Search Results (3,177)

Precision agriculture will continue to heavily rely on data-driven models to enable more intensive crop monitoring and data-driven decisions. The available remote sensing techniques, particularly those based on multispectral Sentinel-2 data, still have major shortcomings due to cloud cover, low temporal resolution, and time lags in data availability. To address these shortcomings, this paper proposes a hybrid approach that combines Sentinel-2 satellite data with real-time data generated by low-cost ground-based single-pixel detectors (SPDs), such as the AS7263. This hybrid approach addresses key shortcomings in existing agricultural monitoring systems and offers a cost-effective, scalable solution for real-time monitoring and prediction of end-of-season yield, moisture, and plant height using simple PLRS models implemented directly in SPDs with an energy-efficient algorithm for deployment on the STM32G030 microcontroller. Full article

Accurate potential energy surfaces (PESs) are the prerequisite for precise studies of molecular dynamics and spectroscopy. The permutationally invariant polynomial neural network (PIP-NN) method has proven highly successful in constructing full-dimensional PESs for gas-phase molecular systems. Building upon over a decade of development, we present CQPES v1.0 (ChongQing Potential Energy Surface), an open-source software package designed to automate and accelerate PES construction. CQPES integrates data preparation, PIP basis generation, and model training into a modernized Python-based workflow, while retaining high-efficiency Fortran kernels for processing dynamics interfaces. Key features include GPU-accelerated training via TensorFlow, the robust Levenberg–Marquardt optimizer for high-precision fitting, real time monitoring via Jupyter and Tensorboard, and an active learning module that is built on top of these. We demonstrate the capabilities of CQPES through four representative case studies: CH₄ to benchmark high-symmetry handling, CH₃CN for a typical unimolecular isomerization reaction, OH + CH₃OH to test GPU training acceleration on a large system, and Ar + H₂O to validate the active learning module. Furthermore, CQPES provides direct interfaces with established dynamics software such as Gaussian 16, Polyrate 2017-C, VENUS96C, RPMDRate v2.0, and Caracal v1.1, enabling immediate application in chemical kinetics and dynamics simulations. Full article

The global food industry is undergoing a critical shift toward sustainability, driven by high postharvest losses—reaching up to 40% for fruits and vegetables—and the need to reduce environmental impact. Sustainable postharvest innovations focus on improving quality, extending shelf life, and minimizing waste through eco-efficient technologies. Advances in non-thermal and minimal processing, including ultrasound, pulsed electric fields, and edible coatings, support nutrient preservation and food safety while reducing energy consumption. Although integrated postharvest technologies can reduce deterioration and microbial spoilage by 70–92%, significant challenges remain, including global losses of 20–40% and the high implementation costs of certain nanostructured materials. Simultaneously, eco-friendly packaging solutions based on biodegradable biopolymers and bio-composites are replacing petroleum-based plastics and enabling intelligent systems capable of monitoring freshness and detecting spoilage. Energy-efficient storage, smart sensors, and optimized cold-chain logistics further contribute to product integrity across distribution networks. In parallel, the circular bioeconomy promotes the valorization of agro-food by-products through the recovery of bioactive compounds with antioxidant and anti-inflammatory benefits. Together, these integrated strategies represent a promising pathway toward reducing postharvest losses, supporting food security, and building a resilient, environmentally responsible fresh produce system. Full article

Tritium (³H) is a low-energy β emitter commonly found in environmental water samples, and its routine monitoring requires highly sensitive techniques capable of achieving low detection limits. Liquid scintillation counting (LSC) is the standard method for low-level ³H analysis; however, quenching significantly affects detection efficiency and minimum detectable activity (MDA), and systematic evaluations across different quench levels and measurement approaches remain limited. This study evaluates quench-related uncertainties in low-level ³H measurement using two ultra-low background liquid scintillation counters, Quantulus 1220 and GCT 6220. High- and low-quench conditions were created by varying sample-to-cocktail ratios, and performance was assessed through detection efficiency, minimum detectable activity (MDA), and stability. Under the relative measurement method with limited quench variation, GCT 6220 achieved higher efficiency, lower background, and lower detection limits. Under the internal standard method with broader quench spans, Quantulus 1220 produced smoother efficiency–quench curves and more stable results. Thus, GCT 6220 is advantageous for sensitivity-demanding scenarios, while Quantulus 1220 is better suited for quench-correction applications. Full article

This paper presents an energy-aware Artificial Intelligence of Things framework designed for intelligent remote device control in residential settings. The system architecture is grounded in the Power Administration Device (PAD), a cost-effective and non-intrusive smart plug prototype that measures real-time electricity consumption and actuates appliance power states. The PAD transmits data to a scalable, cross-platform cloud infrastructure, which powers a web-based interface for monitoring, configuration, and multi-device control. Central to this framework is Cross-Feature Time-MoE, a novel neural forecasting model that processes the ingested data to predict consumption patterns. Integrating a Transformer Decoder with a Top-K Mixture-of-Experts (MoE) layer for temporal reasoning and a Bilinear Interaction Layer for capturing complex cross-time and cross-feature dependencies, the model generates accurate multi-horizon energy forecasts. These predictions drive actionable recommendations for device shut-off times, facilitating automated energy efficiency. Simulation results indicate that this system yields substantial reductions in energy consumption, particularly for high-wattage appliances, providing a user-friendly, scalable solution for household cost savings and environmental sustainability. Full article

Wireless sensor networks (WSNs) consist of distributed nodes to monitor various physical and environmental parameters. The sensor nodes (SNs) are usually resource constrained such as power source, communication, and computation capacity. In WSN, energy consumption varies depending on the distance between sender and receiver SNs. Communication among SNs having long distance requires significantly additional energy that negatively affects network longevity. To address these issues, WSNs are deployed using multi-hop routing. Using multi-hop routing solves various problems like reduced communication and communication cost but finding an optimal cluster head (CH) and route remain an issue. An optimal CH reduces energy consumption and maintains reliable data transmission throughout the network. To improve the performance of multi-hop routing in WSN, we propose a model that combines Multi-Objective Particle Swarm Optimization (MOPSO) and a Decision Tree for dynamic CH selection. The proposed model consists of two phases, namely, the offline phase and the online phase. In the offline phase, various network scenarios with node densities, initial energy levels, and BS positions are simulated, required features are collected, and MOPSO is applied to the collected features to generate a Pareto front of optimal CH nodes to optimize energy efficiency, coverage, and load balancing. Each node is labeled as selected CH or not by the MOPSO, and the labelled dataset is then used to train a Decision Tree classifier, which generates a lightweight and interpretable model for CH prediction. In the online phase, the trained model is used in the deployed network to quickly and adaptively select CHs using features of each node and classifying them as a CH or non-CH. The predicted nodes broadcast the information and manage the intra-cluster communication, data aggregation, and routing to the base station. CH selection is re-initiated based on residual energy drop below a threshold, load saturation, and coverage degradation. The simulation results demonstrate that the proposed model outperforms protocols such as LEACH, HEED, and standard PSO regarding energy efficiency and network lifetime, making it highly suitable for applications in green computing, environmental monitoring, precision agriculture, healthcare, and industrial IoT. Full article

This paper presents the development and validation of an IoT-enabled temperature-sensing device for real-time monitoring of the thermal insulation properties of glass windows. The system integrates contact and non-contact temperature sensors into a compact PCB platform equipped with WiFi connectivity, enabling seamless integration into smart home and building management frameworks. By continuously assessing window insulation performance, the device addresses the challenge of energy loss in buildings, where glazing efficiency often degrades over time. The collected data can be transmitted to cloud-based services or local IoT infrastructures, allowing for advanced analytics, remote access, and adaptive control of heating, ventilation, and air-conditioning (HVAC) systems. Experimental results demonstrate the accuracy and reliability of the proposed system, confirming its potential to contribute to energy conservation and sustainable living practices. Beyond energy efficiency, the device provides a scalable approach to environmental monitoring within the broader future internet ecosystem, supporting the evolution of intelligent, connected, and human-centered living environments. Full article

In the Yangquan coal mining region, China, muffled blasting sounds commonly occur in mine surrounding rocks resulting from instantaneous energy release following the elastic deformation of overlying brittle rock layers; they are related to fracture development. Although these events rarely cause immediate hazards, their acoustic signatures contain critical information about cumulative rock damage. Currently, conventional monitoring of muffled blasting sounds and surrounding rock stability relies on microseismic systems and on-site sampling techniques. However, these methods exhibit low identification efficiency for muffled blasting events, poor real-time performance, and strong subjectivity arising from manual signal interpretation and empirical threshold setting. This article proposes retentive depthwise gated network (RDGNet). By combining retentive network sequence modeling, depthwise separable convolution, and a gated fusion mechanism, RDGNet enables multimodal feature extraction and the fusion of acoustic emission sequences and audio Mel spectrograms, supporting real-time muffled blasting sound recognition and lithology classification. Results confirm model robustness under noisy and multisource mixed-signal conditions (overall accuracy: 92.12%, area under the curve: 0.985, and Macro F1: 0.931). This work provides an efficient approach for intelligent monitoring of coal mine rock stability and can be extended to safety assessments in underground engineering, advancing the mining industry toward preventive management. Full article

To ensure reliable, efficient and sustainable operation of modern power networks, accurate load forecasting is an important task in system planning and control. It is also a crucial task for the efficient operation of smart grids to maintain a balance between load shifting, load management and power dispatch. In this regard, this research study aims to investigate the efficiency of various machine learning models for whole-house energy consumption prediction and appliance-level load disaggregation using Non-Intrusive Load Monitoring (NILM). The primary objective is to determine which model offers the most accurate forecasts for both individual appliance consumption patterns and the total amount of energy used by the household. The empirical study presents comparative performance analysis of machine learning models, i.e., Random Forest, Decision Tree, K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), Gradient Boosting and Support Vector Regressor (SVR) for load forecasting and load disaggregation. This research is conducted on PRECON: Pakistan Residential Electricity Dataset consisting of 42 Pakistani households. The dataset was recorded originally as one minute per sample, but the proposed study aggregated it to hourly samples to evaluate models’ alignment with the typical sampling rate of smart meters in Pakistan. It enables the models to more accurately depict implementation scenarios in real-world settings. The statistical measures MAE, MSE, RMSE and R² have been employed for performance evaluation. The proposed Random Forest algorithm out-performs all other employed models, with the lowest error values (MAE: 0.1316, MSE: 0.0367, RMSE: 0.1916) and the highest R² score of 0.9865. Furthermore, for detecting appliance events from aggregate power data, ensemble models such as Random Forest performed better than other models for ON/OFF prediction. To evaluate the suitability of machine learning models for real-time, appliance-level energy forecasting using Non-Intrusive Load Monitoring (NILM), this study presents a novel evaluation framework that combines learning speed and edge adaptability with conventional performance metrics (e.g., R², MAE). This paper introduces a NILM-based approach for load forecasting and appliance-level ON/OFF prediction, representing its capacity to improve residential energy efficiency and encourage sustainable energy consumption, while emphasizing operational metrics for implementation in embedded smart grid systems—an area mainly neglected in prior NILM-based research articles. The results provide useful information for improving demand-side energy management, facilitating more effective load disaggregation, and maximizing the energy efficiency and responsiveness of smart grids. Full article

The aim of this paper is to investigate the measures adopted by higher education institutions (HEIs) for sustainable water management in university campuses. Rain and storm water harvesting and treatment, rain and storm water reuse, wastewater treatment and reuse and technologies for runoff reduction were found to be frequently undertaken. Sustainable approaches to water supply such as water-efficient appliances, irrigation algorithms and the use of drought-resistant plants have been adopted as well. In support, monitoring of consumed water and of rain and storm waters has been a widespread practice. Important considerations were given to the impact of the identified measures on campuses’ energy consumption and greenhouse gas emissions. Nature-based solutions, employment of renewable energies and sustainable disinfection methods are measures to prioritize. Some wastewater technologies may deserve priority in virtue of their positive contribution to circular economy. Drawbacks such as groundwater and soil contamination due to wastewater reuse and the release of pollutants from fertilized nature-based technologies were identified. Despite their variety, it must be noted that many of these measures have generally involved rather limited portions of campuses, taken more for demonstration or pilot/full-scale research purposes. Additional measures not identified in the current review—for instance the prevention of pollution from micropollutants and waste mismanagement—should be implemented to boost HEIs’ environmental sustainability. The findings of this review pave the way for a more structured implementation of water sustainability measures in university campuses. Full article

In recent decades, significant efforts have been devoted to constructing energy-efficient buildings, providing comfortable indoor environments. However, measures such as enhanced airtightness, while reducing infiltration through the building envelope, might consequently reduce natural ventilation. This reduction is a critical concern because natural ventilation is an essential factor in controlling indoor air quality (IAQ), and its diminution could therefore worsen IAQ. Sick building syndrome has emerged as a term used to describe health hazards linked to the time spent indoors but with no particular cause. Since people spend most of their time indoors, the demand for continuous and real-time IAQ management to reduce human exposure to pollutants has increased considerably. In this context, low-cost sensors (LCS) for IAQ monitoring have become popular, driven by recent technological advancements and increased awareness regarding indoor air pollution and its negative health impacts. Although LCS do not meet the performance requirements of reference and regulatory equipment, they provide informative measurements, offering high-resolution monitoring, emission source identification, exposure mitigation, real-time IAQ assessment, and energy efficiency management. This perspective article proposes a general model for LCS systems (and subsystems) implementation and presents a prospective analysis of their strengths and limitations for IAQ management, reviews the literature regarding sensor system technologies, and offers design recommendations. It provides valuable insights for researchers and practitioners in the field of IAQ and discusses future trends. Full article

This work presents the development and evaluation of a low-cost and low-power IoT system for monitoring slope instabilities, rockfalls, and landslides using LoRa communication. The prototype integrates commercial ESP32-based hardware with an SX1276 transceiver, a triaxial MEMS accelerometer, and a GPS module for real-time tilt and location measurements. A tilt-estimation expression was derived from accelerometer data, enabling adaptation to different terrain inclinations. Laboratory tests were performed to validate the stability and accuracy of the inclination measurement, followed by outdoor LoRa range tests under mixed line-of-sight conditions. A lightweight dashboard was implemented for real-time visualization of GPS position, signal quality, and tilt data. The results show reliable tilt detection, consistent long-range communication, and low power consumption, highlighting the potential of the proposed prototype as a scalable and energy-efficient tool for geotechnical monitoring. Full article

To achieve a substantial enhancement in thermodynamic efficiency, Generation IV nuclear reactors are designed to operate at significantly elevated temperatures compared to conventional reactors. Moreover, they typically employ a fast neutron spectrum, characterized by higher neutron energy and flux. This combination results in a considerably more intense radiation environment within the core relative to traditional thermal neutron reactors. Therefore, the measurement of neutron flux in the core of Generation IV nuclear reactors faces the challenge of a high-temperature and high-radiation environment. Conventional neutron flux monitoring equipment—including fission chambers, gas ionization chambers, scintillator detectors, and silicon or germanium semiconductor detectors—faces considerable challenges in Generation IV reactor conditions. Under high temperatures and intense radiation, these sensors often experience severe performance degradation, significant signal distortion, or complete obliteration of the output signal by noise. This inherent limitation renders them unsuitable for the aforementioned applications. Consequently, significant global research efforts are focused on developing neutron detectors capable of withstanding high-temperature and high-irradiation environments. The objective is to enable accurate neutron flux measurements both inside and outside the reactor core, which are essential for obtaining key operational parameters. In summary, the four different types of neutron detectors have different performance characteristics and are suitable for different operating environments. This review focuses on 4H-SiC, diamond detectors, high-temperature fission chambers, and self-powered neutron detectors. It surveys recent research progress in high-temperature neutron flux monitoring, analyzing key technological aspects such as their high-temperature and radiation resistance, compact size, and high sensitivity. The article also examines their application areas, current development status, and offers perspectives on future research directions. Full article

The growing demand for efficient hydrogen storage solutions highlights the need for reliable and safe composite overwrapped pressure vessels (COPVs). This study investigates the application of an inline monitoring system combining laser-based measurements and photogrammetric line photography to assess COPV quality during fabrication, including quantitative evaluation of liner concentricity and high-resolution line scanning of the composite surface to detect and measure fiber orientations. Fiber detection and angle measurement using the Hough Transform provide detailed assessment of local winding orientation, while global Fourier Transform analysis supports comparative evaluation across vessels or segments, allowing identification of dominant fiber directions and detection of micro-scale deviations. The integrated approach enables early detection of geometric inconsistencies and localized winding irregularities, providing robust performance-based criteria for accept-reject decisions, while filtering out minor noise and ensuring reliable quantitative evaluation. This framework enhances inline quality control, optimizes material usage, and supports the safe deployment of COPVs in hydrogen storage systems, contributing to efficient and reliable energy storage solutions. Full article

Cu₂ZnSnSe₄ is a promising light-absorbing material for cost-effective and eco-friendly thin-film solar cells; however, its synthesis often leads to secondary phases that limit device efficiency. To overcome these challenges, we devised a straightforward and efficient method to obtain single-phase Cu₂ZnSnSe₄ nanocrystalline powders directly from the elements Cu, Zn, Sn, and Se via mechanochemical synthesis followed by vacuum annealing at 450 °C. Phase evolution monitored by X-ray diffraction (XRD) and Raman spectroscopy at two-hour milling intervals confirmed the formation of phase-pure kesterite Cu₂ZnSnSe₄ and enabled tracking of transient secondary phases. Raman spectra revealed the characteristic A₁ vibrational modes of the kesterite structure, while XRD peaks and Rietveld refinement (χ² ~ 1) validated single-phase formation with crystallite sizes of 10–15 nm and dislocation densities of 3.00–3.20 10¹⁵ lines/m². Optical analysis showed a direct bandgap of ~1.1 eV, and estimated linear and nonlinear optical constants validate its potential for photovoltaic applications. Scanning electron microscopy (SEM) analysis showed uniformly distributed particles 50–60 nm, and energy dispersive X-ray (EDS) analysis confirmed a near-stoichiometric Cu:Zn:Sn:Se ratio of 2:1:1:4. X-ray photoelectron spectroscopy (XPS) identified the expected oxidation states (Cu⁺, Zn²⁺, Sn⁴⁺, and Se²⁻). Electrical characterization revealed p-type conductivity with a mobility (μ) of 2.09 cm²/Vs, sheet resistance (ρ) of 4.87 Ω cm, and carrier concentrations of 1.23 × 10¹⁹ cm⁻³. Galvanostatic charge–discharge testing (GCD) demonstrated an energy density of 2.872 Wh/kg⁻¹ and a power density of 1083 W kg⁻¹, highlighting the material’s additional potential for energy storage applications. Full article