Engineering and Technology Horizons

A Novel Weighted Ebola Vector Optimization Based Charging Scheduling of Electric Vehicle Integrated with PV Charging Stations

Nageswara Rao, Shelly Vadhera, Shashi Singh — Mon, 27 Jul 2026 00:00:00 +0700

Electric vehicle (EV) charging scheduling integrated with photovoltaic (PV)-based charging stations is an important aspect of smart energy management. This paper presents an optimal EV charging scheduling framework considering solar-powered charging infrastructure and bidirectional vehicle-to-grid (V2G) and grid-to-vehicle (G2V) power transfer. A hybrid weighted ebola vector algorithm is proposed to improve charging coordination and minimize operational cost. In addition, photovoltaic power generation is forecasted using an artificial neural network (ANN) for accurate 24-hour solar energy prediction. The objective function considers peak shaving, valley filling, power loss, charging coordination, and charging cost minimization under varying load conditions. The proposed method is evaluated using a 24-hour load duration curve with forecasted PV power and power loss is compared with the Ebola Optimization Search Algorithm and Weighted Mean Vector Optimization (WMVO) technique under Normal, High EV plug-in, Low Solar power availability, and Peak Hour scenarios. Simulation results demonstrate that the proposed algorithm achieves superior performance in convergence speed, scheduling accuracy, and power loss reduction. The proposed method reduces charging cost by 28%, whereas the Ebola and WMVO methods achieve reductions of 17.7% and 8.8%, respectively. The results confirm the effectiveness of the proposed optimization approach for smart EV charging management with renewable energy integration.

Comparative Study on the Enhancement of Soil Cement Strength Using Bentonite and Polyurethane Foam

Thu, 03 Sep 2026 00:00:00 +0700

The construction of buildings and infrastructure on soft ground, such as soft Bangkok clay, presents significant geotechnical challenges due to its high compressibility, high settlement potential, and low bearing capacity. Consequently, soil-cement stabilization is widely applied to improve the strength of soft ground. This study evaluated the effects of bentonite and polyurethane foam as additive admixtures to increase the strength of soil cement. The primary purpose was to investigate compressive strength. Moreover, Scanning Electron Microscopy (SEM) was employed to examine the microstructure, which supports the primary purpose. The control specimen consisted of 80% soil and 20% cement by dry weight of the total soil-cement mixture. Bentonite and polyurethane foam contents of 10%, 20%, and 30% by dry weight of the total soil-cement mixture were investigated and added as additive admixtures to the soil-cement mixture. The results showed that adding bentonite and polyurethane foam can increase the strength of soil cement. The suitable ratios of bentonite and polyurethane foam were 30% and 10% by dry weight of the total soil-cement mixture, respectively. Moreover, both bentonite and polyurethane foam can increase compressive strength. This approach offers a pathway to more efficient, sustainable soil-cement stabilization methods to improve soft soils.

An Investigation of the Relationships between Road Geometric Characteristics, Vehicle Speed, Road Lighting Conditions and Traffic Crash Occurrence for Road Safety Design

Sirinapa Jantarakot, Jetsada Kumphong — Thu, 03 Sep 2026 00:00:00 +0700

This study aims to: 1) examine the relationship between the physical characteristics of roads, road lighting conditions, vehicle speed, and road crash data; and 2) propose guidelines for geometric road design to improve road safety. The study area focuses on Sri Chan Road in Khon Kaen Municipality, Khon Kaen Province, Thailand. Field data collection included the physical characteristics of the road, vehicle speed data, road lighting conditions, and road crash data. The data was analyzed using descriptive statistics, the Accident Frequency Method, and Pearson Product Moment Correlation and Simple Linear Regression Models to explore relationships between variables. Geometric designs were also developed according to engineering principles. The study found that the mean speed of private cars was 35.73 km/h per section of the road (SD. = 4.48), and the mean speed of motorcycles was 35.24 km/h per section (SD. = 3.88). The area studied had an average number of injuries of 41.91 people/road sections. Road width was significantly related to the number of lanes, the presence of a median, and the average speed of private cars and motorcycles. The 85th percentile speed of private cars and the 85th percentile speed of motorcycles was also statistically correlated (p-value < 0.05). Additionally, the intensity of road lighting was negatively correlated with sections where severe road crashes occurred, resulting in fatalities (r = -0.702, p-value < 0.05). Similarly, the results of the Simple Linear Regression Models indicate that the mean vehicle speed is significantly related to the number of traffic lanes at a statistically significant level. Based on these results, geometric designs were proposed to reduce vehicle speeds, such as speed-reducing lines, speed limits near important places, and roundabouts. The findings will be submitted to relevant local authorities for future road safety improvements.

Machine Learning–Driven Predictive Maintenance for Smart Beverage Manufacturing Systems

Peerawit Pongtananikorn, Kunlapat Thongkaew — Thu, 03 Sep 2026 00:00:00 +0700

This study proposed a machine learning-based predictive maintenance system in a beverage manufacturing process to enhance failure prediction accuracy and support improved maintenance planning. The approach focused on the beverage filling line, captured short-term temporal process dynamics using rolling-window feature extraction, and addressed the strong class imbalance between normal operation and breakdown events. Significant sensor variables were initially identified through correlation analysis, and the selected sensors and breakdown criteria were subsequently analyzed using various classification models. A 60-minute decision horizon was defined as a classification-based early-warning window derived from the time-to-failure (TTF) variable. Observations were labeled breakdown-proximate state when the next breakdown occurred within 60 minutes (TTF < 60 min) and as normal operation when the next breakdown occurred after 60 minutes (TTF > 60 min). Therefore, the model classifies a breakdown-proximate state rather than predicting the exact remaining time to failure. Model performance was evaluated using precision, recall, F1-score, ROC-AUC, and chronological walk-forward validation. Based on correlation analysis and process relevance, 15 sensors were selected as key features associated with process breakdown. The Random Forest model was the best performer, achieving an accuracy of 0.990, precision of 0.918, recall of 0.722, F1-score of 0.808, and ROC-AUC of 0.995 at the optimized threshold of 0.45. The recall result indicates acceptable detection of breakdown-proximate states, although 27.8% was missed as a false negative. Considering the balance between recall and precision, the precision exceeded 90%, showing that false alarms were maintained at a low level. These results demonstrate a practical trade-off between reducing missed breakdown warnings and avoiding excessive unnecessary inspections. Thus, the Random Forest is suitable for predicting breakdowns with accuracy, stability and robustness. Future work will extend the predictive maintenance results linked with failure-risk predictions to reliability metrics of time between failures (TBF) and mean time between failures (MTBF), improved alignment between maintenance actions and actual machine conditions.

Semi-Automated Air-Cell Image Measurement and Weight-Loss Monitoring for Non-Destructive Assessment of Egg Freshness During Storage

Thu, 03 Sep 2026 00:00:00 +0700

Conventional methods for evaluating egg freshness rely heavily on candling, visual inspection, or destructive laboratory tests. These approaches are often subjective, operator-dependent, and difficult to apply consistently in large-scale food production. To address these limitations, this study developed a non-destructive approach for monitoring egg freshness-related changes during storage by combining cumulative weight-loss measurement with image-based air-cell area measurement. A total of 100 eggs were stored under ambient conditions and repeatedly measured at five time points: Day 1, Day 6, Day 11, Day 16, and Day 21. Repeated-measures ANOVA confirmed significant effects of storage duration on egg weight and air-cell area (p < 0.001). Cumulative weight loss reached 4.22% by Day 21, while the mean air-cell area increased from 90.6 mm² on Day 1 to 274.7 mm² on Day 21 after skipped air-cell annotations were treated as missing data. Cumulative weight loss showed a significant positive association with air-cell area ratio when individual egg-level observations were analyzed. However, the moderate regression strength indicated that air-cell enlargement was not explained by weight loss alone and may also be influenced by egg-specific factors such as shell structure, shell porosity, and initial egg size. These findings indicate that combining weight-loss monitoring with GUI-based air-cell image measurement can support non-destructive assessment of freshness-related changes in eggs during storage.