Search Results (974)

The construction and development of digital infrastructure have emerged as a crucial indicator of national competitiveness, which holds significant importance in driving the sustained growth of the national economy and the comprehensive advancement of society. This paper explores the impact of digital infrastructure on farm households’ scale management, aiming to reveal the role and potential of digital technology in agricultural modernization. Additionally, it seeks to offer a scientific foundation for the government to formulate agricultural policies and advance agricultural modernization. Using the OLS (Ordinary Least Squares) model, moderating effect model, and other methods, this study investigates how digital infrastructure affects farm households’ scale management based on micro-level research data of 2510 farm households from the CRRS (China Rural Revitalization Survey). The following conclusions are drawn: Firstly, the enhancement of digital infrastructure can motivate farm households to expand the land management area and increase the unit output of land. Secondly, farm households’ digital literacy positively moderates the effect of digital infrastructure on their land unit output; moreover, digital skills training for farm households exhibits a positive moderating effect on the influence of digital infrastructure on their management area. Finally, there is a heterogeneity in the impact of digital infrastructure on farm households’ scale management. Specifically, the promotion of farm households’ scale management is stronger in plain areas and weaker in hilly and mountainous areas; stronger for middle-aged and older and small-scale farm households; and weaker for youth groups and large-scale farm households. Based on this, this paper suggests increasing the investment in digital infrastructure construction, improving farm households’ digital literacy, carrying out digital skills training, and formulating differentiated regional policies for reference. Full article

Radon anomalies have long been explored as potential geochemical precursors to seismic activity due to their responsiveness to subsurface stress variations. However, before this study, the scientific progression of this research domain had not been systematically examined through a quantitative lens. This study presents a comprehensive bibliometric analysis of 379 articles published between 1977 and 2025 and indexed in Scopus and Web of Science. Utilizing the Bibliometrix R-package and its Biblioshiny interface, the analysis investigates temporal publication trends, leading countries, institutions, international collaboration networks, and thematic evolution. The results reveal a marked increase in research output since 2010, with China, India, and Italy emerging as the most prolific contributors. Thematic mapping indicates a shift from conventional geochemical monitoring toward the integration of artificial intelligence techniques, such as decision trees and neural networks, for anomaly detection and predictive modeling. Notwithstanding this methodological evolution, core research themes remain centered on radon concentration monitoring and the analysis of environmental parameters. Overall, the findings highlight the coexistence of traditional and emerging approaches, emphasizing the importance of standardized methodologies and interdisciplinary collaboration. This bibliometric synthesis provides strategic insights to inform future research and strengthen the role of radon monitoring in seismic early warning systems. Full article

Advances in technology and changes in the way people entertain themselves have made video games a cultural agent on a par with more traditional games, including language learning. In addition, the use of video games in education is becoming increasingly common and numerous benefits associated with their use have been discovered. The aim of this article is to analyze the search trends in studies dealing with the use of video games in language learning. To this end, a bibliometric analysis was carried out by applying the traditional laws of bibliometrics (Price’s law, Bradford’s law of concentration, Lotka’s law, Zipf’s law and h-index) to documents published in journals indexed in the Core Collection of the Web of Science (WoS). Annual publications between 2009 and 2022 show an exponential growth R2 = 86%. The journals with the most publications are Computer assisted language learning (Taylor & Francis) and Computers and Education (Elsevier). Jie Chi-Yang and Gwo Jen-Hwan were the most cited authors. The United States and Taiwan were the countries with the highest scientific output. The use of video games in language learning has been of particular interest in recent years, with benefits found for students who use them in their classes, although more research is needed to establish criteria and requirements for each video game for its intended purpose. Full article

The significant burden of polypharmacy in clinical settings contrasts sharply with the narrow research focus on drug–drug interactions (DDIs), revealing an important gap in understanding the complexity of real-world multi-drug regimens. The present study addresses this gap by conducting a high-resolution, multidimensional bibliometric and network analysis of 19,151 DDI publications indexed in the Web of Science Core Collection (1975–2025). Using advanced tools, including VOSviewer version 1.6.20, Bibliometrix 5.0.0, and AI-enhanced terminology normalization, global research trajectories, knowledge clusters, and collaborative dynamics were systematically mapped. The analysis revealed an exponential growth in publication volume (from 55 in 1990 to 1194 in 2024), with output led by the United States and a marked acceleration in Chinese contributions after 2015. Key pharmacological agents frequently implicated in DDI research included CYP450-dependent drugs such as statins, antiretrovirals, and central nervous system drugs. Thematic clusters evolved from mechanistic toxicity assessments to complex frameworks involving clinical risk management, oncology co-therapies, and pharmacokinetic modeling. The citation impact peaked at 3.93 per year in 2019, reflecting the increasing integration of DDI research into mainstream areas of pharmaceutical science. The findings highlight a shift toward addressing polypharmacy risks in aging populations, supported by novel computational methodologies. This comprehensive assessment offers insights for researchers and academics aiming to navigate the evolving scientific landscape of DDIs and underlines the need for more nuanced system-level approaches to interaction risk assessment. Future studies should aim to incorporate patient-level real-world data, expand bibliometric coverage to underrepresented regions and non-English literature, and integrate pharmacogenomic and time-dependent variables to enhance predictive models of interaction risk. Cross-validation of AI-based approaches against clinical outcomes and prospective cohort data are also needed to bridge the translational gap and support precision dosing in complex therapeutic regimens. Full article

In this paper, we present the concept of “sequencing economics”, consisting of (A) segmentation, (B) construction sequencing, and (C) functions. An agglomeration is organized into segments, and sequencing economics examines the sequential process of efficiently building such segments. The functions (C) of the segments act as a master switch, an accelerator, a brake, etc. in the implementation of agglomeration policy. In this paper, we identify a master switch and an accelerator in scientific city agglomeration policy and draw two conclusions. First, in agglomeration policy, the construction of the master switch lowers “transport costs”, as derived from the monocentric city model of spatial economics by Fujita and Krugman. Second, the accelerator segment represents the activities of the service sector that have the highest forward-linkage effect in an input–output relationship. Regarding science city agglomeration policy, it can be concluded that the master switch is high-speed rail and the accelerator is research and education activities. In this paper, the new scientific urban agglomeration that emerges from monocentric cities is referred to as railroad-driven agglomeration (RDA), which is a type of transit-oriented development (TOD). This paper demonstrates that the Tsukuba Express, as a case study of RDA, caused the agglomeration of Tsukuba Science City. This paper establishes the concept of sequencing economics, a policy implementation rule that differs from Tinbergen’s rule. The latter is based on the concept of simultaneous equations, whereas the rule of sequencing economics is based on sequential equations. RDA enables middle-income countries to surpass their middle-income status. Full article

Scientific knowledge and international collaboration are critical to achieving the Sustainable Development Goals (SDGs). This study conducts a large-scale bibliometric analysis of 49.4 million publications indexed in the Web of Science (1945–2023) related to the SDGs, with a specific focus on SDG 3 (Good Health and Well-Being). Since 1992, SDG 3 has accounted for 58% of SDG-related scientific output. Using K-means clustering and network analysis, we classified countries/regions by research productivity and mapped core–periphery collaboration structures. Results reveal a sharp concentration: the United States, China, England, and Germany account for 51.65% of publications. In contrast, the group composed of the 195 least productive countries and territories accounts for approximately 5% of the total scientific output on the SDGs, based on the same clustering method. Collaboration patterns mirror this inequality, with 84.97% of partnerships confined to the core group and only 2.81% involving core–periphery cooperation. These asymmetries limit the capacity of developing regions to generate health research aligned with local needs, constraining equitable progress toward SDG 3. Expanding scientific cooperation, fostering North–South and South–South collaborations, and ensuring equitable research funding are essential to promote inclusive knowledge production and support sustainable global health. Full article

This study introduces a predictive model for estimating indoor room temperatures in vernacular building using external environmental factors such as air temperature, humidity, sunshine duration, and wind speed. The dataset was sourced from the Meteorology, Climatology, and Geophysics Agency and supplemented with direct measurements collected from four rooms within a vernacular building in Aceh Province, Indonesia. A Generative Adversarial Network (GAN)-based imputation technique was implemented to address missing data during preprocessing. The prediction model adopts a hybrid framework that integrates Multiple Linear Regression (MLR) and Artificial Neural Networks (ANNs), with both models optimized using Support Vector Regression (SVR) to better capture the nonlinear dynamics between inputs and outputs. The evaluation results show that the ANN-SVR model achieved the lowest average $\overline{MAE}$ and $\overline{RMSE}$ values, at 0.164 and 0.218, respectively, and the highest average $\overline{R}$ and $\overline{R^2}$ values, at 0.785 and 0.618. Evaluation results indicate that the ANN-SVR model consistently achieved the lowest error rates and the highest correlation coefficients across all four rooms, identifying it as the most effective model for forecasting indoor thermal conditions. These results validate the combined use of ANN-SVR for prediction and GAN for preprocessing as a powerful strategy to enhance data quality and model performance. The findings offer a scientific basis for architectural planning to improve thermal comfort in vernacular buildings such as the Rumoh Aceh. Full article

Background/Objectives: Herbal extracts from Betula alba (birch) are traditionally used for their purported diuretic effects, but scientific evidence supporting these claims remains limited. In this pilot study, we evaluated the short-term effects of a standardized B. alba leaf extract in healthy adult rats using an untargeted urinary metabolomics approach based on UPLC-QTOF. Methods: Two doses, 25 or 50 mg/kg, of a standardized B. alba extract were orally administered to rats. The extract contains hyperoside (0.53%), quercetin glucuronide (0.36%), myricetin glucoside (0.32%), and chlorogenic acid (0.28%) as its main constituents. After 3 days of treatment, the 24 h urine output was measured. Results: While no statistically significant changes were observed in the 24 h urine volume or the urinary Na⁺ and K⁺ excretion, multivariate metabolomic analysis revealed treatment-induced alterations in the urinary metabolic profile. Notably, the levels of two glucocorticoids, i.e., corticosterone and 11-dehydrocorticosterone, were increased in treated animals, suggesting that the extract may influence corticosteroid metabolism or excretion, potentially impacting antidiuretic hormone signaling. Elevated bile-related compounds, including bile acids and bilin, and glucuronidated metabolites were also observed, indicating changes in bile acid metabolism, hepatic detoxification, and possibly gut microbiota activity. Conclusions: Although this study did not confirm a diuretic effect of B. alba extract, the observed metabolic shifts suggest broader systemic bioactivities that warrant further investigation. Overall, the results indicate that the approach based on urinary metabolomics may be valuable in uncovering the mechanisms of action and evaluating the bioactivity of herbal extracts with purported diuretic properties. Full article

Due to the dramatic increase in pesticide usage and improper application, large amounts of unused pesticides enter the environment through paddy water, causing severe pesticide pollution. To find a rapid method for identifying pesticide types and predicting their concentrations, the dielectric properties frequency response of pesticides was analyzed in paddy water. A rapid detection method for typical pesticides such as chlorpyrifos, isoprothiolane, imidacloprid and carbendazim was studied based on their dielectric properties. In this paper, amplitude and phase frequency response data for blank paddy water samples and 15 types of paddy water samples containing pesticides were collected at 10 different temperatures. Principal component analysis (PCA) and competitive adaptive reweighted sampling (CARS) were used to extract characteristic frequencies. A species identification model based on support vector machine (SVM) for rapid detection of pesticides in paddy water was established using amplitude and phase frequency response data separately. Frequency response data of 431 sets from nine types of paddy water samples were divided into training and prediction sets in a 3:1 ratio, and a content prediction model based on artificial neural networks (ANN) with multiple inputs and single output was established using amplitude and phase frequency response data after CARS feature extraction. The experimental results show that both PCA-SVM and CARS-SVM species identification models established using amplitude and phase frequency response data have excellent identification effects, reaching over 90%. The PCA-SVM model based on phase frequency response data has the best identification effect for typical pesticides in paddy water with a prediction recognition accuracy range of 97.5–100%. The ANN content prediction model established using phase frequency response data performs well, and the highest R² prediction values of chlorpyrifos, isoprothiolane, imidacloprid and carbendazim in paddy water were 0.8249, 0.8639, 0.9113 and 0.8368 respectively. The research established a dielectric property detection method for the identification and content prediction of typical pesticides in paddy water, providing a theoretical basis for the hardware design of capacitive sensors based on dielectric property and the detection of pesticide residues in paddy water. This provides a new method and approach for pesticide residue detection, which is of great significance for scientific pesticide application and sustainable agricultural development. Full article

This study presents a systematic literature review on the mathematical modeling of resilient and viable supply chains, grounded in the PRISMA methodology and applied to a curated corpus of 235 peer-reviewed scientific articles published between 2011 and 2025. The search strategy was implemented across four major academic databases (Scopus and Web of Science) using Boolean operators to capture intersections among the core concepts of supply chains, resilience, viability, and advanced optimization techniques. The screening process involved a double manual assessment of titles, abstracts, and full texts, based on inclusion criteria centered on the presence of formal mathematical models, computational approaches, and thematic relevance. As a result of the selection process, six thematic categories were identified, clustering the literature according to their analytical objectives and methodological approaches: viability-oriented modeling, resilient supply chain optimization, agile and digitally enabled supply chains, logistics optimization and network configuration, uncertainty modeling, and immune system-inspired approaches. These categories were validated through a bibliometric analysis and a thematic map that visually represents the density and centrality of core research topics. Descriptive analysis revealed a significant increase in scientific output starting in 2020, driven by post-pandemic concerns and the accelerated digitalization of logistics operations. At the methodological level, a high degree of diversity in modeling techniques was observed, with an emphasis on mixed-integer linear programming (MILP), robust optimization, multi-objective modeling, and the increasing use of bio-inspired algorithms, artificial intelligence, and simulation frameworks. The results confirm a paradigm shift toward integrative frameworks that combine robustness, adaptability, and Industry 4.0 technologies, as well as a growing interest in biological metaphors applied to resilient system design. Finally, the review identifies research gaps related to the formal integration of viability under disruptive scenarios, the operationalization of immune-inspired models in logistics environments, and the need for hybrid approaches that jointly address resilience, agility, and sustainability. Full article

This study offers a comprehensive examination of the scientific output related to the integration of Artificial Intelligence (AI) in education using qualitative research methods, which is an emerging intersection that reflects growing interest in understanding the pedagogical, ethical, and methodological implications of AI in educational contexts. Grounded in a theoretical framework that emphasizes the potential of AI to support personalized learning, augment instructional design, and facilitate data-driven decision-making, this study conducts a Systematic Literature Review and bibliometric analysis of 630 publications indexed in Scopus between 2014 and 2024. The results show a significant increase in scholarly output, particularly since 2020, with notable contributions from authors and institutions in the United States, China, and the United Kingdom. High-impact research is found in top-tier journals, and dominant themes include health education, higher education, and the use of AI for feedback and assessment. The findings also highlight the role of semi-structured interviews, thematic analysis, and interdisciplinary approaches in capturing the nuanced impacts of AI integration. The study concludes that qualitative methods remain essential for critically evaluating AI’s role in education, reinforcing the need for ethically sound, human-centered, and context-sensitive applications of AI technologies in diverse learning environments. Full article

Thermal infrared (TIR) ground observations are a well-established method for investigating surface temperature variations in thermally anomalous areas. However, commercially available technical solutions are currently limited, often offering proprietary products with minimal customization options for establishing a permanent TIR monitoring network. This work presents the comprehensive development of a thermal infrared monitoring network, detailing everything from the hardware schematics of the remote monitoring station (RMS) to the code for the final data processing software. The procedures implemented in the RMS for managing TIR sensor operations, acquiring environmental data, and transmitting data remotely are thoroughly discussed, along with the technical solutions adopted. The processing of TIR imagery is carried out using ASIRA (Automated System of InfraRed Analysis), a free software package, now developed for GNU Octave. ASIRA performs quality filtering and co-registration, and applies various seasonal correction methodologies to extract time series of deseasoned surface temperatures, estimate heat fluxes, and track variations in thermally anomalous areas. Processed outputs include binary, Excel, and CSV formats, with interactive HTML plots for visualization. The system’s effectiveness has been validated in active volcanic areas of southern Italy, demonstrating high reliability in detecting anomalous thermal behavior and distinguishing endogenous geophysical processes. The aim of this work is to enable readers to easily replicate and deploy this open-source, low-cost system for the continuous, automated thermal monitoring of active volcanic and geothermal areas and environmental pollution, thereby supporting hazard assessment and scientific research. Full article

The present review summarizes the existing knowledge regarding the afforestation of sand dunes. Our main focus was on the role of trees in stabilizing and rehabilitating these complex ecosystems. We analyzed 937 publications through a systematic bibliometric review and then proceeded to select 422 articles that met our criteria. This methodological approach—combining a comprehensive bibliometric analysis with an in-depth traditional literature review—represents a novel contribution to the field and allows for both quantitative trends and qualitative insights to be captured. This was then complemented by an in-depth literature review. Our results sustain the global importance of this subject, as they include studies from more than 80 countries, with a focus on the USA, China, Australia, and Japan. We have also identified a series of main tree species that are usually used in the afforestation of sand dunes (Pinus, Acacia, Juniperus) and then proceeded to analyze their ecologic and socio-economic impact. As such, we have analyzed case studies from all continents, showcasing a variety of strategies that were successful and adapted to local conditions. This did not exclude challenges, mainly invasive species, low survival rates, and effects on biodiversity and stabilization. The main factors that impact the success of afforestation are represented by topography, soil structure, water dynamics, and climate. Unlike previous reviews, this study offers a global synthesis of both the scientific output and the applied outcomes of sand dune afforestation, bridging the gap between research and practice. As such, afforestation has a positive impact on soil fertility and carbon sequestration but can also present a major risk to native ecosystems. In this context, the present review highlights the need to adopt strategies that are unique for that site, and that must integrate all aspects (ecological, social, economic) to ensure good results. Our ISI-indexed literature review helped us to address the link between the current knowledge, research trends, and future topics that must be addressed. Full article

Background: The increasing complexity of global supply chains has intensified the demand for transparency, traceability, security, and sustainability in logistics and operations. Blockchain technology enables decentralized, immutable frameworks that improve data integrity, automate transactions via smart contracts, and integrate seamlessly with the IoT and AI. Methods: This bibliometric review analyzes 559 peer-reviewed publications retrieved from Scopus and Web of Science using a PRISMA-guided protocol. Data were processed with Bibliometrix and Biblioshiny to examine scientific production, contributing institutions, author countries, collaboration patterns, thematic clusters, and keyword evolution. Results: The analysis reveals a 400% increase in publications after 2020, with China, India, and the USA leading in output but with limited international collaboration. Keyword co-occurrence and thematic mapping reveal dominant topics, including smart contracts, food supply chain traceability, and sustainability, as well as emerging themes such as decentralization, privacy, and the circular economy. Conclusions: The field is marked by interdisciplinary growth, yet it remains thematically and geographically fragmented. This review maps the intellectual structure of blockchain-enabled sustainable supply chains, offering insights for policymakers, developers, and industry leaders and outlining future research avenues centered on global cooperation, platform efficiency, and ethical and regulatory dimensions. Full article

Light curve analysis of W UMa-type contact binary systems using MCMC or MC methods can be time-consuming, primarily because the repeated generation of synthetic light curves tends to be relatively slow during the fitting process. Although various approaches have been proposed to address this issue, their implementation is often challenging due to complexity or uncertain performance. In this study, we introduce the BSN application, whose name is taken from the BSN project. The application is designed for analyzing contact binary system light curves, supporting photometric data, and employing an MCMC algorithm for efficient parameter estimation. The BSN application generates synthetic light curves more than 40 times faster than PHOEBE during the MCMC fitting process. The BSN application enhances light curve analysis with an expanded feature set and a more intuitive interface while maintaining compliance with established scientific standards. In addition, we present the first light curve analyses of four contact binary systems based on the TESS data, utilizing the BSN application version 1.0. We also conducted a light curve analysis using the PHOEBE Python code and compared the resulting outputs. Two of the target systems exhibited asymmetries in the maxima of their light curves, which were appropriately modeled by introducing a cold starspot on one of the components. The estimated mass ratios of these total-eclipse systems place them within the category of low mass ratio contact binary stars. The estimation of the absolute parameters for the selected systems was carried out using the $P-a$ empirical relationship. Based on the effective temperatures and masses of the components, three of the target systems were classified as A-subtype, while TIC 434222993 was identified as a W-subtype system. Full article