The IIUM Engineering Journal, published tri-annually (January, May and September), is a double-blind peer-reviewed open-access journal of the Kulliyyah of Engineering, International Islamic University Malaysia (IIUM). Our next issue will be on May 2026 We are currently indexed in: Scopus (Since 2016) MyCite WoS(Emerging Sources Citation)
Solid-state Fermentation for Production of Bio-coagulant on Rice Bran using Candida utilis, Trichoderma sp., Phanerochaete chrysosporium for Turbidity Removal
Solid-state fermentation is a microbial culture technique used to produce compounds and products of industrial interest. In this study, rice bran, a by-product of the rice processing industry, was used as a coagulant substrate for Candida utilis, Trichoderma sp., and Phanerochaete chrysosporium in solid-state fermentation (SSF). To the best of our knowledge, no study has reported the production of coagulants by Trichoderma sp. and Candida utilis. Moreover, no attempts have been made to utilize rice bran as a substrate for the production of coagulants in solid-state by these three microorganisms. Two different substrate proportions were used to produce natural coagulants. Bio-coagulants produced in this research were used to remove turbidity from the kaolin solution. The experiments were conducted with artificially turbid water and a conventional jar test apparatus. The findings demonstrate that Substrate 2, distinguished by its lower water content, exhibited the highest turbidity removal efficiency compared to Substrate 1, which possessed higher water content. Moreover, Trichoderma sp. showed a maximum turbidity removal of 71%. While the maximum turbidity removal by the coagulants produced from C. utilis and P. chrysosporium was 59 %. The microscopic observation of the microorganism’s growth on rice bran substrate has also been performed. It was revealed that the three microorganisms fully colonized the substrate surface. Further optimization of the SSF process parameters is expected to achieve efficient turbidity reduction in wastewater. ABSTRAK: Penapaian keadaan pepejal ialah teknik kultur mikrob yang digunakan untuk menghasilkan sebatian dan produk yang mempunyai kepentingan industri. Dalam kajian ini, dedak padi, iaitu hasil sampingan daripada industri pemprosesan beras, digunakan sebagai substrat koagulan bagi Candida utilis, Trichoderma sp., dan Phanerochaete chrysosporium dalam penapaian keadaan pepejal (solid-state fermentation, SSF). Sepanjang pengetahuan kami, belum terdapat kajian yang melaporkan penghasilan koagulan oleh Trichoderma sp. dan Candida utilis. Selain itu, belum ada usaha yang dilaporkan untuk memanfaatkan dedak padi sebagai substrat bagi penghasilan koagulan secara keadaan pepejal oleh ketiga-tiga mikroorganisma ini. Dua nisbah substrat yang berbeza telah digunakan untuk menghasilkan koagulan semula jadi. Bio-koagulan yang dihasilkan dalam kajian ini digunakan untuk menyingkirkan kekeruhan daripada larutan kaolin. Eksperimen dijalankan menggunakan air keruh buatan dan radas ujian balang konvensional. Dapatan kajian menunjukkan bahawa Substrat 2, yang dicirikan oleh kandungan air yang lebih rendah, mempamerkan kecekapan penyingkiran kekeruhan yang paling tinggi berbanding Substrat 1 yang mempunyai kandungan air lebih tinggi. Selain itu, Trichoderma sp. menunjukkan penyingkiran kekeruhan maksimum sebanyak 71%, manakala penyingkiran kekeruhan maksimum oleh koagulan yang dihasilkan daripada C. utilis dan P. chrysosporium ialah 59%. Pemerhatian mikroskopik terhadap pertumbuhan mikroorganisma pada substrat dedak padi juga telah dijalankan, dan menunjukkan bahawa ketiga-tiga mikroorganisma tersebut telah mengkolonikan permukaan substrat sepenuhnya. Pengoptimuman lanjut terhadap parameter proses SSF dijangka dapat mencapai pengurangan kekeruhan yang lebih berkesan dalam air sisa.
Copyright (c) 2026 IIUM Press https://creativecommons.org/licenses/by-nc/4.0
Optimization and Scale-Up of Solid-State Bioconversion for Microbial Coagulant Production toward Sustainable Water Treatment
Microbial coagulants are emerging as an eco-friendly alternative to chemical coagulants owing to their non-toxicity, biodegradability, and sustainability. However, their use is limited by high production costs linked to expensive nutrient media and scalability issues. To address these challenges, this study developed a cost-effective microbial coagulant based on a fungus using cocopeat as a substrate. Growth conditions (malt extract, glucose, and pH) were optimized using the Face-Centered Central Composite Design (FCCCD) under Response Surface Methodology (RSM) to maximize coagulant yield. The performance in water treatment was evaluated using jar tests, and large-scale production techniques were investigated using a tray bioreactor and an agitated drum bioreactor (ADB). The fungal strain used in this study was identified as Phanerochaete concrescens, which demonstrated high turbidity-reducing efficiency, achieving a maximum flocculating activity of 93.06% in a standard jar test. These results were obtained under statistically optimized growth conditions, specifically 5 days of incubation at 25°C, 70% moisture content, 3% malt extract, 2.5% glucose, and pH 7, confirming model predictions. Phanerochaete concrescens was successfully scaled using a tray fermenter, achieving a flocculation rate of 92% after 5 days of incubation at 70% moisture, pH 7, and a 3 cm substrate thickness. However, in a 30 L agitated bioreactor drum, flocculating activity was recorded at 29% due to insufficient fungal growth at the tested agitation rate. This study confirms the feasibility of a cost-effective microbial coagulant that can be scaled appropriately, offering a sustainable and biodegradable alternative to chemical coagulants. ABSTRAK: Koagulan mikrob muncul sebagai alternatif mesra alam pada koagulan kimia kerana sifatnya yang tidak toksik, terbiodegradasi, dan mampan. Walau bagaimanapun, penggunaannya terhad kerana kos pengeluaran tinggi disebabkan oleh media nutrien yang mahal dan isu kebolehskalaan. Bagi menangani cabaran ini, kajian ini membangunkan koagulan mikrob kos efektif berdasarkan kulapuk kokopit sebagai substrat. Keadaan pertumbuhan (ekstrak malt, glukosa, dan pH) dioptimumkan menggunakan Reka Bentuk Komposit Berpusat Muka (FCCCD) di bawah Kaedah Gerak Balas Permukaan (RSM) bagi memaksimum hasil koagulan. Prestasi dalam rawatan air dinilai menggunakan ujian balang, dan teknik pengeluaran skala besar dikaji menggunakan bioreaktor dulang dan bioreaktor dram bergoncang (ADB). Strain kulat yang digunakan dalam kajian ini dikenal pasti sebagai Phanerochaete concrescens, menunjukkan kecekapan tinggi dalam mengurangkan kekeruhan, mencapai aktiviti pengflokulan maksimum 93.06% melalui ujian piawai balang. Keputusan ini diperoleh di bawah keadaan pertumbuhan yang optimum secara statistik, iaitu 5 hari pengeraman pada suhu 25°C, kandungan lembapan 70%, ekstrak malt 3%, glukosa 2.5%, dan pH 7, mengesahkan model ramalan. Phanerochaete concrescens berjaya diskala menggunakan penapai dulang, dengan kadar pengflokulan 92% dicapai selepas 5 hari pengeraman di bawah lembapan 70%, pH 7, dan ketebalan substrat 3 cm. Walau bagaimanapun, dalam dram bioreaktor bergoncang 30 L, aktiviti pengflokulan direkodkan pada 29% disebabkan oleh pertumbuhan kulat yang tidak mencukupi pada kadar goncangan yang diuji. Kajian ini mengesahkan kebolehlaksanaan koagulan mikrob kos efektif pada skala wajar, menawarkan alternatif mampan dan terbiodegradasi pada koagulan kimia.
Optimum Alert For In-Vehicle Safety Technologies
Vehicle safety technologies have played a critical role in protecting vehicle drivers for several decades. Auditory signals, for example, convey relevant data to the driver without interfering with the driving task. Vehicle auditory alerts help ensure the safest driving experience possible by providing warnings, thereby reducing road accidents caused by reckless or careless driving. Nonetheless, auditory alerts have long been a source of contention in vehicle safety features due to driver annoyance and false-alarm issues. This study aims to investigate drivers’ awareness and perceptions of in-vehicle safety technology and auditory alerts, and how these factors affect drivers’ reactions to the driving environment. A structured questionnaire was designed and distributed to 150 licensed drivers to collect data on their exposure to safety features, perceived usefulness and urgency of alerts, and preferred auditory characteristics. Descriptive and comparative analyses were conducted to evaluate drivers’ perceptions alongside benchmark data from Original Equipment Manufacturers (OEMs). Results show that 87% of respondents are aware of major in-vehicle safety technologies, yet 48.7% perceive them as potentially distracting while driving. Findings also identify 1000 Hz at 65 dB as the preferred auditory frequencies for safety alerts based on user responses. This study provides insights for policymakers, manufacturers, and safety stakeholders to enhance the design, acceptance, and effectiveness of in-vehicle auditory warning systems. ABSTRAK: Teknologi keselamatan kenderaan memainkan peranan penting dalam melindungi pemandu melalui ciri amaran seperti isyarat auditori yang menyampaikan maklumat keselamatan tanpa mengganggu tugas pemanduan. Walaupun amaran auditori dapat membantu mengurangkan risiko kemalangan jalan raya, isu seperti gangguan kepada pemandu dan amaran palsu masih menjadi cabaran utama. Kajian ini meneliti tahap kesedaran dan persepsi pemandu terhadap teknologi keselamatan dalam kenderaan serta amaran auditori, termasuk kesannya terhadap tindak balas pemandu dalam persekitaran pemanduan. Data diperoleh melalui soal selidik berstruktur yang melibatkan 150 pemandu berlesen, merangkumi pendedahan terhadap ciri keselamatan, persepsi kegunaan, tahap kecemasan amaran dan pilihan ciri auditori. Analisis deskriptif dan perbandingan turut dijalankan berdasarkan data penanda aras daripada Original Equipment Manufacturers (OEM). Dapatan menunjukkan bahawa 87% responden menyedari kewujudan teknologi keselamatan utama dalam kenderaan, namun 48.7% menganggapnya berpotensi mengganggu pemanduan. Kajian ini juga mengenal pasti bahawa frekuensi 1000 Hz pada 65 dB merupakan ciri auditori yang paling digemari untuk amaran keselamatan. Dapatan ini dapat membantu pembuat dasar, pengeluar kenderaan dan pihak berkepentingan meningkatkan reka bentuk, penerimaan dan keberkesanan sistem amaran auditori dalam kenderaan.
Field and Laboratory Evaluation of Reliability in a Novel Cone Deployment System for Safer Highway Operations
This study evaluates the reliability and maintainability of the Cone Collecting and Laying (C2L) machine, developed to enhance safety and efficiency in highway maintenance operations by automating the deployment of traffic cones. The objective is to investigate the use of reliability metrics, which are Mean Time to Failure (MTTF), Mean Time to Repair (MTTR), and Mean Time Between Failures (MTBF), as a systematic approach to assess the operational efficiency and failure behavior of the C2L machine in real highway maintenance scenarios. The methodology encompasses controlled laboratory and field testing conducted over 68 hours in Test Motion A and 3,192 hours in Test Motion B. During these tests, real-time failure data were recorded for six critical components of the C2L machine, namely the bearing, cone lifter, chain, sensor, PLC, and hook spring. Findings from Test Motion A revealed component vulnerabilities, including a low MTTF in bearings and a high MTTR in welded joints. In contrast, Test Motion B identified frequent failures in the PLC system, attributed to vibration stress, as well as a rare but critical hook spring failure caused by material fatigue. All reliability metrics were calculated from empirical failure and repair times, enabling accurate quantification of component durability and overall system availability. Failure data were further categorized into repairable and non-repairable types to inform targeted design improvements. In conclusion, integrating MTTF, MTTR, and MTBF into the evaluation of the C2L machine proved essential for identifying weaknesses and enhancing operational performance. The study recommends proactive maintenance scheduling, structural reinforcement, and redesign of vulnerable components to ensure system resilience. These findings contribute to improved worker safety, reduced operational downtime, and the enhanced long-term deployment potential of the C2L machine in highway maintenance applications. ABSTRAK: Kajian ini menilai kebolehpercayaan dan kebolehselenggaraan mesin Pemungut dan Meletak Kon (C2L) bagi meningkatkan keselamatan dan kecekapan operasi penyelenggaraan lebuh raya melalui automasi proses meletak dan mengutip kon trafik. Objektif kajian ini adalah meneliti penggunaan metrik kebolehpercayaan seperti Masa Purata Sebelum Kegagalan (MTTF), Masa Purata Membaiki (MTTR) dan Masa Purata Antara Kegagalan (MTBF) sebagai satu pendekatan sistematik bagi menilai kecekapan operasi serta tingkah laku kegagalan mesin C2L dalam situasi penyelenggaraan lebuh raya sebenar. Kaedah ini merangkumi ujian makmal dan ujian lapangan terkawal yang dijalankan selama 68 jam bagi Ujian Gerakan A dan 3,192 jam bagi Ujian Gerakan B. Sepanjang ujian tersebut, data kegagalan masa nyata direkodkan pada enam komponen kritikal mesin C2L, iaitu pengalas, pengangkat kon, rantai, pengesan, PLC dan spring cangkuk. Penemuan daripada Ujian Gerakan A menunjukkan kelemahan komponen termasuk MTTF yang rendah pada galas serta MTTR yang tinggi pada sambungan kimpalan. Sebaliknya, Ujian Gerakan B mengenal pasti kegagalan kerap pada sistem PLC akibat tekanan getaran, serta kegagalan kurang tetapi kritikal pada spring cangkuk disebabkan oleh kelemahan bahan. Semua metrik kebolehpercayaan dikira daripada data empirikal masa kegagalan dan masa pembaikan, sekaligus membolehkan pengukuran ketahanan komponen serta ketersediaan keseluruhan sistem. Data kegagalan turut dikategori pada jenis boleh dibaiki dan tidak boleh dibaiki bagi menyokong penambahbaikan reka bentuk terfokus. Kesimpulan, integrasi MTTF, MTTR dan MTBF dalam penilaian mesin C2L terbukti penting bagi mengenal pasti kelemahan serta meningkatkan prestasi operasi. Kajian ini mengesyorkan jadual penyelenggaraan proaktif, pengukuhan struktur, dan reka bentuk semula komponen yang lemah bagi memastikan daya tahan sistem. Dapatan ini menyumbang kepada peningkatan keselamatan pekerja, pengurangan masa henti operasi, serta potensi penggunaan jangka panjang mesin C2L dalam aplikasi penyelenggaraan lebuh raya.
Analytical and Flight Test-Based Dynamic Characteristic Identification of a Small Fixed-Wing UAV
Reliable and efficient UAV flight control design requires accurate knowledge of the vehicle’s dynamic characteristics, which are rarely provided by manufacturers and must therefore be determined independently. These characteristics can be obtained through analytical calculations, wind tunnel testing, and flight testing. However, in small UAV development, the process is often simplified by skipping directly to flight tests for control tuning, without first examining the underlying dynamic behavior. To address this gap, this research compares the dynamic characteristics derived from analytical calculations and flight testing. The case study focuses on BRIN’s bMFE-UAV, in which longitudinal and lateral/directional dynamics are identified using control-surface deflections as inputs and measured responses of speed, altitude, attitude, acceleration, and rotational rate as outputs. Results reveal consistent oscillatory behaviors: two oscillatory modes in longitudinal dynamics and, in lateral/directional dynamics, one oscillatory mode accompanied by two non-oscillatory modes. Only the damping ratio of the short-period mode showed close agreement (<10% difference), while other parameters exhibited substantial discrepancies. The discrepancies demonstrate the limitations of analytical methods in accurately reflecting real flight dynamics, reinforcing the necessity of flight testing to ensure reliable data for small UAV flight-control design. ABSTRAK: Reka bentuk kawalan penerbangan UAV yang boleh dipercayai dan cekap memerlukan pengetahuan tepat pada ciri dinamik kenderaan. Ianya jarang didapati daripada pengeluar dan oleh itu mesti ditentukan secara bebas. Ciri-ciri ini boleh diperolehi melalui pengiraan analitika, ujian terowong angin, dan ujian penerbangan. Walau bagaimanapun, dalam pembangunan UAV kecil, prosesnya sering dipermudahkan dengan melangkau terus ke ujian penerbangan bagi kawalan penalaan, tanpa terlebih dahulu memeriksa tingkah laku dinamik. Kajian ini bertujuan membandingkan ciri dinamik yang diperoleh daripada pengiraan analitika dan ujian penerbangan. Kajian kes memfokuskan pada bMFE-UAV BRIN, di mana dinamik membujur dan sisi/arah dikenal pasti menggunakan pesongan permukaan kawalan sebagai input dan tindak balas diukur pada kelajuan, ketinggian, sikap, pecutan dan kadar putaran sebagai hasil pengeluaran. Dapatan mendapati tingkah laku berayun yang konsisten: dua mod berayun dalam dinamik membujur dan dalam dinamik sisi/arah, satu mod berayun disertai dengan dua mod bukan berayun. Hanya nisbah redaman mod jangka pendek menunjukkan perbezaan terdekat (iaitu <10%), manakala parameter lain menunjukkan ralat yang besar. Ralat menunjukkan batasan kaedah analisis dalam mencerminkan dinamik penerbangan sebenar, mengukuhkan keperluan ujian penerbangan bagi memastikan data boleh dipercayai untuk reka bentuk kawalan penerbangan UAV kecil.
Impact of Aggressive and Moderate Driving Intensity on Vehicle Air–Fuel Ratio Stability
Aggressive driving behavior occurs when drivers neglect traffic rules, leading to violations, accidents, and increased road hazards. Such behavior endangers the driver and places other road users at risk. In contrast, moderate driving reflects disciplined patterns that reduce risks and improve vehicle efficiency. This study examines the influence of aggressive and moderate driving behaviors on engine performance, with a focus on air–fuel ratio (AFR) stability. The investigation used On-Board Diagnostics (OBD-II) data collected during 11.7 km driving sessions under two conditions: aggressive and moderate. The test vehicle, equipped with a 1.8-liter engine, was driven at controlled speeds. Aggressive driving was defined as driving in the 60–90 km/h range, while moderate driving occurred below 60 km/h, representing typical urban operation. Results show distinct differences in AFR behavior. Aggressive driving produced greater instability, with richer AFR outliers near 12.5, higher global wavelet spectrum (GWS) peaks of about 40 units compared to 20, and longer time in fuel-rich regions (12–17% versus 5–14%). These fluctuations indicate unstable combustion, increased fuel use, and higher emissions. In contrast, moderate driving maintained a more stable AFR distribution, with a greater proportion of leaner readings (~36% versus 30%) and closer alignment with stoichiometric values. This stability supports improved combustion efficiency, reduced emissions, and better fuel economy. Overall, the findings show that driving intensity significantly influences AFR stability, with moderate driving promoting cleaner and more efficient engine operation. ABSTRAK: Tingkah laku pemanduan agresif berlaku apabila pemandu mengabai peraturan lalu lintas, menyebabkan pelanggaran, kemalangan dan peningkatan bahaya di jalan raya. Tingkah laku sedemikian membahayakan pemandu dan membahayakan pengguna jalan raya lain. Sebaliknya, pemanduan sederhana mengurangkan risiko dan meningkatkan kecekapan kenderaan. Kajian ini mengkaji pengaruh tingkah laku pemanduan agresif dan sederhana terhadap prestasi enjin, dengan memberi tumpuan kepada kestabilan nisbah udara-bahan api (AFR). Kajian ini menggunakan data Diagnostik Atas Papan (OBD-II) yang dikumpul semasa sesi pemanduan 11.7 km di bawah dua keadaan: agresif dan sederhana. Kenderaan ujian, dilengkapi enjin 1.8 liter, dipandu pada kelajuan terkawal. Pemanduan agresif ditakrifkan pada julat 60–90 km/j, manakala pemanduan sederhana berlaku di bawah 60 km/j, mewakili pemanduan tipikal dalam bandar. Dapatan kajian menunjukkan perbezaan ketara dalam tingkah laku AFR. Pemanduan agresif menghasilkan ketidakstabilan terbesar, dengan AFR terpisah yang lebih tinggi iaitu hampir 12.5, puncak spektrum gelombang global (GWS), iaitu 40 unit lebih tinggi berbanding 20, dan masa lebih lama di kawasan kaya bahan api (12–17% berbanding 5–14%). Turun naik ini menunjukkan pembakaran yang tidak stabil, peningkatan penggunaan bahan api dan pelepasan karbon lebih tinggi. Sebaliknya, pemanduan sederhana mengekalkan taburan AFR lebih stabil, dengan kadar bacaan lebih rendah (~36% berbanding 30%) dan penjajaran hampir pada nilai stoikiometrik. Kestabilan ini menyokong kecekapan pembakaran yang lebih baik, mengurangkan pelepasan dan penjimatan bahan api yang lebih baik. Secara keseluruhan, dapatan kajian menunjukkan bahawa pemanduan yang baik mempengaruhi kestabilan AFR dengan ketara. Pemanduan sederhana menggalakkan operasi enjin yang lebih bersih dan cekap.
Design Optimization and Structural Evaluation of a Hybrid Frame for Next-Generation Electric Scooters
The increasing adoption of electric scooters has introduced unique challenges in chassis design, where packaging constraints imposed by predefined outer shells often conflict with structural efficiency. This study presents the conceptual design, structural analysis, and validation of a hybrid chassis tailored for an electric scooter within such constraints. The frame integrates a step-through central region with a trellis-inspired rear subframe and a reinforced headstock. Three design iterations were modeled in SolidWorks and evaluated using finite element analysis (ANSYS) for static loads (1–3G), fatigue under cyclic loading, equivalent static impact loads, and modal behavior. Analytical stress checks and literature comparisons supported the simulations. Under 3G static loading, the final steel frame exhibited a maximum deformation of 3.16 mm with a minimum factor of safety of 1.48. Fatigue analysis predicted a life of 1.75 × 10? cycles, while modal analysis confirmed the first natural frequency at 28.56 Hz, above the expected excitation frequencies, and thus free from resonance. Although crash simulations showed localized yielding in severe impacts, the frame maintained integrity under operational conditions. These findings demonstrate that high structural performance can be achieved within strictly styling-defined packaging envelopes, validating the hybrid trellis–step-through configuration for electric scooters. ABSTRAK: Peningkatan penggunaan skuter elektrik telah menyebabkan cabaran unik pada reka bentuk casis, di mana kekurangan pada pembungkusan, yang ditentukan oleh struktur luaran, sering bercanggah dengan kecekapan struktur. Kajian ini membentangkan reka bentuk konseptual, analisis struktur, dan pengesahan bagi casis hibrid yang direka khusus untuk skuter elektrik. Rangka casis ini mengintegrasi bahagian tengah jenis telus langkah dengan subrangka belakang berinspirasi jalaran serta stok kepala yang diperkukuh. Tiga iterasi reka bentuk dimodel menggunakan SolidWorks dan dinilai melalui analisis unsur terhingga menggunakan ANSYS bagi beban statik (1–3G), keletihan di bawah beban berkitar, beban impak statik setara, serta tingkah laku mod. Pengesahan analitikal terhadap tegasan serta perbandingan dengan kajian terdahulu turut menyokong dapatan simulasi. Di bawah beban statik 3G, rangka keluli akhir menunjukkan ubah bentuk maksimum sebanyak 3.16 mm dengan faktor keselamatan minimum 1.48. Analisis keletihan meramalkan jangka hayat sebanyak 1.75 × 10? kitaran, manakala analisis mod mengesahkan frekuensi asli pertama pada 28.56 Hz, iaitu melebihi frekuensi yang diuji dan bebas resonans. Walaupun simulasi perlanggaran menunjukkan luluh setempat di bawah impak teruk, rangka masih kukuh ketika beroperasi. Dapatan ini menunjukkan bahawa prestasi struktur yang tinggi boleh dicapai dalam persekitaran pembungkusan yang ketat berasaskan reka bentuk estetik, sekaligus mengesahkan keberkesanan konfigurasi hibrid jalaran–telus langkah pada skuter elektrik.
Scalable Multi-Objective Optimization for Facility Location Using A Metaheuristic Technique
Rapid growth in e-commerce and service expectations forces large-scale logistics networks to balance facility operating cost, service coverage, and delivery equity simultaneously. This study aims to develop a scalable discrete multi-objective particle swarm optimization (MOPSO-FLP) framework for constraint-rich facility location problems, where traditional exact approaches become impractical. The proposed method integrates discrete encoding, feasibility repair, adaptive parameter control, and an archive-based leader selection strategy, and it is evaluated using benchmark instances and a national postal logistics network case. Across 30 independent runs, MOPSO-FLP achieves superior convergence and diversity compared with representative multi-objective metaheuristics (e.g., NSGA-II and related baselines), and the deployment validation yields a Pareto set of 47 non-dominated solutions that clearly exposes cost–coverage–equity trade-offs. Overall, the results demonstrate that the proposed framework provides decision makers with interpreted alternatives and actionable policies for large-scale logistics planning. ABSTRAK: Pertumbuhan pesat dalam e-dagang serta peningkatan jangkaan perkhidmatan memaksa rangkaian logistik berskala besar untuk mengimbangi kos operasi fasiliti, liputan perkhidmatan, dan keadilan penghantaran secara serentak. Kajian ini bertujuan membangunkan satu kerangka pengoptimuman zarah berbilang objektif diskret yang berskala, iaitu Multi-Objective Particle Swarm Optimization bagi masalah penentuan lokasi fasiliti (MOPSO-FLP), khusus untuk persekitaran berkekangan tinggi di mana pendekatan tepat tradisional menjadi tidak praktikal. Kaedah yang dicadangkan mengintegrasikan pengekodan diskret, mekanisme pembaikan kebolehlaksanaan, kawalan parameter adaptif, serta strategi pemilihan pemimpin berasaskan arkib, dan dinilai menggunakan set data penanda aras serta kajian kes rangkaian logistik pos kebangsaan. Merentasi 30 ulangan bebas, MOPSO-FLP menunjukkan penumpuan dan kepelbagaian penyelesaian yang lebih baik berbanding metaheuristik berbilang objektif sedia ada seperti NSGA-II dan kaedah asas berkaitan. Pengesahan pelaksanaan menghasilkan satu set Pareto yang mengandungi 47 penyelesaian tidak didominasi, yang secara jelas menggambarkan kompromi antara kos, liputan, dan keadilan. Secara keseluruhan, dapatan kajian ini menunjukkan bahawa kerangka yang dicadangkan mampu menyediakan alternatif yang boleh ditafsir serta dasar yang boleh dilaksanakan oleh pembuat keputusan dalam perancangan logistik berskala besar.
Magnetic Resonance Imaging and Ultrasound-based Breast Tumor Classification using Soft Voting Ensemble Deep Learning Method
In recent years, breast cancer (BC) has been considered the most common cause of death in women worldwide. Commonly, BC occurs in breast cells or fatty tissues within the breast, and these tissues tend to grow faster and lead to death. The survival of patients with BC can be enhanced with early and accurate diagnosis. Therefore, this research proposes an ensemble learning approach that uses Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and a soft-voting mechanism to make early predictions and diagnoses of BC. The input sample images are initially passed through the convolutional layer to extract features from each modality separately. The Multi-Modality Adaptive Feature Fusion (MMAFF) technique combines generic features across modalities. Compared with existing techniques such as Deep Convolutional Neural Networks (DCNN) and Resolution Adaptive Network (RANet), the proposed ensemble learning technique achieves 89.6% accuracy for clinical indicator prediction and 99.25% accuracy for final tumor classification on the MRI-US dataset. ABSTRAK: Dalam beberapa tahun kebelakangan ini, kanser payudara (Breast Cancer, BC) telah dikenal pasti sebagai antara punca utama kematian dalam kalangan wanita di seluruh dunia. Secara umum, BC berlaku pada sel-sel payudara atau tisu lemak dalam payudara, di mana sel-sel ini cenderung membiak dengan cepat dan berpotensi membawa kepada kematian. Kadar kelangsungan hidup pesakit BC dapat ditingkatkan melalui diagnosis awal yang tepat dan boleh dipercayai. Sehubungan itu, kajian ini mencadangkan satu pendekatan pembelajaran ansambel yang menggabungkan Rangkaian Neural Berulang (Recurrent Neural Networks, RNN), Rangkaian Neural Konvolusi (Convolutional Neural Networks, CNN), Memori Jangka Pendek Panjang (Long Short-Term Memory, LSTM), serta mekanisme pengundian lembut (soft voting) bagi tujuan ramalan awal dan diagnosis BC. Imej sampel input pada peringkat awal diproses melalui lapisan konvolusi untuk mengekstrak ciri daripada setiap modaliti secara berasingan. Seterusnya, teknik Multi-Modality Adaptive Feature Fusion (MMAFF) digunakan untuk menggabungkan ciri-ciri umum daripada setiap modaliti tersebut. Berbanding dengan teknik sedia ada seperti Rangkaian Neural Konvolusi Mendalam (Deep Convolutional Neural Networks, DCNN) dan Resolution Adaptive Network (RANet), pendekatan pembelajaran ansambel yang dicadangkan menunjukkan prestasi yang lebih baik dengan mencapai ketepatan sebanyak 89.6% bagi ramalan penunjuk klinikal dan 99.25% bagi pengelasan akhir tumor menggunakan set data MRI-US.
New Control Strategy of Battery Energy Storage System in Microgrids Integrated with Wind and Solar Power Plants
The global shift toward renewable energy, particularly wind and solar, has led to the rapid growth and diversification of distributed energy resources (DERs). However, integrating these sources sustainably and efficiently presents major challenges for researchers, investors, and power system operators. Their impact on distribution grids, along with operational uncertainties and disturbances, requires a comprehensive, system-wide perspective. This study proposes targeted strategies to enhance renewable energy utilization, offering both economic and environmental benefits while maintaining technical reliability and energy security. The study develops a microgrid model that includes a solar plant, a wind farm, an energy storage system, a distribution grid, and loads. As the core component, the battery energy storage system (BESS) plays a vital role in grid stabilization, power quality enhancement, and voltage/frequency regulation through advanced control strategies. Simulation results are analyzed to assess grid performance, evaluate the influence of renewable sources, and verify the effectiveness of the BESS in maintaining stable microgrid operation under realistic operating conditions. ABSTRAK: Peralihan global ke arah tenaga boleh diperbaharui, khususnya angin dan solar, telah membawa kepada pertumbuhan pesat serta kepelbagaian sumber tenaga teragih (DERs). Walau bagaimanapun, pengintegrasian sumber-sumber ini secara mampan dan cekap menimbulkan cabaran besar kepada para penyelidik, pelabur, dan pengendali sistem kuasa. Kesan sumber tersebut terhadap grid pengagihan, bersama-sama dengan ketidakpastian operasi dan gangguan, memerlukan perspektif menyeluruh pada skala sistem. Kajian ini mencadangkan strategi berfokus bagi meningkatkan penggunaan tenaga boleh diperbaharui, yang menawarkan manfaat ekonomi dan alam sekitar sambil mengekalkan kebolehpercayaan teknikal serta keselamatan tenaga. Kajian ini membangunkan model mikrogrid yang merangkumi loji solar, ladang angin, sistem penyimpanan tenaga, grid pengagihan, dan beban. Sebagai komponen teras, sistem penyimpanan tenaga bateri (BESS) memainkan peranan penting dalam penstabilan grid, peningkatan kualiti kuasa, serta pengawalan voltan/frekuensi melalui strategi kawalan lanjutan. Dapatan simulasi dianalisis bagi menilai prestasi grid, menilai pengaruh sumber boleh diperbaharui, dan mengesahkan keberkesanan BESS dalam mengekalkan operasi mikrogrid yang stabil pada keadaan operasi realistik.
Comparative Performance Analysis of Forward Error Correction Schemes in AX.25-Based APRS Communication Systems with AFSK Modulation using GNU Radio Simulation
This paper presents a comparative analysis of Forward Error Correction (FEC) schemes in AX.25-based Automatic Packet Reporting System (APRS) communications using AFSK modulation through GNU Radio simulations. The study evaluates uncoded transmission, Reed–Solomon (255,223), several convolutional code configurations, and concatenated Reed–Solomon + convolutional coding under both Additive White Gaussian Noise (AWGN) and Rayleigh fading channels. Results show that convolutional coding with rate 1/3 and constraint length K=7 achieves the best trade-off, reaching error-free performance at Eb/No = -2.5 dB under AWGN and maintaining robustness under fading. Reed–Solomon codes proved effective for correcting burst errors, while concatenated coding showed strong potential but required interleaving for stable operation. These findings highlight that convolutional coding offers the most practical balance between performance and complexity for real-world APRS and satellite systems, providing insights for future enhancements in robust low-SNR communication links. ABSTRAK: Kajian ini membentangkan analisis perbandingan bagi skim Forward Error Correction (FEC) dalam komunikasi Automatic Packet Reporting System (APRS) berasaskan protokol AX.25 dengan modulasi AFSK melalui simulasi GNU Radio. Penyelidikan ini menilai penghantaran tanpa FEC, kod Reed–Solomon (255,223), beberapa konfigurasi kod konvolusional, serta kod gabungan Reed–Solomon + konvolusional pada saluran Additive White Gaussian Noise (AWGN) dan Rayleigh fading. Dapatan menunjukkan bahawa kod konvolusional dengan kadar 1/3 dan panjang kekangan K=7 memberikan prestasi terbaik, mencapai komunikasi bebas ralat pada Eb/No = –2.5 dB dalam saluran AWGN dan kekal teguh di bawah keadaan fading. Kod Reed–Solomon berkesan untuk pembetulan ralat berurutan, manakala kod gabungan menunjukkan potensi yang kukuh namun memerlukan interleaver untuk operasi yang stabil. Hasil ini menegaskan bahawa kod konvolusional menawarkan keseimbangan paling praktikal antara prestasi dan kompleksiti bagi aplikasi APRS dan sistem satelit, di samping memberikan panduan untuk pembangunan komunikasi yang lebih boleh dipercayai dalam keadaan SNR yang rendah.
Quantitative Assessment of Coffee Quality Using Microwave Non-Destructive Testing
The geographical origin, processing, roast level, and post-roasting resting time affect coffee quality in multiple ways. The traditional quality assessments in the coffee industry (sensory and chemical analyses) are subject to analysts' biases and are destructive, requiring the coffee to be destroyed to run tests. This analysis uses Microwave Non-Destructive Testing (MNDT) to study the dielectric characteristics of specialty coffee (Coffea arabica L.) associated with roasting and resting period. Coffee beans were roasted to produce light, medium, and dark roasts, and the beans were tested for dielectric properties over 30 days in 5-day intervals. The dielectric permittivity was negatively correlated with the level of roasting (r = -0.590, p = 0.005) and the resting period (r = -0.483, p = 0.027). The multiple linear regression showed strong predictive ability (R = 0.762, Adjusted R² = 0.534), explaining the effects of roasting and resting period, and their combined effect on the coffee's dielectric properties. This study shows, for the first time, how MNDT can be applied to the coffee industry to evaluate and assess coffee quality. ABSTRAK: Kualiti kopi dipengaruhi oleh pelbagai faktor seperti asal geografi, teknik pemprosesan, tahap panggang dan tempoh rehat selepas dipanggang. Kaedah tradisional bagi penilaian kualiti, termasuk penilaian deria dan analisis kimia, terdedah kepada subjektiviti dan keperluan ujian yang merosakkan sampel. Kajian ini menggunakan kaedah Microwave Non-Destructive Testing (MNDT) bagi menilai sifat dielektrik kopi istimewa (Coffea arabica L.) yang dipengaruhi oleh tahap pemanggangan dan tempoh rehat selepas dipanggang. Sampel kopi ini dipanggang pada tahap terang, sederhana dan gelap, dengan sifat dielektrik yang diukur selama 30 hari pada selang 5 hari. Keputusan menunjukkan korelasi negatif ketara antara kebolehtelapan dielektrik dan kedua-dua tahap panggang (r = -0.590, p = 0.005) dan tempoh rehat (r = -0.483, p = 0.027). Model regresi berganda menunjukkan keupayaan ramalan yang kuat (R = 0.762, R² Terlaras = 0.534), memberi kesan gabungan pemanggangan dan tempoh rehat pada sifat dielektrik. Penemuan ini menggariskan potensi MNDT sebagai alat berskala, bukan invasif bagi penilaian kualiti kopi, menyumbang kepada amalan pemprosesan lebih baik dan kawalan kualiti dalam industri kopi.
Experimental Validation of a Labview-Based SFCW Software-Defined Radar System
Software-defined radar (SDR) provides a flexible and cost-effective platform for implementing and testing diverse radar techniques without hardware redesign. Among these techniques, Stepped-Frequency Continuous Wave (SFCW) radar has shown promise in addressing some of the limitations of Frequency Modulated Continuous Wave (FMCW). This paper presents the experimental validation of an SDR system based on the SFCW technique. The radar system was developed using a USRP N210 with a WBX daughterboard, fully controlled through LabVIEW for waveform generation, data acquisition, and signal processing. Experimental validation was conducted in a controlled indoor environment using a 30 × 30 cm metal target placed at distances from 0.7 m to 1.25 m. According to the data, this research has shown accurate detection at longer ranges, with error rates below 0.5%. However, the increasing inaccuracy at lower ranges highlighted issues in near-field measurements. The findings have confirmed the feasibility of SFCW-based SDR for compact, reconfigurable radar platforms and suggest opportunities for enhancing near-field accuracy through advanced signal processing and calibration. ABSTRAK: Perisian takrifan radar (SDR) menyediakan platform fleksibel dan kos efektif bagi melaksana dan menguji pelbagai teknik radar tanpa reka bentuk semula perkakas. Antara teknik ini, radar gelombang berterusan frekuensi lanjutan (SFCW) telah menunjukkan kebolehan dalam menangani beberapa batasan gelombang berterusan modul berfrekuensi (FMCW). Kajian ini membentangkan pengesahan eksperimen sistem SDR berdasarkan teknik SFCW. Sistem radar dibangunkan menggunakan USRP N210 bersama papan WBX. Ia dikawal sepenuhnya melalui LabVIEW bagi penjanaan bentuk gelombang, perolehan data dan prosesan isyarat. Pengesahan eksperimen dijalankan dalam persekitaran dalaman terkawal menggunakan sasaran logam 30 × 30 cm yang diletakkan pada jarak 0.7 m hingga 1.25 m. Dapatan penyelidikan ini menunjukkan pengesanan tepat pada julat lebih panjang, dengan kadar ralat di bawah 0.5%. Walau bagaimanapun, ketidaktepatan semakin meningkat pada julat lebih rendah, menyerlahkan isu pengukuran medan. Penemuan ini mengesahkan kebolehlaksanaan SDR berasaskan SFCW pada platform radar yang padat dan boleh dikonfigurasikan semula. Cadangan bagi meningkatkan ketepatan medan adalah melalui pemprosesan dan penentu ukuran isyarat lanjutan.
Raindrop Size Distribution and Wind-Driven Rain Specific Attenuation: CCDF-Based Analysis at 18 and 38 GHz in Tropical Country
Seasonal monsoons, frequency dependence, and wind effects were investigated for rain-specific attenuation at millimeter-wave (mm-wave) frequencies in a tropical environment. Using one-minute RD-69 DSD data and matched wind observations, CCDFs of specific attenuation were evaluated at 18 and 38 GHz for three monsoon phases (SW, NE, IM) and four wind-direction sectors under unconstrained and constrained winds (9–11 m/s). The CCDF results show clear seasonal variability, with the SW monsoon producing the strongest attenuation and the largest mismatch from ITU-R P.838-3, particularly at 38 GHz. Error statistics show that the maximum deviation occurs in the SW 270°–360° sector, with an RMSE of 1.550 dB/km and a positive bias, confirming ITU-R underestimation. Meanwhile, the NE monsoon exhibits smaller overall deviations (worst-case RMSE below about 0.6 dB/km at 38 GHz). The IM period exhibits mixed behavior, where wind direction can shift the deviation between under- and overestimation under constrained winds. Exceedance levels of 0.01% and 0.001% were emphasized as design-relevant extremes, corresponding to approximately 52.6 min/year and 5.26 min/year, respectively, under one-minute sampling. Overall, the results indicate that ITU-R P.838-3 accuracy in tropical conditions depends on monsoon phase and wind direction, supporting monsoon-aware, direction-sensitive fade-margin planning and link-orientation considerations for robust 5G/6G terrestrial mm-wave systems. ABSTRAK: Monsun bermusim, kebergantungan frekuensi, dan kesan angin telah dikaji terhadap pelemahan spesifik hujan pada frekuensi gelombang milimeter (mm-wave) dalam persekitaran tropika. Menggunakan data DSD RD-69 beresolusi satu minit dan melalui pemerhatian padanan angin, pelemahan CCDF spesifik dinilai pada 18 dan 38 GHz untuk tiga fasa monsun (SW, NE, IM) dan empat sektor arah angin di bawah angin tidak terkekang serta angin terkekang (9–11 m/s). Penyimpangan terbesar daripada ITU-R P.838-3 berlaku pada 38 GHz, khususnya semasa monsun barat daya (SW), apabila sektor 270°–360° di bawah angin tidak terkekang menunjukkan RMSE = 1.550 dB/km dan bias = +1.193 dB/km, menandakan ITU-R bernilai lebih rendah; di bawah angin terkekang, penyimpangan kekal tinggi (RMSE = 1.383 dB/km). Tempoh peralihan (IM) juga menunjukkan kebergantungan pada sektor angin terkekang, dengan RMSE = 1.212 dB/km dan bias = +0.936 dB/km pada 38 GHz, manakala monsun timur laut (NE) menunjukkan penyimpangan lebih kecil (RMSE terburuk adalah kurang daripada kira-kira 0.6 dB/km pada 38 GHz di bawah angin tidak terkekang). Tahap kebarangkalian lebihan 0.01% dan 0.001% ditekankan sebagai ekstrem yang relevan bagi reka bentuk, bersamaan kira-kira 52.6 min/tahun dan 5.26 min/tahun, masing-masing, pada pensampelan satu minit. Secara keseluruhan, dapatan kajian menunjukkan ketepatan ITU-R P.838-3 dalam keadaan tropika bergantung pada fasa monsun dan arah angin, sekaligus menyokong perancangan margin-pudaran yang peka monsun dan peka arah angin serta menimbang orientasi pautan untuk sistem gelombang-mm 5G/6G terestrial yang lebih berdaya tahan.
MultiModal Explainable AI and Blockchain Integration for Automated Halal Verification of Cosmetic Products
This research develops a framework that integrates blockchain technology and MXAI (Multimodal Explainable Artificial Intelligence) to automate the authentication and verification of halal cosmetic products. Two data sources were used: text extracted by OCR (Optical Character Recognition) and text manually input from cosmetic labels. After the pre-processing stage, the text data are analyzed using zero-shot classification to determine the inspection results, namely halal, haram, or syubhat. The inspection is conducted using MXAI, with decisions based on confidence scores and SHAP values. The inspection results are converted into digital reports as SHA-256 hashes and stored as Merkle roots on the blockchain, allowing users to download certificates as QR codes. The halal status experiment on the cosmetics dataset achieved an accuracy of 94.5% for classification, with a 3.3% improvement over baseline models, evaluated using stratified 5-fold cross-validation. This system enhances transparency, accountability, and public trust in automated halal certification. The contribution of this research is the integration of MXAI and blockchain technology into a single intelligent halal verification system, which can be extended to other supply chain sectors. ABSTRAK: Kajian ini menggabungkan teknologi rantaian blok dan MXAI (Kecerdasan Buatan Penjelasan Multimodal) bagi automasi dan jaminan pengesahan produk kosmetik halal. Dua sumber data digunakan: teks yang diekstrak oleh OCR (Pengesahan Optik Karakter) dan teks yang dimasukkan secara manual daripada label kosmetik. Selepas peringkat pra-pemprosesan, data teks dianalisis menggunakan klasifikasi sifar-tembakan bagi menentukan keputusan pemeriksaan, iaitu halal, haram, atau syubhat. Pemeriksaan dijalankan menggunakan MXAI, dengan keputusan berdasarkan skor keyakinan dan nilai SHAP. Keputusan pemeriksaan ditukar menjadi laporan digital dalam bentuk hash SHA-256 dan disimpan sebagai punca Merkle pada rantaian blok, membolehkan pengguna memuat turun sijil dalam bentuk kod QR. Eksperimen status halal pada set data kosmetik menunjukkan ketepatan 94.5% untuk prestasi pengelasan, dengan peningkatan 3.3% berbanding model asas, dinilai menggunakan pengesahan silang 5-lipatan berstrata. Sistem ini meningkatkan ketelusan, akauntabiliti, dan kepercayaan awam dalam pensijilan halal automatik. Sumbangan penyelidikan ini ialah melalui integrasi MXAI dan teknologi rantaian blok dalam satu sistem pengesahan halal pintar tunggal, yang boleh diperluas kepada sektor rantaian bekalan lain.
Generative AI Models: A Comparison of Application Analysis on Web AI-Based Decision Support Systems for Satellite Anomaly Identification
The rapid innovation of Generative Artificial Intelligence (GenAI) has transformed Decision Support Systems (DSS) across various domains, including satellite operations. This paper presents a comparative analysis of four free Generative AI models, including Gemma 3 by Google, Llama 4 Maverick by Meta AI, Nemotron Nano 2 by NVIDIA, and Devstral Small by Mistral, in the context of integrating them into a Web AI-based Decision Support System (DSS) for satellite anomaly identification. Using a dataset of over 4,455 satellite anomaly records covering 1957 to 2024 provided by Seradata, with scalability and adaptability to diverse mission profiles. We evaluate these models by generating anomaly analyses for clarity, accuracy, completeness, and relevance across the Incident Overview, Reliability Trend, Insight, and Stakeholder Recommendation categories, using a 5-point Likert scale and Fleiss' Kappa for internal consistency. The comprehensive evaluation of GenAI models for Web AI-based DSS delineates a clear performance stratification, with scores of 4.44 and 4.39 for Nemotron Nano 2 and Llama 4 Maverick, respectively, confirming their positions as the leading systems based on overall Likert scores. However, the analysis further revealed a critical trade-off between absolute quality and internal consistency (Fleiss' Kappa). The superior models, Nemotron Nano 2 and Llama 4 Maverick, achieved high Likert scores by displaying pronounced performance peaks but suffered the lowest internal predictability, with ? = 0.18 on Llama 4 Maverick, indicating a highly volatile output structure in which strong clarity often masked critical incompleteness. Conversely, Devstrall Small, despite its suboptimal mean score of 3.44, demonstrated the highest internal consistency, with ? = 0.66. This robust predictability, even at a lower level, underscores a significant implication for DSS development. The model selection must prioritize the required balance between absolute performance ceiling and the predictability of the output structure. The findings highlight the potential of GenAI implementation in DSS to enhance the reliability of satellite operations, while exploring future directions for research and development in this area. This research contributes to the development of more resilient and intelligent satellite anomaly identification systems, with broader implications for space mission safety, resource optimization, cost reduction, and the future of AI-driven aerospace technologies. ABSTRAK: Inovasi pesat Kecerdasan Buatan Generatif (GenAI) telah mengubah Sistem Sokongan Keputusan (DSS) merentasi pelbagai domain, termasuk operasi satelit. Kajian ini membentangkan analisis perbandingan empat model AI Generatif percuma termasuk: Gemma 3 oleh Google, Llama 4 Maverick oleh Meta AI, Nemotron Nano 2 oleh NVIDIA dan Devstral Small oleh Mistral, dalam menyepadu Sistem Sokongan Keputusan (DSS) berasaskan web AI bagi mengenal pasti anomali satelit. Mengguna pakai lebih 4,455 rekod set data anomali satelit meliputi tahun 1957 hingga 2024 oleh Seradata, set data ini mempunyai ciri kebolehskalaan dan kebolehsuaian pada profil misi yang pelbagai. Kajian ini menilai kesemua model melalui penjanaan analisis anomali melalui kejelasan, ketepatan, kelengkapan dan kerelevanan merentasi kategori Gambaran Keseluruhan Insiden, Trend Kebolehpercayaan bagi, Wawasan dan Cadangan Pihak Berkepentingan, mengguna skala Likert 5-mata dan Kappa Fleiss kekonsistensi dalaman. Penilaian komprehensif model GenAI DSS berasaskan web AI menggariskan stratifikasi prestasi yang jelas pada skor 4.44 dan 4.39 untuk Nemotron Nano 2 and Llama 4 Maverick masing-masing, mengesahkan keupayaan sebagai sistem utama berdasarkan skor Likert keseluruhan. Walau bagaimanapun, analisis selanjutnya mendedahkan pertukaran kritikal antara kualiti mutlak dan kekonsistensi dalaman (Fleiss' Kappa). Model terbaik, Nemotron Nano 2 and Maverick Llama 4, mencapai skor Likert tertinggi dengan puncak prestasi yang tinggi tetapi keboleh ramalan dalaman terendah iaitu ?»0.18 pada Maverick Llama 4, menandakan dapatan struktur yang sangat tidak menentu di mana kejelasan yang kuat sering menutupi ketidaklengkapan kritikal. Sebaliknya, Mistral Devstrall Small menunjukkan kekonsistensi dalaman tertinggi ?»0.66 walaupun skor min suboptimumnya 3.44. Keboleh ramalan yang mantap ini, walaupun pada tahap lebih rendah, menggariskan implikasi ketara bagi pembangunan DSS. Pemilihan model mesti mengutamakan keseimbangan yang diperlukan antara siling prestasi mutlak dan keboleh ramalan dapatan struktur. Penemuan ini mengetengah potensi pelaksanaan GenAI DSS dalam meningkatkan kebolehpercayaan operasi satelit sambil meneroka hala tuju masa depan bagi tujuan penyelidikan dan pembangunan. Penyelidikan ini menyumbang kepada pembangunan sistem mengenal pasti anomali satelit berdaya tahan dan pintar, dengan implikasi yang lebih luas bagi keselamatan misi angkasa lepas, mengoptimum sumber, mengurangkan kos operasi dan masa depan teknologi aero angkasa yang dipacu AI.
PromptLessSAM: From Foundational Model to Domain Expert via Lightweight Decoder Adaptation for Crack Segmentation
Maintaining infrastructure such as roads and bridges is very important for safety. This is called Structural Health Monitoring (SHM). A key part of SHM is automatically finding cracks in pavements. Recent foundation models, such as the Segment Anything Model (SAM), can segment a wide range of object types. However, they are not well-suited for specific tasks such as detecting thin cracks in complex environments. They also require user input to function, making them unsuitable for fully automatic inspection systems. Therefore, this paper proposes PromptLessSAM to transform the general SAM model into a domain expert for crack segmentation. We use a lightweight method in which we freeze the powerful image encoder of SAM, except for the encoder’s neck, which remains trainable, and we add a new, small decoder. Our model is very efficient and has only about 1.1 million trainable parameters. We trained and tested our model on the public Crack500 dataset. The experimental results show that our model achieved high performance (a Dice score of 72.7% and an IoU of 57.11% on Crack500), outperforming many state-of-the-art models, including TransUNet and PaveSAM. Also, our model showed good generalization capabilities on the DeepCrack dataset (Dice score of 74.7% and IoU of 62.43%), which it did not see during training. PromptLessSAM provides a new, efficient, and effective method. It shows how to adapt large foundation models for a specific task. Our work provides a strong, high-performance solution trained on a very small number of parameters. ABSTRAK: Penyelenggaraan infrastruktur seperti jalan raya dan jambatan sangat penting bagi tujuan keselamatan. Ini dikenali sebagai Pemantauan Kesihatan Struktur (Structural Health Monitoring, SHM). Salah satu komponen utama pada SHM ialah mengesan rekahan pada turapan secara automatik. Model asas terkini, seperti Model Sesuatu Bahagian (SAM), boleh melakukan segmentasi pelbagai objek. Namun, model ini kurang berkesan bagi tugas khusus seperti mengesan rekahan yang nipis pada persekitaran kompleks. Selain itu, SAM memerlukan arahan daripada pengguna untuk berfungsi, menjadikannya kurang sesuai bagi sistem pemeriksaan sepenuhnya automatik. Oleh itu, kajian ini mencadangkan PromptLessSAM untuk menukarkan model SAM yang umum kepada model pakar domain bagi segmentasi rekahan. Kajian ini menggunakan kaedah yang ringan dengan membekukan pengekod imej SAM yang berkuasa, kecuali pada bahagian ‘leher’ pengekod yang kekal dilatih, dan menambah sebuah penyahkod baharu yang kecil. Model ini sangat cekap dan hanya mempunyai kira-kira 1.1 juta parameter boleh latih. Model ini dilatih dan diuji menggunakan set data awam Crack500. Keputusan eksperimen menunjukkan model ini mencapai skor prestasi tinggi (iaitu skor Dice sebanyak 72.7% dan IoU sebanyak 57.11% pada set data Crack500), mengatasi banyak model tercanggih (state-of-the-art) seperti TransUNet dan PaveSAM. Selain itu, model ini menunjukkan keupayaan generalisasi yang baik pada set data DeepCrack (skor Dice 74.7% dan IoU 62.43%), yang tidak pernah dilihat semasa latihan. PromptLessSAM menyediakan kaedah baharu yang cekap dan berkesan, serta menunjukkan cara menyesuaian model asas berskala besar bagi tugas khusus. Kajian ini menawarkan penyelesaian kukuh dengan prestasi tinggi namun dilatih menggunakan bilangan parameter paling rendah.
Stability-Aware Evaluation of a CNN–LSTM–DQN Intrusion Detection System for Zero-Day and Drifted Network Traffic
Intrusion Detection Systems (IDS) deployed in real-world environments must operate under severe class imbalance, evolving attack strategies, and non-stationary traffic distributions. Conventional supervised and deep learning–based IDS rely on fixed decision functions, limiting their adaptability to zero-day attacks and concept drift. This paper proposes a hybrid CNN–LSTM–DQN framework combined with a stability-aware evaluation methodology. The CNN–LSTM backbone extracts spatio-temporal representations, while a Deep Q-Network (DQN) learns adaptive detection policies using an ARMF-aware reward formulation. The framework is evaluated on eleven experimental stages (E1-11), including supervised baselines, reinforcement learning optimization, zero-day generalization (LOAO), and drift scenarios. Experimental results show that supervised models have high recall (up to 98.39%) but generate too many alerts (ARMF up to 44,845). The reinforcement learning model with prioritized experience replay (E7) achieves a more balanced performance with a recall of 91.40% and an ARMF of 1,031. The proposed PER-based approach significantly improves detection performance while maintaining low alert rates, achieving a recall of 42.47% compared to naive reinforcement learning (E5). Further evaluation in real drifting conditions showed robust recall (89-92%) with a tolerable number of alerts (ARMF ? 1,189). These results indicate that adaptive policy learning enables a more effective trade-off between detection performance and operational cost, while ARMF-based evaluation provides a practical complement to accuracy metrics for real-world IDS deployment. ABSTRAK: Sistem Pengesanan Pencerobohan (IDS) dalam dunia nyata perlu beroperasi di bawah ketidakseimbangan kelas yang teruk, strategi serangan berevolusi, dan taburan trafik tidak pegun. IDS berasaskan pembelajaran penyeliaan dan pembelajaran mendalam konvensional bergantung pada fungsi keputusan tetap, mengehadkan kebolehsuaian terhadap serangan sifar hari dan hanyutan konsep. Kajian ini mencadangkan gabungan rangka kerja hibrid CNN–LSTM–DQN dan metodologi penilaian yang mementingkan kestabilan. CNN–LSTM mengekstrak representasi ruang-masa, manakala Rangkaian Q Mendalam (DQN) mempelajari dasar pengesanan adaptif menggunakan formulasi ganjaran ARMF. Rangka kerja ini dinilai dengan sebelas peringkat eksperimen (E1-11) termasuk garis dasar penyeliaan, pengoptimuman pembelajaran pengukuhan, generalisasi sifar hari (LOAO) dan senario hanyutan. Dapatan eksperimen menunjukkan bahawa model penyeliaan mempunyai kadar ingat semula (recall) yang tinggi (sehingga 98.39%) tetapi menjana terlalu banyak amaran (ARMF sehingga 44,845). Model pembelajaran pengukuhan dengan ulangan pengalaman berprioriti (E7) mencapai prestasi lebih seimbang dengan kadar ingat semula 91.40% dan ARMF sebanyak 1,031. Pendekatan berasaskan PER yang dicadangkan meningkatkan keupayaan pengesanan dengan ketara sambil mengekalkan kadar amaran yang rendah berbanding pembelajaran pengukuhan naif (E5) dengan kadar ingat semula 42.47%. Kajian selanjutnya dalam keadaan hanyutan sebenar menunjukkan kadar ingat semula yang teguh (89-92%) dengan bilangan amaran yang boleh diterima (ARMF ? 1,189). Dapatan ini menunjukkan bahawa pembelajaran dasar adaptif membolehkan imbangan yang lebih berkesan antara prestasi pengesanan dan kos operasi, manakala penilaian berasaskan ARMF menyediakan pelengkap praktikal pada metrik ketepatan bagi penempatan IDS dunia nyata.
A Robust Low-Complexity Star Centroiding Algorithm for Autonomous Navigation under Lunar Noise Conditions
Star-based navigation on the lunar surface is severely degraded by strong regolith reflections, abrupt illumination transitions, and sensor-induced noise, all of which lower the signal-to-noise ratio (SNR) and impair centroiding accuracy. Existing methods address this trade-off poorly: classical center-of-mass (COM) and Gaussian fitting are computationally light but noise-sensitive, whereas iterative weighting and learning-based approaches improve accuracy at the cost of high computational load and large training data. This study addresses this gap by proposing a low-complexity yet robust star centroiding algorithm tailored for lunar-surface imagery. The pipeline integrates median filtering for impulse-noise suppression, an adaptive global threshold (set at 30% of peak intensity) for star-region segmentation, and an intensity-weighted COM estimator for sub-pixel localization. The method was implemented in MATLAB R2023b and evaluated on 30 Stellarium-derived star fields, each corrupted by Gaussian, Poisson, salt-and-pepper, speckle, and solar-glare noise spanning SNRs from ?4.71 dB to 0.72 dB. Benchmarked against standard COM, Gaussian fitting, and the Sieve Search Algorithm (SSA), the proposed method achieves the lowest average root-mean-square error (RMSE = 1.218 pixels), the lowest Euclidean distance error (1.143 pixels), and the lowest false detection rate (FDR = 6.716%), corresponding to relative reductions of 21.5%, 16.7%, and 32.7% over the best baseline, respectively. The algorithm’s favorable accuracy–complexity trade-off makes it well-suited for resource-constrained onboard processors in future lunar exploration and autonomous spacecraft navigation missions. ABSTRAK: Navigasi berasaskan bintang di permukaan bulan terjejas dengan ketara akibat pantulan regolit yang kuat, perubahan pencahayaan yang mendadak, dan hingar daripada penderia, yang kesemuanya menurunkan nisbah isyarat kepada hingar (SNR) dan menjejaskan ketepatan penganggaran sentroid. Kaedah sedia ada gagal mengimbangi keseimbangan ini: kaedah pusat jisim (COM) klasik dan pemadanan Gaussian adalah ringan dari segi pengiraan tetapi sensitif terhadap hingar, manakala kaedah pemberat berulang dan berasaskan pembelajaran mesin meningkatkan ketepatan tetapi memerlukan beban pengiraan tinggi dan data latihan yang besar. Kajian ini mengisi jurang tersebut dengan mencadangkan satu algoritma penganggaran sentroid bintang yang teguh dan berkompleksiti rendah, khusus untuk imej permukaan bulan. Saluran pemprosesan menggabungkan penapisan median untuk menindas hingar impuls, ambang global mudah suai (ditetapkan pada 30% daripada keamatan puncak) untuk segmentasi kawasan bintang, dan penganggar COM berwajaran keamatan untuk penyetempatan subpiksel. Algoritma dilaksanakan dalam MATLAB R2023b dan dinilai ke atas 30 medan bintang Stellarium yang dicemari oleh hingar Gaussian, Poisson, garam-dan-lada, bintik, dan silau solar dengan SNR dari ?4.71 dB hingga 0.72 dB. Berbanding COM standard, pemadanan Gaussian, dan Algoritma Carian Ayak (SSA), kaedah yang dicadangkan mencapai purata RMSE terendah (1.218 piksel), jarak Euclidean terendah (1.143 piksel), dan kadar pengesanan palsu terendah (FDR = 6.716%), masing-masing mewakili pengurangan relatif 21.5%, 16.7%, dan 32.7% berbanding garis dasar terbaik. Imbangan ketepatan-kompleksiti yang baik menjadikan algoritma ini sesuai untuk pemproses dalam-pesawat dengan sumber terhad bagi misi penerokaan bulan dan navigasi pesawat angkasa autonomi pada masa hadapan.
Efficiency-Aware Multi-Class Spinal Disorder Classification Using CBAM-Enhanced Lightweight CNNs with Dual-Branch Fusion
Spinal X-rays are still often read through manual measurements, yet the patients who most need timely assessment cannot afford delay, inconsistency, or heavy computational pipelines. Motivated by this clinical tension, this study proposes an efficiency-aware deep learning framework for three-class spinal disorder classification that asks a practical question rarely centered in prior work: not only which model is most accurate, but which model is accurate enough, light enough, and fast enough to matter in real screening settings. Using a public dataset of 338 subjects, five lightweight backbones, CBAM-enhanced variants, and a dual-branch fusion model were evaluated through stratified 5-fold cross-validation under multiple balancing strategies, with performance measured by accuracy, precision, recall, F1-score, parameter count, FLOPs, model size, latency, and throughput. The results reveal an unexpected pattern: bigger models do not win. MobileNetV3Small delivers the strongest efficiency-performance balance, reaching an F1-score of 0.962 with only 1.0 million parameters, while the best overall result is achieved by the Fusion_MNv3_MNAS model under augmentation-only training, with an F1-score of 0.976. Ablation findings further show that attention and fusion are not universally beneficial, but become most effective when paired with sufficient data-driven regularization, and that fine-tuning about 30% of backbone parameters yields the most favorable adaptation. Taken together, these findings show that performance in spinal X-ray classification depends less on model size alone than on the fit between architecture and training strategy. The study therefore offers a concrete and clinically relevant message: lightweight, well-regularized models can match or surpass heavier alternatives while remaining more practical for scalable deployment. ABSTRAK: Radiograf tulang belakang masih kerap dinilai melalui pengukuran manual, sedangkan pesakit yang memerlukan rawatan awal tidak dapat menanggung kelewatan, ketidakselarasan, atau kebergantungan pada sistem pengiraan yang berat. Berpunca pada masalah klinikal ini, kajian ini mengemukakan satu rangka kerja pembelajaran mendalam berpaksikan kecekapan bagi pengelasan tiga kelas gangguan tulang belakang, dengan menumpukan persoalan praktikal yang jarang diberi perhatian dalam kajian terdahulu, iaitu bukan pada model mana yang paling tepat, tetapi model mana yang cukup tepat, cukup ringan, dan cukup pantas bagi persekitaran saringan klinikal. Dengan menggunakan satu set data awam yang melibatkan 338 subjek, lima model asas ringan, varian yang dipertingkatkan dengan CBAM, serta model gabungan dwi-cabang telah dinilai melalui pengesahan silang berstrata lima lipatan di bawah beberapa strategi pengimbangan kelas, dengan prestasi diukur menggunakan ketepatan, kejituan, keboleh ingatan, skor F1, bilangan parameter, FLOPs, saiz model, kependaman, dan kadar pemprosesan. Dapatan kajian menunjukkan satu corak yang tidak dijangka, iaitu model yang lebih besar tidak semestinya memberi prestasi terbaik. MobileNetV3Small memperlihatkan keseimbangan paling kukuh antara kecekapan dan prestasi dengan mencapai skor F1 sebanyak 0.962 hanya menggunakan 1.0 juta parameter, manakala prestasi keseluruhan terbaik dicapai oleh model Fusion_MNv3_MNAS di bawah latihan berasaskan augmentasi sahaja dengan skor F1 sebanyak 0.976. Analisis ablasi seterusnya menunjukkan bahawa mekanisme perhatian dan gabungan tidak sentiasa memberikan manfaat secara menyeluruh, sebaliknya menjadi paling berkesan apabila dipadankan dengan regularisasi berasaskan data yang mencukupi, dan penalaan halus sekitar 30% parameter rangka asas menghasilkan penyesuaian yang terbaik. Secara keseluruhan, dapatan ini menunjukkan bahawa prestasi dalam pengelasan sinar-X tulang belakang kurang bergantung pada saiz model semata-mata, sebaliknya lebih dipengaruhi oleh kesesuaian antara seni bina model dan strategi latihan. Oleh itu, kajian ini membawa mesej yang jelas dan signifikan dari sudut klinikal, iaitu model ringan yang diregularisasikan dengan baik mampu menandingi malah mengatasi model yang lebih berat, iaitu kekal lebih praktikal bagi pelaksanaan berskala dalam persekitaran saringan klinikal.
TSGL-Rust: Teaching Concurrency Through Compile-Time Enforcement via Rust-Based Visualization of Classical Synchronization Problems
Concurrent programming remains a persistent challenge in undergraduate computer science and engineering education, largely because its failure modes are difficult to observe and reason about. Race conditions and deadlocks often emerge only under specific thread interleavings, leaving students to reconcile correct-looking code with unpredictable runtime behavior. Prior instructional approaches, such as the Thread-Safe Graphics Library (TSGL), address this issue by enabling real-time visualization, but they rely on languages that do not enforce safe access to shared state. This separation between observation and enforcement leaves a gap between how concurrency errors are detected and how they are prevented. This paper presents TSGL-Rust, a Rust-based reimplementation of classical TSGL visualizations that integrates compile-time safety guarantees with runtime visualization. Implemented using the egui/eframe framework, the system reproduces three canonical synchronization problems, namely Producer–Consumer, Reader–Writer, and Dining Philosophers, while shifting certain classes of errors from runtime to compilation through Rust's ownership model and borrow checker. The architecture deliberately separates concurrency logic from rendering through a crossbeam channel, decoupling worker threads from the egui main-thread render loop. This paper is presented as a system and tool description: it focuses on architectural design, the mapping from C++ TSGL primitives to Rust constructs, the channel-based communication pattern, and the instructional affordances that follow from these design choices. The contribution lies in the system itself and in the boundary it makes visible between data-race prevention and logical errors, such as deadlock, rather than in any claim about measured learning gains. ABSTRAK: Pengaturcaraan serentak kekal sebagai cabaran utama dalam pendidikan sarjana muda sains komputer dan kejuruteraan, kerana mod kegagalannya sukar dicari dan difahami secara sistematik. Keadaan perlumbaan dan kebuntuan lazimnya hanya muncul di bawah jalinan pelaksanaan jalur tertentu, sekaligus menyebabkan pelajar perlu menyelaras kod yang kelihatan betul dengan tingkah laku masa jalan yang tidak menentu. Pendekatan pengajaran terdahulu, seperti Koleksi Grafik Jalur-Selamat (TSGL), menangani isu ini melalui visualisasi masa nyata, tetapi bergantung pada bahasa yang tidak menguatkuasa capaian selamat pada keadaan berkongsi. Pemisahan antara pemerhatian dan penguatkuasaan ini mewujudkan jurang antara bagaimana ralat serentak dikesan dan bagaimana ia dicegah. Kajian ini membentangkan TSGL-Rust, iaitu pelaksanaan semula TSGL berasaskan Rust yang mengintegrasi jaminan keselamatan masa kompilasi dan visualisasi masa jalan. Melalui rangka kerja e-GUI/e-rangka, sistem ini menjana semula tiga masalah menyegerakan kanonik, iaitu Pengeluar-Pengguna, Penulis-Pembaca, dan Dining Philosophers, sambil mengalihkan sebahagian kelas ralat daripada masa jalan kepada peringkat kompilasi melalui model pemilikan dan pemeriksa pinjaman Rust. Seni bina sistem ini secara sengaja memisahkan logik serentak daripada paparan alur silang, dengan jalur pekerja dipisahkan daripada gelung peparan utama eGUI. Kajian ini dibentangkan sebagai penerangan sistem dan alat: tumpuan diberi pada reka bentuk seni bina, pemetaan primitif TSGL C++ pada konstruk Rust, corak komunikasi berasaskan saluran, dan keupayaan instruksional yang berpunca daripada reka bentuk ini. Sumbangan utama terletak pada sistem ini sendiri dan pada sempadan yang dijelaskan antara pencegahan keadaan perlumbaan dan ralat logik seperti kebuntuan, dan bukan pada sebarang dakwaan terhadap pencapaian pembelajaran yang diukur.
Constant-Time Bitsliced Rijndael-256 on ARM Cortex-M4: On the Limitations of Fixslicing Beyond AES-128
Wider-block ciphers are increasingly needed in high-volume applications, because 128-bit blocks in modes such as Galois/Counter Mode (GCM) limit each invocation to roughly 64 GiB of plaintext per key-nonce pair, forcing complex re-keying strategies. Rijndael-256, the 256-bit-block variant of Rijndael with a 256-bit key, has therefore attracted renewed interest as a natural wider-block companion to Advanced Encryption Standard (AES). At the same time, 32-bit ARM Cortex-M microcontrollers dominate the IoT and embedded landscape, yet, to the best of our knowledge, no constant-time software implementation of Rijndael-256 targeting this platform has been published. This paper addresses that gap. We present a constant-time bitsliced implementation of Rijndael-256 on the ARM Cortex-M4 and provide a systematic structural analysis explaining why fixslicing, the technique that achieves the best-known AES-128 performance on this platform, becomes suboptimal when applied to Rijndael-256. Specifically, the irregular ShiftRows offsets (0, 1, 3, 4) of Rijndael-256 break the uniform register rotation exploited by fixslicing, requiring eight distinct MixColumns compensation variants instead of four. We demonstrate that these compensation variants cost 3.00× as much as executing an explicit, in-place ShiftRows routing using ARM's bitfield instructions. Our macro-inlined assembly variant achieves 6,199 cycles (193.7 cycles/byte) at -O2, including packing and unpacking. We provide benchmarks across five compiler optimization levels, constant-time verification over samples via DUDECT (maximum t-statistic well below the vulnerability threshold), and per-component cycle breakdowns, showing that the optimal bitslicing strategy is inherently cipher-specific and architecture-dependent. ABSTRAK: Sifer blok yang lebih lebar semakin diperlukan dalam aplikasi berisipadu tinggi kerana blok 128-bit dalam mod seperti Galois/Counter Mode (GCM) mengehadkan setiap invokasi kepada kira-kira 64 GiB teks biasa bagi setiap pasangan kunci-nonce, sekali gus memerlukan strategi penukaran kunci yang lebih kompleks. Rijndael-256, iaitu varian Rijndael dengan blok 256-bit dan kunci 256-bit, telah menarik semula perhatian sebagai alternatif pelengkap berblok lebih lebar kepada Advanced Encryption Standard (AES). Pada masa yang sama, mikropengawal ARM Cortex-M 32-bit mendominasi landskap Internet of Things (IoT) dan sistem terbenam. Namun, setakat pengetahuan kami, tiada pelaksanaan perisian Rijndael-256 secara bitslicing dan masa-tetap yang menyasarkan platform ini telah diterbitkan. Makalah ini menangani jurang tersebut. Kami membentangkan pelaksanaan Rijndael-256 secara bitslicing dan masa-tetap pada ARM Cortex-M4 serta menyediakan analisis struktur yang sistematik bagi menjelaskan mengapa fixslicing, iaitu teknik yang mencapai prestasi terbaik yang diketahui bagi AES-128 pada platform ini, menjadi kurang optimum apabila diterapkan pada Rijndael-256. Secara khusus, anjakan ShiftRows Rijndael-256 yang tidak seragam, iaitu (0, 1, 3, 4), mengganggu putaran daftar seragam yang dimanfaatkan oleh fixslicing, lalu memerlukan lapan varian pampasan MixColumns yang berbeza berbanding hanya empat. Kami menunjukkan bahawa varian pampasan ini memerlukan kos 3.00× lebih tinggi berbanding pelaksanaan penghalaan ShiftRows secara eksplisit di tempat menggunakan arahan bitfield ARM. Varian kod himpunan tersisip-makro kami mencapai 6,199 kitaran, bersamaan 193.7 kitaran/bait, pada tahap pengoptimuman -O2, termasuk proses pembungkusan dan penyahbungkusan. Kami turut menyediakan penanda aras merentasi lima tahap pengoptimuman pengkompil, pengesahan masa-tetap ke atas 10? sampel menggunakan DUDECT dengan statistik-t maksimum yang jauh di bawah ambang kerentanan, serta pecahan kitaran bagi setiap komponen. Dapatan ini menunjukkan bahawa strategi bitslicing yang optimum adalah khusus kepada sifer dan sangat bergantung pada seni bina perkakasan.
Editorial Message Vol. 27 No. 2 2026: Engineering for Resilient, Intelligent, and Sustainable Futures
It is with great pleasure that we present the May 2026 edition of the IIUM Engineering Journal, an issue that reflects the expanding role of engineering in shaping a more sustainable, intelligent, safe, and resilient future. The contributions in this volume demonstrate that engineering research today is no longer confined to solving isolated technical problems. It is increasingly directed toward systems, communities, environments, and technologies that must perform reliably in complex real-world conditions. A strong sustainability thread runs throughout this issue. Several contributions explore how waste materials, biological resources, renewable energy systems, and intelligent control can be transformed into practical engineering solutions. Studies on microbial bio-coagulant production, sludge remediation, eco-friendly asphalt mixtures, precision irrigation, and energy storage control for renewable microgrids show that sustainable engineering requires more than good intentions. It requires process optimization, material innovation, system-level thinking, careful validation, and the courage to rethink conventional practices. This issue also highlights the growing maturity of intelligent and data-driven engineering. Artificial intelligence, machine learning, deep learning, foundation models, blockchain, and optimization techniques are applied across diverse problems, including medical image classification, spinal disorder diagnosis, halal verification, satellite anomaly identification, crack segmentation, intrusion detection, facility location, and autonomous navigation. These works show a clear movement beyond algorithmic novelty toward explainability, robustness, efficiency, and practical deployment. In this sense, intelligence is not treated merely as computational performance, but as a means to support better decisions, safer systems, and more trustworthy engineering outcomes. Equally important is the emphasis on safety, resilience, and trust. The papers on communication reliability, software-defined radar, tropical rain attenuation, embedded cryptography, vehicle safety alerts, highway cone deployment, UAV dynamics, electric vehicle control, and lunar navigation demonstrate the importance of engineering systems that can withstand uncertainty, disturbance, failure, and demanding operating environments. These studies remind us that high-performance engineering is incomplete without reliability, accountability, and resilience. The human dimension of engineering is also clearly visible in this volume. Contributions on rehabilitation robotics, EMG-based assessment during prolonged driving, steering control, mobility systems, biomedical classification, and Rust-based visualization for teaching concurrency demonstrate that engineering research ultimately serves people. It supports health, safety, learning, mobility, productivity, and quality of life. This human-centered direction is especially important as engineered systems become more autonomous, more digital, and more deeply embedded in daily life. Taken together, this May 2026 edition presents a compelling picture of engineering as purposeful innovation. The articles are diverse in method, domain, and application, yet they share a common commitment to transforming knowledge into responsible solutions. They also reflect the kind of scholarship that the IIUM Engineering Journal seeks to promote: rigorous in method, relevant in application, interdisciplinary in spirit, and meaningful in contribution. On behalf of the editorial team, we sincerely thank the authors for their valuable contributions, the reviewers for their careful and constructive evaluations, and the editorial board members for their dedication to maintaining the journal's quality and integrity. We hope this issue will inspire further research, strengthen collaboration across disciplines, and encourage engineering innovations that contribute to society and the world in a wise and responsible way. Prof. Ir. Ts. Dr. Teddy Surya Gunawan Executive Editor IIUM Engineering Journal ISSN: 1511-788X E-ISSN: 2289-7860 Published by: IIUM Press, International Islamic University Malaysia Jalan Gombak, 53100 Kuala Lumpur, Malaysia Phone (+603) 6421-5014, Fax: (+603) 6421-6298
Eco-Friendly Asphalt Mixture: Utilization of Waste Polypropylene as a Partial Replacement of Aggregate
Recently, many techniques have been developed to improve the properties of asphalt pavement and to reduce environmental pressure. Adding waste polypropylene (WPP) to asphalt mixtures is one of the most often used and effective techniques. Nevertheless, waste plastic aggregate in flake form exhibited the worst performance in the asphalt mixture, as demonstrated in previous investigations. Therefore, a new recycling process is used in this research to produce crushed and angular WPP aggregate (WPPA) with controlled dimensions, usually ranging from 0.075?mm to 12.5?mm, similar to those of natural aggregate. WPPA is used as a partial replacement for coarse aggregates, fine aggregates, and mineral filler, replacing all aggregate sizes from 12.5 mm to less than 0.075 mm. Modified asphalt mixture was designed with replacement proportions of 4%, 6%, and 8% for coarse WPPA; 2%, 4%, and 6% for fine WPPA; and 0.5%, 0.75%, and 1.0% for filler WPPA by the weight of the asphalt mixtures. An abrasion test was performed to assess the resistance and durability of WPPA to tearing and wear. Marshall and indirect tensile strength tests were employed to assess the performance of control and WPP-modified mixtures. Compared to the control mixture, WPPA incorporation provided greater stability and higher stiffness. For instance, notable increases in stability (up to 34.8%) and stiffness (up to 42.8%) were observed upon replacing fine aggregates with 2% WPPA in the asphalt mixture. In addition, a significant enhancement in the resistance of WPPA-modified mixture against moisture damage was achieved as the tensile strength ratio (TSR) was increased substantially, especially at replacement ratios of 4% coarse WPPA, 2% fine WPPA, and 0.75% filler WPPA, where TSR was increased by 14.4%, 10.9%, and 8.8%, respectively. In conclusion, WPPA-modified mixtures are less susceptible to moisture damage with higher stability and stiffness. ABSTRAK: Dalam beberapa tahun terakhir, banyak teknologi telah dikembangkan untuk meningkatkan sifat-sifat pengaspalan asfalt dan mengurangkan beban alam sekitar . Penambahan sisa polipropilena (WPP) kepada campuran asfalt adalah salah satu teknik yang paling kerap digunakan dan berkesan. Dalam kajian ini, agregat WPP (WPPA) digunakan sebagai penggantian separa agregat kasar, agregat halus, dan pengisi mineral, menggantikan semua kecerunan agregat daripada saiz 12.5 mm kepada saiz kurang daripada 0.075 mm. Kompaun plastik terpakai dalam bentuk kepingan menunjukkan prestasi yang lebih buruk untuk campuran aspal seperti yang dibuktikan dalam penyelidikan sebelum ini. Oleh itu, satu proses kitar semula baru sedang digunakan untuk menghasilkan WPPA dengan dimensi yang terkawal, biasanya antara 0.075 mm hingga 12.5 mm. Campuran asfalt diubah suai telah direka bentuk dengan perkadaran gantian 4%, 6%, dan 8% untuk WPPA kasar; 2%, 4% dan 6% untuk WPPA halus; dan 0.5%, 0.75% dan 1.0% untuk pengisi WPPA mengikut berat campuran asfalt berat. Ujian lelasan dilakukan untuk menilai rintangan dan ketahanan WPPA untuk koyak dan haus. Marshall dan ujian kekuatan tegangan tidak langsung telah digunakan untuk menilai prestasi campuran kawalan dan campuran asfalt yang diubah suai dengan WPP. Berbanding dengan campuran kawalan, penggabungan WPPA memberikan kestabilan yang lebih tinggi dan kekukuhan yang lebih baik, yang masing-masing meningkat sebanyak 7-35% dan 1-43%. Menurut ujian kekuatan tidak langsung, peningkatan ketara dalam rintangan campuran diubah suai WPPA terhadap kerosakan lembapan telah dicapai kerana nisbah kekuatan tegangan (TSR) telah meningkat dengan ketara terutamanya pada nisbah penggantian 4% WPPA kasar, 2% WPPA halus dan 0.75% pengisi WPPA. Secara kesimpulannya, campuran yang diubah suai WPPA kurang terdedah kepada kerosakan lembapan dengan kestabilan dan kekakuan yang lebih tinggi.
Enhancement of Stabilization/Solidification Method with Bioaugmentation for Sludge Remediation
The Stabilization/Solidification (S/S) method encapsulates heavy metals in sludge, while bioaugmentation can degrade organic contaminants. However, each method is limited in its ability to address both contaminants simultaneously. This study aims to enhance the S/S method by integrating bioaugmentation, using either immediate or pre-inoculation of bio-agents. Ordinary Portland Cement (OPC) served as the binder in the S/S process, with the bacteria Sphingobacterium spiritivorum and the fungi Aspergillus brasiliensis acting as bio-agents. Before testing, chemical oxygen demand (COD) and heavy metals in sludge and cement were measured. COD and heavy metal concentrations' leaching behavior were tested using TCLP after 14 and 28 days. The result shows 23.39% of organic contaminants were in the fibreboard sludge. The TOC results for Portland cement were negligible. All heavy metal elements (Cr, Cu, Zn, As, and Pb) in the Portland cement and fibreboard sludge exceeded the standard USEPA limit. COD reduction was evaluated for immediate versus pre-inoculation of fungi and bacteria over 14 and 28 days. Pre-inoculation consistently yielded lower COD levels than immediate inoculation, with fungi pre-inoculation achieving the greatest reduction (579 mg/L at 14 days; 544.7 mg/L at 28 days) and bacteria pre-inoculation showing moderate improvement (579.3 mg/L at 14 days; 570.3 mg/L at 28 days). Concentrations of heavy metal reductions followed a similar trend: after 28 days, pre-inoculated samples contained 0.01 mg/L (Cr), 0.22 mg/L (Cu), 0.06 mg/L (Zn), and 0 mg/L for both As and Pb, whereas immediate-inoculated samples showed 0.04 mg/L (Cr), 0.22 mg/L (Cu), 0.01 mg/L (Zn), and 0 mg/L for As and Pb. Statistical comparison shows significant differences between immediate-inoculation and pre-inoculation, but not between the bio-agents used in this study. To improve the findings, a co-culture method that combines both bio-agents is recommended, offering a viable strategy for industrial sludge management, with future studies needed to scale up the method for practical, sustainable waste treatment applications. ABSTRAK: Kaedah Pengukuhan dan Penstabilan (S/S) bertujuan mengkapsulkan logam berat dalam enap cemar, manakala bioaugmentasi digunakan bagi mengurai bahan pencemar organik. Walau bagaimanapun, setiap kaedah mempunyai keterbatasan dalam menangani kedua-dua jenis pencemar secara serentak. Kajian ini bertujuan menambah baik kaedah S/S dengan menggabungkan bioaugmentasi, sama ada melalui inokulasi segera atau pra-inokulasi mikrob. Simen Portland Biasa digunakan sebagai agen pengikat dalam proses S/S, manakala bakteria Sphingobacterium spiritivorum dan kulat Aspergillus brasiliensis bertindak sebagai agen bio. Sebelum kajian dijalankan, Permintaan Oksigen Kimia (COD) dan logam berat dalam enap cemar serta simen ditentukan. Tingkah laku larut lesap COD dan kepekatan logam berat diuji menggunakan kaedah TCLP selepas 14 dan 28 hari. Dapatan kajian menunjukkan sebanyak 23.39% bahan pencemar organik terdapat dalam enap cemar papan gentian. Kandungan TOC dalam simen Portland didapati sangat rendah dan tidak memberi kesan ketara. Semua unsur logam berat (Cr, Cu, Zn, As dan Pb) dalam Simen Portland Biasa dan enap cemar papan gentian melebihi had piawaian yang ditetapkan oleh USEPA. Pengurangan COD dinilai bagi inokulasi segera berbanding prainokulasi dengan kulat dan bakteria selama 14 dan 28 hari. Prainokulasi secara konsisten menghasilkan tahap COD lebih rendah berbanding inokulasi segera, dan prainokulasi kulat menunjukkan pengurangan paling ketara (579 mg/L pada 14 hari; 544.7 mg/L pada 28 hari) manakala prainokulasi bakteria menunjukkan peningkatan sederhana (579.3 mg/L pada 14 hari; 570.3 mg/L pada 28 hari). Kepekatan pengurangan logam berat mengikuti corak serupa: selepas 28 hari, sampel prainokulasi mengandungi 0.01 mg/L (Cr), 0.22 mg/L (Cu), 0.06 mg/L (Zn), dan 0 mg/L bagi As dan Pb, manakala sampel inokulasi segera menunjukkan 0.04 mg/L (Cr), 0.22 mg/L (Cu), 0.01 mg/L (Zn), dan 0 mg/L bagi As dan Pb. Perbandingan statistik menunjukkan terdapat perbezaan signifikan antara kaedah inokulasi segera dan prainokulasi, namun tiada perbezaan signifikan antara jenis agen bio yang digunakan dalam kajian ini. Bagi meningkatkan keberkesanan rawatan, kaedah ko-kultur menggabungkan kedua-dua bioagen dicadangkan, menawarkan potensi terbaik bagi pengurusan sisa industri. Kajian lanjut diperlukan untuk penskalaan bagi aplikasi praktikal dan rawatan sisa lestari.
A Robust Sensor-Failure-Tolerant Fuzzy Control Framework with Predictive Data Imputation for Sustainable Precision Irrigation
Effective irrigation management under uncertain conditions remains a major challenge in precision agriculture, particularly when sensor performance degrades or extreme environmental variations lead to unreliable data. This study presents a hybrid framework that combines a linear regression model with a Mamdani fuzzy inference system (FIS) to improve the robustness of soil moisture prediction and irrigation control. The proposed approach trains the regression model using synthetically generated data with controlled noise levels to estimate soil moisture conditions. These predictions are then integrated with real-time temperature and humidity data within the fuzzy inference system to generate appropriate irrigation decisions. To represent extreme sensor conditions, additional adaptive noise is introduced when soil moisture values exceed or fall below predefined thresholds, effectively simulating sensor malfunction scenarios. Time-series simulation results indicate that the proposed system can maintain stable, efficient irrigation performance despite significant sensor disturbances. Overall, this work improves irrigation efficiency and provides insights into how hybrid statistical and fuzzy-logic approaches can mitigate the adverse effects of sensor inaccuracies under highly variable environmental conditions. ABSTRAK: Pengurusan pengairan cekap berkeadaan tidak menentu, kekal sebagai cabaran kritikal dalam pertanian, terutama apabila berlaku kerosakan pada sensor dan persekitaran turun naik yang melampau menyebabkan data yang diperolehi tidak boleh dipercayai. Kajian ini, mencadangkan pendekatan hibrid yang mengintegrasi model regresi linear dengan sistem inferens Mamdani kabur (FIS) bagi meramal kelembapan tanah dan mengawal pengairan. Kaedah ini melibatkan latihan model regresi menggunakan data sintetik yang terhasil melalui tahap bunyi yang dikawal dengan teliti bagi meramal kelembapan tanah, sementara sistem inferens kabur secara dinamik menterjemah ramalan tersebut bersama ukuran suhu dan kelembapan masa nyata kepada arahan pengairan yang tepat. Keadaan sensor yang melampau disimulasi dengan memperkenalkan bunyi tambahan secara adaptif apabila tahap kelembapan tanah jatuh di bawah atau melebihi had tertentu, seterusnya meniru senario kegagalan sensor. Melalui simulasi siri masa terperinci, dapatan kajian ini mengekalkan kawalan pengairan yang stabil dan cekap walaupun sensor gagal berfungsi. Kajian ini bukan sahaja meningkatkan kecekapan pengairan malah juga memberi pemahaman tentang bagaimana teknik hibrid statistik dan logik kabur boleh mengurangkan kesan ketidaktepatan sensor dalam keadaan persekitaran yang berubah.
Steering Control Precision in Rack Steering Vehicles Using Finite-Time Prescribed Performance Anti-Windup PI Controller with Nonlinear Error Transformation
This paper addresses the challenge of maintaining steering precision and yaw stability in rack steering vehicles (RSVs) operating under varying inertial loads and aerodynamic disturbances. Conventional control methods often struggle to ensure bounded tracking performance in the presence of actuator saturation and nonlinear dynamics. To overcome these limitations, a Finite-Time Prescribed Performance Anti-Windup PI Controller with Nonlinear Error Transformation (FPPC-API-NET) is proposed. The outer loop employs a finite-time prescribed performance framework with a nonlinear transformation to constrain the error trajectory within predefined decay envelopes, while the inner loop incorporates antiwindup compensation to mitigate actuator saturation effects. Simulation studies benchmarked against the FTPPC-API and API controllers demonstrate that FPPC-API-NET achieves up to a 95% reduction in steady-state error, a 70% reduction in settling time, and a 87% reduction in transient duration. The proposed method also enhances yaw stability, reduces lateral displacement by 35%, minimizes sideslip magnitude, and maintains tire-friction utilization below saturation, resulting in smoother lateral acceleration with up to 48% lower peaks. These results confirm that FPPC-API-NET offers superior robustness and precision during cornering maneuvers across varying inertia levels. ABSTRAK: Kajian ini membincangkan cabaran dalam mengekalkan ketepatan kawalan stereng dan kestabilan sudut gerakan sisi bagi kenderaan jenis rak stering (RSV) yang beroperasi di bawah beban inersia berubah serta gangguan aerodinamik. Kaedah kawalan konvensional sering menghadapi kesukaran mengekalkan prestasi penjejakan apabila berlaku ketepuan aktuator dan kesan dinamik tak linear. Bagi mengatasi kekurangan ini, satu pengawal tirus berasaskan Finite-Time Prescribed Performance dengan Transformasi Ralat Tak Linear dan Antiwindup PI (FPPC-API-NET) telah dicadangkan. Gelung luar menggunakan rangka kerja prestasi terpiawai had masa dengan transformasi ralat tak linear bagi mengehadkan trajektori ralat dalam sempadan peluruhan yang ditetapkan, manakala gelung dalam aplikasi pampasan antiwindup bagi mengawal kesan ketepuan aktuator. Kajian simulasi berbanding pengawal FTPPC-API dan API menunjukkan bahawa FPPC-API-NET mencapai pengurangan ralat keadaan tunak sehingga 95%, masa penetapan 70% lebih pantas, dan tempoh peralihan 87% lebih singkat. Kaedah ini turut meningkatkan kestabilan gerakan sisi, mengurangkan anjakan lateral sebanyak 35%, meminimumkan gelinciran, serta mengekalkan penggunaan geseran tayar di bawah had ketepuan, sekali gus membuktikan keteguhan dan ketepatan unggul FPPC-API-NET dalam manuver membelok di bawah variasi inersia.
Quantifying Activity of the Erector Spinae Longissimus Subgroup Using EMG During Prolonged Driving
Prolonged driving has been associated with localized muscle fatigue, particularly in the lumbar region, posing safety and ergonomic concerns. This study investigates the dynamics of multivariate surface electromyography (sEMG) features to detect fatigue during extended driving sessions. EMG signals were recorded from the erector spinae muscles of seven subjects across two backrest inclination angles (100°, 110°) during real-world, multi-hour driving tasks. Spectrogram-based features, including mean frequency (MNF), median frequency (MDF), and root-mean-square (RMS), were extracted using a sliding-window method for analysis. Co-temporal trends in these features were analyzed to identify fatigue regions, marked by simultaneous decreases in MNF and MDF and/or increases in RMS. Paired t-tests were used to compare the two backrest inclination angles across subjects for each fatigue duration and fatigue event count. Results demonstrate that seat inclination angle significantly affects fatigue duration and number of events, with the 100° angle leading to notably more fatigue than 110° (p<0.01). This work could contribute a framework for time-resolved, EMG fatigue modeling in realistic driving scenarios. ABSTRAK: Pemanduan terlalu lama dikaitkan dengan keletihan otot setempat, khususnya di bahagian lumbar, menimbulkan kebimbangan dari segi keselamatan dan ergonomik. Kajian ini mengkaji dinamika ciri permukaan multivarian elektromiografi (sEMG) bagi mengesan keletihan semasa sesi pemanduan jauh. Isyarat EMG direkodkan daripada otot erektor spina dari 7 subjek pada dua sudut kecondongan penyandar (100°, 110°) semasa aktiviti pemanduan sebenar berlangsung selama beberapa jam. Ciri berasaskan spektrogram, termasuk frekuensi purata (MNF), frekuensi median (MDF) dan purata punca kuasa dua (RMS) telah diekstrak menggunakan tetingkap-gelongsor. Corak serentak dalam ciri-ciri ini dianalisa bagi mengenal pasti kawasan keletihan, dibuktikan dengan penurunan MNF dan MDF serta/atau peningkatan RMS. Ujian-t berkembar digunakan bagi membandingkan dua sudut kecondongan penyandar merentas subjek bagi setiap tempoh keletihan dan bilangan kejadian. Dapatan kajian menunjukkan bahawa sudut kecondongan penyandar memberi kesan signifikan terhadap tempoh keletihan dan bilangan kejadian, dengan sudut 100° menghasilkan lebih banyak keletihan berbanding 110° (p<0.01). Kajian ini berpotensi menyumbang kepada satu rangka kerja pada model keletihan EMG beresolusi masa dalam senario pemanduan realistik.
Integration of Neutrosophic Methods into Adaptive Control of Nonlinear Systems Using Neuro-Fuzzy Networks With B-Spline Functions
This paper addresses the problem of adaptive control of nonlinear dynamic systems operating under parametric uncertainty, external disturbances, and partial or contradictory information about the system state – conditions under which classical linear Model Reference Adaptive Control (MRAC) and conventional neuro-fuzzy controllers exhibit degraded performance, slow adaptation, and oscillatory behavior. To overcome these limitations, a novel Neuro-Neutrosophic Model Reference Adaptive Controller (NN-MRAC) is proposed, implemented using a neutrosophic neuro-fuzzy network with B-spline basis functions. The key innovation of the proposed approach lies in integrating neutrosophic logic into the adaptive control architecture by explicitly using a three-component uncertainty representation – truth, indeterminacy, and falsity – which enables robust control synthesis in the presence of incomplete, noisy, and conflicting data. In contrast to traditional neuro-fuzzy controllers, the proposed NN-MRAC combines localized B-spline approximation with neutrosophic weighting of local models and an adaptive decomposition into lower-dimensional submodels, effectively mitigating the curse of dimensionality and reducing computational complexity. Comparative simulation studies with a classical linear MRAC demonstrate that the proposed controller reduces the mean-square tracking error by approximately 59%, decreases overshoot by more than 3 times, shortens the transient response time by nearly 1.8 times, and lowers control energy consumption by about 18%. The results confirm that the proposed neuro-neutrosophic MRAC ensures stable, smooth, and energy-efficient control in the presence of noise and deep uncertainty, making it a promising solution for intelligent control of complex nonlinear systems. ABSTRAK: Kajian ini mencadangkan masalah kawalan adaptif bagi sistem dinamik bukan linear yang beroperasi pada ketidakpastian parameter, gangguan luaran, serta maklumat separa atau bercanggah mengenai keadaan sistem, di mana kaedah linear klasik Model Kawalan Suai Rujukan (MRAC) dan kawalan konvensional neural-fuzi menunjukkan prestasi terhad, kadar penyesuaian perlahan, dan tingkah laku berayun. Bagi mengatasi kekangan ini, satu Model Kawalan Suai Rujukan Neuro-Neutrosofik (NN-MRAC) yang baharu dicadangkan, dilaksanakan berasaskan rangkaian neural-fuzi neutrosofik dengan fungsi asas alur-B. Inovasi utama pendekatan ini terletak pada pengintegrasian logik neutrosofik ke dalam seni bina kawalan suai melalui penggunaan perwakilan terkecuali pada tiga komponen ketidakpastian – kebenaran, ketidakpastian, dan kepalsuan – membolehkan sintesis kawalan yang teguh dalam keadaan data tidak lengkap, bising, dan bercanggah. Berbeza dengan pengawal neural-fuzi tradisional, NN-MRAC yang dicadangkan menggabungkan penghampiran setempat berasaskan alur-B dengan pemberat neutrosofik bagi model setempat serta penguraian suai kepada submodel berdimensi rendah, sekaligus mengurangkan kerumitan pengiraan dan mengatasi masalah “kutukan dimensi”. Kajian simulasi perbandingan dengan MRAC linear klasik menunjukkan bahawa cadangan kawalan mencapai pengurangan ralat min kuasa dua (MSE) kira-kira 59%, penurunan lebihan (overshoot) lebih daripada tiga kali ganda, pemendekan masa tindak balas sementara hampir 1.8 kali, serta pengurangan penggunaan tenaga kawalan sekitar 18%. Keputusan ini mengesahkan bahawa MRAC neuro-neutrosofik yang dicadangkan mampu memastikan kawalan stabil, licin, dan cekap tenaga di bawah keadaan hingar dan ketidakpastian mendalam, menjadikannya satu penyelesaian berpotensi pada sistem kawalan pintar bukan linear yang kompleks.
A Comprehensive Framework of Ant Colony Optimisation for Optimal Control of Upper Extremity Rehabilitation Robot
Passive rehabilitation of the upper limb requires precise joint positioning to facilitate motor recovery and ensure patient safety. For a two-degree-of-freedom (DOF) elbow robot, the primary challenge is unwanted disturbances arising from human coupling and voluntary and involuntary forces applied by the subject. This study addresses this issue by proposing a comprehensive control framework based on a nature-inspired meta-heuristic approach. The robot dynamics were obtained using the Lagrangian formulation, which was then combined with a realistic actuator model, yielding a closed-loop transfer function that describes system behavior. A two-degree-of-freedom (2-DOF) PID controller was used as the position controller. To determine the optimal PID parameters, an ant colony optimization (ACO) algorithm is used with an appropriate performance index to select the PID parameter values. The resulting ACO-based PID controller showed significant improvement over the conventional Ziegler–Nichols (Z-N) approach, as it could reject significant patient-induced forces, maintain the joint trajectory with the reference, and remain stable even under perturbations. Simulations were performed, confirming that the optimized controller delivers smooth, precise movements, reduces tracking error, and enhances the safety of passive rehabilitation procedures. ABSTRAK: Pemulihan secara pasif di bahagian anggota atas pesakit memerlukan kedudukan sendi yang tepat bagi memudahkan pemulihan motor pesakit daripada bahaya atau cedera. Manakala pada robot siku dua darjah kebebasan (DOF), cabaran utama ialah mengelakkan gangguan yang tidak diingini oleh manusia dan daya sukarela atau tidak sukarela yang dikenakan oleh subjek. Kajian ini mencadangkan rangka kerja kawalan komprehensif berdasarkan pendekatan meta-heuristik yang diilham oleh alam semula jadi. Dinamik robot diwujudkan dengan menggunakan rumusan Lagrangian, diikuti dengan gabungan model penggerak realistik, agar dapat menghasilkan fungsi pemindahan gelung tertutup yang boleh menggambarkan tingkah laku sistem. Pengawal PID 2-DOF pula digunakan sebagai pengawal kedudukan. Bagi mencapai parameter optimum PID, algoritma Pengoptimuman Koloni-Semut (ACO) digunakan dengan indeks prestasi yang sesuai agar dapat memilih PID paling optimum. Pengawal PID berasaskan ACO yang terhasil mengatasi pendekatan Ziegler Nichols (Z-N) konvensional, kerana ia mempunyai keupayaan menolak daya teraruh pesakit yang ketara sambil mengekalkan trajektori bersama rujukan dan kekal stabil walaupun ketika gangguan. Simulasi yang dijalankan dalam kajian ini mengesahkan bahawa pengawal yang dioptimumkan berjaya mencapai pergerakan yang lebih lancar, tepat, seterusnya mengurangkan ralat pengesanan serta mempromosi keselamatan prosedur pemulihan pasif.
Optimal and Robust Speed Control of Electric Vehicles using a Hybrid Ant Colony Optimization Tuned Sliding Mode Control
Electric vehicle (EV) speed control is significantly affected by nonlinear motor dynamics, external disturbances, and parameter uncertainties, which degrade tracking performance and energy efficiency. Conventional controllers often fail to maintain robustness under varying operating conditions. Although Sliding Mode Control (SMC) provides strong robustness, it suffers from chattering and requires proper tuning of control parameters. To address this, a hybrid Ant Colony Optimization-based Sliding Mode Controller (ACO-SMC) is proposed to optimally tune the SMC control parameters. The performance of the ACO-SMC controller is evaluated using the US06 and FTP-75 driving cycles in MATLAB Simulink. The proposed controller outperforms the ACO-tuned proportional-integral-derivative (ACO-PID), conventional SMC, and PID controllers. The ACO-SMC controller significantly improves stabilization and robustness, even in the presence of measurement noise, external disturbance, and varying vehicle parameters. The results reveal that the hybrid ACO-SMC controller effectively minimizes chattering and maintains accurate speed control, even during sudden load disturbance and reference speed variations across all examined driving cycles. The integral absolute error (IAE) and integral squared error (ISE) are used as performance indices, achieving their minimum values over the complete US06 and FTP-75 driving cycles, thereby confirming the high accuracy of the proposed approach. ABSTRAK: Kawalan kelajuan kenderaan elektrik (EV) dipengaruhi secara signifikan oleh dinamik motor tidak linear, gangguan luaran, serta ketidakpastian parameter, seterusnya menjejaskan prestasi penjejakan dan kecekapan tenaga. Pengawal konvensional lazimnya gagal mengekalkan keteguhan di bawah operasi yang berubah-ubah. Walaupun Kawalan Mod Luncur (SMC) menawarkan keteguhan yang tinggi, kaedah ini mengalami masalah chattering dan memerlukan penalaan parameter kawalan yang teliti. Bagi mengatasi isu ini, satu pengawal hibrid berasaskan Pengoptimuman Koloni Semut (ACO-SMC) dicadangkan bagi menala parameter SMC secara optimum. Prestasi pengawal ACO-SMC dinilai menggunakan kitaran pemanduan US06 dan FTP-75 pada MATLAB Simulink. Pengawal yang dicadangkan menunjukkan prestasi yang lebih baik berbanding pengawal PID yang ditala ACO (ACO-PID), SMC konvensional, dan PID biasa. ACO-SMC meningkatkan kestabilan dan keteguhan secara signifikan walaupun dalam kehadiran hingar pengukuran, gangguan luaran, serta variasi parameter kenderaan. Dapatan kajian menunjukkan bahawa pengawal hibrid ini berupaya meminimumkan fenomena chattering dan mengekalkan kawalan kelajuan yang tepat, termasuk semasa gangguan beban mendadak dan variasi rujukan kelajuan merentasi semua kitaran pemanduan yang diuji. Indeks prestasi seperti ralat mutlak berkamiran (IAE) dan ralat kuasa dua berkamiran (ISE) mencapai nilai minimum sepanjang satu kitaran penuh bagi kedua-dua kitaran pemanduan US06 dan FTP-75, sekaligus mengesahkan ketepatan tinggi melalui kaedah yang dicadangkan.