Search Results (53,235)

Reliable environmental monitoring in remote or sparsely instrumented regions is hindered by the cost, maintenance demands, and inaccessibility of dense physical sensor deployments. To address these challenges, this study introduces VSG-SGL, a unified virtual sensor generation framework that integrates Sparse Gaussian Process Regression (SGPR) and Bayesian Ridge Regression (BRR) with deep generative learning via Variational Autoencoders (VAE) and Conditional Tabular GANs (CTGAN). Real meteorological datasets from multiple South Korean cities were preprocessed using thresholding and Isolation Forest anomaly detection and evaluated using distributional alignment (KDE) and sequence-learning validation with BiLSTM and BiGRU models. Experimental findings demonstrate that VAE-augmented virtual sensors provide the most stable and reliable performance. For temperature, VAE maintains predictive errors close to those of BRR and SGPR, reflecting the already well-modeled dynamics of this variable. In contrast, humidity and wind-related variables exhibit measurable gains with VAE; for example, SGPR-based wind speed MAE improves from 0.1848 to 0.1604, while BRR-based wind direction RMSE decreases from 0.1842 to 0.1726. CTGAN augmentation, however, frequently increases error, particularly for humidity and wind speed. Overall, the results establish VAE-enhanced VSG-SGL virtual sensors as a cost-effective and accurate alternative in scenarios where physical sensing is limited or impractical. Full article

Accurate characterization of river channel geometry is essential for hydrological and hydraulic analyses, yet the increasing use of unmanned aerial vehicle (UAV) photogrammetry introduces challenges related to uneven point density, shadow-induced data gaps, and spurious outliers. This study proposed a novel approach for reconstructing 3D river channels from UAV-derived point clouds, emphasizing K-nearest neighbor local regression (KLR), and compared it with the LOWESS model. Method performance was examined through controlled simulations of trapezoidal, triangular, and U-shaped synthetic channels, where KLR consistently preserved morphological fidelity and produced lower RMSE than LOWESS, particularly at channel bends and bed undulations, while a neighborhood selection heuristic approach demonstrated robust results across varying data densities. Synthetic channel experiments show that the proposed K-nearest-neighbor local linear regression (KLR) method achieves RMSE values below 0.06 all tested geometries. In contrast, LOWESS produces substantially larger errors, with RMSE values exceeding 0.9 across all channel shapes. Subsequent application to two South Korean field sites reinforced these findings. In the data-scarce Migok-cheon stream, KLR effectively interpolated missing surfaces while maintaining geomorphic realism, whereas LOWESS generated over-smoothed representations. Within the dense Ogsan Bridge dataset, KLR retained small-scale bed features critical for hydraulic simulations and cross-sectional delineation, while LOWESS obscured local variability. Conclusively, the results demonstrate that KLR provides a more reliable and computationally efficient framework for UAV-based 3D river channel reconstruction, with clear implications for hydraulic modeling, flood risk management, and the advancement of digital-twin systems in operational hydrology. Full article

Background/Objectives: Oleuropein is a bioactive phenolic compound from olive leaves with antimicrobial and antioxidant activity. This study aimed to develop a sprayable oral gel containing an oleuropein-rich aqueous extract and to evaluate its pharmaceutical performance antimicrobial efficacy and in ovo biological response. Methods: Oleuropein content was quantified using a validated chromatographic method. Polymeric systems were screened to select an optimized sprayable formulation. Physicochemical stability, dose uniformity, and antimicrobial activity against major cariogenic bacteria were evaluated. In ovo biological evaluation was conducted using the chick chorioallantoic membrane angiogenesis model together with histopathological examination of embryonic heart and liver tissues. Results: Oleuropein content was determined as 288.6 µg/mL in the olive leaf extract and 255.1 µg/mL in the final formulation. The optimized oral spray showed stable physicochemical properties, with pH maintained at 6.90 ± 0.02 and no relevant changes in viscosity during storage. The mean delivered dose per actuation was 0.128 ± 0.015 g, corresponding to 32.6 µg oleuropein per spray. The formulation exhibited inhibitory activity against all tested cariogenic microorganisms, with MIC values ranging from 13.3 to 170.7 µg/mL and MBC values generally two-fold higher. In the CAM assay, significant concentration- and time-dependent antiangiogenic effects were observed after 24–48 h at moderate and higher concentrations. Histopathological evaluation revealed dose-dependent acute degenerative and congestive changes in heart and liver tissues without evidence of fibrosis or steatosis. Conclusions: The oleuropein-based sprayable oral gel is a promising localized delivery system with adequate stability dose uniformity and antimicrobial efficacy. In ovo findings provide a conservative assessment of systemic exposure and support further development for oral biofilm and caries-related applications. Full article

Gross Domestic Product (GDP) serves as a core indicator for measuring the sustainable economic development of countries and regions. Accurate understanding of its spatio-temporal distribution is crucial for achieving the United Nations Sustainable Development Goals (SDGs). However, current grid-based GDP data for China’s regions predominantly consists of data from specific years, making it difficult to capture fine-grained changes in economic development. To address this, this study proposes a spatial GDP framework integrating cross-scale feature extraction (CSFs) with stacked ensemble learning. Based on China’s county-level GDP statistics and multi-source auxiliary data, it first generates a density-weighted estimation layer. This is then processed through dasymetric mapping to produce China’s Annual High-Resolution Gridded GDP Dataset (CA_GDP) from 2000 to 2021. Evaluation demonstrates the framework’s superior performance in density weight estimation, achieving an R² of 0.82 against statistical data. Compared to traditional single models like Random Forests (RF), it improves R² by 13–54%, reduces mean absolute error (MAE) by 2–26%, and lowers root mean square error (RMSE) by 19–39%, with these advantages remaining stable across time series. The dasymetric mapping of the CA_GDP dataset clearly depicts the economic development patterns and urban agglomeration effects in the southeastern coastal regions, as well as the relatively lagging economic development in western areas. Compared to existing public datasets, CA_GDP offers significant advantages in reflecting the fine-grained economic spatial structure within county-level units, providing a more reliable data foundation for identifying regional economic disparities, policy formulation and evaluation, and related research. Full article

Supercritical CO₂ pipeline transportation is a critical component of the carbon capture, utilization and storage (CCUS) industry chain, where long distance operation introduces inherent risks of accidental leakage. During the leakage process of supercritical CO₂ pipelines, throttling pressure reduction and the Joule–Thomson effect generate distinct negative pressure wave characteristics. The magnitude of the leakage directly impacts localization effectiveness, particularly under small leakage conditions where negative pressure wave signals are less pronounced, so the leakage is difficult to effectively detect. To solve this problem, the mutual correlation function model for pipeline leakage was developed by using the mutual correlation analysis method, and it was verified by the dense-phase CO₂ leakage data from Trondheim University of Technology. Based on the TGNET software, the actual pipeline model of the Yanchang oilfield is established, and the captured leakage signal is imported into MATLAB for differential pressure conversion, using the verified cross-correlation function model of the differential pressure signal to calculate the time difference between the arrival of the negative pressure wave at the two ends of the pipeline. Finally, the actual leakage location was determined. The simulation results indicate that the leakage detection method based on mutual correlation analysis of negative pressure wave signals exhibits varying localization performance under different leakage rates. By enhancing negative pressure wave characteristics and utilizing mutual correlation analysis, this method effectively addresses the challenges of indistinct negative pressure wave features and difficult localization during small leakage conditions. When leakage exceeds 5%, the relative error is controlled within ±5.40%, meeting the preliminary localization requirements for rapid identification and regional determination in engineering applications. Through the application of actual engineering cases, it is shown that this method has high accuracy in pipeline leakage detection. These findings provide theoretical and methodological support for supercritical CO₂ pipeline leakage detection in the CCUS projects currently under construction. Full article

Semiconductor manufacturing equipment such as vacuum pumps, wafer handling mechanisms, etching machines, and deposition systems operates for a long time under high vacuum, high temperature, strong electromagnetic, and high-precision continuous production environments. Its reliability is directly related to the yield and stability of the production line. During equipment operation, the fault signals are often weak, the noise is strong, and the working conditions are variable, so traditional methods are difficult to achieve high-precision recognition. To solve this problem, this paper proposes a fault diagnosis and health monitoring method for semiconductor manufacturing equipment based on deep learning and subspace transfer. Firstly, considering the cyclostationary characteristics of the operating signals of key equipment, the cyclic spectral analysis technology is used to obtain the cyclic spectral coherence map, which effectively reveals the feature differences under different health states. Then, a deep fault diagnosis model based on the convolutional neural network (CNN) is constructed to extract deep feature representations. Furthermore, the subspace transfer learning technology is introduced, and group normalization and correlation alignment unsupervised adaptation layers are designed to achieve automatic alignment and enhancement of the statistical characteristics of deep features between the source domain and the target domain, which effectively improves the generalization and adaptability of the model. Finally, simulation experiments based on the public bearing dataset verify that the proposed method has strong feature representation ability and high classification accuracy under different working conditions and different loads. Because the key components and experimental scenarios of semiconductor manufacturing equipment have similar signal characteristics, this method can be directly transferred to the early fault diagnosis and health monitoring of semiconductor production line equipment, which has important engineering application value. Full article

Anaerobic digestion under a Waste-to-Energy (WtE-AD) framework represents a sustainable alternative for managing organic waste and generating bioenergy in developing countries. However, most life cycle assessment (LCA) studies implicitly assume stable operation, overlooking the environmental implications of process instability. In practice, large-scale WtE-AD plants are frequently affected by methanogenic inhibition events that reduce methane production and compromise their technical, economic, and environmental performance. This study—Part 2 of a two-paper series—addresses this gap by quantifying, from a life cycle perspective, the environmental consequences of recurrent methanogenic inhibition events in large-scale WtE-AD systems, complementing the techno-economic analysis presented in Part 1. Large-scale WtE-AD plants were modeled using design equations based on treatment capacity (60–200 t d⁻¹), considering scenarios with up to ten inhibition events over a 25-year operational period. The LCA was conducted in accordance with ISO 14040:14044 standards, defining as the functional unit one ton of co-digested fruit and vegetable residues with meat industry wastes, under an attributional approach with system boundary expansion and evaluating midpoint indicators through the ReCiPe 2016 method. Results show that inhibition events increase greenhouse gas emissions by up to 400% (from 28.1 to 138.6 kg CO₂ eq t⁻¹ of waste treated), while plants with capacities above 125 t d⁻¹ exhibit environmental credits (negative emission balances), demonstrating greater environmental resilience. Electricity substitution from the Mexican grid generated savings of up to 0.624 kg CO₂ eq kWh⁻¹, although the magnitude of the benefits strongly depends on the regional electricity mix. This dependency was further explored through comparative electricity mix scenarios representative of different levels of power sector decarbonization, allowing the sensitivity of WtE-AD environmental performance to regional grid characteristics to be assessed. Compared to landfill disposal (1326 kg CO₂ eq t⁻¹), WtE-AD plants significantly reduce impacts across all assessed categories. By explicitly integrating operational instability into an industrial-scale LCA framework, this work highlights the importance of evaluating methanogenic inhibition events from a life cycle perspective, providing key insights for the design of more sustainable and resilient WtE-AD processes within a Latin American context. Full article

Vehicle repositioning is essential for improving efficiency and service quality in ride-sharing platforms, yet existing approaches typically optimize proxy rewards that fail to reflect human-centered preferences such as wait time, service coverage, and unnecessary empty travel. We propose the first two-stage Bilevel Reinforcement Learning (RL) from Human Feedback (RLHF) framework for preference-aligned vehicle repositioning. In Stage 1, a value-based Deep Q-Network (DQN)-RLHF warm start learns an initial preference-aligned reward model and stable reference policy, mitigating the reward-model drift and cold-start instability that arise when applying on-policy RLHF directly. In Stage 2, a Kullback–Leibler (KL)-regularized Proximal Policy Optimization (PPO)-RLHF algorithm, equipped with action masking, behavioral-cloning anchoring, and alternating forward–reverse KL, fine-tunes the repositioning policy using either Large Language Model (LLM)-generated or rubric-based preference labels. We develop and compare two coordination schemes, pure alternating (PPO-Alternating) and k-step alternating (PPO-k-step), demonstrating that both yield consistent improvements across all tested arrival scales. Empirically, our framework reduces wait time and empty-mile ratio while improving served rate, without inducing trade-offs or reducing platform profit. These results show that human preference alignment can be stably and effectively incorporated into large-scale ride-sharing repositioning. Full article

This study presents Care-MOVE, a smartphone-based application designed for continuous, passive, and unobtrusive monitoring of mobility, environmental exposure, and cognitive status in older adults within a telemedicine framework. The system integrates movement-related data collected through smartphone sensors (GPS, activity recognition, and caloric expenditure estimation) with contextual air quality information and standardized neuropsychological assessments, resulting in a comprehensive multimodal dataset (Care-MOVE Dataset). An exploratory proof-of-concept study was conducted on a subsample of 53 participants aged over 65, each monitored continuously for five days, contributing on average more than 30,000 longitudinal records. To investigate whether daily motor behavior can serve as a digital biomarker of cognitive functioning, several Machine Learning and Deep Learning models were evaluated using a Leave-One-User-Out (LOUO) cross-validation strategy. The comparative analysis included traditional classifiers (Logistic Regression, Random Forest, Gradient Boosting, K-Nearest Neighbors, and Support Vector Machines) as well as temporal deep learning architectures (1D CNN, LSTM, GRU, and Transformer). Among all of the evaluated approaches, the Support Vector Machine with RBF kernel achieved the best performance, reaching an accuracy of 98.1%, a balanced accuracy of 0.988, and an F1-score of 0.981, demonstrating robust generalization across unseen subjects. For this reason, the study was designed and presented as an exploratory proof-of-concept rather than a definitive clinical validation. This integrated approach not only enables the collection of detailed and contextualized data but also opens new perspectives for proactive digital healthcare, focused on risk prevention, improving quality of life, and promoting autonomy in elderly patients. Full article

The rapid deployment of photovoltaic generation increases uncertainty in power-system operation and strengthens the need for ultra-short-term forecasts with reliable uncertainty estimates. Point-forecasting approaches alone are often insufficient for dispatch and reserve decisions because they do not quantify risk. This study investigates probabilistic forecasting of short-horizon solar generation using quantile regression on a public dataset of solar output and meteorological variables. This study proposes a hybrid attention–convolution model that combines an attention-based encoder to capture long-range temporal dependencies with a causal temporal convolution module that extracts fast local fluctuations using only past information, preventing information leakage. The two representations are fused and decoded jointly across multiple future horizons to produce consistent quantile trajectories. Experiments against representative machine-learning and deep-learning baselines show improved probabilistic accuracy and competitive central forecasts, while illustrating an important sharpness–calibration trade-off relevant to risk-aware grid operation. Key novelties include a multi-horizon quantile formulation at 15 min resolution for one-hour-ahead PV increments, a HybridCNN–STTransformer that fuses causal temporal convolutions with Transformer attention, and a horizon-token decoder that models inter-horizon dependencies to produce consistent multi-step quantile trajectories; reliability/sharpness diagnostics and post hoc calibration are discussed for operational risk-aware use. Full article

Accurate braking-distance prediction for heavy-duty multi-axle trucks remains challenging due to the large gross vehicle weight, tandem-axle interactions, and strong transient load transfer during emergency braking. Recent studies on tire–road friction estimation, commercial-vehicle braking control (EBS/AEBS), and weigh-in-motion (WIM) sensing have highlighted that unmeasured vertical-load dynamics and time-varying friction are key sources of prediction uncertainty. To address these limitations, this study proposes an integrated sensing–simulation–AI framework that combines real-time axle-load estimation, full-scale robotic braking tests, fused road-friction sensing, and physics-consistent machine-learning modeling. A micro-electro-mechanical systems (MEMS)-based load-angle sensor was installed on the leaf-spring panel linking tandem axles, enabling the continuous estimation of dynamic vertical loads via a polynomial calibration model. Full-scale on-road braking tests were conducted at 40–60 km/h under systematically varied payloads (0–15.5 t) using an actuator-based braking robot to eliminate driver variability. A forward-looking optical friction module was synchronized with dynamic axle-load estimates and deceleration signals, and additional scenarios generated in a commercial ASM environment expanded the operational domain across a broader range of friction, grade, and loading conditions. A gradient-boosting regression model trained on the hybrid dataset reproduced measured stopping distances with a mean absolute error (MAE) of 1.58 m and a mean absolute percentage error (MAPE) of 2.46%, with most predictions falling within ±5 m across all test conditions. The results indicate that incorporating real-time dynamic axle-load sensing together with fused friction estimation improves braking-distance prediction compared with static-load assumptions and purely kinematic formulations. The proposed load-aware framework provides a scalable basis for advanced driver-assistance functions, autonomous emergency braking for heavy trucks, and infrastructure-integrated freight safety management. All full-scale braking tests were carried out at approximately 60% of the nominal service-brake pressure, representing non-panic but moderately severe braking conditions, and the proposed model is designed to accurately predict the resulting stopping distance under this prescribed braking regime rather than to minimize the absolute stopping distance itself. Full article

Monitoring the α-biodiversity indicators of mangrove forests and understanding their spatiotemporal trends can guide mangrove restoration strategies. Taking Qinglan Port in Hainan Province, China, as our study area, we compared multiple machine learning methods to predict the spatial distribution of α-biodiversity indicator Shannon’s diversity index (SHDI) by integrating LiDAR points and Worldview-2 images. In addition, the relationship between mangrove forests’ SHDI values and growth years was analyzed. The study extracted 28 spectral features and 99 LiDAR features from Worldview-2 and LiDAR data, respectively. The RReliefF method was adopted to select informative features. Four machine learning methods, including support vector machines (SVMs), extreme gradient boosting (XGBoost), deep neural networks (DNNs), and Gaussian process regression (GPR), were used to establish SHDI prediction models. The leave-one-out cross-validation (LOOCV) method was used to evaluate prediction accuracy, and the optimal model was adopted to generate a spatial map of SHDI. Based on Google Earth and Worldview-2 images, the spatial regions of mangrove forests in 2008, 2013, 2018, and 2023 were identified. The SHDI values within different restoration periods were statistically analyzed by using the mangroves’ spatiotemporal distributions. The results showed that RReliefF selected a total of 30 features, including 13 spectral features and 17 LiDAR features. Using preferred features, GPR had the highest prediction accuracy, with an LOOCV R² of 0.51, followed by SVM (R² = 0.44) and DNN (R² = 0.32); the accuracy of XGBoost (R² = 0.29) was relatively poor. The increased areas of rehabilitated mangrove forests in the periods of 2008–2013, 2013–2018, and 2018–2023 were 0.31 km², 0.13 km², and 1.35 km², respectively. Mangroves growing before 2008 owned the highest mean SHDI value of 0.74, followed by mangroves in 2008–2013 and 2013–2018; mangrove forests restored in 2018–2023 had the lowest mean SHDI value of 0.63. The results indicated that mangrove SHDI can be predicted by integrating LiDAR and Worldview-2. The mangrove population exhibited more diverse α-biodiversity characteristics as growth time increased. In subsequent mangrove restoration processes, planting mangroves of diverse species is beneficial to ensure the stability of the mangrove community. Full article

It is known that rainfall leads to a reduction in driving speed. However, the results of various studies are inconsistent regarding the amount of speed reduction. In this study, we combine high-resolution radar-based rainfall estimates for three days with heavy rainfall with driving speeds derived from floating car data on 1.5 million road sections in Germany. Using linear regression models, we investigate the functional relationship between rainfall and driving speeds depending on road section characteristics like speed limit and number of lanes. We find that the speed reduction due to rainfall is higher at road section with higher speed limits and on multi-lane roads. On highway road section with speed limits of 130 km/h, for example, heavy rainfall of more than 8 L/m${}^2$ in five minutes leads to an average speed reduction of more than 30%, although estimates at very high rainfall intensities are subject to increased uncertainty due to data sparsity. Cross-validation shows that including rainfall as a predictor for driving speed reduces mean squared errors by up 14% in general and up to 50% in heavy rainfall conditions. Furthermore, rainfall as a continuous variable should be preferred over categorical variables for a parsimonious model. Our results demonstrate that parsimonious, interpretable models combining radar rainfall data with floating car data can capture systematic rainfall-related speed reductions across a wide range of road types. However, the analysis should be interpreted strictly as a descriptive, event-specific study. It does not support generalizable inference across time, seasons, or broader traffic conditions. To make this approach suitable for operational applications such as real-time speed prediction, route planning, and traffic management, larger multi-event datasets and the consideration of effects like weekday structure and diurnal demand patterns are required to better constrain effects under heavy rainfall conditions. Full article

Landslides in mountainous regions threaten infrastructure and human safety, making high-accuracy landslide inventories crucial for disaster management. However, fine-grained identification using high-resolution remote sensing imagery is hindered by low small-landslide detection accuracy and bare soil spectral interference. The aim of this study is to propose a lightweight UCTransNet with Triplet Attention and Pyramid Kernel Interaction (UCTransNet-TPKI) deep learning model for accurate multi-scale landslide extraction. The study area is located in Wushan County, Chongqing. GF-2 imagery from 2022 was collected, along with field sampling data and Mengdong dataset as validation data. The model proposed in this study, named UCTransNet-TPKI, is based on an improved UCTransNet architecture. Its key innovations include the introduction of two critical modules: the Pyramid Kernel Interaction module and the Triplet Attention mechanism. The PKI module captures multi-scale local contextual information in parallel under different receptive fields, significantly enhancing the network’s ability to extract landslide features. Concurrently, the Triplet Attention mechanism effectively refines feature representations by capturing the interaction dependencies across the three dimensions of a feature map. This enables the model to focus more precisely on key areas, such as the main body and edges of a landslide, while simultaneously suppressing interference from background noise. The experimental results show that UCTransNet-TPKI achieved the highest F1-score of 0.9008 and an IoU of 0.8252, outperforming MFFENet, TransLandSeg, and Segformer++. Ablation studies confirmed the contributions of each component, with the PKI module improving IoU by 0.72%, the Triplet Attention mechanism increasing IoU by 0.9%, and their combination yielding a clear synergistic enhancement of overall performance. Furthermore, UCTransNet-TPKI demonstrated strong generalization on the Mengdong dataset, achieving an F1-score of 0.9230 and an IoU of 0.8560. These results demonstrate that UCTransNet-TPKI provides an accurate automated landslide mapping solution, offering significant value for post-disaster emergency response and geological hazard management. Full article

Large language models (LLMs) are increasingly used to predict human behavior from plain-text descriptions of experimental tasks that range from judging disease severity to consequential medical decisions. While these methods promise quick insights without complex psychological theories, we reveal a critical flaw: they often latch onto accidental patterns in the data that seem predictive but collapse when faced with novel experimental conditions. Testing across multiple behavioral studies, we show these models can generate wildly inaccurate predictions, sometimes even reversing true relationships, when applied beyond their training context. Standard validation techniques miss this flaw, creating false confidence in their reliability. We introduce a simple diagnostic tool to spot these failures and urge researchers to prioritize theoretical grounding over statistical convenience. Without this, LLM-driven behavioral predictions risk being scientifically meaningless, despite impressive initial results. Full article