Search Results (3,535)

Grasslands represent an essential resource for rural economies and for the provision of ecosystem services, yet they are increasingly affected by anthropogenic pressures, functional land-use changes, and institutional constraints. This study develops a geospatial decision-support framework for assessing grassland suitability in Hunedoara County, Romania, by integrating the Analytic Hierarchy Process (AHP) and Weighted Overlay Analysis (WOA) within a GIS environment. The assessment is based on nine criteria thematically grouped into three dimensions: (A) physical-geographical, including topographic suitability, climatic pressure, and hydrological risk exposure; (B) ecological and conservation-related, reflected by ecological conservation value, ecological carrying capacity, and the anthropic pressure index; and (C) socio-economic and functional, represented by spatial accessibility, recreational value, and policy support mechanisms. Suitability is defined as the integrated capacity of grasslands to sustain productive and multifunctional uses compatible with ecological conservation and the existing policy framework. Results indicate that 0.43% of the grassland area exhibits very high suitability (Class 1), 44.51% high suitability (Class 2), and 54.75% moderate suitability (Class 3), while unfavorable areas account for only 0.31% of the total (Class 4). The proposed methodology is reproducible and transferable, providing support for prioritizing management interventions, agri-environmental payments, and rural planning in mountainous and hilly regions. Full article

Objectives: This study aims to explore the facilitators and barriers in the process of using AI agents for disease management in older COPD patients. Methods: Based on the normalization process theory, a descriptive qualitative study was used to conduct semi-structured interviews with 28 older patients with COPD recruited from June to August 2025 in a Class A tertiary hospital in Wuxi, Jiangsu Province. Results: A total of 28 interviews were conducted. Four themes (Coherence, Cognitive Participation, Collective Action, Reflexive Monitoring), nine subthemes (recognition of intelligent technology;supported by policy discourse and the background of national-level projects; the creation of a family atmosphere; recommendations from HCPs; relief and social connection; new “doctor”–patient relationship and communication; eliminate the burden and return to life; benefit and value perception; right self-decision by AI) in facilitators and nine subthemes (privacy conflicts and trust deficiency; blurred boundaries of human–machine responsibility and authority; non-high-quality services are chosen reluctantly; technical anxiety; lack of motivation for continued engagement; extra burden; limitations of the physical environment; human–machine dialogue frustration; a sense of uncertainty about the future of AI) in barriers were extracted. Conclusions: This study identified key factors influencing the use of AI agents in chronic disease management in older patients with COPD. The results provide directions for improving the implementation and sustainable use of AI health technologies. Full article

Extreme wildfires are becoming increasingly frequent and severe across many regions worldwide, driven by climate change, land-use transitions, and long-standing fire-suppression legacies. In this context, Nature-based Solutions (NbS)—defined as actions that work with ecological processes to address societal challenges while providing biodiversity and socio-economic benefits—offer a promising yet underdeveloped pathway for enhancing wildfire resilience. This Special Issue brings together eleven contributions spanning empirical ecology, landscape configuration, simulation modelling, spatial optimisation, ecosystem service analysis, governance assessment, and community-based innovation. Collectively, these studies demonstrate that restoring ecological fire regimes, promoting multifunctional landscapes, and integrating advanced decision support tools can substantially reduce wildfire hazard while sustaining ecosystem functions. They also reveal significant governance barriers, including fragmented policies, limited investment in prevention, and challenges in incorporating social demands into territorial planning. By synthesising these insights, this editorial identifies several strategic priorities for advancing NbS in fire-prone landscapes: mainstreaming prevention within governance frameworks, strengthening the science–practice interface, investing in long-term socio-ecological monitoring, managing trade-offs transparently, and empowering local communities. Together, the findings highlight that effective NbS emerge from the alignment of ecological, technological, institutional, and social dimensions, offering a coherent pathway toward more resilient, biodiverse, and fire-adaptive landscapes. Full article

Accurate modeling of Battery Energy Storage Systems (BESS) is essential for optimizing system performance, ensuring operational safety, and extending service life in applications ranging from electric vehicles (EV) to large-scale grid storage. However, the simplifications inherent in conventional battery models often hinder optimal system design and operation, leading to conservative performance limits, inaccurate State-of-Energy (SOE) estimation, and reduced overall efficiency. This paper presents a framework for advanced battery modeling, developed to achieve higher fidelity in SOE estimation and improved power-capability prediction. The proposed model introduces a dynamic energy-based representation of the charging and discharging processes, incorporating a functional dependence of instantaneous power on stored energy. Experimental validation confirms the superiority of this modeling framework over existing state-of-the-art models. The proposed approach reduces SOE estimation error to 0.1% and cycle-time duration error to 0.82% compared to the measurements. Consequently, the model provides more accurate predictions of the maximum charge and discharge power limits than state-of-the-art solutions. The enhanced predictive accuracy improves energy utilization, mitigates premature degradation, and strengthens safety assurance in advanced battery management systems. Full article

Organisations increasingly combine social innovation and environmentally orientated technologies in response to sustainability and stakeholder pressures. However, empirical evidence remains limited on how organisational actors perceive and cognitively associate social innovation, green technologies, and practices related to the Sustainable Development Goals (SDGs), particularly in emerging European economies. This study addresses this gap by examining whether employees and managers perceive these dimensions as interconnected and whether green technologies represent a statistically significant indirect association between social innovation and SDG-related organisational practices. Using a cross-sectional online survey of 265 employees and managers from Romanian companies in production, services, IT, and commerce, we estimated an exploratory structural model through partial least squares structural equation modelling (PLS-SEM). The results reveal strong positive associations between perceived social innovation and SDG-related organisational practices, as well as between perceived social innovation and green technologies. Green technologies are also positively associated with SDG-related practices and exhibit a statistically significant indirect association within the observed pattern of associations. Consistent with perception-based research design, these findings suggest that respondents cognitively group social and technological initiatives as complementary components of a broader sustainability orientation, rather than indicating statistical or process-based mechanisms. The study contributes to organisational sustainability research by integrating social innovation and green technologies within a single organisational-level framework and by providing context-specific evidence from Romania, an under-represented central and Eastern European context. Full article

Learning to teach represents a complex process of professional and personal transformation that involves constructing new professional identities, negotiating prior experiences, and engaging with the epistemologies of teaching. For both first- and second-career entrants, ITE is not only a pathway into employment but a significant phase of identity (re)formation and learning within the higher education context. Understanding the needs and challenges that pre-service teachers articulate during this phase therefore provides critical insight into how teacher education functions as a space of learning and belonging. Despite this, empirical research addressing this issue remains limited. Accordingly, the present study sought to address this gap by exploring the differences in the needs and challenges of first- and second-career pre-service teachers. Analysis of qualitative data collected from 123 Australian first- and second-career pre-service teachers revealed that both groups identified a strong need for more authentic, practice-based learning and sustained mentorship to bridge the persistent gap between theory and classroom practice. Despite shared concerns around behaviour management, assessment, and wellbeing, second-career entrants emphasised challenges related to transferring prior professional identities, navigating school cultures, and balancing study with family and financial responsibilities. These findings highlight the importance of differentiated, career-responsive approaches in initial teacher education that recognise diverse life experiences as central to professional learning and teacher identity formation. Full article

Despite the immense potential of digital intelligence technologies to enhance corporate profitability, manufacturing enterprises often face the “digital–green paradox”, which indicates that while companies invest in digital and intelligent transformation, their energy consumption increases rather than promoting green transition. To provide reasonable transformation solutions for manufacturers still caught in this paradox, this paper adopts a single-case study approach from a product lifecycle perspective. Focusing on FAW-Volkswagen—a manufacturing enterprise demonstrating outstanding performance in digital-intelligent green transformation—this study conducts an in-depth investigation into the stage characteristics and underlying mechanisms. The results show that the following: (1) The digital-intelligent green transformation of manufacturing enterprises is an iterative process evolving from “green design, low-carbon production, intelligent service to enterprise spiral value-added”, with distinct digital-intelligent empowerment models at each stage. (2) By leveraging digital-intelligent technologies, manufacturing enterprises can build a multi-tiered “internal-external dual circulation” green development system encompassing the “enterprise—industrial chain—full ecosystem,” driving comprehensive green upgrades across the entire industry and ecosystem. This paper reveals the intrinsic mechanisms through which digital-intelligent technologies facilitate manufacturing enterprises’ green transformation. It expands and enriches the research context and theoretical implications of product lifecycle management, offering management insights and strategic references for other enterprises pursuing green transformation and upgrading pathways in the digital-intelligent economy era. Full article

This article presents a comprehensive bibliometric analysis of the indexed academic literature on the application of distributed ledger technology (DLT) and blockchain in the tourism industry. Using the bibliometrix library within the RStudio environment, key bibliometric indicators were examined in order to characterize the evolution, structure, and thematic focus of this emerging field of research. The systematic literature review, which adhered to PRISMA guidelines, involved retrieving publications from the Web of Science and Scopus databases. A curated dataset of 100 relevant documents was identified and analyzed in terms of annual scientific production, leading journals, influential authors, and highly cited publications. The results indicate that blockchain technology dominates the literature, with a strong emphasis on its potential to enhance trust, transparency, and efficiency in tourism-related processes. In particular, identity management, secure transactions, and disintermediation emerge as central research themes, reflecting blockchain’s capacity to support decentralized, immutable, and privacy-preserving interactions between tourists and service providers. Overall, the findings reveal a rapidly growing and increasingly structured body of knowledge, highlighting emerging research directions and technological challenges for future studies on DLT applications in tourism. Full article

The aim of this article is to present the social–ecological system (SES) as a unit of analysis for sustainable water management under conditions of anthropogenic pressure in Poland. In the face of accelerating climate change and growing human impacts, Polish water systems are exposed to increasing ecological stress and to material and immaterial losses affecting local communities. The SES approach provides an integrative analytical framework that links ecological and social components, enabling a holistic view of adaptive and governance processes at multiple spatial scales, from municipalities to areas that transcend administrative boundaries. Methodologically, this study triangulates three complementary approaches to strengthen explanatory inference. This conceptual SES review defines the analytical categories used in the paper, the bibliometric mapping (Scopus database with VOSviewer) identifies dominant research streams and underexplored themes, and the qualitative Polish case studies operationalize these categories to diagnose mechanisms, feedbacks, and governance vulnerabilities under anthropogenic pressure. The bibliometric analysis identifies the main research streams at the intersection of SES, water management and sustainable development, revealing thematic clusters related to climate change adaptation, environmental governance, ecosystem services and hydrological extremes. The case studies - the 2024 flood, the 2022 ecological disaster in the Odra River, and water deficits associated with lignite opencast mining in Eastern Wielkopolska - illustrate how anthropogenic pressure and climate-related hazards interact within local SES and expose governance gaps. Particular attention is paid to attitudes and social participation, understood as configurations of behaviors, knowledge and emotions that shape decision-making in local self-government, especially at the municipal level. This study argues that an SES-based perspective can contribute to building the resilience of water systems, improving the integration of ecological and social dimensions and supporting more sustainable water management in Poland. Full article

Effective asset management is crucial for improving the reliability, resilience, and cost efficiency of distribution networks throughout the asset life cycle. Distribution transformers are among the most critical components, as their failures can cause extensive service interruptions and substantial economic impacts. Therefore, robust and transparent maintenance prioritization strategies are essential, particularly for utilities managing several transformers. Traditional time-based maintenance, while simple to implement, often results in inefficient resource allocation. Condition-based maintenance provides a more effective alternative; however, its performance depends strongly on the reliability of indicator selection and weighting. This study proposes a systematic weighting framework for distribution transformer maintenance prioritization using a multi-criteria decision-making (MCDM) approach. Each transformer is evaluated across two dimensions, including health condition and operational impact, based on indicators identified from the literature and expert judgment. To address uncertainty and judgmental inconsistency, particularly when the consistency ratio (CR) exceeds the conventional threshold of 0.10, the Fuzzy Analytic Hierarchy Process (FAHP) is employed. Seven condition parameters characterize transformer health, while impact is quantified using five indicators reflecting failure consequences. The proposed framework offers a transparent, repeatable, and defensible decision-support tool, enabling utilities to prioritize maintenance actions, optimize resource allocation, and mitigate operational risks in distribution networks. Full article

Technological innovation plays a vital role in enhancing the economic growth and sustainability of the forestry sector. However, research on the nature, dynamics, and impact of such innovations, particularly within small and medium-sized enterprises (SMEs), remains limited. The forestry sector is often characterised by low levels of technological advancement and a traditionally conservative attitude toward change. Limited expertise, financial constraints, and ownership structures further influence the potential for innovation. This study examines the development of innovation among SMEs in Lithuania’s forestry sector and its contribution to sustainable forest management. Forestry innovations are understood as new processes, products, or services introduced by forest owners and managers to improve management efficiency and sustainability. The study employed the method of a structured questionnaire survey to evaluate technological, organisational, and financial aspects of innovation adoption among small and medium-sized enterprises in the forestry sector. Drawing on comparative survey data from 2005 and 2024, the study analyses the types of innovations implemented by forestry enterprises, the factors driving or hindering their adoption, and the evolving trends in innovation application. The results reveal a significant shift toward digitalisation and technology-based management practices, suggesting that Lithuanian forestry enterprises are gradually transitioning toward a more innovation-driven model. These developments appear to be influenced by the EU Green Deal policy framework, evolving innovation support mechanisms, and broader socio-economic changes. Nonetheless, technological transformation introduces new challenges, including the need for workforce upskilling and enhanced adaptability to rapidly changing market conditions. Full article

Accurate nearshore bathymetry is essential for various marine applications, including navigation, resource management, and the protection of coastal ecosystems and the services they provide. This study presents an approach to enhance the accuracy of bathymetric estimates derived from high-spatial- and high-temporal-resolution optical satellite imagery. The proposed technique is particularly suited for multispectral sensors that acquire spectral bands sequentially rather than simultaneously. PlanetScope SuperDove imagery was employed and validated against bathymetric data collected using a multibeam echosounder. The study area is the Gulf of Sciacca, located along the southwestern coast of Sicily in the Mediterranean Sea. Here, multibeam data were acquired along transects that are subparallel to the shoreline, covering depths ranging from approximately 7 m to 50 m. Satellite imagery was radiometrically and atmospherically corrected and then processed using a simplified radiative transfer transformation to generate a continuous bathymetric map extending over the entire gulf. The resulting satellite-derived bathymetry achieved reliable accuracy between approximately 5 m and 25 m depth. Beyond these limits, excessive signal attenuation for higher depths and increased water turbidity close to shore introduced significant uncertainties. The innovative aspect of this approach lies in the combined use of spectral averaging among the most water-penetrating bands, temporal averaging across multiple acquisitions, and a liquid-facets noise reduction technique. The integration of these multi-layer inputs led to improved accuracy compared to using single-date or single-band imagery alone. Results show a strong correlation between the satellite-derived bathymetry and multibeam measurements over sandy substrates, with an estimated error of ±6% at a 95% confidence interval. Some discrepancies, however, were observed in the presence of mixed pixels (e.g., submerged vegetation or rocky substrates) or surface artifacts. Full article

Source plasma centers sustain hematology therapeutics by safeguarding testing, traceability, and cold-chain integrity before fractionation. Despite regulatory requirements (21 CFR 606/640; EU Directive 2005/62/EC), published pre-operational validation frameworks demonstrating deviation-readiness before first collections remain sparse. We conducted a simulation-based pre-operational validation of an electronic quality management system (eQMS) with an Incident → Deviation → Corrective Action and Preventive Action (CAPA) pathway at a new source plasma center, performing 20 chairside mock runs, 3 freezer-alarm drills, and a document-control stress test. Primary endpoints were anomaly rate, alarm-response time relative to a 15 min service-level agreement (SLA), and deviation-closure SLA compliance. Analyses were descriptive and designed to demonstrate system functionality, not long-term process stability. Minor anomalies occurred in 6/20 mock runs (30.0%; 95% CI 11.9–54.3); no major/critical events were observed (0/20; 95% CI 0–16.8). Deviation-closure SLAs were met in 6/6 tests (100%; 95% CI 54.1–100). Alarm-response times averaged 7.0 min (SD 1.0; range 6–8 min; 95% CI 4.5–9.5), and all drills met the 15 min vendor SLA, illustrating a preliminary readiness margin (Cpu ≈ 2.7) rather than a statistically stable capability estimate. Simulation-based pre-operational validation produced inspection-ready documentation and quantitative acceptance criteria aligned to U.S./EU expectations, supporting reproducible multi-site deployment. By protecting cold-chain integrity and traceability before first collections, the validated QMS helps preserve supply reliability for plasma-derived therapeutics central to hematology care and establishes the measurement infrastructure for post-operational performance validation. Full article

As agricultural labor shifted toward non-farm sectors and the farming population aged, innovative production arrangements became essential to sustain land productivity. While partial agricultural production chain management (PAPM) was widespread, the productivity impact of whole-process agricultural production chain management (WAPM)—a comprehensive model integrating all production stages—remained empirically underexplored. Using nationally representative panel data from the China Labor-force Dynamics Survey (CLDS, 2014–2018) for grain-producing households, this study estimates the differential impacts of WAPM and PAPM with a two-way fixed-effects (TWFE) model, supplemented by propensity score matching (PSM) as a robustness check. The results show that WAPM significantly enhanced land productivity. Notably, the effect size of WAPM (coefficient: 0.486) is substantially larger than that of PAPM (coefficient: 0.214), indicating that systematic integration of service chains offers superior efficiency gains over fragmented outsourcing. Mechanism analysis suggests that WAPM improves productivity primarily by alleviating labor constraints and mitigating the disadvantages of small-scale farming. Furthermore, heterogeneity analysis demonstrated that these benefits are amplified in major grain-producing regions and hilly areas. These findings support policies that facilitate a transition from single-link outsourcing toward whole-process integrated service provision. Full article

We focus on a queueing model in which the sizes of arriving jobs are stochastically dependent and each job may be denied service with a probability determined by the queue size (active management). Both of these effects are known to occur in computer networking and many other real-world realizations of queueing systems. For such a model, we perform a thorough transient and stationary analysis of the job departure process and the job rejection process. The results include theorems on the expected number of jobs that depart within a specified time interval, the departure intensity at a given time, the stationary departure rate, the expected number of jobs rejected within a specified interval, the transient rejection intensity and the stationary rejection rate. Sample numerical calculations are provided for illustration. They include various settings of the level of dependence between jobs, job rejection probabilities, and system load, as well as their impact on the departure and rejection processes. Full article