Search Results (5,317)

Silicon has a striking similarity to carbon and is found in plant cells. However, there is no specific role that has been assigned to silicon in the life cycle of plants. The amount of silicon in plant cells is species specific and can reach levels comparable to macronutrients. Silicon is used extensively in artificial intelligence, nanotechnology, and the digital revolution, and thus can serve as an informational molecule such as nucleic acids. The diverse potential of silicon to bond with different chemical species is analogous to carbon; thus, it can serve as a structural candidate similar to proteins. The discovery of large amounts of silicon on Mars and the moon, along with the recent development of enzyme that can incorporate silicon into organic molecules, has propelled the theory of creating silicon-based life. The bacterial cytochrome has been modified through directed evolution such that it could cleave silicon–carbon bonds in organo-silicon compounds. This consolidates the idea of utilizing silicon in biomolecules. In this article, the potential of silicon-based life forms has been hypothesized, along with the reasoning that autotrophic virus-like particles could be used to investigate such potential. Such investigations in the field of synthetic biology and astrobiology will have corollary benefits for Earth in the areas of medicine, sustainable agriculture, and environmental sustainability. Full article

This study employs k-means clustering to group 30 Chinese provinces into four CO${}_2$ emission patterns, characterized by increasing emission levels and distinct energy consumption structures, and captures their dynamic evolution from 2000 to 2021 using a discrete endogenous Markov chain approach. While Shanghai, Jiangxi, and Hebei retained their original classifications, provinces such as Beijing, Fujian, Tianjin, and Anhui transitioned from higher to lower emission patterns, indicating notable reversals in emission trajectories. To identify the determinants of these transitions, GDP growth rate, population growth rate, and energy investment are incorporated as time varying covariates. The empirical findings demonstrate that GDP growth substantially increases interpattern mobility, thereby weakening state persistence, whereas population growth and energy investment tend to reinforce emission pattern stability. These results imply that policy responses must be tailored to regional dynamics. In rapidly growing regions, fiscal incentives and technological upgrading may facilitate downward transitions in emission states, whereas in provinces where emissions remain persistent due to demographic or investment related rigidity, structural adjustments and long term mitigation frameworks are essential. The study underscores the importance of integrating economic, demographic, and investment characteristics into carbon reduction strategies through a region specific and data informed approach. Full article

Community detection in complex networks plays a pivotal role in modern scientific research, including in social network analysis and protein structure analysis. Traditional community detection methods face challenges in integrating heterogeneous multi-source information, capturing global semantic relationships, and adapting to dynamic network evolution. This paper proposes a novel unsupervised multimodal community detection algorithm (UMM) based on fractal iteration. The core idea is to design a dual-channel encoder that comprehensively considers node semantic features and network topological structures. Initially, node representation vectors are derived from structural information (using feature vectors when available, or singular value decomposition to obtain feature vectors for nodes without attributes). Subsequently, a parameter-free graph convolutional encoder (PFGC) is developed based on fractal iteration principles to extract high-order semantic representations from structural encodings without requiring any training process. Furthermore, a semantic–structural dual-channel encoder (DC-SSE) is designed, which integrates semantic encodings—reduced in dimensionality via UMAP—with structural features extracted by PFGC to obtain the final node embeddings. These embeddings are then clustered using the K-means algorithm to achieve community partitioning. Experimental results demonstrate that the UMM outperforms existing methods on multiple real-world network datasets. Full article

Introduction: Cystic fibrosis (CF) is a genetic condition affecting several organs and systems, including the pancreas, colon, respiratory system, and reproductive system. The detection of a growing number of CFTR variants and genotypes has contributed to an increase in the CF population which, in turn, has had an impact on the overall statistics regarding the prognosis and outcome of the condition. Given the increase in life expectancy, it is critical to better predict outcomes and prognosticate in CF. Thus, each person’s choice to aggressively treat specific disease components can be more appropriate and tailored, further increasing survival. The objective of our narrative review is to summarize the most recent information concerning the value and significance of clinical parameters in predicting outcomes, such as gender, diabetes, liver and pancreatic status, lung function, radiography, bacteriology, and blood and sputum biomarkers of inflammation and disease, and how variations in these parameters affect prognosis from the prenatal stage to maturity. Materials and methods: A methodological search of the available data was performed with regard to prognostic factors in the evolution of CF in children and young adults. We evaluated articles from the PubMed academic search engine using the following search terms: prognostic factors AND children AND cystic fibrosis OR mucoviscidosis. Results: We found that it is crucial to customize CF patients’ care based on their unique clinical and biological parameters, genetics, and related comorbidities. Conclusions: The predictive significance of more dynamic clinical condition markers provides more realistic future objectives to center treatment and targets for each patient. Over the past ten years, improvements in care, diagnostics, and treatment have impacted the prognosis for CF. Although genotyping offers a way to categorize CF to direct research and treatment, it is crucial to understand that a variety of other factors, such as epigenetics, genetic modifiers, environmental factors, and socioeconomic status, can affect CF outcomes. The long-term management of this complicated multisystem condition has been made easier for patients, their families, and physicians by earlier and more accurate identification techniques, evidence-based research, and centralized expert multidisciplinary care. Full article

This study systematically investigates 50 μm-diameter 631 stainless steel fine wires subjected to both sequential and simultaneous electrothermomechanical loading to simulate probe spring conditions in microelectronic test environments. Under cyclic current loading (~10⁴ A/cm²), the 50 μm 631SS wire maintained electrical integrity up to 0.30 A for 15,000 cycles. Above 0.35 A, rapid oxide growth and abnormal grain coarsening resulted in surface embrittlement and mechanical degradation. Current-assisted tensile testing revealed a transition from recovery-dominated behavior at ≤0.20 A to significant thermal softening and ductility loss at ≥0.25 A, corresponding to a threshold temperature of approximately 200 °C. These results establish the endurance limit of 631 stainless steel wire under coupled thermal–mechanical–electrical stress and clarify the roles of Joule heating, oxidation, and microstructural evolution in electrical fatigue resistance. A degradation map is proposed to inform design margins and operational constraints for fatigue-tolerant, electrically stable interconnects in high-reliability probe spring applications. Full article

Previous reviews have examined specific facets of Geographic Information Systems (GIS) in transportation planning, such as transit-focused applications and open source geospatial tools. However, this study offers the first systematic, PRISMA-guided longitudinal evaluation of GIS integration in transportation planning, spanning thematic domains, data models, methodologies, and outcomes from 2004 to 2024. This study addresses this gap through a longitudinal analysis of GIS-based transportation research from 2004 to 2024, adhering to PRISMA guidelines. By conducting a mixed-methods analysis of 241 peer-reviewed articles, this study delineates major trends, such as increased emphasis on sustainability, equity, stakeholder involvement, and the incorporation of advanced technologies. Prominent domains include land use–transportation coordination, accessibility, artificial intelligence, real-time monitoring, and policy evaluation. Expanded data sources, such as real-time sensor feeds and 3D models, alongside sophisticated modeling techniques, enable evidence-based, multifaceted decision-making. However, challenges like data limitations, ethical concerns, and the need for specialized expertise persist, particularly in developing regions. Future geospatial innovations should prioritize the responsible adoption of emerging technologies, inclusive capacity building, and environmental justice to foster equitable and efficient transportation systems. This review highlights GIS’s evolution from a supplementary tool to a cornerstone of data-driven, sustainable urban mobility planning, offering insights for researchers, practitioners, and policymakers to advance transportation strategies that align with equity and sustainability goals. Full article

The rapid evolution of Industry 4.0 technologies has resulted in a scenario in which collaborative networks are essential to overcome the challenges related to their implementation. However, the frameworks to guide such collaborations remain underexplored. This study addresses this gap by proposing Business Rules and Goals Models to operationalize Industry 4.0 solutions through enterprise collaboration. Using the For Enterprise Modeling (4EM) method, the research integrates qualitative insights from expert opinions, including interviews with 12 professionals (academics, industry professionals, and consultants) from Brazilian manufacturing sectors. The Goals Model identifies five main objectives—competitiveness, efficiency, flexibility, interoperability, and real-time collaboration—while the Business Rules Model outlines 18 actionable recommendations, such as investing in digital infrastructure, upskilling employees, and standardizing information technology systems. The results reveal that cultural resistance, limited resources, and knowledge gaps are critical barriers, while interoperability and stakeholder integration emerge as enablers of digital transformation. The study concludes that successfully adopting Industry 4.0 requires technological investments, organizational alignment, structured governance, and collaborative ecosystems. These models provide a practical roadmap for companies navigating the complexities of Industry 4.0, emphasizing adaptability and cross-functional synergy. The research contributes to the literature on collaborative networks by connecting theoretical frameworks with actionable enterprise-level strategies. Full article

Disparities in the functional classification of secretory genes among aphid taxa may be attributed to variations in coding sequences and gene expression profiles. However, the driving factors that regulate sequence evolution remain unclear. This study aimed to investigate the differences in coding sequences and expression patterns of secretory genes between the rose grain aphid (Metopolophium dirhodum) and the pea aphid (Acrythosiphon pisum), with a particular focus on their roles in evolutionary adaptations and functional diversity. The study involved the rearing of aphids, RNA extraction, de novo transcriptome assembly, functional annotation, secretory protein prediction, and comparative analysis of coding sequences and expression patterns across various functional categories using bioinformatics tools. The results revealed that metabolic genes exhibited greater coding sequence divergence, indicating the influence of positive selection. Moreover, significant expression divergence was noted among functional categories, particularly in metabolic and genetic information processing genes, which exhibited higher variability. This study enhances our understanding of the molecular mechanisms that contribute to phenotypic and genetic diversity among aphid species. This study elucidates the relationship between variations in coding sequences and differences in gene expression among functional categories, thereby establishing a foundation for future studies on gene evolution in response to environmental pressures. Full article

During red wine production, managing the pomace cap is key for a successful fermentation, allowing the extraction of phenolics and other metabolites and providing the necessary oxygen for yeast activity. In recent years, automatic cap management systems based on the injection of gases have gained popularity, despite the limited scientific information regarding the outcomes of their use. This trial aimed to evaluate the composition of wine during industrial red wine fermentations using an automatic sequential air injection system (i.e., AirMixing MI^TM). Fourteen lots of Cabernet Sauvignon grapes were fermented using four air injection regimes, where the intensity and daily frequency of air injections were set to either low or high. As expected, the treatment combining high-intensity and high-frequency air injection produced the largest dissolved oxygen peaks reaching up to 1.9 mg L⁻¹ per cycle, compared to 0.1 mg L⁻¹ in the low-intensity and low-frequency treatment. Yet, in all cases, little to no accumulation of oxygen overtime was observed. Regarding phenolics, the highest intensity and frequency of air injections led to the fastest increase in total phenolics, anthocyanins, short polymeric pigments, and tannin concentration, although compositional differences among treatments equilibrate by the end of fermentation. The main differences in phenolic compounds observed during fermentation were mediated by temperature variation among wine tanks. Based on these findings, it is advisable to keep the characterizing kinetics of phenolic extraction and expand the study to the aroma evolution of wines fermented with this technology. Full article

This study explores the challenges and theoretical transformations that the widespread application of AI technology in social governance brings to the protection of citizens’ fundamental rights. By examining typical cases in judicial assistance, technology-enabled law enforcement, and welfare supervision, it explains how AI characteristics such as algorithmic opacity, data bias, and automated decision-making affect fundamental rights including due process, equal protection, and privacy. The article traces the historical evolution of privacy theory from physical space protection to informational self-determination and further to modern data rights, pointing out the inadequacy of traditional rights-protection paradigms in addressing the characteristics of AI technology. Through analyzing AI-governance models in the European Union, the United States, Northeast Asia, and international organizations, it demonstrates diverse governance approaches ranging from systematic risk regulation to decentralized industry regulation. With a special focus on China, the article analyzes the special challenges faced in AI governance and proposes specific recommendations for improving AI-governance paths. The article argues that only within the track of the rule of law, through continuous theoretical innovation, institutional construction, and international cooperation, can AI technology development be ensured to serve human dignity, freedom, and fair justice. Full article

Gully erosion refers to the landform formed by soil and water loss through gully development, which is a critical manifestation of soil degradation. However, research on the spatio-temporal variations in erosion gullies at the county scale remains insufficient, particularly regarding changes in gully aggregation and their driving factors. This study utilized high-resolution remote sensing imagery, gully interpretation information, topographic data, meteorological records, vegetation coverage, soil texture, and land use datasets to analyze the spatio-temporal patterns and influencing factors of erosion gully evolution in Bin County, Heilongjiang Province of China, from 2012 to 2022. Kernel density evaluation (KDE) analysis was also employed to explore these dynamics. The results indicate that the gully number in Bin County has significantly increased over the past decade. Gully development involves not only headward erosion of gully heads but also lateral expansion of gully channels. Gully evolution is most pronounced in slope intervals. While gentle slopes and slope intervals host the highest density of gullies, the aspect does not significantly influence gully development. Vegetation coverage exhibits a clear threshold effect of 0.6 in inhibiting erosion gully formation. Additionally, cultivated areas contain the largest number of gullies and experience the most intense changes; gully aggregation in forested and grassland regions shows an upward trend; the central part of the black soil region has witnessed a marked decrease in gully aggregation; and meadow soil areas exhibit relatively stable spatio-temporal variations in gully distribution. These findings provide valuable data and decision-making support for soil erosion control and transformation efforts. Full article

Amid ongoing societal development, tourists’ travel behavior patterns have been undergoing substantial transformations, and understanding their evolution has emerged as a key area of scholarly interest. Taking Beijing as a case study, this research aims to uncover the spatiotemporal evolution patterns of tourist flows and their underlying driving mechanisms. Based on digital footprint relational data, a dual-perspective analytical framework—“tourist perception–tourist flow network”—is constructed. By integrating the center-of-gravity model, social network analysis, and regression models, the study systematically examines the dynamic spatial structure of tourist flows in Beijing from 2012 to 2024. The findings reveal that in the post-pandemic period, Beijing tourists place greater emphasis on the cultural connotation and experiential aspects of destinations. The gravitational center of tourist flows remains relatively stable, with core historical and cultural blocks retaining strong appeal, though a slight shift has occurred due to policy influences and emerging attractions. The evolution of the spatial network structure reveals that tourism flows have become more dispersed, while the influence of core scenic spots continues to intensify. Government policy orientation, tourism information retrieval, and the agglomeration of tourism resources significantly promote the structure of tourist flows, whereas the general level of tourism resources exerts no notable influence. These findings offer theoretical insights and practical guidance for the sustainable development and regional coordination of tourism in Beijing, and provide a valuable reference for the spatial restructuring of urban tourism in the post-COVID-19 era. Full article

In late 2019, a new virus, SARS-CoV-2, emerged in Wuhan, China, causing COVID-19 and the subsequent global pandemic. As of 30 April 2023, more than 774 million cases of COVID-19 had been reported worldwide, including over 7.5 million in Mexico. Despite advances in vaccination, epidemic surges of COVID-19 continued to occur globally, highlighting the importance of sharing and disseminating the experiences gained during these first years to better understand the virus’s evolution and respond accordingly. For this reason, the National Council for Science and Technology (CONACYT) organized the meeting “Challenges and Opportunities for Genomic Surveillance of SARS-CoV-2 in Mexico” from 15 to 17 August 2022, to present the efforts and results accumulated over more than two years of the pandemic. In this meeting report, we summarize the key findings of each participant and provide their contact information. Full article

This study explores the potential of Intelligent Geographic Information Systems (GISs) in advancing educational practices through the integration of real-time data models. The objective is to investigate how GIS technology can enhance teaching and learning by providing interactive and dynamic learning environments. The research employs a bibliometric analysis based on the Scopus database, covering the period from 2000 to 2024, to identify key trends, the evolution of GIS applications in education, and their pedagogical impact. Findings reveal that GISs, particularly when incorporating real-time data, enable a more immersive learning experience, facilitate data-driven decision-making, and promote student engagement through project-based learning. However, challenges such as the lack of specialized training for educators and limitations in technological infrastructure remain significant barriers to widespread adoption. The study concludes that Intelligent GISs have the potential to transform education by fostering personalized, interdisciplinary learning and enhancing educational management. It emphasizes the need for further research aimed at developing user-friendly systems and addressing ethical concerns to ensure the benefits of GIS technology are accessible to all students. Future studies should examine the long-term effects of GISs on student outcomes and explore their integration into diverse educational contexts. Full article

Background: International migration is a growing global phenomenon involving diverse groups, such as labor migrants, international marriage migrants, refugees, and international students. International migrants face unique mental health challenges influenced by adversities such as social isolation and limited access to mental health services. This study employs bibliometric methods to systematically analyze the global body of literature on international migrants’ mental health. Methods: The literature on the mental health of international migrants published until October 2024 was searched using the Web of Science database. The search terms included (‘International migrants’ OR ‘migrant workers’ OR ‘international students’ OR ‘refugees’ OR ‘asylum seekers’ OR ‘smuggled migrants’) AND ‘mental health’. VOSviewer was used to conduct bibliometric analysis, focusing on co-authorship patterns, keyword co-occurrence, and citation networks. Results: Over the past four decades, research on the mental health of international migrants has grown substantially, with major migration destinations such as the United States, Europe, and Australia playing prominent roles in this field. ‘Post-traumatic stress disorder (PTSD)’ was the most frequent keyword in publications, with strong links to ‘trauma’ and ‘depression’. In recent years, with the impact of global socioenvironmental changes and emergencies such as the COVID-19 pandemic, the research focus has gradually shifted towards social support, service accessibility, and cultural adaptation. Conclusions: International migration is a far-reaching global phenomenon, and addressing the mental health of migrant populations is essential for advancing public health, social cohesion, and sustainable development. This study provides the first bibliometric overview of research in this domain, mapping its thematic evolution and collaborative structure. The findings offer valuable insights into the field’s development and may support future interdisciplinary collaboration and the formulation of culturally informed, evidence-based approaches in migrant mental health. Full article