Search Results (208)

Standalone photovoltaic (PV) systems offer a viable path to decentralized energy access but face limitations during periods of low solar irradiance. While batteries provide short-term storage, their capacity constraints often restrict the use of surplus energy, highlighting the need for long-duration solutions. Green hydrogen, generated via proton exchange membrane (PEM) electrolyzers, offers a scalable alternative. This study proposes a stochastic energy management framework that leverages a Markov decision process (MDP) to coordinate PV generation, battery storage, and hydrogen production under variable irradiance and uncertain load demand. The strategy dynamically allocates power flows, ensuring system stability and efficient energy utilization. Real-time weather data from Goiás, Brazil, is used to simulate system behavior under realistic conditions. Compared to the conventional perturb and observe (P&O) technique, the proposed method significantly improves system performance, achieving a $99.9\%$ average efficiency (vs. 98.64%) and a drastically lower average tracking error of 0.3125 (vs. 9.8836). This enhanced tracking accuracy ensures faster convergence to the maximum power point, even during abrupt load changes, thereby increasing the effective use of solar energy. As a direct consequence, green hydrogen production is maximized while energy curtailment is minimized. The results confirm the robustness of the MDP-based control, demonstrating improved responsiveness, reduced downtime, and enhanced hydrogen yield, thus supporting sustainable energy conversion in off-grid environments. Full article

The downwelling light sensor (DLS) is the industry-standard solution for generating UAV-based digital orthophoto maps (DOMs). Current mainstream DLS correction methods primarily rely on angle compensation. However, due to the temporal mismatch between the DLS sampling intervals and the exposure times of multispectral cameras, as well as external disturbances such as strong wind gusts and abrupt changes in flight attitude, DLS data often become unreliable, particularly at UAV turning points. Building upon traditional angle compensation methods, this study proposes an improved correction approach—FIM-DC (Fitting and Interpolation Model-based Data Correction)—specifically designed for data collection under clear-sky conditions and stable atmospheric illumination, with the goal of significantly enhancing the accuracy of reflectance retrieval. The method addresses three key issues: (1) field tests conducted in the Qingpu region show that FIM-DC markedly reduces the standard deviation of reflectance at tie points across multiple spectral bands and flight sessions, with the most substantial reduction from 15.07% to 0.58%; (2) it effectively mitigates inconsistencies in reflectance within image mosaics caused by anomalous DLS readings, thereby improving the uniformity of DOMs; and (3) FIM-DC accurately corrects the spectral curves of six land cover types in anomalous images, making them consistent with those from non-anomalous images. In summary, this study demonstrates that integrating FIM-DC into DLS data correction workflows for UAV-based multispectral imagery significantly enhances reflectance calculation accuracy and provides a robust solution for improving image quality under stable illumination conditions. Full article

Mine ventilation systems are critical for ensuring operational safety, yet air leakage remains a pervasive challenge, leading to energy inefficiency and heightened safety risks. Traditional tracer gas methods, while effective in simple networks, exhibit significant errors in complex multi-entry systems due to static empirical parameters and environmental interference. This study proposes an integrated methodology that combines multi-path airflow analysis with dynamic longitudinal dispersion coefficient correction to enhance the accuracy of air leakage detection. Utilizing sulfur hexafluoride (SF6) as the tracer gas, a phased release protocol with temporal isolation was implemented across five strategic points in a coal mine ventilation network. High-precision detectors (Bruel & Kiaer 1302) and the MIVENA system enabled synchronized data acquisition and 3D network modeling. Theoretical models were dynamically calibrated using field-measured airflow velocities and dispersion coefficients. The results revealed three deviation patterns between simulated and measured tracer peaks: Class A deviation showed 98.5% alignment in single-path scenarios, Class B deviation highlighted localized velocity anomalies from Venturi effects, and Class C deviation identified recirculation vortices due to abrupt cross-sectional changes. Simulation accuracy improved from 70% to over 95% after introducing wind speed and dispersion adjustment coefficients, resolving concealed leakage pathways between critical nodes and key nodes. The study demonstrates that the dynamic correction of dispersion coefficients and multi-path decomposition effectively mitigates errors caused by turbulence and geometric irregularities. This approach provides a robust framework for optimizing ventilation systems, reducing invalid airflow losses, and advancing intelligent ventilation management through real-time monitoring integration. Full article

This study analyzes precipitation patterns and future trends in the Kuye River Basin in the context of climate change, providing a scientific foundation for water resource management and ecological protection. Using methods such as the Mann–Kendall test, Pettitt test, and complex Morlet wavelet analysis, this study examines both interannual and intra-annual variability in historical precipitation data, identifying abrupt changes and periodic patterns. Future projections are based on CMIP5 models under RCP4.5 and RCP8.5 scenarios, forecasting changes over the next 30 years (2023–2052). The results reveal significant spatiotemporal variability in precipitation, with 88.16% concentrated in the summer and flood seasons, while only 1.07% falls in winter. The basin’s multi-year average precipitation is 445 mm, exhibiting stable interannual variability, but with a significant increase starting in 2006. Projections indicate that the average annual precipitation will rise to 524.69 mm from 2023 to 2052, with a notable change point in 2043. Precipitation is expected to increase spatially from northwest to southeast. This research underscores the importance of understanding precipitation dynamics in managing drought and flood risks. It highlights the role of soil and water conservation and vegetation restoration in improving water resource efficiency, supporting sustainable development, and guiding climate adaptation strategies. Full article

Under the combined influence of climate change and human activities, the non-stationarity of reservoir runoff has significantly intensified, posing challenges for traditional statistical models to accurately capture its multi-scale abrupt changes. This study focuses on Qianping (QP) Reservoir and systematically integrates climate-driven mechanisms with machine learning approaches to uncover the patterns of runoff evolution and develop high-precision prediction models. The findings offer a novel paradigm for adaptive reservoir operation under non-stationary conditions. In this paper, we employ methods including extreme-point symmetric mode decomposition (ESMD), Bayesian ensemble time series decomposition (BETS), and cross-wavelet transform (XWT) to investigate the variation trends and mutation features of the annual runoff in QP Reservoir. Additionally, four models—ARIMA, LSTM, LSTM-RF, and LSTM-CNN—are utilized for runoff prediction and analysis. The results indicate that: (1) the annual runoff of QP Reservoir exhibits a quasi-8.25-year mid-short-term cycle and a quasi-13.20-year long-term cycle on an annual scale; (2) by using Bayesian estimators based on abrupt change year detection and trend variation algorithms, an abrupt change point with a probability of 79.1% was identified in 1985, with a confidence interval spanning 1984 to 1986; (3) cross-wavelet analysis indicates that the periodic associations between the annual runoff of QP Reservoir and climate-driving factors exhibit spatiotemporal heterogeneity: the AMO, AO, and PNA show multi-scale synergistic interactions; the DMI and ENSO display only phase-specific weak coupling; while solar sunspot activity modulates runoff over long-term cycles; and (4) The NSE of the ARIMA, LSTM, LSTM-RF, and LSTM-CNN models all exceed 0.945, the RMSE is below 0.477 × 10⁹ m³, and the MAE is below 0.297 × 10⁹ m³, Among them, the LSTM-RF model demonstrated the highest accuracy and the most stable predicted fluctuations, indicating that future annual runoff will continue to fluctuate but with a decreasing amplitude. Full article

To address the engineering issues of difficult quality control, complex construction processes, and long construction periods in cast-in-place protective walls for manually excavated piles, a prefabricated protective wall structure is proposed. This study aims to investigate its mechanical properties and key influencing parameters through experiments. Six groups of prefabricated wall segment specimens with different wall thicknesses (50 mm, 65 mm) and concrete strengths (C50 concrete, reactive powder concrete RPC) were designed, and two-point bending tests were conducted to systematically analyze their failure characteristics, crack development patterns, and strain distribution laws. The test results show that the peak vertical bending displacements at mid-span of the specimens are 11–18 mm (1.83–2.71% of the radius). The 65-mm-thick specimens exhibit 3–10% higher flexural strength than the 50-mm-thick ones, and reactive powder concrete (RPC) specimens of the same thickness show an 8.3% increase in strength compared to C50 concrete specimens. When the load reaches 80% of the ultimate load, abrupt changes in concrete strain occur at the mid-span and loading points, while the strain at the fixed end is only 15–20% of the mid-span strain. The prefabricated protective wall demonstrates superior deformation resistance, with vertical displacements (3–5% of the radius) significantly lower than those of cast-in-place walls. This research clarifies the influence of wall thickness and concrete strength on the mechanical properties of prefabricated protective walls, providing key mechanical parameters to support their engineering applications. Full article

Fused filament fabrication of thermoplastic composites has grown exponentially owing to its efficiency, thereby meeting numerous engineering demands. However, these materials have limitations owing to their structural vulnerability to elevated temperatures. To address this drawback, this study aims to investigate the tensile behavior of 3D-printed composites in a broad thermal domain from ambient temperature to the crystallization point. For this purpose, a commercial high-temperature-resilient polyamide carbon fiber was selected. To assess the optimal bead configuration and application range, the methodology includes tensile testing of five infill orientations across the four principal thermal domains of the polymers. The results highlight different bead arrangements under constant thermal conditions and demonstrate how temperature effects the tensile performance at similar raster angles, as further correlated with fracture mechanism analysis via scanning electron microscopy. The key findings indicate that raster orientation has a minor influence compared to temperature change. In accordance with the literature, a significantly decreased strength and an abrupt increase in plasticity is observed above the glass transition temperature. Nevertheless, the material retains one-third of its ambient tensile strength at 150 °C, demonstrating its potential for high-temperature applications. Full article

Since the beginning of the 21st century, the supercritical carbon dioxide (sCO₂) Brayton cycle has emerged as a hot topic of research in the energy field. Among its key components, the sCO₂ compressor has received significant attention. In particular, axial-flow sCO₂ compressors are increasingly being investigated as power systems advance toward high power scaling. This paper reviews global research progress in this field. As for performance characteristics, currently, sCO₂ axial-flow compressors are mostly designed with large mass flow rates (>100 kg/s), near-critical inlet conditions, multistage configurations with relatively low stage pressure ratios (1.1–1.2), and high isentropic efficiencies (87–93%). As for internal flow characteristics, although similarity laws remain applicable to sCO₂ turbomachinery, the flow dynamics are strongly influenced by abrupt variations in thermophysical properties (e.g., viscosities, sound speeds, and isentropic exponents). High Reynolds numbers reduce frictional losses and enhance flow stability against separation but increase sensitivity to wall roughness. The locally reduced sound speed may induce shock waves and choke, while drastic variation in the isentropic exponent makes the multistage matching difficult and disperses normalized performance curves. Additionally, the quantitative impact of a near-critical phase change remains insufficiently understood. As for the experimental investigation, so far, it has been publicly shown that only the University of Notre Dame has conducted an axial-flow compressor experimental test, for the first stage of a 10 MW sCO₂ multistage axial-flow compressor. Although the measured efficiency is higher than that of all known sCO₂ centrifugal compressors, the inlet conditions evidently deviate from the critical point, limiting the applicability of the results to sCO₂ power cycles. As for design and optimization, conventional design methodologies for axial-flow compressors require adaptations to incorporate real-gas property correction models, re-evaluations of maximum diffusion (e.g., the DF parameter) for sCO₂ applications, and the intensification of structural constraints due to the high pressure and density of sCO₂. In conclusion, further research should focus on two aspects. The first is to carry out more fundamental cascade experiments and numerical simulations to reveal the complex mechanisms for the near-critical, transonic, and two-phase flow within the sCO₂ axial-flow compressor. The second is to develop loss models and design a space suitable for sCO₂ multistage axial-flow compressors, thus improving the design tools for high-efficiency and wide-margin sCO₂ axial-flow compressors. Full article

To assess structural sealant damage in hidden frame glass curtain walls (HFGCWs) during service, damage states were simulated by controlled cutting with varying incision lengths. Quantitative identification challenges were investigated through natural frequency and curvature modal difference (CMD) analyses at multiple test points. The results indicate that natural frequency decreases with increasing damage severity, while the first-order curvature mode difference (FCMD) exhibits localized abrupt changes in damaged regions. Boundary modes provide more targeted and accurate damage identification. The peak value of the FCMD mutation region enables precise damage localization. A quantitative damage identification threshold of 0.1205 was derived from FCMD distribution characteristics in boundary regions. By leveraging boundary mode features, modal testing efficiency is optimized, reducing the required acquisition nodes and effectively guiding structural sealant damage detection in engineering applications. Full article

In this work, we propose the integration of a mechanism to enable smooth transitions between different locomotion patterns in a hexapod robot. Specifically, we utilize a spiking neural network (SNN) functioning as a Central Pattern Generator (CPG) to generate three distinct locomotion patterns, or gaits: walk, jog, and run. This network produces coordinated spike trains, mimicking those generated in the brain, which are translated into synchronized robot movements via PWM signals. Subsequently, these spike trains are compared using a similarity metric known as SPIKE-synchronization to identify the optimal point for transitioning from one gait to another. This approach aims to achieve three main objectives: first, to maintain the robot’s balance during transitions; second, to ensure that gait transitions are almost imperceptible; and third, to improve energy efficiency by reducing abrupt changes in the robot’s actuators (servomotors). To validate our proposal, we incorporated FSR sensors on the robot’s legs to detect the rigidity of the terrain it navigates. Based on the terrain’s rigidity, the robot dynamically transitions between gaits. The system was tested in real time on a physical hexapod robot across four different types of terrain. Although the method was validated exclusively on a hexapod robot, it can be extended to any legged robot. Full article

Curved-spoke wheels have been proposed as an effective way to overcome stair-like obstacles with smooth, rotation-only motion. However, when the wheel’s contact point shifts, discontinuous changes in its radius of curvature cause abrupt drops in the robot’s linear speed, often leading to reduced payload stability and slip. As a result, maintaining reliable stair climbing becomes more difficult. At higher speeds, these sudden changes become stronger, further reducing dynamic stability. To address these issues, we propose a passive Compliant Spiral Torsional Suspension (C-STS) attached to the wheel’s drive axis. Through camera-based marker tracking, we analyzed wheel trajectories under various stiffness and speed conditions. In particular, we define the deceleration caused by the velocity drop during contact transitions as our dynamic stability metric and demonstrate that the C-STS significantly reduces this deceleration across low-, medium-, and high-speed climbing, based on comparisons both with and without the suspension. It also raises the average velocity, likely due to a brief release of stored elastic energy, and lowers the net torque requirement. Our findings show that the proposed C-STS greatly improves dynamic stability and suggest its potential for enhancing stair-climbing performance in curved-wheel-based robotic systems. Furthermore, our approach may extend to other reconfigurable wheels facing similar instabilities. Full article

This study is the first to use the Bayesian Estimator of Abrupt Change, Seasonality, and Trend (BEAST) algorithm to detect trend change points in the nexuses between the green bond index (Green Bond) and WTI of crude oil, gasoline, as well as natural gas futures. The COVID-19 pandemic and the Russia–Ukraine war are identified as common significant trend change points, and the total sample is subsequently divided into three stages based on these points. Utilizing a skewed MF-DCCA method, this study analyzed the skewed multifractal characteristics between the Green Bond and the energy futures across these stages. The results revealed that both the multifractal characteristics and risk levels experienced significant changes across different periods, exhibiting skewed multifractality. Specifically, from the pre-pandemic period to the post-Russia–Ukraine conflict period, the multifractal features and risk of the Green Bond and WTI and Green Bond and Gasoline groups first declined and then increased, while the Green Bond and Natural Gas group displayed an opposite trend, showing an initial increase followed by a decline. A portfolio analysis further indicated that Green Bond provided effective hedging against all three types of energy futures, particularly during crisis periods. Notably, the portfolios constructed using the Mean-MF-DCCA model, which incorporated multifractal features, outperformed those constructed by traditional portfolio models. These findings offered new insights into the dynamic characteristics of the Green Bond and energy futures markets and provided important policy implications for portfolio optimization and risk management strategies. Full article

How to solve the contradiction between coal mining and soil and water conservation is a key scientific issue in the achievement of high-quality development in the middle reaches of the Yellow River. In this paper, the northern Shaanxi mining area in the middle reaches of the Yellow River is taken as the research area, and the surface loess micro-topography is taken as the entry point. The numerical simulation test and soil loss model calculation are used to reveal the different types of loess natural slope morphology (straight slope, concave slope, convex slope, and composite slope) and the natural slopes (5°, 15°, 25°, 35°, 45°). The influence characteristics and laws of the same mining on the surface loess slope morphology in the coal mining subsidence area are analyzed, and the soil erosion effect on the slope scale is analyzed. The results show that: (1) Coal mining subsidence will lead to an increase in the slope of the loess slope, and the smaller the natural slope, the greater the increase in slope. Among them, the influence of coal mining subsidence on the ‘concave loess slope with natural slope of 15°’ is the most significant, and the natural slope of 15° is the key dividing point for the transformation of the sensitive slope shape of the loess slope in the coal mining subsidence area of northern Shaanxi. (2) Coal mining subsidence will lead to the decrease in slope length of a loess natural slope, and the smaller the natural slope, the greater the decrease in slope length. Among them, coal mining subsidence has the most significant impact on the ‘concave loess slope with a natural slope of 25°’. The natural slope of 25° is the key point of the sudden change rate of the slope length of the loess slope in the coal mining subsidence area of northern Shaanxi. (3) Coal mining subsidence will lead to the increase in the soil erosion modulus on the surface loess slope under the scale of ‘annual erosion rainfall’ and ‘typical field erosion rainfall’, and the smaller the natural slope, the greater the increase in the soil erosion modulus. The natural slopes of 15° and 25° are the key points of the abrupt change in soil erosion intensity on the loess slope in the coal mining subsidence area of northern Shaanxi under the scales of ‘annual erosion rainfall’ and ‘typical erosion rainfall’, respectively. Under the scale of annual erosion rainfall, the increment of the 15° slope was 1.65 times, 1.12 times, 1.11 times, and 1.02 times that of the 5°, 25°, 35°, and 45° slopes, respectively. Under the typical erosion rainfall scale, the increment of the 25° slope was 4.22 times, 1.32 times, 1.04 times, and 1.15 times that of the 5°, 15°, 35°, and 45° slopes, respectively. (4) For the loess subsidence slope with any slope shape, the increase in slope gradient is the main factor for the increase in the soil erosion modulus. Under the annual erosion rainfall scale, the contribution of slope increase was 92.9%. Under the typical erosion rainfall scale, the contribution of slope increase was 79.1%. The research results can provide scientific guidance for soil erosion and control in the northern Shaanxi mining area in the middle reaches of the Yellow River Basin. Full article

Water–sediment evolution and attribution analysis in watersheds is one of the research focuses of hydrogeology. An in-depth investigation into the spatiotemporal variation of water and sediment at multiple spatial scales within the basin, along with a systematic assessment of the respective impacts of climate change and human activities, provides a scientific foundation for formulating effective soil and water conservation practices and integrated water resource management strategies. This research holds significant implications for the sustainable development and ecological management of the basin. In this study, the Mann–Kendall nonparametric test method, double cumulative curve method, cumulative anomaly method, and cumulative slope change rate analysis method were used to quantitatively study the effects of climate change and human activities on runoff and sediment load changes at different spatial scales in the Huangfuchuan River basin. The results show that (1) from 1966 to 2020, the annual runoff and annual sediment load discharge in the Huangfuchuan River basin showed a significant decreasing trend. Among them, the reduction in runoff and sediment in the control sub-basin of Shagedu Station in the upper reaches was more obvious than that in the whole basin. The mutation points of runoff and sediment load in the two basins were 1979 and 1998. The water–sediment relationship exhibits a power function pattern. (2) After the abrupt change, in the change period B (1980–1997), the contribution rates of climate change and human activities to runoff and sediment load reduction in the Huangfuchuan River basin were 24.12%, 75.88% and 20.05%, 79.95%, respectively. In the change period C (1998–2020), the contribution rates of the two factors to the runoff and sediment load reduction in the Huangfuchuan River basin were 18.91%, 81.09% and 15.61%, 84.39%, respectively. Among them, the influence of precipitation in the upper reaches of the Huangfuchuan River basin on the change in runoff and sediment load is higher than that of the whole basin, and the influence on the decrease of sediment load discharge is more significant before 1998. There are certain stage differences and spatial scale effects. (3) Human activities such as large-scale vegetation restoration and construction of silt dam engineering measures are the main reasons for the reduction in runoff and sediment load in the Huangfuchuan River basin and have played a greater role after 1998. Full article

This study presents a novel method for detecting sleep posture changes—specifically tossing and turning—by monitoring variations in humidity using an array of humidity sensors embedded at regular intervals within a memory-foam pillow. Unlike previous approaches that rely primarily on temperature or pressure sensors, our method leverages the observation that humidity fluctuations are more pronounced during movement, enabling the more sensitive detection of posture changes. We demonstrate that dynamic patterns in humidity data correlate strongly with physical motion during sleep. To identify these transitions, we applied the Pruned Exact Linear Time (PELT) algorithm, which effectively segmented the time series based on abrupt changes in humidity. Furthermore, we converted humidity fluctuation curves into image representations and employed a transfer-learning-based model to classify sleep postures, achieving accurate recognition performance. Our findings highlight the potential of humidity sensing as a reliable modality for non-invasive sleep monitoring. In this study, we propose a novel method for detecting tossing and turning during sleep by analyzing changes in humidity captured by a linear array of sensors embedded in a memory foam pillow. Compared to temperature data, humidity data exhibited more significant fluctuations, which were leveraged to track head movement and infer sleep posture. We applied a rolling smoothing technique and quantified the cumulative deviation across sensors to identify posture transitions. Furthermore, the PELT algorithm was utilized for precise change-point detection. To classify sleep posture, we converted the humidity time series into images and implemented a transfer learning model using a Vision Transformer, achieving a classification accuracy of approximately 96%. Our results demonstrate the feasibility of a sleep posture analysis using only humidity data, offering a non-intrusive and effective approach for sleep monitoring. Full article