Search Results (81)

Continuous monitoring of environmental and physiological parameters is essential for early diagnostics, real-time decision making, and intelligent system adaptation. Recent advancements in bio-microelectromechanical systems (BioMEMS) sensors have significantly enhanced our ability to track key metrics in real time. However, continuous monitoring demands sustainable energy supply solutions, especially for on-site energy replenishment in areas with limited resources. Artificial intelligence (AI), particularly large language models, offers new avenues for interpreting the vast amounts of data generated by these sensors. Despite this potential, fully integrated systems that combine self-powered BioMEMS sensing with AI-based analytics remain in the early stages of development. This review first examines the evolution of BioMEMS sensors, focusing on advances in sensing materials, micro/nano-scale architectures, and fabrication techniques that enable high sensitivity, flexibility, and biocompatibility for continuous monitoring applications. We then examine recent advances in energy harvesting technologies, such as piezoelectric nanogenerators, triboelectric nanogenerators and moisture electricity generators, which enable self-powered BioMEMS sensors to operate continuously and reducereliance on traditional batteries. Finally, we discuss the role of AI in BioMEMS sensing, particularly in predictive analytics, to analyze continuous monitoring data, identify patterns, trends, and anomalies, and transform this data into actionable insights. This comprehensive analysis aims to provide a roadmap for future continuous BioMEMS sensing, revealing the potential unlocked by combining materials science, energy harvesting, and artificial intelligence. Full article

Viral infections persistently challenge global health through immune evasion and zoonotic transmission. Dendritic cells (DCs) play a central role in antiviral immunity by detecting viral nucleic acids via conserved pattern recognition receptors, triggering interferon-driven innate responses and cross-presentation-mediated activation of cytotoxic CD8⁺ T cells. This study synthesizes DC-centric defense mechanisms against viral subversion, encompassing divergent nucleic acid sensing pathways for zoonotic DNA and RNA viruses, viral counterstrategies targeting DC maturation and interferon signaling, and functional specialization of DC subsets in immune coordination. Despite advances in DC-based vaccine platforms, clinical translation is hindered by cellular heterogeneity, immunosuppressive microenvironments, and limitations in antigen delivery. Future research should aim to enhance the efficiency of DC-mediated immunity, thereby establishing a robust scientific foundation for the development of next-generation vaccines and antiviral therapies. A more in-depth exploration of DC functions and regulatory mechanisms may unlock novel strategies for antiviral intervention, ultimately paving the way for improved prevention and treatment of viral infections. Full article

This study investigates the impact of rural digital economy development on agricultural carbon emission efficiency, aiming to elucidate the intrinsic mechanisms and pathways through which digital technology enables low-carbon transformation in agriculture, thereby contributing to the achievement of agricultural carbon neutrality goals. Based on provincial-level panel data from China spanning 2011 to 2022, this study examines the relationship between the rural digital economy and agricultural carbon emission efficiency, along with its underlying mechanisms, using bidirectional fixed effects models, mediation effect analysis, and Spatial Durbin Models. The results indicate the following: (1) A significant N-shaped-curve relationship exists between rural digital economy development and agricultural carbon emission efficiency. Specifically, agricultural carbon emission efficiency exhibits a three-phase trajectory of “increase, decrease, and renewed increase” as the rural digital economy advances, ultimately driving a sustained improvement in efficiency. (2) Industrial integration acts as a critical mediating mechanism. Rural digital economy development accelerates the formation of the N-shaped curve by promoting the integration between agriculture and other sectors. (3) Spatial spillover effects significantly influence agricultural carbon emission efficiency. Due to geographical proximity, regional diffusion, learning, and demonstration effects, local agricultural carbon emission efficiency fluctuates with changes in neighboring regions’ digital economy development levels. (4) The relationship between rural digital economy development and agricultural carbon emission efficiency exhibits a significant inverted N-shaped pattern in regions with higher marketization levels, planting-dominated areas of southeast China, and digital economy demonstration zones. Further analysis reveals that within rural digital economy development, production digitalization and circulation digitalization demonstrate a more pronounced inverted N-shaped relationship with agricultural carbon emission efficiency. This study proposes strategic recommendations to maximize the positive impact of the rural digital economy on agricultural carbon emission efficiency, unlock its spatially differentiated contribution potential, identify and leverage inflection points of the N-shaped relationship between digital economy development and emission efficiency, and implement tailored policy portfolios—ultimately facilitating agriculture’s green and low-carbon transition. Full article

The Altay oasis, located at the heart of the transnational ecological conservation zone shared by China, Kazakhstan, Russia, and Mongolia, is a region with tremendous potential for water resource utilization. However, with the continued expansion of agriculture, its ecological vulnerability has become increasingly pronounced. Within this fragile balance lies a critical opportunity: efficient water resource management could pave the way for sustainable development across the entire arid oasis regions. This study uses a decision tree model based on a feature threshold to map the spatial distribution of major crops in the Altay Prefecture oasis, assess their water requirements, and identify the coupling relationships between agricultural water and land resources. Furthermore, it proposed optimization planting structure strategies under three scenarios: water-saving irrigation, cash crop orientation, and forage crop orientation. In 2023, the total planting area of major crops in Altay Prefecture was 3368 km², including spring wheat, spring maize, sunflower, and alfalfa, which consumed 2.68 × 10⁹ m³ of water. Although this area accounted for only 2.85% of the land, it consumed 26.23% of regional water resources, with agricultural water use comprising as much as 82.5% of total consumption, highlighting inefficient agricultural water use as a critical barrier to sustainable agricultural development. Micro-irrigation technologies demonstrate significant water-saving potential. The adoption of such technologies could reduce water consumption by 14.5%, thereby significantly enhancing agricultural water-use efficiency. Cropping structure optimization analysis indicates that sunflower-based planting patterns offer notable water-saving benefits. Increasing the area of sunflower cultivation by one unit can unlock a water-saving potential of 25.91%. Forage crop combinations excluding soybean can increase livestock production by 30.2% under the same level of water consumption, demonstrating their superior effectiveness for livestock system expansion. This study provides valuable insights for achieving sustainable agricultural development in arid regions under different development scenarios. Full article

Artificial Intelligence (AI) has emerged as a transformative force in orthopedic oncology, offering significant advances in the diagnosis, classification, and prediction of treatment response for primary malignant bone tumors (PBT). Through machine learning and deep learning techniques, AI leverages computational algorithms and large datasets to enhance medical imaging interpretation and support clinical decision-making. The integration of radiomics with AI enables the extraction of quantitative features from medical images, allowing for precise tumor characterization and the development of personalized therapeutic strategies. Notably, convolutional neural networks have demonstrated exceptional capabilities in pattern recognition, significantly improving tumor detection, segmentation, and differentiation. This narrative review synthesizes the evolving applications of AI in PBTs, focusing on early tumor detection, imaging analysis, therapy response prediction, and histological classification. AI-driven radiomics and predictive models have yielded promising results in assessing chemotherapy efficacy, optimizing preoperative imaging, and predicting treatment outcomes, thereby advancing the field of precision medicine. Innovative segmentation techniques and multimodal imaging models have further enhanced healthcare efficiency by reducing physician workload and improving diagnostic accuracy. Despite these advancements, challenges remain. The rarity of PBTs limits the availability of robust, high-quality datasets for model development and validation, while the lack of standardized imaging protocols complicates reproducibility. Ethical considerations, including data privacy and the interpretability of complex AI algorithms, also warrant careful attention. Future research should prioritize multicenter collaborations, external validation of AI models, and the integration of explainable AI systems into clinical practice. Addressing these challenges will unlock AI’s full potential to revolutionize PBT management, ultimately improving patient outcomes and advancing personalized care. Full article

Under the dual constraints of rigid water resource management systems and China’s “dual carbon” national strategy, water resource management authorities face pressing practical demands for the coordinated governance of water conservation and carbon dioxide emission reduction. This study comprehensively compiles nationwide data on water supply/consumption, energy use, water intensity, and CO₂ emissions across Chinese provinces. Employing a non-radial directional distance function (NDDF) model with multiple inputs and outputs, we quantitatively assess provincial water saving and carbon reduction performance during 2000–2021; measure synergistic effects; and systematically examine the spatiotemporal evolution, correlation patterns, and convergence trends of three key indicators: standalone water saving performance, standalone carbon reduction performance, and their synergistic performance—essentially addressing whether “1 + 1 > 2” holds true. Furthermore, we analyze the spatial convergence and clustering characteristics of synergistic effect across regions, delving into the underlying causes of inter-regional disparities in water–carbon synergy. Key findings reveal the following: ① Temporally, standalone water saving and carbon reduction performance generally improved, though the water saving metrics initially declined before stabilizing into sustained growth, ultimately outpacing carbon reduction gains. Synergistic performance consistently surpassed standalone measures, with most regions demonstrating accelerating synergistic enhancement over time. Nationally, water–carbon synergy exhibited early volatile declines followed by steady growth, though the growth rate gradually decelerated. ② Spatially, high-value synergy clusters migrated from the western to eastern regions and the northern to southern zones before stabilizing geographically. The synergy effect demonstrates measurable convergence overall, yet with pronounced regional heterogeneity, manifesting a distinct “high southeast–low northwest” agglomeration pattern. Strategic interventions should prioritize water–carbon nexus domains, leverage spatial convergence trends and clustering intensities, and systematically unlock synergistic potential. Full article

The rapid advancement of computing power, combined with the ability to collect vast amounts of data, has unlocked new possibilities for industrial applications. While traditional time–domain industrial signals generally do not allow for direct stability assessment or the detection of abnormal situations, alternative representations can reveal hidden patterns. This paper introduces time-shifted maps (TSMs) as a novel method for analyzing industrial data—an approach that is not yet widely adopted in the field. Unlike contemporary machine learning techniques, TSM relies on a simple and interpretable algorithm designed to process data from standard industrial automation systems. By creating clear, visual representations, TSM facilitates the monitoring and control of production process. Specifically, TSMs are constructed from time series data collected by an acceleration sensor mounted on a robot base. To evaluate the effectiveness of TSM, its results are compared with those obtained using classical signal processing methods, such as the fast Fourier transform (FFT) and wavelet transform. Additionally, TSMs are classified using computed correlation dimensions and entropy measures. To further validate the method, we numerically simulate three distinct anomalous scenarios and present their corresponding TSM-based graphical representations. Full article

Neurodegenerative disorders such as Alzheimer’s disease (AD), the most common form of dementia, represent a growing global health crisis, yet current treatment strategies remain primarily palliative. Recent studies have shown that neurodegeneration through complex interactions within the gut–brain axis largely depends on the gut microbiota and its metabolites. This review explores the intricate molecular mechanisms linking gut microbiota dysbiosis to cognitive decline, emphasizing the impact of microbial metabolites, including short-chain fatty acids (SCFAs), bile acids, and tryptophan metabolites, on neuroinflammation, blood–brain barrier (BBB) integrity, and amyloid-β and tau pathology. The paper highlights major microbiome signatures associated with Alzheimer’s disease, detailing their metabolic pathways and inflammatory crosstalk. Dietary interventions have shown promise in modulating gut microbiota composition, potentially mitigating neurodegenerative processes. This review critically examines the influence of dietary patterns, such as the Mediterranean and Western diets, on microbiota-mediated neuroprotection. Bioactive compounds like prebiotics, omega-3 fatty acids, and polyphenols exhibit neuroprotective effects by modulating gut microbiota and reducing neuroinflammation. Furthermore, it discusses emerging microbiome-based therapeutic strategies, including probiotics, prebiotics, postbiotics, and fecal microbiota transplantation (FMT), as potential interventions for slowing Alzheimer’s progression. Despite these advances, several knowledge gaps remain, including interindividual variability in microbiome responses to dietary interventions and the need for large-scale, longitudinal studies. The study proposes an integrative, precision medicine approach, incorporating microbiome science into Alzheimer’s treatment paradigms. Ultimately, cognizance of the gut–brain axis at a mechanistic level could unlock novel therapeutic avenues, offering a non-invasive, diet-based strategy for managing neurodegeneration and improving cognitive health. Full article

Background/Objectives: Smartphones, with their widespread popularity and diverse apps, have become essential in our daily lives, and ongoing advancements in information technology have unlocked their significant potential in healthcare. Our goal is to identify the future research directions of mobile health (mHealth) by examining its research trends and emerging hotspots. Methods: This study collected mHealth-related literature published between 2005 and 2024 from the Web of Science database. We conducted a descriptive statistic of the annual publication count and categorized the data by authors and institutions. In addition, we developed visualization maps to display the frequency of keyword co-occurrences. Furthermore, overlay visualizations were created to showcase the average publication year of specific keywords, helping to track the changing trends in mHealth research over time. Results: Between 2005 and 2024, a total of 6093 research papers related to mHealth were published. The data have revealed a rapid increase in the number of publications since 2011. However, it was found that research on mHealth has reached a saturation point since 2021. The University of California was the dominant force in mHealth research, with 248 articles. The University of California, the University of London, Harvard University, and Duke University are actively collaborating, which shows a geographical pattern of collaboration. From the analysis of keyword co-occurrence and timeline, the research focus has gradually shifted from solely mHealth technologies to exploring how new technologies, such as artificial intelligence (AI) in mobile apps, can actively intervene in patient conditions, including breast cancer, diabetes, and other chronic diseases. Privacy protection policies and transparency mechanisms have emerged as an active research focus in current mHealth development. Notably, cutting-edge technologies such as the Internet of Things (IoT), blockchain, and virtual reality (VR) are being increasingly integrated into mHealth systems. These technological convergences are likely to constitute key research priorities in the field, particularly in addressing security vulnerabilities while enhancing service scalability. Conclusions: Although the volume of core research in mobile health (mHealth) is gradually declining, its practical applications continue to expand across diverse domains, increasingly integrating with multiple emerging technologies. It is believed that mobile health still holds enormous potential. Full article

This study investigates the influence of human capital and natural resource productivity on achieving sustainable cities and society (SDG-11) within the European Union (EU) while also considering the contribution of renewable energy (RE). This research analyzes data from the European Union between 2011 and 2020 by deploying the first-difference generalized method of moments (FM-GMM) model to distinguish between two different effects of the human capital variable—a low effect (negative influence) and a high effect (positive influence). The analysis has identified an optimal threshold value of 1.867 for the human capital index (HCI) score in the context of European Union countries. This threshold value represents a critical point at which the effect of human capital on achieving SDG-11, which aims to make cities and human settlements inclusive, safe, resilient, and sustainable, undergoes a significant shift. The impact of renewable energy consumption on SDG-11 exhibits a non-linear pattern. There is a negative relationship at lower levels of renewable energy adoption (below a certain threshold), with renewable energy negatively impacting SDG-11 progress at a 1% significance level. However, the relationship becomes significantly positive once renewable energy consumption surpasses this threshold. This non-linearity suggests that achieving mass renewable energy adoption is crucial to unlocking its full potential in promoting the sustainable urban development goals captured by SDG-11. The results also demonstrate a positive effect on natural resource productivity both before and after exceeding a specific threshold, although the magnitude of this effect varies. This robust evidence underscores the necessity for targeted policies in the European Union to enhance human capital, increase renewable energy adoption, and boost natural resource productivity, thereby securing sustainable funding mechanisms for SDG-11. Full article

Adaptive reuse of vacant houses in Japan offers an innovative and sustainable solution to the increase in vacant houses. This approach aligns with principles of sustainable architecture and the circular economy by reducing waste, lowering energy consumption, and extending the lifecycle of existing structures. This study uses purposive sampling, analyzing 262 adaptive reuse cases across Japanese prefectures through partial surveys, municipal records, and online maps. K-prototype clustering identified three distinct patterns. Cluster 1 emphasizes modern businesses, such as food, beverage, and accommodation services, within urban areas to address the needs of densely populated regions and tourist hubs. Cluster 2 blends urban and rural contexts, balancing historical preservation with modern functionality. Cluster 3 highlights rural and scenic accommodations that cater to tourists seeking cultural immersion and authentic experiences, despite challenges like low population density and limited accessibility. These findings contribute to the theoretical understanding of adaptive reuse as a key strategy for repurposing underutilized spaces, promoting both economic and social resilience. In practical terms, it demonstrates how adaptive reuse advances circular economy objectives by preserving cultural heritage, enhancing environmental sustainability, and creating economic opportunities. Further investigation is needed to unlock the unexplored potential of adaptive reuse in broader contexts and functions. Full article

Research shows that family conversations about STEM topics positively influence children’s STEM identity development. This study expands on these findings by exploring how family conversations beyond STEM content contribute to this development. Specifically, we focus on how non-academic forms of family support—as described by students who face systemic racial discrimination in STEM—shape these conversations. In this way, we extend existing work by exploring the extent to which families’ dispositions to talk about a wide range of topics—not just in STEM—might further support youth identification with STEM fields. Using Family Communication Patterns Theory (FCPT) to guide our analysis, we examined data from a survey of first-year college students (n = 1134) attending Minority-Serving Institutions and public universities in the United States. The survey asked students to reflect on their childhood conversations and their current sense of identity in STEM. Using structural equation modeling, we found that family disposition to engage in conversations about a broad range of topics was linked to more frequent STEM-related conversations during childhood and, in turn, greater identification as a “STEM person” in college. These findings highlight the complex ways that family communication patterns can support construction of an individual’s sense of themselves as a STEM person in later years. By interpreting these findings using FCPT, we highlight the nature of family communication patterns that can contribute to STEM identity formation. Full article

Transferring the extensive and varied knowledge contained within multiple complex models into a more compact student model poses significant challenges in multi-teacher knowledge distillation. Traditional distillation approaches often fall short in this context, as they struggle to fully capture and integrate the wide range of valuable information from each teacher. The variation in the knowledge offered by various teacher models complicates the student model’s ability to learn effectively and generalize well, ultimately resulting in subpar results. To overcome these constraints, We introduce an innovative method that integrates both localized and globalized frequency attention techniques, aiming to substantially enhance the distillation process. By simultaneously focusing on fine-grained local details and broad global patterns, our approach allows the student model to more effectively grasp the complex and diverse information provided by each teacher, therefore enhancing its learning capability. This dual-attention mechanism allows for a more balanced assimilation of specific details and generalized concepts, resulting in a more robust and accurate student model. Extensive experimental evaluations on standard benchmarks demonstrate that our methodology reliably exceeds the performance of current multi-teacher distillation methods, yielding outstanding outcomes regarding both performance and robustness. Specifically, our approach achieves an average performance improvement of 0.55% over CA-MKD, with a 1.05% gain in optimal conditions. These findings suggest that frequency-based attention mechanisms can unlock new potential in knowledge distillation, model compression, and transfer learning. Full article

The use of organic semiconductors in electronic devices, including transistors, sensors, and memories, unlocks innovative possibilities such as streamlined fabrication processes, enhanced mechanical flexibility, and potential new applications. Nevertheless, the increasing technical demand for patterning organic semiconductors requires greater integration and functional implementation. This paper overviews recent efforts to pattern organic semiconductors compatible with electronic devices. The review categorizes the contributions of organic semiconductor patterning approaches, such as surface-grafting polymers, capillary force lithography, wettability, evaporation, and diffusion in organic semiconductor-based transistors and sensors, offering a timely perspective on unconventional approaches to enable the patterning of organic semiconductors with a strong focus on the advantages of organic semiconductor utilization. In addition, this review explores the opportunities and challenges of organic semiconductor-based integration, emphasizing the issues related to patterning and interconnection. Full article

The urgency of global climate change and environmental degradation has become increasingly apparent, and green finance, as a pioneering financial tool, is providing critical support to unlock regional green patterns. Based on the data of China’s prefecture level from 2010 to 2021, this study examines the causal relationship and mechanism of green finance (GF) and urban green total factor productivity (GTFP) using the spatial Durbin model. The results show the following: (1) Green finance can not only improve local GTFP, but also has a spatial spillover effect, and it is still valid after a robustness test, which means that the development of GF can significantly promote urban green transformation. (2) The local effect and spatial spillover effect of green finance are more obvious in coastal and developed areas. (3) After deconstructing the mechanism of green transformation, this paper finds that improving urban energy utilization efficiency, mitigating the capital mismatch degree, and enhancing new quality productivity are important impact channels for green finance to enhance urban GTFP. These conclusions not only provide a theoretical reference for GF to help with the construction of a high-quality “Double Cycle” new development pattern, but also promote low-carbon transformation. This study has obvious application value and provides experience for other developing countries to seek green transformation from the perspective of green finance practice. Full article