Search Results (53,748)

Mountain ecosystems, strongly affected by climate-related variability and human impact, are degrading faster than other terrestrial ecosystems. Currently, the United Nations (UN) utilizes Sustainable Development Goal (SDG) 15: Life on Land (Target 15.4 and Sub-indicators 15.4.2a and 15.4.2b), along with the System for Earth Observation Data Access, Processing and Analysis for Land Monitoring, commonly referred to as SEPAL, to track mountain degradation. This SEPAL analysis does not include soil data, which is critical to understanding mountain degradation. The present research focuses on improving the tracking and evaluation of mountain land degradation (LD) utilizing soil data in the state of Colorado (CO) in the United States of America (USA) as an example. Total anthropogenic LD affects an estimated 19% of Colorado’s territory as of 2024, driven mainly by agricultural activities (80%). Between 2001 and 2024, overall LD in CO decreased (−0.4%), but LD from development increased by 23.3%. For mountain areas in CO, the mountain green cover index (MGCI) was 96% for 2024, and it decreased (−0.4%) between 2001 and 2024. The mountain LD proportion was 2.5% as determined by the SEPAL method compared to 4.4% by LULC analysis. Incorporation of soil data into LULC analysis found that between 2001 and 2024 LD increased to 6.6%. All soil types in the mountains exhibited anthropogenic LD due to development with a total developed area of 1385.1 km². Current total mountain LD (inherent + anthropogenic) in CO may be as high as 38.9%. Future estimates of total mountain LD should include both inherent and anthropogenic LD. Full article

Background: Human milk is considered an ideal diet for neonates, and every effort should be made to promote breastfeeding. Donor human milk (DHM) remains the best alternative for neonates when their mother’s own milk (MOM) is not available. We tried to determine whether having easy access to DHM from a Human Milk Bank (HMB) would reduce the pursuit to feed neonates MOM. Methods: A retrospective study was conducted on data from neonates consecutively admitted to the Neonatal Intensive and Intermediate Care Units of the Department of Neonatology of the Medical University of Bialystok between 1 January 2022 and 31 March 2025. The study period covered 2 years before the opening of the HMB and 1 year of its operation. No specific changes in feeding practices occurred simultaneously during the HMB’s first year of operation. Results: In the first year of operation of the HMB, we observed an increase in the percentage of neonates who (1) received mother’s own colostrum (71.88% vs. 52.28% (2023) and 52.05% (2022); p < 0.001), (2) were fed human milk during hospitalization (24.38% vs. 3.57% (2023) and 4.09% (2022); p < 0.001) and (3) were fed MOM at discharge (43.86% vs. 56.25%, p = 0.024). In total, 53.06% of neonates who received DHM were fed MOM at discharge. Conclusions: The possibility of using milk from the HMB did not limit the desire to feed neonates MOM but intensified it. Neonates were more likely to be fed MOM during the first feeding, throughout their hospitalization, and at discharge. It shows the strong potential of HMBs in improving feeding practices in Neonatal Intensive and Intermediate Care Units. Full article

The convergence of Robotics and artificial intelligence (AI) has transformed engineering by enabling the design of intelligent systems capable of learning, adapting, and performing complex tasks. These synergies are driving innovation across multiple engineering disciplines, including mechanical, materials, electrical, industrial, civil, and aerospace engineering. This review provides a comprehensive overview of the knowledge structure and emerging research directions of Robotics and AI in engineering, with the aim of identifying research trends, influential authors, leading institutions, and emerging thematic areas. Data were collected from the Web of Science and Scopus databases, covering the period from 2020 to 2025, and analyzed using bibliometric mapping techniques and performance indicators. The results reveal a sustained growth in research on autonomous systems, collaborative robots, and human–robot interaction within engineering contexts, with a strong emphasis on AI-driven optimization. Bibliometric analyses show that deep learning, reinforcement learning, and computer vision constitute the core enabling technologies structuring the field. In addition, the results highlight a high degree of international collaboration and a concentration of scientific output and impact in a limited number of leading countries, institutions, and journals. Full article

The integration of artificial intelligence (AI) into online education has transformed the digital learning space, offering new ways to enhance learner satisfaction and engagement. This systematic literature review, covering a five-year span from 2020 to 2025, explores how AI technologies, such as chatbots, intelligent tutoring systems (ITS), sentiment analysis, gaze tracking and predictive analytics, support learner engagement across cognitive, emotional, behavioral, and social dimensions. Drawing from 30 peer-reviewed studies, the current review addresses three central research questions: (1) What aspects of AI positively influence learner satisfaction and engagement in online courses within higher education institutions; (2) What potential challenges from using these technologies may arise; and (3) What research approaches are most commonly used to assess AI’s impact in such learning contexts? The findings highlight that adaptive learning, real-time feedback, and emotion-aware systems contribute positively to personalized learning and motivation. However, concerns persist around data privacy, algorithmic bias, over-reliance on automation, and system usability. Experimental and quasi-experimental designs, as well as machine learning, mixed methods, and survey-based approaches are found to dominate in reviewed studies. Based on these insights, this work offers a foundation for future AI-enhanced learning management systems designed primarily to enhance learner engagement across cognitive, emotional, behavioral, and social domains. Full article

The expansion of carbon pricing instruments, such as carbon taxes and emissions trading systems (ETS), has been rapid over the last three decades. However, the global quantitative evidence is often presented in descriptive reports rather than in a unified empirical framework. The present study documents the long-run co-evolution between three factors: firstly, the global diffusion of carbon pricing mechanisms, secondly, the share of global greenhouse gas emissions covered by an explicit carbon price, and thirdly, global carbon-pricing revenues. The present study utilises annual global time-series data spanning the period 1990–2024 (mechanisms) and overlapping samples for coverage and revenues (2005–2024; 2006–2023). Employing correlation analysis, trend modelling and robustness checks tailored to trending series, the study offers a transparent and replicable quantitative synthesis of the data. The findings suggest a robust positive long-term correlation between the number of mechanisms in operation and emissions coverage. Revenues manifest a pronounced non-linear scaling over time; nevertheless, given the aggregate nature of the dataset, the estimates are interpreted as co-movement patterns rather than causal effects of specific instruments. The paper makes a significant contribution to the field by offering a transparent and replicable quantitative synthesis of global carbon-pricing diffusion and fiscal scaling. It is important to note, however, that the paper also explicitly states the limits of causal inference and outlines panel-data extensions for future research. Full article

Background and Objectives: Intermittent exotropia (IXT) is a common childhood strabismus with variable natural history and no universally accepted first-line management. We aimed to characterize long-term real-world outcomes and identify factors associated with alignment stability across surgical and non-surgical strategies. Materials and Methods: This retrospective single-center cohort study reviewed consecutive patients with IXT managed between January 2008 and March 2024. Clinical data included IXT subtype, prism and alternate cover test measurements, binocular control (ECS), stereopsis, refractive error, and treatments (observation, over-minus lenses, and surgery). Postoperative success was defined as ≤10 prism diopters (PD) of eso/exophoria without diplopia. Longitudinal refractive change was analyzed using linear mixed-effects modeling, and time to alignment failure (≤±10 PD at near and distance) was evaluated using Kaplan–Meier and Cox regression analyses. Results: A total of 415 patients were included (mean follow-up 53.2 ± 47.8 months). Over-minus therapy was used in 252 (60.7%) patients for a median of 24 months, and 61 (14.7%) achieved spontaneous alignment. At the final visit, combined near-and-distance alignment success was 41.0% (170/415). Among surgically treated patients (n = 167), motor success was 65.3% (109/167), with reoperation required in 12.6% (21/167). Kaplan–Meier analysis showed cumulative alignment survival of 0.899 at 1 year, 0.563 at 5 years, and 0.302 at 10 years (median 70 ± 4.7 months). In Cox modeling, surgery was strongly protective (HR 0.174), while older age (HR 1.040 per year) and poor baseline distance control (HR 1.421) increased the risk of failure; over-minus therapy was not independently associated with survival. Both treatment groups showed a similar myopic shift over time (β = −0.25 D/year), with no between-group difference. Conclusions: In this longitudinal cohort, intermittent exotropia showed a variable course, with many patients ultimately requiring surgery despite initial non-surgical management. Long-term success was more closely tied to preoperative control quality than age. These findings support an individualized, control-based approach to treatment planning and timing of intervention. Full article

Amazon streams are home to a great richness and diversity of fish, having an essential role in maintaining the aquatic ecosystem multifunctionality and global biodiversity. Here, we investigated the structure of the ichthyofauna of upland streams of the Lower Tapajós River and analyzed ecological descriptors of fish assemblages in different drainages in the rainy and dry seasons. A total of 3715 individuals from 110 species were collected. Species richness was higher during the dry season (99 species) than in the rainy season (66 species). Local environmental variables were measured or obtained from publicly accessible databases. Our results showed that ichthyofauna responds to hydrological changes in upland streams in the eastern Amazon. Abundance and richness were greatest during the dry season, with important contributions from representatives of the order Characiformes. Stream structural variables explained most of the variance in assemblage composition (adjusted R² = 0.102, p = 0.004), with channel width, depth, and canopy cover as key factors. The findings underscore the importance of assessing drainage and seasonality effects not only to understand ichthyofaunal biodiversity but also to adequately design research efforts, conservation strategies, and monitoring programs for aquatic environments in the eastern Amazon. Full article

Against the backdrop of intensified global talent competition, the return of overseas talents has become a key engine driving the enhancement of core competitiveness in developing countries. Accurately identifying its critical driving factors is essential for China to address the challenges of talent introduction. This study constructs a hybrid multiple-criteria decision-making framework to systematically explore the influence mechanism of overseas talent return: first, a 15-criterion decision structure covering economic, policy, educational, technological, and social aspects is established via systematic literature review and two-round Delphi expert surveys; second, the grey DEMATEL-ANP technique is adopted to quantify the inter-relationships and relative weights of the criteria and screen and rank the critical driving factors accurately. Empirical results show that the six core driving factors ranked by importance are talent policy support, economic development level, scientific and technological development strength, public service quality, educational resource supply, and attention to science and technology, with significant synergistic interaction effects among these factors. This research provides a scientific decision-making framework and empirical support for developing countries to formulate targeted talent introduction policies and optimize the talent development ecosystem. Full article

Since software development is in constant evolution, it presents new facets and emerging topics. For example, the integration of qualitative data analysis through Grounded Theory establishes a new approach to categorizing this discipline, allowing it to align with these new contributions. The present work applies an original Glaserian Systematic Mapping Study (GSMS) to explore new ways of categorizing software development using Grounded Theory (GT) and GT elements. The study provides insights from the perspective of human beings, including their trust and doubts, and their attitudes towards work teams. The categorization covers many aspects of software development. One of these relates to Agile development, which has been defined in two pairs: Agile/Non-Agile and Agile/Plan-driven. Although this may seem obvious, this categorization had not been defined in this way before. Data, in all its diversity, accompanies the software development process throughout its entire lifecycle. Another finding of the present work is the concept of bridges, which correspond to the diverse interrelationships within software development. Grounded Theory in the context of software development has enabled the creation of various types of bridges. These bridges could be established between individuals within the same development team, between different areas of software development, or between developers and their tools and artifacts, among others. These findings can be highly diverse and can help software engineers unlock their potential and explore various options in software development. Full article

Background: Tumor-infiltrating lymphocytes (TILs) play a key role in the immune response against melanoma. They act as both markers of an active tumor environment and as treatments in adoptive cell therapy. This narrative review covers what is currently known about TIL biology, their prognostic and predictive value, and the use of TIL-based adoptive cell therapy (TIL-ACT) in advanced melanoma. Methods: We searched PubMed/MEDLINE, Web of Science and clinicaltrials.gov through January 2026 using terms related to melanoma, TILs, adoptive cell therapy, immune checkpoint inhibitors, neoantigens, T-cell receptor clonality, and spatial transcriptomics. We included original research, major clinical trials, translational studies and key reviews. Results: Melanoma often has many neoantigens, which leads to a high number of tumor-resident TILs. These TILs, their arrangement, and their interactions with myeloid cells influence how well they fight tumors. Features of TILs seen under the microscope and through other tests can help predict patient outcomes, even before treatment. Studies show that TIL-ACT leads to objective responses in about 30–50% of patients whose melanoma did not respond to immune checkpoint inhibitors. Some patients achieve lasting complete remissions, though the treatment can cause significant, mostly short-term side effects from lymphodepletion and interleukin-2. New research points to factors related to the patient, tumor, and TIL product that affect treatment success, supporting the use of biomarkers and combination strategies. Conclusions: TIL-based adoptive cell therapy is now a promising, personalized treatment for advanced melanoma after anti-PD-1 therapy has failed. Future studies should focus on identifying reliable biomarkers, improving TIL products, combining therapies to change the tumor environment, and making manufacturing more efficient to ensure more patients can safely access TIL therapy. Full article

The intermittency of renewable-based power is a major barrier for long-term supply of clean energy, which necessitates the development of reliable solutions for clean energy storage and transition towards a carbon-neutral economy. Although hydrogen has emerged as a promising clean energy carrier to address this, its high compressibility requires safe, efficient and practical storage technologies for widespread deployment. Surface storage technologies for hydrogen have garnered attention due to their mobile and stationary applications, paving the way for a future hydrogen-based economy. This review provides a comprehensive review of surface hydrogen storage technologies, covering metal hydrides, metal-organic frameworks (MOFs), liquid organic hydrogen carriers (LOHCs), glass microspheres, capillary arrays, etc. Where previous reviews mostly address the chemistry behind these storage technologies, this study highlights practical integration and techno-economic assessment. Comparative analysis reveals that while LOHC and hydrides dominate in Technology Readiness Level, MOFs and carbohydrate-based systems offer high gravimetric potential, though they are currently quite costly. Other challenges like thermal management and large-scale regeneration remain critical for practical deployment. Moreover, recent advancements in Artificial Intelligence and Machine Learning offer unique insights, demonstrating their growing role in material screening, performance prediction, and the optimization of storage system designs. This review outlines the key challenges and research pathways required to support future deployment. Full article

Populations in areas with limited hydrological data face ongoing challenges related to water supply and management, with climate change increasing the risks of floods and droughts. New remote sensing and modeling tools can improve land and water management in these regions, especially when combined with limited ground measurements and local knowledge of extreme events. This study examined hydrological extremes and land cover change impacts in the Grande Rivière du Nord watershed, Haiti, using satellite and model-based data. Precipitation extremes were obtained from the Global Precipitation Measurement Integrated Multi-satellite Retrievals for GPM (GPM IMERG; 2000–2025), and streamflow data were sourced from the Group on Earth Observation Global Water Sustainability (GEOGLOWS) system and bias-corrected with a small historical hydrologic database. Annual maximum series were created and fitted with Gumbel, Lognormal, and Generalized Extreme Value (GEV) distributions using the L-moment method. Goodness-of-fit tests identified the best models, and precipitation amounts for return periods of 2–100 years were estimated. The precipitation maxima aligned with locally reported extreme events, and GEV provided the best overall fit. Using the bias-corrected streamflow, a hydrologic model was calibrated and validated and then applied to land cover change scenarios. Simulations suggest that moderate land-use change can increase peak flows beyond channel capacity, raising flood risk and informing adaptation planning in northern Haiti, which has limited data. Full article

Typhoon avoidance is critical for ship maneuvering safety under extreme meteo-ocean conditions. This study proposes a data-driven framework that converts AIS trajectories into interpretable course deviation and speed change responses for navigational decision support. After AIS cleaning, temporal resampling, and matching with gridded wind, wave, and current fields, rule-based sliding-window and regression procedures, informed by experienced captains and company staff, automatically generate proxy labels for deviation and speed reduction. Samples are stratified by vessel size to reflect differences in inertia and maneuverability, and XGBoost classifiers are trained with simple resampling to mitigate class imbalance. The framework is demonstrated on a single-event case study of Typhoon Yagi in the South China Sea, covering 8609 vessels and reconstructed sailing fragments. On the test set, the deviation model achieves 89.8% accuracy and high recall for deviation cases, while the speed change model reaches 82% balanced accuracy under the proxy-label setting. Results suggest a scale-dependent response: smaller vessels exhibit more frequent course deviation, whereas larger vessels more often reduce speed under severe wind-wave loading. The framework offers a proof-of-concept approach to derive behavior-based indicators from AIS and environmental data and may support situational assessment under adverse weather. Full article

Land use and land cover (LULC) change strongly influences national carbon dynamics and the effectiveness of forest-based climate mitigation strategies, particularly in mountainous developing countries. This study integrates scenario-based LULC modeling, spatially explicit carbon accounting, and economic valuation to assess how alternative development pathways affect carbon storage and its economic value in Nepal over the 2020–2050 period. LULC projections for four scenarios: Business-as-Usual (BAU), Rapid Urban Development (RUD), Forest Degradation and Terai Contraction (FDTC), and Agricultural Land Abandonment and Ecological Recovery (ALER), were generated using the TerrSet Land Change Modeler, with 2020 as the baseline. These projections were then used as inputs to the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Carbon Storage and Sequestration model to estimate changes in ecosystem carbon stocks, integrating aboveground biomass, belowground biomass, soil organic carbon, and dead organic matter pools. Carbon stock changes were monetized using a constant carbon price of USD 5/tCO₂e and a 3% discount rate to estimate net present values (NPV). Results reveal strong divergence across scenarios. National carbon storage remains near-neutral under BAU (−0.46% by 2050), declines under RUD (−2.42%) and FDTC (−5.32%), and increases substantially under ALER (+11.74%). These biophysical outcomes translate into contrasting economic values: BAU yields a small negative NPV, RUD and FDTC generate large discounted losses, and ALER produces a strongly positive NPV exceeding USD 800 million by 2050. Spatially, forest and other wooded land dominate national carbon dynamics, while urban expansion and forest degradation drive disproportionate losses. Overall, the study results demonstrate that recovery-oriented land-use pathways offer substantially greater long-term carbon and economic benefits than development trajectories dominated by urban expansion or forest degradation, providing a policy-relevant framework to support Reducing Emissions from Deforestation and Forest Degradation, together with conservation, sustainable forest management, and enhancement of forest carbon stocks (REDD+) planning and long-term mitigation assessment in Nepal. Full article

Mitochondria play a crucial role in cellular bioenergetics, signaling, and metabolism; yet, many fundamental mechanisms such as the proton transfer along the membranes, the link between membrane curvature and oxidative phosphorylation, and the nanoscale organization of enzyme supercomplexes remain poorly understood due to the limitations of classical biochemical approaches. This review addresses this gap by systematically analyzing the contemporary physical methods used to investigate the mitochondrial structure and function from the micro to nano scale. It covers advanced fluorescence and super-resolution microscopy, electron and volume electron microscopy, and scanning probe techniques, as well as cryo-electron tomography for resolving supramolecular assemblies in near-native conditions. The review highlights the applications of the modern fluorescent probes, expansion and phase microscopy, and machine-learning-based image analysis for a quantitative assessment of the mitochondrial morphology, membrane potential, and dynamics in living cells and tissues. Complementary spectroscopic and scattering methods, including Raman spectroscopy, NMR, and X-ray and neutron scattering, are discussed as tools for probing the redox state, metabolite composition, and membrane organization. Emphasis is placed on integrating high-resolution experimental data with advanced computational frameworks to test competing models of mitochondrial function and pathology, and to guide the development of biomimetic and biomedical technologies. Full article