Search Results (7,137)

This study projects future Residential Water Use (RWU) under climate change scenarios using a Bayesian Neural Network (BNN) model that quantifies the relationship between observed temperatures and RWU. Eighteen Global Climate Models (GCMs) under the Shared Socioeconomic Pathway 5–8.5 (SSP5–8.5) scenario were used to assess the uncertainties across these models. The findings indicate that RWU in Republic of Korea (ROK) is closely linked to temperature changes, with significant increases projected in the distant future (F3), especially during summer. Under the SSP5–8.5 scenario, RWU is expected to increase by up to 10.3% by the late 21st century (2081–2100) compared to the historical baseline. The model achieved a root mean square error (RMSE) of 11,400 m³/month, demonstrating reliable predictive performance. Unlike conventional deep learning models, the BNN provides probabilistic forecasts with uncertainty bounds, enhancing its suitability for climate-sensitive resource planning. This study also projects inflows to the Paldang Dam, revealing an overall increase in future water availability. However, winter water security may decline due to decreased inflow and minimal changes in RWU. This study suggests enhancing summer precipitation storage while considering downstream flood risks. Demand management strategies are recommended for addressing future winter water security challenges. This research highlights the importance of projecting RWU under climate change scenarios and emphasizes the need for strategic water resource management in ROK. Full article

Between approximately 725 and 518 Ma, a suite of specialized felsic plutons and granitic stocks were emplaced across the Arabian Shield, many of which are now recognized as highly mineralized prospects enriched in rare earth elements (REEs), rare metals, and radioactive elements bearing mineralizations. The current investigation focused on the radiological and geochemical characterization of naturally occurring radionuclides, specifically ²³⁸U, ²²⁶Ra, ²³²Th, and ⁴⁰K, within three strategically selected granitic prospects, namely, J. Tawlah albite granite (TW), J. Hamra (HM), and J. Abu Al Dod alkali feldspar syenite and granites (AD). Concerning the radioactivity levels of the investigated granitic stocks, specifically the activity concentrations of ²³⁸U, ²²⁶Ra, ²³²Th, and ⁴⁰K, the measured average values demonstrate significant variability across the TW, HM, and AD stocks. The average ²³⁸U concentrations are 195 (SD = 38.7), 88.66 (SD = 25.6), and 214.3 (SD = 140.8) Bq/kg for TW, HM, and AD granitic stocks, respectively. Corresponding ²²⁶Ra levels are recorded at 172.4 (SD = 34.6), 75.62 (SD = 25.9), and 198.4 (SD = 139.5) Bq/kg. For ²³²Th, the concentrations are markedly elevated in TW at 5453.8 (SD = 2182.9) Bq/kg, compared to 77.16 (SD = 27.02) and 160.2 (SD = 103.8) Bq/kg in HM and AD granitic stocks, respectively. Meanwhile, ⁴⁰K levels are reported at 1670 (SD = 535.9), 2846.2 (SD = 249.9), and 3225 (SD = 222.3) Bq/kg for TW, HM, and AD granitic plutons, respectively. Notably, these values exceed the global average background levels, indicating an anomalous enrichment of the studied granitic occurrences. The mean radiological hazard indices for each granitic unit generally exceed global benchmarks, except for AEDE_out in the HM and AD stocks, which remain below international limits. The geochemical disparities observed are indicative of post-magmatic alteration processes, as substantiated by the interpretation of remote sensing datasets. In light of the significant radiological burden presented by these granitic stocks, it is essential to implement a rigorous precautionary framework for any future mining. These materials must be categorically excluded from uses that entail direct human exposure, especially in residential construction or infrastructure projects. Full article

Capturing CO₂ emitted by coal chemical enterprises and injecting it into oil reservoirs not only effectively improves the recovery rate and development efficiency of tight oil reservoirs in the Ordos Basin but also addresses the carbon emission problem constraining the development of the region. Since initiating field experiments in 2012, the Ordos Basin has become a significant base for CCUS (Carbon capture, Utilization, and Storage) technology application and demonstration in China. However, over the years, projects have primarily focused on enhancing the recovery rate of CO₂ flooding, while issues such as potential reservoir damage and its extent have received insufficient attention. This oversight hinder the long-term development and promotion of CO₂ flooding technology in the region. Experimental results were comprehensively analyzed using techniques including nuclear magnetic resonance (NMR), X-ray diffraction (XRD), scanning electron microscopy (SEM), inductively coupled plasma (ICP), and ion chromography (IG). The findings indicate that under current reservoir temperature and pressure conditions, significant asphaltene deposition and calcium carbonate precipitation do not occur during CO₂ flooding. The reservoir’s characteristics-high feldspar content, low carbon mineral content, and low clay mineral content determine that the primary mechanism affecting physical properties under CO₂ flooding in the Chang 4 + 5 tight sandstone reservoir is not, as traditional understand, carbon mineral dissolution or primary clay mineral expansion and migration. Instead, feldspar corrosion and secondary particles migration are the fundamental reasons for the changes in reservoir properties. As permeability increases, micro pore blockage decreases, and the damaging effect of CO₂ flooding on reservoir permeability diminishes. Permeability and micro pore structure are therefore significant factors determining the damage degree of CO₂ flooding inflicts on tight reservoirs. In addition, temperature and pressure have a significant impact on the extent of reservoir damage caused by CO₂ flooding in the study region. At a given reservoir temperature, increasing CO₂ injection pressure can mitigate reservoir damage. It is recommended to avoid conducting CO₂ flooding projects in reservoirs with severe pressure attenuation, low permeability, and narrow pore throats as much as possible to prevent serious damage to the reservoir. At the same time, the production pressure difference should be reasonably controlled during the production process to reduce the risk and degree of calcium carbonate precipitation near oil production wells. Full article

Building information modeling (BIM) has emerged as a fundamental component of Industry 4.0 recently. BIM construction engineers (BCEs) play a pivotal role in implementing BIM, and their personal competency is crucial to the successful application and promotion of BIM technology. Existing research on evaluating BIM capabilities has mainly focused on the enterprise or project level, neglecting individual-level analysis. Therefore, this study aims to establish an individual-level competency evaluation model for BCEs. Firstly, the competency of BCEs was divided into five levels by referring to relevant standards and domestic and foreign research. Secondly, through the analysis of literature data and website data, the competency evaluation indicator system for BCEs was constructed, which includes four primary indicators and 27 secondary indicators. Thirdly, variable weight theory was used to optimize the weights determined by general methods and calculate the comprehensive weights of each indicator. Then the competency levels of BCEs were determined by the interval grey clustering method. To demonstrate the application of the proposed method, a case study from a Chinese enterprise was conducted. The main results derived from this case study are as follows: domain competencies have the greatest weight among the primary indicators; the C9-BIM model is the secondary indicator with the highest weight (${\mathsf{\omega }}_j$ = 0.0804); and the competency level of the BCE is “Level 3”. These results are consistent with the actual situation of the enterprise. The proposed model in this study provides a comprehensive tool for evaluating BCEs’ competencies from an individual perspective, and offers guideline for BCEs to enhance their competencies in pursuing sustainable professional development. Full article

In remote sensing building detection tasks, data acquisition remains a critical bottleneck that limits both model performance and large-scale deployment. Due to the high cost of manual annotation, limited geographic coverage, and constraints of image acquisition conditions, obtaining large-scale, high-quality labeled datasets remains a significant challenge. To address this issue, this study proposes an automatic semantic labeling framework for remote sensing imagery. The framework leverages geospatial vector data provided by OpenStreetMap, precisely aligns it with high-resolution satellite imagery from Bing Maps through projection transformation, and incorporates a quality-aware sample filtering strategy to automatically generate accurate annotations for building detection. The resulting dataset comprises 36,647 samples, covering buildings in both urban and suburban areas across multiple cities. To evaluate its effectiveness, we selected three publicly available datasets—WHU, INRIA, and DZU—and conducted three types of experiments using the following four representative object detection models: SSD, Faster R-CNN, DETR, and YOLOv11s. The experiments include benchmark performance evaluation, input perturbation robustness testing, and cross-dataset generalization analysis. Results show that our dataset achieved a mAP at 0.5 intersection over union of up to 93.2%, with a precision of 89.4% and a recall of 90.6%, outperforming the open-source benchmarks across all four models. Furthermore, when simulating real-world noise in satellite image acquisition—such as motion blur and brightness variation—our dataset maintained a mean average precision of 90.4% under the most severe perturbation, indicating strong robustness. In addition, it demonstrated superior cross-dataset stability compared to the benchmarks. Finally, comparative experiments conducted on public test areas further validated the effectiveness and reliability of the proposed annotation framework. Full article

The rapid uptake of electric vehicles (EVs) in the Jawa–Madura–Bali (Jamali) grid produces highly variable charging demands that threaten the supply–demand balance. To forestall instability, we developed a predictive simulation based on long short-term memory (LSTM) networks that combines historical generation and consumption patterns with models of EV population growth and initial charging-time (ICT). We introduce a novel supply–demand balance score to quantify weekly and annual deviations between projected supply and demand curves, then use this metric to guide the machine-learning model in optimizing annual growth rate (AGR) and preventing supply demand imbalance. Relative to a business-as-usual baseline, our approach improves balance scores by 64% and projects up to a 59% reduction in charging load by 2060. These results demonstrate the promise of data-driven demand-management strategies for maintaining grid reliability during large-scale EV integration. Full article

This study investigated the integration of advanced Atmospheric Water Generators (AWGs) within the design process of building energy systems, focusing on the water–energy nexus in the context of a real-life hospital building. It is based on a simulation approach, recognised as a viable means to analyse and enhance AWG potentialities. However, the current state of research does not address the issue of AWG integration within building plant systems. This study contributes to fill such a research gap by building upon an authors’ previous work and proposing an enhanced methodology. The methodology describes how to incorporate a multipurpose AWG system into the energy simulation environment of DesignBuilder (DB), version 7.0.0116, through its coupling with AWGSim, version 1.20d, a simulation tool specifically developed for atmospheric water generators. The chosen case study is a wing of the Mondino Hospital in Pavia, Italy, selected for its complex geometry and HVAC requirements. By integrating AWG outputs—covering water production, heating, and cooling—into DB, this study compared two configurations: the existing HVAC system and an enhanced version that includes the AWG as plant support. The simulation results demonstrated a 16.3% reduction in primary energy consumption (from 231.3 MWh to 193.6 MWh), with the elimination of methane consumption and additional benefits in water production (257 m³). This water can be employed for photovoltaic panel cleaning, further reducing the primary energy consumption to 101.9 MWh (55.9% less than the existing plant), and for human consumption or other technical needs. Moreover, this study highlights the potential of using AWG technology to supply purified water, which can be a pivotal solution for hospitals located in areas affected by water crises. This research contributes to the atmospheric water field by addressing the important issue of simulating AWG systems within building energy design tools, enabling informed decisions regarding water–energy integration at the project stage and supporting a more resilient and sustainable approach to building infrastructure. Full article

This paper proposes a Distributed Adaptive Regularization Algorithm (DARN) for solving composite non-convex and non-smooth optimization problems in multi-agent systems. The algorithm employs a three-phase iterative framework to achieve efficient collaborative optimization: (1) a local regularized optimization step, which utilizes proximal mappings to enforce strong convexity of weakly convex objectives and ensure subproblem well-posedness; (2) a consensus update based on doubly stochastic matrices, guaranteeing asymptotic convergence of agent states to a global consensus point; and (3) an innovative adaptive regularization mechanism that dynamically adjusts regularization strength using local function value variations to balance stability and convergence speed. Theoretical analysis demonstrates that the algorithm maintains strict monotonic descent under non-convex and non-smooth conditions by constructing a mixed time-scale Lyapunov function, achieving a sublinear convergence rate. Notably, we prove that the projection-based update rule for regularization parameters preserves lower-bound constraints, while spectral decay properties of consensus errors and perturbations from local updates are globally governed by the Lyapunov function. Numerical experiments validate the algorithm’s superiority in sparse principal component analysis and robust matrix completion tasks, showing a 6.6% improvement in convergence speed and a 51.7% reduction in consensus error compared to fixed-regularization methods. This work provides theoretical guarantees and an efficient framework for distributed non-convex optimization in heterogeneous networks. Full article

To prevent the occurrence of excavation face instability incidents during shield tunneling, this study takes the Bailuyuan tunnel of the ‘Hanjiang-to-Weihe River Water Diversion Project’ as the engineering background. A three-dimensional discrete element method simulation was employed to analyze the tunneling process, revealing the displacement response of the excavation face to various tunneling parameters. This led to the development of a risk assessment method that considers both tunneling parameters and geological conditions for deep-buried shield tunnels. The above method effectively overcomes the limitations of finite element method (FEM) studies on shield tunneling parameters and, combined with the Analytic Hierarchy Process (AHP), enables rapid tunnel analysis and assessment. The results demonstrate that the displacement of the excavation face in shield tunnel engineering is significantly influenced by factors such as the chamber earth pressure ratio, cutterhead opening rate, cutterhead rotation speed, and tunneling speed. Specifically, variations in the chamber earth pressure ratio have the greatest impact on horizontal displacement, occurring predominantly near the upper center of the tunnel. As the chamber earth pressure ratio decreases, horizontal displacement increases sharply from 12.9 mm to 267.3 mm. Conversely, an increase in the cutterhead opening rate leads to displacement that first rises gradually and then rapidly, from 32.1 mm to 121.1 mm. A weighted index assessment model based on AHP yields a risk level of Grade II, whereas methods from other scholars result in Grade III. By implementing measures such as adjusting the grouting range, cutterhead rotation speed, and tunneling speed, field applications confirm that the risk level remains within acceptable limits, thereby verifying the feasibility of the constructed assessment method. Construction site strategies are proposed, including maintaining a chamber earth pressure ratio greater than 1, tunneling speed not exceeding 30 mm/min, cutterhead rotation speed not exceeding 1.5 rpm, and a synchronous grouting range of 0.15 m. Following implementation, the tunnel construction successfully passed the high-risk section without any incidents. This research offers a decision-making framework for shield TBM operation safety in complex geological environments. Full article

Managed aquifer recharge (MAR) is a complex and hidden process of storing surplus water under the ground surface and extracting it as, when and where needed. Evaluation of the success of any MAR project is challenging due to uncertainty in estimating the hydrogeological characteristics of the subsurface media. This paper demonstrates the use of a groundwater model (MODFLOW) to evaluate a new, large-scale regional MAR project in the agricultural heartland in Punjab, Pakistan. In this MAR project, flood waters have been diverted to the bed of an abandoned canal, where 144 recharge wells (the wells for accelerating the recharge into the aquifer) have been constructed to accelerate the recharge to the aquifer. The model was calibrated for a period of five years from October 2015 to June 2020 on a monthly stress period and the resulting water levels were simulated till 2035. The water balance components and future response of the aquifer to different scenarios up to 2035 including with and without MAR situations are presented. The model simulations showed that MAR can contribute to the replenishment of the aquifer and its potential for the case study site to contribute significantly to the management of groundwater and to enhance supplies for intensive agriculture. It was further established that MODFLOW can help in the evaluation of effectiveness of a MAR scheme. This study is unique as it evaluates a significantly large MAR project in an area where this practice has not been developed for improving groundwater access for large scale irrigation. The model provides guidelines for decision makers in the region as well as for the global community and livelihood benefits for rural communities. Full article

The segmentation of individual trees holds considerable significance in the investigation and management of forest resources. Utilizing smartphone-captured imagery combined with image-based 3D reconstruction techniques to generate corresponding point cloud data can serve as a more accessible and potentially cost-efficient alternative for data acquisition compared to conventional LiDAR methods. In this study, we present a Sparse 3D U-Net framework for single-tree segmentation which is predicated on a multi-head attention mechanism. The mechanism functions by projecting the input data into multiple subspaces—referred to as “heads”—followed by independent attention computation within each subspace. Subsequently, the outputs are aggregated to form a comprehensive representation. As a result, multi-head attention facilitates the model’s ability to capture diverse contextual information, thereby enhancing performance across a wide range of applications. This framework enables efficient, intelligent, and end-to-end instance segmentation of forest point cloud data through the integration of multi-scale features and global contextual information. The introduction of an iterative mechanism at the attention layer allows the model to learn more compact feature representations, thereby significantly enhancing its convergence speed. In this study, Dongsheng Bajia Country Park and Jiufeng National Forest Park, situated in Haidian District, Beijing, China, were selected as the designated test sites. Eight representative sample plots within these areas were systematically sampled. Forest stand sequential photographs were captured using an iPhone, and these images were processed to generate corresponding point cloud data for the respective sample plots. This methodology was employed to comprehensively assess the model’s capability for single-tree segmentation. Furthermore, the generalization performance of the proposed model was validated using the publicly available dataset TreeLearn. The model’s advantages were demonstrated across multiple aspects, including data processing efficiency, training robustness, and single-tree segmentation speed. The proposed method achieved an F1 score of 91.58% on the customized dataset. On the TreeLearn dataset, the method attained an F1 score of 97.12%. Full article

Rectangular box jacking is widely used in densely developed urban areas. However, when conducted with limited clear distance near existing metro tunnels, it introduces considerable structural safety risks. This study investigates a large-section rectangular box jacking project in Suzhou that crosses a double-line metro tunnel with minimal vertical clear distance. Integrated field monitoring and finite element simulations were conducted to analyze the tunnel’s deformation behavior during various jacking phases. The results show that the upline tunnel experienced greater uplift than the downline tunnel, with maximum vertical displacement occurring directly beneath the jacking axis. The affected zone extended approximately 20 m beyond the pipe gallery boundaries. Both the tunnel vault and ballast bed exhibited vertical uplift, while the hance displaced laterally toward the launching shaft. These deformations showed clear stage-dependent patterns strongly influenced by the relative position of the jacking machine. Numerical simulations demonstrated that doubling the pipe–tunnel clearance reduced the vault displacement by 58.87% (upline) and 51.95% (downline). Increasing the pipe–slurry friction coefficient from 0.1 to 0.3 caused the hance displacement difference to rise from 0.12 mm to 0.36 mm. Further sensitivity analysis reveals that when the jacking machine is positioned directly above the tunnel, grouting pressure is the greatest influence on the structural response and must be carefully controlled. The proposed methodology and findings offer valuable insights for future applications in similar tunnelling projects. Full article

In recent years, deep learning (DL) has been demonstrated remarkable capabilities in hyperspectral unmixing (HU) due to its powerful feature representation ability. Convolutional neural networks (CNNs) are effective in capturing local spatial information, but limited in modeling long-range dependencies. In contrast, transformer architectures extract global contextual features via multi-head self-attention (MHSA) mechanisms. However, most existing transformer-based HU methods focus only on spatial or spectral modeling at a single scale, lacking a unified mechanism to jointly explore spatial and channel-wise dependencies. This limitation is particularly critical for multiscale contextual representation in complex scenes. To address these issues, this article proposes a novel Spatial-Channel Multiscale Transformer Network (SCMT-Net) for HU. Specifically, a compact feature projection (CFP) module is first used to extract shallow discriminative features. Then, a spatial multiscale transformer (SMT) and a channel multiscale transformer (CMT) are sequentially applied to model contextual relations across spatial dimensions and long-range dependencies among spectral channels. In addition, a multiscale multi-head self-attention (MMSA) module is designed to extract rich multiscale global contextual and channel information, enabling a balance between accuracy and efficiency. An efficient feed-forward network (E-FFN) is further introduced to enhance inter-channel information flow and fusion. Experiments conducted on three real hyperspectral datasets (Samson, Jasper and Apex) and one synthetic dataset showed that SCMT-Net consistently outperformed existing approaches in both abundance estimation and endmember extraction, demonstrating superior accuracy and robustness. Full article

This paper examines the integration of markerless augmented reality (AR) within the da Vinci Surgical Robot, utilizing artificial intelligence (AI) for improved precision. The main challenge in creating AR for these systems is the small size (5 mm diameter) of the cameras used. Traditional camera-calibration approaches produce significant errors when used for miniature cameras. Further, the use of external markers can be obstructive and inaccurate in dynamic surgical environments. The study focuses on overcoming these limitations of traditional AR methods by employing advanced neural networks for camera calibration and real-time image processing. We demonstrate the use of a dense neural network to reduce the total projection error by directly learning the mapping of a 3D point to a 2D image plane. The results show a median error of 7 pixels (1.4 mm) when using a neural network, as compared to an error of 50 pixels (10 mm) when using a more traditional approach involving camera calibration and robot kinematics. This approach not only enhances the accuracy of AR for surgical procedures but also offers a more seamless integration with existing robotic platforms. These research findings underscore the potential of AI in revolutionizing AR applications in medical robotics and other teleoperated systems, promising efficient and safer interventions. Full article

Fine-grained visual categorization (FGVC) presents significant challenges due to subtle inter-class variation and significant intra-class diversity, often leading to limited discriminative capacity in global representations. Existing methods inadequately capture localized, class-relevant features across multiple semantic levels, especially under complex spatial configurations. To address these challenges, we introduce a Multi-scale Attention-driven Hierarchical Learning (MAHL) framework that iteratively refines feature representations via scale-adaptive attention mechanisms. Specifically, fully connected (FC) classifiers are applied to spatially pooled feature maps at multiple network stages to capture global semantic context. The learned FC weights are then projected onto the original high-resolution feature maps to compute spatial contribution scores for the predicted class, serving as attention cues. These multi-scale attention maps guide the selection of discriminative regions, which are hierarchically integrated into successive training iterations to reinforce both global and local contextual dependencies. Moreover, we explore a generalized pooling operation that parametrically fuses average and max pooling, enabling richer contextual retention in the encoded features. Comprehensive evaluations on benchmark FGVC datasets demonstrate that MAHL consistently outperforms state-of-the-art methods, validating its efficacy in learning robust, class-discriminative, high-resolution representations through attention-guided hierarchical refinement. Full article