Modern Applied Science, Issue: Vol.19, No.2

〖Ca〗^(2+)ion Adsorption and Total Hardness Removal Using Synthesised Iron Oxide – Fused Metakaolin: Batch and Column Studies

Wed, 02 Jul 2025 08:19:21 +0000

Groundwater hardness, primarily caused by calcium and magnesium ions, poses significant challenges to domestic, industrial and agricultural uses, as well as health risks associated with cardiovascular disease, kidney stones and other ailments. This study aimed to investigate the removal of total hardness from groundwater using a novel iron oxide-fused metakaolin composite. Initially, calcium adsorption isotherm studies were conducted to assess the composite’s adsorption capacity and mechanism. The favourable adsorption of calcium ions justified further investigation, leading to batch and column experiments that evaluated the composite’s performance in reducing total hardness from real groundwater samples.The calcium adsorption isotherm studies revealed that the adsorption of calcium ions is best described by the Langmuir isotherm with a maximum adsorption capacity of 24.33 mg/g and a separation factor (R_L value)of 0.398 which shows that adsorption process was favourable (R_L<1).A removal efficiency of 89.78% was achieved for groundwater total hardness using batch studies. Column studies using the adsorbent together with sand and activated carbon as support materials achieved a removal efficiency of 97.37%. The study demonstrates the effectiveness of the synthesised adsorbent in removing total hardness from groundwater, highlighting its potential as a viable treatment option for mitigating health risks and improving water quality.

Machine Learning-Based Proactive Fault Monitoring and Prediction in GPON Networks

Sat, 11 Oct 2025 03:03:21 +0000

Advanced fault management techniques beyond conventional reactive procedures are required due to the widespread use of Gigabit Passive Optical Networks (GPON) as essential infrastructure for high-speed internet services. In order to anticipate connectivity problems before service degradation happens, this study introduces a revolutionary proactive fault detection and monitoring system that combines machine learning algorithms with real-time network analytics. Our hybrid technique addresses class imbalance issues while preserving real-world representativeness by combining meticulously vetted synthetic samples with real failure data from Telekom Malaysia's operational GPON infrastructure. In order to forecast five different fault categories—Line Disconnect, Intermittent Failures, Service Down, Frequent Disconnections, and Normal Operation—the system examines crucial network data such as optical power levels, signal-to-noise ratio, reflectance measures, and signal attenuation. Our Support Vector Machine solution achieved 97% classification accuracy with balanced precision and recall across all fault types after thorough evaluation utilizing several machine learning methods. During a six-month operational trial, the implementation of a web-based monitoring dashboard showed practical success with a mean time to fault resolution reduction of almost 60%. Crucially, this study clearly defines the parameters for model generalizability across various network topologies and operating situations and offers an open discussion of the constraints of synthetic data.

Development of a YOLOv11-Based Deep Learning System for Insect Pest Detection and Classification in Oil Palm Plantation

Tue, 14 Oct 2025 05:25:00 +0000

Pest diseases are serious global agricultural issues that lead to lower crop yields, increased cost of production and excessive pesticide use. Traditional methods of identifying pest infestation (e.g., using field scouting methods) rely on intensive labor, human time, and human errors, thus making them impractical for large-scale and sustainable farming. This project is a structured deep learning-based system for automatically identifying pest diseases and pests through image identification. The system is developed using the YOLOv11 state-of-the-art model for object identification and has been trained on a custom-dataset from the objects of three pest species - bagworms, aphids, and whiteflies. The images representing each pest were pre-processed and augmented in order to equalize data and optimal modeling performance. The experimental evaluation of the trained model archieved a precision of 0.88, recall of 0.80, and mAP@0.5 of 0.85, outperforming conventional detection methods and demonstrating strong reliability even with imbalanced classes., thus demonstrating the proposed system is viable for use in real-world agricultural environment. The proposed system can provide an intervention to pest infestation enabling early and timely diagnoses of pest infestation, which in turn may help reduce over-use of pesticides, and contribute to more targeted use of pesticides and sustainable farming practices.

Adaptive Load Sharing Strategy for Multi-Source Renewable Energy Systems

Tue, 14 Oct 2025 05:43:45 +0000

This research optimizes solar, fuel cell, and battery systems for near-fault current, efficiency, and low-transient charging and discharging to extend battery life. Replicating these energy sources on the grid requires MATLAB Simulink assessment and coordination. Goals include assessing PV, Fuel cell, and battery dependability, maintaining load demand, and controlling power generation to reduce battery stress. Battery power management can improve fuel cell longevity and efficiency, and optimizing peak loads can reduce big spikes. Connecting the PV, fuel cell, and battery systems in MATLAB Simulink will simulate load demand and share electricity proportionally. To balance power output, load fulfilment ratios will be based on source capacity and efficiency. This is 2kW from the photovoltaic system, 6kW from the fuel cell system, and 10 kWh from the battery storage system to supply 100 kW. It charges in 1.5–2 seconds and starts working in 0.5–1.5 seconds with PV and fuel cells. In an ideal world, the energy management system would use PV and fuel cells and the batteries first. By synchronizing PVs, fuel cells, and batteries, efficiency and battery life will improve. Thus, optimization and monitoring will focus on battery burden control, transient charging and discharging control, and system efficiency to extend battery life. Battery will also determine fuel cell power responses. This project uses MATLAB Simulink to analyses power source capacities, synchronize power production, and share load to create a dependable and accurate hybrid power system.

Field Portioning Approach for Lightweight Java Rule-based Anomaly Detection in IPv6 Tunneling Environments

Wed, 15 Oct 2025 06:40:23 +0000

The transition from IPv4 to IPv6 introduces new security risks, particularly through tunneling mechanisms that encapsulate IPv6 traffic within IPv4 headers. Conventional Network Intrusion Detection Systems (NIDS) often fail to detect threats hidden in tunneled or multi-layered packets due to limited protocol awareness and high resource consumption. This paper proposes a lightweight, modular Java-based NIDS that employs a Field Portioning Approach (FPA) for efficient, rule-based anomaly detection in IPv6 tunneling environments. The system architecture integrates real-time packet capture, selective decapsulation, field extraction, and context-aware signature matching. Experimental evaluations conducted in a controlled testbed with enterprise and IoT-like devices, where tunneling attacks such as Denial6, NDPExhaust26, and THCSyn6 were launched alongside benign traffic, confirm that the proposed NIDS achieves detection rates exceeding 98% for most tunneling attack types. Its performance is equivalent to Snort enhanced with adaptive FPA, but with significantly lower CPU and memory usage. The Java-based system also maintains low detection latency, demonstrating suitability for resource-constrained environments such as IoT gateways. The main contribution of this work lies in introducing a selective and context-aware field portioning mechanism tailored for tunneled traffic, enabling lightweight yet accurate detection. The results confirm the effectiveness of the Field Portioning Approach in strengthening security for modern, heterogeneous network infrastructures during the IPv6 transition.

Design, Development and Application of a Drone-Integrated Cutting Mechanism for Agriculture

Mon, 20 Oct 2025 11:37:12 +0000

In Malaysia, one of the industrial crop resources is coconut, where the coconut-based products have varied usage and are being exported to other countries. Hence, it contributes to increasing Malaysia's profit, but the industrial crops face challenges in providing a large scale of coconuts because of high demands from manufacturers. Besides, the manual coconut plucking process is a labour-intensive and time-consuming task, demands skilled climbers, and is exposed to safety risks. Thus, this paper presents the design a cutting mechanism and drone for coconut harvesting and to test the performance of real-time camera feed visualization and the cutting process for the drone. The chosen type of drone for this project is a quadcopter drone, and the drone's body is designed on Autodesk Fusion 360 and printed using a 3D printer with PLA filament. The Arduino UNO board acts as a central controller that connects other components such as the MPU6050, receiver, and ESCs. The ESP32 CAM acts as an eye to display the location of coconuts on the tree. The DC motor and steel saw blade is used as cutting mechanism. The results showed that the ESP32 CAM successfully visualized real-time video streaming with minimal lag, while the cutting mechanism able to cut through the rolled paper and branch within a practical timeframe.

Design, Experimental Evaluation, Thermal Efficiency and Economic Performance of Kapenta Fish Greenhouse Solar Dryer

Mon, 27 Oct 2025 08:57:50 +0000

A natural convection greenhouse solar dryer for Kapenta fish was designed and evaluated for its effectiveness in drying performance, thermal efficiency, specific energy consumption, and economic viability in terms of net present value and payback period. The system featured a 1.0 m × 0.9 m drying tray and a 1.5 m² greenhouse floor area, with 40% of the surface exposed for additional solar heating. Constructed from LDPE film, timber, HDPE components, rocks, mosquito netting, and a zipper, the dryer was optimized for efficient airflow, heat retention, and user convenience. Natural convection facilitated continuous airflow, as heated air exited through a top outlet while cooler ambient air entered from the bottom. Internal temperatures ranged from 49 °C to 60 °C, sustained by heat-retaining rocks that extended drying beyond peak sunlight hours. During testing, a 3 kg batch of fish with an initial moisture content of 76.7% was dried to 2.1% (wet basis) within 4.5 hours, compared to 14.3% moisture under open sun drying. The system achieved a thermal efficiency of 22.3% and a specific energy consumption of 2.81 kWh/kg, with an average airflow rate of 0.021 kg/s. Even under moderate solar radiation (773.9 W/m²) and ambient temperatures (19.1 °C), the dryer performed effectively, allowing up to two drying cycles per day. With a payback period of only 1.2 years and nearly nine years of debt-free operation, the system offers a sustainable, low-cost, and practical solution for Kapenta fish preservation in solar-rich regions with limited low-temperature infrastructure.

An IoT-Based Framework for Wildfire Detection Using Multi-Sensors Integration and CNN Image Classification

Mon, 27 Oct 2025 09:10:49 +0000

Wildfires are a growing threat to ecosystems, property, and human lives, especially in rural and forest-adjacent areas where monitoring infrastructure is limited. Traditional detection methods, such as satellite imaging and human surveillance, often suffer from delayed response and low precision during early fire stages. This study proposes a novel IoT-based wildfire detection framework that combines multi-sensor data with deep learning for rapid and localized fire identification. The system integrates smoke and flame sensors with a YOLOv4-based convolutional neural network (CNN) for image classification, all deployed on a Raspberry Pi 5 platform. A dual-layer detection mechanism enables immediate threshold-based alerts and visual confirmation via AI-driven analysis. Real-time notifications are delivered through a Telegram bot, while environmental data are logged and visualized using the ThingSpeak dashboard. The system, developed in Python, is optimized for deployment in low-resource environments. Experimental results demonstrate high detection accuracy and reliable performance across diverse conditions. This work demonstrates the practical potential of lightweight, AI-enhanced IoT systems for early wildfire detection and offers a scalable solution for remote monitoring. Future enhancements will explore more efficient CNN architectures and predictive analytics for proactive fire management.

An LQR Framework for Simultaneous Optimization of Boeing 707-120 Pitch Control

Fri, 31 Oct 2025 06:51:02 +0000

The goal of the research is to optimize a full-state feedback controller for a Boeing 707-120 pitch control using differently tuned Linear Quadratic Regulators (LQRs) to optimize four conflicting objectives (pitch recovery dynamics, elevator effort, reaction time, and comfort). The step response of the aircraft’s pitch is primarily simulated in MATLAB/Simulink. The aircraft's condition is set at cruising altitude and is preparing for descent by pitching down 10 degrees, with only a short period of pitch oscillation considered. The aircraft's longitudinal dynamics are obtained and modelled under certain assumptions, with elevator deflection as the input. The desired pitch dynamics of the aircraft are determined by a short-period thumbprint chart, where a pole placement is implemented and simulated by assigning the desired eigenvalues from a “satisfactory” region of the short-period thumbprint chart. Later, an LQR controller is implemented and simulated by tuning matrices Q and R in various ways. By normalizing and comparing the step response of the LQR controller with pole placement, it is demonstrated that LQR can optimize conflicting objectives with minimal elevator effort, thereby optimizing multiple conflicting objectives while extending the lifespan of elevators. This research contributes to SDG 9 by advancing innovation in aerospace control technology and supporting the development of resilient and efficient aviation systems infrastructure.

Unmanned Surface Vehicle for Water Quality Monitoring

Fri, 31 Oct 2025 10:31:42 +0000

This paper proposed an Unmanned Surface Vehicle (USV) for water quality monitoring purposes. The USV offered high maneuverability and accurate monitoring result with IOT implementation. The paper discussed the design and development of a USV, identifying the functionality of USV sensory system, and evaluating performance of USV based on stability, velocity, and acceleration. The USV is designed based on hemisphere shape and is equipped with two brushed DC motor propellers for maneuvering purposes. The buoyancy of USV is set at 89.1% positive buoyancy for stability purposes. Dabble Gamepad controller is implemented for USV to move remotely. Temperature, pH and turbidity sensors are embedded into the USV system for monitoring purposes. Internet of Things (IoT) system is coupled with the vehicle for data monitoring via internet as it offers versatility and efficiency. The developed USV shows outstanding results in terms of maneuverability and sensors functionality. The embedded sensors reading shows stable and accurate. This developed USV will have impact in maintaining the sustainability, and wellbeing of ecosystems and health of water resources.

The Role of Psychologists in Highlighting the Importance of School Ergonomics on the Educational Process from the Perspective of School Teachers in Jordanian Society

Sat, 01 Nov 2025 02:38:14 +0000

This study aimed to identify the role played by psychologists in highlighting the importance of school ergonomics to the educational process, from the perspective of school teachers in Jordanian society, to achieve the study’s objective, the researchers used the descriptive analytical approach to suit this study, where a questionnaire was distributed to a sample of teachers amounting to (88) male and female teachers in Irbid Governorate in Jordan. The questionnaire included three fields, which are (equipment and public facilities, school activities, guidance and mental health) at a rate of (27) paragraphs for each, The study results concluded that the psychologist plays a significant role in highlighting the importance of school ergonomics and its components in the educational process and academic achievement, this was evident in all fields, with public facilities and equipment ranking first, the researcher recommends that school administration and relevant authorities, particularly the Ministry of Education, develop school ergonomics to keep pace with the modern era, the spread of digital technology, and its importance to the educational process.

Reviewer Acknowledgements, Vol. 19, No. 2, November 2025

Sat, 01 Nov 2025 05:31:44 +0000

Reviewer Acknowledgements, Vol. 19, No. 2, November 2025