Journal of Advanced Research in Applied Sciences and Engineering Technology
Journal of Advanced Research in Applied Sciences and Engineering Technology (ARASET) is an international forum for the publication and dissemination of original work which contributes to greater scientific understanding in the area of applied sciences and engineering technology. The Journals reports principally the achievements spanning the interdisciplinary field of applied researches. Scope of the journal includes: biology, chemistry, physics, environmental, health science, mathematics and statistics, geology, engineering, computer science, natural and technological sciences, medicine, and architecture. EVENTS UPDATE Innovate, Write & Publish: An International Research Challenge (IWP-IRC2026-Series 2) – MyCite and International indexed Journal (IWP-IRC2026) 30 August 2026, Hybrid https://submit.confbay.com/conf/iwp-irc2026ii Most cited articles (Source: SCOPUS) Renewable Energy of Rice Husk for Reducing Fossil Energy in Indonesia, Lubis Global Energy Security and European Union: A Review, Khattak et al MHD Flow and Heat Transfer of Double Stratified Micropolar Fluid over a Vertical Permeable Shrinking/Stretching Sheet with Chemical Reaction and Heat Source, Khan et al
Modelling The Relationship Between Social Infrastructure Project Success Factor And Their Criteria In Malaysia
Social infrastructure projects (SIPs) are central to social progress and considered to be an important element that influences the quality of life of new community members in the context of urban planning. This idea is crucial as the Malaysia government focuses on the effort to transform Malaysia into a high-income nation because life quality is an important indicator of the population’s general welfare. Understanding the relationship between SIPs success factors and their criteria could help project managers focus on the control of key factors by allocating reasonable resources to achieve the success as measured by predetermined criteria. Despite the topic’s importance, SIPs studies seem to be absent from the research agenda, particularly with respect to the relationships between SIPs success factors and their criteria. In this study, structural equation modelling (SEM) is employed to model the relationship between SIPs success factors and their criteria. The results demonstrate a significant positive relationship between (1) the post-construction factor and both the classical and modern criteria and (2) the change management factor and both the classical and modern criteria. The results provide a useful reference that enables SIPs-related practitioners to focus on fewer factors and criteria to achieve successful outcomes.
Copyright (c) 2026 Journal of Advanced Research in Applied Sciences and Engineering Technology
Development and Validation of The Instrument for Accessibility Model Framework with Integration of Internet-of-Things for Fire Emergency Preparedness
Fire emergencies present significant risks when building safety systems do not adequately address accessibility, preparedness, and inclusive evacuation needs. The increasing use of Internet-of-Things (IoT) technologies provides opportunities to strengthen fire emergency preparedness through real-time alerts, emergency exit guidance, automated lighting, smart fire extinguisher indicators, and voice-assisted evacuation support. This study develops and validates an instrument for assessing an Accessibility Model Framework with IoT integration for fire emergency preparedness. The instrument is structured around two main dimensions: technology acceptance and accessibility-based fire emergency support. The technology acceptance dimension includes performance expectancy, effort expectancy, social influence, facilitating conditions, perceived usefulness, perceived ease of use, attitude, subjective norms, and perceived behavioural control. The accessibility-based fire emergency support dimension includes IoT-based emergency exit guidance, real-time fire alerts on mobile applications, automated emergency lighting, smart fire extinguisher location indicators, and voice-assisted evacuation support. Six experts from fire safety management, facilities management, IoT technology, and emergency response evaluated the instrument using the Content Validity Index method. The results showed strong content validity, with most items achieving acceptable Item-Level Content Validity Index values and the overall Scale-Level Content Validity Index falling within the recommended range. The findings indicate that the instrument has acceptable conceptual coverage and expert-agreed relevance for evaluating accessibility and preparedness in IoT-enabled fire emergency contexts. However, the findings should be interpreted as evidence of content validity only, rather than evidence of field effectiveness or actual user performance. The validated instrument provides a foundation for future pilot testing, empirical implementation, and refinement in real-world IoT-enabled buildings. It may support researchers, building managers, emergency planners, and policymakers in identifying key accessibility and IoT readiness dimensions, although further testing with users and operational building systems is required before broader policy or practice recommendations can be made.
Investigation of Human Robot Collaboration Safety Based on Speed and Separating Monitoring
The increasing focus on robotics safety underscores the importance of establishing rigorous standards to protect human operators. ISO/TS 15066 is a pivotal standard that reflects this concern, providing guidelines for human robot collaboration (HRC). This paper focusing on one out of four main techniques in safety standard, which is speed and separation monitoring. The novelty lies in integration of Machine Learning (ML), Artificial Intelligence (AI) and Robot Operating System (ROS) to enhance the decision accuracy. The test rig involves a 4-degree of freedom (DOF) serial robotic arm and Microsoft Kinect One adept at recognizing gesture signs and tracking hand movements. Moreover, the system ensures safety by adjusting the robot speed dynamically, even coming to stop if necessary. The result shows improving in decision. This demonstrates significant contribution to advancing safety standards in robotics.
Supplying DC Auxiliary System in Power Station by Using a DC Microgrid
Electrical power generation is the first and most important step in producing electricity. Internally, the generation station has an essential sub-system that is called the DC auxiliary system, which is considered the soul of the station and helps in supplying electricity to the essential components in the power station, such as protection elements, monitoring systems, alarms, and control equipment. So, it needs a main and backup source of electrical supply. A challenging problem is discussed in this paper, which is increasing the reliability of the system through the transition to the DC microgrid. This paper aims to provide two different scenarios using renewable energy sources (RESs) for better reliability and economic operation. Both scenarios use photovoltaic (PV) systems with battery energy storage systems (BESSs) to supply the DC auxiliary system in the generation power station. The first scenario supplies the DC loads for 24 hours and the other one only for 8 hours; then, the loads would be supplied by the grid the rest of the day. As a result, both proposed scenarios use DC microgrid, which is applicable, more efficient, reliable, and cost-effective than the conventional source of supply. In addition to choosing, either one of the two scenarios has its own considerations. The first one is more costly than the other, but it is more sustainable over time. Cairo North Power Station Combined Cycle 750 MW in Shoubra, Egypt, is selected in this paper as the case study. Simulation and results are gained using the PVsyst program. The results are manually checked and compared to the simulation report. Also, the economic study over 25 years for the proposed system is added.
Supplier Performance Evaluation in Supply Chain Management Using Fuzzy TOPSIS Based on Vertex Method
Supplier selection is a critical yet complex process in the competitive automotive industry, often hampered by subjective judgment and uncertainty. This study proposes a hybrid fuzzy multi-criteria decision-making (MCDM) framework integrating fuzzy AHP and fuzzy TOPSIS to enhance selection reliability. Within this framework, fuzzy AHP determines the weights of criteria: profitability, relationship closeness, technological capability, conformance quality, and conflict resolution. Fuzzy TOPSIS, utilizing the vertex method for precise distance measurement, is then applied to rank alternatives. A case study of three Malaysian automotive suppliers demonstrates the framework’s effectiveness. Results and comparative analyses confirm that this integrated approach provides a robust, stable, and flexible decision-support tool for managing imprecise information in industrial procurement.
Thermomechanical Optimization of Drill Margin Geometry in Drilling of Ti-6Al-4V: A Coupled FEM-RSM Investigation
Drilling of titanium alloys generates extreme temperatures, cutting forces, and torque due to their poor thermal conductivity and strong tool–workpiece interaction. These extreme thermomechanical outputs could shorten drill span, reduce hole quality, and increase costs. The drill margin dimensions control frictional contact between the tool and the hole wall. However, the influence of drill margin on thermo-mechanical responses has been scarcely investigated in titanium drilling. In this study, a thermomechanical finite element model (FEM) was developed to analyze the effects of drill margin width (Mw) and margin height (Mh) on drilling temperature, thrust force, and torque. A face-centered central composite design was employed to evaluate the influence of the margin parameters. Response surface methodology (RSM) was used to develop predictive models of temperature, thrust force, and torque. The developed FEM was validated against the experimental results of Chatterjee et al. (2016) at various rotational speeds and feeds in the ranges of 400-600 rev/min and 30-50 mm/min, respectively. The force results produced an average prediction error of 2.5 %, which indicates the simulation model is sufficiently accurate to predict the drilling outputs. The results revealed that both Mw and Mhsignificantly influence drilling performance by varying the frictional contact between the drill margin and the hole wall. Increasing the margin dimensions increases temperature, thrust force, and torque due to intensified rubbing contact. Response surface analysis unveiled a strong interaction between Mw and Mh. The optimal configuration within the investigated design space was identified at Mw = 9% and Mh = 2%. The findings provide useful insights for optimizing drill margin geometry to improve drilling efficiency in titanium alloy (Ti-6Al-4V) machining.
Enzyme Assays on Antidiabetic Properties of Tebu Gajah (Albizia myriophylla) Dried Jelly
Tebu Gajah is a plant with high potential as a traditional medicine in Southeast Asia and is believed to have some antidiabetic properties due to a high phytochemical composition, especially phenols and flavonoids. The objective of this research paper was to identify and determine the α-amylase inhibitory property of the Tebu Gajah dried jelly extract in different concentrations and then compare the α-amylase inhibitory property of the extract with the α-amylase inhibitory property of the pure plant extract. The inhibition strength will be further determined by comparing the IC50. Tebu Gajah pure extract and dried jelly were crudely extracted with 70% ethanol. These extracts were further treated with 10, 5, 2.5, 1.25, and 0.625 mg/mL. α-Amylase inhibition was performed using the iodine-starch colorimetric system where the absorbance value was measured at 620 nm. The % inhibition, strength of inhibition (IC50), and % inhibition correlation were determined. The results showed that in all Tebu Gajah samples, inhibitory activity was dose-dependent. Pure extract exhibited the best inhibitory value of 72.09 % at 10mg/mL, then dried jelly extract 2 and 1 with values 56.83 % and 54.29 %, respectively. Even with reduced inhibitory potency due to the presence of the transformation process of dried jelly, it was observed that both forms of the jelly are efficient inhibitors of the enzymes. The level of the extracts and percentage inhibitions showed a positive correlation, which was more pronounced in the dried extracts. Consequently, there was a conclusion drawn to imply that the dried form of Tebu Gajah jelly has the ability to inhibit a-amylase in a functional food in regulating starch and glucose. However, more research would be needed in phytochemicals and in vivo studies concerning its use in antidiabetics.
Optimizing Construction Scheduling across Varying Degrees of Flexibility
In construction project management, it is utmost important to strictly adhere to deadlines. To address this challenge, it becomes essential to have a flexible schedule that can accommodate expected delays during construction. This study delves into the impact of varying degrees of flexibility on overall project outcomes. Flexibility in scheduling was modelled as a function of the activities’ unused modes of construction. The proposed approach integrates flexibility into schedule optimization during the planning phase, with the flexibility degree denoted as “n.” As “n” increases, flexibility decreases, allowing for more crashing options (i.e., expedited construction). Drawing from existing literature, a case study was considered to explore the relationship between the flexibility degree and schedule outcomes, represented in five scenarios. Results reveal that implementing this approach ensures project activities maintain residual flexibility as the project progresses. However, it also leads to higher cost estimates during the planning stage. Conversely, opting for a low value of “n” significantly restricts compression options for activities during planning, resulting in higher total project cost estimates. In extreme scenarios (e.g., Scenario #5), selecting a low “n” value could even cause the project duration to exceed the deadline. Ultimately, the optimal choice of the flexibility degree “n” relies on the unique conditions and requirements of each project. Balancing time, cost, and flexibility becomes critical to mitigate the risk of failing to meet project constraints and enhance the schedule’s ability to absorb unforeseen challenges causing delay during construction.
Spreading and Splashing Dynamics of Bio-Oil Droplets upon Impaction on Inclined Metal Surfaces
Droplet impact on inclined metallic surfaces is critical in heat transfer, coating, and bio-oil delivery systems; however, existing studies have largely focused on low-viscosity fluids and horizontal surfaces, with limited understanding of high-viscosity bio-oils under inclined and rough surface conditions. This study systematically examines the impact dynamics of droplets composed of olive, peanut, sunflower, and a 50% waste–50% olive oil blend on aluminium, copper, mild steel, and stainless steel at room temperature. Droplets with diameters of 2.6–3.2 mm were released at velocities of 3.96 m/s to 4.54 m/s, corresponding to Weber numbers of 923–1214, onto substrates inclined at 5° to 60° with surface roughness Ra ≈ 0.1 to 2.2 µm. High-speed imaging was employed to capture droplet deformation, spreading, and instability during impact. The results display that increasing inclination disrupts spreading symmetry and promotes downslope elongation, while higher inertia enhances deformation and may induce localized splashing. Surface roughness exhibits a regime-dependent effect, where rougher surfaces facilitate contact-line pinning and suppress spreading, whereas smoother surfaces promote lamella mobility and splashing, particularly for low-viscosity liquids. Moreover, fluid viscosity plays a critical stabilizing role, with bio-oils resisting jetting and fragmentation compared to water. These findings demonstrate the coupled effects of inertia, inclination, surface roughness, and fluid properties on droplet impact dynamics, providing a unified physical understanding and practical guidance for optimizing spray cooling, coating, and fuel deposition processes on inclined metallic surfaces.
Proposing LMU-GRUs Based Channel Estimation in Massive MIMO System
A Massive Multiple-Input Multiple-Output (massive MIMO) system relies on channel state information (CSI) feedback to perform precoding and achieve performance gain in frequency division duplex (FDD) networks. However, the transmission of a massive MIMO system is subject to excessive feedback overhead. In this paper, we propose a Deep Learning (DL) approach-based channel estimation technique to enhance recovery quality and improve the trade-off between compression ratio (CR) and complexity of a massive MIMO system. The first proposed technique uses the Channel State Information Network combined with the gated recurrent unit (CsiNet-GRU). The second proposed technique is based upon using CsiNet combined with Legendre Memory Unit in GRUs layers (CsiNet-LMU-GRUs) which improves a novel memory cell for recurrent neural networks that dynamically maintains along by information across long windows of time using relatively few resources. Moreover, the proposed techniques use the dropout method to reduce overfitting during the learning process. The simulation results demonstrate that the proposed (CsiNet-GRU) and (CsiNet-LMU-GRUs) techniques result in a significant performance improvement when compared with existing techniques used in conjunction with massive MIMO systems.
Temperature and Time Effects on Copper Electroplating Performance on Cu-Zn Alloys: Thickness, Conductivity and Corrosion Analysis
In this work, the effects of bath temperature and plating duration on copper (Cu) electrodeposition onto brass (Cu-Zn) substrates from an acidic copper sulfate electrolyte were studied systematically in terms of buildup thickness as well as the electrical conductivity and corrosion resistance by means of depth profiling to identify quantitative processing-property relationships for optimization purposes in industrial settings. Copper electroplating at three temperature levels (25°C, 35°C, 45°C) and three plating times (2, 4, 6 minutes) was conducted using constant electrolyte composition (160 g/L CuSO.5H₂O ₄, 70 g/L H₂SO₄) and applied current (1.5 A), allowing for systematic separation of temperature and time effects. Validation tests showed temperature greater than approximately 35°C were also associated with slightly higher susceptibility to corrosion, as indicated by an increase in the rate of corrosion penetration from salt spray exposure test (ASTM B117) after 72-hour period at temperatures between 25-35°C (4.157 mm/yr), versus 45°C (5.820 mm/yr). In addition, the longer 6-minute plating time led to excellent electrical conductivity with a resistivity of 4.082 mΩ/cm, indicating excellent electronic transport properties for electrical applications, while deposition efficiency decreased with extended plating times due to the onset of mass transport limitations in static bath conditions. The intermediate temperature of 35°C was identified as the optimal operating point, exhibiting suitable performance across various properties, obtaining considerable thickness while maintaining excellent corrosion resistance and conductivity at moderate energy consumption. The comprehensive dataset offers definitive process optimization recommendations, such as maximum productivity at 45°C, ideal corrosion resistance at 25–35°C, a 6-minute duration for optimal conductivity, and a combination of 35°C for 4 minutes for maximum versatility and general-purpose applications that provide balanced overall performance.
Predictive performance of MLP, Random Forest, CNN on knee joint gait angles with single IMU data
Lower limb rehabilitation using active devices faces numerous challenges due to the complexities and uncertainties in human walking patterns. Accurate prediction of gait parameters with minimal subsystems installed in the device is essential. Recent research has focused on gait determination, including locomotion classification, intention detection, and human joint trajectory prediction, which is the focus of this study. This research compares three machine learning algorithms—MLP, CNN, and random forest—for predicting future knee gait angles using data from a single IMU sensor. The MLP model outperformed the other two, achieving the lowest RMSE of 0.0109, compared to 0.0174 for the random forest model and 0.0501 for the CNN. Additionally, the MLP model had a lower execution time than the CNN model. Although the random forest model had the fastest execution time, its large model size was impractical.
Optimization of Pectin Production Parameters from Pineapple Waste
The objective of this study was to optimise the extraction parameters for enhancing the yield of pectin production from pineapple waste. The production of pectin from the pineapple waste was optimised with parameters such as time(120-240s), microwave power (270 W, 360 W, and 700 W), and pH 2-4 to achieve the optimal pectin yield. The Minitab Taguchi approach was used to optimise the parameter to get a high yield of pectin. Additionally, FTIR analysis was utilised to evaluate the quality of the product. The results are a significant yield of 3.7 g and a degree of esterification of 52.4% from 100 g of pineapple waste. These results may be achieved by using the ideal parameters of pH 2, 360 W, and 180 s. To summarise, this study allows for the production of large amounts of high-grade pectin from pineapple waste. The extracted pectin can be utilised in culinary and medical uses as a renewable and eco-friendly component.
Green Synthesis of Copper Nanoparticles using Turmeric (Curcuma longa) Mixed with Activated Carbon for Adsorption of Remazol Brilliant Blue R Dye from Wastewater
Industrialization has surged alongside economic growth, leading to severe wastewater pollution, particularly from dye disposal, which poses risks to water consumers. This study investigates an innovative method for dye removal from wastewater. Previous research has examined the use of nanoparticles mixed with activated carbon to remove dyes such as methyl orange and reactive red 81. However, no studies have specifically addressed the removal of Remazol Brilliant Blue R (RBBR) dye using a combination of copper nanoparticles and activated carbon. RBBR is frequently found in textile industry wastewater. In this research, activated carbon (AC) derived from banana peels was combined with copper nanoparticles (CuNPs) synthesized using a green synthesis method with Curcuma longa extract. The primary goal was to evaluate the adsorption efficiency of this copper nanoparticle-activated carbon (CuNPsAC) composite for the removal of RBBR dye. The synthesis process involved mixing turmeric extract with NaOH and copper sulfate pentahydrate with AC, followed by combining both mixtures. Samples were characterized using UV-Vis spectrometry, Fourier-transform infrared spectroscopy (FTIR) and Differential Scanning Calorimeter (DSC). The findings revealed that CuNPsAC demonstrated significantly higher adsorption efficiency for RBBR dye (90%) compared to AC alone (65.8%) when using 1g of the adsorbent. Other factors, such as adsorbent mass, contact time, dosage, and solution pH, were optimized, with the best results achieved at 1g CuNPsAC, 160 minutes of contact time, 30 mg adsorbent dosage, and pH 9. This study underscores the effectiveness of the CuNPsAC composite in removing dye pollutants from wastewater, offering a sustainable solution for dye wastewater treatment. The green synthesis method using Curcuma longa and banana peels not only boosts dye removal efficiency but also presents an eco-friendly alternative to traditional approaches. These findings suggest a promising strategy for reducing the environmental impact of textile dye effluents.
A Comparative Study of Deep Neural Networks for Aedes Mosquito Species Acoustic Wingbeat Classification
In tropical regions, mosquito borne disease such as dengue and malaria remain major public health challenges. The common approaches to detect mosquito species are inefficient for a large scale surveillance, time consuming and have the risk of human error. To address this issue, this study explores advanced identification approach using deep learning applied to mosquito wingbeat audio. Wingbeat signals were transformed into spectrogram images using the Short Time Fourier Transform (STFT), providing frequency representations as inputs for classification models. This paper examines several deep learning architectures which are convolutional neural networks (CNNs) based, such as ResNet, DenseNet, and MobileNet, as well as Capsule Networks (CapsNet) and Vision Transformer (ViT). Experiments were conducted using the Kaggle mosquito wingbeat audio dataset, with an 80/20 split for training and validation. Results showed that the Vision Transformer achieved the highest classification accuracy at 85%, followed by DenseNet at 83.5%, CapsNet at 80%, MobileNet at 79.5%, and ResNet at 78%. Our findings show that deep learning based analysis of wingbeat spectrograms especially ViT is an effective method for mosquito species identification. Among the tested models, it is proved most effective, highlighting its potential to enhance vector surveillance and support improved public health interventions.
Road traffic accidents continue to pose a critical global challenge, with human error contributing significantly to incident rates, particularly among motorcyclists. One frequently overlooked issue is the failure to deactivate turn signals after maneuvering, which can mislead surrounding road users and increase accident risk. This study addresses the problem through the development of an Advanced Turn Off Signal Alert System (ATSAS) aimed at enhancing rider awareness and reducing signal-related errors. The objective of this research is to design and implement an intelligent, cost-effective alert mechanism capable of notifying riders when turn signals remain active beyond an appropriate duration. The system is developed using an Arduino-based platform integrated with a sensor, vibration motor, and buzzer. The methodology involves system design, circuit simulation, hardware integration, and functional testing. The ATSAS operates through a three-stage alert mechanism activated at 30, 45, and 60 seconds, incorporating progressive vibration intensity and an audible warning at the final stage to ensure effective rider notification. The results demonstrate a high level of effectiveness, with approximately 98% of respondents indicating improved awareness and reduced likelihood of signal misuse. The system successfully enhances rider responsiveness without introducing significant distraction, highlighting its practical applicability in real-world conditions. In conclusion, the ATSAS presents a robust and scalable solution to mitigate human error associated with turn signal usage. Its integration into motorcycle systems has strong potential to advance road safety standards and support the development of smarter, user-centric transportation technologies.
Effect of Air Flow Rate and Temperature on Ash Content, Volatile Ingredients, and Calories through Partial Oxidation Method in Converting Palm Kernel Shells into Adsorbent
One of the biggest commodities in Indonesia is palm oil. Palm oil production in Indonesia continues to increase from year to year, this of course has an impact on increasing Palm Shell (PKSh) production. It should be noted that PKSh is a by-product of palm oil processing in the form of shells which is quite large in quantity, that is, every ton of palm oil produces 65 kg of PKSh. Palm oil shells contain 26.6% cellulose and 27.7% hemicellulose which is good for making activated charcoal. Basically, PKSh has economic value in the palm oil mill itself because it is used as fuel in boilers, but in large quantities and has the potential to be used as active carbon. Activated charcoal is very useful for gas and liquid-based purification technology, where the activated charcoal will function as an adsorbent medium and can also be used in waste processing. Therefore, this research will focus on the activation method, in this case, the activation method is carried out using the partial oxidation technique, namely a pyrolysis process carried out at air flow speeds of 20, 25, 30, 35, and 40 L/minute for 5 hours. This research aims to determine the effect of airflow rate and activated charcoal yield on the values ​​of Ash Content, Volatile Materials, and Fixed Carbon by referring to SNI standard No. 06-3730-1995. The best results were shown at an airflow rate of 35 L/minute with a product yield of 20%, ash content of 7.51%, volatile matter content of 8.73%, and fixed carbon of 77.94%. These results have met SNI 06-3703-1995 standards.
Comparative Study of Physical and Chemical Methods for Synthesizing Zinc Oxide - Loaded Polypropylene Non-Woven Fabrics for Photocatalytic Degradation of Benzophenone-3
Benzophenone-3 (BP-3) is a well-known UV filtering ingredient in chemical sunscreens and other personal care products. However, high levels of BP-3 in discharged sewage water streams have been linked to reproductive damage, abnormal fetal development, endocrine system disruption, and neurotoxicity in experimental animal models. In this study, zinc oxide (ZnO) loaded on polypropylene non-woven fabrics (PNF) as a photocatalyst was created using two different approaches: the physical method and the chemical method. For physical method, ZnO was loaded onto PNF without addition of other chemical while for chemical method, Iminodiacetic acid (IDA) was added as a polymer ligand to hold onto the connection between PNF and ZnO, which helps improve the efficiency and stability of the synthesis process. The study found that PNF/Zn-IDA which is chemically synthesized was effective in degrading BP-3 by up to 93.38% compared to physically synthesized PNF/ZnO which can only remove 18.46% of BP-3. This approach offers a promising solution for the removal of hazardous chemicals from wastewater streams, contributing to a safer and more sustainable environment.
The Effects of using Multimedia in Enhancing Effective Teaching and Learning in Senior High Schools: A Case of Amaniampong Senior High School
This empirical study examined the integration of multimedia tools in teaching and learning at Amaniampong Senior High School (SHS) in the Mampong Municipal area of Ghana. Using mixed-methods research design, the study investigated how videos, interactive animations, digital simulations, and augmented reality applications influenced student engagement, conceptual understanding, and academic performance. Data was collected from 150 students and 20 teachers through surveys, classroom observations, semi-structured interviews, and pre- and post-intervention achievement tests. Findings revealed significant improvements in student motivation, classroom participation, and learning outcomes, particularly in science and mathematics, where multimedia tools enhanced the visualization of abstract concepts. However, the study also identified major implementation barriers, including inadequate ICT infrastructure, unreliable electricity supply, limited internet connectivity, and insufficient teacher training in multimedia-based instruction. The findings suggest that effective multimedia integration in Ghanaian senior high schools requires sustained investment in ICT resources, stable electricity supply, and continuous teacher professional development. The study contributes localized evidence on technology-enhanced learning in resource-constrained educational settings and highlights the importance of contextually relevant multimedia practices for improving teaching and learning outcomes.
Physically Consistent and Unseen-Mix-Validated Machine Learning for Concrete Compressive Strength Prediction
Concrete compressive-strength prediction can support early mix screening, but core engineering use requires evidence beyond random test accuracy. This study develops a physically consistent and unseen-mix-validated machine-learning workflow using the UCI concrete compressive-strength dataset with 1030 records, eight input variables and one strength target. Advanced ensembles, including XGBoost, LightGBM, CatBoost and monotonic gradient boosting, were evaluated using repeated random cross-validation and stricter group validation that held out complete mix-proportion families. Additional evidence included nested group hyperparameter tuning, local physical-response diagnostics, split conformal prediction intervals, conditional coverage, residual diagnostics, permutation importance and SHAP analysis. The best random-validation model was LightGBM unrestricted, with MAE = 2.858 MPa, whereas the best unseen-mix validation model was LightGBM unrestricted, with MAE = 3.839 MPa. The tuned monotonic histogram model achieved unseen-mix MAE = 4.056 MPa and R2 = 0.885 while eliminating tested monotonic-response violations. Conditional conformal analysis revealed high-strength undercoverage, with coverage = 0.834. Regime-transfer stress tests further showed elevated error for high-strength extrapolation (best MAE = 7.872 MPa) and low water-to-binder records (best MAE = 7.115 MPa). The results show that concrete-strength models should be judged by unseen-mixture generalization, physical consistency and conditional uncertainty, not random accuracy alone.
Life Cycle Assessment for Building Materials Constituents using Gabi vs OpenLCA
Cement and paint are two essential construction materials whose manufacturing processes contribute to air pollution and global warming leading to climatic changes. In this research, a life cycle assessment was done using GaBi and OpenLCA simulating tool to study chemical constituents' percentages impact for cement and paint on climate change; acidification; eutrophication, and human toxicity. Results showed that calcium oxide is the main raw material causing pollution in cement manufacturing. From cement charts, the lowest impacts were from the mixture that contained 71.6% calcium oxide. But, from the paint charts, the weakest impacts were from the mixture (WN- RU). These results showed that: reducing the values of the impact categories could be achieved by reducing the participation rates of raw materials with a significant environmental impact in the mixture or replacing the mix of high ecological impact with another mixture, regarding the same quality of the building material.
Secure Privacy Preserving Banking Customer Churn Prediction Using Federated Learning and Fully Homomorphic Encryption
Banking domain is interested in customer churn prediction applications due to the rising competition with financial technologies (FinTech). This fierce competition is impacting banks market share, and it was found that it’s much easier and less costly to keep existing customer rather than acquiring new customers to the bank. Secure privacy preserving Customer Churn Prediction is a challenging and interesting area for research. Federated Machine Learning (FedML) has been proposed to resolve privacy problem, by using federated learning (FL) to apply Machine Learning (ML) prediction at banks locally and was proven to be one of the most effective solutions for this challenge. However, some gaps are identified for using federated machine learning (FedML) like the security attacks targeting the aggregation server or communication with the clients. Accordingly, this research proposes securing FedML vulnerabilities using Fully Homomorphic Encryption (FHE) encryption through a secure privacy preserving framework for customer churn prediction. The proposed framework guarantees the privacy preserving of customer data using Federated Machine Learning (FedML) while securing the aggregation and communication against vulnerabilities by a (FHE) provably secure algorithm. The proposed solution is demonstrated using a public dataset to predict the customer churn of 3 bank clients in different locations. FedML is applied to ensure data privacy for each client by training the model locally while only sharing the updates. FHE is used to encrypt all the updates, model aggregation and model prediction. Prediction accuracy is compared for the global model, the FedML without encryption and the FedML with FHE encryption using neural network binary classifiers. The proposed framework achieved high prediction accuracy, very close to the baseline, in addition to providing privacy and security safeguards that are mandated in banking domain.
IoT-Based Water Quality Monitoring: An Application for Optimal Aquaculture Management
Ineffective water quality monitoring and various farming difficulties causing fish kill events are persistent challenges in aquafarming, significantly impacting fish production. This paper presents a scalable, mobile IoT-based aquaculture monitoring application that tackles the challenges so farmers can enhance fish production, reduce labour costs, and improve food security. With the help of sensors, the application can monitor water quality indicators in real-time for optimal fish growth. In addition, we include predictive analytics and alert messages to mitigate aeration system failures, algal blooms, and unpredictable water changes. The application is a four-module architecture with data acquisition, communication, data processing, and user interface, supported by efficient data gathering, storage, and visualisation. For the application development methodology, we use Evolutionary Prototyping to refine the application repeatedly with the help of user feedback. This work addresses limitations through future work in automation, network backup, and machine learning integration.
MATLAB Implementation of Response Surface Methodology for Structural Damage Identification Using Frequency Response Function Curvature
Structural health monitoring and damage identification are critical for maintaining the integrity and safety of engineering structures. Traditional methods often struggle with accurately identifying damage, particularly in complex structures. This study addresses this challenge by developing a MATLAB-based framework that employs Response Surface Methodology (RSM) and Frequency Response Function (FRF) curvature for model updating in structural damage identification. The methodology involves several key stages: initial finite element (FE) model development, DOE to strategically select parameters, computation of FRF curvature to capture structural responses, construction of primary and secondary response surface models, and an iterative model updating process. The simply supported beam model, with its practical and representative nature, serves as the numerical example. The model's accuracy is validated through comparison with established studies, confirming the low percentage errors in natural frequencies. The response surface models exhibit a strong fit and predictive accuracy, as indicated by high R² values. The study successfully achieves its objective, showing that the RSM-based model updating approach, utilizing FRF curvature, can effectively identify structural damage. In conclusion, this study successfully develops and implements an RSM-based framework for structural damage identification using FRF curvature as the response feature. The findings highlight the method's effectiveness and underscore the need for future research to refine algorithms and validate the approach across a broader range of structural systems, including those exhibiting nonlinear behaviour.
Assessing Hypoxia Tolerance using Rapid Colorimetric Testing of Hypoxia Inducible Factor-1 (HIF-1)
The body reacts by producing a protein called hypoxia-inducible factor (HIF), which triggers a series of physiological changes and serves a central role in the hypoxia response. Its activity is regulated by the oxygen-dependent degradation of the HIF-1α protein (HIF-1A gene). Therefore, the effects of low oxygen tension in hypoxia were explored using rapid and colorimetric tests. The development of the sensor provided more sensitivity for detecting hypoxia by generating a calibration of optimized parameters such as reagent concentrations, reagent volumes, and device dimensions. There is a high sensitivity and specificity in detecting the changes of HIF-1α protein hypoxia using the feasibility hypoxia test. This would enable point-of-care (POC) testing and individual self-administration, resulting in faster and more accurate results that can be used for a robust diagnostic approach and enhanced health surveillance.
Characterization of Bragg Grating Lengths in Single-Mode Fiber for Strain Sensor Applications
This study presents the successful fabrication of Fiber Bragg Gratings (FBGs) using a 248 nm KrF Excimer Laser combined with the optical phase mask technique. The research focuses on investigating the effects of stress on Bragg grating length in single mode fibers (SMFs). A total of five FBGs, each with different grating lengths, were analyzed for strain sensitivity. The findings reveal a linear relationship between strain and Bragg grating length for all tested FBGs. Notably, the FBG with a grating length of 2.50 cm exhibited the highest strain sensitivity, however the strain sensitivity for all FBG grating lengths were found to be consistent. These results suggest the potential for optimizing FBG grating length in multimode fiber to enhance performance in strain-sensitive applications.
Experimental Investigation of Bio-Oil Droplet Impact on Liquid Pools Across Wide Temperature and Weber Number Ranges
Droplet impact on liquid pool surfaces is important in spray cooling, thermal management, and fuel-related applications. However, the combined effects of temperature, Weber number, and liquid configuration on bio-oil droplet impact dynamics remain insufficiently understood, particularly in relation to splashing, jet formation, and vapour explosion behaviour. This study investigates single-droplet impacts on liquid pool surfaces under controlled thermal and dynamic conditions for water-to-water, water-to-oil, and oil-to-water configurations. Distilled water and selected bio-oils, including olive oil, peanut oil, sunflower oil, and a waste-oil/olive-oil blend, were tested over a temperature range from room temperature to 290 °C and at Weber numbers between 172 and 1011. High-speed imaging was employed to capture the transient interfacial dynamics. The results show that Weber number and temperature are dominant parameters governing droplet deformation, jet formation, and instability development. As impact intensity increases, water-to-oil impacts produce greater spreading, crown formation, and jet development, with maximum jet height reaching Hj/D₀ ≈ 9.13 and crown height up to 12.3D₀. In contrast, oil-to-water impacts remain stable and splash-free across all tested conditions, highlighting the stabilizing influence of viscosity and liquid configuration. At 290 °C, vapour explosion events were observed, indicating intensified thermofluid interactions and rapid vapour generation at the liquid interface. These findings provide a unified physical understanding of the coupled effects of inertia, temperature, and liquid properties on bio-oil droplet interactions, while offering useful guidance for improving stability, heat transfer control, and safety in high-temperature industrial and renewable-energy systems.
A 2 x 2 antenna with vary DGS Microstrip for brain tumor detection with high sensitivity
This paper presents a design of four defective ground surface (DGS) - based patch antennas for early detection of stage 0 brain tumours. The four antennas form a 2 x 2 antenna design and are attached to a dielectric ring suitable for being mounted on the head. The proposed 2 x 2 design is composed of four antenna, two of them having an elliptical DGS, while the other two patches have circular DGS. This system is characterized by comprising compact and highly isolated elements. It operates at a frequency of 2.45 GHz and has a wide bandwidth. The antenna's compact dimensions of 50 x 40 x 1.13 mm3 are achieved by employing a meandering geometry, a defective ground structure, and a high permittivity substrate. It is feasible to obtain a bandwidth wider frequency range of 700 MHz (2.38–2.45) GHz. Furthermore, the defects in the ground construction result in a compact size. The performance of the proposed design is demonstrated through simulated and other parameters results using a brain phantom, including the effects of the tumour and without tumour.
A Robust Support Vector Machine Model for Monitoring Methane Emission Levels in Paddy Ecosystems using Electronic Nose Technology
Paddy farming is a significant contributor to global greenhouse gas emissions, particularly methane (CH4) and carbon dioxide (CO2). While accurate monitoring is vital for carbon management, traditional analytical methods are often cost-prohibitive and technically complex. This study develops a robust Support Vector Machine (SVM) model integrated with Electronic Nose (E-Nose) technology to predict carbon emission levels by analyzing multi-dimensional environmental and gas parameters. A comprehensive dataset was collected across three field locations (water inlet, mid-field, and water outlet), comprising CH4 and CO2 concentrations, ambient temperature, humidity, and rice growth stages. The data underwent a rigorous pre-processing pipeline, including median imputation for missing values and outlier removal using the Interquartile Range (IQR) method, which refined the dataset from 1,499 to 1,486 instances. Feature selection via the SelectKBest method identified CH4 and CO2 concentrations, growth stages, and gas fluxes as the most significant predictors. The SVM model utilizing a Radial Basis Function (RBF) kernel was evaluated using two configurations: "All Features" and "Selected Features". The "Selected Features" model demonstrated superior predictive performance on the testing set, achieving a coefficient of determination R2 of 0.9975, a Mean Absolute Error (MAE) of 0.0067, and a Root Mean Square Error (RMSE) of 0.0085. In comparison, the "All Features" model yielded a slightly lower R2 of 0.9918, with a higher MAE of 0.0122 and RMSE of 0.0153. This indicates that isolating key environmental drivers not only reduces computational redundancy but also enhances the model's accuracy and generalizability. The results demonstrate that an optimized SVM model, supported by strategic feature selection, provides a highly reliable and cost-effective tool for monitoring carbon footprints in paddy ecosystems. This framework facilitates the transition toward data-driven sustainable farming and precision emission management.
Inflammatory Markers for Potential Targeted Therapies for Psoriasis: A Review
Psoriasis is characterized by the infiltration of immune cells, epidermal hyperproliferation, and abnormal keratinocyte differentiation. According to recent molecular and cellular research, the key contributors to the pathogenesis of psoriasis are tumor necrosis factor-alpha (TNF-α), interferon-gamma (IFN-γ), interleukin 17 (IL-17), and interleukin 22 (IL-22). Various approaches have been applied to treat psoriasis by suppressing inflammatory expressions, either through conventional therapy or nanotechnology therapy. Conventional therapy shows poor therapeutic effectiveness and requires prolonged treatment durations. These challenges can be overcome with advanced targeted drug delivery systems, which consist of passive targeting and active targeting, offering efficient site-specific delivery. In this review, we discuss the inflammatory markers that contribute to psoriasis and their potential in the development of potent targeted treatment drugs. Moreover, the applications of targeted drug delivery systems for treating psoriasis are summarized.
Developing Blast Prediction Model for Composition 4 (C4) Explosive using Support Vector Machine (SVM)
The Kingery-Bulmash equation has been widely adopted as an explosion prediction model comprising of empirical data for detonation parameters. Theoretically, support vector machine (SVM) is one of the machine learning components that fit small datasets and can generate models with higher accuracy. This study was conducted to develop an explosive prediction model for Composition 4 (C4) using the SVM regression method with explosives weighing in the range from 0.1 to 1.0 kg in burst surface explosion conditions. The experiment was conducted to identify four parameters, namely peak pressure, arrival time, inclined pressure, and reflection pressure, at distances of 0.5 to 5.0 m. The dataset gain was used to develop the prediction model using linear, radial base and polynomial kernel functions. The accuracy for each model was determined using mean square error (MSE), root mean square error (RMSE) and mean absolute error (MAE). The optimisation was conducted using search grid analysis to identify the best configuration for penalty factor (C), epsilon, (ε) and gamma (γ). K-fold cross-validation was used to validate the prediction accuracy. The research focuses on the prediction of blasts, and the model shows significant results with 89% accuracy. The radial basis function (RBF) kernel has better fitting for the dataset and produces a higher accuracy prediction algorithm than linear and polynomial kernel functions.
Nanocoating of Carbon Steel for Anti-Corrosion: A Review
Corrosion is gradual degradation process of metal through chemical reaction. Mostly happen by electrochemical and reaction with the surrounding environment. It affects material properties. Paint industry continuously working on the growing demand on modern coating and strives to improve their products especially to meet requirement in petrochemical industries. Over the past years nanotechnology has become more and more important in the development of coatings. Nanocoatings offer great potential for various applications due to their superior characteristics that are not typically found in conventional coatings. This paper reviews ZnO-SiO2 and CaCO3 as nanopowder in paint formulation as the agent for new and improved corrosion resistant coating by sol-gel method. Focusing more on epoxy as matrix and carbon steel on medium type as common substrate in nanocoating industry.