Search Results (8,288)

A just and sustainable energy transition in historically coal-dependent regions like Kentucky requires more than the adoption of new technologies and market-based solutions. This study uses a stated preferences approach to evaluate public support for various attributes of energy transition programs, revealing broad backing for moving away from coal, as indicated by a negative willingness to pay (WTP) for the status quo (–USD 4.63). Key findings show strong bipartisan support for solar energy, with Democrats showing the highest WTP at USD 8.29, followed closely by Independents/Others at USD 8.22, and Republicans at USD 8.08. Wind energy also garnered support, particularly among Republicans (USD 4.04), who may view it as more industry-compatible and less ideologically polarizing. Job creation was a dominant priority across political affiliations, especially for Independents (USD 9.07), indicating a preference for tangible, near-term economic benefits. Similarly, preserving cultural values tied to coal received support among Independents/Others (USD 4.98), emphasizing the importance of place-based identity in shaping preferences. In contrast, social support programs (e.g., job retraining) and certain post-mining land uses (e.g., recreation and conservation) were less favored, possibly due to their abstract nature, delayed benefits, and political framing. Findings from Kentucky offer insights for other coal-reliant states like Wyoming, West Virginia, Pennsylvania, Indiana, and Illinois. Ultimately, equitable transitions must integrate local voices, address cultural and economic realities, and ensure community-driven planning and investment. Full article

Alexandrium spp. blooms and paralytic shellfish poisoning pose serious economic threats to coastal communities and aquaculture. This study evaluated the removal efficiency of two Alexandrium minutum strains using natural kaolinite clay (KNAC) and kaolinite with polyaluminum chloride (KPAC) at three concentrations (0.1, 0.25, and 0.3 g L⁻¹), two pH levels (7 and 8), and two cell densities (1.0 and 2.0 × 10⁷ cells L⁻¹) in seawater. PAC significantly enhanced removal, achieving up to 100% efficiency within two hours. Zeta potential analysis showed that PAC imparted positive surface charges to the clay, promoting electrostatic interactions with negatively charged algal cells and enhancing flocculation through Van der Waals attractions. In addition, the study conducted a cost estimate analysis and found that treating one hectare at 0.1 g L⁻¹ would cost approximately USD 31.75. The low KPAC application rate also suggests minimal environmental impact on benthic habitats. Full article

This study investigated white wastewater (WW) as a potential source of antimicrobial peptides, employing hydrolysis with Pronase E followed by separation through electrodialysis with ultrafiltration membranes (EDUF) to increase the value of dairy components within a circular economy framework. The WW hydrolysate was divided into two key fractions: the cationic recovery compartment (CRC) and the anionic recovery compartment (ARC). The EDUF process effectively separated peptides, with peptide migration rates reaching 6.83 ± 0.59 g/m²·h for CRC and 6.19 ± 0.66 g/m²·h for ARC. Furthermore, relative energy consumption (REC) increased from 1.15 Wh/g to 2.05 Wh/g over three hours, in line with trends observed in recent studies on electrodialysis energy use. Although 29 peptides were statistically selected from the CRC (20) and ARC (9) compartments, no antibacterial activity was exhibited against Clostridium tyrobutyricum and Pseudomonas aeruginosa; however, antifungal activity was observed in the feed and ARC compartments. Peptides from the ARC demonstrated activity against Mucor racemosus (MIC = 0.156 mg/mL) and showed selective antifungal effects against Penicillium commune (MIC = 0.156 mg/mL). This innovative approach paves the way for improving the recovery of anionic peptides through further optimization of the EDUF process. Future perspectives include synthesizing selected peptides and evaluating their antifungal efficacy against these and other microbial strains, offering exciting potential for applications in food preservation and beyond. Full article

Artificial intelligence (AI) has emerged as a transformative technology in the evolution of massive multiple-input multiple-output (ma-MIMO) systems, positioning them as a cornerstone for sixth-generation (6G) wireless networks. Despite their significant potential, ma-MIMO systems face critical challenges at the receiver end, particularly in signal detection under high-dimensional and noisy environments. To address these limitations, this paper proposes MIMONet, a novel deep learning (DL)-based MIMO detection framework built upon a lightweight and optimized feedforward neural network (FFNN) architecture. MIMONet is specifically designed to achieve a balance between detection performance and complexity by optimizing the neural network architecture for MIMO signal detection tasks. Through extensive simulations across multiple MIMO configurations, the proposed MIMONet detector consistently demonstrates superior bit error rate (BER) performance. It achieves a notably lower error rate compared to conventional benchmark detectors, particularly under moderate to high signal-to-noise ratio (SNR) conditions. In addition to its enhanced detection accuracy, MIMONet maintains significantly reduced computational complexity, highlighting its practical feasibility for advanced wireless communication systems. These results validate the effectiveness of the MIMONet detector in optimizing detection accuracy without imposing excessive processing burdens. Moreover, the architectural flexibility and efficiency of MIMONet lay a solid foundation for future extensions toward large-scale ma-MIMO configurations, paving the way for practical implementations in beyond-5G (B5G) and 6G communication infrastructures. Full article

Critical Internet of Things (IoT) data in Fifth Generation Vehicular Ad Hoc Networks (5G VANETs) demands Ultra-Reliable Low-Latency Communication (URLLC) to support mission-critical vehicular applications such as autonomous driving and collision avoidance. Achieving the stringent Quality of Service (QoS) requirements for these applications remains a significant challenge. This paper proposes a novel framework integrating Software-Defined Networking (SDN) and Network Functions Virtualisation (NFV) as embedded functionalities in connected vehicles. A lightweight SDN Controller model, implemented via vehicle on-board computing resources, optimised QoS for communications between connected vehicles and the Next-Generation Node B (gNB), achieving a consistent packet delivery rate of 100%, compared to 81–96% for existing solutions leveraging SDN. Furthermore, a Software-Defined Wide-Area Network (SD-WAN) model deployed at the gNB enabled the efficient management of data, network, identity, and server access. Performance evaluations indicate that SDN and NFV are reliable and scalable technologies for virtualised and distributed 5G VANET infrastructures. Our SDN-based in-vehicle traffic classification model for dynamic resource allocation achieved 100% accuracy, outperforming existing Artificial Intelligence (AI)-based methods with 88–99% accuracy. In addition, a significant increase of 187% in flow rates over time highlights the framework’s decreasing latency, adaptability, and scalability in supporting URLLC class guarantees for critical vehicular services. Full article

Background: Mesenchymal stem cells (MSCs) are a promising tool in regenerative medicine due to their ability to secrete paracrine factors that modulate tissue repair. Extracellular vesicles (EVs) released by MSCs contain bioactive molecules (e.g., mRNAs, miRNAs, proteins) and play a key role in intercellular communication. Methods: This study compared the transcriptomic profiles (mRNA and miRNA) of equine MSCs derived from adipose tissue (AT-MSCs), bone marrow (BM-MSCs), and ovarian fibroblasts (as a differentiated control). Additionally, miRNAs present in EVs secreted by these cells were characterized using next-generation sequencing. Results: All cell types met ISCT criteria for MSCs, including CD90 expression, lack of MHC II, trilineage differentiation, and adherence. EVs were isolated using ultracentrifugation and validated with nanoparticle tracking analysis and flow cytometry (CD63, CD81). Differential expression analysis revealed distinct mRNA and miRNA profiles across cell types and their secreted EVs, correlating with tissue origin. BM-MSCs showed unique regulation of genes linked to early development and osteogenesis. EVs contained diverse RNA species, including miRNA, mRNA, lncRNA, rRNA, and others. In total, 227 and 256 mature miRNAs were detected in BM-MSCs and AT-MSCs, respectively, including two novel miRNAs per MSC type. Fibroblasts expressed 209 mature miRNAs, including one novel miRNA also found in MSCs. Compared to fibroblasts, 60 and 92 differentially expressed miRNAs were identified in AT-MSCs and BM-MSCs, respectively. Conclusions: The results indicate that MSC tissue origin influences both transcriptomic profiles and EV miRNA content, which may help to interpret their therapeutic potential. Identifying key mRNAs and miRNAs could aid in future optimizing of MSC-based therapies in horses. Full article

Freshwater salinization, an escalating global environmental stressor, poses a significant threat to freshwater biodiversity, including fish communities. This study investigates the grass carp (Ctenopharyngodon idellus), a species with the highest aquaculture output in China, to elucidate the molecular underpinnings of its physiological adaptations to fluctuating salinity gradients. We used high-throughput mRNA sequencing and differential gene expression profiling to analyze transcriptional dynamics in intestinal and kidney tissues of grass carp exposed to heterogeneous salinity stressors. Concurrent serum biochemical analyses showed salinity stress significantly increased Na⁺, Cl⁻, and osmolarity, while decreasing lactate and glucose. Salinity stress exerted a profound impact on the global transcriptomic landscape of grass carp. A substantial number of co-regulated differentially expressed genes (DEGs) in kidney and intestinal tissues were enriched in immune and metabolic pathways. Specifically, genes associated with antigen processing and presentation (e.g., cd4-1, calr3b) and apoptosis (e.g., caspase17, pik3ca) exhibited upregulated expression, whereas genes involved in gluconeogenesis/glycolysis (e.g., hk2, pck2) were downregulated. KEGG pathway enrichment analyses revealed that metabolic and cellular structural pathways were predominantly enriched in intestinal tissues, while kidney tissues showed preferential enrichment of immune and apoptotic pathways. Rigorous validation of RNA-seq data via qPCR confirmed the robustness and cross-platform consistency of the findings. This study investigated the core transcriptional and physiological mechanisms regulating grass carp’s response to salinity stress, providing a theoretical foundation for research into grass carp’s resistance to salinity stress and the development of salt-tolerant varieties. Full article

The large intelligent surface (LIS) concept represents an architectural advance for enhancing the performance of 6G wireless communication systems. In this work, we address the problem of jointly selecting active panels and associating terminals to outputs of such active panels in a panel-based LIS framework to maximise the minimum signal-to-interference-and-noise ratio (SINR) across all terminals. Due to the nature of the mixed-integer linear programming (MILP) formulation, we propose an alternative approach based on a genetic algorithm (GA) that efficiently explores the solution space through tailored crossover via column swapping and adaptive mutation. We compare the GA’s performance against the CPLEX solver under various configurations and time constraints. The performance results show that the GA provides competitive solutions with reduced computational complexity, showcasing its potential for scalable LIS implementations with complex resource allocation. Full article

In plateau wetlands, the interactions of herbaceous roots with ectorhizosphere soil microorganisms represent an important way to realize their ecological functions. Global change-induced aridification of plateau wetlands has altered long-established functional synergistic relationships between plant roots and ectorhizosphere soil microbes, but we still know little about this phenomenon. In this context, nine typical wetlands with three different moisture statuses were selected from the eastern Tibetan Plateau in this study to analyze the relationships among herbaceous plant root traits and microbial communities and functions. The results revealed that drought significantly inhibited the accumulation of root biomass and surface area as well as the development of root volumes and diameters. Similarly, drought significantly reduced the diversity of ectorhizosphere soil microbial communities and the relative abundances of key phyla of archaea and bacteria. Redundancy analysis revealed that plant root traits and ectorhizosphere soil microbes were equally regulated by soil physicochemical properties. Functional genes related to carbohydrate metabolism were significantly associated with functional traits related to plant root elongation and nutrient uptake. Functional genes related to carbon and energy metabolism were significantly associated with traits related to plant root support and storage. Key genes such as CS,gltA, and G6PD,zwf help to improve the drought resistance and barrenness resistance of plant roots. This study helps to elucidate the synergistic mechanism of plant and soil microbial functions in plateau wetlands under drought stress, and provides a basis for evolutionary research and conservation of wetland ecosystems in the context of global change. Full article

Individuals with autism spectrum disorder (ASD) have deficits in social–emotional competence. Most people with ASD have difficulties in emotion recognition, emotion understanding, emotion expression, and emotion regulation, which seriously affects their normal social communication and interaction. The information technology (IT) era has given more possibilities for intervention training for people with ASD, and research has proven that technological interventions have a significant effect on the socio-emotional competence of people with ASD. This study employed a meta-analytic approach using 32 independent effect sizes from 25 studies to investigate the effects of IT interventions on socio-emotional competence in individuals with ASD, using emotion recognition, understanding, expression, and regulation as dependent variables and examining key moderating factors. The results found that information technology has an excellent effect on social–emotional competence in ASD (Hedges’ g = 0.897, CI = 0.676, 1.117, z = 7.967, p < 0.001) and is significantly moderated by the intervention technique (Q = 7.392, p = 0.025) and the intervener (Q = 4.933, p = 0.026). The findings provide insights into further deepening information technology intervention research as well as practical applications. Full article

Agricultural nondestructive testing technology is pivotal in safeguarding food quality assurance, safety monitoring, and supply chain transparency. While conventional optical methods such as near-infrared spectroscopy and hyperspectral imaging demonstrate proficiency in surface composition analysis, their constrained penetration depth and environmental sensitivity limit effectiveness in dynamic agricultural inspections. This review highlights the transformative potential of microwave technologies, systematically examining their operational principles, current implementations, and developmental trajectories for agricultural quality control. Microwave technology leverages dielectric response mechanisms to overcome traditional limitations, such as low-frequency penetration for grain silo moisture testing and high-frequency multi-parameter analysis, enabling simultaneous detection of moisture gradients, density variations, and foreign contaminants. Established applications span moisture quantification in cereal grains, oilseed crops, and plant tissues, while emerging implementations address storage condition monitoring, mycotoxin detection, and adulteration screening. The high-frequency branch of the microwave–millimeter wave systems enhances analytical precision through molecular resonance effects and sub-millimeter spatial resolution, achieving trace-level contaminant identification. Current challenges focus on three areas: excessive absorption of low-frequency microwaves by high-moisture agricultural products, significant path loss of microwave high-frequency signals in complex environments, and the lack of a standardized dielectric database. In the future, it is essential to develop low-cost, highly sensitive, and portable systems based on solid-state microelectronics and metamaterials, and to utilize IoT and 6G communications to enable dynamic monitoring. This review not only consolidates the state-of-the-art but also identifies future innovation pathways, providing a roadmap for scalable deployment of next-generation agricultural NDT systems. Full article

Glycyrrhiza uralensis (G. uralensis), a leguminous plant, is an important medicinal and economic plant in saline–alkaline soils of arid regions in China. Its main bioactive components include liquiritin, glycyrrhizic acid, and flavonoids, which play significant roles in maintaining human health and preventing and adjuvantly treating related diseases. However, the cultivation of G. uralensis is easily restricted by adverse soil conditions in these regions, characterized by high salinity, high alkalinity, and nutrient deficiency. This study investigated the impacts of four multistrain microbial inoculants (Pa, Pb, Pc, Pd) on the growth performance and bioactive compound accumulation of G. uralensis in moderately saline–sodic soil. The aim was to screen the most beneficial inoculant from these strains, which were isolated from the rhizosphere of plants in moderately saline–alkaline soils of the Hexi Corridor and possess native advantages with excellent adaptability to arid environments. The results showed that inoculant Pc, comprising Pseudomonas silesiensis, Arthrobacter sp. GCG3, and Rhizobium sp. DG1, exhibited superior performance: it induced a 0.86-unit reduction in lateral root number relative to the control, while promoting significant increases in single-plant dry weight (101.70%), single-plant liquiritin (177.93%), single-plant glycyrrhizic acid (106.10%), and single-plant total flavonoids (107.64%). Application of the composite microbial inoculant Pc induced no significant changes in the pH and soluble salt content of G. uralensis rhizospheric soils. However, it promoted root utilization of soil organic matter and nitrate, while significantly increasing the contents of available potassium and available phosphorus in the rhizosphere. High-throughput sequencing revealed that Pc reorganized the rhizospheric microbial communities of G. uralensis, inducing pronounced shifts in the relative abundances of rhizospheric bacteria and fungi, leading to significant enrichment of target bacterial genera (Arthrobacter, Pseudomonas, Rhizobium), concomitant suppression of pathogenic fungi, and proliferation of beneficial fungi (Mortierella, Cladosporium). Correlation analyses showed that these microbial shifts were linked to improved plant nutrition and secondary metabolite biosynthesis. This study highlights Pc as a sustainable strategy to enhance G. uralensis yield and medicinal quality in saline–alkali ecosystems by mediating microbe–plant–nutrient interactions. Full article

Microbial carbon use efficiency (CUE) is an important indicator of soil organic carbon accumulation and loss and a key parameter in biogeochemical cycling models. Its regulatory mechanism is highly dependent on microbial communities and their dynamic mediation of abiotic factors. Land-use change (e.g., agricultural expansion, deforestation, urbanization) profoundly alter carbon input patterns and soil physicochemical properties, further exacerbating the complexity and uncertainty of CUE. Existing carbon cycle models often neglect microbial ecological processes, resulting in an incomplete understanding of how microbial traits interact with environmental factors to regulate CUE. This paper provides a comprehensive review of the microbial regulation mechanisms of CUE under land-use change and systematically explores how microorganisms drive organic carbon allocation through community compositions, interspecies interactions, and environmental adaptability, with particular emphasis on the synergistic response between microbial communities and abiotic factors. We found that the buffering effect of microbial communities on abiotic factors during land-use change is a key factor determining CUE change patterns. This review not only provides a theoretical framework for clarifying the microbial-dominated carbon turnover mechanism but also lays a scientific foundation for the precise implementation of sustainable land management and carbon neutrality goals. Full article

The 2025 avian influenza A(H5N1) outbreak has highlighted the urgent need for rapidly generated health communication materials during public health emergencies. Artificial intelligence (AI) systems offer transformative potential to accelerate content development pipelines while maintaining scientific accuracy and impact. We evaluated an AI-generated health communication material on bird flu precautions among 100 U.S. adults. The material was developed using ChatGPT for text generation based on CDC guidelines and Leonardo.AI for illustrations. Participants rated perceived message effectiveness, quality, realism, relevance, attractiveness, and visual informativeness. The AI-generated health communication material received favorable ratings across all dimensions: perceived message effectiveness (3.83/5, 77%), perceived message quality (3.84/5, 77%), realism (3.72/5, 74%), relevance (3.68/5, 74%), attractiveness (3.62/5, 74%), and visual informativeness (3.35/5 67%). Linear regression analysis revealed that all features significantly predicted perceived message effectiveness in unadjusted and adjusted models (p < 0.0001), e.g., multivariate analysis of outcome on perceived visual informativeness showed β = 0.51, 95% CI: 0.37–0.66, p < 0.0001. Also, mediation analysis revealed that visual informativeness accounted for 23.8% of the relationship between material attractiveness and perceived effectiveness. AI tools can enable real-time adaptation of prevention guidance during epidemiological emergencies while maintaining effective risk communication. Full article

This study is the first to systematically integrate supervised machine learning (decision tree) and association rule mining techniques to analyze accident data from the Taipei Metro system, conducting a large-scale data-driven investigation into both passenger injury and train malfunction events. The research demonstrates strong novelty and practical contributions. In the passenger injury analysis, a dataset of 3331 cases was examined, from which two highly explanatory rules were extracted: (i) elderly passengers (aged > 61) involved in station incidents are more likely to suffer moderate to severe injuries; and (ii) younger passengers (aged ≤ 61) involved in escalator incidents during off-peak hours are also at higher risk of severe injury. This is the first study to quantitatively reveal the interactive effect of age and time of use on injury severity. In the train malfunction analysis, 1157 incidents with delays exceeding five minutes were analyzed. The study identified high-risk condition combinations—such as those involving rolling stock, power supply, communication, and signaling systems—associated with specific seasons and time periods (e.g., a lift value of 4.0 for power system failures during clear mornings from 06:00–12:00, and 3.27 for communication failures during summer evenings from 18:00–24:00). These findings were further cross-validated with maintenance records to uncover underlying causes, including brake system failures, cable aging, and automatic train operation (ATO) module malfunctions. Targeted preventive maintenance recommendations were proposed. Additionally, the study highlighted existing gaps in the completeness and consistency of maintenance records, recommending improvements in documentation standards and data auditing mechanisms. Overall, this research presents a new paradigm for intelligent metro system maintenance and safety prediction, offering substantial potential for broader adoption and practical application. Full article