Flooding severely threatens the livelihoods of coastal populations, especially as the effects of climate change become more pronounced. However, coastal adaptations alongside their barriers and enabling factors remain underexplored. This study is aimed at filling this gap by comprehensively examining coastal adaptations and their associated barriers and enabling factors toward enhanced community resilience in the face of intensifying climate change impacts. To achieve this goal, a systematic review is employed, and the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) procedure is followed. The key terms used for searching relevant records in the Scopus database are local adaptation and coastal flooding. After the inclusion/exclusion criteria are applied and the timeline is limited to 2000–2025, the final screening yields 45 records. For analysis, Evidence for Policy and Practice (EPPI)-Reviewer, which is a web-based software renowned for its robustness in assisting systematic reviews, is utilized. This study shows that records with cases in Bangladesh and the U.S. disproportionately dominate the literature. The majority of those records, exploring local knowledge, adaptation practices, and people's adaptive capacities, are qualitative and cross-sectional, indicating a significant lack of longitudinal studies that examine the dynamics of coastal adaptations and their impacts on local social–ecological systems over time. Additionally, accommodation adaptations, soft adaptations, and retreat adaptations are the most frequently mentioned in Global South cases, with institutional barriers and social cohesion serving as the most frequently identified challenges and enabler, respectively. Finally, an actionable framework for coastal adaptations is offered to help guide government and local communities in enhancing their adaptive capacities. This research contributes to efforts to improve coastal community resilience, and it lays the groundwork for future research by providing insights into the specific barriers and enablers of adaptations present in vulnerable areas, particularly in the Global South.
This systematic review synthesises empirical evidence on learner-facing artificial intelligence (AI) in programming education. It examines how intelligent, adaptive, generative, recommender, assessment, and feedback systems support programming-skill acquisition; what motivational, engagement, collaborative, and problem-solving outcomes are reported; and what ethical, technological, accessibility, and access-related challenges arise. Following PRISMA guidelines, a Scopus search identified English-language journal articles published between 2010 and 2025, of which 76 studies met the narrowed eligibility criteria. The findings suggest that AI-supported systems appear most educationally valuable when they combine personalised learning pathways, learner-facing assessment, adaptive scaffolding, and timely process-oriented feedback. Reported outcomes include improvements in programming performance, conceptual understanding, motivation, engagement, self-efficacy, collaboration, and problem-solving, although effects vary according to learner experience, prior knowledge, task type, and instructional design. Motivational benefits were reported across adaptive, conversational, reflective, collaborative, problem-based, and immersive environments; however, explicitly game-based or gamified evidence was limited. The most consistently documented implementation challenges concerned inaccurate or misleading AI outputs, overreliance, academic-integrity uncertainty, unequal access, and differences in learners' capacity to verify generated solutions. Direct empirical evidence on algorithmic bias, formal transparency mechanisms, privacy-preserving design, and institutional data governance remained sparse. Overall, AI should be understood as a complement to sound pedagogy, human judgement, peer interaction, and learner agency. Future research should use stronger comparative and longitudinal designs, evaluate independent learning after AI support is withdrawn, and examine reliability, equity, privacy, and subgroup differences more directly.
This study aims to assess the long-term spatio-temporal dynamics and magnitude of the Surface Urban Heat Island (SUHI) in the intermediate, high-altitude Andean city of Cuenca, Ecuador (∼2560 m a.s.l.). This research addresses a significant gap in urban climatology by analyzing the SUHI evolution over 34 years, utilizing Landsat data from 1989 to 2022. The analysis focused on the correlation between Landsat-derived Land Surface Temperature (LST) and key biophysical indices: the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-up Index (NDBI), which serve as proxies for characterizing surface cooling and warming mechanisms, respectively. The results confirm a strong and intensifying SUHI effect in Cuenca, characterized by a net increase of 1.28 °C in SUHI Intensity (SUHII) over the study period, rising from 4.47 °C (1989) to 5.75 °C (2022). Urban areas consistently exhibited mean temperatures 4.47–5.75 °C higher than surrounding unurbanized regions. The mechanisms driving this thermal anomaly are confirmed by strong correlations: LST exhibits a negative correlation with NDVI (R2 = 0.22 to 0.59), suggesting that vegetative cover provides evaporative cooling, while the strong positive correlation with NDBI (R2 = 0.47 to 0.59) indicates that the increase in impervious, thermally massive surfaces contributes significantly to SUHI intensification. These findings provide specific evidence for the magnitude and long-term trend of surface warming within a high-altitude Andean environment, underscoring the necessity of adopting climate-responsive urban planning measures centered on green infrastructure and surface material selection to mitigate the escalating SUHI effect in this context.
The irresponsible behavior of enterprises can seriously disrupt market order, break market rules, and bring adverse effects to economic development. This study focuses on the supply chain perspective and confirms the impact of supplier corporate social irresponsibility (CSI) on focal firm. We collected data on corporate social irresponsibility events of Chinese listed companies and their suppliers from 2011 to 2021, and proposed research hypotheses based on social contagion theory. The data analysis results indicate that when the supplier's CSI increases, on the one hand, the CSI of the focal firm will also significantly increase; On the other hand, the CSR of focal firm has decreased. In addition, this study further analyzed the impact of cooperation intensity, industry sensitivity, and information disclosure on the contagion process, proposing four specific response mechanisms: “those who approach the dark”, “self-protection”, “moral erosion”, and “moral compensation”. Our conclusion extends the impact of CSI from within the enterprise to between the enterprise and its suppliers, explaining the mechanism of external environmental factors affecting the contagion mechanism in different contexts, and providing theoretical support for regulating CSI.
The study introduces a novel dual-relationship modelling concept for integrated supply chain management, addressing the complex interactions among lean, green, and resilient practices in the automotive industry. The integration of these practices is increasingly recognised as essential for achieving long-term competitiveness and sustainability, yet it requires a structured understanding of both synergistic and conflicting relationships within supply chain systems. To capture this dual nature, the research develops and applies an enhanced methodological framework based on Interpretive Structural Modelling (ISM) and MICMAC analysis, incorporating dual-relationship matrices that simultaneously reflect complementary and conflicting relationships between key logistics and supply chain practices. The developed model enables the identification of leverage practices, including cross-functional teams, information and knowledge sharing, supplier collaboration and partnerships, green monitoring, green ICT system, crisis response teams, enhanced supplier communication, and supplier support and assistance, that drive coordinated transformation, while also revealing practices that may create systemic tensions if not properly managed. The proposed concept offers a decision-support framework for managers in the automotive industry and establishes a transferable methodological foundation applicable to other sectors facing the challenge of sustainable supply chain integration.
This study investigated the spatial distribution and radiological risks of naturally occurring radioactive material (NORM) in soils around the Geita Gold Mine (GGM) in Tanzania and assessed community awareness and perception of associated health and ecological risks. Soil samples were collected from ten farms located across six villages and analyzed using gamma-ray spectrometry to determine the activity concentrations of 226Ra, 232Th, and 40K. The average concentrations were 53 ± 7 Bq kg−1 for 226Ra, 36 ± 5 Bq kg−1 for 232Th, and 205 ± 38 Bq kg−1 for 40K. Radiological indices such as radium equivalent activity (Raeq), hazard indices (Hex, Hin), absorbed dose rate (DR), annual effective dose equivalent (AEDE), organ doses (Dorgan) and excess lifetime cancer risk (ELCR) were computed. All average values were below the global safety limits, although some localized values exceeded the thresholds, indicating the need for continued monitoring. A parallel cross-section survey of 390 residents revealed that only 35.4% were aware of the NORM and its associated risks. Awareness was significantly associated with education level, age and occupation (p < 0.01). Among informed respondents, perceptions of risks were highest for wildlife, water quality, and human health. Moreover, correlations between perceived environmental degradation and impacts on agriculture and biodiversity were also noted. The findings underscore the importance of integrating scientific assessments with public education to inform risk communication and environmental policy. While current radiological risks remain within acceptable limits, the combination of localized elevations in radionuclide levels and low public awareness highlight the need for targeted awareness campaigns and community engagement in mining-impacted regions.
Background Nursing errors in emergency departments significantly threaten patient safety. However, comprehensive data on their types, underlying causes, and reporting barriers remain limited in Iran. This study was conducted to determine the types of nursing errors, contributing factors, and barriers to reporting in emergency departments of educational-therapeutic hospitals in Tabriz. Materials and methods A cross-sectional study was conducted from November 2023 to April 2024 in six educational-therapeutic hospitals in Tabriz, northwestern Iran. A total of 144 nurses were selected via stratified random sampling. Data were collected using three validated questionnaires: Demographic Information Form, Underlying Causes of Nursing Errors (Mashouf), and Barriers to Reporting Nursing Errors (Mardani & Shahraki). Statistical analysis was performed using SPSS version 26, employing independent t-tests, Chi-square, and Fisher's exact tests. A significance level of p < 0.05 was considered. Results Of the participants, 31.3% reported experiencing at least one nursing error in the past year (with a total of 41% error occurrences, as some nurses reported multiple errors). Job dissatisfaction (p = 0.017), witnessing colleagues’ errors (p < 0.001), and prior error reporting (p < 0.001) were significantly associated with error occurrence. Communication errors were most prevalent (35.4%), followed by operational errors (average of three sub-items: medication 25%, care 25.7%, and equipment 19.4%, yielding a mean of 23.4%). Management factors (mean = 2.16, SD = 0.45) and environmental/physical factors (mean = 2.14, SD = 0.46) were identified as the most significant causes. Management-related barriers to reporting (mean = 3.90) were rated higher than employee-related barriers (mean = 3.58). Conclusion It is critical that emergency departments address communication issues, improve work conditions, and promote a non-punitive culture to reduce nursing errors and enhance reporting. Preventive strategies must be prioritized to mitigate potentially irreversible consequences.
Chickpea (Cicer arietinum L.) is the second most widely cultivated food legume crop globally, serving as a vital source of dietary protein and improving soil fertility. However, plant-parasitic nematodes (PPNs) pose a serious threat to the sustainability of chickpea cultivation. Estimates of various studies on major crops indicate that PPNs cause global economic losses up to 157-173 billion US$ worldwide annually. The major challenge to control the PPNs in infected crops is their hidden, soil-dwelling nature and wide host range resulting in early detection and effective management difficult. PPN infection often also increases the severity of other soil-borne pathogens. PPN species causing significant economic damage in chickpea include root-knot (Meloidogyne spp.), cyst (Heterodera spp.), and root-lesion nematodes (Pratylenchus spp.). Identifying and utilizing inherent resistance in host plants, along with agronomic management practices, is considered the most effective approach to controlling PPNs. Accordingly, various studies have screened substantial chickpea germplasm collections against the major PPNs, viz. Meloidogyne incognita (∼1250 accessions), M. javanica (∼700), Heterodera ciceri (∼9700), Pratylenchus thornei (∼2200), P. neglectus (∼1750), and Rotylenchulus reniformis (∼600). These screenings have revealed that resistance sources in the cultivated gene pool are very rare. However, relatively better resistance level is observed in wild Cicer species. In this review, we have discussed the economic importance and distribution of PPNs, the life cycle of different species of PPNs infecting chickpea, PPN-host plant interactions, resistant donors identified, and modern tools to accelerate chickpea crop improvement against PPNs.
Objective Liquid crystal display (LCD) three-dimensional (3D) printing has been increasingly used for fabricating provisional restorations; however, evidence regarding the influence of printing angle on dental applications remains limited. This study evaluated the effects of printing angle on the fracture strength, marginal fit, and surface roughness of single-unit provisional crowns fabricated using an LCD-based 3D printer. Materials and methods Anatomical crown specimens (maxillary second molars, 2 mm occlusal thickness) and disc specimens (5 mm diameter, 2 mm thickness) were fabricated using an LCD 3D printer and a urethane dimethacrylate-based resin at four printing angles (0°, 15°, 30°, and 45°; n = 19/group). Crowns were evaluated for fracture strength (universal testing machine, 1 mm/min) and marginal fit (silicone replica technique). Discs underwent surface roughness evaluation via confocal laser scanning microscopy (ISO 21920). Data were analyzed using One-way ANOVA, Kruskal–Wallis, and Bonferroni post-hoc tests (α = 0.05). Results FS, marginal fit, and surface roughness significantly differed among the groups (p < 0.05). The 15° orientation exhibited the highest mean FS (2220.43 ± 392.97 N), while the 30° orientation showed the lowest (1782.51 ± 111.77 N; p < 0.0001), with all values exceeding the average maximum occlusal load. The 30° orientation demonstrated the optimal marginal fit (97.88 ± 8.49 μm; p = 0.0091); all tested orientations remained below the 120 μm clinical threshold. Surface roughness (Ra) was lowest at 0° (1.10 ± 0.16 μm) and highest at 15° (4.39 ± 0.15 μm; p < 0.0001). Conclusions Printing angles significantly influence the mechanical and surface properties of LCD-printed restorations. The 0° and 15° orientations optimized fracture strength, whereas the 30° orientation minimized marginal gap discrepancy. Although all angles provided clinically acceptable fracture strength and marginal fit, surface roughness varied distinctly depending on the orientation.
Through-thickness reinforcement methods are widely used to enhance the mechanical performance of fibre-reinforced plastics (FRPs), yet their adaptation to wood veneers remains unexamined. This study presents a novel investigation into the feasibility and transferability of thread-based through-thickness reinforcement techniques, such as sewing or tufting, to wood veneers and engineered wood products, providing new insights into potential reinforcement methods and manufacturing approaches. Needle penetration resistance of wood veneers from different species (poplar, birch and beech), under varying moisture conditions, and with a selection of needle geometries were examined. The damage introduced through needle penetration was characterised in a biaxial load case (slow rate penetration testing according to ASTM F1306), and the extent of the damage was assessed with reflected-light microscopy. The overarching goal is to provide the basis for the development of an innovative sewing or tufting based reinforcement method for veneer-based wood laminates, which could function as a sustainable alternative to through-thickness reinforced FRPs. Results indicate that wood veneers can be penetrated by sewing needles at forces comparable to, though higher than, those found in FRP tufting processes. Additionally, needle geometry and moisture conditions of the veneers significantly influence the penetration process, affecting the required penetration forces substantially. While veneer perforation can lead to substantial degradation, the findings highlight key parameters for process optimisation, offering a pathway towards integrating through-thickness reinforcements in wood-based composites.
The cajuzinho-do-cerrado (Anacardium humile St. Hil.) is a native fruit of the Brazilian Cerrado biome, traditionally harvested extractively and valued for its high content of bioactive compounds such as phenolic compounds, anthocyanins, and vitamin C. However, its high perishability limits large-scale commercialization, requiring the development of efficient postharvest conservation strategies. This study aimed to evaluate the effect of different packaging types on the physicochemical, functional, and visual quality of A. humile fruits stored under refrigeration (6 °C) for up to 15 days. A completely randomized design (6 × 6 factorial) was used, consisting of six packaging types and six storage periods, with three replicates. Analyses were performed every three days for weight loss, firmness, respiration rate, color, soluble solids, titratable acidity, total extractable phenolic compounds, and antioxidant activity. Packaging type and storage duration significantly affected the evaluated quality attributes, with responses varying according to the specific variable and storage period. BD, PP, and PET were the most effective packaging systems for minimizing weight loss, maintaining values below 2% at the end of their respective evaluation periods. In contrast, EPS and NY exhibited substantially greater dehydration and lost acceptable commercial quality before the final evaluation at day 15. Firmness, color attributes, total extractable phenolic compounds, and antioxidant activity showed distinct packaging- and time-dependent responses, with no single packaging system consistently exhibiting superior performance for all attributes throughout storage. Pearson correlation and principal component analyses indicated that weight loss and respiration rate were associated with storage progression and quality deterioration, whereas firmness, color attributes, total extractable phenolic compounds, and FRAP antioxidant activity were generally associated with better-preserved samples. Overall, packaging effectiveness depended on both the quality attribute considered and storage duration. Packaging systems with greater moisture retention, particularly BD, PP, and PET, were effective in limiting fruit dehydration, while the preservation of physicochemical and functional attributes varied among packaging systems over time. These findings provide a basis for selecting packaging strategies according to specific postharvest quality requirements and may contribute to the commercial valorization of A. humile and its inclusion in sustainable value chains.
Background Inadequate communication skills can worsen the emotional well-being of patients and their families and reduce trust in nursing care. In this descriptive and correlational study, we examined nurses’ self-reported knowledge and perceived ability to deliver bad news to patients. Methods The study was conducted with 375 nurses in a hospital in southern Turkey. Research data were collected using the “Bad News Delivering Scale”. Data were analyzed using IBM SPSS Statistics version 25.0. The STROBE checklist was used to report this study. Results The mean scale score of the nurses was 54.62 ± 8.28 (possible range: 21–63). Weak but statistically significant positive correlations were found between scale scores and age (r = 0.178, p < 0.01) and years of professional experience (r = 0.186, p < 0.01). Nurses who perceived their skills as good or moderate had significantly higher scores compared to those who perceived them as very poor (p < 0.001). Discussion The findings of this study highlight the importance of effective and empathetic communication skills in nursing practice. Involving patients in the care process, seeking their input, and collaborating with them enable patient-centered care and communication. The results of this study show that nurses' age, as well as their years of professional experience and education level, are related to their perception of their ability at delivering bad news to patients. Conclusions In this study, nurses' age, education level, and length of professional experience were found to be associated with a higher perceived ability to deliver bad news.This study found that most nurses believe breaking bad news is not a nurse's responsibility. This is significant because it may lead to communication gaps, delays in emotional support, and disruptions in continuity of care.
Purpose The DIERS Postura reconstructs spinal curvature from back surface topography using an algorithm derived from the established DIERS Formetric. By replacing rasterstereography with an Azure Kinect depth camera, it offers a cost-efficient, portable alternative. This study assesses how this change affects Postura′s reliability and its agreement with Formetric to identify potential systematic errors. Methods Measurements were taken consecutively with the Postura and Formetric while the subjects maintained the same posture. Thirty-one subjects without spinal deformity were evaluated in habitual standing, with four sets of measurements per subject. Three landmarks were marked using reflective markers. Eleven spinal parameters calculated as outputs by both systems were analyzed using Intraclass Correlation Coefficient (ICC), Standard Error of Measurement (SEM), and Smallest Detectable Change (SDC). System agreement was assessed using the Bland-Altman method. Results The non-commercial Postura prototype (2021) showed moderate reliability for two parameters, good reliability for eight, and excellent reliability for one. SDC ranged from 0.52° to 5.11° for measured angles and from 2.06 mm to 7.46 mm for lengths. The agreement analysis showed for eight parameters a mean difference of less than 1°/1 mm, with Limits of Agreement (LoA) up to 5.6° and 8.3 mm. The kyphotic angle showed highest discrepancies, with an SDC of 5.11°, mean difference of −8.88°, and LoA of 10.70°. Conclusion Systematic errors were found in the determination of the spinous process line (SPL) and kyphotic angle. They were caused by the incompatibility of reflective markers with Postura's depth sensor. However, for parameters not directly relying on the SPL, Postura achieved Formetric′s reliability classification for four parameters, one classification lower for others, and good inter-system agreement for eight parameters. This study represents the only evaluation of a Postura system to date, providing insight into its accuracy and limitations, and a framework for the further validation of the released commercial version.
Although the literature on generative artificial intelligence (GenAI) is expanding, the implementation of GenAI policies in higher education remains under-recognised. In the broader context of GenAI reshaping the higher education sector, this article considers: the patterns in the literature in the nexus between GenAI and university policies; and how universities in Australia and New Zealand (ANZ) have responded to GenAI. This study used: a bibliometric analysis of scholarly literature on GenAI and university policies (n = 91); and a qualitative content analysis and lexical analysis of policies from ANZ universities (n = 49). The findings indicate that the salient themes were academic integrity and ChatGPT, while pedagogy and competency were largely overlooked. Additionally, the concepts of integrity and student perspectives did not feature prominently in the policies, signalling a moderative discourse within universities. These findings are discussed with reference to the need to integrate student voice and adopt a tailored approach to shape and implement GenAI policies. This article contributes to discussions on the disparate ways universities have approached responsible GenAI use, providing opportunities to reimagine and improve policies.
Reference evapotranspiration is essential for effective water resource planning and management. The Penman-Monteith (PM) method, though widely accepted, requires several meteorological variables that are often unavailable in data-scarce regions. This study aimed to develop and calibrate simplified temperature-based models to estimate ETo in the Awash basin, Ethiopia. Using multiple linear regression and optimization of the Hargreaves-Samani (HS) model through modifying both exponent and coefficients of the original model, new locally calibrated models were developed for the upper, middle, and lower parts of the basin. The calibrated coefficients were 0.0025, 0.0022, and 0.0055, respectively. Compared with the original HS model, the calibrated models substantially improved performance, achieving an average coefficient of determination (R2) of 0.60, Nash-Sutcliffe efficiency (NSE) of 0.60, and index of agreement (dr) of 0.67 for daily ETo. The root mean squared error (RMSE) was reduced by 17.6, 3.7, and 30.4% in the upper, middle, and lower regions, respectively. Spatial mapping of ETo indicated higher values in the lower and middle plains and lower values in the upper, eastern, and western highlands, reflecting topographic and climatic variability. Overall, the newly calibrated models provide reliable ETo estimation when only temperature data are available, supporting water resource planning in data-limited environments.
Budget shortfall risk, arising from the gap between required and allocated budgets, presents a major challenge to effective resource allocation in Higher Education Institutions (HEIs). Although previous studies have acknowledged the importance of budget allocation under uncertainty, they have generally relied on scenario-based analyses and have not incorporated explicit risk indicators for quantifying budget shortfalls. This study addresses this gap by developing an integrated quantitative budget allocation model tailored to the context of HEIs. Recognizing the multi-objective nature of budgeting decisions, the proposed framework integrates the Lexicographic and ε-constraint optimization methods to prioritize objectives and systematically explore the Pareto frontier. The model adopts a two-tier budget allocation structure that distributes financial resources across both cost centers and institutional activities while introducing dedicated risk indicators to quantify and support the management of budget shortfall risk at each level. A real-world case study conducted at Yazd University, Iran, demonstrates that the proposed approach effectively reduces budget shortfall risk, improves resource allocation efficiency, and supports financially sustainable decision-making. The proposed framework provides a practical decision-support tool for university administrators to make evidence-based budgeting decisions, strengthen financial sustainability, and improve the effectiveness of institutional resource allocation.
Background Machine-learning (ML) models are increasingly used to predict coronary in-stent restenosis (ISR), but evidence combines prognostic, diagnostic-radiomics, and image-reconstruction tasks and often lacks uncertainty around area-under-the-curve (AUC) estimates. We conducted a task-aware systematic review and meta-analysis with study-level auditing of AUC provenance. Methods PubMed, Embase, and Scopus were searched through 3 November 2024. Tabular models predicting subsequent coronary ISR after PCI were pooled within model families using logit-AUC random-effects REML, with one estimate per cohort and family. Missing 95% confidence intervals (CIs) were reconstructed using Hanley–McNeil variance and the logit-delta method when validation/test case and non-case counts were available or transparently approximated. Modified Hartung–Knapp and sensitivity analyses assessed robustness; diagnostic radiomics was analyzed separately. Results Twelve studies represented 16,964 nominal participants/observations. Pooled AUCs for subsequent ISR prediction were 0.84 (95% CI 0.67–0.94; I2 = 98.4%) for random forest (RF), 0.74 (0.71–0.77; I2 = 0%) for logistic regression (LR), 0.59 (0.46–0.71; I2 = 23.1%) for support-vector machines, and 0.73 (0.70–0.76; I2 = 0%) for DNN/MLP. The modified Hartung–Knapp RF interval widened to 0.52–0.96. Restricting analysis to independent patient-level cohorts reporting conventional published 95% CIs yielded AUCs of 0.89 (0.54–0.98) for RF and 0.74 (0.70–0.77) for LR. RF subgroup AUCs were 0.91 (0.70–0.98) for DES-only and 0.72 (0.71–0.73) for mixed BMS/DES cohorts. Age and lipid-related variables appeared in 50% of studies. Conclusions RF showed the highest discrimination but substantial instability, whereas LR was more consistent. Prospective external validation is required before algorithm ranking or clinical deployment.
The Local Climate Zones (LCZ) classification system has been applied in Brazil's southernmost capital, utilizing Geographic Object-Based Image Analysis segmentation to investigate spatiotemporal Surface Urban Heat Island phenomenon, represented here by area of elevated surface temperature across all seasons for the seasonal LST composites (2000/2004, 2010/2011, and 2019/2022) associated with the LCZ reference years 2002, 2010, and 2023. Time-series temperature averages for each season were derived from Landsat images, while high-resolution images and the Random Forest algorithm were employed for mapping. Main results identified LCZ classes 2, 3, 6, A, B, C, E, and G in the study area, with the first three belonging to the built category and the remainder to the land cover category. In addition, LCZ 6 exhibits a noteworthy increase in its total area by 42.18% from 2010 to 2023, while LCZ A experienced a decline of 5.2 percentage-points from 2002 to 2010, a decrease of 21.22%. Furthermore, temperature data from LCZ 3 reached approximately 40 °C in in the most recent LST composite period associated with the 2023 LCZ map, marking the highest average temperature observed during the summer. The classification accuracy of the 2023 LCZ map was deemed satisfactory at 85.7%, and statistical analyses confirmed significant differences in temperature averages. Consequently, the proposed methodology proves to be effective for detecting Land Surface Temperatures in regions with diverse characteristics, facilitating the identification of areas that require mitigation measures for high temperatures, and aligning with Sustainable Development Goal 11.
Health disparities exist not only between, but also within socioeconomic groups with the greatest variation within the lower socioeconomic status (SES) groups. The sources of this within-SES-group variation are not yet well-understood.We investigate the role of loneliness in within-SES-group variation in health using a sample of 12,918 individuals from SHARE survey waves 3 and 5. To analyse the impact of loneliness on within- and between-SES group variation in health we consider depression, self-assessed health and chronic diseases as health outcomes. Income is used as a primary indicator of SES, but the results are similar if the level of education is used instead.Our results show that loneliness is correlated with SES, but even after controlling for SES, it explains a substantial amount of within-group differences in depression, self-assessed health and chronic diseases. Counterfactual analysis suggests substantial health gains in depression may be achieved if this inequality in loneliness could be eliminated. Better understanding of within-group differences would allow developing effective policy tools for health improvement without the necessity of adjusting the underlying socioeconomic conditions.
