Search Results (160)

Background and Objectives: Acute appendicitis is a common cause of abdominal pain requiring surgery in pediatric patients. Given concerns regarding radiation exposure from computed tomography (CT), ultrasound (US) has become the first-line diagnostic modality. In many emergency and resource-limited settings, non-radiologist physicians often perform these examinations. This study aimed to evaluate the diagnostic accuracy of a non-radiologist-performed ultrasound in detecting acute appendicitis in children. Materials and Methods: We conducted a systematic review and meta-analysis according to the PRISMA guidelines. The literature was searched across PubMed, Ovid MEDLINE, EMBASE, the Cochrane Library, and Google Scholar through June 2024. Studies reporting on the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of non-radiologist-performed ultrasounds in pediatric appendicitis were included. Study quality was assessed using the QUADAS-2 tool, and a bivariate random-effects model was used for statistical analysis. Results: Eight studies, with a total of 1006 pediatric patients, were included. The pooled sensitivity and specificity were 0.87 (95% CI, 0.83–0.90) and 0.93 (95% CI, 0.91–0.95), respectively. The area under the SROC curve was 0.783 (95% CI, 0.708–0.853), suggesting moderate-to-good diagnostic accuracy. Substantial heterogeneity was observed across studies, possibly due to differences in operator training and ultrasound techniques. Conclusions: Non-radiologist-performed ultrasound demonstrates moderate-to-good diagnostic accuracy in identifying pediatric appendicitis. These findings support its implementation in emergency or resource-constrained settings and suggest that incorporating structured ultrasound training for non-radiologists may improve timely diagnosis and optimize clinical decision-making in pediatric emergency care. Full article

The future of marine sustainability depends on public understanding and trust in the policy recommendations that emerge from scientific research. For common pool marine resource decisions made by the people who depend on these resources for their food, employment, and economic future, understanding the current status of these marine systems and change is essential to ensure these resources will persist into the future. As such, the informal learning infrastructure is essential to increasing marine science literacy in a changing world. This mixed-methods research study analyzed the distribution and accessibility of marine science education and research across Alaska’s five geographic regions. Using the PRISMA framework, we synthesized data from 198 institutions and analyzed peer-reviewed literature on marine ecosystems to identify geographic and thematic gaps in access to informal science learning and research focus. In parallel, we undertook geospatial analysis and resource availability to describe the distribution of resources, types of informal learning infrastructure present across the state, regional presence, and resources to support informal marine science learning opportunities. Findings from this multifactor research revealed a concentration of resources in urban hubs and a lack of consistent access to learning resources for rural and Indigenous communities. The configurative literature review of 9549 publications identified topical underrepresentation of the Bering Sea and Aleutian Islands, as well as a lack of research on seabirds across all regions. Considered together, these results recommend targeted investments in rural engagement with marine science programming, culturally grounded partnerships, and research diversification. This review concludes that disparities in learning resource support and government-funded priorities in marine wildlife research have created conditions that undermine the local people’s participation in the sustainability of sensitive resources and are likely exacerbating declines driven by rapid change in Arctic and sub-Arctic waters. Full article

The transition to sustainable, innovation-driven economies has become a global imperative, particularly for resource-dependent regions like the Middle East, where environmental challenges, fossil fuel reliance, and economic diversification pressures intersect. In this context, green innovation plays a pivotal role in mitigating environmental degradation while supporting long-term economic growth. This study examines the short-term and long-term drivers of green innovation across 13 Middle Eastern countries from 1990 to 2023, with a focus on environmental governance, environmental pollution, economic growth, and natural resource abundance. Using a balanced panel dataset, this study applies Frees, Friedman, and Pesaran CSD tests to address cross-sectional dependency and second-generation unit root tests for data stationarity. Both first- and second-generation cointegration tests confirm long-run relationships among variables. The empirical analysis employs the cross-sectional autoregressive distributed lag (CS-ARDL) model, alongside Pooled Mean Group (PMG-ARDL), Average Mean Group (AMG), and Common Correlated Effects CCEMG estimators, ensuring robustness. The findings indicate that, in the long term, environmental governance, economic growth, population size, and natural resource abundance significantly promote green innovation, with respective coefficients of 0.3, 0.01, 0.02, and 0.4. Conversely, human development and environmental pollution exert a negative influence on green innovation, particularly over the long term. These results suggest that, while economic and governance factors drive innovation, human capital development may prioritize immediate growth over sustainability, and pollution may hinder long-term innovation. Enhancing environmental governance, accelerating renewables, using strategic resource revenue for green projects, integrating green growth, and regional collaboration can position Middle Eastern economies as green innovation leaders. Full article

The integration of health and social care is increasingly recognized as essential to address population ageing, the rise in chronic diseases, and persistent health inequities. Across Europe, diverse models have been developed to improve service coordination, resource efficiency, and person-centered care. This paper aims to explore international experiences in integrating health and social care, identify common strategies and challenges, and provide insights to inform policy development in countries where integration remains incipient, with a focus on Portugal. A qualitative comparative approach was employed. A systematic literature review was conducted across PUBMED, MEDLINE, and Google Scholar, including peer-reviewed articles, policy reports, and government documents. Thematic analysis was used to identify integration models, enablers, and barriers across different countries. Different models reveal that joint governance, pooled funding, strong community involvement, and digital innovation are key enablers of integration. However, common challenges persist, including fragmented governance, inconsistent implementation, and financial sustainability. In Portugal, structural separation between the health and social sectors continues to limit strategic alignment. Successful integration depends on political commitment, shared vision, and active stakeholder collaboration. European models offer adaptable lessons for Portugal and similar systems, especially regarding intersectoral coordination and preventive care. Integrating health and social care is vital for building resilient, equitable systems. Portugal must adopt a cohesive national strategy; strengthen local implementation; and embrace person-centered, sustainable solutions to ensure long-term impact. Integrating the health and social sectors is indispensable in navigating the ever-evolving healthcare landscape and promoting holistic well-being. Full article

The application of intelligent video monitoring for natural resource protection and management has become increasingly common in recent years. To enhance safety monitoring during the grazing prohibition and rest period of grassland, this paper proposes a multi-object segmentation algorithm based on mid-infrared images for all-weather surveillance. The approach integrates the Image Timing Features–Gaussian Mixture Model (ITF-GMM) and Convolutional-UNet (Con-UNet) to improve the accuracy of target detection. First, a robust background modelling, i.e., the ITF-GMM, is proposed. Unlike the basic Gaussian Mixture Model (GMM), the proposed model dynamically adjusts the learning rate according to the content difference between adjacent frames and optimizes the number of Gaussian distributions through time series histogram analysis of pixels. Second, a segmentation framework based on Con-UNet is developed to improve the feature extraction ability of UNet. In this model, the maximum pooling layer is replaced with a convolutional layer, addressing the challenge of limited training data and improving the network’s ability to preserve spatial features. Finally, an integrated computation strategy is designed to combine the outputs of ITF-GMM and Con-UNet at the pixel level, and morphological operations are performed to refine the segmentation results and suppress noises, ensuring clearer object boundaries. The experimental results show the effectiveness of proposed approach, achieving a precision of 96.92%, an accuracy of 99.87%, an intersection over union (IOU) of 94.81%, and a recall of 97.75%. Furthermore, the proposed algorithm meets real-time processing requirements, confirming its capability to enhance small-target detection in complex outdoor environments and supporting the automation of grassland monitoring and enforcement. Full article

Boating tourism in coastal–maritime areas often overlaps spatially and temporally with other economic activities, such as fishing, leading to complex interactions. These interactions can create opportunities for positive cooperation or generate conflicts that pressure natural resources and stakeholders. The aim of this study is to show whether or not there is evidence of interactions between fishing (n_f = 43) and tourism/recreation (n_t = 65) vessels. This study focuses on the interaction between maritime tourism activities and fishing in southern Portugal, using a social network analysis (SNA) approach based on automatic identification system (AIS) data to evaluate spatial and temporal patterns. The findings reveal that tourism activities dominate zones closer to the coast, with intermediate areas serving as shared spaces where interactions between vessel activities are more likely to occur. There was evidence of occasional interactions between a few recreational and fishing vessels (two passengers and three seiners), but the inferences from the results are insufficient to demonstrate how beneficial they are for both activities. Full article

Liver cancer is one of the most common malignancies and the second leading cause of cancer-related deaths worldwide, particularly in developing countries, where it poses a significant financial burden. Early detection and timely treatment remain challenging due to the complex mechanisms underlying the initiation and progression of liver cancer. This study aims to uncover key genomic features, analyze their functional roles, and propose potential therapeutic drugs identified through molecular docking, utilizing single-cell RNA sequencing (scRNA-seq) data from liver cancer studies. We applied two advanced hybrid methods known for their robust identification of differentially expressed genes (DEGs) regardless of sample size, along with four top-performing individual methods. These approaches were used to analyze four scRNA-seq datasets, leading to the identification of essential DEGs. Through a protein−protein-interaction (PPI) network, we identified 25 hub-of-hub genes (hHubGs) and 20 additional hHubGs from two naturally occurring gene clusters, ultimately validating a total of 36 hHubGs. Functional, pathway, and survival analyses revealed that these hHubGs are strongly linked to liver cancer. Based on molecular docking and binding-affinity scores with 36 receptor proteins, we proposed 10 potential therapeutic drugs, which we selected from a pool of 300 cancer meta-drugs. The choice of these drugs was further validated using 14 top-ranked published receptor proteins from a set of 42. The proposed candidates include Adozelesin, Tivozanib, NVP-BHG712, Nilotinib, Entrectinib, Irinotecan, Ponatinib, and YM201636. This study provides critical insights into the genomic landscape of liver cancer and identifies promising therapeutic candidates, serving as a valuable resource for advancing liver cancer research and treatment strategies. Full article

Heritage conservation projects involve diverse stakeholders, such as property owners, communities, NGOs, and governments, whose “misaligned” incentives produce high transaction costs and often pose persistent challenges. Considering heritage sites as analogous to common pool resources (CPR), this study examines whether sustainable heritage projects adhere to Elinor Ostrom’s design principles for managing CPRs. The UNESCO Asia-Pacific Awards for Cultural Heritage Conservation are used as a benchmark to identify best practices in private–public collaborations over the past two decades. Through content analysis of award application dossiers, the researchers assess whether the design principles are applied differently between successful and unsuccessful projects and evaluate the sufficiency of Ostrom’s principles in the heritage conservation context. The findings indicate that four design principles are more consistently balanced in award-winning projects: “Collective Choice Arrangements”, “Congruence with Local Conditions”, “Monitoring Mechanisms”, and “Minimum Recognition of Rights”. Additionally, successful projects tend to incorporate “people growth-oriented activities”, such as skills training and education for local workers and youth, benefiting current and future project beneficiaries. These insights suggest that adapting Ostrom’s design principles can enhance the sustainability of heritage conservation efforts by better aligning stakeholder incentives and addressing the “misalignment of incentives” challenge commonly encountered in such projects. Full article

Meningiomas are a common type of brain tumor. Due to their location within the cranial cavity, they can potentially cause irreversible damage to adjacent brain tissues. Clinical practice typically involves surgical resection for tumors that provoke symptoms and exhibit continued growth. Given the variability in the size and location of meningiomas, achieving rapid and precise localization is critical in clinical practice. Typically, meningiomas are imaged using magnetic resonance imaging (MRI), which produces 3D images that require significant memory resources for the segmentation task. In this paper, a lightweight 3D attention mechanism based on entropy–curvature (ECA) is proposed, which significantly enhances both parameter efficiency and inference accuracy. This attention mechanism uses a pooling method and two spatial attention modules to effectively reduce computational complexity while capturing spatial feature information. In terms of pooling, a tri-axis pooling method is developed to maximize information retention during the dimensionality reduction process of meningioma data, allowing the application of two-dimensional attention techniques to 3D medical images. Subsequently, this mechanism utilizes information entropy and curvature filters to filter noise and enhance feature information. Moreover, to validate the proposed method, the meningioma dataset from West China Hospital’s Department of Neurosurgery and the BraTS2021 dataset are used in our experiments. The results demonstrated superior performance compared to the state-of-the-art methods. Full article

This paper explores the intersection of tourism and environmental theories, highlighting how sustainability serves as a critical bridge between the two disciplines. Tourism theories such as Butler’s Tourism Area Life Cycle (TALC) and the Theory of Planned Behavior (TPB) provide insights into destination development and tourist behavior but often lack an ecological perspective. The environmental framework, including Ecological Modernization Theory (EMT) and Common-Pool Resource (CPR) Theory, emphasizes sustainable resource management and the interconnectedness of human activities and natural systems. This paper examines common concepts such as carrying capacity, sustainable development, and behavioral insights while also identifying tensions between economic growth and environmental preservation. Case studies from Costa Rica, Hawaii, and Thailand illustrate practical applications of these theories in real-world settings, revealing how tourism can be both a threat and a tool for conservation. My paper concludes that integrating environmental impact assessment (EIA) and community-based tourism (CBT) models into tourism planning can lead to better long-term resource management. It recommends the adoption of stricter regulations on tourism development in fragile ecosystems, the implementation of eco-certifications, and the promotion of locally governed tourism initiatives. These strategies will ensure the sustainability of both tourism activities and the ecosystems on which they depend. Full article

Intra-individual variation in floral traits is linked to plant fitness, playing a central role in sexual selection. This variation can arise from architectural constraints, such as flower position on the inflorescence axis, and from environmental factors. In relation to the environmental influences on floral traits, the most common causes of variation are linked to the presence of pollinators, to plant resource acquisition strategies and to the availability of local resource pools. We investigated how clonal integration and resource depletion through defoliation affect floral trait stability in Eichhornia crassipes, testing whether clonal integration buffer floral traits against resource limitations. Using greenhouse experiments, we manipulated clonal structure and resource availability. We assessed the effects of floral position and clonal integration on floral traits through model selection. Our results showed that basal flowers generally had larger traits, more attractive to pollinators, and isolated or defoliated ramets exhibited significant reductions in floral traits, especially at distal flowers. Clonal integration stabilized floral traits across positions by mitigating the effects of resource variability. Clonal integration in E. crassipes enhances resilience to resource depletion, likely contributing to this species invasiveness. These findings highlight the significance of clonal and architectural integration in sustaining reproductive traits under environmental stress. Full article

Collective action remains a fundamental approach to governing common pool resources, and Ostrom’s design principles (DPs) have been widely used for analyzing such governance. However, grasslands, as typical common pool resources, have received limited attention within this framework in China. Based on the village-level survey data in Yushu, Qinghai, we utilized DPs as fuzzy sets to discern nuances among cases and identify necessary and sufficient conditions for successful collective governance using the fsQCA method. Our findings are as follows: (i) Diverse combinations of institutional rules can lead to favorable collective governance outcomes. Achieving good outcomes requires a comprehensive approach that encompasses rules, rule enforcement, and rule-making arenas. (ii) Congruence with local conditions (DP2) emerges as a pivotal and foundational condition in collective grassland governance. Therefore, it is crucial to formulate strategic governance rules that adapt to local realities and capitalize on regional advantages to ensure long-term success. (iii) The establishment of ecological husbandry cooperatives is in line with local realities and can help achieve high grassland governance performance. The improvement in ecological, economic, and social benefits brought about by cooperatives constitutes a crucial factor for promoting herders’ active involvement in the collective management of grasslands. Full article

Groundwater depletion in arid regions poses a significant threat to agricultural sustainability and rural livelihoods. This study employs geospatial analysis and economic modeling to address groundwater depletion in the arid Barind region of Northwest Bangladesh, where 84% of the rural population depends on agriculture. Using remote sensing and GIS, we developed an elevation map revealing areas up to 60 m above sea level, exacerbating evaporation and aquifer dryness. Field data collected through Participatory Rural Appraisal tools showed farmers exhibiting “ignorant myopic” behavior, prioritizing short-term profits over resource conservation. To address this, an Increasing Block Tariff (IBT) water pricing model was developed, dividing water usage into three blocks based on irrigation hours: 1–275 h, 276–550 h, and 551+ h. The proposed IBT model significantly increases water prices across the three blocks: 117 BDT/hour for the first block (from current 100–110 BDT/hour), 120 BDT/hour for the second block, and 138 BDT/hour for the third block. A demand function (y = −0.1178x + 241.8) was formulated to evaluate the model’s impact. The results show potential reductions in groundwater consumption: 59 h in the first block, 26 h in the second block, and 158 h in the third block. These reductions align with the principles of integrated water resource management (IWRM): social equity, economic efficiency, and environmental integration. The model incorporates economic externalities (e.g., well lifting costs) and environmental externalities (e.g., crop pattern shifts), with total costs reaching 92,709,049 BDT for environmental factors. This research provides a framework for sustainable groundwater management in arid regions, potentially reducing overextraction while maintaining agricultural productivity. The proposed IBT model offers a locally driven solution to balance resource conservation with the livelihood needs of farming communities in the Barind tract. By combining remote sensing, GIS, and economic modeling, this research provides a framework for sustainable groundwater management in arid regions, demonstrating the power of geospatial technologies in addressing complex water resource challenges. Full article

Drowning is among the most common reasons for children’s death aged one to fourteen around the globe, ranking as the third leading cause of unintentional injury death. With rising populations and the growing popularity of swimming pools in hotels and villas, the incidence of drowning has accelerated. Accordingly, the development of systems for detecting and preventing drowning has become increasingly critical to provide safe swimming settings. In this paper, we propose a comprehensive review of recent existing advancements in automated drowning detection and prevention systems. The existing approaches can be broadly categorized according to their objectives into two main groups: detection-based systems, which alert lifeguards or parents to perform manual rescues, and detection and rescue-based systems, which integrate detection with automatic rescue mechanisms. Automatic drowning detection approaches could be further categorized into computer vision-based approaches, where camera-captured images are analyzed by machine learning algorithms to detect instances of drowning, and sensing-based approaches, where sensing instruments are attached to swimmers to monitor their physical parameters. We explore the advantages and limitations of each approach. Additionally, we highlight technical challenges and unresolved issues related to this domain, such as data imbalance, accuracy, privacy concerns, and integration with rescue systems. We also identify future research opportunities, emphasizing the need for more advanced AI models, uniform datasets, and better integration of detection with autonomous rescue mechanisms. This study aims to provide a critical resource for researchers and practitioners, facilitating the development of more effective systems to enhance water safety and minimize drowning incidents. Full article

In the present work, a subset extracted from a core collection of common beans (Phaseolus vulgaris L.) landrace accessions from the Lazio region in central Italy was used to identify the most suitable drought-tolerant or -susceptible genotypes. By applying several morpho-physiological and agronomic selection criteria recommended by the available literature, we conducted a pre-screening experiment under controlled conditions on a primary group of 24 landraces. These landraces were chosen to represent the diversity in the Lazio region in terms of geographical provenance, elevation, landform, growth habit, customary water management in the field, and native gene pool. Pre-screening under controlled conditions allowed us to identify two subsets of landraces: one exhibiting the most drought-tolerance and one showing the most susceptibility to drought. These two subsets were then tested in field trials using two water treatments, i.e., normal irrigation versus no irrigation. Such field experiments were simultaneously conducted at two sites within the Lazio region, deliberately chosen to maximize their differences in terms of pedo-climatic conditions. This notwithstanding, the core findings from the two separate field experiments were remarkably consistent and coherent among each other, highlighting a notable degree of variability within the group of the tested landraces. In general, the morpho-physiological traits considered were found to be less responsive to water shortage than yield parameters. A statistically significant Landrace × Treatment interaction was found for almost all the yield parameters considered, suggesting that certain genotypes are more susceptible than others to water shortage. By taking into account the concept of “yield stability”, i.e., the ability to maintain comparatively high yields even under conditions of water scarcity, certain common bean landraces were found to be the most promising, in terms of drought tolerance. Two genotype pairs, in particular, could be of interest for studying the morpho-physiological and molecular mechanisms underlying drought tolerance in common bean, as well as for identifying quantitative trait loci associated with water scarcity, which could be beneficially employed in breeding programs. The results reported here also suggest that pre-screening under laboratory conditions, followed by targeted field trials, can constitute a reliable, efficient, and resourceful combined approach, in which morpho-physiological traits measured on juvenile plants might play a role in predicting drought tolerance at the agronomic level. Full article