Search Results (37)

With the development of the Internet of Things (IoT) and smart industry, the demand for high-precision indoor positioning is becoming increasingly urgent. Ultra-ideband (UWB) technology has become a research hotspot due to its centimeter-level ranging accuracy, good penetration, and high multipath resolution. However, in complex environments, it still faces problems such as high cost of anchor node layout, gross errors in observation data, and difficulty in eliminating systematic errors such as electronic time delay. To address the aforementioned problems, this paper proposes a comprehensive UWB indoor positioning scheme. By constructing virtual reference stations to enhance the observation network, the geometric structure is optimized and the dependence on physical anchors is reduced. Combined with a gross error elimination method under short-baseline constraints and a double-difference positioning model including virtual observations, it systematically suppresses systematic errors such as electronic delay. Additionally, a quality control strategy with velocity constraints is introduced to improve trajectory smoothness and reliability. Static experimental results show that the proposed double-difference model can effectively eliminate systematic errors. For example, the positioning deviation in the Xdirection is reduced from approximately 2.88 cm to 0.84 cm, while the positioning accuracy in the Ydirection slightly decreases. Overall, the positioning accuracy is improved. The gross error elimination method achieves an identification efficiency of over 85% and an accuracy of higher than 99%, providing high-quality observation data for subsequent calculations. Dynamic experimental results show that the positioning trajectory after geometric enhancement of virtual reference stations and velocity-constrained quality control is highly consistent with the reference trajectory, with significantly improved trajectory smoothness and reliability. In summary, this study constructs a complete technical chain from data preprocessing to result quality control, effectively improving the accuracy and robustness of UWB positioning in complex indoor environments, and exhibits promising engineering application potential. Full article

The construction of New Energy Demonstration Cities (NEDC) represents a crucial policy initiative in advancing China’s energy transition and serves as an institutional innovation to promote inclusive green growth (IGG) at the urban level. Based on panel data for 278 prefecture-level cities in China from 2011 to 2021, this study employs a double machine learning model to identify the causal impact of the NEDC on IGG and to further explore the underlying mechanisms. The empirical results show that the policy significantly enhances IGG overall. However, the positive effects are mainly observed in non-resource-based and non-old industrial cities, while the impacts in resource-based and old industrial cities are statistically insignificant. This finding indicates that structural constraints such as the resource curse and Dutch disease remain evident in these regions. Mechanism analysis reveals that the NEDC promotes IGG primarily through technological innovation and employment creation, forming a chained mediating pathway of ‘NEDC → technological innovation → employment creation → IGG.’ This study enriches the literature on the economic effects of energy reform pilot policies and provides empirical evidence and policy insights for achieving IGG goals in both China and other countries. Full article

Urban areas globally face the critical challenge of meeting growing energy demands while maintaining environmental sustainability. However, existing research provides limited and often inconsistent evidence on how green finance affects urban energy efficiency, largely due to heterogeneous measurement systems, methodological constraints, and insufficient identification of underlying mechanisms. To address these research gaps, this study investigates two core questions: Does green finance significantly improve urban energy efficiency? If so, what are the specific transmission mechanisms driving this impact? Methodologically, this exploration employs a Double Machine Learning (DML) approach to analyze panel data from 210 Chinese cities between 2006 and 2022. The analysis demonstrates a significant and positive impact of green finance on urban energy efficiency, with an estimated coefficient of 0.1910. Further analysis identifies three constructive mechanisms, including environmental regulations, industrial structures, and green technological innovation, which enhance resource allocation and energy utilization efficiency. Moreover, green finance shows a stronger positive impact in non-resource-dependent cities, regions outside traditional industrial bases, and financially developed areas. These findings recommend establishing standardized green finance frameworks, increasing targeted financial support for key regions, and integrating green innovation with industrial restructuring. These measures help consolidate China’s green finance system and improve regional energy efficiency through market expansion, energy transition, and technological advancement. Full article

This study presents a comprehensive techno-economic assessment of offshore wind projects in the Colombian Caribbean, emphasizing the impact of site-specific parameters on development costs and performance. Wind resource conditions were evaluated in four coastal regions (La Guajira, Magdalena, Atlántico, and Bolívar) using hourly meteorological data from 2015 to 2024, adjusted to 100 m above ground level through logarithmic and power law wind profile models. The analysis included wind speed, bathymetry, distance to shore, distance to substation, foundation type, wind power density (WPD), and capacity factor (C_f). Based on these parameters, annual energy generation was estimated, and both capital expenditures (CAPEX) and operational expenditures (OPEX) were calculated, considering the technical and cost differences between fixed and floating foundations. Results show that La Guajira combines excellent wind conditions (WPD of 796 W/m² and C_f of 61.5%) with favorable construction feasibility (bathymetry of −32 m), resulting in the lowest CAPEX among the studied regions. In contrast, Magdalena and Atlántico, with bathymetries exceeding 200 m, require floating foundations that more than double the investment costs. Bolívar presents an intermediate profile, offering solid wind potential and fixed foundation feasibility at a moderate cost. The findings confirm that offshore wind project viability depends not only on wind resource quality but also on physical site constraints, which directly influence the cost structure and energy yield. This integrated approach supports more accurate project prioritization and contributes to strategic planning for the sustainable deployment of offshore wind energy in Colombia. Full article

Non-negative Tucker decomposition (NTD) is one of the general and prominent decomposition tools designed for high-order tensor data, with its advantages reflected in feature extraction and low-dimensional representation of data. Most NTD-based methods only apply intrinsic and different constraints to the last factor matrix that is a low-dimensional representation of the original tensor information. This processing procedure may result in the loss of the relationship between the factor matrices in all dimensions. To enhance the representation ability of NTD, we propose a consistent regularized non-negative Tucker decomposition for three-dimensional tensor data representation. Consistent regularization is symmetrically presented and mathematically expressed by intrinsic cues in multiple dimensions, that is, manifold structure and orthogonality information. The paired constraint constructed by the double parameter operator is utilized to unlock hidden semantics and maintain the consistent geometric structure of the three-dimensional tensor. Moreover, we develop the iterative updating method based on the multiplicative update rule to solve the proposed model and provide its convergence and computational complexity. The extensive numerical results of unsupervised image clustering experiments on eight real-world datasets demonstrated the feasibility and efficiency of the new method. Full article

Sustainability assessment has emerged as a critical research area given the pressing challenges of balancing economic growth, environmental protection, and social equity. This study aims to develop an objective and reproducible framework to evaluate sustainability efficiency across countries by integrating multiple development dimensions into a unified decision model. Despite substantial prior research, inconsistencies often arise due to data heterogeneity and conflicting criteria. To address this gap, a hybrid multi-criteria decision-making (MCDM) framework was developed by combining the Double Normalization Method based on Removal Effects of Criteria (DNMEREC) for objective weighting and the Double Normalization Measurement of Alternatives and Ranking according to Compromise Solution (DNMARCOS) method for ranking alternatives. This integration ensures balanced consideration of beneficial and non-beneficial criteria while minimizing subjectivity. The model was empirically validated through a comparative assessment of G7 and BRICS countries using twelve sustainability indicators covering economic, environmental, and social dimensions. Results show significant variations in sustainability efficiency, with G7 countries generally demonstrating higher overall performance, while BRICS nations exhibit strong growth potential but face environmental and structural constraints. These findings confirm the robustness of the DNMEREC-DNMARCOS framework and highlight its adaptability to complex, multidimensional datasets. The study contributes a transparent methodological tool for researchers and policymakers seeking evidence-based strategies to enhance global sustainability performance and bridge development gaps. Full article

On GPU-based clusters, the training workloads of machine learning (ML) models, particularly neural networks (NNs), are often structured as Directed Acyclic Graphs (DAGs) and typically deployed for parallel execution across heterogeneous GPU resources. Efficient scheduling of these workloads is crucial for optimizing performance metrics such as execution time, under various constraints including GPU heterogeneity, network capacity, and data dependencies. DAG-structured ML workload scheduling could be modeled as a Nonlinear Integer Program (NIP) problem, and is shown to be NP-complete. By leveraging a positive correlation between Scheduling Plan Distance (SPD) and Finish Time Gap (FTG) identified through an empirical study, we propose to develop a Running Time Gap Strategy for scheduling based on Whale Optimization Algorithm (WOA) and Reinforcement Learning, referred to as WORL-RTGS. The proposed method integrates the global search capabilities of WOA with the adaptive decision-making of Double Deep Q-Networks (DDQN). Particularly, we derive a novel function to generate effective scheduling plans using DDQN, enhancing adaptability to complex DAG structures. Comprehensive evaluations on practical ML workload traces collected from Alibaba on simulated GPU-enabled platforms demonstrate that WORL-RTGS significantly improves WOA’s stability for DAG-structured ML workload scheduling and reduces completion time by up to 66.56% compared with five state-of-the-art scheduling algorithms. Full article

Global efforts to achieve carbon neutrality increasingly rely on institutional green policy that reshape corporate environmental behavior. This study examines whether green policy improves corporate environmental performance (EP). Using panel data of the A-share listed firms from 2010 to 2022, we exploit the rollout of pilot cities as a quasi-natural experiment and apply a difference-in-differences (DID) framework, supplemented by double machine learning (DML) and robustness tests. The results show that the New Energy Demonstration City (NEDC) policy notably increases EP, with stronger effects for state-owned enterprises, large firms, and regulated industries. Mechanism analysis indicates that artificial intelligence innovation capacity and the stringency of regional environmental regulation amplify the policy’s effectiveness, revealing a “innovation–regulation” dual mechanism. By focusing on integrated EP rather than single outcomes, this paper extends the literature on green policy instruments. It demonstrates that structural policies combining fiscal incentives and regulatory constraints can correct market failures and foster long-term green transition. Beyond China, the findings provide insights for other developing economies where market-based instruments alone may be insufficient to trigger low-carbon transformation. Full article

In the context of the “Dual Carbon” goals and ecological civilization development, enhancing forestry ecological total factor productivity (FETFP) has become vital for advancing green development and environmental governance. Confronted with tightening resource constraints and pressure to transform traditional growth models, whether digital intelligence integration can effectively empower improvements in FETFP requires in-depth empirical validation. Based on publicly available panel data from 30 Chinese provinces spanning 2012 to 2022, this study constructs an index system for measuring digital intelligence integration and FETFP. Using the Double Machine Learning (DML) framework, the study empirically identifies the impact of digital intelligence development on FETFP and explores its internal mechanisms. The key results show that (1) digital intelligence integration significantly enhances FETFP. For every unit increase in digital and intelligent integration, FETFP rises by an average of 19.97%; (2) mechanism analysis reveals that digital intelligence improves FETFP by optimizing the forestry industrial structure, promoting green technological innovation, and amplifying the synergistic effects of fiscal support; (3) and heterogeneity analysis suggests that the positive impact of digital intelligence integration is more pronounced in regions with higher environmental expenditures and stronger green finance support. Accordingly, this study proposes several policy recommendations, including accelerating digital infrastructure development, strengthening foundational digital intelligence capabilities, enhancing support for green innovation, leveraging the ecological multiplier effects of digital transformation, tailoring digital strategies to local conditions, and improving the precision of regional environmental governance. The findings provide robust empirical evidence for improving FETFP in developing and developed economies. Full article

This paper presents a comprehensive deep reinforcement learning (DRL) framework for developing strategic trading models tailored to the Indian NIFTY 50 index, leveraging the temporal and nonlinear nature of financial markets. Three advanced DRL architectures deep Q-network (DQN), double deep Q-network (DDQN), and dueling double deep Q-network (Dueling DDQN) were implemented and empirically evaluated. Using a decade-long dataset of 15-min interval OHLC data enriched with technical indicators such as the exponential moving average (EMA), pivot points, and multiple supertrend configurations, the models were trained using prioritized experience replay, epsilon-greedy exploration strategies, and softmax sampling mechanisms. A test set comprising one year of unseen data (May 2024–April 2025) was used to assess generalization performance across key financial metrics, including Sharpe ratio, profit factor, win rate, and trade frequency. Each architecture was analyzed in three progressively sophisticated variants incorporating enhancements in reward shaping, exploration–exploitation balancing, and penalty-based trade constraints. DDQN V3 achieved a Sharpe ratio of 0.7394, a 73.33% win rate, and a 16.58 profit factor across 15 trades, indicating strong volatility-adjusted suitability for real-world deployment. In contrast, the Dueling DDQN V3 achieved a high Sharpe ratio of 1.2278 and a 100% win rate but with only three trades, indicating an excessive conservatism. The DQN V1 model served as a strong baseline, outperforming passive strategies but exhibiting limitations due to Q-value overestimation. The novelty of this work lies in its systematic exploration of DRL variants integrated with enhanced exploration mechanisms and reward–penalty structures, rigorously applied to high-frequency trading on the NIFTY 50 index within an emerging market context. Our findings underscore the critical importance of architectural refinements, dynamic exploration strategies, and trade regularization in stabilizing learning and enhancing profitability in DRL-based intelligent trading systems. Full article

In freeform surface grid structures, quadrilateral meshes offer high visual transparency and simple joint connections, but their structural stability is relatively limited. To enhance stability, designers often introduce additional structural elements along the diagonals of the quadrilateral mesh, forming double-layer quadrilateral grid systems such as cable-braced gridshells. However, current design methodologies do not support the simultaneous optimization of both layers. As a result, the two layers are often designed independently in practical applications, leading to complex joint detailing that compromises construction efficiency, architectural aesthetics, and overall structural performance. To address these challenges, this study presents a weighted multi-objective geometry optimization framework based on a Guided-Projection algorithm. The proposed method integrates half-edge data structure and multiple geometric and structural constraints, enabling the simultaneous optimization of quadrilateral mesh planarity (i.e., panels lying on flat planes) and the orthogonality (i.e., angles approaching 90°) of diagonal cable layouts. Through multiple case studies, the method demonstrates significant improvements in panel planarity and cable orthogonality. The results also highlight the algorithm’s rapid convergence and high computational efficiency. Finite element analysis further validates the structural benefits of the optimized configurations, including reduced peak axial forces in cables, more uniform cable force distribution, and enhanced overall stiffness and buckling resistance. In conclusion, the method improves structural stability, constructability, and design efficiency, offering a practical tool for optimizing freeform cable-braced gridshells. Full article

Based on sample data from A-share listed heavy polluters from 2012 to 2021, this paper adopts the double-difference method to explore the influence of the construction of national pilot zone for ecological conservation on the ESG performance of heavily polluting enterprises. Following several robustness tests, this study argues that the ESG performance of heavy-polluting companies is significantly enhanced by the construction of the national pilot zone for ecological conservation. Specifically, the construction of the pilot zone enhances the ESG performance of heavy polluters by easing financing constraints. The enhancing effect of the construction of the pilot zone on the ESG performance of heavy polluters is more prominent in terms of strengthening social responsibility and optimizing governance structure. Additionally, improving heavily polluting enterprises’ ESG performance is demonstrated to effectively enhance their financial performance. The facilitating effect of the construction of the pilot zone on ESG performance is more obvious among state-owned enterprises, enterprises with high media attention, enterprises established at a late stage, and enterprises with high-quality environmental information disclosure. This study offers an empirical foundation for the government to develop policies regarding the establishment of pilot zones and for heavily polluting enterprises to enhance their ESG performance. Full article

As the development of oilfields in China enters its middle-to-late stage, the old oilfields still occupy a dominant position in the production structure. The seepage process of reservoirs in the high Water Content Period (WCP) presents significant nonlinear and non-homogeneous evolution characteristics, and the traditional seepage-modeling methods are facing the double challenges of accuracy and adaptability when dealing with complex dynamic scenarios. In recent years, Deep Learning technology has gradually become an important tool for reservoir seepage field prediction by virtue of its powerful feature extraction and nonlinear modeling capabilities. This paper systematically reviews the development history of seepage field prediction methods and focuses on the typical models and application paths of Deep Learning in this field, including FeedForward Neural networks, Convolutional Neural Networks, temporal networks, Graphical Neural Networks, and Physical Information Neural Networks (PINNs). Key processes based on Deep Learning, such as feature engineering, network structure design, and physical constraint integration mechanisms, are further explored. Based on the summary of the existing results, this paper proposes future development directions including real-time prediction and closed-loop optimization, multi-source data fusion, physical consistency modeling and interpretability enhancement, model migration, and online updating capability. The research aims to provide theoretical support and technical reference for the intelligent development of old oilfields, the construction of digital twin reservoirs, and the prediction of seepage behavior in complex reservoirs. Full article

Artificial Intelligence (AI) plays a dual role in the clean energy transition, acting both as a major energy consumer and as a driver of sustainability. While AI enhances renewable energy forecasting, optimizes smart grids, and improves energy storage efficiency, the rapid growth of AI-driven data centers has significantly increased global electricity demand. AI-related energy consumption is projected to double by 2026 and triple by 2030, accounting for approximately 1.3% of global electricity use. This study adopts a multidisciplinary approach, synthesizing engineering, business, and policy insights to evaluate AI’s energy footprint and contributions to sustainability. The findings reveal that AI-driven optimization enhances smart grid efficiency and forecasting accuracy; however, infrastructure limitations, regulatory gaps, and economic constraints hinder AI’s alignment with sustainability goals. The results are systematically structured across five key themes: key findings, impact on energy consumption, risks and challenges, potential solutions, and policies and regulations. Supported by thematic tables and an original infographic, this study provides a comprehensive analysis of AI’s evolving role. By integrating AI with global sustainability policies, stakeholders can leverage its potential to accelerate the clean energy transition while minimizing the ecological footprint. Full article

Current deep excavation applications, which pose risks for constructing high-rise buildings and infrastructures, are increasing. Therefore, the increasing urbanization, underground infrastructure requirements, and time and cost constraints in construction projects have led to a growing demand for rapid, economical, and safe deep excavation designs. Although numerical modeling tools enable rapid analyses, the reliability of soil engineering parameters remains a challenge due to natural variability, sample disturbances, and differences between laboratory and field test conditions. In this study, PLAXIS 2D (Version 24) was used to model a deep excavation, allowing for the assessment of soil–structure interaction and excavation-induced deformations. The objectives are to compare field data with the numerical model and identify which soil parameters are critical for excavation. Through the sensitivity analysis, the study highlighted that the variations in shear strength parameters, such as cohesion and internal friction angle, are crucial and shall be precisely determined. The performed analyses revealed that even minor changes in the internal friction angle can dramatically impact displacements by doubling them and highlight the significant disparity between the minimum and maximum margins. The numerical analysis underscores the need for precise parameter measurement and careful analysis to achieve reliable results and ensure safer, more effective designs. The comparison of numerical results with field measurements confirmed the model’s accuracy. Full article