Engineering and Technology Horizons

Development of a Smart Irrigation System for Highland Durian Seedlings Integrating Fog Harvesting and Atmospheric Water Generation via IoT

2026-03-09T08:31:57+07:00

Severe drought conditions in Thailand, particularly in highland areas with limited water access, have significantly impacted durian cultivation in Nakhon Thai District, Phitsanulok Province. This study develops a smart irrigation system for highland durian seedlings by integrating fog harvesting technology and atmospheric water generation (AWG) via an Internet of Things (IoT) framework. The system consists of a fog collector using 0.8 mm nylon mesh and a condensation–based AWG unit, managed by an ESP8266 microcontroller with cloud-based data logging and solar power integration. A 90–day field experiment on Monthong durian seedlings demonstrated that the system produced an average of 0.7 L/day from fog harvesting and 1.2 L/day from AWG, totaling 1.9 L/day. This output was sufficient to sustain the growth of one–year–old seedlings, achieving a 100% survival rate throughout the trial period with a low energy consumption of 0.3 kWh/day. The results confirm that this integrated IoT–based architecture provides a sustainable and efficient water management solution for remote and drought–prone agricultural regions.

Effect of Sorting Bin Geometry and Operational Parameters on Impurity Separation Efficiency in Jasmine Rice Using Rotating Drum Sorter

2026-02-23T13:19:27+07:00

Small-scale rice mills in rural Thailand lack advanced sorting equipment for removing impurities from jasmine rice. This study investigated sorting bin geometry and operational parameter effects on separation efficiency using a rotating drum sorter with three-layer concentric screens (2.51 mm, 1.55 mm, 8.0 mm apertures). Three geometries (cylindrical, octagonal, hexagonal) were evaluated at three inclination angles (2°, 3°, 4°) and rotation speeds (50, 55, 60 rpm) using 3³ factorial design (n = 81). ANOVA revealed bin geometry as the dominant factor (61.2% contribution, F = 287.45, P < 0.001), with cylindrical bins achieving optimal performance at 55 rpm and 3° inclination: 82.0 ± 2.1% separation efficiency, 3.02 ± 0.15 min/kg processing time, 96.3 ± 1.1% purity, and 10.4 ± 1.2% material loss. Cylindrical geometry outperformed hexagonal (64.0%, 28% improvement) and octagonal (58.0%, 41% improvement) configurations through four mechanisms: uniform centrifugal force distribution, laminar material flow, minimal dead zones (<2% versus 8-12%), and consistent grain-screen contact dynamics. Compared to manual methods (72 ± 8% efficiency, 8.5 ± 2.1 min/kg), the cylindrical sorter offers 14% higher efficiency, 2.8× faster processing, and 6-8 month payback period, providing cost-effective mechanization for approximately 15,000 small rice mills in Thailand.

Design and Development of Pneumatic Controlled Seed Sowing Machines in Seedling Trays

2026-03-04T13:58:09+07:00

The objective of this study was to design and develop a pneumatically controlled seed sowing machine for seedling trays in order to reduce labor requirements and improve planting efficiency. The system integrates a pneumatic actuation mechanism with a control system developed using FluidSIM 4.2. The experimental setup utilized seedling trays with dimensions of 34 × 54 cm, while the prototype machine measured 50 × 90 × 60 cm. Five types of seeds—melon, lettuce, kale, chili, and tomato were tested using three nozzle diameters (2.6 mm, 1.34 mm, and 0.83 mm), with planting conditions ranging from one to four seeds per hole. The results indicate that nozzle size significantly affects planting accuracy depending on seed characteristics. Larger and elongated seeds (melon) achieved the highest accuracy with a 2.6 mm nozzle, whereas small and round seeds (kale and lettuce) performed best with a 1.34 mm nozzle. For small and lightweight seeds (chili and tomato), the 0.83 mm nozzle yielded the highest accuracy due to improved suction concentration and stability. Overall, the system achieved a planting accuracy of 96% across 300 experimental trials, with an error rate of 4%. In terms of operational efficiency, the proposed machine can complete seed sowing for a 50-hole tray within approximately 1 minute, compared to approximately 15 minutes required for manual operation. These results demonstrate that the developed system provides a cost-effective and efficient alternative to conventional manual seed planting methods.

The Empirical Equations for Estimating Spanwise Loading Distribution on a Straight-Trapezoidal Wing Planform Formulated by using Data Generated from Lifting-Line Theory and Least Squares Approximation Method

2026-02-17T10:09:34+07:00

The empirical equations for estimating a span loading distribution on a straight-trapezoidal wing planform are presented in this paper. The main concept is that the spanwise loading coefficient can be presented as a function of aspect ratio, taper ratio and spanwise position on a wingspan. The lifting-line theory is used to generate data of spanwise loading coefficient at each condition of these three parameters and the curve fitting process is applied to find the suitable equations that can represent the data. These equations can be used to determine the location of center of pressure and spanwise loading distribution on a wingspan. They can be applied in the case of a straight-trapezoidal wing planform that has no geometric and no aerodynamic twist along the spanwise direction. The flow condition is incompressible flow and the range of angle of attack is at the low to moderate which viscosity has no significant impact in the flow field. The results from the empirical equations are compared to those from Anderson’s method and experimental data. The comparison shows that the results from these empirical equations agree very well with the data from both theoretical and experimental data which the detail is discussed in this paper.

Automated Instrument Control for Low-Pass Filter PCB Testing Using SCPI Commands and Python

2026-02-12T08:00:59+07:00

In laboratory classes and research settings, instruments are typically controlled manually, and data is often saved to a flash drive for later analysis. However, this method does not support real-time monitoring and lacks continuity. Some approaches involve using LabVIEW or MATLAB for instrument control and interfacing. However, these tools are typically not freely available and may require a paid license. This paper presents the use of instrument programming for automated testing of PCBs to determine whether they pass or fail, supporting the industry 4.0 initiative in Thailand, where the electronics market has been rapidly growing. In this experiment, a 4th-order low-pass filter PCB is investigated using a DC power supply, an oscilloscope, a function generator, and a digital multimeter. Although this approach is not new, it provides a good foundation and enhances flexibility for education, research, instrument control, and laboratory automation. Furthermore, it can significantly reduce costs associated with commercial software packages such as MATLAB and LabVIEW.

HalluCVE: A Multi-Signal Benchmark for Hallucination Detection in LLM-Generated in Cyber Threat Intelligence

2026-03-04T14:04:12+07:00

Large Language Models (LLMs) are increasingly utilized for automated Cyber Threat Intelligence (CTI) tasks, such as vulnerability analysis and security advisory generation. However, LLMs are susceptible to hallucination, which refers to the generation of plausible yet factually incorrect content, posing significant risks in security-critical contexts. Although concerns have increased, there is currently no dedicated benchmark for the systematic evaluation of hallucination in LLM-generated cyber threat intelligence (CTI). This study introduces HalluCVE, a multi-signal benchmark designed to detect hallucinations in LLM-generated Common Vulnerabilities and Exposures (CVE). HalluCVE incorporates four complementary detection components: 1) Natural Language Inference-based entailment scoring, 2) lexical factual alignment, 3) LLM-as-a-Judge self-reflection, and 4) cross-model consensus divergence. Five state-of-the-art LLMs are evaluated on 1000 CVE entries as the dataset, from 2022 to 2026, encompassing both known (pre-training cutoff) and unknown (post-cutoff) vulnerabilities. The results indicate pervasive hallucination across all models, with mean Hallucination Index values ranging from 0.480 to 0.820. Notably, models demonstrate near-universal confabulation, reaching up to 100%, when queried about post-cutoff vulnerabilities, and frequently respond with high confidence instead of appropriate refusal. HalluCVE establishes a rigorous evaluation framework for assessing LLM reliability in security-sensitive CTI applications and provides insights into potential mitigation strategies.

A Measurement-Based Study of Data Transmission Energy in Long-Term IoT Environmental

2026-03-19T09:21:23+07:00

Long-term Internet of Things (IoT) deployments require predictable and realistic energy consumption estimates to ensure sustainable operation and effective maintenance planning. While many existing studies focus on energy-efficient protocols duty cycling or adaptive sensing strategies communication energy consumption is often estimated using analytical models or datasheet-based assumptions rather than direct hardware measurements. This limits the reliability of long-term energy estimation in real-world deployments. This paper presents a measurement-based study of data transmission energy consumption in a long-term IoT environmental monitoring system using an ESP32-based platform. The system was deployed continuously for approximately one year to collect environmental data while transmission current was measured experimentally using an external ammeter. By isolating the incremental current associated with each data transmission from baseline standby and sensing consumption, the per-transmission energy cost was experimentally characterized. Statistical analysis of the measured transmission current demonstrates low variability and stable behavior across multiple transmission samples enabling reliable estimation of communication energy over extended operational periods. Based on the measured per-transmission energy a practical framework is established to estimate monthly and annual communication energy consumption under a fixed transmission interval. Rather than proposing new optimization algorithms, this work provides empirical energy measurements derived from real hardware and long-term operation, offering a realistic reference for system designers and researchers. The results support informed battery capacity planning lifetime estimation, and energy-aware system design in environmental and agricultural IoT monitoring applications.

An Integrated LoRa-Enabled IoT Sensing System for Precision Agriculture: Design Algorithm and Practical Evaluation

2026-03-31T15:39:20+07:00

Precision agriculture increasingly relies on Internet of Things (IoT) technologies for continuous environmental monitoring; however, practical deployment remains constrained by energy efficiency, long-range communication, and system scalability. This paper presents an integrated IoT-based environmental sensing system that combines multi-parameter sensors with a microcontroller-based node and LoRa based long-range communication through a hardware algorithm co design approach. A lightweight device level algorithm is developed to manage sequential sensing, data validation, compact payload construction, and duty cycled transmission under resource constrained conditions. Experimental evaluations under simulated and representative field conditions demonstrate that sensor measurements remain within acceptable accuracy ranges for precision agriculture, with stable repeatability and limited drift over time. Communication experiments show packet delivery ratios above 90% over extended distances, while scalability analysis confirms reliable multi node operation with moderate latency increase. Energy profiling reveals that duty cycled operation significantly reduces power consumption without substantially degrading communication reliability. Overall, the proposed system provides a practical, energy efficient, and scalable solution for long term environmental monitoring in precision agriculture.

Investigation of Electrogelation of Low Methoxyl Pectin and Its Potential Applications

2026-02-25T09:01:32+07:00

Electrogelation is a promising technique for producing biopolymer gels with controllable structures and properties; however, its behavior in low-methoxyl pectin (LMP) systems remains insufficiently understood. In this study, a new electrogelation setup was developed to investigate the effects of applied voltage (4–12 V), pectin concentration (2–4%w/v), and processing time (up to 120 min) on LMP electrogel formation. The electrogel exhibited controlled growth rates that increased with voltage from approximately 1.0 mg/min at low voltage to 3.13 mg/min at 12 V, producing progressively denser and stiffer gels. Higher gel density was associated with greater calcium incorporation, as confirmed by post-furnace residue analysis. The system also enabled the successful fabrication of multilayer hydrogels with controlled layer composition and structure. In addition, the electrogelation process demonstrated potential for copper ion removal from water. At an initial Cu²⁺ concentration of 2700 mg/L and 8 V, reducing the pectin concentration from 1%w/v to 0.5%w/v significantly improved removal performance, achieving near-complete copper removal within 50 min (68 mg/L min). These findings provide new insights into the electrogelation behavior of LMP and demonstrate the potential of this approach for applications in food structuring, biomedical scaffolds, and environmentally friendly water treatment.

A Comprehensive Bibliometric Analysis of Material Cost Escalation in Building Construction

2026-03-17T15:52:27+07:00

Rising costs of construction materials, particularly steel, concrete, and other materials, have become a huge international issue, as they have affected the budgets of the project and its viability as well as its affordability. The recent inflation, supply-chain turmoil, and political unrest are increasing the cost volatility, and escalating material prices have become the focus of construction research. The Dimensions AI database was used to conduct a bibliometric analysis (2002-2025). A specific search query has been used to obtain literature on cost increases, price fluctuations, and construction materials. After data cleaning, VOSviewer provided the possibility to analyze the trends of publications, performance on citations, the largest institutions, journals, and clusters of keywords. Publication trends showed substantial research growth after 2012, with Nigeria, China, Malaysia, and India emerging as major contributing countries, while leading institutional and journal contributions were associated with Hanyang University, Obafemi Awolowo University, the International Journal of Project Management, and the Journal of Construction Engineering and Management. Network measures pointed out common themes and patterns of research. Two large clusters were identified: (1) a problem-focused cluster centered on cost, overrun, risk, and time; and (2) a solution-focused cluster emphasizing strategy, knowledge, tools, and implementation. To enhance analytical depth, a temporal burst analysis was performed using document-level bigram extraction and structure break detection (PELT algorithm) in RStudio. Results reveal a structural shift around 2012 toward intensified project-level cost overrun research, followed by post-2023 acceleration in governance and digital-oriented themes such as stakeholder collaboration and BIM-based approaches, highlighting evolving mitigation strategies for cost volatility.