Advanced and Sustainable Technologies (ASET)
Journal of Advanced and Sustainable Technologies (ASET) [eISSN: 2976-2294] is an international, peer-reviewed engineering technology journal dedicated to the dissemination of high-quality research at the intersection of technological innovation and environmental stewardship. Published biannually (June and December) by UniMAP Press (Universiti Malaysia Perlis), ASET was established in September 2021 by the Faculty of Mechanical Engineering Technology. As a strategic initiative of the Malaysian Technical University Network (MTUN), the journal serves as a global bridge between advanced engineering applications and sustainable development. ASET publishes original research articles and reviews that advance knowledge in the fields of Advanced Applied Mechanics, Electronics, Sustainable Infrastructure, and Environmental Engineering. Our editorial mission is to support technology-driven solutions for industrial, infrastructural, and agricultural contexts, with a specific emphasis on resource efficiency and the circular economy. To accommodate growing contributions and ensure faster dissemination of research, ASET is expanding its publication schedule: starting in 2026, the journal transitions to three issues annually (June, September, and December), followed by a quarterly schedule of four issues (March, June, September, and December) beginning in 2027.
PENERBIT UniMAP, UNIVERSITI MALAYSIA PERLIS
Analysis of Factors Contributing to Road Accidents in Penang, Malaysia
Road accidents in Malaysia impose significant public health and economic burdens. Despite national road safety initiatives, Penang’s high vehicle density, complex traffic conditions, and role as a major tourist hub continue to contribute to elevated fatality rates, emphasizing the need to examine underlying risk factors. This study aims to explore the factors contributing to road accidents in Penang to inform targeted interventions and improve road safety in the region and beyond. Factors considered in this study include driving behavior, vehicle condition, traffic violation, road condition, weather condition, and traffic condition. A cross-sectional study was conducted, and convenience sampling was used to collect data from 400 licensed drivers in 5 districts of Penang via a questionnaire survey. The data were entered and analyzed via SPSS software. The results demonstrated that traffic conditions is the most significant factor impacting road accidents, while road conditions and driving behavior also contributing positively. In contrast, traffic violations have a slight negative impact.
Copyright (c) 2026 Advanced and Sustainable Technologies (ASET)
Enhancing Process Safety in Ammonia-Based Refrigeration for The Food Industry: A Mini Review
This mini-review evaluates sustainable technological strategies to enhance process safety and environmental performance in ammonia-based refrigeration systems in the food industry. Ammonia has zero ozone-depletion potential (ODP) and negligible global warming potential (GWP ≈ 0), supporting climate mitigation objectives aligned with the Sustainable Development Goals (SDGs) 9, 12, and 13. However, its toxicity and flammability present occupational and environmental risks that require integrated engineering and digital safety controls. A structured review of 1,568 peer-reviewed publications from Scopus, Web of Science, and Google Scholar was conducted, but only 32 publications met the criterion for empirical and review articles on enhancing process safety in ammonia-based refrigeration for the food industry. The findings indicate that IoT-enabled monitoring, real-time gas detection, automated shutdown systems, and predictive maintenance analytics reduce the likelihood of leaks, energy intensity, and operational disruptions. Low-charge ammonia systems, secondary loop configurations, and hybrid refrigeration architectures further minimize refrigerant inventory and potential Scope 1 emission exposure. Despite measurable progress, implementation barriers persist, including capital investment constraints, fragmented regulatory frameworks, and limited Environmental, Social, and Governance-based safety performance metrics. Advancing sustainable refrigeration requires harmonized standards, digitized risk governance, and quantifiable carbon-reduction indicators to achieve resilient, climate-aligned food production systems.
Design and Development of a Rotary Dryer-Based Palletizing Machine with Multi-Purpose Drying and Cooling
High initial moisture content (≈70%) in poultry feed and herbal fertilizer pellets poses significant challenges in agro-industrial processing, leading to prolonged drying times, high energy consumption, and inconsistent product quality. This study designs and analytically validates a three-stage rotary dryer integrated with palletizing and in-line cooling to reduce moisture content from 70% to 9% at a throughput of 0.5 T/h within a 10 × 10 m footprint. A systematic product development approach was employed, incorporating agreed specifications with stakeholders, market benchmarking, and weighted Pugh concept selection. Thermal verification was conducted using heat and mass balance calculations to determine evaporation rate, heat load, airflow requirement, residence time, and power demand. The final configuration comprises a 0.5 m diameter, 21 m long rotary drum operating at 5 rpm with controlled air injection at 70–80 °C. Results indicate improved drying control, integrated cooling efficiency, and enhanced process sustainability for agro-industrial applications.
A Bibliometric Analysis of Drone Technology in Healthcare Logistics
The integration of drone technology into healthcare logistics has emerged as a strategic innovation to enhance the speed, accessibility, and resilience of medical supply chains, particularly in emergency and remote settings. Despite rapid technological progress, the intellectual structure and global research dynamics of this domain remain fragmented and insufficiently synthesized. This study conducts a comprehensive bibliometric analysis to map the evolution, thematic structure, and collaborative landscape of drone technology in healthcare logistics. Bibliographic data were retrieved from the Scopus database and refined through systematic screening, yielding 830 peer-reviewed journal articles published between 2016 and 2025. Data cleaning was performed using OpenRefine, while performance analysis and science mapping were conducted using the Scopus analyzer and VOSviewer. The findings reveal exponential publication growth, with more than 60% of outputs produced in the most recent 4 years. The United States, India, and China lead in productivity, while emergency medical services, unmanned aerial vehicles, and Internet of Things emerge as dominant research themes. The results demonstrate a rapidly consolidating, interdisciplinary field with strong international collaboration, highlighting its expanding role in advancing resilient, intelligent healthcare logistics systems.
Characterization of Different Coffee (Coffea) Blends using Ultraviolet-Visible Spectroscopy Technique and Principal Component Analysis
This study investigates the characterization of coffee blends using ultraviolet-visible (UV-Vis) spectroscopy combined with principal component analysis (PCA). Given the complexity of quality control in coffee production with differences in bean type, roasting degree, and processing methods, this research focuses on the comparative analysis of colour intensity, total soluble solids (°Brix), and pH of All Day (A), Mocca (M), and Espresso (E) coffee styles. The results indicate significant differences (p<0.05) across all samples, with All Day coffee having among the highest mean colour intensity (L* = 26.61; a* = 7.84; b* = 10.7), Mocca coffee has the highest mean for pH level (pH = 6.08), and Espresso coffee has the highest mean for °Brix value (0.81° Bx). The UV-Vis spectral data, further analyzed using the PCA, showed distinct groupings among the coffee types, with pH and colour showing as key discriminative variables. The integration of UV-Vis spectroscopy with chemometric techniques provides a reliable, rapid analytical approach for coffee profiling. The results may promote sustainable quality assurance strategies and strengthen brand protection efforts in the coffee industry.
Design and Development of Topology Optimized Three-Dimensional Printed Furniture Joint
This study focuses on the design and development of a three-dimensional (3D) printed furniture joint that is versatile and applicable across various furniture designs and styles, addressing the inherent limitations of traditional furniture joints. Conventional methods often rely on custom-made joints, which are not only time-intensive but also costly to produce, modify, or repair. To overcome these challenges, this research aims to create a furniture joint optimized for both strength and functionality through the application of topology optimization analysis. A comprehensive methodology was employed, beginning with a questionnaire survey to identify user needs and establish product design specifications. Following this, the design concept was developed and evaluated to finalize the joint design. Based on the selected design, a topology optimization analysis was performed to enhance its structural efficiency. The analysis revealed a 45.6% reduction in mass compared to the original joint design. While the structural analysis indicated that the optimized joint experienced slightly higher stress levels than the original design, the maximum stress values remained well below the yield strength of ABS material. This finding confirms the optimized joint’s structural integrity and low likelihood of functional failure. The optimized furniture joint, manufactured using 3D printing, demonstrated excellent compatibility and fit with furniture components, including tabletops and legs. This adaptability enables the production of a wide range of furniture designs, including coffee tables, side tables, bookshelves, shoe racks, and honeycomb bookshelves. Furthermore, integrating 3D printing and topology optimization offers several advantages, including reduced material waste, shorter production times, and a more environmentally sustainable approach to furniture manufacturing.
Identifying Critical Post-Processing Criteria for Enhancing Surface Finish of FDM-Printed ABS Automotive Exterior Parts
The increasing adoption of Fused Deposition Modeling (FDM) in automotive manufacturing is constrained by poor surface quality, primarily due to inherent layer-based defects and process-parameter variability. This study aims to identify the critical post-processing criteria that influence the surface finish of FDM-printed ABS automotive exterior parts. A qualitative research approach was employed, involving semi-structured interviews with domain experts, followed by systematic analysis in ATLAS.ti. The data were evaluated through groundedness (Gr) and network density (Gs) to capture both frequency-based importance and structural relationships among factors. A total of 36 codes were generated and subsequently synthesized into six key themes: surface finish duration, factors influencing quality, surface finishing process, mechanical finishing, chemical finishing, and surface quality. The findings indicate that surface finishing is a highly interdependent system, with chemical finishing techniques and process control parameters emerging as dominant contributors to improved surface quality. Surface quality was identified as the central outcome, influenced by both upstream fabrication and downstream finishing processes. This study provides a structured understanding of the critical factors governing finishing effectiveness, offering preliminary insights to support consistent improvement of visual and functional quality in automotive applications, while contributing to sustainable manufacturing practices (SDG 9 and SDG 12).
Simulation-Based Optimization of Plastic Transfer Molding Parameter for IC Package Encapsulation Process
This study focuses on the simulation-based optimization of plastic transfer molding parameters in the Quad Flat No-Lead (QFN) encapsulation process, which is widely used in the semiconductor industry due to its compact size and reliable performance. However, process-related issues such as air traps, incomplete filling, and uneven curing can compromise product quality, leading to performance failures and higher rejection rates. To address these challenges, this study aims to analyze and optimize critical molding parameters to minimize such defects and improve the consistency of the QFN package. Four key numerical parameters, such as mold temperature, melt temperature, curing time, and injection pressure, were selected based on their significant influence on flow behaviour and material curing. The study employs Response Surface Methodology (RSM) with a Central Composite Design (CCD) to systematically evaluate the effect of each parameter and its interactions. A total of 30 simulation runs were generated using Design Expert software, and Autodesk Moldflow was used to simulate the transfer molding process. The results demonstrated that optimized settings significantly reduced defects, with validation showing minimal percentage differences (<10%) in key responses, including 0.0000% for Fill time, 0.0000% for Air traps, 0.0607% for Curing level, 1.7179% for Shear rate, and 0.1246% for Shear stress. This study concludes that simulation-based optimization effectively improves the QFN encapsulation process, ensuring better quality and process consistency. Future research could focus on experimental validation, integration of advanced materials, and real-time monitoring to further enhance the process.
Structural and Functional-Symbolic Roles of Traditional Malay Tanggam Joinery in Sustainable Timber Construction: A Case Study in Peninsular Malaysia
Traditional Malay wooden houses embody an integrated architectural response to climate conditions, material efficiency, and construction knowledge developed over generations. A key element of this architecture is the tanggam joinery system, a mechanical interlocking technique that connects structural components without nails or metal fasteners. This study examines typological patterns and functional-symbolic roles of traditional Malay tanggam joinery in traditional Malay houses across four regional house typologies of Peninsular Malaysia: Kedah, Perak, Melaka, and the East Coast. A qualitative multiple case study approach involving 11 representative houses was employed, with an integrated literature review, field observations, photographic documentation, geometric assessment, and cross-regional comparative analysis conducted over six months. The findings reveal three principal roles of tanggam: (i) structural load-bearing, (ii) structural non-load-bearing, and (iii) integrated structural-symbolic applications. The results demonstrate that tanggam functions not merely as a carpentry technique but as an embedded structural logic within traditional Malay timber construction. Its mechanical interlocking configuration facilitates adaptability, disassembly, maintenance, and material longevity. The findings highlight the relevance of traditional joinery knowledge as a conceptual and technical foundation for sustainable timber architecture, which contributes to SDG 11 (Sustainable Cities and Communities) and heritage conservation initiatives.
CFD Simulation Analysis of a Rotary Dryer-Based Pelletizing Machine for Agro-Industrial Applications
This study presents a Computational Fluid Dynamics (CFD) analysis of a rotary dryer-based pelletizing system for agro-industrial applications. The objective is to evaluate airflow behaviour, velocity distribution, pressure characteristics, and turbulence intensity under different operating configurations. Simulations were performed using ANSYS Fluent 2021 R2 with the realizable k–ε turbulence model and SIMPLE pressure–velocity coupling scheme. Two working fluids, water and milk, were selected to represent different material properties. Three configurations were analyzed: (i) rotating impeller with water, (ii) rotating impeller with milk, and (iii) rotating drum with milk. The results indicate that the rotating drum configuration produces significantly higher velocity (0.1043 m/s) compared to the impeller-driven system (0.03743 m/s). Similarly, pressure distribution is more pronounced and uniform in the rotating drum case, reaching up to 1.003 Pa, while impeller cases remain below 0.05043 Pa. Furthermore, turbulence kinetic energy (TKE), which ranged from 2.863 × 10⁻⁶ to 8.814 × 10⁻⁵ m²/s², is more evenly distributed in the rotating drum configuration, indicating enhanced mixing and momentum transfer. These findings demonstrate that drum rotation provides superior flow uniformity and hydrodynamic performance. The study confirms that CFD is an effective tool for design evaluation and supports the selection of rotating drum mechanisms to improve drying efficiency in agro-industrial applications.
Effect of Machining Parameters on Surface Roughness during Milling of Steel
Surface roughness has been the most extensively studied parameter in machining and remains a key indicator of process efficiency, surface quality, energy consumption, and productivity. With the growing emphasis on sustainable manufacturing, this study investigates the effect of a biodegradable lubricant (sunflower oil) on surface roughness during milling, as an alternative to conventional synthetic cutting fluids. An experimental analysis was conducted on SS 304, a widely used yet difficult-to-machine engineering material, using the Response Surface Methodology with a Central Composite Design. A total of 45 experiments were conducted by varying the depth of cut, feed per tooth, cutting speed, and cutting conditions (sunflower oil, conventional lubricant, and dry machining). The results were analyzed using ANOVA, which revealed depth of cut as the most significant factor influencing surface roughness with a contribution of 58.49%, followed by cutting speed (7.53%) and feed per tooth (5.89%), while cutting condition showed the least significance. Although the improvement in surface quality with sunflower oil was marginal compared to that with the conventional lubricant, it was notably superior to dry machining, demonstrating that sunflower oil can be effectively used as a sustainable lubricant without compromising surface quality.
A Digital Twin Framework for Corrosion Predictive Analysis
In today’s dynamic environment, digital twins are gaining attraction for predictive maintenance. Existing research lacks the solutions that integrate advanced computer vision with intuitive visualization for corrosion analysis. This study aims to develop a Digital Twin framework for predictive corrosion analysis to improve asset integrity and operational performance from an Industry 4.0 perspective. The proposed framework focuses on offshore and onshore oil and gas platforms. In order to automatically identify the corrosion, YOLOv8 and YOLOv11 object detection models have been used to pre-process and analyze a curated corrosion dataset. The findings of the study reveal that with 93.6% accuracy and an F1-score of 0.92, YOLOv11 performs better than YOLOv8 (88.1% accuracy and 0.71 F1-score). It demonstrates the robustness of the YOLOv11 in diverse platform environments. Further, in the proposed Digital Twin framework, a corrosion data visualization dashboard has been designed to convert detection outputs into practical insights. The dashboard comprises corrosion severity indicators, spatial mapping, and performance metrics, enabling support for Reliability-Centered Maintenance (RCM) and Risk-Based Inspection (RBI). The study contributes to knowledge by benchmarking state-of-the-art detection models and presenting a corrosion-focused Digital Twin framework that integrates AI-enabled sensing with decision-support visualization.
Integrating Emerging Technologies for Sustainable Banking: Insights into Digital Transformation amid the Digital Divide
Digital transformation in the banking sector has changed the banking operations by improving operational efficiency, strengthening transparency, and supporting sustainable practices. Leveraging technologies such as artificial intelligence, cloud computing, green financial technologies, and blockchain promotes sustainability goals and helps tackle ongoing challenges arising from the digital divide. Despite the increasing adoption of emerging technologies in the banking sector, their contribution to fostering sustainability is still hindered by limitations arising from digital divide barriers, such as digital skills gaps, infrastructure constraints, and an unclear regulatory framework. This study evaluates the moderating role of this limitation on the relationship between innovative technologies and digital transformation sustainability in the developing countries' context, particularly in Palestine. This study utilizes a qualitative research method to address the research problem by developing semi-structured interviews with leaders from sustainability and information technology departments, in which the collected data are thematically analyzed. The thematic analysis extracted four major themes from the collected data: operational efficiency using AI, trust and integrity using Blockchain, scalability using cloud computing, and a sustainable system using green financial technologies. indicate that the emergence of advanced digital transformation strategies remains constrained by the digital divide. The study proposes a context model to foster banking sustainability in developing countries, while acknowledging the digital divide as a significant moderator. The study also concluded that the emergence of digital strategy can successfully align technology with sustainability, using the lens of the Technology–Organization–Environment framework and Digital Divide Theory.
Integrating Mobile Apps into University Leadership: Evidence from Palestinian Universities
Digitalization and the accelerating shift toward integrating digital tools into management and leadership systems in higher education institutions have been intensified following the outbreak of COVID-19. Smartphones and mobile applications played a vital role in digital transformation, serving as effective tools for university leaders and administrators to enhance decision-making, communication, and stakeholders’ engagement. In the Palestinian context, political instability, infrastructure readiness, and limited digital skills have been identified as constraints on the effective use of mobile applications in leadership practices. This study aims to examine the level of integration of mobile applications into university leadership in Palestinian universities. A qualitative approach was adopted to address the study objective by conducting semi-structured interviews with university IT directors who support presidents, deans, and administrative units. The study examined how mobile applications are incorporated into leadership practices across seven Palestinian universities. Data were analyzed thematically using an inductive–deductive approach. The study revealed that there’s a clear digital divide among Palestinian universities regarding the integration of mobile applications into leadership practices. While institutions are advancing toward full integration, others remain at early or fragmented stages. Greater collaboration among Palestinian universities is needed to adopt a unified strategy for using mobile applications to enhance transparency, efficiency, and accountability, thereby supporting sustainability in the higher education sector. Further research is recommended across different cultural contexts, using mixed-methods research methodologies.
Evaluation of Digital Transformation of the Regional Manufacturing Industry Based on the Entropy Weight TOPSIS Method
Manufacturing digital transformation capability is a key factor in the orderly advancement of digital upgrading in the manufacturing sector. Effective identification and evaluation of this capability can help grasp the core aspects of manufacturing digital transformation and upgrading, thereby improving the efficiency and effectiveness of the transformation process. Based on the definition of manufacturing digital transformation capability, this study constructs an evaluation index system across three dimensions: digital transformation support, digital transformation potential, and digital transformation benefits. The entropy-weighted TOPSIS method is adopted, with empirical analysis based on digital transformation data from the manufacturing industry across 30 mainland Chinese provinces. The study finds that digital innovation capability is critical to the digital transformation of the manufacturing industry, and that the benefits of digital transformation are more directly reflected in economic than in environmental outcomes. While individual provinces and cities are exceptions, the overall level of manufacturing digital transformation in China shows a pattern of eastern regions leading, central regions following, and western regions lagging. In terms of specific digital transformation dimensions, provinces and cities exhibit varying performance in digital transformation support, potential, and benefits.
Green Synthesis of Bio-ZnO Nanoparticles via Aloe Vera Powder-Assisted Sol–Gel Method with Antibacterial Properties
This study presents the green synthesis of zinc oxide nanoparticles (bio-ZnO NPs) using Aloe vera powder as a natural reducing and stabilizing agent through the sol–gel method. The use of Aloe vera powder eliminates the need for toxic chemicals and simplifies the synthesis process, thereby promoting sustainability in nanomaterial fabrication. The obtained bio-ZnO NPs were characterized by UV–Vis spectroscopy, FTIR, XRD, FESEM, TEM, and EDX analyses. Results confirmed the successful formation of wurtzite hexagonal ZnO with uniform nanoscale morphology. The optical band gap was found to decrease slightly with increasing Aloe vera concentration, indicating the influence of phytochemical compounds on crystal growth and electronic structure. Antibacterial activity against Escherichia coli demonstrated significant inhibition zones, suggesting strong biological functionality of the synthesized nanoparticles. This work highlights the effectiveness of Aloe vera powder as a sustainable and low-cost precursor for producing high-quality ZnO nanoparticles with potential applications in environmental and antimicrobial technologies.
Performance Evaluation of a Battery Pack Assembled from Used Li-Ion Cells of Electronic Vape for Small-Scale Applications
Lithium-ion cells are well known for their low mass and high energy density. However, improper disposal of Li-ion cells from disposable vape devices contributes significantly to battery waste, even though many of these cells still retain substantial usable capacity. This study explores the second-life potential of such cells by assembling a 3P4S battery pack using reclaimed 13300-type Li-ion cells for low-power applications. Three configurations were tested: without a Battery Management System (BMS), and with two passive BMS units (BMS-40A-4S-E and HXYP-C47-MA18). The repurposed pack exhibited a nominal voltage of 14.8 V and a rated capacity of 1.2 Ah. Constant current discharge at 0.6 A showed high remaining capacity across all setups, with measured capacities of 1.199 Ah (99.9% SOH) without BMS, 1.255 Ah (100% SOH) with BMS-40A-4S-E, and 1.241 Ah (100% SOH) with HXYP-C47-MA18. Thermal testing revealed safe operating temperatures, peaking at 35 °C during 1.5C discharge. All configurations reached full charge within 67–69 minutes and managed to power a 28 W load for up to 40 minutes. Voltage uniformity, low thermal rise, and stable discharge curves confirm the feasibility of repurposing vape cells into reliable battery packs. While all setups demonstrated acceptable short-term performance, the inclusion of a proper BMS remains essential to ensure safety, protect against overcharge/discharge, and prolong pack lifespan for long-term usage.
Design and Analysis of Simple, Low-Cost Solar Dryers for Rural Applications: An Integrated Review
Simple, low-cost solar dryers can reduce post-harvest losses, improving product quality and increasing rural incomes by using locally available materials and renewable solar energy. This paper synthesises the experimental, numerical, and techno-economic literature on solar drying technologies, with a special focus on rural applications and simple constructions. The review includes optimised collector geometries, such as V-groove and V-corrugated collectors, double-pass systems, and finned absorbers, as well as natural and forced convection systems, sensible and latent thermal storage (phase change materials, PCMs), and protection and usability features. Reported results show high thermal efficiencies for advanced solar air collectors, significant reductions in drying time when PCMs or conduction enhancement are used, and favourable energy and economic performance for well-designed systems. The discussion relates such technical findings to implementation realities using design-oriented guidelines for the utilisation of solar thermal energy in rural areas.
IoT-Driven Vibration Sensing and Decision-Tree Analytics for Multi-Class Tyre Pressure Diagnostics
Maintaining correct tire pressure is essential for ensuring vehicle safety, fuel efficiency, and ride stability. However, conventional tire pressure monitoring systems commonly rely on direct pressure sensors or indirect wheel-speed estimation, which limits diagnostic resolution and scalability for multi-level pressure conditions. This study introduces an IoT-enabled vibration-based monitoring framework that leverages machine learning for improved accuracy and interpretability to assessing tire pressure states. Four MPU-9250 triaxial accelerometers were mounted near each tire and interfaced with a Raspberry Pi 5 using a TCA9548A I²C multiplexer. Vibration signals were collected at nine distinct tire pressure levels, ranging from optimal to progressively lower pressure. Time-domain features and frequency-domain characteristics were extracted to capture tire–road interaction dynamics influenced by pressure changes. A decision tree classifier was employed to evaluate the separability and diagnostic performance across all classes. The model achieved clear discrimination between optimal and low-pressure states, confirming that vibration signatures contain sufficient information for reliable, multi-class pressure detection. The system integrates a MariaDB backend, a Flask-based dashboard, and secure remote connectivity to enable real-time monitoring. The results demonstrate that IoT-driven vibration sensing, combined with interpretable machine-learning analytics, offers a low-cost, scalable, and accurate alternative to conventional TPMS for intelligent, adaptive vehicle maintenance.
Reconstruction of Broken Parts Utilizing 3D Printing
One of the applications of 3D printing, like fused deposition modelling (FDM), is for the reconstruction of broken parts or features. The reconstruction of a part or feature proceeds mainly to extend its life cycle. The most common challenges in the reconstruction include achieving acceptable dimensional accuracy and good surface roughness. In this project, the focus is to observe the effect of process parameters, like layer heights to the dimensional accuracy and surface finish. Four layer heights (0.1, 0.2, 0.3, and 0.4 mm) are used, and other parameters such as wall thickness, printing temperature, hot-bed temperature, print speed, and infill density are set to 0.8 mm, 2200C, 50.0 0C, 80.0 mm/s, and 20%, respectively. This study begins by generating a digital model of the broken features, followed by modification of the G-code program, and ends with a repair operation by printing the broken features on the real part. A plastic gear was used as a case study. These results showed high dimensional accuracy, with deviations of 10 to 1 microns at a layer height of 0.2 mm. Similarly, acceptable surface roughness was observed at a layer height of 0.2 mm, with an average and root-mean-square roughness of Ra-21.2392µm and Rq-25.6308µm, respectively. The developed method shows a very promising approach to be applied in various applications.
Analyzing Nitrogen Dioxide at Peninsular Malaysia Ports and Its Exclusive Economic Zone (EEZ) in 2023 Using Sentinel-5P Satellite
The rise in nitrogen dioxide (NO₂) emissions, driven primarily by both land and sea transport, has become a major contributor to worsening air pollution. This study examines NO₂ pollution levels at major ports in Peninsular Malaysia and its Exclusive Economic Zone in 2023, utilizing data from the Sentinel-5P satellite. The Tropospheric Monitoring Instrument aboard Sentinel-5P offers high-resolution atmospheric measurements, enabling comprehensive spatial and temporal analysis of NO₂ concentrations across the region. Ground-based air quality data from the Department of Environment serve as a reference for validating satellite data. The study employs interpolation techniques, such as Inverse Distance Weighted, to visualize NO₂ distribution and assess its correlations with geographical and demographic factors. The findings reveal a significant increase in NO₂ levels, particularly in key maritime hubs, underscoring the urgent need for stricter regulatory measures and the adoption of sustainable transportation practices. This research not only supports Malaysia’s ongoing air pollution monitoring efforts but also aligns with the nation’s commitment to the International Maritime Organization’s target of achieving net-zero emissions by 2050. By leveraging advanced satellite technology, this study contributes to improving air quality management and informing policy decisions aimed at reducing NO₂ emissions across the region.
Influence of Drilling Parameters on Temperature and Hole Roundness in PTFE Machining
This study examines the influence of drilling parameters and techniques on the machining performance of polytetrafluoroethylene (PTFE), a polymer known for its low friction, high chemical resistance, and thermal sensitivity. PTFE’s low thermal conductivity and high deformability often lead to dimensional inaccuracy, burr formation, and heat-induced defects during drilling. To address these challenges, the effects of spindle speed, feed rate, and drilling methods (straight, peck, and dwell) were investigated using an 8 mm HSS drill bit under controlled dry-machining conditions. Temperature distribution along the drilling path was monitored using dual K-type thermocouples positioned at entry and exit points, while an optical microscope was used to evaluate hole roundness. This study presents an analysis and strategic optimization of drilling parameters for PTFE to resolve conflicting performance objectives between geometric accuracy and thermal stability. Using Signal-to-Noise ratio analysis, the most critical quality characteristic, hole roundness, was found to demand a high spindle speed to ensure stable cutting conditions. This requirement imposes a significant thermal penalty, as high spindle speed is simultaneously the least optimal setting for minimizing both Entry and Exit temperatures. The final process configuration addresses this trade-off by selecting parameters that maximize thermal mitigation: a high feed rate is chosen to reduce the time-in-cut and dissipate heat efficiently, and the Dwell technique is implemented. The Dwell technique is identified as the optimal thermal moderator, as it provides robust hole roundness while simultaneously maximizing the SN ratio to minimize the critical Exit Temperature. This strategic compromise effectively achieves the primary objective of superior geometric tolerance while preventing catastrophic material failure caused by thermal degradation at the hole exit.
CFD Studies of Flow Dynamics in Exhaust Manifolds Configuration
Efficient internal combustion engines require optimized exhaust flow to minimize backpressure and emissions. Complex pulse interference in the manifold can affect engine performance and catalyst light-off time. In this context, two common four-cylinder manifold configurations – the 4-1 and 4-2-1 types – were compared via computational fluid dynamics. The study uses steady-state ANSYS Fluent simulations on a simplified geometry to examine velocity and pressure profiles in each design. Results show that the 4-1 layout generates higher peak gas velocity but at the expense of a greater overall pressure drop, indicating stronger flow pulses from converging runners. In contrast, the 4-2-1 configuration smooths pressure gradients and yields more uniform flow along the runner network. These findings align with analyses highlighting the 4-2-1 manifold’s ability to mitigate flow interference and enhance exhaust scavenging. The more uniform flow in the 4-2-1 case implies faster catalyst warm-up and the potential to advance ignition timing, which can improve engine performance and reduce emissions. By contrast, the 4-1 design may support high-power operation but incurs higher backpressure. Overall, this comparative CFD study clarifies how manifold choice influences flow characteristics and engine efficiency. These insights, though based on idealized steady simulations, may guide manifold selection in high-efficiency engines and downsizing strategies.
Optimizing Layer Configuration in Banana Bomb Blankets for Enhanced Blast Resistance
This study investigates the influence of layer configuration on the blast resistance of banana fiber bomb blankets, a sustainable alternative to conventional synthetic protective materials. Twill-woven banana fiber blankets were fabricated with varying numbers of layers ranging from one to five layers, while all other parameters were kept constant to isolate the effect of layer configuration. Controlled explosive blast tests were conducted, and the thermal response of each configuration was measured using Type-K thermocouples connected to a data logging system. The experimental results demonstrate that increasing the number of layers significantly enhances blast mitigation performance by reducing peak temperature transmission and improving energy dissipation across the blanket structure. Among the tested configurations, the five-layer banana bomb blanket exhibited the lowest thermal penetration and superior resistance to blast effects. These findings confirm that optimized multilayer stacking plays a critical role in improving blast resistance by promoting progressive energy attenuation. The five-layer configuration reduced peak temperature transmission from 387.89 °C to 114.80 °C, a 70% reduction, demonstrating superior blast-mitigation performance. The results highlight the potential of banana fiber bomb blankets as an eco-friendly and cost-effective solution for blast protection in industrial and safety-related applications.
Digital Transformation in Structural Engineering: Integrating Peridynamic Modelling for Smart Damage Assessment
Digitalization in construction, known as Construction 4.0, depends on high-fidelity Digital Twins (DT). Current DT models are good at showing geometry and data. However, they struggle to simulate real-time structural failure and complex cracks using physics. This paper introduces a new framework that adds Peridynamic (PD) theory to smart damage assessment. Traditional methods like the Finite Element Method (FEM) have limitations, often struggling with mathematical errors at the tip of a crack. In contrast, Peridynamics uses an integral-based formula that works perfectly even where the material is broken. This study shows how a PD-driven Digital Twin uses sensor data to predict exactly how cracks will grow. This allows us to calculate the Remaining Useful Life (RUL) with higher accuracy. This research successfully connects advanced mechanics with data-driven Structural Health Monitoring (SHM).
Coupling Peridynamic Simulations with Data-Driven Approaches for Intelligent Fracture Prediction in Structures
Predicting fracture propagation in complex structures remains a significant challenge in computational mechanics. While Peridynamics (PD) has emerged as a robust nonlocal theory capable of naturally handling discontinuities like cracks, its high computational cost often hinders real-time application and large-scale optimization. Conversely, data-driven approaches, particularly Deep Learning (DL), offer rapid inference but often lack physical interpretability and generalizability. This paper proposes a novel hybrid framework, the Peridynamic-Informed Neural Network (PD-Net), which couples high-fidelity peridynamic simulations with a Convolutional Neural Network (CNN) to achieve intelligent, real-time fracture prediction. We utilize a bond-based peridynamic solver to generate a comprehensive dataset of damage evolution under varying loading conditions. While the data-generation process incurs a high initial computational cost, the trained model reduces inference time by an order of magnitude compared to direct numerical simulation, enabling real-time fracture prediction. This work bridges the gap between high-fidelity physics-based modeling and data-driven intelligence, offering a promising path for digital twin applications in structural health monitoring.
Development of Green Concrete incorporating Recycled Glass Powder for Sustainable Construction and Material Conservation
This study explores the innovative reuse of recycled glass powder (GP) as a sustainable partial substitute for fine aggregates in concrete production. In line with research on Supplementary Cementitious Materials (SCM) and Malaysia’s carbon-neutrality aspirations, the study aims to reduce both environmental impact and reliance on raw materials. Aimed at reducing both environmental impact and reliance on raw materials in construction, M30 grade concrete mixes were designed using the Department of Environment (DOE) method with 0%, 5%, 10%, and 15% GP incorporation. Twenty-four cube specimens were cast and tested for key performance indicators: workability, compressive strength, ultrasonic pulse velocity (UPV), and water absorption. Results revealed that mixes with 0%–5% GP achieved enhanced workability (28–36% increase) and reduced water absorption (by 1.54–1.67%), suggesting better fresh and durability properties. Meanwhile, 10%–15% GP mixes excelled in mechanical performance, with UPV improving by 4.7–5.7% and compressive strength rising by 5.7–20.9% (1.8–7.5 MPa). These findings confirm that GP, when optimally dosed, enhances both the fresh and hardened properties of concrete, promoting its use as a high-performance, eco-friendly material in sustainable construction. This study provides practical insights into green concrete design strategies that align with circular-economy goals and resource-conservation efforts.
Redesigning Lean for Sustainability: A Risk-Aware VSM Framework for Continuous Improvement in Manufacturing
Manufacturing systems face the dual imperative of achieving operational excellence while meeting stringent sustainability and resilience goals. Traditional Lean and Value Stream Mapping (VSM) approaches, however, often lack structured risk management and fail to account for the impact of process variability on holistic sustainability performance. This study proposes and validates a novel Sustainability–Risk–Variability Value Stream Mapping (Sus-Risk-VVSM) framework, designed to bridge this gap. The framework systematically integrates the ISO 31000 risk management cycle and Triple Bottom Line (TBL) metrics into the Lean Six Sigma DMAIC structure. Validation involved a two-round Delphi study with industry and academic experts, confirming the clarity and relevance of the findings. A Monte Carlo simulation of a discrete manufacturing process demonstrated the framework’s superior performance, reducing process variability by 26%, lowering operational risk exposure by 35%, and improving a composite Sustainability Index by 30.2% compared to conventional VSM. Statistical analysis identified process variability (r = -0.71) and defect rate (r = -0.73) as the strongest predictors of sustainability outcomes. The study contributes a validated, integrated model that transforms Lean into a proactive, risk-aware tool for sustainable decision-making, supporting the transition to resilient, sustainable Industry 4.0 operations.
Performance Evaluation of Conventional Lattices Additively Manufactured Using PCL for Bone Scaffold Applications
The internal architecture of the bone scaffold is crucial for bone tissue engineering; i.e., the bone scaffold must be high-strength and lightweight. Besides architecture, materials are also influential parameters in designing the bone scaffold. This study aims to investigate the impact of different pore shapes on mechanical properties, which will exclusively apply to bone scaffolds. Dog-bone specimens made of Polycaprolactone (PCL) with four different porous structures, i.e., triangular, circular, hexagonal, and square, were tested on a Universal Testing Machine (UTM). These specimens were additively manufactured through the Fused Deposition Modelling (FDM) technique. Tensile and compression tests on a universal testing machine revealed that the hexagonal lattice specimen can sustain and bear more load under compression than the triangular and circular lattices. While under tensile load conditions, circular lattices can bear more load than triangular and hexagonal lattice specimens. The square-lattice specimen was found to be the least favorable for tensile and compressive loads. Hence, this research will aid in selecting the lattice structure for the bone scaffold design.
Expert Consensus on Post-Processing Techniques for Enhancing the Visual Appearance of FDM 3D-Printed Parts Using the Fuzzy Delphi Method
The growing use of Fused Deposition Modeling (FDM) for automotive exterior components is constrained by poor surface quality and the “staircase effect,” which fails to meet “Class A” visual standards. To overcome fragmented, trial-and-error manual finishing, this study uses the Fuzzy Delphi Method (FDM) to establish an expert-driven consensus framework. An eleven-member panel evaluated 36 items across six themes using a 7-point Likert scale, which was converted into triangular fuzzy numbers. The findings show that finishing is an interdependent system in which mechanical and chemical sequences govern light reflection, thereby dictating paint smoothness and gloss. Mechanical sandblasting with angular aluminum oxide (A = 0.821) serves solely as a preparatory step to level the substrate topography and optimize bonding morphology. This establishes an anchor for chemical finishing, where standardized automotive coating systems, including 2K primers, layer thickness control, and topcoat distribution (A), drive final visual enhancements. These layers eliminate micro-voids, directly optimizing glossiness and aesthetic quality. Ultimately, this transition leads to a standardized engineering pathway, accelerating commercial FDM integration while advancing sustainable manufacturing practices (SDG 9 and SDG 12).