Search Results (36,008)

Glutamate dehydrogenase (GDH) is ubiquitous in organisms and crucial for amino acid metabolism, energy production, and redox balance. The gdhA and gudB genes encoding GDH were identified in Bacillus altitudinis AS19 and shown to be regulated by iron. However, their functions remain unclear. In this study, gdhA and gudB were analyzed using bioinformatics tools, such as MEGA, Expasy, and SWISS-MODEL, expressed with a prokaryotic expression system, and the induction conditions were optimized to increase the yield of soluble proteins. Phylogenetic analysis revealed that GDH is evolutionarily conserved within the genus Bacillus. GdhA and GudB were identified as hydrophobic proteins, not secreted or membrane proteins. Their structures were primarily composed of irregular coils and α-helices. SWISS-MODEL predicts GdhA to be an NADP-specific GDH, whereas GudB is an NAD-specific GDH. SDS-PAGE analysis showed that GdhA was expressed as a soluble protein after induction with 0.2 mmol/L IPTG at 24 °C for 16 h. GudB was expressed as a soluble protein after induction with 0.1 mmol/L IPTG at 16 °C for 12 h. The proteins were confirmed by Western blot and mass spectrometry. The enzyme activity of recombinant GdhA was 62.7 U/mg with NADPH as the coenzyme. This study provides a foundation for uncovering the functions of two GDHs of B. altitudinis AS19. Full article

Insects and their bacterial endosymbionts form intricate ecological relationships, yet their role in host–pathogen interactions are not fully elucidated. The small brown planthopper (Laodelphax striatellus), a polyphagous pest of cereal crops, acts as a key vector for rice stripe virus (RSV), a significant threat to rice production. This study aimed to compare the endosymbiont community structures of nonviruliferous and RSV-viruliferous L. striatellus populations using 16S rRNA gene sequencing with high-throughput sequencing technology. Wolbachia was highly dominant in both groups; however, the prevalence of other endosymbionts, specifically Rickettsia and Burkholderia, differed markedly depending on RSV infection. Comprehensive microbial diversity and composition analyses revealed distinct community structures between nonviruliferous and RSV-viruliferous populations, highlighting potential interactions and implications for vector competence and virus transmission dynamics. These findings contribute to understanding virus-insect-endosymbiont dynamics and could inform strategies to mitigate viral spread by targeting symbiotic bacteria. Full article

Rising volumes of textile waste necessitate the development of more efficient recycling systems, with a primary focus on the optimization of sorting technologies. Near-infrared (NIR) spectroscopy is a state-of-the-art method for fiber identification; however, its accuracy in quantifying textile blends, particularly common polyester/cotton blend textiles, still requires refinement. This study explores the potential and limitations of NIR spectroscopy for quantifying cotton content in post-consumer textiles. A lab-scale NIR sorter and a handheld NIR spectrometer in complementary wavelength ranges were applied to a diverse range of post-consumer textile samples to test model accuracies. Results show that the commonly assumed 10% accuracy threshold in industrial sorting can be exceeded, especially when excluding textiles with <35% cotton content. Identifying and excluding the range of non-linearity significantly improved the model’s performance. The final models achieved an RMSEP of 6.6% and bias of −0.9% for the NIR sorter and an RMSEP of 3.1% and bias of −0.6% for the handheld NIR spectrometer. This study also assessed how textile characteristics—such as color, structure, product type, and alkaline treatment—affect spectral behavior and model accuracy, highlighting their importance for refining quantification when high-purity inputs are needed. By identifying current limitations and potential sources of errors, this study provides a foundation for improving NIR-based models. Full article

Enhancing the specificity and applicability of PCR-based genome-walking methods is highly desirable. A new and universal genome-walking tool, called center degenerated walking-primer PCR (CDWP-PCR), is presented in this study. CDWP-PCR involves adopting a center degenerated walking primer (cdWP) in the secondary/tertiary round of amplification. This cdWP is generated by degenerating the seven central nucleotides of the normal walking primer (nWP) used in primary PCR to NNNNNNN (where N includes the bases A, T, C, and G). Clearly, a partially complementary structure is formed between the two primers. Accordingly, the primary CDWP-PCR non-target products defined by the nWP are diluted in secondary/tertiary CDWP-PCR, as these non-targets have difficulty in annealing with the cdWP; conversely, the primary target product can still be efficiently amplified. The working performance of the proposed CDWP-PCR is verified through cloning of the unknown flanks of three known genes. All the clear DNA bands in the tertiary CDWP-PCRs are confirmed to be correct, and the largest DNA band is 8.0 kb. Overall, CDWP-PCR can be considered as a reliable supplement to existing genome-walking methods. Full article

Microbial feed additives can effectively promote the healthy development of aquaculture, and Lactobacillus acidophilus can be utilized to mitigate disease risks and enhance productivity while minimizing antibiotic use. This article summarizes research on the application of L. acidophilus in aquaculture, focusing on growth and nutrient utilization, intestinal structure and microbial communities, disease prevention and control in aquatic organisms, and the regulation of water quality. This review holds significant implications for the development of compound feed additives and environmental regulators involving L. acidophilus, as well as for future aquatic food safety. Full article

Catalysis research is complex and interdisciplinary, involving diverse physical effects and challenging data practices. Research data often captures only selected aspects, such as specific reactants and products, limiting its utility for machine learning and the implementation of FAIR (Findable, Accessible, Interoperable, Reusable) workflows. To improve this, semantic structuring through ontologies is essential. This work extends the established ontologies by refining logical relations and integrating semantic tools such as the Web Ontology Language or the Shape Constraint Language. It incorporates application programming interfaces from chemical databases, such as the Kyoto Encyclopedia of Genes and Genomes and the National Institute of Health’s PubChem database, and builds upon established ontologies. A key innovation lies in automatically decomposing chemical substances through database entries and chemical identifier representations to identify functional groups, enabling more generalized reaction classification. Using new semantic functionality, functional groups are flexibly addressed, improving the classification of reactions such as saponification and ester cleavage with simultaneous oxidation. A graphical interface (GUI) supports user interaction with the knowledge graph, enabling ontological reasoning and querying. This approach demonstrates improved specificity of the newly established ontology over its predecessors and offers a more user-friendly interface for engaging with structured chemical knowledge. Future work will focus on expanding ontology coverage to support a wider range of reactions in catalysis research. Full article

Soil degradation resulting from intensive agricultural practices, the excessive use of agrochemicals, and climate-induced stresses has significantly impaired soil fertility, disrupted microbial diversity, and reduced crop productivity. Plant growth-promoting bacteria (PGPB) represent a sustainable biological approach to restoring degraded soils by modulating plant physiology and soil function through diverse molecular mechanisms. PGPB synthesizes indole-3-acetic acid (IAA) to stimulate root development and nutrient uptake and produce ACC deaminase, which lowers ethylene accumulation under stress, mitigating growth inhibition. They also enhance nutrient availability by releasing phosphate-solubilizing enzymes and siderophores that improve iron acquisition. In parallel, PGPB activates jasmonate and salicylate pathways, priming a systemic resistance to biotic and abiotic stress. Through quorum sensing, biofilm formation, and biosynthetic gene clusters encoding antibiotics, lipopeptides, and VOCs, PGPB strengthen rhizosphere colonization and suppress pathogens. These interactions contribute to microbial community recovery, an improved soil structure, and enhanced nutrient cycling. This review synthesizes current evidence on the molecular and physiological mechanisms by which PGPB enhance soil restoration in degraded agroecosystems, highlighting their role beyond biofertilization as key agents in ecological rehabilitation. It examines advances in nutrient mobilization, stress mitigation, and signaling pathways, based on the literature retrieved from major scientific databases, focusing on studies published in the last decade. Full article

Water is essential for ecological balance, environmental sustainability, and food security, particularly in arid regions where effective water management increasingly depends on farmer cooperation. The Samarkand region of Uzbekistan, known for its favorable climate and leading role in grape production, is facing rising drought conditions. This study explores the factors influencing grape farmers’ willingness to collaborate on water management in the districts of Ishtikhan, Payarik, and Kushrabot, which together produce 75–80% of the region’s grapes. A quantitative survey of 384 grape-producing households was conducted across 19 county citizens’ gatherings (38.7% of such gatherings), and structural equation modeling was employed to analyze a framework consisting of four dimensions: norms, environmental concerns, economic barriers, and the intention to adopt sustainable practices. The results indicate that norms and environmental concerns positively influence collaboration, suggesting a collective orientation toward sustainability. In contrast, economic barriers such as high costs and limited financial capacity significantly hinder cooperative behavior. Furthermore, a strong individual intention to adopt sustainable practices was associated with a greater likelihood of collaboration. These findings highlight the critical drivers and constraints shaping collective water use in agriculture and suggest that targeted policy measures and community-led efforts are vital for promoting sustainable water governance in drought-prone regions. Full article

MXenes, a class of two-dimensional transition metal carbides and nitrides, emerged as a promising material for next-generation energy storage and corresponding applications due to their unique combination of high electrical conductivity, tunable surface chemistry, and lamellar structure. This review highlights recent advances in MXene-based composites, focusing on their integration into electrode architectures for the development of supercapacitors, batteries, and multifunctional devices, including triboelectric nanogenerators. It serves as a comprehensive overview of the multifunctional capabilities of MXene-based composites and their role in advancing efficient, flexible, and sustainable energy and sensing technologies, outlining how MXene-based systems are poised to redefine multifunctional energy platforms. Electrochemical performance optimization strategies are discussed by considering surface functionalization, interlayer engineering, scalable synthesis techniques, and integration with advanced electrolytes, with particular attention paid to the development of hybrid supercapacitors, triboelectric nanogenerators (TENGs), and wearable sensors. These applications are favored due to improved charge storage capability, mechanical properties, and the multifunctionality of MXenes. Despite these aspects, challenges related to long-term stability, sustainable large-scale production, and environmental degradation must still be addressed. Emerging approaches such as three-dimensional self-assembly and artificial intelligence-assisted design are identified as key challenges for overcoming these issues. Full article

The structural design of rail vehicle bodies significantly influences rail vehicle performance, passenger comfort, and operational efficiency. This study presents a comparative analysis of three key concepts of a rail vehicle body, namely a differential, an integral, and a hybrid structure, with a focus on their structural principles, material utilization, and implications for manufacturability and maintenance. Three rail vehicle body variants were developed, each incorporating a low-floor configuration to enhance accessibility and interior layout flexibility. The research explores the suitable placement of technical components such as a power unit and an air-conditioning system, and it evaluates interior layouts aimed at maximizing both passenger capacity and their travelling comfort. Key features, including door and window technologies, thermal comfort solutions, and seating arrangements, are also analyzed. The study emphasizes the importance of compromises between structural stiffness, reparability, production complexity, and passenger-oriented design considerations. A part of the research includes a proposal of three variants of a rail vehicle body frame, together with their strength analysis by means of the finite element method. These analyses identified that the maximal permissible stresses for the individual versions of the frame were not exceeded. Findings contribute to the development of more efficient, accessible, and sustainable regional passenger rail vehicles. Full article

Global supply chains often face uncertainties in production lead times, fluctuating exchange rates, and varying tariff regulations, all of which can significantly impact total profit. To address these challenges, this study formulates a multi-country supply chain problem as a Semi-Markov Decision Process (SMDP), integrating both currency variability and tariff levels. Using a Q-learning-based method (SMART), we explore three scenarios: (1) wide currency gaps under a uniform tariff, (2) narrowed currency gaps encouraging more local sourcing, and (3) distinct tariff structures that highlight how varying duties can reshape global fulfillment decisions. Beyond these baselines we analyze uncertainty-extended variants and targeted sensitivities (quantity discounts, tariff escalation, and the joint influence of inventory holding costs and tariff costs). Simulation results, accompanied by policy heatmaps and performance metrics, illustrate how small or large shifts in exchange rates and tariffs can alter sourcing strategies, transportation modes, and inventory management. A Deep Q-Network (DQN) is also applied to validate the Q-learning policy, demonstrating alignment with a more advanced neural model for moderate-scale problems. These findings underscore the adaptability of reinforcement learning in guiding practitioners and policymakers, especially under rapidly changing trade environments where exchange rate volatility and incremental tariff changes demand robust, data-driven decision-making. Full article

In the digital era, understanding the intention to use generative AI is critical, as it enhances productivity, transforms workflows, and enables humans to focus on higher-value tasks. Drawing upon the unified theory of acceptance and use of technology (UTAUT) and the technology threat avoidance theory (TTAT), this research integrates perceived AI literacy into the AI acceptance–avoidance framework as a central variable. This study gathered 583 valid survey responses from China and validated its model using a dual-phase, combined method that integrates structural equation modeling and artificial neural networks. Research findings indicate that the model explains 51.6% of the variance in generative AI usage intention. Except for social influence, all variables within the extended framework significantly impact the usage intention, with perceived AI literacy being the strongest predictor (β = 0.33, p < 0.001). Additionally, perceived AI literacy mitigates the adverse effect of perceived threats on the intention to use AI. Practical implications suggest that enterprises adopt a tiered strategy, as follows: maximize perceived benefits by integrating AI skills into reward systems and providing task-automation training; minimize perceived costs through dedicated technical support and transparent risk mitigation plans; and cultivate AI literacy via progressive learning paths, advancing from data analysis to innovation. Full article

Indoor horticulture requires a substantial quantity of electricity to meet crops extended photoperiodic requirements for optimal photosynthetic rate. Simultaneously, global electricity costs have grown dramatically in recent years, endangering the sustainability and profitability of indoor vertical farms and/or modern greenhouses that use artificial lighting systems to accelerate crop development and growth. This study investigates the growth rate and physiological development of cherry tomato plants cultivated in a pilot indoor vertical farm at the Agricultural University of Athens’ Laboratory of Farm Structures (AUA) under continuous and disruptive lighting. The leaf physiological traits from multiple photoperiodic stress treatments were analyzed and utilized to estimate the plant’s tolerance rate under varied illumination conditions. Four different photoperiodic treatments were examined and compared, firstly plants grew under 14 h of continuous light (C-14L10D/control), secondly plants grew under a normalized photoperiod of 14 h with intermittent light intervals of 10 min of light followed by 50 min of dark (NI-14L10D/stress), the third treatment where plants grew under 14 h of a load-shifted energy demand response intermittent lighting schedule (LSI-14L10D/stress) and finally plants grew under 13 h photoperiod following of a load-shifted energy demand response intermittent lighting schedule (LSI-13L11D/stress). Plants were subjected also under two different light spectra for all the treatments, specifically WHITE and Blue/Red/Far-red light composition. The aim was to develop flexible, energy-efficient lighting protocols that maintain crop productivity while reducing electricity consumption in indoor settings. Results indicated that short periods of disruptive light did not negatively impact physiological responses, and plants exhibited tolerance to abiotic stress induced by intermittent lighting. Post-harvest data indicated that intermittent lighting regimes maintained or enhanced growth compared to continuous lighting, with spectral composition further influencing productivity. Plants under LSI-14L10D and B/R/FR spectra produced up to 93 g fresh fruit per plant and 30.4 g dry mass, while consuming up to 16 kWh less energy than continuous lighting—highlighting the potential of flexible lighting strategies for improved energy-use efficiency. Full article

The intersection of green finance and artificial intelligence (AI) represents a rapidly emerging and high-impact research domain with the potential to reshape sustainable economic systems. This study presents a comprehensive bibliometric and network analysis aimed at mapping the scientific landscape, identifying research hotspots, and highlighting methodological trends at this nexus. A dataset of 268 peer-reviewed publications (2014–June 2025) was retrieved from the Web of Science Core Collection, filtered by the Business Economics category. Analytical techniques employed include Bibliometrix in R, VOSviewer, and science mapping tools such as thematic mapping, trend topic analysis, co-citation networks, and co-occurrence clustering. Results indicate an annual growth rate of 53.31%, with China leading in both productivity and impact, followed by Vietnam and the United Kingdom. The most prolific affiliations and authors, primarily based in China, underscore a concentrated regional research output. The most relevant journals include Energy Economics and Finance Research Letters. Network visualizations identified 17 clusters, with focused analysis on the top three: (1) Emission, Health, and Environmental Risk, (2) Institutional and Technological Infrastructure, and (3) Green Innovation and Sustainable Urban Development. The methodological landscape is equally diverse, with top techniques including blockchain technology, large language models, convolutional neural networks, sentiment analysis, and structural equation modeling, demonstrating a blend of traditional econometrics and advanced AI. This study not only uncovers intellectual structures and thematic evolution but also identifies underdeveloped areas and proposes future research directions. These include dynamic topic modeling, regional case studies, and ethical frameworks for AI in sustainable finance. The findings provide a strategic foundation for advancing interdisciplinary collaboration and policy innovation in green AI–finance ecosystems. Full article

Industrial Vision Inspection Systems (VISs) often struggle to adapt to increasing variability of modern manufacturing due to the inherent rigidity of their hardware architectures. Although the Reconfigurable Manufacturing System (RMS) paradigm was introduced in the early 2000s to overcome these limitations, designing such reconfigurable machines remains a complex, expert-dependent, and time-consuming task. This is primarily due to the lack of structured methodologies and the reliance on trial-and-error processes. In this context, this study proposes a novel theoretical framework to facilitate the design of fully reconfigurable handling systems for VISs, with a particular focus on fixture design. The framework is grounded in Model-Based Definition (MBD), embedding semantic information directly into the 3D CAD models of the inspected product. As an additional contribution, a general hardware architecture for the inspection of axisymmetric components is presented. This architecture integrates an anthropomorphic robotic arm, Numerically Controlled (NC) modules, and adaptable software and hardware components to enable automated, software-driven reconfiguration. The proposed framework and architecture were applied in an industrial case study conducted in collaboration with a leading automotive half-shaft manufacturer. The resulting system, implemented across seven automated cells, successfully inspected over 200 part types from 12 part families and detected more than 60 defect types, with a cycle below 30 s per part. Full article