ASEAN Engineering Journal (AEJ) is an official publication of the JICA Project for AUN/SEED-Net as a fruit of collaboration with the main support from the Government of Japan through Japan International Cooperation Agency (JICA), Member Governments from 10 ASEAN countries and 26 Member Institutions (MIs) selected from leading institutions in engineering field with support from 14 leading Japanese Universities. Over the years, AEJ developed a strong international academic network from extensive human resources established by network of professionals and continuous collaborations via the journal. ASEAN Engineering Journal is published online with a frequency of four (4) issues per year (March, June, September, and December). A double-blind peer review system is adopted to ensure the transparency and integrity of the review process. The journal is open access and does not charge any publication fee for AUN/SEED.net Member of Institution in ASEAN. Scope of Publication AEJ intended to be an international platform for scholars, researchers, and students from all over the world to publish innovative knowledge addressing ASEAN common issues from academic research outcomes and to improve research capacity through review process. AEJ publishes full academic papers covering 10 different engineering fields including Chemical Engineering (ChE), Civil Engineering (CE), Computer and Information Engineering (CIE), Electrical and Electronics Engineering (EEE), Energy Engineering (EneE), Environmental Engineering (EnvE), Geological and Geo-Resource Engineering (GeoE), Materials Engineering (MatE), Mechanical and Manufacturing Engineering (ME/ManuE) and Natural Disaster (ND). Indexed by: SCOPUS, Asean Citation Index (ACI), Thai Citation Index (TCI), GOOGLE SCHOLAR
DESIGN AND COMPARATIVE ANALYSIS OF FINFET BASED 6 T AND 7T-SRAM CELLS FOR IN-MEMORY COMPUTING APPLICATIONS
Since memories are the core components of most digital systems, lowering their power usage can significantly affect the system's stability, performance, and efficiency. SRAM cells are suitable for embedded and portable electronics due to their lower leakage. FinFETs have demonstrated their potential as a low-power, high-performance option at lower technological nodes. One of the main priorities for designers working on semiconductor memories is low power consumption, which can be achieved by reducing transistor leakage current. This article design and compares the average power consumption and noise voltage of 6T as well as 7T SRAM cells using FinFET approach for In-memory computing applications. It has been found that the stability and power handling capacity of an SRAM cell constructed using 45 nm FinFET technology are improved. The proposed 7T FinFET SRAM reduces average power dissipation by approximately 12% and increases the average noise voltage by approx. 7% as compared to 6T SRAM cells. Cadence Virtuoso ADE is used for all designs and simulations.
Average power dissipation, static noise margin, FinFET, SRAM, Multi-Gate Transistors, Memory Density
Copyright (c) 2025 ASEAN Engineering Journal
MESOSCALE PERSPECTIVE OF ENVIRONMENTAL FLOW ASSESSMENT IN THE MIDDLE REACH OF SEKAMPUNG RIVER, INDONESIA
Minimum environmental flow (E-flow) is a river discharge that is essential to maintain the environmental capacity along the river and support its ecological systems. A mesoscale approach is critical for a more focused understanding of river dynamics within the affected river reach. This paper discusses the mesoscale perspective of E-flow studied on the middle reach of a natural river, Sekampung in Indonesia. The wetted perimeter method was used to assess the minimum environmental flows at both the upstream and the downstream sides of the concerned reach of the river. An analytical approach was also used as a comparative method. The Average Reciprocal Distance (ARD) approach was used to obtain the E-flow at the specific site within the reach. Based on the mesoscale approach, a minimum E-flow of between 2.5 m3/s and 3.6 m3/s was obtained for the upstream and downstream boundaries, respectively. Using the ARD method, a minimum E-flow of 2.6 m3/s must be provided at the new Sekampung Dam site. The water use and river dynamic nexus need to be further studied to make an integrated decision regarding the E-flow along the mesoscale boundaries.
SMART VEHICLE CYBERSECURITY: IMPLEMENTING AN AUTONOMOUS AND ADAPTIVE INTRUSION RESPONSE SYSTEM
As smart vehicles are becoming more common, protecting them from cyberattacks has become very important. To address this issue, this paper introduces an automatic intrusion response system (IRS) designed especially for intelligent vehicles. The system is able to quickly analyze the effect of a cyberattack and choose the best response method in real time, making vehicle operations more secure. The proposed IRS offers several key features. First, it provides a clear analysis of different response methods that can be used during cyber intrusions. Second, it introduces a framework that evaluates both the cost and the impact of each response. Third, it applies decision-making tools such as Simple Additive Weighting (SAW), Linear Programming (LP), game theory, and artificial intelligence–based approaches to select the most effective response strategy. Extensive testing shows that the system performs strongly in terms of response quality, time efficiency, and resource usage. This study proposes a hybrid SAW-LP method that combines rapid multi-criteria ranking with constraint-based optimization. Experimental evaluation shows that the hybrid approach reduced response selection time by 32% and improved decision accuracy by 12% compared to standalone algorithms.
Autonomous vehicles, Intrusion response system, Cybersecurity, Intelligent vehicles, Linear Programming, AI-based mechanisms.
SOUND TRANSMISSION LOSS PERFORMANCE OF BIO-COMPOSITE MICRO-PERFORATED PANELS BACKED BY NATURAL FIBERS
Micro-perforated panels (MPPs) with natural fiber backing hold great potential for the development of acoustic panels due to their various advantages over conventional porous materials. Some aesthetic restrictions and the bulky nature posed by conventional porous materials have made natural fiber-reinforced MPP a more adaptable and compelling solution for acoustic efficacy. Therefore, in this paper, MPPs backed by coconut coir, kenaf fiber, and palm leaf fiber are the focused for assessing the sound transmission loss (STL) performance. The selection of these natural fibers is largely due to their favorable inherent natural characteristics and acoustic properties. To assess STL, the current study employed the impedance tube approach to analyze the performance of materials in attenuating sound pressure across varying frequencies. Results reveal that the MPP reinforced with coconut coir achieved the maximum STL at 42 dB in the mid-frequency range of 190 to 230 Hz, demonstrating that it may be a highly suitable material for applications that require soundproofing. Undoubtedly, natural fiber-backed panels offer a viable alternative and an effective solutions to mitigate different forms of sound transmission, including airborne and structure-borne noise, thereby enhancing overall acoustic insulation.
IMPROVING THE EFFICIENCY OF DETECTING AND CATEGORIZING BRAIN TUMORS FROM DIVERSE MRI IMAGES THROUGH THE IMPLEMENTATION IN DEEP CONVOLUTION NEURAL NETWORK
Deep learning is a popular and effective approach to medical imaging detection and classification. Various brain tumor classifications are related to an ongoing research topic made possible by the diversity of cancer features. Local brain images testing in the latest brain images datasets BRAT can be challenging due to their processing time, lower accuracy, and the possibility of overfitting and underfitting. In this paper, Xception DCNN model can be implemented to test the local images or real images and apply three datasets in this model, analyse the individual result and improve the training speed and stability of neural networks by normalizing the activations of each layer. The Xception model can significantly reduce the number of parameters and computational complexity without compromising the model’s accuracy. Moreover, this system used popular benchmark datasets such as Kaggle and BRATS, the suggested research MRI image datasets were assessed, and the local validation dataset was proposed. In this research work, this model achieved the best performance results with training accuracy and loss 99±0.005%, validation accuracy and loss 98±0.2%. The main objectives are improving the accuracy of classification, avoiding overestimation along with underestimating, and decreasing down on processing time.
Brain Tumor, Local Brain images, Xception DCNN model, MRI, Classification accuracy
SUSTAINING THE DISTRIBUTION GRID NETWORK RELIABILITY WITH DISTRIBUTED WIND TURBINE GENERATIONS
Distributed generations are practically operated at the rated maximum power output. The locking off and on into the grid network of the intermittent power generating units is on availability basis. There are significant integration challenges posed by wind power generation due to its intermittency nature which seriously affect power grid stability and reliability issues related to grid power quality and voltage profile. Several reliability indices can be established to assess the distribution network reliability; they include ASAI, SAIFI, AENS and EENS. This study had the main purpose of optimizing placement and sizing of wind distributed generations through the application of the Particle Swarm Optimization algorithm to attain voltage profile improvement as well as network reliability enhancement by power loss reduction for radial distribution network - IEEE 33-bus system. The proposed PSO-based algorithm adequacy was investigated using MATLAB simulation on a radial distribution network - IEEE standard 33-bus test system, with a case study of Genetic Algorithm being used for validation of the solution techniques and model developed. The RDN test system is connected to a total of 3.32 MW and 2.71 MVAr; both real and reactive loads respectively. Modelling of the wind power generation considered for grid integration was variable reactive power model. A cut-off wind speed of ≥ 6 m/s on average was considered for power generation. Results from the analysis yield 0.2115 p.u of average active power produced by the wind turbine generator. 206.87 kW and 139.13 kVAr were the APL and RPL initial network configurations obtained respectively at 0.83pf when no DG was integrated. When wind DG is integrated at optimal placement and capacity, the reduction of the overall network power loss is 68.12% and 61.43% for real and reactive power loss accordingly. The voltage profile improved by 8.17% on installation of wind DG. The network ASAI reliability index value before wind DG integration was 0.99743 p.u. which improved by 1.81% after their installation. In general, network power losses are minimized besides acceptable voltage profile being maintained for a sustained distribution system reliability when several wind DGs are integrated.
Active Power Loss, Distribution Network, Optimization Function, Reliability index, Wind Turbine
USER STORY QUALITY EVALUATION: ANALYZING FRAMEWORKS AND APPLICATION METHODS
Quality issues in User Stories (US) frequently arise during implementation, with common problems including ambiguity, integration challenges, and inconsistencies in templates or formal structure. These challenges emphasize the critical need to evaluate the quality of the US, prompting extensive research focused on developing methods and innovations to improve the requirement elicitation process. This study aims to analyze the frameworks, application methods, and tools employed in previous research to assess US quality. Utilizing the Kitchenham framework, 26 relevant studies were systematically reviewed. Next, the analysis identified six prominent quality evaluation frameworks: INVEST, Quality User Story (QUS), ISO 25010, IEEE 830, USQM, and linguistic-based approaches. The application of these frameworks often involves experts, practitioners, and artificial intelligence (AI). Meanwhile, among the tools reviewed, AQUSA emerged as the most frequently used due to its alignment with QUS standards. These findings highlight the adaptability of existing frameworks and tools while underscoring the potential for further integrating generative AI to enhance the accuracy and efficiency of US quality evaluations. Subsequently, future research should explore innovative AI-based methods to advance this critical area of requirement engineering.
DEVELOPMENT OF AN ADVANCED BLIND STICK FOR ENHANCED MOBILITY AND SAFETY OF VISUALLY IMPAIRED AND ELDERLY INDIVIDUALS USING IOT
With the growing challenges faced by visually impaired and elderly individuals, particularly regarding mobility and safety, the development of advanced assistive technologies has become crucial. This paper presents the design and implementation of an Advanced Blind Stick (ABS) leveraging Internet of Things (IoT) technology. The ABS integrates multiple sensors, including ultrasonic sensors for obstacle detection, infrared sensors for fire and pothole/stair detection, a moisture sensor for puddle detection, a GPS module for real-time location tracking, and a thermistor for temperature monitoring. An Arduino microcontroller processes sensor data, providing real-time feedback via a smartphone application. The system also includes an emergency save our soul (SOS) button, allowing users to alert caregivers or emergency services during critical situations. The developed ABS enhances mobility and safety by incorporating advanced features absent in previous models. It uniquely detects puddle, monitors both ambient and user temperature, and retains essential functionalities such as potholes/stair detection, IoT integration, caregiver reporting, SOS, and fire detection. This comprehensive assistive device ensures real-time assistance, improved usability, and greater independence for users. Tested with visually impaired and elderly participants, the ABS demonstrated significant improvements in mobility, safety, and confidence. Its lightweight, cost-effective design makes it particularly beneficial for resource-constrained settings, offering an invaluable tool to enhance the independence and well-being of visually impaired and elderly individuals.
Blind Stick, IoT, obstacle detection, elderly assistance, assistive technology
HYBRID MEMBRANE PHOTOCATALYTIC REACTORS FOR POLLUTED RIVER WATER TREATMENT: A REVIEW ON ADVANCEMENTS AND PILOT-SCALE PROSPECTS
The increasing discharge of untreated effluents into rivers, particularly from agricultural and industrial sectors, has led to severe degradation of surface water quality. In response, global and national authorities have established various water quality standards, such as Malaysia’s Water Quality Index (WQI) and National Water Quality Standards (NWQS), to ensure cleaner water resources. However, conventional treatment methods such as coagulation, flocculation, and filtration are often inadequate in removing micropollutants and emerging contaminants. This review outlines the current landscape of river water treatment in Malaysia, with a specific focus on emerging hybrid membrane-photocatalytic reactor (MPR) technologies. The integration of membrane separation and photocatalytic degradation in MPR systems offers a synergistic solution to enhance pollutant removal efficiency, reduce membrane fouling, and promote sustainable operation. Recent advancements at the laboratory scale have demonstrated promising pollutant removal efficiencies for both organic and inorganic contaminants. However, scaling to pilot-level remains limited due to catalyst recovery issues and operational complexity. By analyzing regulatory frameworks, treatment performance, and system configurations, this review highlights the potential of hybrid MPRs as a transformative approach for polluted river water remediation. The findings support the development of integrated and high-performance treatment strategies suited for complex aquatic environments.
DESIGN OF AN INTEGRATED MODEL USING MULTIAGENT BIOINSPIRED DEEP REINFORCEMENT LEARNING FOR SCALABLE BLOCKCHAIN-BASED MACHINE LEARNING SYSTEMS
With the increasing complexity and scale of ML workloads, the necessity to develop secure and efficient consensus mechanisms for blockchain-based ML is now becoming a crucial requirement. Among the two existing consensus protocols, that include PoW and PoS, both are relatively incapable of handling heterogeneous ML tasks because of both high computationally expensive overhead and its energy inefficiency as well as an inability to dynamically adapt itself in varying workloads. In doing so, this work proposes a state-of-the-art suite of consensus designs based on five optimization techniques: the ABC algorithm, MADRL, GA-based sharding, ACO, and DS-RDA. The ABC algorithm optimizes node assignment based on network states and task complexity, which produces 20-30% latency reduction and up to a 25% improvement in energy efficiency. MADRL enables decentralized resource allocation across heterogeneous ML workloads, enhancing throughput by up to 40% and timestamp to complete tasks by 35%. GA-based sharding optimizes the dynamic partitioning process, provides 50% better shard balance and improvements in using resources of up to 30%. ACO can off-chain the computation tasks efficiently; thereby, it reduces 40% of the load from the chain and improves parallel processing by increasing 25%. Last but not the least, DS-RDA improves scalability due to the dynamic nature of reconfiguring shards in line with real-time fluctuations in workload, thus increasing up to 45% throughput under high-load conditions. Collectively, they significantly enhance computation efficiency, resource distribution, and scalability with blockchain security in order to provide a new and robust solution to the present limitations within the existing consensus protocol of machine learning operations.
Consensus Algorithms, Machine Learning, Resource Optimization, Dynamic Sharding, Scenarios
ECO DRAINAGE MODEL USING POROUS PAVING BLOCK WITH SIEVE WASTE CORAL FILLER AND PUMICE STONE
Environmental problems in the form of changes in land use have an impact on the degradation of land carrying capacity. Rapid urban growth with various paving materials causes various problems such as reduced water absorption, increased surface runoff and puddles and reduced infiltration and percolation capacity. The most extreme condition of paving use is flooding. In overcoming the problem of reducing the impact on waterproof pavement in residential, office and industrial areas, pavement modification with the eco-drain concept is needed. The eco-drain concept is to use porous paving blocks with fillers. The paving blocks used are grass blocks whose cavities/pores are filled with natural stone in the form of sifted waste rock and pumice. The purpose of the study is to obtain an environmentally friendly pavement model that can reduce surface runoff, prevent puddles and conserve groundwater. The modeling results show an average reduction in surface runoff on paving blocks with sieved waste filler of up to 20%, while with pumice filler the reduction in runoff is greater, up to 25 - 30%. The value of the surface runoff coefficient (C) for hexagonal paving block land cover ranges from 0.327 - 0.492. While the value of the runoff coefficient for porous paving blocks with sieved waste stone filler is: 0.302 ˗ 0.411 and for paving block land cover with pumice filler is 0.267 ˗ 0.396.
paving, grass block, waste, pumice, runoff coefficient.
SHAPING THE FUTURE OF WIRELESS COMMUNICATION: AN ANALYSIS OF REFLECTARRAY ANTENNAS FOR 5G/6G
The rapid development of intelligent emerging applications and technologies requires a huge data rate along with reliable and efficient connectivity. Each new generation is better than the last one in several ways. Fifth Generation (5G) communication represented various features as compared to 4th generation communication. However, 5G specifications severely limit on demands of innovative emerging technologies. Which include a high data rate, more capacity, low latency, reliability, resource sharing, and energy per bit. While testing phase for 5G is ongoing in most countries, the 6G technology has become the focus of modern research. In this paper, we discuss the implementation of 6G projects worldwide, requirements, and 6G emerging technologies applications such as Multi-Sensory XR, Robotics and Autonomation, Smart and Remote Healthcare, Internet of Everything (IoE), Wireless power transfer and Wireless Brain Computer Intercommunication. Reflectarray antenna (RAs) are a potential antenna for 5G and 6G systems because they have many advantages including high gain, beam modelling, beam scanning, reconfigurability, and multiple beams. This study represents a comprehensive review of the design of single and dual band reflectarray antenna in some specific research areas. The development of several design operations for improving the reflection phase, gain, and efficiency are covered in detail. Several methods of enhancing the unit cell reflectarray featured properties to make them 5G-compatible.
CLASSIFICATION OF HYDROGEN CONCENTRATIONS BASED ON TIO2 GAS SENSOR RESPONSES USING ARTIFICIAL NEURAL NETWORK
In this research endeavor, a TiO2 gas sensor was employed to discern the TiO2 gas sensor response to varying hydrogen gas concentrations across three distinct temperature settings: 150℃, 200℃, and 250℃. The concentration levels spanned from 100 to 1000 ppm. The primary objective of this investigation was twofold: firstly, to eliminate the noise from the captured response, thereby clustering the gas sensor response at various hydrogen concentrations using principal component analysis, and secondly, to classify the hydrogen concentration using an artificial neural network. Five distinct hydrogen concentration values were extracted from each set of samples, in the range of 100 to 1000 ppm. All the values were acquired at different operational temperatures. The ensuing analytical phase utilizes the Principal Component Analysis (PCA) method in conjunction with an Artificial Neural Network (ANN). Remarkably, classification accuracy achieved a median testing accuracy of 88.8% in 70% of the training data and 15% of the testing strategy
THE UTILIZATION OF ROOM AND BUILDING COMFORT EQUATION FOR REPORTING THERMAL COMFORT: A CASE STUDY AT LABORATORY BUILDINGS
As a tropical country, the government of Indonesia issued energy conservation standards for air conditioning systems in buildings through the Indonesian National Standard (SNI) 6390:2020. However, this SNI has not provided specific calculations to identify the comfort level of parameters in a building. This study aims to provide clear limits by providing conditions for fulfilling comfort conditions for indoor temperature parameters and buildings using a new proposed equation, namely the Room Comfort Equation (RCE) and Building Comfort Equation (BCE). These methods are based on the average ratio of room data parameters to SNI limited by a widening factor of 1% to 5% as comfort temperature tolerance threshold values. RCE is an Indoor temperature measurement compared to the SNI, resulting in a temperature parameter ratio . At the same time, BCE is the sum of the RCE across the building. The room temperature was measured for 4 weeks manually using a digital Environmental meter in four rooms , , and at the Integrated Laboratory and Technology Innovation Center (LTSIT) Building, University of Lampung. The calculation results obtained a temperature parameter ratio (RCE) of 0.958, 1.012, 1.002, and 0.990, so a BCE of 3.96 was obtained. This result shows that the building temperature is ideal by a 5% widening factor, but there are two rooms ( and ) has a passing tolerance limit of 1% widening factor. This finding successfully identified that the cause was the behavioural factor of busy users during practical hours. These results also show that room temperature comfort is more straightforward to measure and report. Therefore, additional efforts are needed to obtain the ideal/optimal temperature, such as upgrading HVAC systems with distributed adaptive control, which can be an effective temperature control solution while compensating for disturbances due to occupant behaviour and the influence of differing ambient weather conditions.
DEEP STAIRS DETECTION BASED ON CONVOLUTIONAL NEURAL NETWORKS FOR VISUALLY IMPAIRED PERSONS
In 2022, an alarming 2.2 billion people worldwide suffered from some form of vision impairment, with 237 million individuals experiencing moderate to severe visual impairment, putting them at a heightened risk of accidents and injuries. Clinical studies have shown that individuals with significant vision loss are at heightened risk of accidents, particularly during activities involving motion and orientation. Recent research on assistive devices has leveraged deep learning techniques to enhance safety, particularly by detecting stairs and preventing falls. This project aims to evaluate and compare the performance of well-known convolutional neural networks (CNNs) in detecting and classifying stairs, specifically MobileNetV2, GoogleNet, and AlexNet. A dataset of 3,000 RGB images, captured at a resolution of 2268 x 4032 pixels, was used, featuring images of stairs (upstairs, downstairs) and non-stair elements. The labeled dataset was augmented to match the input layer size of the pre-trained models and processed using MATLAB R2022b. Model performance was assessed by analyzing training and validation accuracy and loss through a training progress graph. Additionally, testing accuracy, precision, recall, and F1 score were evaluated using a confusion matrix. The results demonstrated that AlexNet outperformed GoogleNet and MobileNetV2, achieving an impressive 99% accuracy across all performance metrics.
Visual impairment, Assistive technology, Convolutional neural networks, Stair fall risk, Deep learning
STRUCTURAL BEHAVIOUR OF ALUMINIUM ALLOY BEAMS TO EUROCODE 9
The increasing demand for lightweight, corrosion-resistant, and sustainable construction materials highlights the need for exploring alternatives to conventional steel, such as aluminium alloys. Despite their advantages, including a high strength-to-weight ratio and environmental friendliness, aluminium alloys face challenges such as lower modulus of elasticity, reduced buckling resistance, and higher deflection compared to steel. This study investigates the structural behaviour of aluminium alloy beams under Eurocode 9 design guidelines, aiming to address these limitations and identify optimisation strategies for their application in construction. Using manual calculation spreadsheet and SCIA Engineer software, structural analysis, validation, and parametric studies were conducted. In the first case study, 24 aluminium alloy specimens with varying cross-sections, alloy series, and beam lengths were compared to steel beams. The findings reveal that while steel beams generally exhibit higher flexural resistance, aluminium beams EN AW 7020 show superior performance within its series due to its high yield strength. Increased cross-section areas enhance flexural resistance, while longer beam lengths reduce buckling moment resistance and increased deflection. To address deflection and buckling challenges caused by lower modulus of elasticity of aluminium alloy, an optimal increase in flange width by a factor of 1.6 was proposed in second case study. In the third case study, conventional aluminium alloy sections (CS RHS and CS SHS) were evaluated for structural feasibility compared to standard hollow sections (RHS and SHS). Aluminium alloy sections outperformed hollow sections in bending and deflection, whereas CS RHS exhibited lower shear and buckling resistance than RHS due to smaller torsional constants and shear areas. This study underscores the potential of aluminium alloys as a viable alternative to steel, providing critical insights into their structural optimisation. The results offer valuable guidance for improving aluminium alloy beam designs, promoting their adoption in construction, and advancing sustainable engineering practices.
AN ANALYSIS OF THE EVOLUTION IN THE MICROSTRUCTURE AND MECHANICAL PROPERTIES OF ALUMINUM ALLOY FOLLOWING THE ECAP PROCESS
An analysis of the changes in the microstructure and mechanical properties of Aluminum Alloy 6016 after the ECAP process is presented in this study. It involves designing an easy-to-use die set and having it manufactured for the ECAP procedure. After heating and nitrogen permeation to achieve the required hardness and strength, the A6016 Aluminum extrusion experiment was conducted. The extruded product was tested for hardness, tensile strength, and microstructure to examine the effect of the processing on the microstructure and mechanical properties of the aluminum alloy. Experimental results show that the die can only press each billet for a maximum of 2 times and cracks on the 3rd pass. The hardness of the billet increased from 36.5 HB (as-received sample) to 67.4 HB and reached 78.8 HB after the first and second pass, respectively. The tensile strength of aluminum also increased significantly; particularly, the maximum tensile stress rose from 50 bar from the initial billet to 65 bar and 70 bar after the first and second pass, correspondingly. The microstructure of the part also changed considerably. From the initial spherical shape structure, the grains were elongated in the pressing direction after the first pressing; and in the second pressing, some grains continued to be elongated further while some others were broken into smaller grains. The overall results show that after only 2 presses through the ECAP channel, aluminum 6016 has significantly improved its microstructure and mechanical properties.
ROUTING PROTOCOLS IN FANET FOR DISASTER AREA NETWORKS: A REVIEW
A group of small, unmanned aerial vehicles (UAVs) linked together in an ad hoc fashion is called a Flying Ad hoc Network (FANET). The main characteristics of FANETs are the absence of central control, mobility, self-organizing, and flexibility, which makes FANET a suitable solution for disaster relief applications where the typical communication infrastructure is likely to be malfunction. UAVs can be used to scan the afflicted region in search and rescue missions and help set up communication network for survivors of disasters, emergency responders, and the nearest cellular infrastructure. To create a stable and robust connection between numerous flying UAVs in an ad-hoc network, suitable communication routing protocols that could be implemented on highly dynamic networks are required. This paper reviews and investigates current FANET routing protocols to determine which routing protocol is the most appropriate in a particular disaster scenario. In addition, reviewing the mechanism, the performance of existing routing protocols, the simulators utilized, routing metrics, performance metrics, advantages and disadvantages, and the potential application in disaster areas of those protocols. The result of the analysis and investigation of this work indicates that for each disaster situation, there is no particular routing protocol; protocol selection is depending on a number of criteria, including network size, network density, UAV velocity, and disaster application.
IOT-BASED INTELLIGENT TRAFFIC MANAGEMENT SYSTEM WITH DYNAMIC GREEN CORRIDORS FOR EMERGENCY VEHICLE PRIORITIZATION
Traffic congestion has become a critical issue in urban areas worldwide, leading to increased accidents and delays for emergency vehicles. This paper proposes a solution utilizing IoT-enabled technology to alleviate these challenges by implementing "Green Corridors" for emergency vehicles. By leveraging Intelligent Traffic Management Systems (ITMS), which integrate sensors, cameras, and data analysis, this research aims to evaluate the effectiveness of such systems in reducing congestion and improving road safety. A comprehensive evaluation framework will be employed to analyze the impact of ITMS implementation on traffic flow patterns and safety outcomes. The system utilizes UWB technology to identify emergency vehicles, triggering traffic lights to switch to green and notifying nearby vehicles to clear lanes, facilitating unimpeded passage for emergency services. This integrated approach addresses the dual challenges of traffic congestion and emergency response, offering valuable insights for policymakers and urban planners seeking effective solutions for smart city transportation management.
EXPERIMENTAL AND COMPARATIVE STUDY OF TWO SOLAR COOKERS: BOX COOKER AND PARABOLIC COOKER
The aim of this work is to experiment and compare the operation of two solar cookers, namely a parabolic solar cooker and a box solar cooker in order to encourage the population to use solar thermal equipment. For this purpose a parabolic cooker fitted with a mirror as a reflector and a box cooker were created. These two sensors are tested under the same conditions following the international standard procedure for testing solar cookers and reporting performance. Under a solar flux varying between 250 W/m2 and 1009.78 W/m2, the maximum water temperature is 108.87°C and 98.02°C at the parabolic cooker and the box cooker, respectively. The parabolic cooker has a standardized cooking power than the box cooker. The specific boiling time is 0.41 h.m2.Kg-1 and 0.54 h.m2.Kg-1 for the parabolic cooker and the box cooker respectively. The output of the parabolic cooker is higher than the canned one in the morning. The two efficiencies decrease as a function of time and in the evening the efficiency of the parabolic collector becomes lower compared to that of the box cooker. In view of these results, solar cookers can be used in our climate zone to meet certain cooking energy needs.
Solar cooker, Efficiency, Temperature, cooking power, boiling time
MULTI-DIMENSIONAL SQUARE WAVE EVALUATION AND SIMULATION EMPLOYING THE CUBIC INTERPOLATION PSEUDO-PARTICLE METHOD
The Cubic Interpolation Pseudo-Particle (CIP) method is used in various papers to simulate different phenomena. It solves hyperbolic-type equations and is more efficient than a first-order upwind scheme. This verification gives one the future to apply CIP to other or more complex geometries. In this paper, we simulate the numerically square wave propagation based on characteristic equations, using the CIP method using 1D-CIP and 2D-CIP. Based on the idea that the wave field and its spatial derivative propagate along the same characteristic curves obtained from a hyperbolic differential equation. In this research, we simulate the numerical propagation of the acoustic wave based on characteristic equations, using the CIP method with two dimensions 1D-CIP & and 2D-CIP. In addition, provides several numerical simulation behaviors in demonstrating how the CIP can accurately model the propagation of acoustic waves without much numerical dispersion. Moreover, the mean square error displayed the superiority of 2D-CIP with 0.5% over 1D-CIP. However, the characteristic-based CIP method is a particularly effective way to handle wave propagation to tackle the fluid's dynamic challenges and give accuracy, managing nonlinearities, and flexibility, making it a useful tool in numerical analysis.
Cubic Interpolation pseudo-Particle (CIP)
LOAD VARIATIONS BASED OPTIMAL REACTIVE POWER SUPPORT FOR HIGH ECONOMIC GAIN IN DISTRIBUTION ELECTRIC POWER NETWORK
Problems such as high impedance, below normal bus voltages, and Load density of the Distribution Electric Power Network (DEPN) lead to a rise in power losses (real and reactive power losses) with large deviations in node voltage profile. For DEPN planning and operation studies, the power and voltage deviation have been significantly apprehensive. For the past five decades, several researchers have been focusing on DEPN to reduce Power losses to the minimum level. Sitting and sizing of Shunt Capacitor Banks (SSSCB) optimally in the DEPN is of greatest importance in reducing the power losses, power factor improvement, and voltage profile upgradation thereby more Economic Gain (EG). The target of this study is to enhance the performance of the DEPN using Reactive Power Support (RPS) under three different load levels to minimize the capacitor investment cost and maximize the difference in kWh and kVARh using the Sea-Horse Optimizer Algorithm (SHOA). The proposed algorithm has been witnessed with the help of two general test systems (PG&E 69 bus and Indian 118 bus) and one real DEPN (28 bus system, PEA, Thailand). The overall % power loss reduction ranging between 29% and 37% has been observed across the three load variations for all the three test systems. Simulation results reveal that significant improvements in technical and EG are fulfilled which confirms the prospective of the proposed RPS procedure.
Economic Gain, Distribution Electric Power Network, Power losses reduction, Sea-Horse Optimizer Algorithm, Sitting and Sizing of Shunt Capacitor Banks, Reactive Power Support
OPTIMIZATION OF OPERATIONAL PARAMETERS TO ENHANCE SYNGAS YIELD AND SELECTIVITY IN THE GASIFICATION PROCESS WITHIN A FLUIDIZED BED REACTOR
One of the gasifiers used in the gasification process is the fluidized bed reactor. Determining the optimal process parameters for a fluidized bed reactor through direct experimentation is both time-consuming and costly. These challenges can be addressed through a computational fluid dynamics (CFD) based numerical approach. In this study, the Eulerian-Eulerian two-phase model coupled with kinetic model will be employed to predict the amount of product produced by chemical reaction. To achieve the best performance of the fluidized bed reactor and optimize the production of syngas, variations in temperature, airflow rate, biomass flow rate, and biomass humidity will be explored to determine the optimal operational conditions for syngas production and selectivity. The simulation results indicate that high-intensity reactions occur around the biomass feed, increasing the temperature in that region. Changes in process parameters influence syngas yield. H2 yield enhanced 3% at 12 L/min air flow rate. Conversely H2 production decreased at inlet air temperature 823 K due to consumed to produce CH4. A high biomass feed rate extends retention time, thereby increasing the likelihood of secondary reactions that produce CO and CH4. Additionally, high water content in the gasification process decreases CO production and increases CO2 production.
Optimization, Gasification, Fluidized Bed Reactor, Syngas, Computational Fluid Dynamics
ENHANCED CONGESTION CONTROL IN FUTURE-GENERATION 5G/6G NETWORKS: A NOVEL HYBRID DEEP LEARNING MODEL
Next-generation networks, such as millimeter-wave LAN, broadband wireless access systems, and 5th or 6th generation (5G/6G) networks, require enhanced security, diminished latency, and augmented reliability. Efficient congestion management is crucial for 5G/6G technologies, enabling operators to monitor many network instances on a unified infrastructure to provide enhanced quality of service (QoS). The increasing network traffic generated by these systems requires advanced methods for load balancing, preventing network slice failures, and offering alternatives when overloads or slice failures. This study introduces a reliable and efficient hybrid deep learning-based method for congestion reduction. The model combines Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) techniques to improve traffic prediction and resource distribution. The model achieved an overall accuracy of 93.23% during a one-week simulation with unidentified gadgets and variable settings. Additional metrics, such as specificity, recall, time efficiency, and F-score, further demonstrate the model's effectiveness in mitigating congestion and enhancing network performance.
Network slicing, 5G/6G networks, Hybrid deep learning model, Congestion control, LSTM, SVM
CARBONIZATION OF SPENT COFFEE GROUNDS: A PATHWAY TO HIGH-ENERGY BIOMASS PELLETS
This research investigates the use of spent coffee grounds (SCG) for producing pellet fuel (PF), with a focus on enhancing their energy properties through carbonization. Thermogravimetric analysis (TGA) was employed to determine the optimal carbonization temperatures. Three types of SCG samples were prepared for pelletization: (1) Dry spent coffee grounds (DSCG), (2) Carbonized spent coffee grounds at 250 °C (CSCG250), and (3) Carbonized spent coffee grounds at 350 °C (CSCG350). The pellets were formed using a custom-built single pellet press, utilizing a cold pressing technique with tapioca starch as a binder. The physical properties (length, diameter, durability index, bulk density, and weight) and fuel properties (moisture content, ash content, volatile matter, fixed carbon, and heating value) were analyzed in accordance with ASTM (American Society for Testing and Materials) standards. The analysis showed that the length, diameter, durability index, bulk density, and ash content of all three pellet types met established biomass pellet standards. However, moisture content analysis revealed that DSCG-PF and CSCG250-PF exceeded the standard threshold, likely due to the cold pressing technique, which does not reduce moisture content. Although sun-drying was employed post-production, the resulting moisture levels were inconsistent. Nonetheless, the residual moisture did not significantly impact fuel quality. The heating value analysis revealed a substantial increase from 21.9 MJ/kg in DSCG-PF to 30.9 MJ/kg in CSCG350-PF, representing a 40.1% improvement. All pellet types met the standard heating value criteria, with CSCG350 displaying the most favorable energy properties. The production of pellet fuel from SCG presents an effective solution for coffee waste management. Furthermore, the carbonization process substantially enhances the fuel properties and heating value of the resulting pellets, with the CSCG350 sample showing the most promising results. Therefore, producing SCG into pellet fuel is another good way to solve the problem of SCG waste. The carbonization process is a way to make the fuel properties and heating value of the pellets more efficient. This research contributes to the development of sustainable, high-efficiency biomass fuels from SCG, while addressing the issue of coffee waste disposal.
Solid fuel, Biomass pellet fuel, Energy properties, Heating value
MODELING AND OUTDOOR CHARACTERIZATION OF TWO THIN-FILM PHOTOVOLTAIC MODULES: A CASE STUDY OF A-SI AND CIGS MODULES IN A SEMI-ARID CLIMATE ZONE
Reliable modeling of photovoltaic (PV) module performance under field conditions is important for optimizing system design. This study evaluates two promising thin-film technologies, an amorphous silicon (a-Si) 5W module and copper indium gallium diselenide (CIGS) 7W module, under Baghdad’s semi-arid climate over six months. Outdoor current-voltage and power-voltage curves were measured at irradiation levels of 500-1000 W/m2 and module temperatures of 25-50°C. A validated five-parameter model was used to simulate module performance. At 1000 W/m2 irradiation and 25°C, measurements showed maximum power outputs of 4.05W and 7.11W for the a-Si and CIGS modules respectively. The model predicted these values within 5% (3.9W) and 4% (7.37W). Model errors were below 10% for all test conditions. Specifically, at 500 W/m2 and 50°C, measured powers were 1.75W (a-Si) and 3.11W (CIGS), while modeled outputs were 1.8W and 3.07W respectively. This analysis provides a comprehensive long-term experimental dataset and accurate modeling of these thin-film technologies under real Middle Eastern operating conditions. It can aid optimization of PV systems based on these promising thin-film technologies
Photovoltaics, Thin-film solar modules, Five-parameter model, Outdoor characterization.
EXPERIMENTAL AND NUMERICAL INVESTIGATION ON THE EFFECT OF PCM AND EXTERNAL REFLECTORS ON THE PERFORMANCE OF A CONVENTIONAL SOLAR STILL
The conventional solar still (CSS) is a device that utilises solar energy to generate distilled water by employing thermal processes such as evaporation and condensation of brackish water The CSS is a desalination system in the development stage; further enhancement is required to increase its output yield. In the current study, an experimental and numerical investigation was conducted to explore the impact of using PCM and external reflectors on the performance of conventional solar still during different months in the climate of Baghdad. Also, the impact of water depth was examined for 2 cm, 3 cm, and 4cm. Furthermore, a series of numerical simulations using ANSYS-FLUENT commercial software was conducted. The results showed that using PCM improves the yield by 8% on average while using both PCM and external reflectors improves the yield by 24%. The results also revealed that increasing the water depth from 2 cm to 4 cm reduces the yield by 16%. The numerical simulation revealed that the numerical and experimental yield are in good agreement, with less than 5% as a maximum deviation.
UTILIZATION OF BIM MODEL FOR OSH COST ESTIMATION ON LOW AND MEDIUM RISE BUILDING PROJECTS
The Occupational Safety and Health (OSH) accident in Indonesia has reached its highest level in five years. The development of OSH cost includes the project budget to reduce accidents. Building Information Modeling (BIM) can detect potential causes of accidents prior to the construction starts so that the accident can be reduced. Many utilizations of BIM in OSH focus on planning and prevention rather than on OSH cost estimation. This study aims to conduct OSH cost component estimation using BIM for a building project in Indonesia. Two projects in the planning phase, tower building and warehouse building, are studied. The result indicates BIM utilization contributes to a to a more detailed safety equipment budget compared to the actual case. OSH cost reaches 2.01% from total budgeted project cost for tower building and 0.37% from total budgeted project cost for warehouse building. This shows the potential of estimating the OSH budget using the BIM model to achieve the ideal value of the OSH budget (2.01%–3.70%), particularly for mid-rise buildings.
STUDIES OF MECHANICAL PROPERTIES OF BOTTOM ASH FILLED PBT COMPOSITES
Particulate filled polymer composites have been very useful due to their low cost and wide engineering applications. Fly Ash as well as Bottom Ash emerging from thermal power plants have been an inevitable environmental hazard. Polybutylene Terephthalate (PBT) polymer has outstanding mechanical, electrical, chemical properties, better dimensional stability and low moisture absorption properties. Therefore, it has been very widely used for automobile and industrial applications. PBT composite of Bottom Ash, with and without a coupling agent has been made in this work. It is found that flexural modulus and flexural strength of this composite have increased for 90 microns Bottom Ash with the 15% (by wt%) loading and also a cost saving of 15% is reported. However tensile strength and impact strength are found to be decreasing marginally with the increase in % loading of Bottom Ash in the polymer. Scanning electron microscope (SEM) was used to characterize the samples for microstructure.
Bottom ash, Untreated bottom ash (UBA), PBT composite, Scanning electron microscope (SEM), Fly ash.
ANALYZING THE DYNAMICS OF CONTRACTOR FAILURES IN E-PROCUREMENT BASED CONSTRUCTION TENDERS: A CASE STUDY IN BANDA ACEH, INDONESIA
The inefficiencies in the e-procurement tender process have become a critical issue for government construction projects in Banda Aceh, Indonesia, leading to a high incidence of contractor failures. This research investigates the underlying factors contributing to these failures, aiming to identify the primary influences and their relative impacts. The study employs a comprehensive methodological approach, including descriptive analysis, factor analysis, and multiple linear regression analysis. Key findings reveal that administrative factors (X1) and qualification factors (X4) significantly contribute to contractor failures. However, the analysis shows that technical competence (X2) is the most influential factor. The regression analysis results in the equation Y = 5.213 + 0.797X1 + 1.061X2 + 0.609X3 + 0.280X4, with a coefficient of determination (R2) of 0.955. This indicates that 95.50% of the variability in contractor failure rates can be explained by the variables of administration, technical competence, pricing strategy, and qualification. This study contributes to a better understanding of contractor failures in e-procurement dynamics and provides actionable insights that can enhance the efficiency and success of future tender processes.