Journal of Energy and Safety Technology (JEST)
SCOPE OF PUBLICATION Journal of Energy & Safety Technology (JEST) provides a platform to publish new findings and innovation, original research & development and systematic review for two crossed-disciplines in energy and safety technology. Journal of Energy & Safety Technology (JEST) welcomes quality research in the area of Hydrogen & Biomass, Fuel & Combustion, Ocean & Other Renewable Energy, Energy Supply & Utilization, Energy Management, Energy Material, Process Safety and Occupational Safety and Health The journal will be peer-reviewed and published in two regular annual issues (January and July). Indexed by: MyJurnal & Google Scholar eISSN: 2637-1030
Copyright of articles that appear in Journal of Energy and Safety Technology belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions or any other reproductions of similar nature.
INHERENT SAFETY ASSESSMENT OF PROTON EXCHANGE MEMBRANE (PEM) WATER ELECTROLYSIS FOR HYDROGEN PRODUCTION
Hydrogen is increasingly recognized as a clean energy carrier and proton exchange membrane water electrolysis (PEMWE) has attracted significant attention as a pathway for green hydrogen production because of its ability to deliver high efficiency and rapid response. However, despite advancements in performance, the inherent safety aspects of PEMWE remain insufficiently addressed. Hydrogen is categorized under a wide flammability range, low ignition energy and risks of leakage, fire, explosion and corrosion that threaten its reliability. This project aims to identify and analyze potential hazards in PEMWE systems to evaluate their inherent safety level by using Heikkilä’s Inherent Safety Index (ISI) and proposing design improvements based on the principles of inherent safety: minimization, substitution, moderation and simplification. The study applied ISI scoring for both chemical and process hazards by evaluating factors such as hydrogen-oxygen mixing, flammability, explosiveness, corrosiveness, inventory, pressure and equipment complexity. Results showed PEM to fall within the medium hazard category with hydrogen-oxygen mixing and corrosion as key concerns. Design improvements were proposed based on the four principles such as reducing hydrogen inventory, using corrosion-resistant materials, operating at lower pressures and simplifying stack design. Based on the findings, it demonstrated that inherent safety assessment provides a pathway to enhance PEMWE reliability and align with hydrogen production with industrial safety standards and sustainability goals.
Copyright (c) 2026 Journal of Energy and Safety Technology (JEST)
PROCESS HAZARD SCENARIO COMPLETENESS AS A KEY RESULT INDICATOR FOR HAZOP STUDY QUALITY – THREE PHASE SEPARATOR CASE
Scenario completeness refers to the extent to which all credible process hazard scenarios have been identified and documented. Ensuring the completeness of process hazard scenarios is foundational to the quality and credibility of a Hazard and Operability (HAZOP) study. As the cornerstone of qualitative risk assessment in the process industry, HAZOP’s effectiveness depends on the systematic identification of all credible deviations and their associated hazardous consequences. This paper proposes hazard scenario completeness as a key result indicator for evaluating the quality of HAZOP studies, based on the recognition that omitted scenarios can significantly underestimate risk and compromise safeguard adequacy. A structured framework is presented for assessing scenario completeness, including benchmarking against established scenario taxonomies – such as API RP 14C – and analysing coverage through cause-consequence pairs. The approach emphasizes the role of historical data, expert knowledge, and consistency in the identification of representative and credible hazard scenarios across different process nodes. The findings suggest that incorporating scenario completeness as a quantifiable metric can enhance the reliability, traceability, and defensibility of HAZOP outputs. This approach offers a practical basis for both internal quality assurance and external regulatory validation of process hazard analyses.
COLLECTION AND EVALUATION OF INDONESIA’S ONSHORE PROCESS SAFETY FAILURES AND ACCIDENTS: A BASIS FOR IMPROVING AND VALIDATING QUANTITATIVE RISK ASSESSMENT
Real-world data on onshore process safety failures and accidents offer significant potential as a learning tool for validating and refining Quantitative Risk Assessment (QRA). This paper presents a curated dataset of incidents—sourced from official reports, equipment failure maintenance records, and change management documentation—and a structured methodology for their evaluation. Incidents are classified by industry sector, accident type, and facility age, then analyzed in terms of causes and consequences. By comparing actual events with publictly available failure rate database, the study identifies mismatches and provides evidence-based recommendations for improving hazard recognition and risk estimation. The findings are distilled into case studies designed for use in QRA validation as best practices for embedding incident data into ongoing QRA validation, emphasizing continuous incident database updates as a means to maintain relevance beyond fixed review cycles. The ultimate aim is to support both safer industrial operations and more credible QRA activity.
COMPUTATIONAL FLUID DYNAMICS (CFD) VALIDATION FOR ELECTRIC AND CONVENTIONAL VEHICLE FIRES IN A TUNNEL
This study justifies the application of a Computational Fluid Dynamics (CFD) model, developed using Fire Dynamics Simulator (FDS), for validating and extending the analysis of experimental data from full-scale tunnel fires involving battery electric vehicles (BEVs) and internal combustion engine vehicles (ICEVs). The model was rigorously calibrated using experimental heat release rate (HRR) data and demonstrated a strong capability to reproduce the where and when temperature trends observed across multiple fire scenarios. Quantitative validation through Root Mean Square Error (RMSE) analysis confirmed acceptable engineering accuracy, with contextualized percentage errors providing clear boundaries of model performance. Notably, the model performed with higher accuracy for ICEV fires (~12% error) compared to BEV fires (~20-26% error), highlighting the increased complexity of battery fire dynamics. The CFD model successfully captured critical fire dynamics, including thermal stratification and decay patterns with distance from the fire source. This work establishes that the validated CFD model serves as a reliable and cost-effective tool for supplementing physical experiments, enabling detailed analysis of fire behavior and supporting tunnel safety design where experimental data is limited. The validation is limited to gas temperature predictions in a single tunnel geometry, using experimentally measured HRR as the prescribed fire source together with simplified combustion and material models. The model does not resolve detailed species concentrations or battery degradation chemistry. The reported error ranges (approximately 11–15% for ICEVs and 20–26% for BEVs) should therefore be interpreted as engineering accuracy bounds for similar tunnel configurations, rather than as universal performance indicators.
VOLTAGE SAG MITIGATION USING DVR AND DSTATCOM IN POWER SYSTEM NETWORK
A good power quality is crucial to avoid any disruptions or damages to equipment, especially sensitive equipment as most consumers in the industrial sector, utilities, residential, and commercial use a lot of modern technology and devices. The power quality issues that the consumer faced are voltage sag, voltage swell, flickering, fluctuations, and more. This project will focus on voltage sag as it is the most common issue in modern distribution systems. Some causes of these issues are unavoidable, as weather conditions are also one of the factors that contribute to voltage sags. For instance, lightning, storms, heavy rain, and more. Besides equipment failure, consumers could also face an increase in electricity bills, wasted energy, communications interference, and disruption of the whole system. Thus, an effective device or method is needed to mitigate voltage sag. A modelling of Dynamic Voltage Restorer (DVR) and Distribution Static Compensator (DSTATCOM) is done in MATLAB/Simulink to simulate the comparison of effectiveness between two devices. The three-phase test model represents the typical distribution system, incorporating three phase source, step-down transformers, transmission line, and two types of loads, which are inductive load and capacitive load. The simulation is done under three-phase fault, double line-to-ground fault, and single line-to-ground fault. From the analysis of voltage recovery, THD improvement, and power injection, it is proven that DVR is much superior in mitigating voltage sag than DSTATCOM.
Copyright (c) 2025 Journal of Energy and Safety Technology (JEST)
RISK ASSESSMENT OF THE MALAYSIAN AGRICULTURE, FORESTRY, AND FISHING FATALITIES USING FAULT TREE ANALYSIS
Malaysia’s agriculture, forestry and fishing sector has persistent fatality risks, shaped by mechanisation, remote work settings, and informal employment. This study analysed 103 fatal AgFF accidents investigated by the Malaysian Department of Occupational Safety and Health (DOSH) between 2010 and 2020, using structured extraction from case summaries to derive accident type, activity, employment status, state, exposure set, and source of injury. Descriptive analysis summarised distributions, and associations were tested using chi square with effect size measures. A fault tree model was then constructed to represent plausible causal pathways linking exposure sets and sources of injury to accident types. In descriptive counts, “struck by object” was the most frequent fatal accident type. In the fault tree results, “fall from height” ranked higher because the model estimates scenario contributions along dominant exposure source pathways, rather than reporting raw marginal frequencies only. The findings prioritise prevention efforts around transport related tasks and tractor or trailer scenarios, alongside targeted controls for struck by object events.
DESIGN AND IMPLEMENTATION OF A MULTI-SWITCHING CONTACTLESS SOLUTION USING RF AND WI-FI CONTROL FOR SMART AUTOMATION
This study addresses the need for safer, more hygienic, and energy-efficient control of electrical loads in residential, healthcare, and industrial environments by developing a multi-switching contactless system. Conventional single-mode switching systems are limited by network dependency, restricted range, and inconsistent reliability, creating a demand for a multi-switching solution that ensures redundancy and responsive operation. The proposed system integrates an Arduino Uno as the central controller, a 433 MHz RF sensor for local wireless control, an ESP-32 Wi-Fi module for remote management, and a 5V relay module to switch multiple electrical loads. Users can operate the system via a mobile application or an RF remote, providing flexible and contactless interaction. The development involved hardware assembly, Arduino and ESP-32 firmware programming, mobile app integration, and structured testing including unit, integration, and functional evaluations to ensure performance and reliability. Experimental results demonstrated mean response times of 118 ms for RF and 176 ms for Wi-Fi control, an RF open-space range of up to 98 meters, command execution success rates of 98.5% (RF) and 97.1% (Wi-Fi), and safe load handling up to 1200 W with relay temperatures remaining below 48°C. These findings confirm that the system operates reliably under diverse conditions while maintaining low latency and thermal safety. In conclusion, the multi-switching contactless system offers a scalable, dependable, and practical solution for smart automation, enhancing hygiene, convenience, and energy management, with potential for future enhancements including voice control, improved security, and expanded load capacity.
COMBINED EFFECTS OF WATER DEPTH, ELECTRODE SPACING, AND ADDITIONAL SUBSTRATE IN A SOIL MICROBIAL FUEL CELL FOR SUSTAINABLE BIOELECTRIC
The Soil Microbial Fuel Cell (SMFC) is a technology that utilizes the metabolic activity of soil microorganisms to produce bioelectricity. SMFC provides a sustainable and environmentally friendly energy solution that has the capacity to alleviate the negative consequences of fossil fuel consumption. Unfortunately, there are many factors such as water depth, electrode spacing and types of additional substrate used will affect the performance of a soil microbial fuel cell (SMFC). Therefore, in order to improve and enhance the efficiency of SMFC, factors that contribute to low bioelectricity production need to be studied. In this study, there were 3 different parameters that were investigated for their effect towards the performance of soil microbial fuel cell (SMFC), which were the water depth, electrode spacing and extra substrate types added. SMFC’s configuration in this study was built in a 10cm x 10 cm x 12cm PVC box, with attached stainless steel’s activated carbon felt as electrodes and rice washing water (RWW) as control substrate and with high clay content soil in fixed 10 cm soil level. The experiment was conducted for 3 months duration and multimeter was used to record the voltage output of SMFC. Total Bacteria Count (TBC) analysis was also conducted to study the relationship between number of microorganisms and voltage generation (mV). Combined effects of SMFC were observed at the end of the experiment. Throughout the study, it was found that the SMFC with 10 cm water depth, 5 cm electrode spacing and using only rice washing water (RWW) as substrate without any addition of other substrate was to be the best condition with the maximum voltage output of 711 mV. Moreover, with TBC analysis, this setup has proven that the more the number of microorganisms in the fuel cell, the higher the voltage output (mV) as it has the highest amount of microorganisms up to 141 × 10⁶ of microorganisms at its highest voltage output’s peak.
ESTIMATION OF SOLAR PHOTOVOLTAIC GENERATION YIELD BASED ON ROOFTOP TILT ANGLE
Solar photovoltaic (PV) based power systems are being deployed more often in order to satisfy increasing energy requirements and global decarbonisation campaigns. Precise estimation of PV yield is essential for sizing systems, performance optimization and long-term planning. One limitation of existing models, including the Sustainable Energy Development Authority (SEDA) Malaysia formula, is that it does not provide a good approximation to different roof slopes and orientations. As a result, this led to less accurate performance estimates e.g. on tilted or multi-roof-top plate configurations. As such, this paper proposes a better method for predicting PV output by generating accurate factors and fitting formulas with respect to roof tilt and azimuth. LossFactor models were produced using PVSyst for different tilt and azimuth settings for all the Malaysian state capitals. Key parameters including global irradiance and temperature dependence, as well as module and system losses were deduced to study the influence of geometrical parameters on overall performance. Tilt angles ranging from 0° to 60° and azimuth orientations of south, east, and west were analyzed. A polynomial-based predictive model using a compiled data set was developed to estimate the optimal tilt–azimuth pairs for an annual energy production. The accuracy of the model was validated through on-site measurements and this model was consistent with the simulation output. Many researchers state a consistent and significant interaction between roof structure and the PV efficiency, where the improvement in energy output deviations resulting in measurable losses was determined by the optimal configuration. This study introduces a robust and practical method to enhance PV yield estimation through integration of simulated data, mathematical modelling and field validation.
A REVIEW OF EFFICIENCY IMPROVEMENT OF HYDRO-TURBINE GENERATOR
Endowed with favorable topography and tremendous amount of rainfall annually, allowed Malaysia to boost its hydroelectric power plant with Bakun Hydroelectric Dam in Sarawak as the largest supplier of hydroelectric energy. Hydroelectric not only acts as flood mitigation but also an alternative of electricity that slowly replaces conventional electric sources like natural gases and coal. However, efficiency of hydro-turbine should be taken into consideration to minimize energy loss while simultaneously optimizing total output power. The review also highlights major challenges, such as material limitations, infrastructure constraints, and the complexities of integrating artificial intelligence into control and optimization processes. Overall, the findings show that improving system efficiency requires simultaneous advancements in material selection, monitoring technologies, control strategies, and predictive tools to create more reliable and higher‑performing hydropower systems.
EFFECT OF THE GEOMETRY AND MOORING LINE ON THE HYDRODYNAMICS OF A PONTOON MODEL FOR MARINE FLOATING PHOTOVOLTAICS
This study aimed to analyze the effects of different pontoon models, including variations in pontoon geometry and mooring lines, on the hydrodynamic responses of marine floating photovoltaic (FPV) systems using numerical models. The examined variables included the geometric shapes of the pontoons, the number of mooring lines, irregular wave patterns, and water depth. The results demonstrate that adding pontoons with breakwater geometries—specifically cylindrical and rectangular shapes—results in lower hydrodynamic responses than the Pure float model. Among the models tested, the breakwater configuration achieved the most significant reduction in hydrodynamic responses, with a decrease of 59% across the six degrees of freedom (DOF). The rectangular model was closely followed, with a 56% reduction, whereas the cylindrical model showed a 47% reduction. The use of six mooring lines can further minimize the effects of wave excitation and hydrodynamic responses across the six DOFs. On average, adding the mooring lines resulted in a 27% reduction in the response amplitude operator (RAO) values and a 9% decrease in the mooring line tension. This research provides valuable insights for designing and developing efficient and reliable marine FPVs, serving as a practical guide for the renewable energy industry to enhance performance and system sustainability. Keywords—Marine Floating Photovoltaic (Marine FPV), Breakwater Mooring Line, Response Amplitude Operator (RAO).
THE CORRELATION BETWEEN WORK-LIFE BALANCE AND SAFETY CULTURE AMONG OIL & GAS GEO-SURVEY WORKERS IN MALAYSIA
Introduction: The oil and gas industry has adopted many prevalent safety initiatives to ensure a safer working environment. However, the effectiveness of these initiatives is very depending on the organization's safety culture. Many previous studies have highlighted the importance of Work-Life Balance (WLB) for employees in the oil and gas industry, the problem is most acute for workers in geo-survey, who are highly susceptible to poor WLB. Therefore, the aim of this study is to investigate the influence of WLB on safety culture and contributing factors of WLB among on-shore and offshore geo-survey workers. Methods: A cross-sectional study was conducted across 89 offshore and on-shore geo-survey workers in a Malaysian oil and gas company. The study employed convenience and snowball sampling methods, with data collected via online questionnaire survey. Data analysis was done using the Mann-Whitney test, which was selected after a normality test. All calculations were performed in SPSS and SEM PLS. Results: The WLB rating for the geo-survey company is 2.74; rating for home induces is 2.73, and the job induces 2.74. Although the level is the same, but the contributory factors for offshore workers arise from job-induced activities. Additionally, years of experience showed a statistically significant relationship with WLB of the offshore employees. Nevertheless, there is no statistically significant relationship between WLB and safety culture. Conclusion: This research identified a low rating of WLB within the geo-survey company and revealed specific concerns based on location and years of experience. A failure to manage this situation could significantly affect productivity and employees’ health.
IMPLEMENTATION OF KNOWLEDGE TRANSFER OF FIRE SAFETY AT HOME THROUGH A COMMUNITY PROGRAM
Fire safety is commonly enforced and practised across all industries in Malaysia. However, fire safety practices among individuals are often not applied in residential areas because they feel or perceive homes are safer (i.e., it is where they live and they believe they are familiar with what goes on in their own home) than workplaces. Because of such perception, house fires remain a serious threat, especially in densely populated areas. Faulty wiring, overloaded power sockets, and unattended stoves or candles are common causes of house fires, according to the Malaysian Fire and Rescue Department. This paper presents a community program aimed at raising awareness and providing basic knowledge of home safety, enabling residents to practice preventive measures early to reduce the risk of serious accidents. The program addressed significant gaps in fire safety awareness and preparedness, targeting residents of Taman Sri Pulai in Skudai. Researchers from UTM collaborated with the mosque committee and Komuniti Rukun Tetangga (KRT) to implement a comprehensive program involving knowledge transfer from agency and industry experts. The program featured exhibitions, talks, and demonstrations conducted by industry representatives and the Malaysian Fire and Rescue Department. A survey on home fire safety–safety awareness, preparedness, and practices–was conducted during the program and participated in by 116 individuals. The analysis of responses indicated that 84 percent of participants knew the causes of fire, and 72 percent knew how to put out fires at home. The analysis also revealed that many residents lacked a fire extinguisher at home. The program highlights the importance of targeted education in promoting community safety and underscores the value of leveraging industry expertise for public education.
INFLUENCING FACTORS OF OCCUPATIONAL INJURY IN THE CONSTRUCTION INDUSTRY: EVALUATING THE EFFECTIVENESS OF NEW OSHA 2022 AMENDMENTS
The construction sector is always seen as an extremely dangerous business because of the complexity of the work involved in. In Malaysia, the non-fatal occupational injuries in the construction sector increased by 59.65% in 2022 compared to the previous year. Additionally, with 6.30 deaths per 100,000 workers, the construction industry has the greatest risk of worker fatalities. Due to the construction sector consistently reporting the highest number of fatal accidents among all industries in Malaysia, this study will specifically focus on identifying the underlying factors that influence occupational injuries in this sector. On the other hand, the government implemented the new OSHA amendment in June 2024. It is essential to assess the beneficial impact of the latest amendment act. The effectiveness of risk assessments stands as another critical area of this study. A total of 90 respondents were involved in this study. Questionnaires were distributed to construction workers to obtain data. Subsequently, a set of quantitative analysis was performed in order to analyze the variables. The study employed several statistical analyses, including T-test, Analysis of Variance (ANOVA), Pearson Correlation, Multiple Linear Regression (MLR) and Linear Regression. Results: This study identified that economic pressure and safety investment are significant contributory factors of occupational injury. The enforcement of new OSHA amendments and workplace risk assessments contributed to significantly lower numbers of injuries and improved safety performance in the construction industry. The present study has addressed the research gap concerning the key factors contributing to occupational injuries in the Malaysian construction industry and the effectiveness of the new OSHA (2022) amendments.
PERFORMANCE STUDY ON THE HYBRID SYSTEM OF PIEZOELECTRIC-TRIBOELECTRIC NANOGENERATORS
The global shift towards renewable and sustainable energy sources has intensified interest in modern energy harvesting technologies, particularly for low-power electronic applications. However, conventional single-mode energy harvesters, such as standalone piezoelectric nanogenerators (PENGs) or triboelectric nanogenerators (TENGs), face limitations in energy conversion efficiency and consistency under varying mechanical conditions. This study addresses these limitations by proposing a hybrid nanogenerator system that integrates both piezoelectric and triboelectric mechanisms to enhance overall performance. The main objective is to evaluate the energy harvesting efficiency and output characteristics of the hybrid nanogenerator system under different operating scenarios. The hybrid design aims to maximize electrical output and improve system reliability by combining the piezoelectric effect, which converts mechanical stress into electrical energy with the triboelectric effect, which harnesses kinetic energy through contact electrification and electrostatic induction. To achieve this, the system is modeled and simulated using COMSOL Multiphysics and MATLAB Simulink. The PENG component is evaluated based on different materials (PVDF and PZT-5H) and applied mechanical forces, while the TENG component is analyzed under various operational modes (single-electrode and contact-separation). A circuit-level prototype simulation is developed using full-wave bridge rectifier (FWBR) configurations to examine AC-to-DC conversion efficiency and potential for energy storage in lithium-ion batteries. The simulation results reveal that PVDF-based PENGs generate higher voltage output than those made from PZT-5H. For TENGs, the contact-separation mode yields significantly better voltage output than the single-electrode mode. The integration of both nanogenerator types, along with effective rectification, demonstrates the system's capability to produce stable DC output suitable for powering portable and wearable electronics. This study contributes to the advancement of self-powered technologies by providing valuable insights into the design and optimization of hybrid nanogenerators for practical, scalable energy harvesting solutions.
ECONOMIC INVESTIGATION AND PERFORMANCE EVALUATION OF AN INSTALLED HYBRID PV/BATTERY SYSTEM IN NAJAF CITY, IRAQ
Iraq experiences chronic electricity shortages despite having abundant solar energy potential, with most cities receiving utility power for less than 15 hours per day. To mitigate reliance on expensive and polluting private generators, this study evaluates the performance and cost-effectiveness of a residential hybrid photovoltaic (PV)/battery system in Najaf City. The research aims to assess the technical, economic, and environmental feasibility of the system, focusing on its ability to supply electricity during both grid-connected and blackout periods. A 5.7 kW PV system integrated with a lithium battery storage unit and smart inverter was installed at a household site. Load estimation, system design, and component specification were conducted based on actual consumption patterns. HOMER Pro software was used to simulate performance data, solar irradiance, and temperature profiles. Cost comparisons were made between the hybrid system and private diesel generators over a 20-year project lifespan. The results demonstrated that the PV/battery system generated more electricity than consumed, with a total production of 5.85 MWh compared to 5.55 MWh of usage over a 7-month period. The system reduced CO₂ emissions by 3.14 tons and offered significant economic savings. While the private generator's projected cost was USD 13,420, the hybrid system cost only USD 9,088 under normal conditions or USD 11,488 in worst-case scenarios, representing up to 40% in savings. Payback period analysis showed a return on investment in less than one year. Compared to previous studies that primarily relied on simulations, this research uniquely incorporates real-world performance data from an operational system, offering empirical validation of technical and economic viability. The significance of this study highlights the practical effectiveness and financial attractiveness of hybrid PV/battery systems for residential use in Iraq and similar regions with frequent power outages. Adoption of such systems can support national sustainability goals, reduce dependency on fossil fuels, and promote cleaner energy access for communities.
A COMPREHENSIVE LAYERS OF PROTECTION ANALYSIS (LOPA) OF AMMONIA EXPOSURE IN HYBRID OCEAN THERMAL ENERGY CONVERSION (H-OTEC) SYSTEMS
Ocean Thermal Energy Conversion (OTEC) (Figure 1) harnesses the temperature difference between warm surface seawater and cold deep seawater to generate renewable energy. This innovative technology relies on ammonia as a working fluid in closed-cycle systems, where its favourable thermodynamic properties enable efficient energy transfer. However, ammonia usage introduces significant hazards, necessitating comprehensive risk evaluation and mitigation strategies to ensure safe and sustainable operations. Ammonia poses risks to personnel and the environment due to its toxic and corrosive nature. Exposure can result in respiratory distress, eye irritation, or severe health consequences, while accidental releases may lead to water contamination and ecological damage. Common scenarios for ammonia hazards in OTEC systems include leaks from piping or storage tanks, safety valve failures, operator errors, and structural damage from extreme weather. These events have immediate consequences such as injury or environmental harm, as well as long-term impacts, including reputational damage and operational disruptions. These risks, the Layers of Protection Analysis (LOPA) methodology provides a structured framework to identify cause-consequence pathways and evaluate the adequacy of safeguards. Current protective layers include corrosion-resistant materials, ammonia detection systems, safety valves, regular maintenance, and operator training. Additional measures, such as real-time leak detection, automated shutoff systems, predictive maintenance using AI, and enhanced secondary containment, further strengthen system resilience. By systematically assessing risks and implementing robust safeguards, OTEC systems can achieve operational safety, environmental compliance, and long-term reliability, advancing the adoption of this promising renewable energy technology.
HUMAN FACTORS ENGINEERING FOR MITIGATING INDUSTRIAL HYGIENE HAZARDS IN MALAYSIAN LABORATORIES AND MANUFACTURING ENVIRONMENTS
This study explores the integration of human factors engineering (HFE) into industrial hazard mitigation, with focus on ergonomic adjustment, noise reduction, and chemical exposure. Recognizing the limitations of conventional safety measures, the research identifies ergonomic risks and auditory hazards as critical factors affecting worker safety and operational efficiency. Through a mixed-method approach, including observational studies and statistical analysis, effective HFE interventions are designed and assessed. Results indicate that tailored ergonomic solutions and noise control strategies significantly enhance hazard mitigation. The study advocates for a systemic application of HFE principles in industrial settings, emphasizing the role of comprehensive ergonomic assessments and proactive noise management in mitigating risks. The conclusions drawn underscore the potential of HFE to revolutionize safety protocols and suggest avenues for future research in advancing industrial health and safety measures.
Isu Keselamatan dan Etika Penggunaan Kecerdasan Buatan
Artificial intelligence (AI) has become an integral part of modern life, impacting various sectors. While it offers significant benefits, AI also presents safety, ethical and social challenges. This literature review examines these challenges, focusing on safety of data privacy, algorithmic bias, decision reliability, and workforce displacement due to automation. The widespread use of AI requires robust data protection measures to safeguard personal information from breaches and misuse. Algorithmic bias, often unintentional, can lead to discriminatory outcomes. The lack of transparency in some AI systems raises concerns about the trustworthiness of their decisions. Furthermore, AI-driven automation has the potential to displace workers, requiring careful planning and strategies for workforce transition. This review explores the complex interplay between AI's potential and its risks. It examines the ethical implications of data collection and usage, the challenges of ensuring algorithmic fairness, and the socioeconomic impact of automation. Finally, it proposes strategies for responsible AI development and deployment, aiming to maximize benefits while mitigating potential harms. These strategies include promoting AI literacy, developing transparent and accountable AI systems, and fostering a balanced approach to AI adoption.
A Bibliometric Review on Air Pollution, Health, and Safety Within Factories and Mills
This bibliometric review presents a comprehensive analysis of research on air pollution and its impact on health and safety, focusing on factory and mill environments. The study examined 401 documents published between 2019 and 2023, sourced from 247 distinct outlets, with an annual growth rate of 12.32%. Key research contributions come from China and the USA, with strong international collaboration evident in 35.41% of the publications. Core sources include prominent journals such as Science of the Total Environment, Environment International, and the International Journal of Environmental Research and Public Health. Leading authors, including Kan H. and Chen R., have made significant contributions to the literature on the health impacts of particulate matter and nitrogen dioxide. This study identifies three (3) major trending topics: (1) human; (2) particulate matter; and (3) China. A shift towards broader discussions on “sustainable development” and “climate change” is observed, influencing recent research agendas. The co-occurrence network of keywords reveals three main research clusters: (1) human–air pollution interaction; (2) clinical studies on gender and occupational health; and (3) health risks and environmental monitoring. Analyses of the aforementioned clusters have demonstrated the role of interdisciplinary collaboration in advancing research in this field. The findings emphasise the value of interdisciplinary and international collaboration, particularly between China and the USA, in addressing health risks from air pollution. These partnerships aid in policy development, support technology sharing, and foster further research opportunities. Future studies should refine methods to better link pollutant exposure with health impacts, particularly among vulnerable populations.
MODELING SOLAR COLLECTORS: A REVIEW OF MODERN METHODS TO IMPROVE ENERGY CONVERSION EFFICIENCY UNDER CHANGING CLIMATIC CONDITIONS
Efficiency and reliability in solar energy systems are largely influenced by the development of solar collector technologies and methodologies adopted for performance modeling under variable climatic conditions. This paper reviews recent approaches to modeling solar collectors, with a primary emphasis on the integration of solar tracking systems with advanced computational techniques such as artificial intelligence and hybrid models. The review highlights the use of long short-term memory (LSTM) models, neural networks, and AI-driven optimizations for enhancing energy conversion efficiency. It also provides an overview of the contributions of computational fluid dynamics (CFD) and thermodynamic models to the structural and thermal performance of solar collectors, mainly parabolic trough collectors (PTCs) and systems utilizing nanofluids as working mediums. Various case studies demonstrate the significant impacts of these modeling methods on improving efficiency and adapting to real-time environmental changes. The review concludes with recommendations for future work in integrating artificial intelligence (AI), hybrid model development, new material explorations, and long-term field studies to refine and enhance solar collector performance. These advancements are expected to contribute significantly to the future of solar energy, fostering sustainable and efficient energy solutions.
False Work Accident: Forensic Engineering Investigation Approach
On June 19, 2021, at 7:15 PM, the collapse of a falsework structure at a construction site in Selangor caused one fatality, injured another worker, and severely damaged the falsework. Prior to the collapse, five workers were engaged in pouring concrete into a formwork box at channel P116R. Method of forensic engineering investigation as such by triangulation of approach visual inspections and measurements were conducted on the components used for the falsework structure. A review of technical documents was performed, focusing on the design specifications, engineering drawings, and procedures for the installation of the falsework. A structural analysis was carried out based on the technical data obtained. This study investigates the collapse of a falsework structure at a construction site in Selangor, resulting in one fatality and one injury. Forensic analysis revealed that the structure failed under a concrete load of 32.08 cubic meters (50.74 kN/m²), exceeding its capacity. The investigation included onsite inspections, structural analysis, and review of technical documents, concluding that inadequate bracing and joint integrity were primary causes of failure.
DESIGN AND SIZING OF STAND-ALONE PHOTOVOLTAIC SYSTEMS FOR REMOTE AREAS
As solar photovoltaic technology becomes more affordable globally, Stand-Alone Photovoltaic (SAPV) systems are being recognized as a viable solution for powering essential services in areas lacking grid connectivity. The paper begins with a comprehensive review of SAPV systems, highlighting their categorization, benefits, and typical applications, especially in remote areas. Subsequently, it delves into the design and sizing aspects of these systems, emphasizing the importance of choosing appropriate components, including PV modules, batteries, charge controllers, inverters, and cables. A detailed methodology outlines the intuitive design process, incorporating factors like site selection, solar potential assessment, daily energy needs calculation, and component sizing. This systematic yet straightforward methodology bridges the gap between theoretical design principles and practical implementation. To demonstrate the applicability of the proposed approach, a case study is included, offering real-world insights and best practices for SAPV system deployment. This study contributes to the field of renewable energy by addressing specific challenges associated with SAPV system design in off-grid areas, such as the need for accurate solar irradiation data and precise component sizing. By providing a structured framework and practical guidance, this work serves as a valuable reference for researchers, engineers, and policymakers aiming to optimize SAPV systems for sustainable development and to enhance energy access in remote regions.
WIND FARM DESIGN THROUGH OPTIMAL WIND TURBINE POSITIONING USING GREEDY ALGORITHM
Finding the optimal design method for offshore wind turbines in wind farms poses a challenge due to various optimization considerations. This study addresses the optimization problem of wind turbine placement using the Greedy Algorithm. The problem incorporates a linear wake model and a power output function, with an incremental computation method devised to account for the impact of additional turbines on existing ones, thereby enhancing the wind power assessment process. The objective function of the proposed study is the power output, factoring in the wake effect from nearby turbines. The optimal wind farm layout is determined through two optimization stages applied to the existing designs. The proposed wind farm aims to generate 3 MW of power from the turbines. Two scenarios with constant wind velocities and different rotor diameters were utilized to test the proposed method, employing Ansys 2022 as the simulation software. The findings demonstrate that the proposed Greedy Algorithm, coupled with repeated adjustments, yields more accurate results. By optimizing the placement of turbines while considering their mutual influence on power output, the proposed method validates the effectiveness of the Greedy Algorithm for wind turbine position optimization. The insights gained from this research can serve as a foundation for future work in large-scale offshore wind energy projects, contributing to the global transition toward sustainable energy solutions.
INNOVATIVE APPROACHES TO DUAL AXIS SOLAR TRACKING SYSTEMS
This review discusses the latest design approaches to dual-axis solar trackers by underlining their role in the development of solar energy efficiency and sustainability. Major areas of innovations reviewed include new design proposals, choice of materials, control systems, incorporation of Artificial Intelligence, Internet of Things (IoT), and Machine Learning (ML) in optimizing energy production. A comparison of traditional and modern dual axis tracking systems is made, focusing on how these systems are improved to increase energy yield, improve environmental adaptability, and enhance the durability of the systems. Challenges include increased complexity and cost of modern systems, while future trends will be discussed for predictive analytics, energy storage integration, and smart grid connectivity. With these developments in the background, this review assesses the prospects of dual-axis solar tracking systems as a cornerstone for sustainable energy solutions.
THE IMPACT OF PROLONGED OCCUPATIONAL NOISE ON STAFF WELL-BEING IN HOSPITAL KITCHENS
Occupational noise in hospital kitchens can significantly impact staff well-being due to prolonged exposure. This study evaluated noise levels in cooking, washing, and preparation areas of a hospital kitchen to assess compliance with the 2019 Noise Exposure Regulation and to understand how noise exposure varies by location and time. Measurements were conducted during peak hours during working activities showed that all areas were within the safe exposure limits, with the cooking area (77.620 dB) typically experiencing higher noise levels than the washing (76.338 dB) and preparation areas (75.302 dB). Noise levels significantly varied between peak and non-peak hours in the cooking and washing areas (p>0.05) but remained consistent in the preparation area (p=0.001). A weak yet significant positive correlation (p=0.001, r=0.145) was observed between noise levels and proximity to noise-generating distance of equipment, suggesting that reducing distance from noise sources may slightly increase exposure risks. These findings underline the need for ongoing monitoring and targeted noise reduction strategies in hospital kitchens to safeguard staff health, not hospital patients.
INVESTIGATION OF THE PHYSICO-CHEMICAL CHARACTERISTICS OF IRRIGATION WATER QUALITY AT AN AGRICULTURAL SITE IN PASIR PUTEH, KELANTAN, MALAYSIA
This study was conducted to assess the physico-chemical characteristics of irrigation water quality, focusing on both chemical and physical elements at an agriculture site in Pasir Puteh, Kelantan, Malaysia. Eight sampling points were selected near four different crops namely corn, cucumber, longbean and luffa, with two sampling points located 10 meters apart for each crop. Water samples were analyzed in-situ and ex-situ in evaluating their physico-chemical characteristics including temperature, pH, salinity, conductivity, total dissolved solids (TDS), turbidity, chemical oxygen demand (COD), dissolved oxygen (DO) and biochemical oxygen demand (BOD). The findings revealed that the values of the observed water quality parameters did not exceed the specified standards aligning with the National Water Quality Standards (NWQS) water quality index Class IV – Irrigation. TDS levels at all sampling points were below the NWQS standard for Class I water (<500 mg/L). Although there is no specific turbidity standard for Class IV water, the highest turbidity value was recorded at sampling point 2 (246.50 ± 0.71 NTU). The average COD concentration across all sampling points was 43.63 ± 9.72 mg/L, with a DO value of 3.45 ± 0.55 mg/L, and a mean BOD of 5.80 ± 6.43 mg/L, all falling within Class III NWQS. In conclusion, all measured water quality parameters met the NWQS Class IV irrigation standards, indicating that the water is suitable for irrigation purposes.
SOCIODEMOGRAPHIC FACTORS ASSOCIATED WITH MUSCULOSKELETAL SYMPTOMS IN TRUCK DRIVERS EXPOSED TO WHOLE-BODY VIBRATION: A STUDY AT PORT KLANG, SELANGOR
Prime mover truck operations are associated with a significant risk of musculoskeletal disorders (MSDs) among drivers due to prolonged exposure to whole-body vibration (WBV). This study aimed to explore the potential link between WBV and the prevalence of MSDs in truck drivers at a major port in Port Klang, Selangor. A cross-sectional analysis was conducted involving 205 prime mover drivers, utilizing data from the Extended Standardized Nordic Questionnaire (NMQ-E) to assess sociodemographic factors and the prevalence of MSDs. Additionally, WBV exposure was measured in a subset of 32 drivers using the Larson Davis HVM100 device. The findings indicated that WBV exposure levels were within the limits set by the European Directive 2002/44/EC, with a maximum of 1.15 m/s² over an 8-hour work period. Despite this, 25.93% of drivers reported lower back pain, 19.88% reported upper back pain, and 16.61% reported neck pain. Statistical analysis revealed significant correlations between work experience and back pain (p=0.038), work experience and hip pain (p=0.033), and body mass index (BMI) and shoulder pain (p=0.027). The study suggests that drivers should be encouraged to take regular breaks to mitigate vibration exposure and that maintaining a healthy weight could reduce the strain on musculoskeletal systems.
COMPARATIVE ANALYSIS OF BUILDING ENERGY PERFORMANCE IN MALAYSIA: A MULTIPLE LINEAR REGRESSION STUDY ON GREEN VS. CONVENTIONAL BUILDINGS
This study presents a detailed comparison of energy consumption between green and conventional commercial buildings by analyzing actual energy usage against benchmarks derived using Multiple Linear Regression (MLR) and Building Energy Intensity (BEI). MLR, selected for its statistical robustness and ease of implementation, incorporates variables such as Cooling Degree Days (CDD), temperature, humidity, rainfall intensity, wind speed, and number of working days as independent factors. The study evaluates energy consumption across 20 office buildings in Klang Valley, Malaysia, from 2019 to 2023. The findings indicate that both green and conventional buildings generally consumed less energy than their benchmarks. In terms of the difference between actual energy consumption and the benchmarks; green buildings recorded a narrower gap (26.49%) than conventional buildings (34.99%). However, green buildings consistently achieved superior energy efficiency, with BEI values ranging from 110 to 150, compared to 170 to 220 for conventional buildings. A notable reduction in energy usage occurred from 2020 to 2021 due to COVID-19-related remote work environment, though BEI slightly increased in 2023 with the normalization of operations. Energy label ratings further highlighted green buildings' superior performance, with 42% achieving a 4-star rating, while 80% of conventional buildings received only 2-star ratings. The results emphasize the importance of green building practices, demonstrating their long-term benefits in reducing energy consumption and achieving higher energy efficiency for sustainable living. This study offers valuable insights for policymakers, developers, and stakeholders, underscoring the need for sustained efforts in energy saving and innovation to enhance building energy performance.
KNOWLEDGE, ATTITUDE, AND PRACTICE ON BIOMEDICAL WASTE MANAGEMENT AMONG MEDICAL LABORATORY TECHNOLOGISTS
Biomedical waste management (BMW) is crucial in healthcare due to its significant impact on public health and environmental safety. Malaysian hospitals are expected to generate up to 33,000 tons of BMW annually, with increased volumes during crises like the COVID-19 pandemic. This highlights the urgent need for effective waste management strategies. A cross-sectional study was conducted among 102 medical laboratory technologists (MLTs) at the Universiti Sains Malaysia Health Campus. Participants were assessed using a modified questionnaire to evaluate their knowledge, attitude, and practice (KAP) regarding biomedical waste management. Descriptive and inferential statistics, including Pearson's correlation and Multiple Linear Regression (MLR), were used to analyse the data. The mean scores for knowledge, attitude and practices were 70.0%, 89.61% and 82.19% respectively. A significant positive correlation was found between knowledge and attitude (p = 0.012, r = 0.248). MLR analysis revealed that training significantly influenced knowledge (Adj β = 0.634, 95% CI: 0.015, 1.252), and the duration of employment significantly influenced practices (Adj β = 0.031, 95% CI: 0.002, 0.060). The findings underscore the critical role of training in improving biomedical waste management knowledge and practices among healthcare workers. Regular and comprehensive training programs are essential for enhancing the competency of MLTs, leading to safer and more effective waste management practices. This study provides empirical evidence on the importance of training in healthcare waste management, which is crucial for reducing health risks associated with improper waste handling and disposal.