Search Results (6,213)

Background: Frailty is a multidimensional syndrome characterized by reduced physiological reserve and resilience and has become a crucial predictor of outcomes in liver surgery. Unlike chronological age, frailty reflects broader vulnerabilities that significantly influence postoperative recovery. Aim: To review and synthesize current evidence on the relationship between frailty and postoperative outcomes following liver resection, with an emphasis on short-term complications, mortality, and long-term survival. Methods: A comprehensive literature review was performed, drawing on recent meta-analyses, large-scale cohort studies, and prospective observational data. Frailty was evaluated using a range of assessment tools, including the Modified Frailty Index (mFI), Clinical Frailty Scale (CFS), Kihon Checklist (KCL), and claims-based measures such as the Johns Hopkins Frailty Indicator. Results: Across studies, frailty has been consistently linked to a higher incidence of postoperative complications, such as post-hepatectomy liver failure (PHLF), infections, extended hospital stays, and increased mortality. In patients undergoing liver resection for cancer, frailty is also associated with poorer long-term survival. Importantly, frailty serves as an independent risk factor, even after adjusting for age, comorbid conditions, and tumor characteristics. Preoperative identification of frailty enhances risk stratification, informs surgical planning, potentially favoring parenchymal-sparing or minimally invasive approaches, and highlights patients who may benefit from prehabilitation. Conclusions: Frailty is a strong and independent predictor of poor outcomes after liver resection. Incorporating frailty assessment into routine preoperative evaluation can improve surgical decision-making, facilitate informed patient counseling, and optimize perioperative care strategies. Full article

Access to clean, hygienic, and sufficient potable water is a concern in many countries. To ensure this, asset management, planning, and structured pipe renewal are crucial in providing an adequate level of service. However, there is a significant backlog in municipal pipe renewal, which needs to be addressed to raise the standard of potable water supply to an acceptable level in countries across most continents. Therefore, the objective of this research was to improve decision-making to reduce this backlog. Competent personnel are a scarce resource and not easily replaced. Standardized decision-making is considered an efficient approach to addressing the shortage of skilled personnel in pipe renewal. However, its effectiveness depends on its adaptability to the varying complexity and scale of such projects during implementation. This research is based on a literature review that explores decision theories, project definitions, and project models, and compares the typical characteristics of pipe renewal projects with those of other infrastructure projects. The research highlights that structured and standardized decision-making processes are essential to ensure appropriate asset management of the pipe network and sufficient pipe renewal. The main outcome of this research is a tailored project model that supports better front-end decision-making in pipe renewal projects through improved information flow. Full article

The study presents a scalable decision-support framework to assess energy-sharing strategies within mixed-use urban districts, with a focus on planning, sustainability, and policy relevance. Two renewable energy-sharing mechanisms—energy sharing (ES) and net metering (NM)—are compared through a techno-economic analysis applied to a real neighborhood in Montréal, Canada. The workflow integrates irradiance-aware PV simulation, archetype-based urban building modeling, and financial sensitivity analysis adaptable to local regulatory conditions. Key performance indicators (KPIs)—including Self-Consumption Ratio (SCR), Self-Sufficiency Ratio (SSR), and peak load reduction—are used to evaluate technical performance. Results show that ES outperforms NM, achieving higher SCR (77% vs. 66%) and SSR (40% vs. 35%), and seasonal analysis reveals that peak shaving reaches 30.3% during summer afternoons, while PV impact is limited to 15.6% in winter mornings and negligible during winter evenings. Although both mechanisms are currently unprofitable under existing Québec tariffs, scenario analysis reveals that a 50% CAPEX subsidy or a 0.12 CAD/kWh feed-in tariff could make the system viable. The novelty of this study lies in the development of a replicable, archetype-driven, and policy-oriented simulation framework that enables the evaluation of renewable energy communities in mixed-use and data-scarce urban environments, contributing new insights into the Canadian energy transition context. Full article

Zambia continues to experience increasingly frequent and intense droughts, with the 2023/2024 season among the most severe in recent history. These events have threatened livelihoods, strained water and food systems, and placed immense pressure on already limited national and local resources. Given the limited knowledge in the literature on drought management in Zambia, this study investigated the state of localized district efforts across the country. By using mixed methods with a total of 161 interviews, it assessed the participation of district governments and sector players across key components of drought governance, including early warning, monitoring, vulnerability and impact assessment, mitigation, and response. Although Zambia has made notable progress in establishing national institutional frameworks and climate policies, key findings reveal a pattern of limited proactive engagement, with most participation occurring only in response to extreme events like the 2023/2024 drought. This reactive posture at the district level is further compounded by inadequate resources, limited coordination, a lack of localized drought planning, and systemic bureaucratic constraints that undermine a timely and effective response. Nonetheless, numerous opportunities exist to strengthen drought management by localizing decision-making, integrating indigenous knowledge into existing early warning systems, and leveraging community-based infrastructures to maximize scarce resources and build long-term resilience. The paper concludes with recommendations for enhancing Zambia’s drought preparedness and response capacity through inclusive, risk-based, and proactive strategies; insights that can be adapted to other developing country contexts. Full article

Efficient water resource management in arid and semi-arid regions is a critical challenge due to persistent scarcity, climate change, and unsustainable agricultural practices. This review synthesizes recent advances in applying remote sensing (RS), geographic information systems (GIS), and machine learning (ML) to monitor, analyze, and optimize water use in vulnerable agricultural landscapes. RS is evaluated for its capacity to quantify soil moisture, evapotranspiration, vegetation dynamics, and surface water extent. GIS applications are reviewed for hydrological modeling, watershed analysis, irrigation zoning, and multi-criteria decision-making. ML algorithms, including supervised, unsupervised, and deep learning approaches, are assessed for forecasting, classification, and hybrid integration with RS and GIS. Case studies from Central Asia, North Africa, the Middle East, and the United States illustrate successful implementations across various applications. The review also applies the DPSIR (Driving Force–Pressure–State–Impact–Response) framework to connect geospatial analytics with water policy, stakeholder engagement, and resilience planning. Key gaps include data scarcity, limited model interpretability, and equity challenges in tool access. Future directions emphasize explainable AI, cloud-based platforms, real-time modeling, and participatory approaches. By integrating RS, GIS, and ML, this review demonstrates pathways for more transparent, precise, and inclusive water governance in arid agricultural regions. Full article

Urban wetlands provide cultural ecosystem services (CES) that are essential to human well-being. However, their study remains limited in Colombia and across Latin America, particularly in terms of quantitative assessments of CES in urban ecosystems. This research examines the perception and valuation of CES among visitors to three urban wetlands designated as Ramsar sites in Bogotá, Colombia—Santa María del Lago, Juan Amarillo, and Córdoba. We assessed how sociodemographic profiles influence the appreciation and valuation of CES employing a mixed-methods approach that combined structured surveys, hierarchical cluster analysis (HCA), and principal component analysis (PCA). Results revealed notable differences among the wetlands: Santa María del Lago attracts younger visitors and is characterized by strong appreciation for landscape aesthetics and spirituality; Juan Amarillo exhibits a mixed visitor profile with lower appreciation for spirituality; and Córdoba receives older visitors with higher education levels, who prioritize recreation and tourism. HCA and PCA identified distinct visitor segments: “passive visitors,” oriented toward contemplation and learning, and “active visitors,” focused on recreation and tourism. Across all sites, the most valued services were aesthetic appreciation of the landscape and knowledge of nature. This study provides empirical evidence to support the integration of CES into decision-making and environmental policy for urban planning, recommending differentiated governance strategies, targeted environmental education programs, and low-impact tourism initiatives aligned with Ramsar principles and nature-based solutions. Full article

This paper presents a comprehensive design framework for Enterprise Architecture aimed at facilitating decision-driven analytics in smart factories. The motivation behind this research lies in challenges faced by manufacturing companies, such as skilled labor shortages and increasing global competition, alongside the imperative for sustainable production. This journal provides a novel approach for designing and documenting prescriptive analytics use cases in manufacturing environments. The framework addresses the need for effective integration of advanced data analytics and prescriptive analytics solutions within existing production environments, thereby enhancing operational efficiency and decision-making processes. A Design Science Research approach is used to iteratively derive a framework based on stakeholder needs and activities along the prescriptive analytics use case development cycle. The resulting framework is demonstrated and evaluated in an IoT Factory setup in a research facility. From a practical perspective, the framework supports manufacturing companies in systematically designing prescriptive analytics use cases. From a research perspective, it contributes to the body of knowledge on Enterprise Architecture Management (EAM) by operationalizing the design of prescriptive analytics use cases in manufacturing contexts. The main contributions of this study include the development of a framework that supports the planning, design, and integration of prescriptive analytics use cases. This framework fosters interdisciplinary collaboration and aids in managing the complexity of data-driven projects. Full article

Digital mining has been evolving in recent years under the Industry 4.0 paradigm. In this sense, technological tools such as sensors aid the management and operation of mining projects, reducing the risk of accidents, increasing productivity, and promoting business sustainability. DT is a technological tool that enables the integration of various Industry 4.0 technologies to create a virtual model of a real, physical entity, allowing for the study and analysis of the model’s behavior through real-time data collection. A digital twin of an underground mine is a real-time, virtual replica of an actual mine. It is like an extremely detailed “simulator” that uses data from sensors, machines, and personnel to accurately reflect what is happening in the mine at that very moment. Some of the functionalities of an underground mining DT include (i) accurate geometry of the real physical asset, (ii) real-time monitoring capability, (iii) anomaly prediction capability, (iv) scenario simulation, (v) lifecycle management to reduce costs, and (vi) a support system for smart and proactive decision-making. A digital twin of an underground mine offers transformative benefits, such as real-time operational optimization, improved safety through risk simulation, strategic planning with predictive scenarios, and cost reduction through predictive maintenance. However, its implementation faces significant challenges, including the high technical complexity of integrating diverse data, the high initial cost, organizational resistance to change, a shortage of skilled personnel, and the lack of a comprehensive, multi-layered conceptual framework for an underground mine digital twin. To overcome these barriers and gaps, this paper proposes a strategy that includes defining an advanced, multi-layered conceptual framework for the digital twin. Simultaneously, it advocates for fostering a culture of change through continuous training, establishing partnerships with specialized experts, and investing in robust sensor and connectivity infrastructure to ensure reliable, real-time data flow that feeds the digital twin. Finally, validation of the advanced multi-layered conceptual framework for digital twins of underground mines is carried out through a questionnaire administered to a panel of experts. Full article

Context: For decades, maize monoculture practices dominated Northeast China, causing significant damage to the local soil and ecological environment. Crop rotation has, in recent years, been promoted as an environmentally friendly and sustainable technology in China. Despite its numerous benefits for the environment and crop productivity, farmers’ willingness to adopt crop rotation remains low. Objective: This study aims to investigate the social–psychological factors influencing farmers’ intentions to adopt maize–soybean rotation, with the goal of informing strategies for promoting sustainable agricultural practices. Methods: Based on a farm-level survey of 298 rural households in Northeast China, this study integrates value orientation into the Theory of Planned Behavior and employs structural equation modeling to investigate the social–psychological factors that affect farmers’ willingness to adopt soybean-based rotation. Results and Conclusions: The findings confirm the applicability of the extended Theory of Planned Behavior in explaining farmers’ decision-making. Farmers’ attitudes (0.384) and perceived behavioral control (0.323) had significant positive effects on adoption intentions, whereas subjective norms (0.018) were not significant. More favorable attitudes and greater perceived behavioral control, reflecting higher risk tolerance and better access to external support, promoted adoption. Value orientations strongly shaped farmers’ attitudes: altruism (0.148) and biospheric values (0.180) had positive effects, while egoism (0.044) showed no significant impact. These results offer guidance for policymakers to design targeted interventions promoting sustainable crop rotation. Significance: These results can help policymakers better understand what factors influence farmers’ adoption of rotation and what targeted measures can be taken to popularize the improved agricultural system. To foster farmers’ adoption of rotation, it is important to go beyond traditional supporting policies and to leverage innovative approaches to promote value orientation on sustainable farming practices. Full article

In Korea, rural regions increasingly function as peri-urban zones integrated into urban systems. To assess vulnerabilities in these transitional areas characterized by mixed land use and uneven access to infrastructure, this study developed a three-tiered peri-urban livability vulnerability framework by integrating the analytic hierarchy process and the entropy weight method. The results indicated that medical facilities, childcare and education centers, and village communities consistently emerged as key indicators, linking peri-urban livability directly to the stability of settlement environments and the quality of life of residents. Contrastingly, expert evaluations and data-driven outcomes related to road networks and agricultural infrastructure displayed substantial discrepancies, revealing gaps between perceived importance and actual provision levels. Such differences highlight the risk of underestimating infrastructure-related vulnerabilities when subjective assessments are employed exclusively. By synthesizing subjective and objective weights, this study advances urban and environmental analysis and supports evidence-based decision-making for policy prioritization. The findings demonstrate that peri-urban vulnerability is shaped less by productive capacity than by social infrastructure and community stability. This conclusion offers crucial insights for enhancing livability and guiding urban planning strategies. Full article

Urban ecosystem services (ESs) are increasingly recognized as critical determinants of residents’ quality of life and well-being. This study develops a data-driven demand–supply matching framework to integrate ES concepts into community-level planning and service performance evaluation. Based on 312 resident surveys across 10 communities in Wuhan, China, we identify the key environmental attributes shaping perceived service quality. A random forest (RF) algorithm is employed to assess the relative importance of environmental features, while a multinomial logit (Mlogit) model quantifies their specific effects. The results highlight that community autonomy, neighborhood relations, environmental awareness, and infrastructure—such as broadband networks and security systems—play pivotal roles in improving service quality. Although provisioning and regulating ESs, such as safety and infrastructure, are relatively well established, cultural services that promote social cohesion and civic participation remain under-supported. These findings uncover the heterogeneity of residents’ environmental expectations and provide actionable insights for incorporating ES-oriented thinking into community planning and fiscal decision-making. By bridging ecological theory with operational urban governance, this study contributes a replicable approach for advancing more inclusive and sustainable community development. Full article

Ensuring the resilience of water distribution networks (WDNs) remains a critical challenge for utilities, as aging infrastructure and inadequate planning can compromise drinking water quality (DWQ) and increase customer dissatisfaction. This study aims to strengthen existing DWQ monitoring schemes utilized by utilities by introducing a GIS-based framework that integrates structured customer feedback into the selection of sampling locations. Using a district metering area in Jūrmala, Latvia, as a case study, consumer-reported complaints were collected through an online survey and geoprocessed to identify problem hotspots. Based on these insights, the sampling program was refined and linked with asset data to improve previously established maintenance routines. Targeted sampling confirmed elevated iron and turbidity levels in several locations, validating the reliability of customer feedback. Embedding spatial context throughout the data pipeline enables systematic evaluation and optimization of sampling locations, enhancing operational awareness and supporting informed decision-making during incidents. The proposed approach can be widely adopted by utilities to develop priority-based monitoring campaigns, particularly in regions with significant seasonal demand fluctuations, such as tourism hubs and vacation destinations. Full article

Background: In a fast-changing sector, apparel distribution centers (DCs) are under increasing pressure to meet labor intensive operational requirements, short delivery windows, and variable demand in the rapidly changing apparel industry. Traditional labor forecasting methods often fail in dynamic environments, leading to inefficiencies, inadequate staffing, and reduced responsiveness. Methods: This comprehensive review discusses AI-enhanced labor forecasting tools that support flexible workforce planning in apparel DCs using a PRISMA methodology. To provide proactive, data-driven scheduling recommendations, the model combines machine learning algorithms with workforce metrics and real-time operational data. Results: Key performance indicators such as throughput per work hour, skill alignment among employees, and schedule adherence were used to assess performance. Apparel distribution centers can significantly benefit from real-time, adaptive decision-making made possible by AI technologies in contrast to traditional models that depend on static forecasts and human scheduling. These include LSTM for time-series prediction, XGBoost for performance-based staffing, and reinforcement learning for flexible task assignments. Conclusions: The paper demonstrates the potential of AI in workforce planning and provides useful guidance for the digitization of labor management in the clothing distribution industry for dynamic and responsive supply chains. Full article

This paper investigates the joint scheduling problem of unmanned aerial vehicles (UAVs) and trucks for community logistics, where UAVs act as service providers for last-mile delivery and trucks serve as mobile storage platforms for drone deployment. To address the complexity of decision variables, this paper proposes a three-stage solution scheme that divides the problem into the following: (1) UAV mission set generation via clustering, (2) truck-drone route planning, and (3) collaborative scheduling via a Mixed-Integer Linear Programming (MILP) model. The MILP model, solved exactly using Gurobi, optimizes truck movements and drone operations to minimize total delivery time, representing the core contribution. In the experimental section, to verify the correctness and effectiveness of the proposed Mixed-Integer Linear Programming (MILP) model, comparative experiments were conducted against a heuristic algorithm based on empirical intuitive decision-making. The solution results of experiments with different scales indicate that the joint scheduling model outperforms the scheduling strategies based on empirical experience across various scenario sizes. Additionally, multiple experiments conducted under different parameter settings within the same scenario successfully demonstrated that the model can stably be solved without deteriorating results when parameters change. Furthermore, this study observed that the relationship between the increase in the number of drones and the decrease in the total consumed time is not a simple linear relationship. This phenomenon is speculated to be due to the periodic patterns exhibited by the drone scheduling sequence, which align with the average duration of individual tasks. Full article

Recalcitrant species are highly sensitive to drought and climate stress, posing urgent challenges for their conservation. Propagation for ex situ management and habitat restoration depends on adequate fruit handling, yet postharvest protocols remain insufficiently examined to support practical implementation. Cryptocarya alba, a dominant tree of the Chilean Mediterranean biome, reflects this gap. Despite its ecological relevance and central role in forest planning, the biological basis of its recalcitrant behavior has yet to be fully elucidated, constraining informed decision-making on its propagation. Accordingly, this study examined the progressive breakdown of fruit integrity under two contrasting storage conditions—refrigeration (5 °C) and room temperature (20 °C)—over 150 days, using a multiscale approach combining physical measurements, histology, and scanning electron microscopy. Fruit weight, moisture, pericarp thickness, and cotyledon starch exhibited a significant linear decline over time. The rate was consistently higher at room temperature—except for starch, which showed no quantitative differences across treatments, though the severity of granule alterations was greater. Overall evidence indicates a close association among these variables, suggesting that desiccation and metabolism-driven degradation result in the structural collapse of C. alba fruits. These findings highlight the need to integrate environmental conditions alongside complementary strategies targeted at physiological regulation, guiding the development of robust, science-based handling protocols to support the species’ conservation. Full article