Search Results (12,807)

Covalent organic frameworks (COFs) have emerged as promising candidates for solid-state electrolytes (SSEs) in lithium-ion batteries (LIBs) due to their tunable pore sizes, high surface areas, and exceptional thermal stability. However, the rational design of COF-based SSEs is hindered by the vast combinatorial chemical space, synthetic complexity, and the need for precise control over structure-property relationships. Machine learning (ML) has revolutionized the development of COF materials by enabling high-throughput screening, predictive modeling, and optimization of synthesis conditions. This review systematically explores the integration of ML in COF-based SSE development, focusing on structure prediction, synthesis-performance optimization, and the application of digital twin strategies. We highlight the role of ML in accelerating the discovery of high-performance COF-based solid-state electrolytes, optimizing ionic conductivity, and enhancing interfacial stability. By summarizing the synergistic pathways between computational simulations and experimental validation, this review offers strategic guidelines for overcoming traditional “trial-and-error” R&D bottlenecks, paving the way for the next generation of high-energy-density LIBs. Full article

The Chinese black truffle (Tuber sinense) is an economically vital ectomycorrhizal fungus threatened by unsustainable harvesting. Cultivating truffles using mycorrhizal seedlings is essential for sustainable production, yet the rhizosphere microbiome dynamics remain unclear. This study explored microbial community succession in the rhizosphere of Chinese chestnut (Castanea mollissima) seedlings inoculated with T. sinense over 8 months. High-throughput sequencing and soil physicochemical analysis were conducted at 1, 3, and 8 months post-inoculation. Significant changes in soil properties, such as decreased pH and increased total nitrogen (TN), total potassium (TK), available phosphorus (AP), and calcium (Ca), influenced microbial assembly. Tuber relative abundance rose from 0.02% in non-inoculated samples to 8.81% at 8 months. Inoculation altered microbial structures, enriching fungal genera like Tuber, Staphylotrichum, and Sphaerosporella. Network analysis showed 79.23% positive bacterial-fungal interactions, crucial for rhizosphere stability. Tuber correlated positively with Staphylotrichum and Spizellomyces, indicating potential synergies in mycorrhizal development and nutrient cycling. Tuber also showed significant positive correlations with TN, TK, AP, and Ca, highlighting its preference for nutrient-enriched conditions. This study provides the first comprehensive profile of microbial succession during the mycorrhizal development of T. sinense on chestnut, offering a scientific basis for optimizing truffle seedling production and supporting sustainable cultivation. Full article

Healthcare systems worldwide face increasing pressure to deliver accurate, affordable, and scalable diagnostic services while maintaining long-term sustainability. Chest X-ray screening is considered one of the most cost-effective methods for detecting lung disease. However, many deep learning approaches are computationally intensive and difficult to interpret, which limits their adoption in high-throughput, resource-constrained clinical settings. This study proposes a hybrid CNN–tree framework for automated lung disease classification from chest X-ray images, which targets COVID-19, pneumonia, tuberculosis, lung cancer, and normal cases. To ensure robustness and generalization, four publicly available chest X-ray datasets from different sources are merged into a unified five-class dataset, which introduces realistic variations in imaging conditions and patient populations. A ResNet-18 model is fine-tuned to extract domain-specific deep feature representations. Feature dimensionality and redundancy are reduced using Principal Component Analysis, while class imbalance is addressed through the Synthetic Minority Over-sampling Technique. The resulting compact feature vectors are used to train interpretable tree-based classifiers, which include Decision Tree, Random Forest, and XGBoost. Experiments conducted using five-fold stratified cross-validation demonstrate substantial and consistent performance gains. When trained on fine-tuned and preprocessed deep features, all evaluated tree-based classifiers achieve weighted F1-scores between 0.977 and 0.982 using five-fold cross-validation, with a significant reduction in inter-class confusion. In addition, the proposed framework maintains low per-sample inference latency, which supports energy-efficient and scalable deployment. These results indicate that combining deep feature learning with interpretable tree-based models provides a practical and reliable solution for sustainable chest X-ray screening in real-world clinical environments. Full article

Using AI to analyze metabolite profiles provides critical insights into health, aging, and disease. Metabolomic signatures reveal how lifestyle and therapy impact organ function and cancer progression. This review highlights emerging toolkits for high-throughput data analysis, emphasizing their integration with other omics. Advanced AI approaches facilitate metabolic pathway mapping and accelerate biomarker discovery. By combining AI with multi-omics, researchers can optimize interventions and enhance precision medicine. This article serves as a resource demonstrating AI’s potential in diagnostics and drug discovery. Full article

Small and medium enterprises (SMEs) are critical actors in housing supply chains; however, they often struggle to adopt industrialized construction. High variability, limited infrastructure, and skill constraints can reduce repeatability and quality. This study shows that SMEs can start with targeted standardization of prefabricated wood panels. A panel library and coded kits support scalable production, repeatable quality, and a structured workflow for light timber framing. Evidence is provided by a Chilean industrial case study using a time-study campaign. The campaign quantified processing, setup, and internal movement times across a five-station manual layout. Results indicate that a standardized panel set for larger housing typologies stabilizes manual operations. Throughput improves only after key bottlenecks are addressed as staffing increases from 12 to 18 operators, enabling production above 200 homes per year. When two of eight activities are automated at Station 2 using CNC (fixing and cutting), annual capacity can approach 300 homes. Overall, the findings suggest a staged pathway for SMEs: standardize first, add selective automation once constraints are removed, and then integrate internal logistics to sustain the transition from craft-based to industrialized housing production. Full article

Cross-medium acoustic–radio collaborative networks enable integrated communication among underwater, surface, and aerial nodes for marine observation and detection. However, heterogeneous propagation characteristics of acoustic and radio channels significantly degrade the performance of conventional single-medium routing protocols, resulting in excessive control overhead, a low packet delivery ratio (PDR), and high latency. To address these challenges, this paper proposes an optimized AODV protocol for Cross-medium Acoustic–Radio Collaborative Networks (CACN-OAODV). The proposed protocol incorporates a medium-aware routing initiation mechanism to reduce unnecessary broadcasts, a link stability factor that jointly considers hop count and channel quality for reliable path selection, and a lightweight control optimization scheme to limit routing overhead in acoustic environments. Extensive simulations conducted in NS-3 with realistic multi-channel propagation models demonstrate that CACN-OAODV significantly outperforms the standard AODV protocol, achieving improved PDR, higher throughput, and reduced end-to-end delay. These results indicate that CACN-OAODV provides an effective routing solution for heterogeneous cross-medium marine communication networks. Full article

Confronted with the severe imperatives to food security posed by a growing population and the urgent need for sustainable development amid climate change, traditional agricultural models face significant resource-intensive efficiency bottlenecks. Deep learning-based image recognition is driving a future-oriented intelligent agricultural revolution by enabling high-throughput phenotyping and autonomous decision-making across the production chain. This paper systematically reviews key advancements in image recognition within modern agriculture, mapping the fundamental paradigm shift from traditional hand-crafted feature engineering to adaptive deep feature learning. We critically analyze technological implementation and performance across five core application scenarios: high-precision pest and disease diagnosis, spatio-temporal growth monitoring and yield prediction through multi-source image fusion, agricultural robots for automated harvesting, non-destructive quality inspection of products, and intelligent precision management of farmland. The review further identifies critical challenges hindering large-scale technology adoption, primarily centered on the high costs of constructing high-quality agricultural datasets and model robustness in complex field environments. Consequently, this study provides a comprehensive and forward-looking reference for advancing the deep integration of vision technology, thereby offering a strategic path toward achieving more intelligent, efficient, and sustainable global agricultural production systems in the digital era. Full article

Breast cancer is a heterogeneous malignancy that often changes during diagnosis and treatment, so timely monitoring of tumors, patients and treatment responses is crucial to improve the prognosis of patients. With the development of precision oncology, early patient stratification and the formulation of tailored therapeutic approaches have become essential strategies to maximize treatment efficacy. Several techniques, such as molecular pathology and genomics analysis have been thoroughly studied in the diagnosis and treatment of breast cancer, but they only evaluate and analyze from the perspective of patients or tumors in isolation. Metabolomics uses high-throughput analytical techniques to provide a functional readout of the biological phenotype, reflecting the sum of alterations occurring at the DNA, RNA, and protein levels. Therefore, through the detection of tumor tissues and peripheral blood of patients, metabolomics could describe the bidirectional interaction between the tumor and its microenvironment, as well as the systemic metabolic changes in patients to evaluate cancer progression from both tumor and patient aspects in a more comprehensive way. In this review, we summarize the currently available techniques for metabolomics and how metabolomics can be used to improve the clinical management of breast cancer patients, including diagnosis, treatment, and prognosis. We also discuss current challenges and future directions in metabolomics research. Full article

The bioactive compounds that are produced by leeches combine traditional and modern treatment since the saliva of the animal contains proteins and peptides with anticoagulant, anti-inflammatory, antimicrobial, antioxidant, and regenerative properties. In this review, their biochemical profile, mechanisms and clinical uses are considered with a special focus on the fact that they are utilized to combine traditional practices with the modern developments in biomedical approaches. Proteomic and transcriptomic research has recently found more than 100 bioactive molecules, such as hirudin, calin, eglins, bdellins and destabilase, which are related to the blood-feeding process and therapeutic processes. These compounds control blood clotting, control inflammatory mediators, block microbes and enhance wound healing and the development of new blood vessels. In clinical practice, leech therapy is common in the reconstruction and microsurgical practice to reduce venous congestion and enhance graft success. They are also shown to be useful in wound healing, cardiovascular health, musculoskeletal conditions and regenerative medicine, as well as emerging drug delivery systems of recombinant proteins and nanocarriers. Some of the challenges involve biological variation, infection or bleeding risks and stringent regulations on purity and standardization. Biotechnology has improved through other developments such as recombinant protein production, high-throughput omics, and nanotechnology, which will help resolve these problems, making them safe and scalable for clinical use. Altogether, leech bioactives are the prime examples of the sophisticated pharmacology of nature, which have the potential of being used as therapeutic agents in the future. The recent approach and incorporation in personalized medicine and bioengineering models reflect the leech’s capacity to address complicated illness and unmet healthcare requirements to reassert its significance in preventive medicine and recent biomedicine. Full article

Environmental health and biosecurity in pig farms and surroundings are increasingly threatened by pathogenic bacteria carried by fine particulate matter with an aerodynamic diameter of 2.5 μm or less (PM_2.5) in enclosed piggeries. However, limited attention has been given to these pathogens and their association with the respiratory microbiome of pigs. Using high-throughput sequencing, we investigated the overall and pathogenic bacterial communities attached to PM_2.5 in pig houses, as well as those in the upper (URT) and lower respiratory tracts (LRT) of healthy fattening pigs. Concentrations of PM_2.5, particulate matter with an aerodynamic diameter of 10 μm or less (PM₁₀), ammonia (NH₃), total volatile organic compounds (TVOCs), and hydrogen sulfide (H₂S) were significantly higher inside the piggery than in the surrounding environment. The composition of PM_2.5-associated bacteria varied with sampling height and showed greater similarity to the microbiota of the URT, particularly the oropharynx, than to that of the LRT. Additionally, 140 core potential bacterial pathogens were identified via Venn analysis in both PM_2.5 and respiratory tracts. Co-occurrence network analysis and community assembly patterns revealed that microbial communities in PM_2.5 and the respiratory tract exhibit distinct interaction and assembly characteristics. These findings highlight the potential role of PM_2.5 as a vector for respiratory pathogens and underscore the importance of air quality management in pig farming to safeguard environmental health. Full article

Background/Objectives: Rheumatoid arthritis (RA) is a chronic autoimmune disorder characterized by persistent synovial inflammation and vascular abnormalities. Emerging evidence suggests that dysbiosis of the microbiome contributes to the pathogenesis of this disease, while seasonal and meteorological variations represent significant factors influencing microbial community dynamics. However, the specific pathological mechanisms mediated by microbial populations within knee joint effusions of RA patients remain poorly elucidated. The present study employs 16S rRNA high-throughput sequencing technology to characterize seasonal variation patterns affecting microbial communities in knee joint effusions of RA patients and to investigate the relationship between microbial community structures and climatic lag effects. Methods: Microbial communities in knee joint effusion samples obtained from RA patients were analyzed using 16S rRNA high-throughput sequencing methodologies. A Distributed Lag Non-linear Model (DLNM) was applied to quantify the delayed effects of climatic variables on microbial community composition. The correlation patterns between meteorological parameters and community structure were elucidated through the integration of ridge regression and redundancy analysis (RDA). Preliminary identification of potential biomarkers was conducted using random forest algorithms. Results: According to research findings, the microbial composition of knee joint effusions in RA patients shows seasonal fluctuation patterns that are compatible with those seen in RA patients, even though there is no discernible seasonal change in β-diversity. Compared with samples obtained during other seasons, spring specimens exhibited significantly elevated relative abundances of both beneficial microorganisms and opportunistic pathogenic taxa. Random forest modeling identified Escherichia-Shigella and Curtobacterium as preliminary candidate biomarkers; however, external validation is required to establish their specificity as disease indicators. Further analysis revealed that although short-term meteorological fluctuations exert minimal influence on overall microbial diversity, specific alterations in mean wind speed (MWS) and relative humidity (RH) drive compositional changes in the microbial community, manifested as rapid responses from dominant bacterial taxa and compensatory buffering effects from rare taxa. Conclusions: This study suggests that the synovial cavity microbiota in RA patients may exhibit seasonal variation patterns that are statistically associated with environmental parameters, particularly humidity and temperature. Due to the inherent limitations of the cross-sectional study design, the preliminary candidate biomarkers identified herein require validation through external cohorts. Additional investigations incorporating healthy controls and osteoarthritis (OA) cohorts are necessary to confirm specificity and to elucidate the therapeutic potential of these microbial targets for RA microbiome interventions. Currently, insufficient evidence exists to establish causal relationships among microbial populations, joint pathology, and climatic factors. Longitudinal cohort studies are imperative to validate the temporal dynamics and clinical significance of these associations. Full article

The transition toward market-oriented renewable energy policies has increased the demand for flexible operation of biogas plants (BGPs), particularly under Japan’s Feed-in Premium (FIP) scheme. This study evaluates the technical performance and revenue potential of integrating hydrogen production into a dairy-manure-based BGP, focusing on steam reforming (SR) and electrolysis (EL) pathways. An energy system optimization model was developed using the Open Energy Modelling Framework (OEMOF) to simulate coordinated operation of biogas combined heat and power (CHP), hydrogen production, heat supply, and storage under electricity spot market conditions in Hokkaido, Japan. Sensitivity and scenario analyses were conducted to examine hydrogen production behavior, system-level resource allocation, and revenue performance under varying hydrogen prices and FIP levels. The results show that EL enables price-responsive switching between electricity supply and hydrogen production, resulting in dynamic hydrogen output and high sensitivity to conditions. In contrast, SR provides stable hydrogen production through continuous biogas utilization, achieving biogas throughput but limited responsiveness to price fluctuations. A System-level trade-off between conversion flexibility and direct fuel utilization efficiency was identified. These findings indicate that hydrogen pathway selection in farm-scale BGPs should be treated as a system design decision shaped by market exposure, operational objectives, and risk tolerance under the FIP framework. Full article

Many existing healthcare facilities still rely on the legacy Wi-Fi 5 (IEEE 802.11ac) standard, which is based on Orthogonal Frequency-Division Multiplexing (OFDM). OFDM supports single-user-per-channel access, leading to increased contention, higher latency, jitter, and packet loss under dense device deployments commonly found in clinical environments. This study presents a quantitative performance evaluation of Wi-Fi 5 and Wi-Fi 6/7 by comparing the effectiveness of OFDM with Orthogonal Frequency-Division Multiple Access (OFDMA) and Target Wake Time (TWT) in a simulated dense IoMT environment. Simulations were conducted using Network Simulator 3 (NS-3), and relevant Quality of Service (QoS) metrics. The results demonstrated that OFDMA reduces average network delay by up to approximately 37%, improves throughput by approximately 20%, and reduces packet loss ratio by up to 85% compared to OFDM under high-density operations, while exhibiting marginally improved jitter performance (approximately 2%). In addition, the use of TWT achieved substantial reductions in device power consumption of up to approximately 90%, at the cost of reduced aggregate throughput of up to approximately 75% under high station densities. These results demonstrated that Wi-Fi 6/7 technologies can offer significant advantages in terms of QoS and energy efficiency over legacy Wi-Fi 5 for dense IoMT environments. Full article

Limited terrestrial network coverage in rural and remote areas constitutes a significant barrier to the digital transformation of the agricultural sector. Smart and precision farming applications, ranging from conventional environmental monitoring systems to advanced Digital Twin solutions, rely on the reliable transmission of sensor data, images, and video streams from geographically isolated farms. Such data-intensive services cannot be effectively supported without a robust communication infrastructure. Non-Terrestrial Networks (NTNs), particularly satellite systems, offer both narrowband and broadband connectivity, enabling the transmission of low-rate sensor measurements, as well as high-throughput multimedia data from the field. This paper presents an experimental performance evaluation of two satellite backhauling solutions: a Geostationary Earth Orbit (GEO) system provided by SES and a Low Earth Orbit (LEO) system from Starlink. The networks were first deployed and tested in a laboratory environment and subsequently validated in an operational agricultural field setting. Their performance is benchmarked against a terrestrial cellular network to assess their suitability for supporting advanced agricultural applications. The performance assessment results indicate that both satellite backhauling solutions are reliable and capable of meeting the bandwidth and latency requirements of delay-tolerant agricultural applications. In addition to the technical evaluation, this work presents a cost–benefit analysis that further underscores the advantages of NTN-based solutions. Despite higher initial expenditures, they provide extended coverage in remote areas and enable cost sharing across multiple users, improving overall economic viability. Full article

Multi-wavelength lensless microscopy enables high-speed, wide-field, and high-throughput imaging, making it highly attractive for modern biomedical applications. However, its practical performance is often limited by unreliable autofocusing and wavelength-dependent phase inconsistencies, which together degrade reconstruction fidelity in complex environments. To explicitly address these two limitations, we present a fully scanning-free computational microscopy framework using a static four-wavelength Light-Emitting Diode (LED) illumination module that sequentially switches between wavelengths to provide strong spectral constraints. For robust geometric parameter estimation, we develop an Adaptive-Weighted Multi-wavelength Autofocus (A-WMAF) scheme that exploits the differential defocus sensitivities of multiple wavelengths to yield a single, sharply peaked autofocus curve and thereby reliably determines the sample–sensor distance. To mitigate chromatic phase inconsistencies, we further introduce an iterative optical-path-difference (OPD)–domain adaptive fusion strategy that fuses multi-wavelength phase estimates in a physically consistent OPD space, suppressing wavelength-dependent artifacts and reconstruction noise. With only four raw holograms acquired within seconds, the proposed method achieves high-fidelity quantitative phase reconstruction with a Phase Structural Similarity Index Measure (SSIM) of 0.9942 and a quantitative OPD accuracy of 95.0%, as well as a measured lateral resolution of 1.23 µm, surpassing the Nyquist–Shannon sampling limit. Experimental demonstrations on fixed biological samples and long-term live-cell monitoring validate that the proposed framework simultaneously achieves reliable autofocusing and chromaticity-robust phase fusion, highlighting its potential for high-throughput biomedical imaging and clinical diagnostics. Full article