Search Results (5,961)

Irrigation water management in arid systems requires spatially distributed estimates of crop evapotranspiration (ET) that fixed crop coefficients cannot provide. The actual ET of flood-irrigated rice (Oryza sativa L.) on the arid northern coast of Peru was mapped with the METRIC surface energy balance model (Mapping EvapoTranspiration at high Resolution with Internalized Calibration) on Google Earth Engine (GEE). Ten cloud-free Landsat 8/9 scenes (January–July 2022) were processed over 113 ha at Ferreñafe (Lambayeque) on the 30 m product grid, onto which the 100 m native thermal observation was resampled, with internal calibration based on automatic anchor-pixel selection and hourly ERA5-Land data. Daily field-mean ET ranged from 4.2 to 8.1 mm d⁻¹, peaking during flooding and establishment and declining towards harvest. Because the same reference $E{T}_o$ underlies the METRIC internal calibration and the FAO-56 estimate, this is a comparison between two modelling approaches rather than an independent validation. Against the FAO-56 reference ET, METRIC showed a positive bias of $+0.65$ mm d⁻¹ (percent bias (PBIAS) $=+13\%$; root mean square error (RMSE) $=1.23$ mm d⁻¹; $r^2=0.57$; $n=9$, after excluding one date with anomalous reanalysis forcing), concentrated during flooding and after harvest, whereas at full canopy cover the two estimates converged. Two global ET products that share neither the METRIC formulation nor the ERA5-Land forcing reproduce the same seasonal decline once the canopy closes ($r=0.63$ and $0.91$) but stay far below in magnitude, as expected from their 500 m pixel. ET did not differ between sowing methods and varied only slightly among cultivars (∼0.3 mm d⁻¹), against marked intra-field variability. The METRIC–GEE workflow offers a low-cost, high-resolution tool for monitoring water use in data-scarce arid rice systems. Full article

The convective drying of agricultural materials is an energy-intensive process, and optimizing dryer design is critical for improving efficiency and product quality. This study presents a comprehensive heat and mass transfer model for the convective drying of alfalfa leaves in a rotary drum dryer. Freshly harvested leaves with an initial moisture content of approximately 70% (w.b.) were used as the test material. The proposed system features a simplified drum design aimed at enhancing process efficiency while reducing equipment complexity. The primary objective was to reduce the moisture content of alfalfa leaves to below 50% to ensure their quality during subsequent storage and transportation. To determine the optimal operating conditions, the kinematics of leaf motion inside the rotating drum and the associated heat and mass transfer phenomena were investigated through analytical modeling, numerical simulation, and experimental studies on a laboratory-scale physical model. An analytical model was developed to establish relationships between transverse kinematic characteristics (detachment condition, Froude number, drum inclination angle), average longitudinal velocity, and residence time. Numerical simulations based on the Navier–Stokes equations (continuity, momentum, and energy) provided detailed moisture content distributions within individual leaves under varying airflow orientations and drying durations. The novelty of this work lies in the integrated determination of optimized operating parameters through combined analytical, numerical, and experimental approaches. A regression model relating final moisture content to key process variables (air velocity, temperature of 60 °C, drum rotation frequency and mass of loaded material) was developed from experimental data, yielding practical recommendations for the design and operation of rotary drum dryers for alfalfa and similar agricultural materials. Full article

Low-grade thermal energy is widely available in industrial waste-heat streams and natural temperature fluctuations, yet its utilization remains limited because of weak thermodynamic driving forces and the complexity of conventional heat-engine technologies. This study presents a physics-based framework for modular thermoelastic energy harvesting using the reversible thermal expansion and contraction of structural materials. Analytical models are established to quantify thermoelastic work, structural constraints, thermodynamic and exergy efficiencies, and long-term energy production. Material selection and thermo-mechanical limitations are evaluated through parametric analysis and finite-element verification. The results indicate that extractable work is fundamentally constrained by yield strength, buckling resistance, temperature swing, and the limited exergy content of low-grade heat. Scaling laws show that annual energy generation scales approximately linearly with active structural mass while remaining strongly dependent on column diameter, thermal-cycle frequency, and material performance indices. Thermodynamic and exergy efficiencies remain well below the Carnot limit, highlighting the inherent limitations of solid-state thermoelastic conversion. A techno-economic assessment further indicates that economic viability depends primarily on multi-cycle operation and low-cost implementation. Although the achievable energy density remains modest compared with conventional renewable technologies, the proposed framework provides quantitative performance limits and practical design guidelines for evaluating thermoelastic energy harvesting from low-grade heat. Full article

The integration of low-Earth-orbit (LEO) satellites with unmanned aerial vehicles (UAVs) promises high-throughput and flexible wireless connectivity, yet it faces critical challenges in simultaneously guaranteeing data rates and long-term energy harvesting under mobility and imperfect channel state information (CSI). Additionally, the rate–energy trade-off imposed by simultaneous wireless information and power transfer (SWIPT) further complicates per-slot resource allocation. In this paper, we propose a Lyapunov-based scheduling framework that stabilizes UAV data and virtual energy queues while maximizing weighted throughput. The framework employs a custom inner solver combining successive convex approximation (SCA) and weighted minimum mean-square error (WMMSE) optimization to efficiently compute per-slot beamformers and power-splitting ratios. Our approach explicitly accounts for UAV mobility, Rician fading channels with Doppler, and circuit nonlinearities in energy harvesting, ensuring feasible and energy-aware SWIPT operation. A LEO satellite–UAV integrated communication system is considered, where multiple satellites provide wireless connectivity to energy-constrained UAVs operating in a dynamic three-dimensional environment. The satellites employ multi-antenna transmission, while the UAVs rely on energy harvesting mechanisms to sustain their operation. The communication links are characterized by dominant line-of-sight propagation conditions, and UAV trajectories are adaptively optimized to improve network performance and energy efficiency. Simulation results demonstrate that the proposed Lyapunov-based SCA-WMMSE framework significantly outperforms a fixed baseline approach, providing substantial improvements in signal quality, achievable data rates, and harvested energy. Moreover, the proposed method maintains stable energy management behavior and guarantees long-term energy sustainability for the UAVs. Full article

Precision agriculture and sustainable farming practices require autonomous environmental monitoring systems capable of operating in remote areas with limited energy and connectivity. However, the high cost of existing professional technology remains a significant barrier to widespread adoption. This study presents the development of a solar-powered smart sensing node designed for autonomous operation that integrates TinyML and dual-mode wireless connectivity via LoRaWAN and Wi-Fi for intelligent monitoring. The system features a custom-designed cup anemometer and multispectral sensing capabilities integrated into a compact single-tower architecture. All structural components, including radiation shields and a modular PVC frame, were designed for low-cost manufacturing and mass production. A single hermetic housing protects the core control electronics and is designed to improve durability in harsh outdoor environments. A Multi-Layer Perceptron model was implemented on the edge to enable intelligent data fusion and compensation, while a dynamic sampling strategy optimized power consumption. Experimental results demonstrate the feasibility of the proposed architecture through adaptive spectral acquisition over a daily illumination cycle, embedded MLP-based sensor fusion, and telemetry-oriented data compression that substantially reduces the number of transmitted samples. The main contribution of this work is a system-level architecture that integrates sensing, embedded intelligence, solar-energy harvesting, hybrid wireless communication, and telemetry optimization into a compact, low-cost, and field-deployable prototype IoT platform for sustainable precision agriculture. Full article

The reciprocating cutterbar is evolving from a conventional harvesting mechanism into an intelligent end-effector for crop harvesting, mechanical weeding, and selective cutting. However, plant anisotropy, moisture-dependent fracture, root-soil constraints, vibration, and wear still hinder low-energy cutting, long service life, and robust control. This review integrates harvesting and mechanical weeding within a unified analysis of reciprocating cutterbar technologies. It first links plant tissue structure and dynamic fracture to blade penetration, fiber stretching, crack propagation, and energy dissipation. It then examines how cutting speed, sliding-cut angle, blade clearance, and root-soil anchorage jointly affect performance. Advanced testing, response surface methodology, discrete element method, finite element method, and multiphysics simulations are compared for failure analysis, parameter optimization, and contact modeling. The review further assesses bionic blade design, surface strengthening, composite coatings, novel transmissions, multisource perception, and adaptive control. Key barriers include inconsistent plant-mechanics datasets, computationally intensive models, limited field robustness, and conflicts among performance objectives. We therefore identify digital twins, modular electric cutterbars, and closed-loop control as priorities for translating mechanistic insight into reliable field performance. Full article

Measuring the water table level is a critical factor in irrigated agriculture in arid regions, as it can significantly influence the exchange of water and nutrients with crops. This work presents the design, implementation, and field validation of an open-source, solar-powered IoT device for autonomous groundwater level monitoring, combining long-range low-power LoRa communication, a non-contact pressure-based level sensor using the trapped-air capillary method, and an efficient power management stage that seamlessly switches between solar and battery power. Unlike existing commercial leveloggers, which are costly and lack integrated wireless telemetry and solar-based autonomy, the proposed platform is presented as a fully open-source, low-cost alternative purpose-built for unattended deployment in areas without grid power or cellular coverage. The system was validated through a multi-day field trial and dedicated communication tests, demonstrating a stable power conversion efficiency of 84–90%, a five-day autonomous operation without any deep-discharge event, high linearity ($R^2$ = 0.9998) of the level module over a 0–2 m range with a resolution of approximately 1.94 mm per ADC count, and a reliable LoRa link of up to 8.51 km in an urban/suburban environment despite non-line-of-sight conditions. With an estimated hardware cost of approximately $100 USD per unit, the device represents a low-cost, low-maintenance tool capable of generating knowledge about water resources to optimize irrigation and crop management in the face of climate change. Full article

Iced conductor galloping poses a critical threat to high-voltage transmission line safety and stability, yet existing monitoring systems are constrained by single-sensor dependency, inadequate signal denoising, and limited early warning accuracy. This paper presents a field-validated framework integrating multi-source sensor fusion, adaptive signal denoising, and deep learning-based early warning for iced conductor galloping. A five-layer Internet of Things (IoT) monitoring architecture is designed, fusing fiber Bragg grating (FBG) arrays, MEMS inertial sensors, and micro-meteorological stations, with dual-spectrum cameras providing auxiliary visual verification. Self-powered MEMS nodes utilizing electromagnetic induction energy harvesting are shown to have achieved year-round zero-external-power maintenance. An improved hummingbird local optimization algorithm (IHLOA) adaptively optimizes variational mode decomposition (VMD) parameters, combined with wavelet threshold denoising (WTD) for joint signal preprocessing, achieving a 17.78 dB signal-to-noise ratio improvement [95% CI: 17.2–18.3 dB]. A 26-dimensional multi-domain feature vector is constructed and reduced to 12 discriminative features via ReliefF selection. A Temporal Adaptation Gated Recurrent Unit with Attention (TA-GRU-Attention) model incorporating an adaptive irregular time series perception module is developed for galloping early warning, with all models evaluated exclusively on real-event test samples. Experimental validation through 1:20 scale wind tunnel aeroelastic tests and a 15-month field demonstration on an operating 220 kV transmission line at 2800–3200 m elevation demonstrates a galloping prediction accuracy of 91.3% [95% CI: 81.5–97.2%] under stratified time series split, a missed alarm rate of 12.2%, and a median advance warning time of 38.5 min (range: 18–65 min, IQR: 26–52 min), with 97.3% system availability over the deployment period. Full article

This paper presents an experimental study of vibration energy recovery from a horizontal seat suspension system in which a brushless direct current (BLDC) motor is used as both an active force actuator and a controllable regenerative braking element. The novelty of the study lies in the experimental validation of an active/regenerative switching strategy for a horizontal seat suspension and in the quantitative comparison of passive, fully active and regenerative operating modes under random vibration excitation and different inertial loads. The proposed system was evaluated using transmissibility functions, seat effective amplitude transmissibility (SEAT) factors, suspension travel, and electrical quantities generated in the braking branch. The results show that the fully active mode provides the highest vibration attenuation, whereas the regenerative mode reduces the SEAT factor compared with the passive suspension while simultaneously producing measurable electrical power in the braking resistor network. The maximum measured electrical power in the braking branch reached 7.692 W for the WN3 excitation signal and an 80 kg load. The obtained results confirm the practical potential of regenerative braking for potentially reducing the net energy demand of active seat suspension systems, while also highlighting the trade-off between vibration attenuation, suspension travel, and recoverable electrical power. Full article

Agrivoltaic (AV) systems represent a promising strategy for integrating renewable energy production and agricultural activity on the same land unit, while contributing to soil water conservation under increasingly frequent drought conditions. This preliminary, single-site case study investigates the effects of a horizontal biaxial tracking AV system on soil water dynamics in a durum wheat field in the Po Valley (Borgo Virgilio, Mantua, Italy) over a full monitoring period, covering the final crop growth stages and the post-harvest bare soil phase (May–December 2024). Monitoring of soil temperature, volumetric water content (VWC), and soil water potential (SWP) was conducted at four depths (15, 30, 45, and 60 cm) at one representative monitoring station per treatment, comparing soil under AV cover (AVC) and in unshaded conditions (UC), located 10 m apart. Paired VWC and SWP measurements were used to derive site-specific soil water characteristic curves (SWCCs) and to calibrate the agro-hydrological model CRITERIA-1D, which was used to estimate available water (AW) in the first 80 cm of depth for both treatments. Measured VWC values were higher in the AVC profile than in the UC profile at all monitored depths throughout the May–September period, with differences persisting, although at lower values through October–December. Estimated AW was consistently higher under AVC than in UC during both the dry and wet periods. Despite higher VWC, the AVC profile showed more negative average SWP values at all depths during summer. This pattern is consistent with the shape of the derived SWCCs and may point to differences in water-retaining capacity between the two profiles, possibly related to structural modifications induced by 13 years of AV system operation. These preliminary findings suggest that AV systems could potentially improve soil water availability in the root zone of rainfed cereal crops and propose the hypothesis that long-term AV cover may act as a driver of changes in soil hydraulic properties, with implications for the sustainability and climate resilience of dryland farming systems. However, given the design of this case study, with only one monitoring point per treatment, the observed differences reflect the specific monitored locations and cannot fully disentangle the AV treatment effect from pre-existing spatial heterogeneity in soil properties. The preliminary results obtained in this study should therefore not be generalised beyond the specific conditions of this case study, and the interpretations proposed here should be treated as unproven hypotheses rather than established conclusions. Further studies with spatial replication and multi-year monitoring are needed to confirm these patterns. Full article

Developing efficient, stable, and low-cost photocatalysts is the key to achieving large-scale photocatalytic hydrogen production. Herein, a universal fluoride-induced sulfur vacancy engineering strategy is proposed for the full Mn_xCd_1−xS solid solution series (x = 0.1–0.9), with Mn_0.6Cd_0.4S selected as the representative optimal sample. By introducing ammonium fluoride during the hydrothermal process, controllable sulfur vacancies are generated to enable efficient separation and transfer of photogenerated charge carriers for high-efficiency hydrogen production. Impressively, the optimal fluoride-modified Mn_0.6Cd_0.4S (F-MCS) photocatalyst shows the fastest hydrogen production rate up to 8.08 mmol·g⁻¹·h⁻¹, which is 1.5 times that of pure MCS nanoparticles, as well as enhanced photochemical stability. Quantitative EDS elemental analysis verifies that 1.2 at.% fluorine is incorporated into the lattice of F-MCS, rather than being physically adsorbed as residual ammonium fluoride precursors. Experimental results reveal that the introduction of NH₄F can effectively facilitate the sulfur vacancy formation in MCS, which alters the band position of MCS nanoflakes for increased light harvesting, and serves carrier separation centers for promoting the efficient transfer of photogenerated charge carriers. This study provides valuable insights into the design of a solid solution-based photocatalyst for efficient solar-driven hydrogen production for sustainable energy applications. Full article

This paper develops a reduced-order electromechanical model for piezoelectric energy harvesting from a beam traversed by a moving mass. The beam is described by the Euler–Bernoulli theory, and the coupled equations of motion are derived through modal expansion combined with the linear piezoelectric constitutive relations. Unlike most existing formulations, the model accounts for non-uniform transit by including moving-mass acceleration, accommodates an arbitrary number of piezoelectric patches distributed along the span, and incorporates von Kármán strain–displacement relations. So, moderately large deflections and mid-plane stretching as well as various boundary conditions may be investigated within the same framework. The resulting coupled nonlinear ordinary differential equations are integrated in time using a numerical solver. On the other hand, predictions of midpoint deflection, output voltage, and harvested power are validated against the COMSOL Multiphysics Finite element model. The experimental setup has been established, and a dedicated laboratory experiment provides additional verification under controlled conditions. Parametric analyses investigating the individual and combined effects of the mass ratio, velocity ratio, acceleration profile, patch length, patch position, number of patches, and external load resistance are elaborated. Distributed multi-patch configurations are shown to recover more energy than a single-centered patch once higher modes contribute appreciably to the response. Design charts relating the governing parameters to the harvested power are constructed for each set of support conditions. These results are intended to assist the preliminary sizing and placement of piezoelectric transducers on some practical energy harvesting applications. Full article

This article reviews the reactive magnetron sputtering of piezoelectric Aluminium Nitride (AlN) thin films, with a focus on process parameter optimisation and system-level deposition challenges. AlN is a leading material for MEMS and RF applications owing to its c-axis (002) orientation, high acoustic velocity, wide bandgap (∼6.2 eV), and CMOS compatibility. We review the influence of sputtering power, nitrogen flow ratio, substrate temperature, and target-to-substrate distance on crystallographic quality and document practical hardware challenges, including vacuum leakage, grounding faults, target erosion, and mass flow controller drift, that critically affect reproducibility but are systematically underreported in the literature. A perspective is provided on emerging application domains where optimised AlN films address current performance gaps, including next-generation RF/telecom systems towards 6G and Future Networks, harsh environment sensing and actuation, biomedical ultrasound, and IoT energy harvesting. The complementarity between AlN and Silicon Carbide (SiC) is discussed for high-temperature, high-power, and radiation-hard MEMS, where AlN/SiC heterostructures combine the piezoelectric activity of AlN with the mechanical and chemical robustness of SiC. It also incorporates a discussion of dopant- and heteroepitaxy-based AlN engineering, AlN deposition on a wider range of substrates, the role of seed and electrode underlayers, and pulsed-DC sputtering as a third power supply mode alongside RF and conventional DC. Full article

In high-density cities, limited roof and ground areas constrain conventional photovoltaic (PV) deployment. Vertical bifacial photovoltaic (bPV) modules offer an alternative by making good use of building and infrastructure surfaces while harvesting irradiance on both sides. This study develops an integrated module-level framework for evaluating tilted and vertical bPV modules. It couples two-sided anisotropic irradiance calculations with five-parameter electrical and steady-state thermal models. Unlike irradiance-only or configuration-specific assessments, the framework consistently compares bPV and monofacial PV (mPV) modules across tilt and azimuth configurations while jointly quantifying power output, module temperature, bifacial gain, and angular losses. Predicted power output agreed well with outdoor measurements across four representative mounting configurations, and annual predictions were comparable to PVsyst and SAM results. Applied to Hong Kong, the framework identified optimum tilt angles of approximately 20° for bPV and 18° for mPV modules. A vertical west-facing bPV module achieved 96.3% of the annual energy yield of optimally tilted mPV, with a bifacial gain of 67.2% and an angular-loss-related power loss of 4.8%. These results show that vertical bPV can approach optimally tilted mPV performance while utilizing otherwise unused building surfaces, supporting preliminary design decisions in land-constrained cities. Full article

Pyropiezoelectric energy harvesting has the potential to utilize both environmental vibrations and time-dependent temperature changes to increase the amount of energy harvested compared to harvesting from only one of the two sources. So far, the investigation of such hybrid energy harvesting approaches has mainly been experimental. This makes it difficult to distinguish between their individual physical effects and complicates the optimization of such harvesters. This work presents a numerical framework for solving transient coupled pyropiezoelectric equations using the finite element method. The numerical method can be applied to simulate hybrid energy harvesters by taking into account external electrical circuits. The numerical simulations enable a targeted analysis of the contributions of mechanical, electrical and thermal effects to the harvested energy. This is illustrated by various numerical examples, such as a simple piezoelectric cuboid, a unimorph, a bimorph and a bimetallic beam with a piezoelectric patch. These are subjected to oscillating deformations and/or temperature changes. The simulations calculate the harvested energy resulting from the individual physical effects, depending on the excitation frequency, the external resistance, and the geometric configuration of the harvester. For a bimetallic beam with a piezoelectric patch, which is used as a low frequency hybrid energy harvester, a geometric optimization based on the simulation results showed a possible increase in the harvested energy of up to 386% under idealized circuit conditions compared to the initial design from the literature. Full article