Search Results (1,824)

Vegetation phenology serves as a crucial indicator reflecting vegetation responses to the growth environment and climate change. Existing studies have demonstrated that in permafrost regions, the impact of frozen soil changes on vegetation phenology is more direct and pronounced compared to climate factors. Amid the slowdown of global warming in the 21st century, permafrost dynamics continued to drive uncertain variations in vegetation phenological stages across the Qinghai–Tibet Plateau (QTP). Using MODIS Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) data during 2001–2024, this study derived vegetation phenological parameters and analyzed their spatiotemporal patterns on the QTP. The results indicate that overall, the start of growing season (SOS) was advanced, the end of growing season (EOS) was delayed, and the length of growing season (LOG) was extended throughout the study period. Additionally, divergent phenological trends were observed across three distinct phases, and regarding frozen soil types, vegetation phenology in permafrost and seasonally frozen ground regions exhibited distinct characteristics. From 2001 to 2024, both permafrost and seasonally frozen ground regions showed an advanced SOS and prolonged LOG, but significant differences were observed in EOS dynamics. For vegetation types, alpine meadow displayed advanced SOS and EOS, alongside an extended LOG. The alpine steppe exhibited advanced SOS and delayed EOS with an extended LOG. Alpine desert displayed SOS advancement and EOS delay, alongside LOG extension. These findings revealed variations in vegetation phenological changes under different frozen soil types and highlighted divergent responses of distinct frozen soil types to climate change. They suggested that the influence of frozen soil types should be considered when investigating vegetation phenological dynamics at the regional scale. Full article

Visible Light Positioning (VLP) has emerged as a pivotal technology for industrial Internet of Things (IoT) and smart logistics, offering high accuracy, immunity to electromagnetic interference, and cost-effectiveness. However, fluctuations in signal gain caused by target motion significantly degrade the positioning accuracy of current VLP systems. Conventional approaches face intrinsic limitations: propagation-model-based techniques rely on static assumptions, fingerprint-based approaches are highly sensitive to dynamic parameter variations, and although CNN/LSTM-based models achieve high accuracy under static conditions, their inability to capture long-term temporal dependencies leads to unstable performance in dynamic scenarios. To overcome these challenges, we propose a novel dynamic VLP algorithm that incorporates a Spatio-Temporal Feature Information Network (STFI-Net) for joint localization and orientation estimation of moving targets. The proposed method integrates a two-layer convolutional block for spatial feature extraction and employs modern Temporal Convolutional Networks (TCNs) with dilated convolutions to capture multi-scale temporal dependencies in dynamic environments. Experimental results demonstrate that the STFI-Net-based system enhances positioning accuracy by over 26% compared to state-of-the-art methods while maintaining robustness in the face of complex motion patterns and environmental variations. This work introduces a novel framework for deep learning-enabled dynamic VLP systems, providing more efficient, accurate, and scalable solutions for indoor positioning. Full article

Emotion alters the way humans walk, yet most prior studies have relied on laboratory-based 3D motion capture systems. While accurate, these approaches limit translation to real-world settings and have largely focused on spatiotemporal parameters and joint motions. This study evaluated the feasibility of using inertial measurement units (IMUs) to detect emotion-related changes in gait variability as well as spatiotemporal gait parameters. Fourteen healthy young adults completed overground gait trials while wearing two ankle-mounted IMUs. Five target emotions, anger, sadness, neutral emotion, joy, and fear, were elicited using an autobiographical memory paradigm. The IMUs measured stride length, stride time, stride velocity, cadence, and gait variability. The results showed that stride length, stride time, stride velocity, and cadence significantly differed across emotions. Anger and joy were associated with longer strides and faster velocities, while sadness produced slower walking with longer stride times and reduced cadence. Interestingly, gait variability did not differ significantly across emotional states. These findings demonstrate that IMUs can capture emotion specific gait changes previously documented with motion capture, supporting their feasibility for use in natural and clinical contexts. This work advances understanding of how emotions shape gait and highlights the potential of wearable technology for unobtrusive emotion and mobility research. Full article

The uneven expansion of renewable energy generation in different regions highlights the necessity of accurately assessing the available transfer capability (ATC) in power systems. This paper proposes a distributed probabilistic inter-regional ATC assessment framework. First, a spatiotemporally correlated wind power output model is established using wind speed forecast data and correlation matrices, enhancing the accuracy of wind power forecasting. Second, a two-stage probabilistic ATC assessment optimization model is proposed. The first stage minimizes both generation cost and risk-related costs by incorporating conditional value-at-risk (CVaR), while the second stage maximizes the power transaction amount. Thirdly, a privacy-preserving two-level iterative alternating direction method of multipliers (I-ADMM) algorithm is designed to solve this mixed-integer optimization problem, requiring only the exchange of boundary voltage phase angles between regions. Case studies are performed on the 12-bus, the IEEE 39-bus and the IEEE 118-bus systems to validate the proposed framework. Hence, the proposed framework enables more reliable and risk-aware intraday ATC evaluation for inter-regional power transactions. Moreover, the impacts of risk parameters and wind farm output correlations on ATC and generation cost are further investigated. Full article

Conventional grain monitoring systems often rely on isolated data points (e.g., point-based temperature measurements), limiting holistic condition assessment. This study proposes a novel Mechanism and Data Driven (MDD) framework that integrates physical mechanisms with real-time sensor data. The framework quantitatively analyzes solar radiation and external air temperature effects on silo boundaries and introduces a novel interpolation-optimized model parameter initialization technique to enable comprehensive grain condition perception. Rigorous multidimensional validation confirms the method’s accuracy: The novel initialization technique achieved high precision, demonstrating only 1.89% error in Day-2 low-temperature zone predictions (27.02 m² measured vs. 26.52 m² simulated). Temperature fields were accurately reconstructed (≤0.5 °C deviation in YOZ planes), capturing spatiotemporal dynamics with ≤0.45 m² maximum low-temperature zone deviation. Cloud map comparisons showed superior simulation fidelity (SSIM > 0.97). Further analysis revealed a 22.97% reduction in total low-temperature zone area (XOZ plane), with Zone 1 (near south exterior wall) declining 27.64%, Zone 2 (center) 25.30%, and Zone 3 20.35%. For dynamic evolution patterns, high-temperature zones exhibit low moisture (<14%), while low-temperature zones retain elevated moisture (>14%). A strong positive correlation between temperature and relative humidity fields; temperature homogenization drives humidity uniformity. The framework enables holistic monitoring, providing actionable insights for smart ventilation control, condensation risk warnings, and mold prevention. It establishes a robust foundation for intelligent grain storage management, ultimately reducing post-harvest losses. Full article

Traffic flow forecasting plays a significant role in intelligent transportation systems (ITSs) and is instructive for traffic planning, management and control. Increasingly complex traffic conditions pose further challenges to the traffic flow forecasting. While improving the accuracy of model forecasting, the parameter effectiveness of the model is also an issue that cannot be ignored. In addition, existing traffic prediction models have failed to organically integrate data with well-designed model architectures. Therefore, to address the above two issues, we propose the STAE-BiSSSM model as a solution. STAE-BiSSSM consists of Spatio-Temporal Adaptive Embedding (STAE) and Bidirectional Selective State Space Model (BiSSSM), where STAE aims to process features to obtain richer spatio-temporal feature representations. BiSSSM is a novel structural design serving as an alternative to Transformer, capable of extracting patterns of traffic flow changes from both the forward and backward directions of time series with much fewer parameters. Comparative tests between baseline models and STAE-BiSSSM on five real-world datasets illustrates the advance performance of STAE-BiSSSM. This is especially so on METRLA and PeMSBAY datasets, compared with the SOTA model STAEformer. In the short-term forecasting task (horizon: 15 min), MAE, RMSE and MAPE of STAE-BiSSSM decrease by 1.89%/13.74%, 3.72%/16.19% and 1.46%/17.39%, respectively. In the long-term forecasting task (horizon: 60 min), MAE, RMSE and MAPE of STAE-BiSSSM decrease by 3.59%/13.83%, 7.26%/16.36% and 2.16%/15.65%, respectively. Full article

The temperature-based indicators heating and cooling degree days, are frequently utilized to quantitatively link indoor energy demand and outdoor thermal conditions, especially in the context of climate change. We present a comprehensive study of the heating and cooling degree-days and the degree-days categories for the near past (1976–2005), and the AR5 RCP4.5 and RCP8.5 scenario-driven future (2066–2095) over Southeast Europe based on an elaborated methodology and performed using a 19 combinations of driving global and regional climate models from EURO-CORDEX with horizontal resolution of 0.11°. Alongside the explicit focus of the degree-days categories and the finer grid resolution, the study benefits substantially from the consideration of the monthly, rather than annual, time scale, which allows the assessment of the intra-annual variations of all analyzed parameters. We provide evidences that the EURO-CORDEX ensemble is capable of simulating the spatiotemporal patterns of the degree-days and degree-day categories for the near past period. Generally, we demonstrate also a steady growth in cooling and a decrease in heating degree-days, where the change of the former is larger in relative terms. Additionally, we show an overall shift toward warmer degree-day categories as well as prolongation of the cooling season and shortening of the heating season. As a whole, the magnitude of the projected long-term changes is significantly stronger for the ’pessimistic’ scenario RCP8.5 than the ’realistic’ scenario RCP4.5. These outcomes are consistent with the well-documented general temperature trend in the gradually warming climate of Southeast Europe. The patterns of the projected long-term changes, however, exhibit essential heterogeneity, both in time and space, as well as among the analyzed parameters. This finding is manifested, in particular, in the coexistence of opposite tendencies for some degree-day categories over neighboring parts of the domain and non-negligible month-to-month variations. Most importantly, the present study unequivocally affirms the significance of the anticipated long-term changes of the considered parameters over Southeast Europe in the RCP scenario-driven future with all subsequent and far-reaching effects on the heating, cooling, and ventilation industry. Full article

In recent years, controlling the integrity of shallow-buried natural gas pipelines within surface subsidence zones caused by high-intensity underground longwall mining in the Daniudi Gas Field of China’s Ordos Basin has emerged as a critical challenge impacting both mine planning and the safe, efficient co-exploitation of coal and deep natural gas resources. This study included field measurements and an analysis of surface subsidence data from high-intensity longwall mining operations at the Xiaobaodang No. 2 Coal Mine, revealing characteristic ground movement patterns under intensive extraction conditions. The subsidence basin was systematically divided into pipeline hazard zones using three key deformation indicators: horizontal strain, tilt, and curvature. Through ABAQUS-based 3D numerical modeling of coupled pipeline–coal seam mining systems, this research elucidated the spatiotemporal evolution of pipeline Von Mises stress under varying mining parameters, including working face advance rates, mining thicknesses, and pipeline orientation angles relative to the advance direction. The simulations further uncovered non-synchronous deformation behavior between the pipeline and its surrounding sand and soil, identifying two distinct evolutionary phases and three characteristic response patterns. Based on these findings, targeted pipeline integrity preservation measures were developed, with numerical validation demonstrating that maintaining advance rates below 10 m/d, restricting mining heights to under 2.5 m within the 260 m pre-mining influence zone, and where geotechnically feasible, the maximum stress of the pipeline laid perpendicular to the propulsion direction (90°) can be controlled below 480 MPa, and the separation amount between the pipe and the sand and soil can be controlled below 8.69 mm, which can effectively reduce the interference caused by mining. These results provide significant engineering guidance for optimizing longwall mining parameters while ensuring the structural integrity of shallow-buried pipelines in high-intensity extraction environments. Full article

Artificial water replenishment has improved the ecological environment of West Lake by introducing external clean water, but pollution issues still persist in some local regions. However, whether enhancing water exchange through internal water diversion within the lake can improve local water quality remains unverified. This study employed the Delft3D hydrodynamic model to simulate the spatiotemporal distribution of local water renewal time in West Lake, revealing that regions with prolonged water renewal times exhibited diminished resilience to water quality disturbances. This study utilized the Random Forest algorithm to determine the responsiveness of West Lake’s water transparency to parameters such as local water renewal time, and further discussed the impact of reducing local water renewal time on transparency under different water quality conditions. The results indicate that the sensitivity of West Lake’s transparency to water quality parameters closely resembles that of lakes with rainwater storage. The primary mechanism by which external water diversion improves transparency is through pollutant dilution, whereas enhanced local water exchange capacity contributes minimally to this effect. This conclusion demonstrates that localized internal water diversion within the lake is only suitable for preventing ecological issues such as regional eutrophication and algal blooms, but cannot effectively improve the overall lake ecosystem. Furthermore, this study identifies key factors affecting water transparency in artificially managed waters, highlighting priority monitoring indicators for similar water bodies. It also provides evidence to support research on aquatic optics and the development of remote sensing algorithms for such waters. Full article

Background/Objectives: Cognitive impairment in older adults is a growing public health concern due to global population aging. Early detection is crucial, yet current screening methods are time-consuming and require clinical expertise. Gait analysis has emerged as a promising alternative for cognitive screening. The aim of the study was to identify gait variables associated with cognitive impairment and to develop predictive algorithms capable of distinguishing between cognitively impaired and non-impaired older adults using gold-standard gait analysis. Methods: A cross-sectional study was conducted with 42 adults aged > 60 years. Cognitive function was assessed using the Mini-Mental State Examination (MMSE), and participants were divided into high (MMSE > 24) and low (MMSE ≤ 24) cognitive function groups. Spatiotemporal gait parameters were recorded at participants’ usual pace using the Optogait system. Logistic regression models were developed using half of the sample (training group) and validated in the remaining participants (verification group). Results: Algorithms based on stride length and double support time demonstrated high classification performance. In the training group, the best-performing model achieved an AUC-ROC of 0.91, with a sensitivity of 71.4% and specificity of 92.3%. Validation in the verification group yielded an AUC-ROC of 0.84 and accuracy of 81.0%. Alternative models showed acceptable to excellent predictive power. Conclusions: Gait analysis using gold-standard methods can effectively identify cognitive impairment in older adults. The developed algorithms offer a rapid, objective, and accurate screening alternative with strong potential for clinical application. Full article

Background and Objectives: This study aimed to compare gait patterns between young adults and physically active older adults. Additionally, the relation between these parameters and age was explored. Materials and Methods: Transversal case and control study, recruiting 81 participants divided into two groups: young adults (18–45 years) and physically active older adults (60+ years). Participants were assessed using the PodoSmart Insole^® system, which recorded spatiotemporal and kinematic gait data. Gait parameters were measured during a self-selected walking test. Data analysis included descriptive statistics, t-tests for group comparisons, and Pearson’s correlation to explore relationships between age and gait parameters. Results: Significant differences in gait parameters were found between young and older adults, particularly in stride length (right foot: p = 0.009, left foot: p = 0.001), cadence (p < 0.001), contact time (p < 0.001), swing time (p < 0.001), and support phase duration (p < 0.001), with moderate to large effect sizes. Sex differences were also observed within each group for several gait variables. Correlation analysis evidenced worsened parameters with increasing age, with moderate to strong associations in terms of cadence (r = −0.590), contact time (r = −0.504, r = −0.462), swing time (r = −0.662), and support phase duration (r = −0.524, r = −0.439). Conclusions: Evident differences in gait parameters are observed between young adults and active older adults. Although these results follow the trend of previous studies that employed more sophisticated lab-based protocols for gait analysis, slight differences between our study and these others could be attributed to the regular physical activity performed by these participants, which should be explored in more detail in future studies. Full article

Given that the increasing non-agricultural conversion of cultivated land (NACCL) endangers food security, studying the spatial and temporal variation characteristics and driving mechanisms of NACCL in Sichuan Province can offer a scientific foundation for developing local farmland preservation measures and controlling further conversion. Guided by the theoretical framework of land use transition, this study utilizes land use datasets spanning multiple periods between 2000 and 2023. Comprehensively considering population scale factors, natural geographical factors, and socioeconomic factors, the county-level annual NACCL rate is calculated. Following this, the dynamic evolution and underlying driving forces of NACCL across 183 counties in Sichuan Province are examined through temporal and spatial dimensions, utilizing analytical tools including Nonparametric Kernel Density Estimation (KDE) and the Geographical Detector model with Optimal Parameters (OPGD). The study finds that: (1) Overall, NACCL in Sichuan Province exhibits phased temporal fluctuations characterized by “expansion—contraction—re-expansion—strict control,” with cultivated land mainly being converted into urban land, and the differences among counties gradually narrowing. (2) In Sichuan Province, the spatial configuration of NACCL is characterized by the expansion of high-value agglomerations alongside the dispersed and stable distribution of low-value areas. (3) Analysis through the OPGD model indicates that urban construction land dominates the NACCL process in Sichuan Province, and the driving dimension evolves from single to synergistic. The findings of this study offer a systematic examination of the spatiotemporal evolution and underlying drivers of NACCL in Sichuan Province. This analysis provides a scientific basis for formulating region-specific farmland protection policies and supports the optimization of territorial spatial planning systems. The results hold significant practical relevance for promoting the sustainable use of cultivated land resources. Full article

Water quality management is crucial for sustainable whiteleg shrimp (Litopenaeus vannamei) aquaculture, though little research has comprehensively investigated the spatiotemporal fluctuation of trace elements in tropical semi-intensive ponds. This study investigated the water quality variations and trace element concentrations in an earthen pond across a 56-day culture cycle during the dry season. Physicochemical parameters (temperature, pH, salinity, dissolved oxygen, ammonia, nitrite, and nitrate) and trace elements (Cu, Zn, Mn, Fe, and Mg) were measured concurrently with shrimp growth and survival. The DO and pH readings were observed to fluctuate significantly during the mid-to-late stages of culture, with DO nearing critical thresholds (<5.0 mg L⁻¹). A sudden increase in ammonia and nitrite levels suggested the accumulation of organic matter and a microbial imbalance. Zinc concentrations (0.28–1.00 mg L⁻¹) approached stress-inducing levels, while magnesium remained low (10.44–10.72 mg L⁻¹). Pearson’s correlation revealed strong positive associations between ammonia and nitrate (r = 0.95) and between DO and pH (r = 0.94), while Mg was negatively correlated with Fe (r = −0.99) and nitrite (r = −0.88). Shrimp achieved 13.43 ± 0.73 g mean weight, with 77.8% survival and an FCR of 1.08. These results provide baseline evidence that combined water quality and trace element monitoring can become an early warning framework for pond management. Future studies integrating shrimp physiology and immune responses are needed to establish direct causal relationships. Full article

To understand how landscapes have changed in Yanqing District, Beijing, during its urban development over the past 15 years, we referred to the Landscape Character Assessment (LCA) theory, selecting altitude, slope, roughness, forest type, land cover, and forest vegetation cover as characteristic factors, identified nine types of landscape character types (LCTs) from 2005 to 2020 through unsupervised clustering. Then, we applied multi-model interpreters, including the Optimal Parameter-Based Geographical Detector (OPGD) and SHapley Additive exPlanations (SHAP), to analyze how social and natural factors impact the spatiotemporal changes of these LCTs. The results indicate that over the past 15 years, the landscape character of Yanqing District has undergone significant changes, with more frequent changes occurring in the “piedmont” areas where mountains meet plains. Slope and precipitation are the main factors affecting the intensity of LCT changes. In contrast, the transformation of different landscape characters is affected by factors such as altitude, slope, precipitation, and distance to artificial surfaces. This study reveals the dynamic changes in landscape character and their driving mechanisms, helping to develop more targeted strategies for landscape management in Yanqing District to promote sustainable regional development. Full article

Multi-view data, captured from various perspectives, is crucial for training view-invariant human action recognition models, yet its acquisition is hindered by spatio-temporal constraints and high costs. This study aims to develop the Pose Scene EveryWhere (PSEW) framework, which automatically generates temporally consistent, multi-view 3D human action data from a single monocular video. The proposed framework first predicts 3D human parameters from each video frame using a deep learning-based Human Mesh Recovery (HMR) model. Subsequently, it applies tracking, linear interpolation, and Kalman filtering to refine temporal consistency and produce naturalistic motion. The refined human meshes are then reconstructed into a virtual 3D scene by estimating a stable floor plane for alignment, and finally, novel-view videos are rendered using user-defined virtual cameras. As a result, the framework successfully generated multi-view data with realistic, jitter-free motion from a single video input. To assess fidelity to the original motion, we used Root Mean Square Error (RMSE) and Mean Per Joint Position Error (MPJPE) as metrics, achieving low average errors in both 2D (RMSE: 0.172; MPJPE: 0.202) and 3D (RMSE: 0.145; MPJPE: 0.206) space. PSEW provides an efficient, scalable, and low-cost solution that overcomes the limitations of traditional data collection methods, offering a remedy for the scarcity of training data for action recognition models. Full article