This paper presents a genetic algorithm (GA) tuned Mamdani type fuzzy logic control (FLC) framework for trajectory tracking of a quadrotor unmanned aerial vehicle (UAV) using a nonlinear rigid body model. The proposed architecture adopts a cascaded structure in which an outer loop position controller generates attitude and thrust references $(\phi_{\mathrm{ref}},\theta_{\mathrm{ref}},T_{\mathrm{ref}})$, while an inner loop attitude controller generates body torques $(\tau_\phi,\tau_\theta,\tau_\psi)$. Both loops employ a shared Mamdani fuzzy inference system with normalized inputs (tracking error and error-rate) and a normalized control output. The GA automatically tunes scaling gains $(K_e,K_d,K_u)$ across all axes to minimize a robust objective that averages tracking error, control effort, and constraint violations over multiple scenarios with mass uncertainty and wind disturbances. Simulation results on a three dimensional figure eight trajectory indicate that GA tuning can reduce position and attitude errors while respecting actuator saturation and tilt safety limits, demonstrating a practical route to performance enhancement without requiring a high fidelity aerodynamic model. The methodology leverages the interpretability of fuzzy rules and the global search capabilities of evolutionary optimization within a UAV modeling framework consistent with established quadrotor dynamics literature.
Cost-schedule control in construction projects is inherently a continuous decision-making process conducted under conditions of uncertainty, rather than a purely technical or accounting activity. Conventional approaches, which rely on retrospective performance measurement and fragmented indicators, provide limited support for timely managerial intervention and often lead to delayed or suboptimal decisions. This study develops a decision-centric framework that integrates earned value analytics with organizational decision processes to enable proactive and structured cost–schedule control in small and medium-sized construction projects. The proposed framework conceptualizes cost control as a four-stage decision process—situational awareness, diagnostic analysis, predictive assessment, and intervention execution—and establishes explicit linkages between analytical signals and managerial actions. Within this structure, earned value metrics are reinterpreted as decision triggers rather than passive evaluation tools, while organizational roles are reconfigured to support timely interpretation and coordinated response. The framework is examined through an in-depth case study of a gas station construction project exposed to significant environmental and operational uncertainty. The findings indicate that cost overruns are primarily associated with delayed decision responses, fragmented information flows, and misaligned responsibility structures. By embedding real-time performance evaluation within a coherent decision architecture, the proposed approach enables earlier identification of deviations and more targeted managerial interventions. The study contributes to the literature on intelligent management decision-making by demonstrating how analytical tools can be operationalized within organizational contexts to enhance decision quality under uncertainty. It further provides a transferable framework for structuring data-informed decision processes in resource-constrained project environments.
Rolling bearings are critical components of marine shafting power transmission systems, and accurate prediction of their vibration signal trends is essential for predictive maintenance. To address the limited adaptability of conventional time-series forecasting models under varying operating conditions and their insufficient ability to capture strong noise and abrupt changes, this study proposes a vibration signal prediction method that integrates particle swarm optimization (PSO) with an improved Informer model. PSO is used to adaptively optimize key Informer hyperparameters for different operating conditions, while a rolling time-window mechanism is introduced to enhance the capture of abrupt signal variations. In addition, a mixture of sparse attention (MoSA) encoder with a collaborative dense-head/sparse-head structure is designed to balance global temporal dependency modeling and local fault feature extraction. Experimental results on the Case Western Reserve University (CWRU) bearing fault dataset show that the proposed model outperforms Long Short-Term Memory (LSTM), Transformer, Informer, iTransformer, and Flowformer in terms of Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Erro (RMSE). The model achieves an MSE of 0.2015, which is 25.5% lower than that of the second-best iTransformer model. It also demonstrates robust performance under four different bearing operating states, confirming its adaptability to complex operating conditions. The proposed method provides a promising technical route for the predictive maintenance of rolling bearings in marine shafting systems.
Decentralization is often justified on the grounds that local governments are closer to citizens and therefore better able to respond to local needs. Yet, much of the existing literature has approached decentralization mainly in terms of administrative performance and service delivery, leaving its implications for community development less clearly understood. This study revisits the issue by bringing together empirical findings from a wide range of contexts. Rather than asking whether decentralization performs better than centralization in general terms, attention is directed to the conditions under which it makes a difference at the community level. The evidence points to a pattern that is far from uniform. Where local authorities operate with sufficient resources, administrative competence, and room for decision-making, decentralization tends to support more responsive and locally grounded forms of service provision. In contrast, where these conditions are weak, especially in smaller or under-resourced jurisdictions, similar arrangements often produce uneven access, limited participation, and fragile outcomes. Taken together, the findings suggest that decentralization cannot be treated as a universally beneficial reform. Its contribution depends on how responsibilities are matched with local capacity, how different scales of governance are organized, and whether institutional arrangements allow communities to exercise meaningful influence over local affairs.
Retailers frequently face stockouts and overstocking due to inaccurate demand forecasting, leading to financial losses and reduced customer satisfaction. This study proposes a data-driven framework to improve weekly sales forecasting at both aggregate and store levels using Walmart’s historical sales data. A hybrid methodology integrating time series models, regression techniques, deep learning, and a hierarchical structure is developed to capture temporal patterns and external demand factors. The proposed approach achieves high predictive accuracy, with a Mean Absolute Error (MAE) of 306,361.11, Root Mean Square Error (RMSE) of 528,096.34, and an R² of 0.99, outperforming traditional models. Beyond accuracy, the study emphasizes the role of forecasting as a decision-support tool. The results demonstrate that improved forecasts enable better operational decisions such as replenishment planning and safety stock optimization, while also supporting tactical and strategic decisions related to distribution, workforce planning, and supply chain design. Overall, the findings highlight that integrating hybrid forecasting models with decision-making processes can reduce inventory costs, enhance service levels, and support more efficient and sustainable retail operations.
Electronics and mechatronics have transformed railway vehicles into complex cyber–physical systems integrating mechanical assemblies, electrical architectures, and embedded software across multi-decade lifecycles. Maintaining configuration integrity across these domains is particularly challenging under phased retrofits, supplier substitutions, and stringent safety requirements. This paper presents a structured narrative review of Product Lifecycle Management (PLM)-enabled approaches for governing electronics and mechatronics lifecycle data in railway manufacturing. Guided by explicit research questions, the review synthesizes literature from 2010–2025 using transparent search and inclusion criteria aligned with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting principles. Drawing on systems engineering, lifecycle management, and configuration management theory, the analysis examines multi-domain BOM governance, hardware–software traceability, variant and effectivity control, digital thread continuity, and supplier collaboration. Cross-industry implementation archetypes are evaluated to clarify contextual boundary conditions and transferability limits. The findings indicate that configuration-centric governance—rather than tool integration alone—is the primary determinant of PLM effectiveness in long-lifecycle, safety-regulated environments. A structured future research agenda and explicit engineering management implications are proposed to strengthen digital continuity, fleet-level effectivity discipline, and safety-aligned lifecycle governance.
Efficient regulation of the direct-current (DC) voltage plays a vital role in increasing the stability and reliability of the renewable energy systems when they are operating under variable wind speed and solar irradiation. The single-ended primary-inductor converter (SEPIC) can operate efficiently in buck-boost mode without inverting the output voltage if a robust control technique is used to mitigate variation in the input voltage. This paper proposed a modified discrete-time sliding mode controller (MDTSMC) that can externally generate a reference switching variable trajectory generator to ensure fast and accurate voltage regulation for the SEPIC converter while minimizing the effects of disturbances and reducing the quasi-sliding-mode bandwidth. Initially, the effectiveness of the proposed approach is evaluated in a MATLAB/Simulink environment across four distinct test scenarios, demonstrating its capability to maintain voltage regulation under the influence of disturbances, unmodelled dynamics, and system parameter variations. To further validate the practical feasibility of the proposed strategy, hardware-in-the-loop (HIL) simulations are conducted by using the OPAL-RT platform under multiple operating cases. The HIL results confirm that the MDTSMC provides excellent dynamic response and resilience against load and input fluctuations, highlighting its suitability for real-time digital control implementation in SEPIC converters.
This review of the current literature highlights the barriers present within cavities and their contribution to heat dissipation and cooling of various activities. This study shows a set of factors that affect the function of the obstacle (shape, length, size, thickness, and location of the obstacles). The variation in boundary conditions between obstacles and cavity walls has opened up broad horizons for scientific research in the field of heat transfer (HT) and fluid flow. Despite significant progress, research gaps remain. Most previous studies have focused on simple shaped obstacles within cavities with uniform boundaries. There is a distinct lack of studies exploring the effect of complex such as U/L/H shapes or orientable obstacles within complex cavities or under dynamic conditions such as non-uniform heating or varying magnetic fields. It was found that the Nusselt number increased by 15.56% depending on the shape of the internal obstacle, which gives an advantage to some obstacle shapes over others and highlights the importance of choosing the obstacle and cavity shape so that the best HT is obtained. This study is the first to compare simple and complex shapes of obstacles, and this is the innovative point of this review.
This study presents the development and performance evaluation of a photoelectrochemical (PEC) cell designed for sustainable hydrogen production, emphasizing a cost-effective and reproducible approach to clean energy generation. The PEC system was fabricated using an n-type TiO$_2$ photoanode and Pt cathode in an aqueous Na$_2$SO$_4$ electrolyte (0.5 M), operating under simulated solar irradiation of 100 mW/cm$^2$ (AM 1.5 G) within a controlled temperature range of 25–45 ℃. Experimental testing demonstrated that the system sustained hydrogen evolution through an automated electrolyte refilling and pump control mechanism, achieving 51% H$_2$ saturation within an average of 2.8 seconds over 172 activation cycles, indicating responsive system logic. However, prolonged operation led to efficiency decline, with pump activation time extending to 833 seconds and only 56% hydrogen recovery, signifying material and control degradation. The temperature monitoring subsystem malfunctioned, registering persistent –127 ℃ readings, which impeded accurate thermal regulation and safety evaluation. Sensor drift and inconsistent pump actuation were also observed, reflecting calibration deficiencies. Three operational phases were identified—initial instability (0–300 s), stabilization (300–600 s), and performance degradation ($\geq$800 s). Overall, while the PEC system demonstrates promising short-term hydrogen generation efficiency under defined light and electrolyte conditions, long-term stability remains constrained by electrode durability, thermal control accuracy, and system integration challenges, requiring further optimization for sustained hydrogen production.
Assessing sustainable development performance is essential for understanding national progress. This study evaluates the sustainable development performance of G20 member countries (excluding the EU) using an integrated set of economic, social, and environmental indicators. Economic performance is captured by GDP per capita and the unemployment rate, while social performance is assessed through the education index, health expenditure, and income inequality (Gini coefficient). Environmental performance is represented by CO$_2$ emissions and the share of renewable energy in total energy consumption. Objective, data-driven weights were derived using the Criteria Importance Through Intercriteria Correlation (CRITIC) method, and countries were subsequently ranked using two multi-criteria decision-making (MCDM) approaches: Additive Ratio Assessment (ARAS) and Grey Relational Analysis (GRA). Correlation analysis (Spearman, Kendall, and Pearson) was conducted to examine the consistency and reliability of the rankings. The findings provide a comparative assessment of sustainability performance across G20 countries, highlighting relative strengths and weaknesses across economic, social, and environmental dimensions. The results offer a structured reference for decision-makers in engineering management and policy design in formulating evidence-based strategies.
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