Using spatially explicit machine learning to enhance assessment of the Global Gravity-based Groundwater Product for groundwater storage change in Germany

A comparative transcriptomics reveals stage-specific molecular mechanisms during early zoeal development in the Chinese mitten crab, Eriocheir sinensis.

Profitability structure of tourist companies during and after crises

Artificial Intelligence-Based ABI Dynamic Fluctuation Patterns Predict Adverse Vascular Events in PAD: A Multicenter Prospective Study.

Proteomic analysis reveals the mechanism of cold tolerance in black porgy (Acanthopagrus schlegelii) via ribosome hibernation, metabolic remodeling, and antioxidant coordination.

Cause-variations in neonatal mortality across Europe and Africa; evidence from a 20-year retrospective dataset and clinical practice guidelines.

Startup mentoring has become an essential pillar of entrepreneurial ecosystems, supporting founders in navigating challenges, accessing resources, and achieving sustainable growth. Despite its recognized value, the research landscape on startup mentoring remains fragmented and context-dependent. This study provides a comprehensive bibliometric and thematic trend analysis of startup mentoring literature from 1986 to 2025, drawing on data from Scopus and Web of Science. The final dataset comprised 1081 peer-reviewed journal articles after screening 3470 initial records and removing 412 duplicates in accordance with the PRISMA 2020 protocol. Employing advanced bibliometric and co-word analysis techniques, the study maps the intellectual structure, identifies influential authors, institutions, and journals, and visualizes the evolution of key research themes. Results reveal a clear developmental trajectory spanning four decades. Longitudinal analysis delineates a thematic progression: from an early focus on education and mentor-mentee relationships (pre-2018), to a strategic emphasis on mentoring within accelerators and digital platforms (2018–2022), and toward emerging research on its role in fostering sustainability and inclusivity (2023–2025). Thematic mapping shows that entrepreneurship education, human-centered mentoring, and innovation are core “motor themes,” while digital mentoring, equity, and sustainability are promising but underdeveloped areas. Geographical analysis highlights the United States as the leading contributor, with growing research contributions from Europe, Asia, Africa, and the Middle East. The paper advances theoretical understanding by integrating perspectives from social capital, human capital, and entrepreneurial learning theories, and outlines practical implications for designing effective, inclusive, and adaptive mentoring programs. • Comprehensive bibliometric analysis of startup mentoring (1986-2025). • Identifies thematic evolution from education-focused to ecosystem-driven research. • Digital mentoring and equity emerge as critical but underdeveloped areas. • Provides strategic thematic map with motor, niche, and emerging themes. • Offers evidence-based roadmap for researchers, practitioners, and policymakers.

Piemonte Plain (upper Po plain), northwestern Italy. The relationship between meteorological and groundwater droughts of shallow aquifers is investigated in the Piemonte Plain, characterized by widespread irrigation, mainly supplied by Alpine-fed streamflow distributed through a complex irrigation network. Groundwater-level trends (2000–2023) were analyzed considering also seasonal behaviors. Anomalies in precipitation and groundwater levels were studied through the Standardized Precipitation Index (SPI) and Standardized Groundwater Index (SGI) across multiple time-windows and lags. A correlation-weighted lag was introduced to assess SPI–SGI response times during and outside the irrigation period. A conditional frequency analysis was carried out to study the propagation of meteorological drought into groundwater drought. The aquifer system exhibits a widespread decline over the 2000–2023 period. The analysis of the SPI-SGI correlations shows different results for the irrigation and non-irrigation periods. Irrigation weakens the relationship between precipitation and groundwater levels and the rice-cultivated area, mostly irrigated with flooding, shows the lowest SPI-SGI correlation values. The newly introduced weighted lag allows for a better characterization of groundwater response time to precipitation, overcoming the use of a single lag corresponding to maximum correlation. The propagation of meteorological drought into groundwater drought is disentangled during the irrigation period, mitigating SGI negative values in case of scarce precipitation. • Groundwater levels declined in last twenty years in the region (according to monthly trend analysis). • The SPI-SGI correlation describes the propagation of meteorological to groundwater drought. • A novel weighted-lag approach is developed to analyze SPI-SGI relationship. • Current irrigation practices recharge shallow aquifers thus buffering groundwater droughts.

• Constrained and full historic data provide substantial differences in true colour trend. • Robust equation developed for the conversion of apparent colour into true colour data. • Inclusion of full historic colour data substantially reduced the rate of true colour change. • Full historic trends showed no or a decrease in ∼ 90% of Greater Sydney’s water supply. • Catchment characteristics showed no substantial effect on colour trend categories. True colour in drinking water poses no direct health risk; however, visibly coloured water (>15 TCU) often leads to consumer complaints. Long-term analyses of colour trends in drinking water reservoirs are limited by historical industry-wide shift from apparent to true colour monitoring techniques. This study established a conversion equation to estimate true colour (400 nm), from apparent colour, iron and turbidity, and applied this to data from 1931 to 1990 across eight reservoirs in New South Wales, Australia. A Bayesian hierarchical generalised linear model was then used to analyse full historic record trends (∼80 years) and compare the findings to constrained historic record trends derived only from direct sampled true colour values only. While observed true colour constrained historic record trends showed substantial increases across seven of eight reservoirs, incorporating full historic record converted true colour data resulted in either decreases or no change in four major reservoirs that collectively supply ∼ 90% of the region’s total drinking water volume. Notably, full historic record trends substantially reduced the rate of change compared to constrained historic record trends. These contrasting results highlight the opportunity of applying methods to make use of the full historic record for trend analysis. Qualitatively, we found that reservoirs showing decreasing colour trends were the largest in both surface area and storage volume, providing evidence that morphometry can be a driver of colour, through processes such as photooxidation, settling and mixing. Future research should prioritise understanding catchment condition change analysis, as well as long-term rainfall cycles and their relationship with reservoir colour, and integrating lake-specific biogeochemical processes to better explain in-lake true colour dynamics.

Innovations in targeted drug delivery: From nanotechnology to clinical applications

• A ‘sandwich’ model of automated window detection workflow for digital twin buildings. • 105 multidisciplinary articles focused on design, construction, and operation phases. • Deep learning overtaking rule-based methods in diverse data processing since 2018. • Windows’ digital twins enable energy simulation, robotics, and semantic models. • A three-step guideline is proposed for automated window detection in digital twin. Windows play a significant role in the livability and sustainability of buildings, such as energy efficiency, natural lighting, and ventilation; therefore, they are increasingly important subjects of surveys and analysis for both new and existing buildings. Many automated window detection (AWD) workflows have been studied to extract windows’ properties; however, a generalized model of the underlying technologies and practical guidelines remains lacking for both newcomers and Industry practitioners. Therefore, this paper aims to address the following questions: (1) the scope and general workflow of AWD; (2) the trends regarding AWD key components; and (3) the pathways of key component (e.g., data source, attribute, storage, and method) selections to fit the application scenario. Four decision trees, trained on 105 technical papers collected in accordance with the PRISMA standard, achieved an average F1-score of 80.3% and accuracy of 88.1%. This paper contributes the first comprehensive review of AWD, with a general ‘sandwich’ model, a comprehensive analysis of advanced technologies, trends, and future directions, as well as three-step guidelines for practitioners.

Comparison of early-onset and later-onset cervical cancer clinical characteristics and trends in Eswatini.

Evolution of teacher training in gamification: a bibliometric analysis of global trends and collaborations

Brain temperature, a fundamental modulator of neural function, remains dramatically understudied despite its critical role in health and disease. This review synthesizes current understanding of brain thermoregulation and its disruption in neurological conditions, addressing a significant knowledge gap in neuroscience. We examined the physiological mechanisms maintaining brain temperature homeostasis, including the interplay between cerebral blood flow, metabolism, and cerebrospinal fluid dynamics. Analysis of publication trends reveals that brain temperature research is underrepresented by 7- to 37-fold compared with other brain physiological parameters, despite comparable clinical relevance. We evaluated current noninvasive measurement techniques, particularly magnetic resonance-based thermometry, highlighting advances and limitations for clinical application. The review presents evidence for distinct temperature dysregulation patterns in neurological diseases. In Alzheimer's disease, we propose a theoretical framework of early-stage hyperthermia driven by neuroinflammation and hypermetabolism, transitioning to late-stage hypothermia with metabolic decline. Brain tumors exhibit contrasting thermal profiles: glioblastomas frequently present as hypothermic due to necrotic cores acting as metabolic voids, whereas melanoma metastases show hyperthermia from sustained metabolic activity. These temperature alterations may influence disease progression through effects on protein aggregation, cellular metabolism, and neuron-glial interactions. Looking forward, brain temperature monitoring could provide biomarkers for disease staging and treatment response. In addition, understanding thermal limits becomes urgent as climate change exposes vulnerable populations with compromised thermoregulation to extreme heat. This review establishes brain temperature as an overlooked but essential axis in neurophysiology, calling for increased research attention to address fundamental questions about thermal regulation in health and disease.

Energy efficiency and energy conservation: An analysis of global research trends, disparities, and future directions

Research trends in microRNAs in glioma tumors: A data-driven exploration using a bibliometric approach.

Real-world complications, utilization patterns, and patient selection for the vertiflex interspinous process device: A national cohort analysis.

This study proposes a two-level stacking machine learning approach for predicting rainfall erosivity ( R m ) in Taiwan, providing a flexible alternative to traditional empirical methods. Conventional models rely on limited high-resolution rainfall data and are often region-specific, which limits their accuracy elsewhere. In contrast, the proposed ensemble framework captures complex, non-linear interactions among climatic and topographic variables to improve prediction accuracy. In the first level, six base models were combined, and in the second level, each base model was used as a meta-model to form the ensemble structure. Twenty-eight predictor variables, including climatic and topographic factors, were derived from Coupled Model Intercomparison Project Phase 6 (CMIP6) high-resolution global climate data and a digital elevation model (DEM). To ensure robustness, the modeling procedure was repeated five times using different train–test splits, and final performance metrics were calculated as averages across five datasets. Feature selection using Boruta identified rainfall-related variables as the most important contributors. The ensemble approach significantly improved predictive performance, achieving a root mean square error (RMSE) of 5317 . 92 ± 261 . 23 MJ ⋅ mm ⋅ ha − 1 ⋅ hour − 1 ⋅ year − 1 and a Nash–Sutcliffe efficiency (NSE) of 0 . 67 ± 0 . 02 . The analysis revealed an increasing trend in R m , particularly under higher emission scenarios (SSP3-7.0 and SSP5-8.5), with increases projected in the latter half of the century. These findings highlight the importance of targeted climate mitigation and adaptation strategies for soil conservation and watershed management. This study supports Sustainable Development Goals 13 (Climate Action) and 15 (Life on Land) by improving R m prediction to reduce land degradation and enhance climate resilience. • Two-level stacking ensemble ML framework predicts rainfall erosivity ( R m ) in Taiwan. • Combined six base and meta models with 28 climate and DEM predictors. • Random forest (RF) meta-model achieved best accuracy (NSE = 0.67, RMSE = 5317.92 MJ ⋅ mm ⋅ ha −1 ⋅ hour −1 ⋅ year −1 ). • R m shows increasing trends under high-emission scenarios in late 21st century.

To use bibliometric methods to deeply analyze the application of WeChat in China's mHealth field, outline its research panorama, and clarify research frontiers. Literature from January 2011 to December 2024 was sourced from the Web of Science Core Collection, and PubMed databases. The work has been registered in PROSPERO (ID: CRD420251060517). Bibliometrix, Biblioshiny, VOSviewer, GraphPad Prism, and Adobe Photoshop were used for bibliometric analysis and visualization of the included literature. A total of 1633 publications were initially retrieved, and 379 eligible documents were finally included after rigorous screening based on inclusion and exclusion criteria (1254 excluded). The annual publication output showed a sharp growth from 2016 to 2021 and entered a stable phase thereafter, while the citation count has maintained a steady upward trend since 2020. China was identified as the core contributing country in this research field. Among institutional contributors, Fujian Medical University ranked first in publication output, and Sun Yat-sen University exhibited outstanding citation impact. Keyword co-occurrence analysis identified two distinct research clusters, with one focusing on clinical practice and disease-oriented research, and the other centering on research scientificity and population health research. It reveals a growing and interdisciplinary research area, and the trends in publication outputs, citations, and research hotspots can serve as a guide for future research. This research thoroughly outlined the current state of WeChat in China's mHealth, addressed research gaps, and suggested future research directions to encourage innovative applications.

Trends in acute decompensated heart failure admissions and healthcare costs in U.S. emergency departments.