Search Results (411)

Short-term fluctuations in dissolved oxygen are difficult to capture in warm outdoor Nile tilapia (Oreochromis niloticus) ponds using periodic manual measurements, yet they strongly influence fish performance and farm management. This study presents a preliminary field evaluation of a low-cost IoT workflow for dissolved oxygen monitoring and short-horizon forecasting in pond-based tilapia culture. An ESP32-based sensing node continuously measured dissolved oxygen, temperature, and pH, transmitted readings to a cloud backend, and generated short-horizon forecasts from 5 min aggregated windows. During live validation from 1 to 10 April 2026, the 30 min forecast achieved a mean absolute error of 0.783 mg/L and directional accuracy of 60.23%, with only modest improvement over a persistence baseline. The 6 h forecast achieved 1.109 mg/L and 53.82%, respectively, indicating limited predictive value at the extended horizon. An extended 47-day field deployment (May–June 2026) captured four sensor-recorded low-DO events and two documented power outages, causing sensor downtime and providing additional field-deployment evidence. These results demonstrate the engineering feasibility of the integrated workflow, but they do not establish robust operational forecasting validity because the data were collected from one pond, high-frequency records were temporally correlated, and independent reference-meter validation was not available. The study is, therefore, best interpreted as a proof-of-concept field evaluation that identifies practical requirements for future low-cost aquaculture forecasting systems. Full article

This study presents an energy-based assessment of a battery electric sport utility vehicle (SUV) tested under high-altitude and high-gradient real-world conditions in Ambato, Ecuador, at approximately 2500 m above sea level. A low-cost instrumentation setup composed of a Global Navigation Satellite System (GNSS) device, a Fluke 393 FC clamp meter, and an On-Board Diagnostics II (OBD-II) interface was used to evaluate zero, positive, and negative road-gradient conditions in Normal and Sport driving modes. The results show that positive gradients increased the acceleration energy from 0.0454 to 0.0658 kWh in Normal mode and from 0.0351 to 0.0535 kWh in Sport mode. In contrast, negative gradients favored regenerative braking, with Normal mode reaching a net energy balance of $-0.0249$ kWh and a segment-level recovery ratio of 194.38%. This value reflects the contribution of gravitational potential energy. Sport mode showed lower regenerative performance, particularly during uphill operation, where the recovery ratio decreased to 8.96%. These findings demonstrate that low-cost instrumentation can capture representative route-level energy trends and support real-world electric vehicle (EV) energy assessment in topographically complex high-altitude environments. Full article

This study presents an Indoor Environmental Quality (IEQ)-aware framework for residential valuation by integrating low-cost IoT sensing, transparent scoring, and hedonic price modeling. The analysis uses a dataset of 244 apartments across 12 districts in Tehran. It combines indicators of thermal comfort, particulate exposure, lighting, acoustics, stability, exceedance, and uncertainty with conventional housing covariates (area, age, bedrooms, floor level, renovation status, amenities, and accessibility proxies). Results show that pooled IEQ–price relationships are weak and confounded, whereas controlled specifications produce modest but consistent improvements in explanatory fit after IEQ features are introduced. Conventional location and structural attributes remain the dominant determinants of price per square meter. Still, IEQ contributes a non-redundant information layer that improves within-segment differentiation and interpretability for inspection and listing workflows. Methodologically, the framework extends beyond average comfort metrics by incorporating volatility, threshold exceedance duration, and sensor uncertainty, enabling uncertainty-aware reporting rather than single-point scoring. In practice, the workflow supports portable sensing, reproducible analytics, and privacy-preserving edge aggregation, suitable for PropTech deployment. The findings support a cautious but actionable conclusion: IEQ should be treated as an incremental valuation signal rather than a standalone pricing determinant. In this context, IEQ is conceptualized as a supplementary attribute block that may add explanatory value beyond conventional housing covariates rather than as a standalone pricing determinant. Full article

To address the difficulty of real-time detection of seed-filling performance in pneumatic suction seed metering devices under high-speed operation—where seed targets are tiny, prone to adhesion, and affected by motion blur—this paper proposes a lightweight online detection algorithm, YOLOv8n-MA. First, according to the seed adsorption characteristics of the suction holes, the detection targets are divided into three categories: none, one, and two. Second, based on YOLOv8n, the backbone network is replaced with MobileNetV1 to reduce computational cost, and an ACmix attention module is integrated into the Neck to enhance feature representation for the three suction-hole states. Finally, to meet the demand for low-latency inference on resource-constrained devices, the model is deployed on an edge computing controller to achieve real-time detection. Experimental results show that, compared with the original YOLOv8n, the parameters and FLOPs of YOLOv8n-MA are reduced by 34.4% and 59.8%, respectively, while the mean average precision (mAP) is improved by 2.0% to 96.8%, achieving a superior trade-off between accuracy and efficiency over other detection models of the same category, such as YOLOv5n, YOLOv9n, and YOLOv10n. In field tests, the detection accuracy reaches 95.02% at 12 km/h and 92.65% at 15 km/h. The proposed method provides effective technical support for the intelligent monitoring and control of precision seeding under high-speed operation. Full article

Garlic, a significant global specialty economic crop, is currently facing severe challenges from labor shortages and escalating production costs. Achieving full-process mechanized production is the core approach to ensuring sustainable industrial development and enhancing international competitiveness. This paper systematically reviews the research progress and application status of mechanized equipment throughout the entire crop cycle of garlic production, including seeding, field management, harvesting, and post-harvest processing and sorting. The study reveals that garlic equipment is undergoing a profound transformation from traditional mechanization to “opto-electro-mechanical integration” and intelligence. In the seeding phase, breakthroughs have been made in pneumatic precision seed-metering and machine vision-based clove bud orientation technologies, significantly improving the quality of upright planting. In field management, precise variable-rate application and targeted weeding have been preliminary realized through plant protection Unmanned Aerial Vehicle (UAV) downwash airflow field simulation (CFD) and deep learning-based image segmentation. In the harvesting phase, relying on 3D Discrete Element Method (3D-DEM) soil-cutting simulation and adaptive profile root-trimming technology, the industry is accelerating the transition from inefficient segmented harvesting to low-damage combined harvesting. In the post-harvest phase, hyperspectral imaging (HSI) and multi-label convolutional neural networks (CNNs) have been utilized to achieve high-speed non-destructive detection of internal and external quality. However, industry still faces critical bottlenecks such as the insufficient integration of machinery and agronomy, poor robustness of intelligent perception algorithms in complex environments, and high damage rates of core soil-engaging components. Future research should focus on lightweight algorithm deployment, digital twin-driven virtual prototyping, and the construction of regional standardized machinery–agronomy systems, aiming to build an efficient and universal intelligent production closed-loop for garlic. Full article

Surface quality in turning is still evaluated mainly by post-process profilometry, which limits the use of sensor feedback during machining. This article examines whether perpendicular vibration displacement can be used as a practical indirect indicator of surface roughness in the turning of aluminum alloys. The study is based on 204 synchronized segment-level vibration–roughness observation pairs collected during 408 s of turning. The vibration meter operated in displacement mode, continuously measuring vibration while the SD logger stored one perpendicular displacement p-p reading every 2 s; R_a and R_z were then associated with the corresponding machined segment. The analysis combined descriptive time-domain statistics, low-frequency FFT/STFT descriptors of process-state evolution, phase segmentation, correlation analysis, and linear regression. Very strong within-dataset relationships were obtained between perpendicular vibration displacement and surface roughness, with R² = 0.992 for R_a and R² = 0.988 for R_z. Entry, steady-state, and exit phases showed different variability levels, and the steady-state segment provided the most stable basis for roughness estimation. Because the logger sampling interval was 2 s, the spectral results should be interpreted as low-frequency process-state descriptors rather than as direct chatter measurements. Within this scope, the results support the use of perpendicular displacement sensing as a low-cost feasibility approach for in-process roughness indication. Broader transfer to CNC production, other alloys, and higher-bandwidth monitoring requires additional validation. Full article

In this paper a new mathematical optimization model is proposed to optimize the number of data aggregators in smart grids and assign each smart meter to at least one data aggregator. The model is based on a Set-Covering Problem, which aims to find the minimum number of sets that cover all elements. In the case of smart grids, these elements will be the smart meters. We used a branch–bound algorithm for the new optimization model to solve several instances considering different smart grid scenarios. In the scenarios, real-world parameters were used for a lot of smart meters (which ranged from 15 to 900, with output powers of 23 and 30 dBm used in the theoretical analysis), data aggregator costs, dispersions, maximum budget, and signal propagation losses. The tests reached the best values for the objective function with small, medium and large-scale instances in low computational times. Theoretical analysis was used to evaluate the signal received from data aggregators, indicating that they can receive information with a high-quality signal.The proposed model provides insights for stakeholders involved and can aid in smart grid implementation. In addition, it can offer a blueprint for engineers to optimize data flow within hierarchical grid structures with smart meters and data aggregators. Full article

Low-voltage distribution networks (LVDNs) in urban and semi urban areas face increasing challenges in maintaining supply reliability due to limited automation, growing demand, and high penetration of distributed energy resources (DERs). This paper proposes a coordinated framework for supply restoration, demand management, and DER control using a novel multi parameter optimization framework with a priority-based objective function and real-time smart meter data. The approach integrates essential and non-essential load separation, smart field devices, and coordinated control strategies to enable adaptive network operation under normal and contingency conditions. The framework is evaluated using a modified IEEE 33 bus-based LVDN model with data derived from real smart meter measurements from a Sri Lankan LVDN. Results demonstrate that restoration time is reduced from approximately 2.5 h to a few minutes through automated switching. In addition, selective curtailment of non-essential loads improves service continuity, while coordinated DER control mitigates voltage rise under high photovoltaic penetration. A quantitative comparison confirms significant reductions in energy not served (ENS) and associated economic costs based on Public Utilities Commission of Sri Lanka values. The proposed framework provides a practical and scalable solution for enhancing reliability and enabling advanced distribution management in modern LVDNs. Full article

The rapidly increasing availability of low-cost commercial UAVs poses significant security challenges for critical infrastructure and law enforcement agencies. This paper presents an integrated LiDAR-based detection and vision-based verification system for an autonomous drone-on-drone aerial interception system. To eliminate the threat of possible dangerous target drones, the interception UAVs presented in this paper use a net to capture them safely in the air. The system addresses the critical limitation of ground-based sensors, which provide insufficient precision for reliable net-based capture operations. Moving beyond simulation-only approaches, the core novelty of this work lies in the successful real-world integration of these sensors on a strictly constrained aerial platform in size, weight and power to achieve sub-meter terminal guidance precision. The developed system uses real-time point cloud processing, DBSCAN clustering, and Moving Horizon Estimation tracking for the detection and tracking of the target. Vision-based verification uses a custom-trained YOLO neural network and achieves over 90% detection rates. The evaluation demonstrates a detection accuracy of less than 0.4 m at ranges exceeding 40 m during dynamic interception scenarios using RTK-GNSS ground truth. The dual-sensor approach successfully completed multiple autonomous interception missions with target detection ranges of up to 60 m, validating the capability of the system for safe, autonomous civilian UAV interception. Full article

In electric propulsion for space applications, searching for alternative propellants is increasingly important due to limited resources and economic considerations. Among the candidates, iodine has emerged as promising thanks to its favorable chemical and physical properties for propulsion and its lower cost and simpler storage compared with xenon. However, its corrosive behavior is a drawback, as iodine reacts with many aerospace materials, and its condensable nature prevents using propellant management systems like those for noble gases. At the University of Pisa, activities on fluidics for iodine-fed electric propulsion systems and material compatibility studies led to the development of a mass flowmeter within the “iFACT-MP” Horizon EU project. The device is a spectrophotometric flow meter measuring instantaneous mass flow through a cell in series with the iodine feeding line, upstream of a thermal throttle. A beam splitter directs part of the light to a reference photodiode to compensate for laser intensity variations. Temperature and absorption measurements allow inferring iodine pressure in the cell, while the thermal throttle ensures sonic conditions, enabling correlation with instantaneous mass flow. The mass flow meter shows good behavior and repeatability, especially at low mass flow rates. Full article

Comprehensive mechanized solid backfilling technology exhibits significant advantages in solid waste disposal, “three-under” coal mining, and dynamic disaster control. However, its large-scale application is constrained by low production efficiency, high unit production cost, and high labor intensity. Therefore, industrial upgrading through intelligent technologies is urgently required. In this study, methods including literature review, theoretical analysis, and field measurements are employed to propose three backfilling modes. The configurations of the six core subsystems under each mode are systematically summarized, and the core definition of an intelligent backfilling mine is established. Furthermore, a key technology framework for intelligent backfill mining is developed, based on PLC control and PID algorithms, with a closed-loop architecture centered on “perception–decision–execution.” Engineering applications demonstrate that the surface gangue intelligent pretreatment system achieves functions including automatic vehicle washing, intelligent dust suppression spraying at discharge points, dynamic metering during conveying, and adaptive adjustment of feeding systems. The intelligent surface-to-underground coal gangue vertical feeding system enables full silo alarm and level regulation. The underground jigging intelligent separation system realizes intelligent jigging ratio adjustment, intelligent bed layer measurement and control, and intelligent air volume regulation, with the coal content in gangue discharge maintained below 4%. At the working face, the intelligent solid backfilling system doubles monthly coal output, boosts backfilling efficiency by 50%, and cuts the workforce by 8–10 workers. The intelligent backfilling effectiveness monitoring system operates stably, with a working face weighting factor of 1.12 and precise ground deformation control within Grade I limits. Full article

Soil apparent electrical conductivity (EC_a), volumetric water content (VWC), and temperature are important parameters for evaluating soil condition and supporting irrigation and crop management practices. This study presents the design and experimental evaluation of a ultra-low-hardware-cost soil sensing system capable of estimating these three parameters through impedance-based measurements at different frequency ranges. The proposed system uses sinusoidal excitation in the kHz range for EC_α estimation and in the MHz range for VWC estimation, while temperature is also considered as a relevant factor affecting the electrical behavior of soil. The sensor was experimentally tested on three soil types under two moisture conditions, namely water addition with and without mixing, and the results were compared with those obtained from a commercial instrument (5TE Meter Group). The overall mean error of the developed system, without calibration, was 20.2%, with mean errors of 16.3% for EC_a and 24.2% for VWC. Although the accuracy achieved is lower than that of commercial instruments, the results demonstrate that the proposed system can provide a satisfactory preliminary assessment of soil conditions in applications where low cost, simplicity and ease of implementation are important. The results can be significantly improved if calibration is made initially for the soil type of the field to be measured. Electrode geometry, lack of calibration with a larger set of soil samples and PCB implementation issues are the main limitations affecting performance. Overall, the proposed approach shows potential as a supportive tool for low-cost agricultural monitoring and decision-making applications. The implementation of a system that measures soil conductivity and moisture in two frequency ranges measurement (kHz for EC_α/MHz for VWC), with synchronous soil temperature measurement, at a particularly low cost, is the innovation of the sensor system. Full article

At the Second Water Intake Pump Station of the Chenhang Reservoir in Shanghai, suboptimal pump scheduling resulted in electricity consumption cost attributable to pump-motor equipment accounting for an exceptionally large proportion of the total power expenditure. In response to the economical operation issues, a mathematical model of power consumption cost for the pump station was established by introducing time-of-use electricity pricing and constraint suppression terms. Taking the minimum cost as the research objective, the differential evolution (DE) algorithm was employed to establish a fitness function for electricity cost, aiming to find the most economical and reliable scheduling strategy. However, owing to its low computational speed and high complexity, machine learning was introduced to establish neural network surrogate models of the DE algorithm. By comparing three surrogate models, the Multilayer Perceptron (MLP) neural network model was adopted as the most appropriate surrogate model. It was optimized for robustness improvement and verified on site. The results demonstrate that implementing the surrogate model achieves over 25% savings in electricity cost per thousand cubic meters of water, while slashing the solution time by 88.53% compared to the standard DE algorithm. Furthermore, the overall power consumption is reduced by 2.20% under a cost-priority strategy and by 15.89% under a power-priority strategy, thereby directly mitigating the carbon footprint of the pump station. The proposed hybrid computational framework in this study bridges the gap between the computationally expensive heuristic optimization and the strict real-time control requirements in engineering, highlighting its significant contribution to the sustainable and low-carbon operation of water infrastructure. Full article

This study addresses the critical issues of rigid energy use and insufficient demand-side responsiveness in office buildings’ Variable Refrigerant Flow (VRF) systems under complex summer conditions. Existing research lacks fine-grained characterisation of short-term load fluctuations and often fails to accurately couple energy efficiency with humidity-adapted thermal comfort. To fill this gap, this paper proposes an integrated Model Predictive Control (MPC) framework driven by load characteristic identification and a novel hybrid prediction model. First, based on actual hourly metered data (683,280 records), K-means clustering was employed to identify three typical load patterns, pinpointing short-term peak loads in core office zones as the primary target for flexible regulation. Second, a high-precision GS-DBO-ELM prediction model—integrating Grid Search and Dung Beetle Optimisation—was developed to capture the nonlinear dynamics of VRF energy consumption and Predicted Mean Vote (PMV). The model achieved an R² of 0.99 with relative errors constrained within ±5%. Finally, a multi-objective MPC strategy, solved via an improved Artificial Hummingbird Algorithm (HAGSAHA) and weighted by the Analytic Hierarchy Process (AHP), was implemented to dynamically adjust zone-level temperature setpoints. Results demonstrate that the proposed MPC strategy reduces daily cooling energy consumption by 7.95–10.69% and peak loads by 15.3%, while maintaining strict thermal comfort (PMV within ±0.5). Under a time-of-use pricing mechanism, the flexible scheduling strategy achieved a 12.37% total electricity reduction and a 9.54% reduction in operating costs. This work provides a highly replicable, climate-tailored solution for low-carbon, flexible energy management in public buildings. Full article

Indoor localization in GNSS-denied environments remains a significant challenge due to the low sampling frequency and high variability of wireless signal measurements. This paper presents a wireless communication-based indoor localization method that integrates Wi-Fi received signal strength indication (RSSI) measurements with optical initialization and inertial sensor fusion. The proposed approach eliminates the need for labor-intensive fingerprinting and specialized infrastructure by leveraging existing Wi-Fi networks. Optical pose estimation using ArUco markers provides accurate initial position and orientation, enabling alignment between sensor coordinate systems and reducing inertial drift. During tracking, inertial measurements compensate for motion between sparse Wi-Fi observations by virtually translating historical RSSI samples, allowing statistically consistent averaging and improved distance estimation. A simplified factor graph framework is employed to fuse heterogeneous measurements while maintaining computational efficiency suitable for real-time operation on mobile devices. Experimental validation using a robot-based ground-truth reference system demonstrates sub-meter localization accuracy with an average positioning error of approximately 0.40 m. The proposed method provides a low-cost and scalable solution for indoor positioning and navigation applications such as access-controlled environments, exhibitions, and large public venues. Full article