One of the most important items in feasibility reports used in investment decisions of industrial facilities is to make capital cost estimates. The most reliable way of making this cost estimate used in investment decisions directly affects investment decisions and is vital for the future of companies. Therefore, cost estimates are a more strategic step in large-scale facilities. In this study, Advanced System for Process Engineering (ASPEN) software was used to design the process in 3D and for process optimization, examples were given for "Sour Water Stripping" and "Gas Dehydration-Regeneration" and for cost estimation, the classification of "Association for the Advancement of Cost Engineering" was studied. During the 3D design for both processes, the relationships between parameters such as "Water to Saturate Mass Flow" and "Sales Gas Compression Mass Flow" were examined and the results obtained were exemplified.
The integration of waste electrical and electronic equipment (WEEE) into construction materials represents a promising circular economy strategy for sustainable building design. However, the indoor hygrothermal performance of such eco-friendly buildings under real climatic conditions remains insufficiently understood. This study investigates the indoor temperature and humidity behavior of buildings incorporating different proportions of WEEE-derived glass materials and develops a predictive framework for indoor environmental conditions. Three experimental buildings were constructed: a control building (C100PV0) and two eco-friendly configurations, C70PV30 (30% glass powder) and GV50Sa50 (50% glass aggregate). Indoor and outdoor data were collected using IoT-based sensors combined with external NASA atmospheric data. An XGBoost model incorporating lagged and rolling features was developed and evaluated using a chronological train–test split to ensure realistic validation. The results show that indoor environments exhibit reduced variability, reflecting the thermal and hygrometric damping effect of construction materials. The C70PV30 building demonstrates the highest thermal stability, while GV50Sa50 exhibits the most stable humidity evolution with minimal fluctuations. From a predictive perspective, the model achieves excellent performance for temperature in both buildings and for humidity in C70PV30 (R² ≈ 0.99), with error levels of RMSE ≤ 0.10 for temperature and ≤ 0.22 for humidity. In contrast, humidity prediction in GV50Sa50 yields a low R² (≈ 0.12) due probably to the near-constant nature of the signal; however, error-based metrics (MedAE ≈ 0.04) confirm that predictions remain accurate in absolute terms. Overall, the findings demonstrate that incorporating recycled glass materials improves indoor hygrothermal stability while maintaining high predictability. The proposed IoT–machine learning framework provides a robust approach for modeling and optimizing indoor environmental conditions in sustainable buildings.
The development of Hainan Free Trade Port has created significant opportunities for the tourism industry while also placing new and higher demands for talent cultivation in tourism management. This paper analyzes emerging trends in tourism development within the free trade port context, identifies key challenges in current training models, and proposes strategies to enhance the competitiveness of tourism management professionals. These strategies focus on reorienting educational objectives, optimizing curriculum systems, strengthening faculty development, deepening industry-education collaboration, and elevating internationalization levels. The findings aim to provide actionable insights for professional reforms in relevant academic institutions. Furthermore, the paper seeks to improve the existing tourism management education system to enhance talent competitiveness and better serve the construction of the free trade port.
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 (RL value)of 0.398 which shows that adsorption process was favourable (RL<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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
This study explores the design, CFD simulation, experimental validation, exergy, and economic performance of a natural convection solar tunnel dryer for drying maize ears. The dryer, designed to address postharvest losses and promote sustainable drying practices, has a capacity of 114.2 kg and a total area of 5.0 m². It reduced the maize moisture content from 23.4% to 12.5% (wet basis) in 5 days, compared to 12 days under open sun drying. Experimental performance closely aligned with CFD simulations performed using SOLIDWORKS 2023, which predicted airflow and heat transfer. The dryer effectively heated the air from an average of 25°C at the inlet to 55°C at the collector’s outlet end. The central and top sections exhibited the highest temperatures due to direct solar radiation, while slight cooling occurred near the outlet as heat was absorbed by the drying material and lost through convection and radiation. The chimney, designed as a vertical solar collector, enhanced airflow by increasing buoyancy pressure. Exergy analysis identified losses due to irreversibility, suggesting chimney design modifications to improve airflow and exergy utilization. Economically, the dryer offered a payback period of 3.7 years, demonstrating its value in preventing postharvest losses and enhancing food safety. This integrated approach highlights the feasibility and sustainability of natural convection solar dryers as effective solutions for postharvest maize drying, particularly in regions with similar climatic conditions.
The Matang Mangrove Forest Reserve (MMFR) in Malaysia, known for its sustainable management, However, the specific relationships between tidal dynamics, seasonal changes, and water quality parameters within MMFR remain understudied. This study investigates the effects of tidal and seasonal fluctuations on water quality by examining seven parameters—Dissolved Oxygen, Salinity, Temperature, Total Dissolved Solids, pH, Turbidity, and Electric Conductivity—alongside river characteristics such as width, depth, and velocity. In-situ measurements were conducted across dry and wet seasons at both high and low tides to capture variability in water quality. The findings indicate that tidal cycles and seasonal changes significantly influence the parameters studied, with distinct patterns observed in relation to tidal conditions. For instance, salinity and turbidity levels were found to increase during high tide, influenced by seawater intrusion, while dissolved oxygen and temperature varied with seasonal rainfall and evaporation. These fluctuations not only reflect the hydrological processes within MMFR but also highlight the sensitivity of water quality to environmental conditions. Understanding these relationships is essential for developing adaptive management strategies that address the challenges posed by climate change and human impacts.
Machine vision plays an important role in the development of agriculture to improve productivity. In the current research, this project plans to develop an automated navigation system that can carry an autonomous vehicle that will travel between rows in the orchard. The system focuses on identifying the straight lines of the tree rows by identifying the center line for robot navigation in garden rows using the Hough Transform and image processing techniques such as morphing, thresholding and edge detection with the Canny operator. The system is intended for outdoor use only as it is designed for garden navigation. MATLAB is the main software used in this project to simulate a visual approach that provides an image processing workspace with different features. The algorithm was then evaluated using several image parks with different characteristics, and the result showed that the proposed method can successfully detect center lines as autonomous vehicle guidance to travel between rows with different tree heights and sizes.
The main limitation of passive suspension system lies in their inherent compromise between ride comfort and car handling, resulting from their inability to dynamically adjust to varying road conditions. Efforts to enhance riding comfort often led to trade-offs that may compromise safety, and vice versa. This duality necessitates a more adaptable and flexible solution. Active suspension systems emerge as a transformative methodology, allowing real-time adjustments and dynamic modifications to damping characteristics. This capability effectively separates the compromise between ride enjoyment and safety, enabling an optimal equilibrium by adaptively responding to fluctuations in road conditions. This paper presents a quarter-car active suspension system to improve comfort under various road conditions. A PSO optimized PID controller is implemented to minimize both the sprung mass displacement, and the sprung mass acceleration subjected to single bump and dual bump road profile. The performance of the PSO-based PID controller is illustrated by simulation results in MATLAB, demonstrating significant improvements in body displacement and body acceleration, thereby enhancing the ride comfort by adaptively responding to road conditions in real time.
Piliostigma reticulatum (D.C.) Hochst is a plant belonging to the legume family. The population widely uses it as medicine, food, feed, and many others. This study aimed to enlighten the status of P. reticulatum in the Maradi region through an ethno-nutritional survey. Three localities in the region were concerned by the study, namely the city of Maradi, the rural commune of Sarkin Hausa (Mayahi department), and the Aguie department. A population of 920 people were interviewed, mostly constituted of Hausa ethnic group (89.5%). The respondents were classified according to gender with 36.2% female and 63.8% male. However, 55.5% of them were aged between 20 and 40 years old. The main activity of respondents was mainly agriculture (30%) followed by pupils/students (24.6%). This study shows that P. reticulatum 97.4% of the population in the study area surveys stated to recognize the plant. Furthermore, it was noted that 99% of the surveys that recognized the plant were ≥ 40 years old. On the other hand, this wild plant is better known in rural areas (100% in the rural commune of Sarkin Hausa) than in urban areas (93.53% in the city of Maradi). In addition, the tree is better used in rural areas (99% in Sarkin Hausa) than in urban areas (73.23% in Maradi). It was found that P. reticulatum is an available tree throughout the study zone (92.7%). Indeed, 89.5% of respondents know that the tree is used either in human food or in animal feed and that these pods and seeds are the most used parts accordingly 27.9% and 15.3% respectively. Indeed, highly appreciated by this population, up to its usage in traditional medicine; thus, the study revealed that 35.3% of respondents disclosed that all organs of the plant are useful.
