Search Results (673)

In today’s dynamic and highly uncertain environment, organizations, particularly in the aviation sector, face increasing challenges that demand resilient, flexible, and data-driven change management decisions. Responding to the growing need for structured approaches to managing complex uncertainties—geopolitical tensions, economic volatility, social shifts, rapid technological advancements, environmental pressures and regulatory changes—this research proposes a theoretical change management model for aviation service providers, such as airports. Integrating three analytical approaches, the model offers a robust, multi-method approach for supporting sustainable transformation under uncertainty. Normative analysis using Bayesian decision theory identifies influential external environmental factors, capturing probabilistic relationships, and revealing causal links under uncertainty. Prescriptive planning through scenario theory explores alternative future pathways and helps to identify possible predictions, offer descriptive evaluation employing fuzzy comprehensive evaluation, and assess decision quality under vagueness and complexity. The proposed four-stage model—observation, analysis, evaluation, and response—offers a methodology for continuous external environment monitoring, scenario development, and data-driven, proactive change management decision-making, including the impact assessment of change and development. The proposed model contributes to the theoretical advancement of the change management research area under uncertainty and offers practical guidance for aviation organizations (airports) facing a volatile external environment. This framework strengthens aviation organizations’ ability to anticipate, evaluate, and adapt to multifaceted external changes, supporting operational flexibility and adaptability and contributing to the sustainable development of aviation services. Supporting aviation organizations with tools to proactively manage systemic uncertainty, this research directly supports the integration of sustainability principles, such as resilience and adaptability, for long-term value creation through change management decision-making. Full article

This paper presents a data-driven energy management scheme for fuel cell and battery electric vehicles, formulated as a constrained optimal control problem. The proposed method employs a co-state network trained using real-time measurements to estimate the control law without requiring prior knowledge of the system model or a complete dataset across the full operating domain. In contrast to conventional reinforcement learning approaches, this method avoids the issue of high dimensionality and does not depend on extensive offline training. Robustness is demonstrated by treating uncertain and time-varying elements, including power consumption from air conditioning systems, variations in road slope, and passenger-related demands, as unknown disturbances. The desired state of charge is defined as a reference trajectory, and the control input is computed while ensuring compliance with all operational constraints. Validation results based on a combined driving profile confirm the effectiveness of the proposed controller in maintaining the battery charge, reducing fluctuations in fuel cell power output, and ensuring reliable performance under practical conditions. Comparative evaluations are conducted against two benchmark controllers: one designed to maintain a constant state of charge and another based on a soft actor–critic learning algorithm. Full article

The increasing penetration of renewable energy sources (RESs), particularly solar photovoltaic (PV) sources, has introduced significant uncertainty into power system operations, challenging traditional scheduling models and threatening system reliability. This study proposes a Fuzzy Unit Commitment Model (FUCM) designed to address uncertainty in load demand, solar PV generation, and spinning reserve requirements by applying fuzzy linear programming techniques. The FUCM reformulates uncertain constraints using triangular membership functions and integrates them into a mixed-integer linear programming (MILP) framework. The model’s effectiveness is demonstrated through two case studies: a 30-generator test system and a national-scale power system in Thailand comprising 171 generators across five service zones. Simulation results indicate that the FUCM consistently produces stable scheduling solutions that fall within deterministic upper and lower bounds. The model improves reliability metrics, including reduced loss-of-load probability and minimized load deficiency, while maintaining acceptable computational performance. These results suggest that the proposed approach offers a practical and scalable method for unit commitment planning under uncertainty. By enhancing both operational stability and economic efficiency, the FUCM contributes to the sustainable management of RES-integrated power systems. Full article

Smoke appears earlier than flames, so image-based fire monitoring techniques mainly focus on the detection of smoke, which is regarded as one of the effective strategies for preventing the spread of initial fires that eventually evolve into serious fires. Smoke monitoring in mine fires faces serious challenges: the underground environment is complex, with smoke and backgrounds being highly integrated and visual features being blurred, which makes it difficult for existing image-based monitoring techniques to meet the actual needs in terms of accuracy and robustness. The conventional ground-based methods are directly used in the underground with a high rate of missed detection and false detection. Aiming at the core problems of mixed target and background information and high boundary uncertainty in smoke images, this paper, inspired by the principle of information entropy, proposes a method for recognizing smoke from mine fires by integrating entropy-enhanced image processing and improved YOLOv8. Firstly, according to the entropy change characteristics of spatio-temporal information brought by smoke diffusion movement, based on spatio-temporal entropy separation, an equidistant frame image differential fusion method is proposed, which effectively suppresses the low entropy background noise, enhances the detail clarity of the high entropy smoke region, and significantly improves the image signal-to-noise ratio. Further, in order to cope with the variable scale and complex texture (high information entropy) of the smoke target, an improvement mechanism based on entropy-constrained feature focusing is introduced on the basis of the YOLOv8m model, so as to more effectively capture and distinguish the rich detailed features and uncertain information of the smoke region, realizing the balanced and accurate detection of large and small smoke targets. The experiments show that the comprehensive performance of the proposed method is significantly better than the baseline model and similar algorithms, and it can meet the demand of real-time detection. Compared with YOLOv9m, YOLOv10n, and YOLOv11n, although there is a decrease in inference speed, the accuracy, recall, average detection accuracy mAP (50), and mAP (50–95) performance metrics are all substantially improved. The precision and robustness of smoke recognition in complex mine scenarios are effectively improved. Full article

The growing demand for agile and reliable Unmanned Aerial Vehicles (UAVs) has spurred the advancement of advanced control strategies capable of ensuring stability and precision under nonlinear and uncertain flight conditions. This work addresses the challenge of accurately tracking UAV position by proposing a neural-network-based approach designed to replicate the behavior of classical control systems. A complete nonlinear model of the quadcopter was derived and linearized around a hovering point to design a traditional proportional derivative (PD) controller, which served as a baseline for training a nonlinear autoregressive exogenous (NARX) artificial neural network. The NARX model, selected for its feedback structure and ability to capture temporal dynamics, was trained to emulate the control signals of the PD controller under varied reference trajectories, including step, sinusoidal, and triangular inputs. The trained networks demonstrated performance comparable to the PD controller, particularly in the vertical axis, where the NARX model achieved a minimal Mean Squared Error (MSE) of $7.78\times {10}^{-5}$ and an $R^2$ value of 0.9852. These results confirm that the NARX neural network, trained via supervised learning to emulate a PD controller, can replicate and even improve classical control strategies in nonlinear scenarios, thereby enhancing robustness against dynamic changes and modeling uncertainties. This research contributes a scalable approach for integrating neural models into UAV control systems, offering a promising path toward adaptive and autonomous flight control architectures that maintain stability and accuracy in complex environments. Full article

With the frequent occurrence of global floods, the demand for emergency rescue equipment has grown rapidly. The development and technological innovation of digital hydraulic drive systems (DHDSs) for emergency drainage pumps (EDPs) have become key to improving rescue efficiency. However, EDPs are prone to being affected by random and uncertain loads during operation. To achieve intelligent and efficient rescue operations, a DHDS suitable for EDPs was proposed. Firstly, the configuration and operation mode of the DHDS for EDPs were analyzed. Based on this, a multi-field coupling dynamic simulation platform for the DHDS was constructed. Secondly, the output characteristics of the system under alternating loads were simulated and analyzed. Finally, a test platform for the EDP DHDS was established, and the dynamic characteristics of the system under alternating loads were explored. The results show that as the load torque of the alternating loads increases, the amplitude of the pressure of the motor also increases, the output flow of the hydraulic-controlled proportional reversing valve (HCPRV) changes slightly, and the fluctuation range of the rotational speed of the motor increases. The fluctuation range of the pressure and the rotational speed of the motor are basically not affected by the frequency of alternating loads, but the fluctuation amplitude of the output flow of the HCPRV reduces with the increase in the frequency of alternating loads. This system can respond to changes in load relatively quickly under alternating loads and can return to a stable state in a short time. It has laudable anti-interference ability and output stability. Full article

Standalone photovoltaic (PV) systems offer a viable path to decentralized energy access but face limitations during periods of low solar irradiance. While batteries provide short-term storage, their capacity constraints often restrict the use of surplus energy, highlighting the need for long-duration solutions. Green hydrogen, generated via proton exchange membrane (PEM) electrolyzers, offers a scalable alternative. This study proposes a stochastic energy management framework that leverages a Markov decision process (MDP) to coordinate PV generation, battery storage, and hydrogen production under variable irradiance and uncertain load demand. The strategy dynamically allocates power flows, ensuring system stability and efficient energy utilization. Real-time weather data from Goiás, Brazil, is used to simulate system behavior under realistic conditions. Compared to the conventional perturb and observe (P&O) technique, the proposed method significantly improves system performance, achieving a $99.9\%$ average efficiency (vs. 98.64%) and a drastically lower average tracking error of 0.3125 (vs. 9.8836). This enhanced tracking accuracy ensures faster convergence to the maximum power point, even during abrupt load changes, thereby increasing the effective use of solar energy. As a direct consequence, green hydrogen production is maximized while energy curtailment is minimized. The results confirm the robustness of the MDP-based control, demonstrating improved responsiveness, reduced downtime, and enhanced hydrogen yield, thus supporting sustainable energy conversion in off-grid environments. Full article

Per- and polyfluoroalkyl substances (PFAS) have been reported to contaminate soil as a result of improper management of waste, wastewater, landfill leachate, biosolids, and a large and indiscriminate use of aqueous film-forming foams (AFFF), posing potential risks to human health. However, their high chemical and thermal stability pose a great challenge for remediation. As a result, there is an increasing interest in identifying and optimizing very effective and sustainable technologies for PFAS removal. This review summarizes both traditional and innovative remediation strategies and technologies for PFAS-contaminated soils. Unlike existing literature, which primarily focuses on the effectiveness of PFAS remediation, this review critically discusses several techniques (based on PFAS immobilization, mobilization and extraction, and destruction) with a deep focus on their sustainability and scalability. PFAS destruction technologies demonstrate the highest removal efficiencies; however, thermal treatments face sustainability challenges due to high energy demands and potential formation of harmful by-products, while mechanical treatments have rarely been explored at full scale. PFAS immobilization techniques are less costly than destruction methods, but issues related to the regeneration/disposal of spent sorbents should be still addressed and more long-term studies conducted. PFAS mobilization techniques such as soil washing/flushing are hindered by the generation of PFAS-laden wastewater requiring further treatments, while phytoremediation is limited to small- or medium-scale experiments. Finally, bioremediation would be the cheapest and least impactful alternative, though its efficacy remains uncertain and demonstrated under simplified lab-scale conditions. Future research should prioritize pilot- and full-scale studies under realistic conditions, alongside comprehensive assessments of environmental impacts and economic feasibility. Full article

Manufacturing serves as a vital indicator of a nation’s economic strength, technological advancement, and comprehensive competitiveness. In the context of the VUCA (Volatility, Uncertainty, Complexity, Ambiguity) business environment and globalization, uncertain market demand has intensified supply chain disruption risks, necessitating resilience strategies to enhance supply chain stability. This study proposes five resilience strategies—establishing an information sharing system, multi-sourcing, alternative suppliers, safety stock, and alternative transportation plans—while integrating sustainability requirements. A multi-objective mixed-integer optimization model was developed to balance cost efficiency, resilience, and environmental sustainability. Comparative analysis reveals that the resilience-embedded model outperforms traditional approaches in both cost control and risk mitigation capabilities. The impact of parameter variations on the model results was examined through sensitivity analysis. The findings demonstrate that the proposed optimization model effectively enhances supply chain resilience—mitigating cost fluctuations while maintaining robust demand fulfillment under uncertainties. Full article

This study presents a reinforcement learning–based approach to optimize replenishment policies in the presence of uncertainty, with the objective of minimizing total costs, including inventory holding, shortage, and ordering costs. The focus is on single-level assembly systems, where both component delivery lead times and finished product demand are subject to randomness. The problem is formulated as a Markov decision process (MDP), in which an agent determines optimal order quantities for each component by accounting for stochastic lead times and demand variability. The Deep Q-Network (DQN) algorithm is adapted and employed to learn optimal replenishment policies over a fixed planning horizon. To enhance learning performance, we develop a tailored simulation environment that captures multi-component interactions, random lead times, and variable demand, along with a modular and realistic cost structure. The environment enables dynamic state transitions, lead time sampling, and flexible order reception modeling, providing a high-fidelity training ground for the agent. To further improve convergence and policy quality, we incorporate local search mechanisms and multiple action space discretizations per component. Simulation results show that the proposed method converges to stable ordering policies after approximately 100 episodes. The agent achieves an average service level of 96.93%, and stockout events are reduced by over 100% relative to early training phases. The system maintains component inventories within operationally feasible ranges, and cost components—holding, shortage, and ordering—are consistently minimized across 500 training episodes. These findings highlight the potential of deep reinforcement learning as a data-driven and adaptive approach to inventory management in complex and uncertain supply chains. Full article

Unmanned Aerial Vehicles (UAVs) have become increasingly prevalent in both governmental and civilian applications, offering significant reductions in operational costs by minimizing human involvement. There is a growing demand for autonomous, scalable, and intelligent coordination strategies in complex aerial missions involving multiple Unmanned Aerial Vehicles (UAVs). Traditional control techniques often fall short in dynamic, uncertain, or large-scale environments where decentralized decision-making and inter-agent cooperation are crucial. A potentially effective technique used for UAV fleet operation is Multi-Agent Reinforcement Learning (MARL). MARL offers a powerful framework for addressing these challenges by enabling UAVs to learn optimal behaviors through interaction with the environment and each other. Despite significant progress, the field remains fragmented, with a wide variety of algorithms, architectures, and evaluation metrics spread across domains. This survey aims to systematically review and categorize state-of-the-art MARL approaches applied to UAV control, identify prevailing trends and research gaps, and provide a structured foundation for future advancements in cooperative aerial robotics. The advantages and limitations of these techniques are discussed along with suggestions for further research to improve the effectiveness of MARL application to UAV fleet management. Full article

Background: Infective endocarditis after transcatheter aortic valve replacement (TAVR-IE) is a rare but severe complication associated with high morbidity and mortality. The optimal treatment strategy—surgical explantation versus medical therapy—remains uncertain, particularly given the technical demands of TAVR removal and the advanced age of many affected patients. Methods: We conducted a systematic review and meta-analysis of studies comparing the surgical and medical management of TAVR-IE. Primary outcomes included 30-day mortality and 1-year survival. Secondary analyses explored microbiological profiles, patient demographics, prosthesis type, postoperative complications, and surgical indications. A qualitative synthesis of surgical explantation techniques and reconstructive strategies was also performed based on recent consensus recommendations. Results: Three studies comprising 1557 patients with TAVR-IE were included; 155 (10.0%) underwent surgical treatment. Thirty-day mortality was comparable between groups (surgical: 9.7%; medical: 8.4%), while the pooled odds ratio for one-year survival did not reach statistical significance (OR: 1.91, 95% CI: 0.36–10.22; I² = 88%). However, single-center outcomes demonstrated markedly improved survival with surgery (96% vs. 51%). The most common surgical indications included severe valvular dysfunction (50.3%), aortic root abscess (26.5%), and large vegetations (21.3%), in line with current guideline recommendations. Postoperative complications included acute renal failure (10%) and longer hospitalizations (19.8 vs. 18 days), although these were not statistically different. Contemporary explant strategies—such as the Double Kocher, Tourniquet, and Y-incision aortic enlargement techniques—were highlighted as critical tools for surgical success. Conclusions: While underutilized, surgical intervention for TAVR-IE may offer significant survival benefits in select patients, particularly when guided by established indications and performed at high-volume centers. Outcomes depend heavily on timing, surgical expertise, and appropriate patient selection. As TAVR expands to younger populations, TAVR-IE will become increasingly relevant, necessitating early multidisciplinary involvement and broader familiarity with advanced explant techniques among cardiac surgeons. Full article

This study introduces a stochastic optimization framework designed to effectively manage power flows in flexible medium-voltage DC (MVDC) link systems within distribution networks (DNs). The proposed approach operates in coordination with a battery energy storage system (BESS) to enhance the overall efficiency and reliability of the power distribution. Given the inherent uncertain characteristics associated with forecasting errors in photovoltaic (PV) generation and load demand, the study employs a distributionally robust chance-constrained optimization technique to mitigate the potential operational risks. To achieve a cooperative and optimized control strategy for MVDC link systems and BESS, the proposed method incorporates a stochastic relaxation of the reliability constraints on bus voltages. By strategically adjusting the conservativeness of these constraints, the proposed framework seeks to maximize the cost-effectiveness of DN operations. The numerical simulations demonstrate that relaxing the strict reliability constraints enables the distribution system operator to optimize the electricity imports more economically, thereby improving the overall financial performance while maintaining system reliability. Through case studies, we showed that the proposed method improves the operational cost by up to 44.7% while maintaining 96.83% bus voltage reliability under PV and load power output uncertainty. Full article

Uncertainty in consumer purchasing behavior within online markets propels manufacturers to adopt blockchain for risk mitigation, reshaping supply chain operational dynamics. This study investigates the sales mode selection and blockchain adoption strategies of a risk-averse manufacturer in platform supply chain under uncertain market demand. By integrating Stackelberg game theory with mean-variance analysis, we analyze supply chain equilibrium across four scenarios: RN, RB, AN, and AB. Our findings highlight the significance of a critical commission rate threshold in the manufacturer’s sales mode choice, emphasizing that blockchain adoption enhances the preference for the agency mode. Importantly, highly risk-averse manufacturers are inclined to absorb higher costs associated with blockchain adoption, while those with lower risk aversion only consider it when costs are minimal. Notably, the “agency mode with blockchain adoption” (AB) creates mutual benefits under low adoption costs and risk aversion. When both parties exhibit risk aversion, the platform’s risk aversion significantly influences resale-mode decisions, leading to a transition from the scenario AN to the RB, thereby optimizing synchronized profits. Full article

Sensorineural hearing loss occurs when cochlear hair cells fail to convert mechanical sound waves into electrical signals transmitted via the auditory nerve. Cochlear implants (CIs) restore hearing by directly stimulating the auditory nerve with electrical impulses, often while preserving residual hearing. Over the past two decades, robotic-assisted techniques in otologic surgery have gained prominence for improving precision and safety. Robotic systems support critical procedures such as mastoidectomy, cochleostomy drilling, and electrode array (EA) insertion. These technologies aim to minimize trauma and enhance hearing preservation. Despite the outpatient nature of most CI surgeries, surgeons still face challenges, including anatomical complexity, imaging demands, and rising costs. Robotic systems help address these issues by streamlining workflows, reducing variability, and improving electrode placement accuracy. This review evaluates robotic systems developed for cochlear implantation, focusing on their design, surgical integration, and clinical outcomes. This review concludes that robotic systems offer low insertion speed, which leads to reduced insertion forces and lower intracochlear pressure. However, their impact on trauma, long-term hearing preservation, and speech outcome remains uncertain. Further research is needed to assess clinical durability, cost-effectiveness, and patient-reported outcomes. Full article