Search Results (1,075)

This paper presents a fully native optimization framework, implemented within DIgSILENT PowerFactory, which is aimed at solving the optimal conductor sizing problem (OCSP) in electrical distribution systems under realistic operating conditions. Our methodology integrates a tabu search algorithm (TSA) directly with the three-phase power flow routines and database objects available in the DigSILENT programming language (DPL), thereby eliminating the need for external data exchange and synchronization between independent optimization and network simulation environments. Our framework considers balanced and unbalanced operating conditions while incorporating peak demand, multilevel demand, and hourly demand load profiles. The optimization process minimizes annual investment and operating costs while satisfying voltage regulation and conductor ampacity constraints. The methodology was validated using a 27-bus benchmark system and the IEEE 33- and 123-bus distribution systems under different operating scenarios. The numerical results indicate that chronological demand scenarios significantly influence conductor allocation decisions and annual operating costs. Compared to the conventional peak demand load profile, the multilevel and hourly load profiles produced lower annual costs by distributing conductor sizing decisions across multiple operating states instead of considering worst-case loading conditions. Additionally, the unbalanced scenarios increased the operating losses and modified the conductor selection patterns due to unequal phase loading and current asymmetries. The proposed TSA-DPL implementation maintained stable convergence behavior and low statistical dispersion under all the evaluated benchmark systems and operating conditions. Even for the IEEE 123-bus feeder under unbalanced hourly operating conditions, the standard deviation remained below 0.70% of the average annual cost, confirming the robustness and repeatability of the methodology. Although the detailed three-phase chronological simulations increased the computational requirements, the proposed implementation demonstrated computational applicability to the evaluated benchmark systems. Overall, the proposed TSA-DPL framework constitutes a robust native implementation for realistic conductor sizing studies in modern three-phase distribution systems. Full article

Quantifying water and soil resource (WSR) carrying capacity is crucial for sustainable regional development. However, humid hilly regions like Hunan Province remain understudied, facing ecological vulnerability despite abundant water resources. This study constructs a comprehensive evaluation index system based on the Production–Living–Ecological Space (PLES) framework and applies an improved entropy weight technique for order preference by similarity to ideal solution (TOPSIS) method; a coupling coordination model among production, living, and ecological space subsystems; and an obstacle degree model to annual-scale data from 2011 to 2022 for Hunan’s 14 prefecture-level cities. The results show the following: (1) The overall WSR carrying capacity index increased from 0.28 in 2011 to 0.52 in 2022, indicating a continuous improvement in carrying capacity during the study period. Spatially, the carrying capacity is higher in central and eastern regions and lower in western and southern areas, with most cities falling into low, relatively low, or medium categories. (2) The coupling coordination degree among the three PLES subsystems improved but remained in a transitional stage (0.30–0.45), indicating unbalanced development. (3) The top five obstacle factors are water yield modulus, length of water supply pipelines, ecological water use rate, gross domestic product (GDP) per capita, and per capita water resources—with the ecological water use rate exceeding 19% in every year of the study period. These findings highlight the need to coordinate production, living, and ecological subsystems to improve WSR carrying capacity. Targeted strategies, including efficient water use, optimized resource allocation, and ecological protection, are recommended for sustainable water–soil resource management in Hunan Province. Full article

Based on agency theory, this study examines the association between the optimistic tone of earnings conference calls and the cost of equity capital using an unbalanced panel of 342 non-financial FTSE All-Share companies (987 firm-year observations) over the period 2010–2024. Earnings conference call tone is measured using the financial sentiment dictionary and analysed using NVivo 14 software. The cost of equity capital is estimated using an implied cost of equity model. Panel specification is determined using appropriate panel-data diagnostic tests, while robustness is assessed through lagged-tone regressions, an alternative cost of equity measure, and two-stage least-squares (2SLS) estimation to address potential endogeneity. The results show a significant negative association between optimistic earnings conference call tone and the cost of equity capital (β = −7.787, p < 0.01). A statistically significant reverse association is also documented: A statistically significant reverse association is also documented: a lower cost of equity is associated with a more optimistic tone in subsequent conference calls (β = −0.001, p < 0.01). This result is interpreted as evidence of an association rather than a causal effect. Both results remain robust across alternative model specifications, lagged-tone analyses, alternative cost of equity measures, and endogeneity controls. The findings indicate that positive and transparent voluntary communication, particularly through earnings conference calls, is associated with lower information asymmetry and a lower cost of equity capital. Firms that have not yet adopted this communication channel may consider incorporating earnings conference calls into their investor-relations strategies to enhance voluntary communication with investors. This study contributes to the disclosure literature by documenting statistically significant associations between earnings conference call tone and the cost of equity capital under two model specifications in the UK market and by providing comprehensive robustness evidence supporting the stability of the reported associations. Full article

This paper proposes a dynamic intelligent method for voltage violation management in high-renewable-penetration distribution networks. The method employs a dual-agent architecture: DERMS_Agent coordinates task scheduling, data management, and computational resource allocation, while Solution_Agent performs three-phase unbalanced power flow calculation and MIQP-based voltage violation joint optimization. Four key technical contributions are presented. (i) An asymmetric nodal admittance matrix is developed to incorporate transformer tap-phase-shift and capacitor branches within a unified formulation. (ii) Five categories of analytical sensitivities are systematically derived, covering transformer tap, phase shift, and capacitor compensation effects for both voltage regulation and harmonic suppression. (iii) A three-parameter MIQP joint optimization model is constructed with voltage deviation minimization as the objective and three-phase unbalance and resonance avoidance as constraints. (iv) A two-stage hybrid solution strategy combining Ipopt continuous relaxation with Gurobi neighborhood enumeration is designed to achieve real-time solvability. Validation on a real 10 kV feeder with 91 transformer areas over 768 time sections (8 days) demonstrates a 95.6% voltage violation resolution rate within the first three polling cycles and an average single-section solution time of 0.83 s, satisfying the real-time requirements of 15 min operational control cycles. Full article

The collaborative transformation of digitalization and greening has become a core trend for modern enterprises to build sustainable development systems. As a typical voluntary environmental regulation in China, green factory certification plays an important role in promoting the integration of digital technology and green production. Based on the unbalanced panel data of Chinese A-share listed companies from 2012 to 2022, this paper adopts a multi-period difference-in-differences model to empirically investigate the impact, internal mechanisms and heterogeneous characteristics of green factory certification on corporate digital-green collaborative transformation. The results show that green factory certification can significantly boost the level of corporate digital-green synergy, with an average policy effect of 2.07%. Mechanism tests confirm that this policy realizes the dual transformation through four core paths: stimulating green technological innovation, raising public environmental awareness, improving corporate environmental, social, and governance (ESG) performance and easing financing constraints. Heterogeneity analysis indicates that the promotion effect varies distinctly across regions, enterprise life cycles, pollution types and executive backgrounds. This study reveals the systemic logic of voluntary environmental regulation enabling digital-green collaborative transformation, and provides empirical support and targeted policy suggestions for enterprises and governments to accelerate the integrated development of digitalization and greening. Full article

In the information-supported penetration scenario, the predicted encounter point and encounter time can provide the future spatial threat position and the time margin for avoidance maneuver, respectively, which are important prior information for dangerous-area judgment, avoidance triggering, and penetration decision making. However, the data-driven prediction method based on absolute coordinates is likely to depend on the fixed training airspace, resulting in insufficient cross-space generalization ability. Meanwhile, the unconstrained prediction space will lead to an excessively large sample size and an unbalanced sample distribution. Aiming at the above problems, this paper proposes a physics-constrained relative-state prediction framework for the rapid prediction of the encounter point and encounter time. Firstly, the relative-state input centered on the maneuvering vehicle is adopted to reduce the dependence of the model on the fixed global coordinate system. Secondly, a concentric double-layer spherical-shell detectable threat domain is constructed to limit the approximately unbounded prediction space to a finite region that satisfies the sensor detection condition and the maneuvering constraint of the maneuvering vehicle. Furthermore, a physical geometric stratified sampling strategy based on relative distance, azimuth angle, and pitch angle is designed, and a sample-weight correction mechanism is combined to improve the balance of sample coverage under different distance layers and incoming directions. Finally, a ResNet-MLP joint regression model is constructed and trained using offline numerical simulation samples, which is used as an online rapid predictor. The simulation results show that, on the stratified training subset, the mean absolute error of the proposed model for encounter time is 0.1775 s, and the three-dimensional Euclidean error of the encounter point is 129.89 m. The tests with spatial position variation and bounded measurement noise further verify the generalization ability and robustness of the model. The proposed method can provide rapid spatial threat information and time-margin information for dynamic penetration decision making and reduce the computational requirement of repeated online numerical propagation. Full article

This paper presents the conceptual foundation and software architecture of a proactive system for real-time cyber-attack detection. The system is based on a nested approach of building two-level chains of machine learning models. It is composed of two components—one for learning and one for evaluating real-time data. Both components are written in the Python programming language. The data has been processed and manipulated using PySpark. The initial experiments showed a combined increase of up to 25% in accuracy, compared to using non-combined ML models and unbalanced data. Full article

This article examines the performance of negative-sequence overcurrent protection during short circuits in the presence of current asymmetry caused by single-phase loads, such as those encountered in AC railway traction systems. The impact of unbalanced loads on the generation of negative-sequence currents is analyzed using field test data and a mathematical model. Various operating modes of an electric power network under unbalanced loading conditions are simulated in MATLAB Simulink R2015a. It is shown that under significant load asymmetry, negative-sequence currents can reach magnitudes comparable to those of short-circuit currents, thereby increasing the risk of false protection operation. To address this issue, a modified negative-sequence overcurrent protection scheme is proposed that ensures both sensitivity and selectivity. The modification is based on analyzing the ratio of negative-sequence to positive-sequence current phasors and monitoring the rate of change of the negative-sequence current. A faulted phase selector is also incorporated into the protection scheme. Simulation results confirm the effectiveness of the modified protection in reliably identifying unsymmetrical short circuits under varying unbalanced load conditions, including remote faults with high fault resistance. Full article

Ice–snow tourism in Northeast China is developing rapidly. Against this backdrop, revealing the spatial network structure of ice–snow tourism cities and assessing their disturbance resistance capacity is of great significance for achieving high-quality development of regional ice–snow tourism. This study takes 25 cities across the three northeastern provinces as network nodes, using data covering the period from January 2024 to March 2025. Integrating a complex network analysis framework, this paper comprehensively employs an accessibility model, tourism symbiotic linkage intensity model, and core–periphery model to distinguish core and peripheral cities within the network, analyze its structural characteristics and spatial patterns, and evaluate network vulnerability by simulating two scenarios: random attacks and deliberate attacks. The results indicate that: (1) Accessibility presents a concentric zonal pattern that attenuates gradually from the center to the periphery, accompanied by pronounced north–south disparities. Urban symbiosis intensity is strongly influenced by transportation distance, exhibiting a distinct proximity symbiosis pattern. (2) An ice–snow tourism symbiotic network has initially taken shape among northeastern cities. The network displays small-world properties; however, urban development is unbalanced, with marked hierarchical differentiation. Based on geographic location and resource endowments, the network can be divided into four cohesive subgroups. (3) The symbiotic network proves robust under random attacks, whereas connectivity declines sharply under deliberate attacks, embodying typical “robust-yet-vulnerable” structural characteristics. Both expanding the scale of core nodes and optimizing inter-node connection weights can significantly enhance network robustness. The static identification and dynamic dependency evaluation framework constructed in this study can effectively identify key nodes and vulnerable links within ice–snow city networks, and can serve as a reference for the coordinated development and structural optimization of ice-snow tourism in Northeast China. Full article

Modern distribution networks operate under increasingly demanding conditions, characterized by the integration of distributed energy resources, unbalanced three-phase operation, low measurement redundancy, variable topologies, and data uncertainty. In this context, distribution system state estimation (DSSE) is a key tool for operational monitoring; however, its practical deployment is often hindered by topological inconsistencies and gross measurement errors. This paper proposes an integrated and modular deep learning-based methodological framework that combines active topology identification (ATI), gross error detection (GED), error-type identification (ETI), error-location identification (ELI), measurement reconstruction and correction (MRC), and DSSE. The ATI module is formulated as a global multiclass classifier, whereas the subsequent modules are trained as topology-specific models. Compromised measurements are handled through an iterative GED–ETI–ELI–MRC loop that detects, identifies, locates, and corrects one anomalous measurement per iteration before re-evaluating the input vector. The proposed methodology was validated by using simulation-based scenarios generated in OpenDSS for a real unbalanced three-phase 240-node distribution feeder. The results show that no single architecture is dominant across all subproblems: WaveNet1D achieved the best relative performance in ATI, GED, and ETI; EncDec-CNN in ELI; NBEATS1D in MRC; and EncDec-GRU in DSSE. Additionally, WLS estimators based on both nodal voltages and branch currents failed to achieve numerical convergence on the 240-node test system under the evaluated conditions, a finding consistent with recent literature reporting analogous convergence failures in distribution networks of similar or smaller scale. Furthermore, the integrated evaluation shows that omitting ATI increases the voltage-magnitude MAE by a factor of 12.3 and the voltage-angle MAE by a factor of 8.1 with respect to the complete framework, whereas omitting only the compromised-measurement treatment increases these errors by factors of 1.8 and 1.9, respectively. The total offline computational cost was approximately 1587.9 h (66.2 GPU-days), while the online inference latency was approximately 0.45 ms per sample, making the framework compatible with AMI- and SCADA-based monitoring cycles. These findings confirm that topological consistency is the dominant factor in DSSE accuracy and that iterative measurement correction meaningfully improves estimator robustness under anomalous measurement conditions. Full article

This study aims to examine the relationship between related party sales (RPS) and earnings management (EMN), as well as to investigate the moderating effects of institutional ownership (IO), single institutional ownership (SIO), and dispersed institutional ownership (DIO) on this relationship. This study examines non-service and non-financial firms listed on the Indonesia Stock Exchange during the 2016–2024 period. The sample selection included firms with IO and RPS. The final sample consisted of 68 firms (585 firm-year observations) out of a total of 601 firms and was analyzed using moderated regression with unbalanced panel data. The findings indicate that RPS has a positive effect on EMN. IO weakens the positive effect of RPS on EMN. However, in the group consisting only of SIO, the positive effect of RPS on EMN becomes stronger. In contrast, in the DIO group, DIO is unable to moderate the relationship. Furthermore, EMN in firms engaging in RPS with parent firms does not differ from EMN in firms engaging in RPS with non-parent related parties. Finally, we conclude that, in the Indonesian context, RPS provides opportunities for management to engage in opportunistic behavior. The presence of SIO may increase earnings management, whereas DIO is unable to mitigate earnings management. Full article

Coastal and island destinations increasingly rely on data-driven governance to manage seasonality, resource scarcity, overtourism, and climate risk. This article analyzes 30 official documents issued between 2017 and 2025 by nine Spanish and Portuguese Sustainable Tourism Observatories in the UN Tourism INSTO network. Rather than treating observatory language as verified policy implementation, the study uses computational content analysis to measure institutional discourse about monitoring and action. The Actionability Index (ActI), action-share, and log-ratio metrics are interpreted as exploratory, compositional text indicators, not direct evidence of governance effectiveness. Results show a negative document-level association between monitoring intensity and the ActI (r = −0.3951, p = 0.0307, alpha = 0.05), but denominator coupling and report-genre heterogeneity require a restrained interpretation: the pattern signals a monitoring-action imbalance that warrants follow-up validation rather than causal proof of a governance failure. Quartile-based profile labels are treated as sample-internal descriptors, with Barcelona interpreted as a boundary case whose action discourse is concentrated around tourism carrying capacity. A pre/post-2020 comparison is reported only as an unbalanced descriptive contrast. The contribution is a transparent, reproducible framework for auditing whether monitoring intelligence is connected to actionable discourse, together with explicit limits and validation priorities for future work. Full article

The high energy demand driven by industrial development has transformed the power system from a single energy source to multiple energy systems (MESs). These systems, which involve thermal generators, combined heat-and-power (CHP) units, electrolyzers, fuel cells, etc., with realistic forecast uncertainty, are very operationally challenged. This paper proposes a Distributionally Robust Optimization (DRO) based on a Wasserstein-metric ambiguity set, which simultaneously optimizes the annual maintenance schedules and short-term operational dispatch across MESs. The ambiguity set is constructed using joint samples of forecast errors for the three carriers’ demand, allowing for a data-driven worst-case distribution approach that mitigates the excessive conservatism typically associated with conventional robust optimization (CRO). The penalties are explicitly enforced for load and renewable energy curtailments across each of the MESs with source-specific value-of-lost-load coefficients. The Wasserstein radius is improved by sensitivity analysis, obtaining a θ value of 0.20 as the cost reduction radius for a 40% RES penetration. Five RES penetration levels are implemented here on the IEEE 39-bus New England network, with CHP, electrolyzer, fuel cell, thermal storage, and hydrogen storage. The DRO reduces the total annual system cost by 56% compared to CRO, while reducing the unbalanced energy. Full article

This study investigates the causal relationship between profit efficiency and bank risk in Southeast Asian commercial banks using a Panel Vector Autoregression framework. The banking data is unbalanced panel data collected from BankFocus from 2007 to 2022 from the data of financial institutions in 11 Southeast Asian countries. The author excluded commercial bank data from three countries, including Brunei, East Timor, and Myanmar, due to their lack of financial reports. Therefore, the number of commercial banks obtained is 118 banks from eight countries including Cambodia, Indonesia, Laos, Malaysia, the Philippines, Singapore, Thailand, and Vietnam. Profit efficiency is measured by ROA and ROE, and bank risk is proxied by Z-score. The results reveal a bidirectional causal relationship between profit efficiency and bank risk. Bank risk positively affects ROA at a 10% significance level, while ROA has a negative effect on bank risk at a 1% level. In contrast, bank risk exerts a negative and significant impact on ROE at a 1% level, whereas changes in ROE do not significantly influence bank risk. These findings imply that Vietnamese commercial banks need to maintain a balance between traditional operations and diversification strategies. Simultaneously, evidence of a causal relationship between profitability and risk supports hypotheses of poor management and austere behavior, thereby highlighting the need to strengthen governance capacity, improve operational quality, and implement appropriate development strategies to optimize efficiency and ensure sustainable risk control. Full article

Cloud computing and mobile edge computing address the growing demand for computing power driven by the rise in data-intensive applications, but they are prone to creating computing silos, resulting in unbalanced resource utilization. To address this issue, the computing power network (CPN) has been introduced to enable the centralized management and scheduling of resources across the entire network. However, task scheduling in the CPN requires joint selection of computation nodes and routing paths, which greatly increases the complexity of the scheduling problem. In existing studies, heuristic methods are difficult to satisfy real-time requirements, whereas deep reinforcement learning methods ignore the collaborative optimization of network resources, making them difficult to adapt to complex CPN scenarios. To this end, we propose a task scheduling method for the CPN, called TS-DQNF. First, the method uses the Deep Q-Network (DQN) to determine the computation node for the computation task. Then, it introduces a dynamic congestion-aware mechanism to determine a low-cost routing path. Finally, it gradually obtains an effective task scheduling scheme through multiple rounds of alternating iterations. Simulation results show that the TS-DQNF improves the task success rate by 2.47–60.71% and reduces the average processing delay by 1.92–16.94% compared with other methods, while demonstrating good convergence performance. Full article