Search Results (525)

This study presents a multiscale, uncertainty-aware hybrid deep learning approach addressing the short-term wind speed prediction problem, which is critical for the reliable planning and operation of wind energy systems. Wind signals are decomposed using adaptive variational mode decomposition (VMD), and the resulting wind components are processed together with meteorological data through a dual-stream CNN–BiLSTM architecture. Based on this multiscale representation, probabilistic forecasts are generated using quantile regression to capture best- and worst-case scenarios for decision-making purposes. Unlike fixed prediction intervals, the proposed approach produces adaptive prediction bands that expand during unstable wind conditions and contract during calm periods. The developed model is evaluated using four years of meteorological data from the Afyonkarahisar region of Türkiye. While the proposed model achieves competitive point forecasting performance (RMSE = 0.700 m/s and MAE = 0.54 m/s), its main contribution lies in providing reliable probabilistic forecasts through well-calibrated uncertainty quantification, offering decision-relevant information beyond single-point predictions. The proposed method is compared with a classical CNN–LSTM and several structural variants. Furthermore, SHAP-based explainability analysis indicates that seasonal and solar-related variables play a dominant role in the forecasting process. Full article

With the development of manufacturing industry, traditional fixed process processing methods cannot adapt to the changes in workshop operations and the demand for small batches and multiple orders. Therefore, it is necessary to introduce multiple robots to provide a more flexible production mode. Currently, some Job Shop Scheduling Problems with Transportation (JSP-T) only consider job scheduling and vehicle task allocation, and does not focus on the problem of collision free paths between vehicles. This article proposes a novel solution framework that integrates workshop scheduling, material handling robot task allocation, and conflict free path planning between robots. With the goal of minimizing the maximum completion time (Makespan) that includes handling, this paper first establishes an extended JSP-T problem model that integrates handling time and robot paths, and provides the corresponding workshop layout map. Secondly, in the scheduling layer, an improved Deep Q-Network (DQN) method is used for dynamic scheduling to generate a feasible and optimal machining scheduling scheme. Subsequently, considering the robot’s position information, the task sequence is assigned to the robot path execution layer. Finally, at the path execution layer, the Priority Based Search (PBS) algorithm is applied to solve conflict free paths for the handling robot. The optimized solution for obtaining the maximum completion time of all jobs under the condition of conflict free path handling. The experimental results show that compared with algorithms such as PPO, the scheduling algorithm proposed in this paper has improved performance by 9.7% in Makespan, and the PBS algorithm can obtain optimized paths for multiple handling robots under conflict free conditions. The framework can handle scheduling, task allocation, and conflict-free path planning in a unified optimization process, which can adapt well to job changes and then flexible manufacturing. Full article

Hot-fluid injection in thermal-enhanced oil recovery (thermal-EOR, TEOR) imposes temperature-driven volumetric strains that can substantially alter in situ stresses, fracture geometry, and wellbore/reservoir integrity, yet existing TEOR modeling has not fully captured coupled thermo-poroelastic (thermo-hydro-mechanical) effects on fracture aperture, fracture-tip behavior, and stress rotation within a displacement discontinuity method (DDM) framework. This study aims to examine the influence of sustained hot-fluid injection on stress redistribution, hydraulic-fracture deformation, and fracture stability in thermal-EOR by accounting for coupled thermal, hydraulic, and mechanical interactions. This study develops a fully coupled thermo-poroelastic DDM formulation in which fracture-surface normal and shear displacement discontinuities, together with fluid and heat influx, act as boundary sources to compute time-dependent stresses, pore pressure, and temperature, while internal fracture fluid flow (Poiseuille-based volume balance), heat transport (conduction–advection with rock exchange), and mixed-mode propagation criteria are included. A representative scenario considers an initially isothermal hydraulic fracture grown to 32 m, followed by 12 months of hot-fluid injection, with temperature contrasts of ΔT = 0–100 °C and reduced pumping rate. Results show that the hydraulic-fracture aperture increases under isothermal and modest heating (ΔT = 25 °C) and remains nearly stable near ΔT = 50 °C, but progressively narrows for ΔT = 75–100 °C despite continued injection, indicating potential injectivity decline driven by thermally induced compressive stresses. Hot injection also tightens fracture tips, restricting unintended propagation, and produces pronounced near-fracture stress amplification and re-orientation: minimum principal stress increases by 6 MPa for ΔT = 50 °C and 10 MPa for ΔT = 100 °C, with principal-stress rotation reaching 70–90° in regions adjacent to the fracture plane and with markedly elevated shear stresses that may promote natural-fracture activation. These findings show that temperature effects can directly influence injectivity, fracture containment, and the risk of unintended fracture or natural-fracture activation, underscoring the importance of temperature-aware geomechanical planning and injection-strategy design in field operations. Incorporating these effects into project design can help operators anticipate injectivity decline, improve fracture containment, and reduce geomechanical uncertainty during long-term hot-fluid injection. Full article

This study introduces the Sustainable Urban Mobility Maturity Model (SUM-MM) to assess and enhance the maturity of sustainable urban mobility in cities. The SUM-MM comprises 3 main dimensions (enablers, sustainability, and transport modes) and 11 sub-dimensions (strategic and spatial planning, organization and human resources, information and communication technologies, environment, economy, social, walking, micromobility, public transport, paratransit systems, and multimodal integration), evaluated at 5 levels (beginner, initial, integrated, managed, and mature). Developed through a literature review and validated using a questionnaire-based expert opinion method, the model was tested in Konya, Türkiye. The results show that Konya’s overall maturity falls between integrated and managed, with significant variability across sub-dimensions. The enablers dimension demonstrated the highest maturity, driven by strong organizational and technological capabilities, whereas the transport modes dimension had the lowest—particularly in paratransit systems. The SUM-MM serves as both a benchmarking tool and a policy guidance framework, facilitating targeted strategies for sustainable urban mobility improvements. Unlike existing smart city or transport maturity models, the SUM-MM specifically focuses on sustainable urban mobility, offering a structured, operational, and decision-oriented framework for policy-makers and city administrations. The results can be used by local and national authorities to support comparative benchmarking, strategic planning, and the prioritization of sustainable urban mobility investments. Full article

The technological triangle is a non-mathematical representation of the relationship between the characteristics of transport infrastructure, modes of transport, and the operational concept in a specific region. It is only through the synergistic effect of these three vertices that the railway undertaking, infrastructure manager, and authority can achieve optimal resource utilisation. Concurrently, it is imperative to exert pressure on the authorities to implement conceptual, systematic, and predictable measures. The process of implementing changes to transport infrastructure is a protracted one, typically spanning several years from the initial stages of preparation through to the project’s execution. The application of the technological triangle is possible on various parts of the infrastructure. Based on previous research, the authors prepared this Article to address intermediate stations, which were identified as the key focus of this article. Therefore, the authors in this article answer the question of what typical solutions exist for intermediate station configurations in relation to the operational concept and financial costs. Twenty different configurations were selected, and each was examined from the perspectives of financial, operational, planning, automation, and user pillars. The weights of the individual pillars were then assessed from the perspective of the infrastructure manager, the carrier, and the customer. The result is a comprehensive assessment of all wayside station configurations from different perspectives. Each user of this workflow can determine the weights of the individual pillars according to their needs and financial capabilities. This also gives the article a general use. The final part of the article presents specific examples of existing structures in the Czech Republic, which were not built with the perspective of this article in mind. The authors point out that if our method were applied, not only would large platform stations be built, which is the case for many intermediate stations in the Czech Republic; instead, more efficient solutions would be developed and adapted to the specific case. Full article

Citizen-centric smart city construction (SCC) has been the crucial mode for enhancing citizens’ well-being with rapid urbanization. While smart cities are constructed to improve urban operational safety, the concomitant low resilience of infrastructure, data breaches, and other issues also lead to physical, financial, and legal consequences, which therefore have the complicated the impact on citizens’ security perception of smart city construction (CSPSCC). To achieve sustainable smart city construction, it is important to clarify the influencing factors on CSPSCC. Although the enhancement of CSPSCC needs the joint efforts of citizens, government, and social organizations, the previous studies mostly focus on influencing factors from the single stakeholder. To address this gap, the theory of planned behavior was expanded to examine factors influencing CSPSCC from the perspective of participatory governance. Taking Nanjing as a case, hypotheses testing, mediating testing, and heterogeneity analysis were carried out for this theoretical model. The results show that the security governance of citizens, the government, and social organizations all had a positive impact on CSPSCC, with citizens’ behavioral intention being the most significant influencing factor. In addition, CSPSCC is also significantly affected by the citizens’ age, educational level, and usage frequency of smart city services. Full article

Planning serves as a vital tool for achieving orderly land management and utilization. The success of conservation planning hinges on its ability to translate cultural heritage preservation needs into rational allocation and guidance of land resources, ultimately realizing a win–win outcome that fosters cultural continuity, social harmony, and economic development. Historic and cultural towns are highly representative urban and rural historic and cultural heritage sites. However, the participation components in the conservation planning of historic towns are complex, and the misalignment of the functions, rights and responsibilities, and interest demands of the participants often leads to a loss of actual benefits. To help achieve a reasonable transformation of the protection needs of historic towns and guide the cultural inheritance and socially harmonious development of urban and rural construction, based on game theory and the logic of planning rights games, this paper begins with an understanding of the relevant laws and regulations, conducts an empirical analysis of the game processes and situations of conservation planning in two provinces and four towns, and incorporates publicly available data from the internet for argumentation to explore the game states and operation mechanisms of conservation planning in historic and cultural towns. The findings reveal the following regarding historic town conservation planning: (1) it proceeds lawfully and rationally, reflecting collective rationality; (2) it exhibits two equilibrium modes: relatively static and dynamic; (3) game conflicts mainly manifest as multi-planning conflicts and the resulting conflicts among systems and inter-systems. The game dynamics are influenced by the value of the historic town, resource allocation, and the relationship between rights, responsibilities, and interests. To overcome the game dilemma, it is essential to establish effective cooperative mechanisms at the legal and regulatory levels based on the value of the historic town, allocate resources reasonably, and achieve a balance between rights, responsibilities, and interests. Full article

How does digital technology mediate decision-making and shape our understanding of disaster recovery? I address this question by examining both the administrative and cinematic uses of digital images in the reconstruction process following the 2011 Great East Japan Earthquake. Post-disaster digital mediation is characterized by the administrative use of what has been termed “operational images,” designed not for interpretation but for action, particularly in disaster response and prevention. I connect the social and ethical dimensions of post-disaster recovery with the ontological dimensions of the technological characteristics of digital photography. By comparing Japanese independent filmmaker Haruka Komori’s digital filmmaking practice with the operational images utilized by administrative and research bodies, I aim to demonstrate how her particular digital aesthetics elicit the latent capacity of the “digital unconscious” and offer new modes of perceiving post-disaster recovery, in contrast to both other forms of post-disaster digital mediation and to analog photography. Through close analyses, I argue that her work articulates an alternative vision of recovery—one rooted not in spatial management or predictive planning, but in physical attachment to place, trust in the future, and imaginative engagement with survivors and the dead. Full article

Data sparsity, particularly the partial loss of diagnostic data caused by sensor failures, transmission errors, or missed inspections, frequently occurs in practical power transformer operations and significantly degrades the accuracy and reliability of health index (HI) assessments. In this study, a machine learning-based HI evaluation framework is developed using key diagnostic input parameters systematically derived from failure mode and effect analysis (FMEA) and established transformer diagnostic practices. To compensate for missing data, an unsupervised autoencoder (AE)-based imputation method is introduced and benchmarked against conventional statistical supplementation techniques, namely mean and mode imputation. The experimental results, obtained using real inspection-based transformer diagnostic records, demonstrate that the AE-based approach effectively preserves inter-variable correlations and latent data structures by learning nonlinear feature relationships. As a result, the proposed method maintains robust and consistent HI classification performance under varying missing-data conditions. Furthermore, validation using confirmed transformer failure cases shows that the AE method more accurately reconstructs missing dissolved gas analysis indicators and improves the identification of high-risk equipment compared with statistical imputation. Overall, the proposed approach provides decision-consistent HI evaluations even when diagnostic data are incomplete, thereby reducing uncertainty in maintenance planning and minimizing the need for additional follow-up inspections solely to compensate for missing information. Full article

This article examines whether, and under what conditions, there is room for new mosques in Belgian cities by analyzing how media controversies around mosque projects are assembled. We study a corpus of press articles (2014–2024) using a two-step approach: First, keyword mapping identifies dominant discursive patterns across six themes (mobility, legality, size and visibility, social cohesion and integration, security and extremism, financing). Second, argument coding links lexical signals to public modes of judgment through actor–network theory (ANT) and controversy registers. Applied to five case studies across Flanders, Wallonia, and the Brussels-Capital Region, this framework offers comparative depth. The results show that identity and security controversies frequently outweigh strict urban planning controversies; neutral planning criteria (e.g., traffic congestion, permit compliance) are often recoded as symbolic markers of alterity. Regional contrasts provide nuance to this pattern: in Flanders, politicization through security/identity is salient; in Wallonia, debates emphasize size, form, and spatial integration; in Brussels-Capital, technico-legal compliance intertwines with aesthetic visibility. Media operate as boundary objects that hierarchize registers and amplify controversies. We conclude that mosques are treated less as ordinary urban infrastructure than as contested symbols of belonging and visibility. Moving toward negotiated pluralism requires institutional mechanisms that ensure transparency, equal treatment, local anchoring, and symbolic requalification. Full article

In the centralized scheduling mode of automated container terminals, Automated Guided Vehicles (AGVs) often experience decision-making delays caused by system information-processing bottlenecks, which significantly affect path-planning efficiency and are particularly evident in sudden-traffic scenarios. To address this issue, this paper incorporates the artificial potential field (APF) into the cost function of Model Predictive Control (MPC) and develops a dual-trigger mechanism for lane-change and lane-return MPC obstacle-avoidance framework (Event-Triggered Model Predictive Control, EMPC). This framework integrates an obstacle-triggered local optimization mechanism and a lane-change trigger, enabling AGV to perform autonomous and dynamically responsive local obstacle avoidance, thereby improving local path-planning efficiency. Furthermore, a Proximal Policy Optimization (PPO)-based strategy is introduced to adaptively adjust the obstacle-weighting parameters within the EMPC cost function, enhancing both obstacle-avoidance and lane-keeping performance. Under multi-lane overtaking conditions, a lane-change trigger—implemented as a dual-phase “lane-change–return” mechanism—is employed, in which lateral optimization is activated only during critical phases, reducing online computational load by at least 28% compared with conventional MPC strategies. The experimental results demonstrate that the proposed PPO–EMPC architecture exhibits high robustness, real-time performance, and scalability under dynamic and partially observable environments, providing a practical and generalizable decision-making paradigm for cooperative AGV operations in automated container terminals. Full article

In the digital era, rapid urban growth and the demand for sustainable mobility are placing increasing pressure on transport systems, where congestion, energy consumption, and schedule variability complicate intermodal journey planning. This work proposes an AI-enhanced transfer-graph framework that models each transport mode as an independent subnetwork connected through explicit transfer arcs. This modular structure captures modal interactions while reducing graph complexity, enabling algorithms to operate more efficiently in time-dependent contexts. A Deep Q-Network (DQN) agent is further introduced as an exploratory alternative to exact and meta-heuristic methods for learning adaptive routing strategies. Exact (Dijkstra) and meta-heuristic (ACO, DFS, GA) algorithms were evaluated on synthetic networks reflecting Casablanca’s intermodal structure, achieving coherent routing with favorable computation and memory performance. The results demonstrate the potential of combining transfer-graph decomposition with learning-based components to support scalable intermodal routing. Full article

Logistics involves planning and managing resources to meet customer demands. Its effectiveness depends not only on time and process coordination but also on the performance of logistics operators, whose actions directly affect customer satisfaction. Although operational risks are inherent to logistics, customer-oriented service failures are often overlooked in traditional risk assessment. To address this gap, this study proposes an integrated approach that combines a Pareto Diagram and Failure Mode and Effects Analysis (FMEA) within the ISO 31000 risk assessment framework. This structured method enables the identification and prioritization of logistics failures based on customer complaints, thereby supporting data-driven decision-making and continuous service improvement. Applied to a real-world case in a ceramic production line specializing in tableware manufacturing, the method identified and evaluated key logistics failures; particularly those related to late deliveries and damaged goods. Based on these findings, improvement actions were proposed to reduce the recurrence of these issues. This study contributes a structured, practical, and replicable approach for organizations to introduce risk assessment practices and enhance the service quality of logistics management. This study advances the literature by shifting the focus from internal production failures to customer-driven service risks, offering strategic insights for improving reliability and operational performance. Full article

The rapid development of renewable energy has highlighted the issue of its accommodation, which has become a critical challenge for power grids with high renewable energy penetration. Accurately assessing a grid’s renewable energy accommodation capability is essential for ensuring power grid operational security, as well as for the rational planning and efficient operation of renewable energy sources and adjustable power resources. This paper adopts a long-term chronological power balance simulation approach, integrating the dynamic balance among multiple types of power sources, loads, and outbound transmission. Dispatch schemes suitable for different types of power sources, including hydropower, thermal power, wind power, solar power, and nuclear power, were designed based on their operational characteristics. Key operational constraints, such as output limits, staged water levels, pumping/generation modes of pumped storage, and nuclear power regulation duration, were considered. A refined analysis model for renewable energy accommodation in regional power grids was constructed, aiming to maximize the total accommodated renewable energy electricity. Using actual data from the Northeast China Power Grid in 2024, the model was validated, showing results largely consistent with actual accommodation conditions. Analysis based on next-year forecast data indicated that the renewable energy utilization rate is expected to decline to 90.6%, with the proportion of curtailment due to insufficient peaking capacity and grid constraints expanding to 8:2. Sensitivity analysis revealed a clear correlation between the renewable energy utilization rate and the scale of newly installed renewable capacity and energy storage. It is recommended to control the expansion of new renewable energy installations while increasing the construction of flexible power sources such as pumped storage and other energy storage technologies. Full article

This paper develops accurate three-dimensional trajectory tracking and anti-sway control strategies for offshore container cranes operating in an open-sea environment. A 5-DOF nonlinear dynamic model is developed that simultaneously accounts for the crane’s structural motion, trolley movement, spreader hoisting with variable rope length, and both lateral and longitudinal payload sway. The model further incorporates external disturbances induced by wave-excited ship motions. To ensure smooth, efficient, and accurate load transportation from the initial to the target position, an effective trajectory-planning scheme is proposed using a quintic polynomial trajectory refined by a ZVD shaper to suppress residual oscillations. A sliding mode control method is then designed to achieve accurate trajectory tracking and load-sway suppression under external disturbances. Numerical simulations demonstrate that the proposed trajectory planning method effectively reduces the residual oscillations and verifies the effectiveness and robustness of the proposed sliding mode control strategy. Full article