Search Results (288)

Monitoring avocado (Persea americana Mill., cv. Semil-34) in tropical mountain landscapes of Cambita, San Cristóbal, Dominican Republic is inherently complex due to the pronounced topographical and climatic heterogeneity that modulates the crop’s ecophysiological responses, specifically vegetative vigor, carbon allocation, and the synchronization of reproductive flushes. This study integrates 5-year (2020–2025) Sentinel-2 time series, ERA5-Land climatic variables (air temperature, total precipitation, and radiation), and geomorphometric covariates to explain variability in yield and fruit quality. Multispectral indices, including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Normalized Difference Red Edge (NDRE), and Normalized Difference Moisture Index (NDMI), were analyzed using Partial Least Squares Regression (PLSR) to characterize phenological dynamics and rank dominant predictors. The results revealed coherent spectral phenological trajectories; however, a significant inverse relationship was detected between canopy vigor and yield during reproductive phases. High vegetation index values were significantly and negatively associated with lower production (r = −0.58, p < 0.0021), reflecting a potential source–sink imbalance. Topography functioned as a structural filter, regulating root drainage and productive stability across the landscape. While yield variability was partially explainable (R² = 0.38), internal fruit quality, measured as dry matter content, exhibited comparatively high environmental stability. A central contribution of this research lies in identifying the “vigor paradox” in cv. Semil-34 and the suggestion that topography may exert a stronger influence than direct spectral signals under tropical hillside conditions. These findings provide an exploratory framework for anticipating yield and fruit quality through satellite remote sensing or UAVs, supporting site-specific management decisions in mountain agricultural systems. Full article

Background: The landscape of paediatric oncology has undergone a remarkable transformation over recent decades. Advances in both oncological and supportive therapies have dramatically improved survival in children with haematological malignancies and solid tumours, with current survival rates exceeding 80% for many childhood cancers. However, this therapeutic success has brought with it an unexpected consequence: the intensification of treatment protocols has led to a parallel increase in life-threatening complications requiring intensive care support. Current evidence indicates that up to 40% of paediatric oncology patients will require admission to a Paediatric Intensive Care Unit (PICU) at some point during their disease trajectory. Objectives: This comprehensive review synthesises current evidence to provide an updated framework for PICU admission decision-making in oncology haematology patients. We have integrated the most recently published international guidelines, including the groundbreaking Phoenix 2024 sepsis criteria and the updated PALICC-2 2023 recommendations for paediatric acute respiratory distress syndrome. Beyond establishing admission criteria, we critically analyse the efficacy of advanced support strategies and examine emerging therapeutic approaches in this uniquely vulnerable population. Methods: Our methodology encompassed a systematic review of the literature published between 2011 and 2024, complemented by a detailed analysis of current international guidelines and expert consensus statements. We included randomised controlled trials, observational studies, meta-analyses, and consensus conference proceedings specifically addressing the intensive care management of paediatric patients with oncological or haematological conditions. Main Results: Several key findings emerge from our analysis. The Phoenix 2024 criteria represent a fundamental reconceptualisation of paediatric sepsis diagnosis, validated through an unprecedented dataset encompassing more than 3 million paediatric encounters. In the realm of respiratory support, early implementation of non-invasive ventilation (NIV) or continuous positive airway pressure (CPAP) has demonstrated remarkable efficacy, reducing the need for invasive mechanical ventilation by 45% (RR 0.45, 95% CI 0.26–0.78) when applied to appropriately selected patients. Extracorporeal membrane oxygenation (ECMO), whilst increasingly utilised, shows survival to decannulation ranging from 52% to 64%, though survival to hospital discharge remains less encouraging at 36–42%. Continuous renal replacement therapy (CRRT) has proven highly effective for tumour lysis syndrome, achieving metabolic correction in 90% of severe cases. Perhaps most promisingly, emerging biomarkers—particularly interleukin-6, interleukin-10, and procalcitonin—have substantially enhanced our ability to stratify infection risk, demonstrating sensitivity exceeding 85% for bacteraemia detection. Conclusions: The evidence unequivocally supports several core principles for optimising outcomes in this population. Early identification of deterioration through validated scoring systems enables timely intervention before irreversible organ failure develops. Prompt implementation of non-invasive respiratory support, when appropriately applied, can obviate the need for mechanical ventilation with its attendant complications. Perhaps most critically, centralisation of care in centres with dedicated expertise and comprehensive support capabilities fundamentally improves survival. These findings argue compellingly for the establishment of a formal national network of reference centres, implementing standardised protocols and structured care pathways specifically designed for critically ill paediatric oncology haematology patients. Full article

Built heritage is increasingly affected by climate-driven processes, yet its capacity to inform broader understandings of urban environmental change remains insufficiently explored. Here, we synthesize the recent literature (2020–2024) on the application of the Historic Urban Landscape (HUL) approach to the integrated management of cultural heritage under climate risk, reframing the historic built environment as a multi-scale diagnostic medium for climate–urban interactions. We analyze the steps and tools employed to support decision-making across territorial planning, risk assessment, and heritage governance in the papers selected from Web of Science, Science Direct, and Scopus databases. Results show that the approach is a flexible analytical framework that allows the integration of heterogeneous data, multi-criteria evaluations, and diverse stakeholder perspectives across spatial and temporal scales. Information modeling tools are shown to play a central role in structuring territorial knowledge, identifying patterns of vulnerability, and supporting comparative analyses across urban contexts. Nonetheless, significant challenges persist, including limited quantification of climate-induced degradation mechanisms, uncertainties in linking vulnerability assessments to predictive models, structural constraints on participatory implementation, and a tendency to apply the approach as a checklist due to inadequate understanding of its holistic dimensions. Overall, the HUL approach emerges as a scalable and transferable framework for embedding cultural heritage within climate research, advancing the conceptual integration of built heritage into resilience science and sustainability-oriented urban systems. Full article

Renewable energy sources, and wind energy in particular, constitute a central pillar of energy policy at both national and European levels. Nevertheless, the deployment of onshore wind farms is frequently associated with spatial, environmental, and social conflicts, making the evaluation of existing projects imperative. The present study aimed to assess the sustainability of existing onshore wind farms in the Region of Thessaly, with particular emphasis on their spatial planning, technical characteristics, and environmental impacts. The methodological framework consists of four distinct stages: (i) identification and spatial mapping of existing wind farms in the study area, (ii) assessment of the compliance of existing wind installations with the Specific Framework for Spatial Planning and Sustainable Development for Renewable Energy Sources (SFSPSD–RES), (iii) application of the Rapid Impact Assessment Matrix (RIAM) to enable a systematic and comparable evaluation of the impacts of wind installations on specific environmental and anthropogenic parameters, and (iv) estimation of project hazard and operational vulnerability through the application of Operational Risk Management (ORM). Geographic Information Systems (GISs) were employed for data processing and spatial analysis. The assessment showed that 40% of the evaluated wind farms fully comply with all eleven exclusion criteria of the SFSPSD-RES, whereas the remaining 60% show partial compliance, failing to meet between one and three criteria. RIAM results indicate that the most significant adverse impacts (−D and −C) during construction are associated with morphology/soils and the natural environment, mainly due to loss/fragmentation of vegetation and disturbance of fauna, and, in some cases, in areas of increased sensitivity. During operation, the main negative effects (−D and −C) relate to landscape and visual quality, as well as continued disturbance to the natural environment. At the same time, the operation generates important positive effects (+E) on the atmospheric environment through reduced CO₂ emissions. The ORM analysis further shows that the most important risks for most wind farms arise during construction (ORM = 2 and 3), particularly from serious worker accidents during lifting, roadworks, and foundation activities. The study demonstrates that the sustainability of existing wind installations depends on a complex set of spatial, environmental, and technical factors. The proposed framework integrates spatial compliance screening, RIAM-based environmental impact assessment, and ORM-based risk and opportunity evaluation. This connection links the importance of impacts with their operational manageability during construction and operation phases, as well as across sustainability dimensions. Consequently, the study provides a more decision-focused approach for assessing existing wind farms and supporting policy development. Full article

With accelerating global urbanization and increasingly diverse demands for public spaces, promoting urban low-carbon transitions and enhancing residents’ quality of life have become central missions of modern urban development. As one of the city’s primary arteries, streets—through their green landscapes, slow-moving transportation systems, and public facilities—play an indispensable role in reducing carbon emissions, promoting healthy living, and improving residents’ well-being. In this study, the Yubei District of Chongqing was selected as the research area, and an automated evaluation framework was proposed for street visual quality, based on multi-source street view data and ensemble learning. PSP-Net semantic segmentation model was employed to extract eight key visual indicators from street view images, including green view index, Visual Entropy (Entropy), sky view factor (SVF), drivable space, sidewalk, safety facilities, buildings, and enclosure. Based on these features, a Stacking-based ensemble learning model was constructed, integrating multiple base models such as Random Forest, XGBoost, and LightGBM, with Linear Regression as the meta-learner, to predict street visual quality. The results demonstrate that the ensemble model significantly outperforms any single model, achieving a correlation coefficient (r) of 0.77 and effectively capturing the complex perceptual features of street environments. This study provides a reliable, intelligent, and quantitative method for large-scale evaluation of urban street visual quality, while supplying data support and decision-making references for street renewal and spatial optimization. Full article

This paper develops a rigorous algebraic framework for quasi-substructures in Sheffer-based Nelson algebras, extending the landscape of fuzzy algebraic theory. By systematically introducing $(\in ,\in \vee q)$-bipolar fuzzy quasi-subalgebras and ideals, we analyze their structural properties through generalized belongingness and quasi-coincidence relations. We formalise invariant threshold symmetry as the condition $g^{+}\left(\chi \right)+\left|g^{-}\left(\chi \right)\right|=c$ for a constant $c\in [0,2]$ and every $\chi \in \Omega$ (Definition 10) and prove its structural preservation within $(\in ,\in \vee q)$-bipolar fuzzy quasi-subalgebras (Theorem 4, supported by Theorems 3, 15 and 16). This enables a balanced dual evaluation of positive and negative information. Characterization theorems are established via level subsets, revealing how quasi-substructure properties are governed by bounds at critical membership values. Equivalence results unify classical and bipolar fuzzy perspectives, demonstrating that algebraic constraints preserve structural coherence across crisp and fuzzy environments. Algorithmic verification procedures are provided for practical validation in finite systems, and illustrative examples highlight applications in uncertainty modeling and decision support. Overall, the proposed theory formalizes bipolar fuzzy structures in Sheffer-based Nelson algebras, utilizing invariant threshold symmetry, level-set decomposition, and crisp equivalence to evaluate dual information. Full article

Cultural and natural heritage are increasingly framed as components of territorial governance rather than isolated conservation elements; yet, a structural gap persists between their strategic recognition in planning documents and their measurable integration into statutory land-use systems that guide spatial decision-making. This gap is particularly pronounced in protected areas, where ecological integrity, cultural and symbolic values, tourism functions, and socio-economic expectations converge within environmentally sensitive landscapes. This study develops and empirically applies a compatibility-based analytical framework that embeds Multi-Criteria Decision Analysis (MCDA) within the statutory spatial planning system of Kozara National Park. The framework combines (i) institutional analysis of legally binding planning instruments, (ii) zoning-aligned analytical units derived from the Special-Purpose Spatial Plan and Management Plan, and (iii) a weighted multi-criteria model incorporating ecological integrity, cultural–historical significance, tourism and recreation capacity under controlled use, and socio-economic feasibility. Climate-related disturbance exposure is incorporated as a planning-relevant modifier of ecological compatibility. Composite compatibility scores under the baseline configuration range from 2.55 to 3.85 across analytical units. Rank correlation analysis suggests a high degree of structural consistency across both alternative weighting configurations relative to the baseline scenario (Spearman’s ρ ≈ 0.90), with only limited rank reordering observed, primarily between the two highest-ranked analytical units. Dispersed low-intensity recreational configurations demonstrate the highest structural robustness, whereas infrastructure-intensive zones exhibit management-dependent compatibility. The findings show how spatial planning in protected areas can operationalize compatibility as a measurable decision-support principle without substituting statutory zoning logic. Full article

The urgency to mitigate greenhouse gas emissions and address the accelerating impacts of climate change has placed renewable energy as a core part of global climate strategies. However, the expansion of renewable infrastructures with a focus on solar systems often generates competition with other land uses, raising concerns about land availability, environmental integrity, and social acceptance. Renewable energy solutions deployment must be aligned with sustainable land-use planning, particularly in diverse and multifunctional landscapes. This study presents a GIS-based Multi-Criteria Decision-Making (MCDM) methodology to identify the most suitable areas for implementing a set of six land-use-based adaptation and mitigation solutions (LAMSs) focused on solar energy. Using Python-based processing algorithms and high-resolution spatial datasets, the methodology integrates technical, environmental, and socioeconomic criteria to generate suitability maps for three different case studies across Europe: Almería (Spain), Valle d’Aosta (Italy), and the Azores (Portugal). Results reveal significant geographical disparities in suitability due to the different land constraints. Almería and the Azores demonstrate high potential for photovoltaic and agrovoltaic farms, while Valle d’Aosta’s mountainous terrain is more limited for these measures. Floating solar and solar land management measures show limited applicability across all sites. The analysis highlights the value of place-based approaches in energy planning and the utility of GIS-MCDM tools to support evidence-based decision-making, enabling context-sensitive deployment of renewable energy infrastructure. Full article

Background: Artificial intelligence (AI) holds transformative potential for mental health services. However, existing reviews have predominantly focused on algorithmic accuracy, with limited attention to how these technologies are implemented and integrated into real-world service delivery. This scoping review addresses this gap by examining the contexts in which AI technologies—including large language models (LLMs) and machine learning—are implemented, as well as the factors influencing their sustainable adoption within real-world mental health service systems. Methods: Following the established methodological framework, a systematic search (2015–2026) was conducted in PubMed and Scopus. Two independent reviewers screened an initial pool of 829 records using Zotero and Rayyan to minimize selection bias. Following title, abstract, and full-text screening based on predefined eligibility criteria, 26 studies focusing on real-world AI applications (e.g., clinical settings, community services, and case management) were included in the final synthesis. Results: The findings indicate a rapid acceleration in research, with 50% of included studies (n = 13) published since 2024. AI-driven decision support systems were the most prevalent (50%, n = 13), followed by predictive machine learning models (27%) and generative AI applications (15%). Most tools were designed for clinician use (77%) and implemented in hospital-based settings (46%). Although 46% of studies reported real-world implementation, more than half remained at the pilot stage. Notably, research emphasis has shifted from technical efficacy toward feasibility, and implementation contexts (n = 17). Conclusion: AI in mental health is transitioning from laboratory validation to real-world integration. However, the current landscape remains heavily centered on clinician workflows and screening functions, with limited expansion into community-based recovery and long-term prevention. To move beyond the pilot stage, future initiatives should prioritize seamless workflow integration and the application of structured ethical and implementation frameworks that support clinician–patient relationships. This review provides an evidentiary basis for advancing sustainable, AI-enhanced mental health service delivery. Full article

Remote sensing has become a cornerstone of modern agricultural monitoring, addressing the dual challenges of increasing production while ensuring environmental sustainability. Based on a conceptual framework developed over the past decade, key application areas include yield estimation, phenology, stress assessment (e.g., drought), crop mapping, and land-use change detection. In Central Europe, regionally specific conditions such as fragmented land ownership, small and irregular plots, and high climate variability shape these applications. Annual field crops, such as cereals, oilseeds, maize, and forage crops dominate production and represent the primary focus of monitoring efforts. Optical data from Sentinel-2 are effective for mapping crop types and analyzing phenology, especially when dense time series are available. However, persistent cloud cover during critical growth phases limits the effectiveness of optical approaches, prompting the integration of radar data from Sentinel-1. Multi-sensor strategies increase the robustness of classification and temporal continuity, supporting monitoring under adverse conditions. Reliable reference data from systems such as the Land Parcel Identification System enables parcel-level validation and facilitates object-oriented analyses in line with management needs. Future developments will increasingly rely on advanced time-series analysis, machine learning, and the integration of agrometeorological and crop model data. As climate change intensifies drought frequency and yield variability, remote sensing will play a pivotal role in enabling near-real-time monitoring and decision support within the evolving landscape of digital agriculture ecosystems. The aim of this review article is to provide an overview of crop monitoring in the Central European region over approximately the past fifteen years, emphasizing trends in subsequent technological and procedural developments. Full article

Although urban streetscape visual esthetic quality (VAQ) assessment has progressed markedly, its findings are rarely operationalized in urban planning policy-making. The resulting discontinuity in the assessment–policy linkage is a critical impediment to streetscape VAQ enhancement. We propose an eye-tracking-enabled, end-to-end decision support framework that links evidence acquisition, intervention prioritization, design strategy formulation, and outcome feedback. Eye tracking is integrated to establish a three-dimensional assessment system spanning spatial, psychological, and physiological dimensions. Within this integrated system, we construct a three-level eye-tracking-based visual characteristics (ET-VC) framework across streetscape elements, formal characteristics, and public esthetic perception (PAP). Together, the three-dimensional system provides a theoretical basis for acquiring the multi-modal data required for VAQ enhancement. Building on this integrated assessment, we embed scenario planning theory to construct a planning facing decision model with PAP as the core outcome. The model combines importance-performance analysis (IPA) with the coupling coordination degree model (CCDM) to guide resource allocation decisions and intervention prioritization, and further uses eye-tracking evidence to support the development of refined, actionable enhancement strategies. A case study in Wudadao validates the framework’s robustness and feasibility. The ET-VC results provide additional evidence for interpreting esthetic perception: (1) ET-VC indicators differ significantly across streetscape elements, and “being viewed more” does not necessarily correspond to higher esthetic ratings; (2) four groups of key formal characteristic indicators—color configuration, naturalness, historicity and planning/regulatory control, and visual scale—systematically reshape fixation onset and maintenance patterns; and (3) PAP appears to involve partially nonlinear relationships between material landscape features and additional top-down influences (e.g., historical narratives and individual experience), rather than being fully explained by linear associations alone. Overall, this study provides both a theoretical basis and an applied demonstration for evidence-based streetscape VAQ enhancement. Full article

Schools are increasingly recognised as critical public infrastructure for urban climate adaptation, particularly in heat-vulnerable and park-poor neighbourhoods. This study examines schoolyards as distributed cooling systems, social spaces, and educational landscapes and proposes an integrated decision support approach for programme comparison and prioritisation. A comparative review of nine international schoolyard transformation programmes (Paris, Barcelona, Madrid, Milan, Rotterdam, Los Angeles, New York, Melbourne, and Santiago de Chile) was conducted using municipal plans, reports, and implementation guidance. Design strategies, governance configurations, and monitoring approaches were synthesised through a CAME (Correct, Adapt, Maintain, Explore) framework. Building on this synthesis, a Multicriteria Analysis framework was developed to support prioritisation across four criteria families: environmental and climatic performance, social and educational equity, urban integration and accessibility, and feasibility and co-benefits. The results highlight a recurrent toolkit of interventions—depaving, tree planting, shade provision, cool and permeable surfaces, nature-based drainage systems, and monitoring practices—that is consistently associated in the reviewed evidence with improved thermal comfort, stormwater performance, biodiversity, and community use beyond school hours. It is concluded that a combined CAME–Multicriteria Analysis structure provides a transferable basis for transparent, criteria-based prioritisation of schoolyard interventions by local governments and school authorities. Full article

Manufacturing enterprises face increasing complexity in managing the complete life cycle of production systems, requiring integration of information from diverse sources to support timely maintenance, diagnostics, and operational decisions. This paper presents a comprehensive service-oriented architecture (SOA) for decision support in industrial life-cycle management, integrating real-time monitoring, predictive maintenance, and collaborative problem-solving across extended manufacturing enterprises. The architecture implements a three-layer service model comprising eight core collaborative services, three application services, and six life-cycle management services, orchestrated through a risk assessment module that monitors life-cycle parameters and triggers appropriate maintenance, diagnostics, or hazard prevention actions. The system was developed in the context of a European research project and validated in two industrial settings: automotive assembly lines at a German SME and air conditioning manufacturing at a Portuguese company. Results demonstrated substantial operational improvements, including reduced problem resolution time, lower diagnostic travel requirements, reduced spare-parts consumption, and increased structured problem registration. The original SOAP-based web-services implementation is further contextualized within the contemporary Industry 4.0 landscape through comparison with microservices architectures and discussion of integration paths involving OPC UA, Asset Administration Shells, and digital twins. The paper contributes a validated reference architecture for service-based industrial life-cycle management and clarifies its relevance as an early precursor of contemporary smart manufacturing approaches. Full article

Amid rapid urbanization in China, widespread gated residential districts have created physical and visual isolation from surrounding nature, undermining environmental benefits and daily accessibility. The emergence of a twenty-first-century “sharing” paradigm reshapes how buildings and landscapes are used and experienced, opening new opportunities for diversified sharing between communities and natural systems. Yet, despite mature research on city-scale landscape sharing, micro-scale tools to balance sharing versus exclusive route allocation—and to operationalize cross-system sharing-route design—remain limited. This study examines nature-integrated community design through the Landscape Route Sharing Ratio (LRSR), a metric derived from the Length and Density of Sharing Landscape Route (L_s/D_s), the Length and Density of Non-shared Landscape Route (L_ns/D_ns). It analyzes eight cases using a mixed-methods approach (field surveys, spatial mapping, planning-document review and quantitative measurement), and identifies five core cross-system features through typological analysis: extension to surrounding landscapes (ENL), cross-boundary landscape axes (CBLA), multi-scale hierarchy (MSH), multi-elevation systems (MES), and non-motorized priority (NMP). This study demonstrates that higher LRSR values significantly enhance landscape integration and pedestrian experiences. By establishing actionable target ranges (0.50–0.70), the research provides a practical decision-support tool for nature-integrated community design, advancing the methodological understanding of how shared routes foster ecological and social vitality in contemporary urban environments. The framework effectively bridges the gap between quantification with design guidance for nature-integrated communities. Full article

Background: Chronic kidney disease (CKD) and end-stage renal disease (ESRD) represent a growing global health burden, affecting nearly one in ten adults worldwide. CKD is associated with high morbidity, premature mortality, reduced quality of life and enormous healthcare costs, and is primarily driven by dialysis and kidney transplantation. The silent and progressive nature of CKD means that most patients are diagnosed late, when irreversible damage has already occurred and costly kidney replacement therapies (KRT) become necessary. Dialysis services are resource-intensive, requiring significant infrastructure, specialized staff, and consumables, which makes them especially challenging to sustain in low- and middle-income countries. Traditional models of nephrology, care center-based dialysis and fragmented follow-up are increasingly inadequate in meeting the demands of a rising CKD population. These challenges highlight the urgent need for innovative approaches that enhance efficiency, improve patient outcomes, and expand access. Objective: This review aims to analyze the current landscape of information and communication technology (ICT) applications in nephrology and to evaluate how digital innovations are reconfiguring kidney therapy. Specifically, it seeks to identify the major ICT tools that are currently in use, assess their clinical and operational impact, and discuss their role in creating more sustainable, patient-centered kidney care models. This study reviews and analyzes ICT tools that are reconfiguring nephrology, including remote monitoring, AI, wearables, patient engagement apps and data dashboards. Methods: Narrative and scoping review of recent innovations in nephrology, including remote patient monitoring (RPM), telehealth, artificial intelligence (AI) analytics, wearable sensors, and clinical decision support platforms. Results: ICT tools such as Sharesource, Versia, telenephrology platforms, medical assistant for Chronic Care Service (MACCS), AI-based predictive analytics, wearable devices and patient engagement apps have improved patient outcomes, adherence, and early detection of complications. Key metrics include technique survival, hospitalization rate, patient-reported outcomes, workflow efficiency, and prediction accuracy. The relevant literature describing the potential of digital health technologies, including ICT platforms, artificial intelligence tools, and remote monitoring systems, to transform nephrology care was retrieved and screened for inclusion in this narrative review. Conclusions: ICT has shifted nephrology from reactive to proactive care, enhancing accessibility, patient empowerment and clinical efficiency. Future directions include precision nephrology, fully wearable kidneys, AI integration and large language models for education and triage. Challenges include digital divide, regulatory heterogeneity, cost and the need for long-term evidence. Full article