Search Results (5,224)

Accurate solar irradiance forecasting is important for efficient planning of solar energy systems, renewable energy integration, and data-driven energy management in smart cities. This becomes more essential in regions with limited measured data availability and varying climatic conditions, where reliable forecasting can support urban energy planning and smart grid operation. Pakistan faces a scarcity of available solar data and has varying climatic conditions, which makes it ideal for such a study. This study utilizes nine geographically diverse stations to develop a benchmark framework for direct one-step-ahead hourly solar irradiance forecasting. The dataset was subjected to data preprocessing, feature engineering, and multi-model evaluation. A staged approach was adopted for feature selection, starting from a base model comprising three input variables: extraterrestrial radiation, solar zenith angle, and relative humidity. Features were added in an incremental order, which resulted in an optimized four-variable input set through the addition of a lagged clearness index to the base model. The forecasting models evaluated in this study, using these input variables, were ANN, NAR, NARX, LSTM, GRU, SARIMA, and Prophet. Deep learning models outperformed the other considered approaches, with LSTM showing the best overall benchmark performance with an average RMSE of 92.93 W/m², MAE of 66.56 W/m², and R-Squared of 0.872. The performance trends were broadly consistent across the evaluated stations, indicating stable behaviour within the adopted dataset and experimental setup. The study shows that a compact and physically interpretable input feature set, used with recurrent deep learning models, provides an effective solution for hourly solar irradiance forecasting, especially in locations with varying climatic conditions. The proposed benchmark can support smart city applications related to distributed solar generation, energy-aware urban planning, and intelligent operation of renewable-rich power systems. Full article

The past few years has witnessed the rapid development of China’s logistics industry. However, the industry still faces problems such as uneven regional development, low-cost efficiency, insufficient technology application, and pressure for green transformation. To support more effective policy and strategic planning, this study used composite location entropy, spatial autocorrelation analysis, and kernel density estimation to analyze the spatiotemporal evolution of logistics industry agglomeration based on China’s city-level panel data from 2010 to 2023. Geographic detectors and geographically weighted regression were used to explore its driving mechanisms from multiple perspectives. The results indicated that (1) China’s logistics industry agglomeration exhibited a decreasing gradient from east to west and the regional disparities gradually narrowed down over time. (2) China’s logistics industry showed significantly positive spatial autocorrelation, characterized mainly by high-high and low-low clusters. Northeastern China experienced the most active and tortuous local spatial evolution of logistics agglomeration, while Eastern China exhibited high tortuosity but stable spatial structure. Western China showed a smooth evolution, and Central China followed a relatively independent evolutionary path. Spatially, China’s logistics industry presented a pattern of high concentration in the southeast and sparse distribution in the northwest, with high-value zones expanding toward the central and western regions. (3) Transportation accessibility was the primary factor influencing logistics industry agglomeration, and the interaction among factors was stronger than the effect of individual factors. Specifically, the degree of openness exhibited a driving pattern centered on coastal areas and decreasing towards inland regions; the level of commercial development showed a positive correlation in the west and a negative correlation in the east; the spatial pattern of transportation capacity shifted from a pronounced east–west polarization to a more fragmented multi-cluster distribution; and transportation accessibility demonstrated spatial heterogeneity, with positive correlation in the southeast coastal areas and negative correlation in the west. Full article

This paper investigates engineering decision optimization in uncertain environments. Subject to constraints on cost and expected returns, engineering decisions optimize material input, equipment selection and process arrangement to minimize costs and maximize economic benefits. As an efficient formal verification technique, model checking offers a new approach to addressing this problem. Traditional model checking focuses on qualitative verification, while quantitative approaches, including probabilistic and possibilistic model checking, have been gradually developed. Among them, possibilistic model checking is more applicable to systems with fuzzy uncertainty. However, existing possibilistic model-checking techniques have notable limitations: they are only designed for closed systems and ignore interactions between the system and external environments; their simplistic information aggregation leads to information asynchrony and loss; and they cannot model and verify systems with incomplete information. Model checking based on possibilistic decision processes enables the selection of uncertain actions and initially resolves the modeling and verification of open systems. In our previous work, we introduced quality constraints into possibilistic temporal logic to mitigate information asynchrony and loss. We also established the theories of intuitionistic fuzzy Kripke structure (IFKS) and Intuitionistic Fuzzy Computation Tree Logic (IFCTL), which support the modeling and verification of systems with incomplete information. To improve the practicality and accuracy of engineering decisions, this study adopts the ideas of uncertain decision-making behavior selection, quality constraints and incomplete information modeling. It extends IFKS to the Weighted Intuitionistic Fuzzy Kripke Structure (WIFKS) and evolves IFCTL into the intuitionistic fuzzy decision tree logic (IFDTL). We further propose an IFDTL model-checking algorithm and a multi-attribute engineering decision algorithm based on the proposed method, along with corresponding correctness proofs and complexity analysis. A case study on Qinling health-preserving tourism planning verifies the rationality and effectiveness of the presented approach. This research provides a novel formal solution for engineering decision-making under uncertainty. Full article

This paper presents a comparative analysis of three workspace discretization strategies, Cartesian, cylindrical and spherical, integrated into a graph-based path planning application developed in Python and connected to RoboDK. The study starts from the observation that the workspace of an articulated industrial robot is not naturally aligned with a uniform Cartesian partitioning, and this aspect can influence the internal structure of the graph and the planning effort. For the initial analysis, the three discretizations were tested for the same start-goal pair and for resolutions ranging from 1500 mm to 600 mm. All three variants led to the same validated route, with a length of 3292.215 mm, which shows that the main differences did not occur at the level of the final geometric solution, but at the level of the internal structure of the graph. On average, the spherical discretization generated the most compact graph, with 101.7 nodes and 256.4 edges, compared to 277.3 nodes and 724.9 edges for the Cartesian discretization. The average planning time was also shorter for the spherical discretization, 0.0069 s, compared to 0.0150 s for the Cartesian discretization and 0.0127 s for the cylindrical discretization. At the 600 mm resolution, the spherical discretization used approximately 63% fewer nodes and 66% fewer edges than the Cartesian discretization, while retaining a larger number of candidate routes. The evaluation was then extended by 180 additional trials, performed on two scenarios and on several start-goal pairs. Of these, 151 led to valid routes, corresponding to an overall success rate of 83.9%. The results show that the spatial representation influences the graph size, connectivity, planning time and length of validated routes. However, additional tests also show that these effects depend on the scenario and the criterion analyzed. The spherical discretization produced the most compact graphs, but did not lead in all cases to the shortest routes or the highest success rate. Therefore, the contribution of the paper consists in a controlled comparative evaluation of the influence of the spatial representation on a graph-based planning pipeline, not in demonstrating the universal superiority of a single discretization. Full article

The seamless integration of real-time operational technology (OT) with big data architectures remains a critical bottleneck in developing robust robotic Digital Twins. Furthermore, evaluating stochastic motion planners strictly within pristine simulations obscures vital real-world challenges such as sensor noise, communication latency, and mechanical stress. This study presents a high-throughput, real-time Hardware-in-the-Loop (HIL) pipeline integrating ROS 2, Apache Kafka, and Functional Mock-up Units (FMUs). Using a UR10e manipulator in a constrained industrial environment, we conducted extensive physical benchmarking of 11 Open Motion Planning Library (OMPL) algorithms across 10 repetitions, generating a comprehensive dataset of 785,192 samples. The proposed IT/OT architecture achieved deterministic millisecond-level synchronization, bounding end-to-end communication latency between 0.09 and 15.51 ms. Physical executions revealed a macroscopic “topological divergence” between simulation and reality, with spatial deviations peaking at 457.65 mm due to real-world point-cloud noise. While algorithms like EST and KPIECE demonstrated optimal geometric efficiency (e.g., a mean path length of 14.57 m) and hardware-friendly dynamics, traditional planners like RRT generated severe inertial spikes of up to 100 N, demonstrating substantial unsuitability for continuous industrial deployment. The primary contribution is a methodologically novel, rigorously validated big data pipeline and the release of an open-source, 50 Hz multimodal dataset (spatial, temporal, and dynamic forces), bridging the sim-to-real gap and providing a foundational benchmark for future data-driven robotic applications. Full article

Indonesia, located along the equatorial belt, has consistently high solar irradiance, offering strong potential for renewable energy development. However, limited availability of high-resolution solar radiation data constrains accurate system design. This study aims to evaluate eight empirical models for estimating hourly solar radiation under tropical conditions and identify the most suitable approach for data-scarce regions. The novelty lies in a comparative assessment tailored to Indonesia’s tropical climate and its application to sustainable energy planning. Model performance is assessed using MBE, RMSE, and R² against measured data. The results identify the most accurate model, which serves as the basis for developing a modified model that better represents local atmospheric characteristics. The proposed model improves estimation accuracy and supports more reliable solar resource assessment for sustainable energy applications in tropical regions. These findings support improved solar resource assessment and contribute to more reliable and sustainable solar energy system development in tropical regions. Full article

The global change towards renewable energy presents significant challenges, particularly for local communities in developing countries. Although many of these countries have adopted various clean energy initiatives, numerous wind power projects have faced considerable delays and implementation obstacles. Key issues include conflicts with local inhabitants, environmental licensing bottlenecks, and difficulties integrating projects into national electricity grids. These setbacks highlight misaligned stakeholder interests and the lack of robust tools for impact assessment. This paper introduces a multi-criteria decision-making model designed to evaluate the sustainability impacts of wind power megaprojects, using a case study from Colombia. The model integrates stakeholder perspectives and assesses projects across four dimensions: environmental, social, economic, and institutional. Its application demonstrates the model’s effectiveness in capturing complex social dynamics and improving the understanding of stakeholder concerns. It offers a structured framework to support more inclusive and informed decision-making processes. This study proposes a practical tool for enhancing the planning and governance of renewable energy initiatives, the stakeholder-oriented multidimensional assessment framework designed to evaluate social, economic and environmental challenges associated with wind power projects. The findings underscore the importance of incorporating these dimensions into impact assessments to foster stronger alignment with local communities and increase the likelihood of project success in the energy transition. Full article

Failure Mode and Effects Analysis (FMEA) remains a fundamental risk management methodology in the automotive industry. This review provides a structured comparative analysis of the classical AIAG FMEA (4th edition, 2008) and the harmonized AIAG & VDA FMEA (1st edition, 2019) across Design (DFMEA), Process (PFMEA), and System (SFMEA) levels. Unlike conventional descriptive reviews, this study presents an integrative analytical synthesis that systematically evaluates methodological differences, decision-making logic, and structural transformations between the two frameworks. The analysis focuses on key developments, including the transition from Risk Priority Number (RPN) to Action Priority (AP), the introduction of a mandatory seven-step methodology, the formalization of structure–function–failure relationships, and enhanced traceability to downstream quality documentation such as Control Plans. The findings demonstrate that the harmonized framework represents a conceptual shift from a primarily scoring-based approach to a structured systems engineering methodology, improving consistency, completeness, and auditability of risk analysis. Particular emphasis is placed on the implications of AP-based prioritization, which alters traditional decision logic by preventing the suppression of safety-critical risks. The paper contributes to the literature by providing a comprehensive cross-level comparison (DFMEA–PFMEA–SFMEA) within a single analytical framework, identifying both strengths and limitations of the harmonized approach, and outlining its practical implications for industrial implementation. Future research directions include quantitative validation, application-based case studies, and integration with digital and AI-driven FMEA systems. Full article

This paper investigates machine learning (ML)-supplemented workflows integrated within a modular parametric modelling framework derived from a typological analysis of stadiums. The objective of the research is to address a gap between numerous isolated computational studies and the realities of early stadium design within the Royal Institute of British Architects (RIBA) Plan of Work (PoW) Stages 0–3. From a practical perspective, the proposed design framework aims to embed supervised learning, semi-supervised learning, and evolutionary optimisation into stadium design development to support site appraisal, brief preparation, concept development, spatial coordination, and stadium bay or stand optimisation based on quantifiable design characteristics. The framework addresses the inefficiencies and limitations of the traditional stadium design process by allowing rapid design space exploration defined by typological drivers, evaluation of a large set of solutions based on performance metrics such as circulation distances, sightline quality, and layout distribution, and the validation of concepts against benchmarks. Within the applicable design pipelines, and where labels are derived from deterministic performance criteria, the supervised approaches achieved prediction accuracies above 85%, while evolutionary optimisation reduced the number of seats with restricted views by approximately 95%. The value of the study is that it demonstrates that the integration of parametric modelling based on shared typological characteristics and the mapping of ML methods to the RIBA PoW has the potential to support stadium design in a novel way. Full article

To achieve scientific cost forecasting, this study investigates structural changes in mining cost driven by the widespread adoption of mechanized mining, increased mining depths, and significant operational variations. Based on the backpropagation (BP) neural network, this study systematically analyzes the cost-composition characteristics of modern mining operations and applies activity-based costing to achieve refined cost accounting for each mining operation unit. Ten key influencing factors, including working space, stope temperature, stope depth, haulage distance, worker seniority and work efficiency, scraper efficiency, equipment service life, fuel and lubricant consumption rates, are identified by analyzing cost variation patterns. Principal component analysis (PCA) is used to reduce the dimensionality of the ten factors to simplify this model and enhance prediction accuracy. The PCA-BP neural network mining cost forecasting model is built with the principal components extracted as input variables. Actual cost data from an underground metal mine in Shandong Province is used for our model training and validation, with adopting linear regression, eXtreme Gradient Boosting (XGBoost), and a traditional BP neural network as the comparison models for performance evaluation. Our prediction results indicate that the PCA-BP model achieves an average relative error of 3.80% and a root mean square error of 1.43, both significantly outperforming the comparison models. The results demonstrate superior predictive accuracy and stability of our model. Validated with data from a typical deep mechanized gold mine in eastern China, the PCA-BP cost forecasting model requires parameter retraining based on local production conditions for applications in other regions. This study confirms that the model aligns well with the cost characteristics of modern underground metal mines and produces effective predictions, offering reliable quantitative support for the development of cost control strategies and optimization of cost planning in mining enterprises. Full article

Disruptions in urban supply chains increase the risk of reduced access to medicines whose continuous availability is important for public health. This article develops an open-data decision support system (DSS) framework for assessing medicine availability under shortage and node-failure scenarios. The empirical application combines redeemed e-prescription data from the Ukrainian reimbursement program for 2022–2025 with the registry of dispensing points under National Health Service of Ukraine contracts and applies a unified scenario design to Kyiv and Lviv. The results show that demand is more concentrated in Lviv: the top 10 dispensing nodes account for 29.7% of redeemed e-prescriptions, compared with 14.2% in Kyiv. The proposed DSS supports the redistribution of limited available volume across spatial zones; it does not generate additional supply. Its value lies in identifying where lower-tail coverage, service coverage gaps, and redistribution-distance constraints should be monitored under explicitly defined stress-test assumptions. The framework is therefore positioned as a scenario-based planning tool rather than as a real-time inventory-management system. Full article

Path planning for autonomous robots is a key problem area, particularly when faced with complicated, dynamic, or uncertain environments. Even though traditional techniques (grid-based, graph-based, sampling, and optimization-based) have already been developed to solve this problem, there are notable limitations to scalability, adaptability, and responsiveness with these methods. In this paper, we explore an alternative approach based on chaotic dynamical systems, specifically chaotic attractors like those produced by the Lorenz and Rössler systems. Chaotic systems are defined by several properties that could be leveraged: non-linearity, sensitivity to initial conditions, and dense coverage of the state space are three notable properties that could be used to generate trajectories that are organized, yet ultimately unpredictable. By applying numerical integration (Runge–Kutta) directly to robot motion through MATLAB R2025b simulations, chaotic states support more effective exploration, better obstacle avoidance, and more robust navigation in dynamic or adversarial environments. The paper also examines whether chaotic path planning can be applied in multi-robot systems through state coupled robots that emerge coordinated behavior while maintaining autonomous movement. This paper is a framework for chaos theory supporting adaptable, robust navigating behaviors for purposes such as autonomous vehicles, swarm robotics, and search and rescue and surveillance applications. Full article

The increasing interconnectivity and digital transformation of Communication, Navigation, and Surveillance (CNS) systems have expanded their attack surface, rendering traditional perimeter-based security models inadequate for protecting these critical infrastructures. Zero Trust Architecture (ZTA), founded on the principle of “never trust, always verify,” offers a paradigm shift towards continuous, context-aware security. This paper presents a literature review investigating the application of ZTA principles to secure modern CNS ecosystems, following the guidelines of the International Civil Aviation Organization (ICAO) through its Cybersecurity Strategy and Plan. We analyze the alignment of ZTA core tenets—such as least-privilege access, micro-segmentation, and continuous authentication—with the unique operational requirements of CNS systems. This paper also presents a cybersecurity framework, under development within the Future Communications Digital Infrastructure (FCDI) project of the SESAR JU program, which aims to assist CNS stakeholders in collaboratively identifying cybersecurity threats within their scope of responsibility. The review critically examines implementation challenges for specific CNS subsystems: secure aeronautical communications (e.g., LDACS), resilient PNT (Positioning, Navigation, and Timing) services, and integrated surveillance networks (e.g., ADS-B, multilateration). Furthermore, we identify and evaluate domain-specific challenges, including integration with legacy avionics and ground systems, managing stringent latency and reliability constraints, and protecting against sophisticated threats targeting supply chains and data fusion processes. By synthesizing current research and practical deployment insights, this review aims to provide a foundational reference for aerospace engineers, cybersecurity specialists, and policymakers, offering a roadmap to enhance the cyber-resilience of vital CNS infrastructure in an era of evolving digital threats. Full article

Coastal urban environments are increasingly exposed to natural hazards, including storm surges, tsunamis, coastal erosion, and flooding, which threaten lives, livelihoods, and infrastructure. Despite their widespread use, conventional hard and soft engineering measures have often proved insufficient to address the escalating risks posed by climate change and rapid urbanisation. This study explores the potential of Nature-Inspired Solutions (NiS) as a complementary pathway to advance resilience in architecture, urban design, and planning. Unlike Nature-Based Solutions that utilise existing ecosystems directly, NiS draw design principles from both biotic and abiotic natural systems, offering innovative models for resilient settlements, coastal infrastructure, and adaptive urban planning. Using a mixed-methods approach that includes systematic and narrative reviews, semi-structured expert interviews, analysis of urban development plans, a panel discussion, and expert brainstorming, this research examines how natural coastal systems inform design interventions. Sri Lanka was selected as the primary case study context due to its exceptional coastal vulnerability, significant climate adaptation policy gaps, and status as a small island developing state representative of the coastal challenges faced by similar contexts globally. Furthermore, Sri Lanka was selected as the case study in accordance with the original research proposal submitted to the University of Huddersfield, which identified the country as a suitable context due to its significant vulnerability to coastal hazards, as outlined above. Field investigations in the Lunawa coastal area documented community-based adaptive practices emerging from multi-generational environmental observation. Analysis reveals how dune morphologies, root structures, living shorelines, and rock pool formations translate into architectural and engineering applications. Findings identify critical implementation challenges, including context-specific requirements, technical knowledge gaps, insufficient policy frameworks, limited practitioner awareness, and uncertainties about economic feasibility, as well as key enablers such as demonstrated ecological effectiveness and the potential of multifunctional infrastructure. The study demonstrates that embedding NiS into risk-informed planning and resilient urban design contributes to climate change adaptation, ecological sustainability, and inclusive governance, while highlighting persistent barriers that require strategic intervention. By bridging ecological wisdom and architectural innovation, NiS offers transformative opportunities to reimagine resilient coastal cities and communities facing escalating climate-induced hazards. Full article

The development of accessible and experimentally validated robotic systems for engineering education is a challenge, especially in academic environments where industrial manipulators are economically inaccessible. This paper presents the design, mechanical validation, and experimental evaluation of a robotic arm-based didactic module developed for the classification of objects according to color and morphology. The proposed system integrates a five-degree-of-freedom articulated configuration, a servomotor drive, motion planning with a trapezoidal speed profile, and a web-based control interface, enabling local and remote operation within an educational environment aligned with Industry 4.0 principles. The mechanical structure was designed using CAD modeling and validated through static structural analysis to ensure mechanical integrity and adequate safety factors. The selection of actuators was made considering the torque, angular velocity, and load requirements. A trapezoidal speed profile was implemented in order to ensure smooth trajectories and minimize positioning errors. Experimental validation was carried out through repetitive tests under controlled laboratory conditions, evaluating the accuracy and repeatability metrics. Statistical indicators such as mean error, standard deviation, and root mean square error (RMSE) were calculated. The results show the stable performance of the system, with low variability in multiple test cycles, confirming the viability of the proposed architecture for its implementation in automation and educational robotics laboratories. The integration of structural validation, motion control strategy, and experimental quantitative evaluation contributes to bridging the gap between theoretical teaching of robotics and its practical application, offering a scalable, low-cost platform for engineering training. Full article