Search Results (39,905)

Background: Failure to rescue (FTR), defined as death after major postoperative complications, is a critical quality indicator in pancreatic cancer surgery. Despite advances in surgical techniques and perioperative care, FTR rates remain high and vary across institutions. Methods: This systematic review uses a narrative synthesis followed by PRISMA 2020. A PubMed search (1992–2025) identified 83 studies; after screening, 52 studies (2010–2025) were included. Eligible designs were registry-based, multicenter, single-center, or prospective audits. Given substantial heterogeneity in study designs, FTR definitions, and outcome measures, a narrative synthesis was performed; no formal risk-of-bias assessment or meta-analysis was conducted. Results: Definitions of FTR varied (in-hospital, 30-day, 90-day, severity-based, and complication-specific cases). Reported rates differed by definition: average reported rates were 13.2% for 90-day CD ≥ III (G1); 10.3% for in-hospital/30-day CD ≥ III (G3); and 7.4% for 30-day “serious/major” morbidity (G8). Absolute differences were +3.0 and +2.9 percentage points (exploratory, descriptive comparisons). Five domains were consistently associated with lower FTR: (i) centralization to high-volume centers; (ii) safe adoption/refinement of surgical techniques; (iii) optimized perioperative management including early imaging and structured escalation pathways; (iv) patient-level risk stratification and prehabilitation; and (v) non-technical skills (NTSs) such as decision-making, situational awareness, communication, teamwork, and leadership. Among NTS domains, stress and fatigue management were not addressed in any included study. Limitations: Evidence is predominantly observational with substantial heterogeneity in study designs and FTR definitions; the search was limited to PubMed; and no formal risk-of-bias, publication-bias assessment, or meta-analysis was performed. Consequently, estimates and associations are descriptive/associative with limited certainty and generalizability. Conclusions: NTSs were rarely used or measured across the included studies, with validated instruments; quantitative assessment was uncommon, and no study evaluated stress or fatigue management. Reducing the FTR after pancreatic surgery will require standardized, pancreas-specific definitions of FTR, process-level rescue metrics, and deliberate strengthening of NTS. We recommend a pancreas-specific operational definition with an explicit numerator/denominator: numerator = all-cause mortality within 90 days of surgery; denominator = patients who experience major complications (Clavien–Dindo grade III–V, often labeled “CD ≥ 3”). Addressing the gaps in stress and fatigue management and embedding behavioral metrics into quality improvement programs are critical next steps to reduce preventable mortality after complex pancreatic cancer procedures. Full article

The selection of an appropriate formwork system represents a critical decision in the planning of reinforced concrete multi-story buildings. While this decision has traditionally been deferred to the construction phase, increasing evidence of time and cost overruns in construction projects has highlighted the necessity of addressing it during earlier stages, particularly in design and planning. Early identification and selection of the optimal formwork system enhances the likelihood of achieving significant improvements in both time efficiency and cost effectiveness. To facilitate this process, a decision-support framework based on the Analytic Hierarchy Process (AHP) and Simple Additive Weighting (SAW) methods has been developed. This framework provides decision-makers with a structured and systematic approach for evaluating alternatives and selecting the most suitable formwork system for a given project. By offering an analytical foundation for the decision-making process, the framework assists designers and engineers in mitigating risks associated with delays and potential standstills during construction. The findings indicate that the proposed decision-support framework ensures both clarity and consistency in decision-making outcomes, irrespective of the analytical method employed. Consequently, it contributes to more robust planning and execution of construction projects. Full article

Bias in artificial intelligence is a critical issue because these technologies increasingly influence decision-making in a wide range of areas. The recommender system field is one of them, where biases can lead to unfair or skewed outcomes. The origin usually lies in data biases coming from historical inequalities or irregular sampling. Recommendation algorithms using such data contribute to a greater or lesser extent to amplify and perpetuate those imbalances. On the other hand, different types of biases can be found in the outputs of recommender systems, and they can be evaluated by a variety of metrics specific to each of them. However, biases should not be treated independently, as they are interrelated and can potentiate or mask each other. Properly assessing the biases is crucial for ensuring fair and equitable recommendations. This work focuses on analyzing the interrelationship between different types of biases and proposes metrics designed to jointly evaluate multiple interrelated biases, with particular emphasis on those biases that tend to mask or obscure discriminatory treatment against minority or protected demographic groups, evaluated in terms of disparities in recommendation quality outcomes. This approach enables a more comprehensive assessment of algorithmic performance in terms of both fairness and predictive accuracy. Special attention is given to Graph Neural Network-based recommender systems, due to their strong performance in this application domain. Full article

This paper introduces a formal mathematical framework for Digital Risk Twins (DRTs) as an extension of traditional digital twin (DT) architectures, explicitly tailored to the needs of safety-critical systems. While conventional DTs enable real-time monitoring and simulation of physical assets, they often lack structured mechanisms to model stochastic failure processes; evaluate dynamic risk; or support resilient, risk-aware decision-making. The proposed DRT framework addresses these limitations by embedding probabilistic hazard modeling, reliability theory, and coherent risk measures into a modular and mathematically interpretable structure. The DT to DRT transformation is formalized as a composition of operators that project system trajectories onto risk-relevant features, compute failure intensities, and evaluate risk metrics under uncertainty. The framework supports layered integration of simulation, feature extraction, hazard dynamics, and decision-oriented evaluation, providing traceability, scalability, and explainability. Its utility is demonstrated through a case study involving an aircraft brake system, showcasing early warning detection, inspection schedule optimization, and visual risk interpretation. The results confirm that the DRT enables modular, explainable, and domain-agnostic integration of reliability logic into digital twin systems, enhancing their value in safety-critical applications. Full article

Digital innovation is the core driving force to enhance the competitiveness of enterprises and promote sustainable development, and is a key enabler for achieving corporate ability goals. Executives with information technology (IT) backgrounds who have rich knowledge and skills in digital technology are the backbone of promoting the digital transformation of enterprises and optimizing the allocation of auditing resources. And they can lay the technological foundation for sustainable corporate development and play an important role in corporate audit decision-making. Based on the data of China’s A-share listed companies from 2015 to 2023, the impact of executives with IT backgrounds on auditor selection is empirically analyzed. The study shows that (1) the higher the proportion of executives with IT backgrounds in the executive team, the more the companies tend to choose high-quality auditors; (2) the degree of corporate digital innovation positively moderates the relationship between executives with an IT background and high-quality auditors; (3) the level of corporate internal control plays a mediating effect in the relationship between executives with an IT background and auditor selection; (4) for non-state-owned, large-scale, short executive tenures, and labor-intensive firms, executives with IT backgrounds exert a more significant influence on auditor selection. This study broadens previous research on corporate auditing behaviors from the perspective of executives with IT backgrounds, providing insights for companies to select suitable auditors, to make scientifically sound decisions regarding auditor selection in the context of digital innovation, further optimize internal management, enhance risk response capabilities, and thereby achieve sustainable corporate development. Full article

This paper develops a dual-channel environmental hotel supply chain equilibrium model to investigate the impact of mergers and acquisitions (M&A), decision makers’ altruistic preferences, and consumers’ low-carbon preferences under demand uncertainty. The model incorporates hotels, online travel agency (OTA) platforms, and demand markets and captures interactions under both the merchant and agency models. Variational inequalities are employed to describe interdependent decision-making behaviors, and the projection gradient algorithm is used to obtain equilibrium solutions. The numerical analysis reveals that sustainability factors directly affect the profitability of both hotels and OTAs. Under the agency model, higher commission rates reduce hotel profits while significantly increasing OTA gains. When comparing the merchant and agency models without M&A, the merchant model yields higher hotel profits, whereas the agency model favors OTAs. Furthermore, when M&A is combined with altruistic preferences, the utility of hotels and OTAs under the agency model reaches the highest level, while M&A offers limited advantages for OTAs. Overall, the findings highlight that sustainability factors, altruistic preferences, and M&A jointly interact with merchant and agency governance structures to shape profitability and utility. These results provide theoretical insights and managerial implications for developing resilient, cooperative, and low-carbon hotel supply chains under demand uncertainty. Full article

One of the main challenges faced by DC microgrid (DCMG) is their low inertia, which leads to rapid and significant voltage fluctuations during load or generation changes. These fluctuations can negatively impact sensitive loads and protection devices. Previous studies have addressed this by enabling battery converters to mimic the behavior of synchronous generators (SGs), but this approach becomes ineffective when the converters or batteries reach their current or energy limits, leading to a loss of inertia and potential system instability. In interconnected multi-microgrid (MMG) systems, the presence of multiple batteries offers the potential to enhance system inertia, provided there is a coordinated control strategy. This research introduces a hierarchical control method that combines decentralized and centralized approaches. Decentralized control allows individual converters to emulate SG behavior, while the centralized control uses Internet of Things (IoT) technology to enable real-time coordination among all Energy Storage Units (ESUs). This coordination improves inertia across the DCMMG system, enhances energy management, and strengthens overall system stability. IoT integration ensures real-time data exchange, monitoring, and collaborative decision-making. The proposed scheme is validated through MATLAB simulations, with results confirming its effectiveness in improving inertial response and supporting the integration of renewable energy sources within DCMMGs. Full article

Background: Pancreatic cancer is a challenging malignancy with an aggressive biology and limited treatment options, contributing to low survival rates. Supportive and palliative care play a key role in improving the quality of life and psychological distress for patients and their families. However, appropriate delivery and effectiveness of these interventions may be influenced by social determinants of health (SDOH). These factors create significant barriers for patients, influencing their access to care and ability to make informed decisions. This review explores the role of SDOH in supportive and palliative care of pancreatic cancer patients and identifies areas for improvement to enhance this type of care for vulnerable populations. Methods: A thorough narrative review was carried out to evaluate the influence of social determinants of health on supportive and palliative care in the management of pancreatic cancer, focusing on symptom management, psychosocial support, nutritional support, advance care planning, rehabilitation, functional support, and care coordination. Results: This review demonstrates that disparities exist. Black and Asian patients receive less pain medications; those with lower level of education struggle to access psychological support; Hispanic and Black patients often do not receive needed nutritional care; and end-of-life planning is less common among non-White and less-educated patients. Conclusions: SDOH significantly affects the experience and delivery of supportive and palliative care in pancreatic cancer patients, exacerbating inequities across multiple domains of care. Addressing these disparities requires coordinated efforts at clinical, organizational, and policy levels to ensure equitable access to care for all patients in their final phase of life. Integrating attention to SODH into care delivery models can improve outcomes and enhance quality of life for these patients. Full article

This study investigates how process manufacturing enterprises can optimize semi-finished inventory (SFI) distribution in delayed production models, with particular attention to differences in cost volatility between single- and multi-period planning scenarios. To address this research gap, we develop a mixed-integer programming model that determines optimal customer order decoupling point (CODP)/product differentiation point (PDP) positions and SFI quantities (both generic and dedicated) for each production period, employing particle swarm optimization for solution derivation and validating findings through a comprehensive case study of a steel manufacturer with characteristic long-period production processes. The analysis yields two significant findings: (1) single-period operations demonstrate marked cost sensitivity to service level requirements and delay penalties, necessitating end-stage inventory buffers, and (2) multi-period optimization generates a distinctive cost-smoothing effect through strategic order deferrals and cross-period inventory reuse, resulting in remarkably stable total costs (≤2% variation observed). The study makes seminal theoretical contributions by revealing the convex cost sensitivity of short-term inventory decisions versus the near-flat cost trajectories achievable through multi-period planning, while establishing practical guidelines for process industries through its empirically validated two-period threshold for optimal order deferral and inventory positioning strategies. Full article

Advances in machine learning have improved the ability to predict critical environmental conditions, including solar radiation levels that, while essential for life, can pose serious risks to human health. In Ecuador, due to its geographical location and altitude, UV radiation reaches extreme levels. This study presents the development of a chatbot system driven by a hybrid artificial intelligence model, combining Random Forest, CatBoost, Gradient Boosting, and a 1D Convolutional Neural Network. The model was trained with meteorological data, optimized using hyperparameters (iterations: 500–1500, depth: 4–8, learning rate: 0.01–0.3), and evaluated through MAE, MSE, R², and F1-Score. The hybrid model achieved superior accuracy (MAE = 13.77 W/m², MSE = 849.96, R² = 0.98), outperforming traditional methods. A 15% error margin was observed without significantly affecting classification. The chatbot, implemented via Telegram and hosted on Heroku, provided real-time personalized alerts, demonstrating an effective, accessible, and scalable solution for health safety and environmental awareness. Furthermore, it facilitates decision-making in the efficient generation of renewable energy and supports a more sustainable energy transition. It offers a tool that strengthens the relationship between artificial intelligence and sustainability by providing a practical instrument for integrating clean energy and mitigating climate change. Full article

Over 35% of bridges in the United States are currently rated in fair or poor condition, highlighting ongoing challenges in maintaining safety and performance amid aging infrastructure, limited budgets, and extended repair timelines. While Accelerated Bridge Construction (ABC) offers a faster solution, its adoption requires comprehensive decision frameworks. This paper presents a multi-criteria decision support tool (DST) that builds on the Connecticut Department of Transportation (CTDOT) ABC decision matrix. This DST quantifies the benefits of ABC for road and work zone safety, social equity, and environmental justice (SEEJ) and integrates them with structural, traffic, and construction factors to provide a comprehensive approach for determining the suitability of ABC techniques in bridge construction projects. Crash costs and corresponding safety benefits are quantified based on crash severity and frequency. While the tool incorporates both safety and SEEJ criteria, it also allows decision makers to consider either criterion individually based on their preferences. To demonstrate the applicability and benefits of the tool, it was applied to case studies in Connecticut. The results demonstrated how the considerations of safety and SEEJ can affect ABC decision-making. The presented DST is simple (Excel-based) and offers a practical and flexible tool that utilizes readily available data from national databases, making it applicable to all state DOTs across the United States. Full article

Accurate spleen segmentation in abdominal CT scans remains a critical challenge in medical image analysis due to variable morphology, low tissue contrast, and proximity to similar anatomical structures. This paper presents an enhanced U-Net architecture that addresses these challenges through multi-slice contextual integration and interpretable deep learning. Our approach incorporates three-channel inputs from adjacent CT slices, implements a hybrid loss function combining Dice and binary cross-entropy terms, and integrates Grad-CAM visualization for enhanced model interpretability. Comprehensive evaluation on the Medical Decathlon dataset demonstrates superior performance, with a Dice similarity coefficient of 0.923 ± 0.04, outperforming standard 2D approaches by 3.2%. The model exhibits robust performance across varying slice thicknesses, contrast phases, and pathological conditions. Grad-CAM analysis reveals focused attention on spleen–tissue interfaces and internal vascular structures, providing clinical insight into model decision-making. The system demonstrates practical applicability for automated splenic volumetry, trauma assessment, and surgical planning, with processing times suitable for clinical workflow integration. Full article

The paper contributes to the debate on the holistic sustainability assessment of real estate projects, integrating economic, financial, environmental, and social aspects. A methodological study is presented to support decision-making processes involving the preferability ranking of alternative investment scenarios: new building production vs. retrofitting the existing stock, in the context of urban transformation interventions. The study integrates life cycle approaches by introducing the social components besides the economic and environmental ones. Firstly, a composite unidimensional (monetary) indicator calculation is illustrated. The sustainability components are internalized in the NPV calculation through a Discounted Cash-Flow Analysis (DCFA). Life Cycle Costing (LCC) and Life Cycle Assessment (LCA) are suggested to assess the economic and environmental impacts, and the Social Return on Investment (SROI) to assess the intervention’s extra-financial value. Secondly, a methodology based on multicriteria techniques is proposed. The Hierarchical Analytical Process (AHP) model is suggested to harmonize various performance indicators. Focus is placed on the criticalities emerging in both the methodological approaches, while highlighting the relevance of multidimensional approaches in decision-making processes and for supporting urban policies and urban resilience. Full article

Oral cancer remains a critical global health challenge, with delayed diagnosis driving high morbidity and mortality. Despite progress in artificial intelligence, computer vision, and medical imaging, early detection tools that are accessible, explainable, and designed for patient engagement remain limited. This study presents a novel system that combines a patient-centred chatbot with a deep learning framework to support early diagnosis, symptom triage, and health education. The system integrates convolutional neural networks, class activation mapping, and natural language processing within a conversational interface. Five deep learning models were evaluated (CNN, DenseNet121, DenseNet169, DenseNet201, and InceptionV3) using two balanced public datasets. Model performance was assessed using accuracy, sensitivity, specificity, diagnostic odds ratio (DOR), and Cohen’s Kappa. InceptionV3 consistently outperformed the other models across these metrics, achieving the highest diagnostic accuracy (77.6%) and DOR (20.67), and was selected as the core engine of the chatbot’s diagnostic module. The deployed chatbot provides real-time image assessments and personalised conversational support via multilingual web and mobile platforms. By combining automated image interpretation with interactive guidance, the system promotes timely consultation and informed decision-making. It offers a prototype for a chatbot, which is scalable and serves as a low-cost solution for underserved populations and demonstrates strong potential for integration into digital health pathways. Importantly, the system is not intended to function as a formal screening tool or replace clinical diagnosis; rather, it provides preliminary guidance to encourage early medical consultation and informed health decisions. Full article

Under the background of global climate change, lichens as a staple fallback food source for the endangered Yunnan snub-nosed monkey (Rhinopithecus bieti) exert a critical influence on the survival of Yunnan snub-nosed monkey populations through their distribution dynamics. This study focused on the contiguous habitats of the Yunnan snub-nosed monkey in the southern Hengduan Mountains. By species distribution models (SDMs) and landscape pattern analysis, we investigated the changes in suitable habitats of lichens under four Representative Concentration Pathway (RCP) scenarios and their implications for the habitat utilization of the Yunnan snub-nosed monkey until 2050. The results indicate that the current suitable habitat for lichen spans approximately 16,821.96 km², with highly suitable habitats predominantly located in Deqin County and Weixi County. Altitude and vegetation type emerged as primary factors influencing lichen distribution. The overlap rate of suitable habitats between lichens and the Yunnan snub-nosed monkey is 72.24%. Furthermore, the Yunnan snub-nosed monkey exhibits a preference for selecting habitats characterized by the largest patch index (LPI) of lichen distribution. By 2050, the suitable habitat for lichen is projected to marginally increase in the southern Hengduan Mountains, particularly under the RCP 6.0 scenario, by 22.20% compared to the current expansion. However, both the suitable habitat and the LPI of lichen face potential decline within the habitat of the Yunnan snub-nosed monkey. Therefore, we recommend conducting a quantitative investigation into the correlation between the actual productivity of lichen radiata and the population dynamics of Yunnan snub-nosed monkey as a priority. This research will offer a more precise scientific foundation for conservation decision-making for Yunnan snub-nosed monkey. Full article