Search Results (2,346)

This article proposes a multi-agent path planning and decision-making solution for high-tempo field robotic operations, such as search-and-rescue, in large-scale unstructured environments. As a representative example, the subterranean environments can span many kilometers and are loaded with challenges such as limited to no communication, hazardous terrain, blocked passages due to collapses, and vertical structures. The time-sensitive nature of these operations inherently requires solutions that are reliably deployable in practice. Moreover, a human-supervised multi-robot team is required to ensure that mobility and cognitive capabilities of various agents are leveraged for efficiency of the mission. Therefore, this article attempts to propose a solution that is suited for both air and ground vehicles and is adapted well for information sharing between different agents. This article first details a sampling-based autonomous exploration solution that brings significant improvements with respect to the current state of the art. These improvements include relying on an occupancy grid-based sample-and-project solution to terrain assessment and formulating the solution-search problem as a constraint-satisfaction problem to further enhance the computational efficiency of the planner. In addition, the demonstration of the exploration planner by team MARBLE at the DARPA Subterranean Challenge finals is presented. The inevitable interaction of heterogeneous autonomous robots with human operators demands the use of common semantics for reasoning across the robot and human teams making use of different geometric map capabilities suited for their mobility and computational resources. To this end, the path planner is further extended to include semantic mapping and decision-making into the framework. Firstly, the proposed solution generates a semantic map of the exploration environment by labeling position history of a robot in the form of probability distributions of observations. The semantic reasoning solution uses higher-level cues from a semantic map in order to bias exploration behaviors toward a semantic of interest. This objective is achieved by using a particle filter to localize a robot on a given semantic map followed by a Partially Observable Markov Decision Process (POMDP)-based controller to guide the exploration direction of the sampling-based exploration planner. Hence, this article aims to bridge an understanding gap between human and a heterogeneous robotic team not just through a common-sense semantic map transfer among the agents but by also enabling a robot to make use of such information to guide its lower-level reasoning in case such abstract information is transferred to it. Full article

The existence of fixed-sum output constraints in real-world situations is widespread, such as market share and carbon dioxide emissions, etc. However, existing fixed-sum output data envelopment analysis (DEA) methods mostly focus on individual decision-making units (DMUs) and ignore the interactions between groups. Therefore, this study first establishes a systematic framework to quantify group performance by the average criterion, and constructs the equilibrium efficient frontier (EEF) to evaluate all groups on a common platform. To address the non-uniqueness issue of EEF, we further introduce the aggressive cross-efficiency mechanism, ultimately proposing a novel group cross-EEF methodology that explicitly accounts for competitive intergroup dynamics. The proposed method is applied in the assessment of carbon emission efficiency in the industrial sector for 30 provinces in China, and the validity of the method is verified. The result shows that (1) even though the average industrial carbon efficiency stands at 1.2015, half of the provinces exhibit values below 1; (2) significant regional heterogeneity is observed, with North China and East China exhibiting higher efficiency levels, while the Northeast and Northwest regions lag behind; (3) provinces such as Beijing, Guangdong, and Zhejiang demonstrate superior performance, in contrast to Ningxia, Hebei, and Qinghai, which remain at relatively low efficiency levels. This study provides theoretical and policy insights to support the advancement of low-carbon development in China’s industrial sector. Full article

Recent research has increasingly emphasized the synergies between Supply Chain Management Practices (SCMPs) and Quality Management Principles (QMPs), particularly through the emerging concept of Supply Chain Quality Management (SCQM). Despite this recognition, empirical evidence on how these practices interact to influence performance remains very limited, especially in the context of developing countries. This study addresses the gap by interviewing 70 Tunisian agri-food companies to investigate the relationships between five dimensions of SCMP, strategic supplier partnerships, customer relationship, information sharing, information quality and postponement, and the seven principles of ISO9001 QMP: leadership, engagement of people, improvement, customer focus, process approach, evidence-based decision making, and relationship management. Using factor analysis and structural equation modelling, the study explores the mediating role of competitive advantage (CA): price/cost, product quality, product innovation, delivery dependability and time-to-market—on operational performance. The findings indicate that analyzing SCMP, QMP, and CA as aggregated blocks does not produce significant explanatory correlations. Instead, judiciously reorganizing their sub-constructs into five integrated groups provides a more effective model: (1) information and decision capacity, (2) customer-centric innovation, (3) process management and agility, (4) supplier and network management, and (5) leadership and workforce engagement. This integrated classification offers managers a coherent framework for implementing SCMP and QMP to enhance competitiveness results. Full article

Understanding the systemic synergy of peer effects on digital transformation is essential for overcoming development bottlenecks and stimulating digital vitality across industrial and regional ecosystems. Utilizing data from Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges from 2010 to 2024, this study empirically investigates the impact of peer effects on corporate digital transformation and its underlying influencing factors from a systems perspective. The findings reveal significant industry and regional peer effects in corporate digital transformation, indicating that firms’ decision-making is interdependent within broader ecosystems. A greater distance between a focal firm’s prior digital transformation level and that of its peers is associated with a higher level of enthusiasm for such transformation. Similarly, the more a focal firm’s prior performance falls below that of its peers, the stronger its impetus for digital transformation becomes. Furthermore, the influence of the transformation distance on digital transformation enthusiasm exhibits a non-linear threshold effect, which varies with the performance gap. Finally, further analysis indicates that peer effects exert a multiplier effect and that industry-level peer effects in digital transformation significantly enhance firm performance. These conclusions contribute to a deeper understanding of the systemic mechanisms and pathways of corporate digital transformation and offer both theoretical and empirical support for fostering resilient digital economic ecosystems across industries and regions. Full article

Thoracic aortic atresia in adults represents a rare and extreme manifestation of aortic coarctation, marked by complete luminal occlusion and frequently compensated by extensive collateral circulation. This narrative review critically evaluates existing literature and institutional experience concerning surgical and endovascular repair strategies for aortic atresia, synthesizing evidence from related aortic arch pathologies due to the absence of direct comparative studies. Both treatment modalities—open surgical repair and catheter-based recanalization with stenting—have evolved significantly, presenting distinct advantages and limitations influenced by patient anatomy, age, and comorbidities. While surgical repair remains the standard for neonates, infants, and complex cases due to superior long-term durability and blood pressure control, endovascular procedures using chronic total occlusion technique and covered stents offer less invasive alternatives with rapid recovery, particularly in adults with suitable anatomic characteristics. The review highlights procedural considerations, including technical approaches, stent selection, and potential complications such as restenosis, hypertension, and vascular injury. Individualized, multidisciplinary decision-making remains essential, with shared consensus guiding therapy in the absence of randomized trials. The article identifies critical gaps in knowledge, emphasizing the need for multicenter, long-term studies and technological advances—including hybrid and personalized strategies for optimal management and for improving outcomes in this challenging congenital condition. Full article

This study explores the impact of government subsidy on blockchain traceability leadership (manufacturer vs. retailer) in a technology-supported supply chain including a manufacturer, a retailer, and a technical service firm. Methodology: We built Stackelberg game models for different scenarios (non-blockchain, manufacturer/retailer-led blockchain, and subsidized blockchain) to derive equilibria. Results: First, blockchain adoption is not always optimal unless consumers exhibit low acceptance of non-blockchain products and construction costs are low. Second, a party (manufacturer/retailer) tends to lead blockchain construction if the technical service firm shares more of its costs than the other party. Finally, government subsidies benefit the manufacturer and the retailer, but the technical service firm does not always benefit from subsidies. With suitable rates and lower costs, the manufacturer or the retailer prefers to lead the construction, potentially creating a win–win scenario in the supply chain. Novelty: We quantified leadership-switching conditions via the technical service firm’s cost sharing and took its decision-making licensing fees into account, addressing gaps in multi-stakeholder blockchain adoption research. Full article

The research explores how artificial intelligence-based chatbots transform psychological and legal assistance in situations of gender-based violence, evaluating their effect on women’s digital empowerment. A cross-sectional design with a mixed approach was used, combining a 25-item survey of 1000 women and a quantitative analysis using multiple correspondences and clustering techniques, supplemented by semi-structured interviews. The findings show that 64% considered the use of chatbots useful for accessing information, although only 27% used them to report incidents due to structural and digital barriers. Participants from rural areas faced severe connectivity limitations and expressed distrust of artificial intelligence, while those who interacted frequently demonstrated greater autonomy, decision-making capacity, and confidence in seeking support. Qualitative analysis showed that users valued confidentiality and anonymity as essential elements for sharing experiences of violence that they did not reveal in face-to-face settings. They also highlighted that immediate interaction with chatbots created a perception of constant support, reducing isolation and motivating users to seek formal help. The conclusion is that designing gender-focused chatbots and integrating them into care systems is an innovative and effective way to expand access to justice and psychological care. Full article

Financial risk early warning systems provide critical corporate financial status information to stakeholders, including corporate managers, investors, regulatory agencies, and other interested parties, enabling informed decision-making. This study proposes a corporate financial risk early warning model based on a bagging–cascading–boosting architecture, which can be used to predict the financial risk of a firm. The model performance is improved by integrating the residual fitting characteristics of LightGBM, the variance suppression mechanism of bagging, and the adaptive expansion ability of the cascade framework. Evaluated on 46 financial indicators from 2826 A-share-listed companies, the model demonstrates superior performance in AUC and F1-score metrics, outperforming traditional statistical methods and standalone machine-learning models. The methodological innovation lies in its tripartite mechanism: LightGBM ensures low-bias prediction, bagging controls variance, and the cascading structure dynamically adapts to data complexity, maintaining 94.09% AUC robustness, even when training data is reduced to 50%. Empirical results confirm this “ensemble-of-ensembles” framework effectively identifies Special Treatment (ST) firms, delivering early risk alerts for management while supporting investment decisions and regulatory risk mitigation. Full article

Background/Objectives: Rectal cancer is a significant global health concern, requiring precise staging and response assessment to make treatment decisions. Magnetic resonance imaging (MRI) is the standard imaging modality for evaluating tumor stage and treatment response. Positron emission tomography/computed tomography (PET/CT) offers complementary insights into pelvic lymph node involvement, tumor response, and distant metastases. Integrating PET and MRI into a hybrid PET/MRI modality can provide superior assessment of tumor staging and response compared to conventional imaging techniques. This review shares an update on the role of PET/MRI in rectal cancer staging and treatment response assessment. Methods: A systematic review of the current literature was conducted by two independent reviewers. This study utilized databases including Embase, Biosis, PubMed, Scopus, and Web of Science, employing the following keywords as eligibility criteria: “PET/MRI” OR “PET/MR” AND “rectal cancer” OR “colorectal cancer” AND “staging” AND “treatment assessment” OR “planning”. The inclusion criteria were that studies must examine cancer staging and response assessment. The exclusion criteria for the search were letters to the editors, abstracts, and case reports; studies that included fewer than five patients; studies that included cancer other than rectal or colorectal cancer; studies that did not utilize PET/MRI for rectal cancer staging and assessment; and non-human studies. Results: PET/MRI demonstrates potential advantages over conventional imaging, providing superior soft tissue contrast, functional imaging capabilities, and improved lesion characterization. A total of ten studies suggest that PET/MRI may enhance tumor staging accuracy and better assess pelvic lymph node involvement than PET/CT and MRI alone; in four studies, PET/MRI also showed higher response accuracy. Challenges remain in standardizing imaging protocols, validating PET tracers, and encouraging widespread clinical adoption. Conclusions: PET/MRI has the potential to offer a superior imaging solution for rectal cancer staging and treatment response assessment. While preliminary studies highlight its advantages over PET/CT and MRI alone, further research is needed to establish standardized protocols, validate PET tracers for routine clinical use, and improve imaging quality through attenuation and motion correction. Full article

There is a lack of systematic research on the behavioral design of charging decision-making for Shared Autonomous Electric Vehicles (ASEVs), and the thresholds of “when to charge and where to charge” have not been clarified. Therefore, this paper investigates the optimization of charging decisions of SAEVs and the impact of different decision-making objectives to provide theoretical support and practical guidance for intelligent operation. A multi-agent simulation model (which accurately simulates complex interaction systems) is constructed to simulate the operation and charging behavior of SAEVs. Four charging decision optimization objective functions are defined, and a weighted multi-objective optimization method is adopted. A comprehensive solution process combining the multi-agent simulation model and genetic algorithm (efficiently solving complex objective optimization problems) is applied to approximate the global optimal solution among 35 scenarios through 100 iterative runs. In this paper, factors such as passenger demand (e.g., average remaining battery power, demand response time) and operator demand (e.g., empty vehicle mileage, charging cost) are considered, and the impacts of different objectives and decision variables are analyzed. The optimization results show that (1) when a single optimization objective is selected, minimizing the total charging cost effectively balances the overall fleet operation; (2) there are trade-offs between different objectives, such as the conflict between the remaining battery power and charging cost, and the balance between the demand response time and the empty vehicle mileage; and (3) in order to satisfy the operational requirements, the weight distribution, charging probability, stopping probability, and recommended battery power should be adjusted. In conclusion, this study provides optimal charging decision strategies for the intelligent operation of SAEVs in different scenarios, which can optimize target weights and charging parameters, and achieve dynamic, balanced fleet management. Full article

The article addresses key challenges and opportunities within the high jewelry industry. It explores how brands can target sustainability and diversification demands by consumers from the supply side. It details sustainability-oriented market trends and resulting challenges and opportunities for high jewelry brands, in particular regarding artificial lab-grown diamonds, without being solely focused on this option alone. The study implements a qualitative research methodology, using semi-structured interviews with eight professionals active in different parts of the high jewelry industry, thus covering a large share of all high jewelry companies. The interviews provide an in-depth understanding of the obstacles and opportunities faced by both traditional and new high jewelry brands. The findings of this study reveal the changing trends in the high jewelry industry. Sustainability has emerged as a key driver in consumer decision-making, with ethical concerns now taking a central role in brand strategy and supply chain practices. Diversification has emerged as a strategy to meet this demand without losing the brand’s luxury essence and to build brand power without compromising exclusivity and creativity. The study concludes by proposing an SOR-type framework for the interplay of sustainability and diversification deduced from the interviews. Full article

Polypharmacy is typically seen as an unavoidable consequence of multimorbidity and aging, with clinicians addressing complex medication lists unsystematically. In this perspective, we argue that polypharmacy should be managed as a chronic condition. Like diabetes or hypertension, for example, the medication burden shows persistence, progression in its absence despite active management, predictable complications (such as falls, delirium, renal injury, functional decline), and a need for structured surveillance. We introduce a pragmatic diagnostic framework that moves beyond pill counts to modality-agnostic, regimen-level risk across prescribed and non-prescribed medicines. Diagnosis rests on prolonged exposure, composite burden indices (e.g., anticholinergic/sedative load), medication-related complications or prescribing cascades, and the need for a planned review. As biologics, gene therapies and long-acting formulations can lower tablet numbers while increasing monitoring, administration, and interaction complexity. We treat polypharmacy as cumulative pharmacodynamic and operational burden. We advocate stage matched care with unique, functional aims—decreasing the harmful burden instead of mass deprescribing—and position a structured medication review as the standard for polypharmacy with support from pharmacists, shared decision making, and safety netted taper plans. The framework fosters patient-centred care, embedding continuity and equity, and outlines a concise outcome set that integrates pharmacometric measures with patient-reported function and treatment burden. At the systems level, the framework enables registries, recall systems, decision support, and audit/feedback mechanisms to shift from sporadic medication list clean-up to a structured, measurable long-term program. Redefining polypharmacy in this way aligns clinical practice, education, and policy with real-world evidence, fostering a cohesive pathway to safer, streamlined, and more patient-centred care in community settings. Full article

Turnouts are critical components of railway infrastructure, ensuring operational flexibility but also representing a significant share of track maintenance costs. The frog, as the most vulnerable part of a turnout, is subject to severe wear and degradation, requiring frequent inspection and maintenance. Traditional manual inspection methods are costly, labour-intensive, and susceptible to subjectivity. This study explores a data-driven approach to condition monitoring of fixed-turnout frogs using standard track recording car measurements. By leveraging over 20 years of longitudinal level and rail surface signal data from the Austrian track-recording measurement car, we assess the feasibility of using existing measurement data for predictive maintenance. Six complementary approaches are proposed to evaluate frog condition, including track geometry assessment, ballast condition analysis, rail surface irregularity detection, and axle box acceleration-based monitoring. Results indicate that data-driven monitoring enhances maintenance decision-making by identifying deterioration trends, reducing reliance on manual inspections, and enabling predictive interventions. The integration of standardised measurement data with advanced analytical models offers a cost-effective and scalable solution for turnout maintenance. Full article

The adoption of artificial intelligence (AI) in healthcare requires not only accurate predictions but also a clear understanding of its reliability. In safety-critical domains such as medical imaging and diagnosis, clinicians must assess the confidence in model outputs to ensure safe decision making. Uncertainty quantification (UQ) addresses this need by providing confidence estimates and identifying situations in which models may fail. Such uncertainty estimates enable risk-aware deployment, improve model robustness, and ultimately strengthen clinical trust. Although prior studies have surveyed UQ in centralized learning, a systematic review in the federated learning (FL) context is still lacking. As a privacy-preserving collaborative paradigm, FL enables institutions to jointly train models without sharing raw patient data. However, compared with centralized learning, FL introduces more complex sources of uncertainty. In addition to data uncertainty caused by noisy inputs and model uncertainty from distributed optimization, there also exists distributional uncertainty arising from client heterogeneity and personalized uncertainty associated with site-specific biases. These intertwined uncertainties complicate model reliability and highlight the urgent need for UQ strategies tailored to federated settings. This survey reviews UQ in medical FL. We categorize uncertainties unique to FL and compare them with those in centralized learning. We examine the sources of uncertainty, existing FL architectures, UQ methods, and their integration with privacy-preserving techniques, and we analyze their advantages, limitations, and trade-offs. Finally, we highlight key challenges—scalable UQ under non-IID conditions, federated OOD detection, and clinical validation—and outline future opportunities such as hybrid UQ strategies and personalization. By combining methodological advances in UQ with application perspectives, this survey provides a structured overview to inform the development of more reliable and privacy-preserving FL systems in healthcare. Full article

The need to address long-term sustainability goals while ensuring short-term resilience to unexpected disruptions is placing an increasing challenge on urban mobility systems. This study organizes an analytical framework that compares and integrates the concepts of sustainability and resilience in urban mobility. A scoping review and thematic analysis were conducted to identify and compare the definitions, dimensions, and operational features of these two paradigms. The results reveal that, although they are conceptually distinct, sustainability and resilience share subjects of analysis, including multimodality and diversity of transport modes, the impacts of climate change, and social equity issues. However, they also present tensions between the dimensions of efficiency and redundancy, speed of recovery and sustainability of implemented solutions, and new vulnerabilities introduced by sustainable technologies. These synergies and trade-offs underscore the necessity of an integrated, systemic and holistic approach to urban mobility planning. The study emphasizes that building resilient and sustainable urban mobility requires coherent policies across government levels, technical capacity, public engagement, and comprehensive indicators. Recommendations for future research include developing integrated metrics and planning tools to support evidence-based decision-making. Full article