Search Results (903)

Teacher wellbeing is a matter of social justice since burnout syndrome disproportionately affects those working in under-resourced and diverse educational contexts by limiting their ability to foster inclusive and equitable learning. To this situation, art museums respond as pedagogical spaces for wellbeing while contributing to socially just and sustainable arts education. School teachers are offered new opportunities for ongoing professional development tailored to their well-being needs, such as burnout prevention. A two-round international Delphi study with experts from universities, schools, museums, and arts-and-wellbeing organizations (n = 26 1st round, n = 17 2nd round)—rather than focusing on teachers’ personal accounts—develops consensus on a pedagogical framework for art-based programs designed to prevent teacher burnout and enhance wellbeing. The findings identify nine pedagogical guidelines highlighting participatory approaches—audience, objectives, content, methodology, scheduling, facilitators, activities, evaluation, and program adherence. By positioning art museums as democratic, inclusive, and relational spaces, the framework advances the role of the arts in addressing systemic challenges in education, such as supporting teachers’ wellbeing. This research contributes to the international debate on socially just arts education by demonstrating how teacher wellbeing can be fostered through innovative, evidence-based museum practices aligned with SDG 4. Full article

Atmospheric electricity measurements are very sensitive to weather conditions. Fair weather for atmospheric electricity in Kamchatka (Russia) was determined by the method of expert assessment at an observatory. After the transition to automated digital methods for measuring meteorological parameters, the necessity to determine the criteria of fair weather appeared. In this paper we developed the criteria for fair weather based on digital measurements in summer and winter observation periods in view of a limited set of meteorological instruments. A database of fair weather since 2009 up to the present was created. We suggest the algorithm to determine fog during a day on the basis of air humidity measurements. The morning convective generator effect occurs sometimes in diurnal variations in atmospheric electricity. The morning convection maximum is determined by the sunrise time. This entails the problems of averaging the electric field diurnal variation over a long time period. We suggest taking into account the days with morning convective generator effect and the days without this effect separately when processing a long series of data. Full article

Transcatheter aortic valve implantation (TAVI) is the default therapy for most elderly patients with severe aortic stenosis, but outcomes in complex anatomy depend on imaging-guided planning and disciplined technique. This article aims to present our institutional approach, supported by the current literature, in managing several challenging anatomical scenarios. We focus on seven high-impact scenarios—bicuspid aortic valve (BAV), hostile transfemoral access, iliofemoral/aortic tortuosity, adverse aortic angulation, heavy annulus/Left Ventricular Outflow Tract (LVOT) calcification, small annulus, and risk of coronary obstruction—and propose a practical approach to minimize the risk of complications. In BAV, current generation transcatheter heart valves (THV) achieve favorable early outcomes when sizing accounts for supra-annular constraints and implantation depth is tailored. Transfemoral access remains dominant in contemporary registries, yet a meaningful minority of cases require adjunctive peripheral vascular intervention to enable THV delivery-system passage. In case of annulus or LVOT calcification, small annuli, complex aortic anatomy, high risk for coronary obstruction, and pre-procedural Computed Tomography (CT) allow for an accurate sizing of THV and tailored procedural planning. A structured, CT-driven pathway that links anatomic findings to specific facilitation and bailout steps can standardize decision-making and improve safety across these challenging scenarios. We strongly highlight the importance to build a network where most complex procedures are carried out in Valve Centers where expert operators are trained to manage high volume, high complexity, and difficult complications. Full article

The Amazon wetlands are the largest and most diverse freshwater ecosystem globally, characterized by various flooded vegetation and the Amazon River’s estuary. This critical ecosystem is vulnerable to land use changes, dam construction, mining, and climate change. While several studies have utilized remote sensing to map wetlands in this region, significant uncertainty remains, which limits the assessment of impacts and the conservation priorities for Amazon wetlands. This study aims to enhance wetland mapping by integrating existing maps, remote sensing data, expert knowledge, and cloud computing via Earth Engine. We developed a harmonized regional wetland classification system adaptable to individual countries, enabling us to train and build a random forest model to classify wetlands using a robust remote sensing dataset. In 2020, wetlands spanned 151.7 million hectares (Mha) or 22.0% of the study area, plus an additional 7.4 Mha in deforested zones. The four dominant wetland classes accounted for 98.5% of the total area: Forest Floodplain (89.0 Mha; 58.6%), Lowland Herbaceous Floodplain (29.6 Mha; 19.6%), Shrub Floodplain (16.7 Mha; 11.0%), and Open Water (14.1 Mha; 9.3%). The overall mapping accuracy was 82.2%. Of the total wetlands in 2020, 52.6% (i.e., 79.8 Mha) were protected in Indigenous Territories, Conservation Units, and Ramsar Sites. Threats to the mapped wetlands included 7.4 Mha of loss due to fires and deforestation, with an additional 800,000 ha lost from 2021 to 2024 due to agriculture, urban expansion, and gold mining. Notably, 21 Mha of wetlands were directly affected by both reduced precipitation and surface water in 2020. Our mapping efforts will help identify priorities for wetland protection and support informed decision-making by local governments and ancestral communities to implement conservation and management plans. As 47.4% of the mapped wetlands are unprotected and have some level of threats and pressure, there are also opportunities to expand protected areas and implement effective management and conservation practices. Full article

Tax practitioners (such as accountants and bookkeepers) are important enablers of tax compliance. Taxpayers, particularly small businesses, look to tax practitioners for expert advice because of increasingly complex tax legislation. This study’s purpose was to examine tax practitioners’ perspectives on tax policy and SME compliance in South Africa. This study looks at the perspective of tax practitioners to extend information on tax policy and its effect on the tax compliance of SMEs. A total of 90% of companies in South Africa are classified as SMEs, which account for more than 80% of employment in the economy. Despite the importance of the SME sector in job creation, tax policies and the costs associated with them are major issues affecting the overall regulatory environment and they are identified as a major threat to SMEs’ growth. This study seeks to close this gap by examining practitioners’ perspectives on tax policy and SME compliance in South Africa. This study adopted a quantitative approach using a self-administered questionnaire which was emailed to a sample of 255 tax practitioners by using a link through QuestionPro, and this study applied descriptive statistics in analysing data. This study indicated that tax practitioners have sufficient experience and qualifications to prepare and handle tax matters for SMEs. This study demonstrated that SMEs register for taxes, file annual returns, and pay tax liability within the period stipulated by tax law. It further indicated that being tax-compliant has certain benefits for SMEs. This research is intended to assist tax authorities and the government in better creating measures to address the problem of tax compliance among SMEs in South Africa. This article adds to the body of knowledge because it uses the opinion of tax practitioners to extend debate on tax policy in tax compliance and its effect on the functioning of SMEs. Full article

Claims are a significant cause of delays and increased costs in international construction projects, yet contract provisions on claims remain inconsistent, narrative, and difficult to apply in practice. This study presents a concise, knowledge-driven checklist for effective claim management in major standard forms, including International Federation of Consulting Engineers (FIDIC), the New Engineering Contract (NEC4), the American Institute of Architects (AIA), and Singapore’s Public Sector Standard Conditions of Contract (PSSCOC). The research mapped 22 entitlement clauses and 12 procedural clauses, then prioritized items through expert interviews and surveys. The final checklist comprises 16 items selected through transparent criteria (mean scores ≥ 4.0 or above group averages) that address critical risk areas. Application to two complex projects demonstrates that a few key clauses, such as those governing variations and timing requirements for requests, supporting documents, and decisions, account for most claim-related risks. Experts indicate that practical periods for submitting claim requests and proofs, and making decisions, are approximately 31, 65, and 61 days, respectively. The proposed checklist converts fragmented contract requirements into an actionable and auditable tool. It enhances clarity, transparency, and fairness in both pre-award reviews and daily project administration, which supports better risk management and minimizes disputes in global construction projects. Full article

The application of the same global legal regulations to areas with different climates, landscapes, and cultural and urban conditions may ultimately lead to decisions that are unsuitable for the region, which could result in poor investment and development decisions for the municipality. This article examines how sustainability regulations established locally, in response to local conditions, differ from global regulations created without considering the differences between the areas to which they apply. Selected criteria were assessed in relation to global and local regulations, and then, based on these criteria and their weights, rankings of the strengths and weaknesses of municipalities were proposed in relation to the selected criteria, the weights of which were evaluated depending on the adopted global or local regulations. The AHP method was used to conduct this multi-criteria assessment, based both on expert group opinions and artificial intelligence tools. The aim of this analysis was to demonstrate differences in the hierarchies of sustainable development aspects implemented globally and locally, as well as local conditions. The assessment results indicate discrepancies between expert knowledge, which takes into account local conditions, and the priorities resulting from general legal regulations. Some areas important from a local perspective, such as building density or mixed-use development, are insufficiently addressed in legal regulations, both under Polish and EU law and local law. This also contradicts current trends in urban planning theory, which advocates a shift away from zoning. Others, such as energy efficiency in buildings and renewable energy sources, are strongly present in both national and EU law but are not implemented in local regulations. Full article

West Nile Virus (WNV) is an emerging arboviral threat in Europe, with rising incidence in Spain since 2004. In 2024, Spain experienced its largest outbreak, primarily in small urban areas of south-western regions. We report a subset of an emergency integrated vector management program, focusing on six municipalities accounting for one-third of all human WNV cases nationwide. Over four months, 725 potential larval sites were inspected during 4026 visits. Adult mosquitoes (n = 2553) were collected with suction traps, and immature stages (n = 4457) with dipper techniques, yielding 11 species. Culex pipiens s.l. was predominant, while Cx. perexiguus, though less abundant, was epidemiologically significant. Cytochrome Oxidase I (COI) gene phylogenetic analysis confirmed Cx. perexiguus, forming a distinct clade from Cx. univittatus. Immature mosquitoes were found in 18.6% of sites, especially irrigation canals, ditches, and backwaters near urban areas. Habitat differences in larval abundance were analyzed using generalized linear mixed models. Targeted larviciding with Bacillus thuringiensis var. israelensis (Bti) and focal adulticiding with cypermethrin totaled 259 interventions (70.4% larviciding, 29.6% adulticiding). A significant 63.9% reduction in larval abundance was observed after five consecutive Bti treatments, with some variation among treatment cycles (52.2–75.5%). Adult activity persisted into late autumn. This study provides the first comprehensive characterization of larval mosquitoes in Spain’s main WNV hotspot, highlighting the need for rapid, coordinated expert interventions and extended seasonal control to prevent future outbreaks. Full article

Introduction: A considerable body of evidence shows significant racial inequities in health and health care, affecting access, care and treatment for patients, as well as the wellbeing of employees. Many hospitals and health care organizations have committed to anti-racist change within their systems. Still, there is limited systematic knowledge regarding organizational anti-racist practices, the conditions under which they can be implemented successfully and their effectiveness. This research aims to identify anti-racist practices within health care organizations with a special focus on three areas: (1) increasing workforce diversity, (2) reducing racial health disparities and (3) responding to discriminatory behavior. Moreover, the role of different stakeholders in implementing anti-racist change will be analyzed, as well as the challenges organizations have encountered and strategies they have utilized to implement change. Methods: Primary (n = 11) and secondary qualitative data (n = 26) were used to gain insights from anti-racism diversity experts and health equity officers within organizations across the US in the beginning of 2024. A qualitative content analysis was used to identify anti-racist practices in organizations. Results: Findings reveal a broad range of anti-racist practices in use across these organizations. These practices include (1) collecting patient and staff data, (2) actively normalizing and implementing anti-racist work standards and guidelines, (3) developing organizational policies and tools to address racism, (4) creating accountability procedures for addressing racist behavior and (5) building safe and culturally appropriate spaces for racialized communities. By embedding a structural anti-racist lens across these organizations, stakeholders acknowledge their role in (past) harms and commit to addressing disparities in health care and creating a vision for health equity. Conclusion: The identification of anti-racist practices makes solutions visible to a broader audience and identifies the potential influence and responsibility each stakeholder in health care has to address racism. In order to apply these practices to other health care organizations, there is a need to rigorously evaluate the interventions and analyze their effectiveness. Full article

Higher Education Institutions (HEI) face increasing expectations to engage in sustainability reporting despite limited resources and heterogeneous practices. This study explores how Joint Sustainability Reports (JSR), built on the EU Voluntary Sustainability Reporting Standard for non-listed SMEs (VSME), can serve as a cooperative and digitally supported framework to enhance transparency, comparability, and efficiency while strengthening universities’ third mission of societal engagement and knowledge transfer. Qualitative interviews with six sustainability experts from German and Austrian universities of applied sciences (UAS) highlight persistent challenges such as data gaps, staffing shortages, and weak strategic anchoring. The findings show that VSME-based JSRs, through shared data collection, centralised coordination, and modular reporting, enable resource and data pooling, standardised indicators, and cross-university synergies. By making social contributions more visible and credible, JSRs reinforce accountability and advance universities’ third mission in fostering community outreach and sustainable development. Full article

Context: Population segmentation is a critical health system planning activity that enables more integrated, needs-responsive, and sustainable care. This paper describes the development and evaluation of a Systemic Health System Population Segmentation Model based on the person-centred and needs-based Unified Care Model by Yishun Health, a regional population health system in Singapore. We highlight three implications to enhance health systems operational relevance: (i) psychosocial factors as key determinants of outcomes, (ii) accountability and resource allocation across differentiated segments, and (iii) integration of lifelong and episodic care needs. Methods: Three interdependent models were developed, a Lifelong Care Segmentation Model, a Needs-Based Sub-Segmentation Model, and an Episodic Care Segmentation Model, all underpinned by the Unified Care Model. These models systematically stratify residents into mutually exclusive and collectively exhaustive population groups based on biopsychosocial needs across different health system levels. An expert-driven design process was used, supported by integrated administrative and clinical data. Model evaluation examined the ability to stratify patients into distinct risk groups using healthcare utilisation, costs, and readmission outcomes. Findings: In 2022, 78,810 residents were segmented into seven lifelong care segments, with 43,473 residents with chronic conditions further stratified into sub-segments reflecting varying complexity and psychosocial needs. Additionally, 14,335 emergency admissions were categorised into six episodic care segments. Healthcare utilisation and annual healthcare costs differed significantly across needs-based sub-segments (p < 0.001). Higher episodic care needs were associated with longer hospital stays, higher rates of emergency readmissions, and admission costs (p < 0.001). Psychosocial issues consistently emerged as a key determinant of poorer outcomes, underscoring implications for more systemic and systematic accountability assignment and more deliberate resource planning, especially for care integration horizontally. The integration of lifelong and episodic care needs further enabled operational redesign for vertically integrated health systems. Conclusions: By incorporating psychosocial drivers, focusing on clarifying accountability and resource allocation, and lifelong-episodic care integration, our Systemic Health System Population Segmentation Model strengthens the operational utility of segmentation as a foundation for population health system transformation and provided a robust framework for health systems governance and leadership system redesign globally. Full article

Digital transformation has driven the use of artificial intelligence (AI) in local government financial reporting to improve efficiency, transparency, and accountability. This study employs a systematic literature review (SLR) approach to analyze 20 relevant articles, identifying common characteristics of publications, research focus, methods, AI technologies used, key findings, research gaps, and future research directions. The analysis results show the dominance of machine learning and expert systems in detecting fraud, predicting financial performance, and improving reporting accuracy. However, limitations in infrastructure, regulations, and system integration across government agencies remain significant challenges to implementing AI in the public sector. This study proposes the need for the development of practical implementation models, collaboration between academics, government, and technology developers, as well as the formulation of policies that support ethical and responsible AI governance. These findings make a significant contribution to shaping the strategic direction of AI utilization to strengthen local government financial reporting systems sustainably. Full article

To address the limitation that single-index evaluation fails to fully reflect the development performance of reservoirs of different types and at various development stages, a multi-index comprehensive evaluation system featuring the workflow of “index screening–weight determination–model evaluation–strategy guidance” was established. Firstly, the grey correlation analysis method (with a correlation degree threshold set at 0.65) was employed to screen 12 key evaluation indicators, including reservoir physical properties (porosity, permeability) and development dynamics (recovery factor, water cut, well activation rate). Subsequently, the fuzzy analytic hierarchy process (FAHP, for subjective weighting, with the consistency ratio (CR) of expert judgments < 0.1) was coupled with the attribute measurement method (for objective weighting, with information entropy redundancy < 5%) to determine the indicator weights, thereby balancing the influences of subjective experience and objective data. Finally, two evaluation models, namely the fuzzy comprehensive decision-making method and the unascertained measurement method, were constructed to conduct evaluations on 308 reservoirs in the Liaohe Oilfield (covering five major categories: integral medium–high-permeability reservoirs, complex fault-block reservoirs, low-permeability reservoirs, special lithology reservoirs, and thermal recovery heavy oil reservoirs). The results indicate that there are 147 high-efficiency reservoirs categorized as Class I and Class II in total. Although these reservoirs account for 47.7% of the total number, they control 71% of the geological reserves (154,548 × 10⁴ t) and 78% of the annual oil production (738.2 × 10⁴ t) in the oilfield, with an average well activation rate of 65.4% and an average recovery factor of 28.9. Significant quantitative differences are observed in the development characteristics of different reservoir types: Integral medium–high-permeability reservoirs achieve an average recovery factor of 37.6% and an average well activation rate of 74.1% by virtue of their excellent physical properties (permeability mostly > 100 mD), with Block Jin 16 (recovery factor: 56.9%, well activation rate: 86.1%) serving as a typical example. Complex fault-block reservoirs exhibit optimal performance at the stage of “recovery degree > 70%, water cut ≥ 90%”, where 65.6% of the blocks are classified as Class I, and the recovery factor of blocks with a “good” rating (42.3%) is 1.8 times that of blocks with a “poor” rating (23.5%). For low-permeability reservoirs, blocks with a rating below medium grade account for 68% of the geological reserves (8403.2 × 10⁴ t), with an average well activation rate of 64.9%. Specifically, Block Le 208 (permeability < 10 mD) has an annual oil production of only 0.83 × 10⁴ t. Special lithology reservoirs show polarized development performance, as Block Shugu 1 (recovery factor: 32.0%) and Biantai Buried Hill (recovery factor: 20.4%) exhibit significantly different development effects due to variations in fracture–vug development. Among thermal recovery heavy oil reservoirs, ultra-heavy oil reservoirs (e.g., Block Du 84 Guantao, with a recovery factor of 63.1% and a well activation rate of 92%) are developed efficiently via steam flooding, while extra-heavy oil reservoirs (e.g., Block Leng 42, with a recovery factor of 19.6% and a well activation rate of 30%) are constrained by reservoir heterogeneity. This system refines the quantitative classification boundaries for four development levels of water-flooded reservoirs (e.g., for Class I reservoirs in the high water cut stage, the recovery factor is ≥35% and the water cut is ≥90%), as well as the evaluation criteria for different stages (steam huff and puff, steam flooding) of thermal recovery heavy oil reservoirs. It realizes the transition from traditional single-index qualitative evaluation to multi-index quantitative evaluation, and the consistency between the evaluation results and the on-site development adjustment plans reaches 88%, which provides a scientific basis for formulating development strategies for the Liaohe Oilfield and other similar oilfields. Full article

The real estate industry, known for its complexity and exposure to systemic and idiosyncratic risks, requires increasingly sophisticated investment risk assessment tools. In this study, we present the Real Estate Construction Investment Risk (RECIR) model, a machine learning-based framework designed to quantify and manage multi-dimensional investment risks in construction projects. The model integrates diverse data sources, including macroeconomic indicators, property characteristics, market dynamics, and regulatory variables, to generate a composite risk metric called the total risk score. Unlike previous artificial intelligence (AI)-based approaches that primarily focus on forecasting prices, we incorporate regulatory compliance, forensic risk assessment, and explainable AI to provide a transparent and accountable decision support system. We train and validate the RECIR model using structured datasets such as the American Housing Survey and World Development Indicators, along with survey data from domain experts. The empirical results show the relatively high predictive accuracy of the RECIR model, particularly in highly volatile environments. Location score, legal context, and economic indicators are the dominant contributors to investment risk, which affirms the interpretability and strategic relevance of the model. By integrating AI with ethical oversight, we provide a scalable, governance-aware methodology for analyzing risks in the real estate sector. Full article

Background: Dementia refers to a spectrum of clinical conditions characterized by impairments in memory, language, and cognitive function. Alzheimer’s Disease (AD) is the most common cause of dementia and it accounted for 60–70% of the estimated 57 million cases worldwide as of 2021. The exact pathology of this neurodegenerative condition is not fully understood. While it is currently incurable, progression to more critical stages can be slowed, and early diagnosis is crucial to alleviate and manage some of its symptoms. Contemporary diagnostic practices hinder early detection due to the high costs and inaccessibility of advanced neuroimaging tools and specialists, particularly for populations with resource-constrained clinical settings. Methods: This paper addresses this challenge by developing and evaluating computationally efficient lightweight deep learning models, MobileNetV2 and EfficientNetV2B0, for early AD detection from 2D slices sourced from standard structural magnetic resonance imaging (MRI). Results: For the challenging multi-class task of distinguishing between Cognitively Normal (CN), Early Mild Cognitive Impairment (EMCI), and Late Mild Cognitive Impairment (LMCI), our best model, EfficientNetV2B0, achieved 88.0% mean accuracy across a 5-fold stratified cross-validation (std = 1.0%). To enhance clinical interpretability and build trust, we integrated explainability methods, Grad-CAM++ and Guided Grad-CAM++, to visualize the anatomical basis for the models’ predictions. Conclusions: This work delivers an accessible and interpretable neuroimaging tool to support early AD diagnosis and extend expert-level capabilities to routine clinical practice. Full article