Search Results (1,886)

Scorpion venom peptides, with their stable disulfide backbone, compact structural framework, and highly selective regulation of ion channels, have long been regarded as important molecular probes in neuropharmacology. However, recent studies have revealed their potential for regulating oxidative stress, inflammation, and neuroprotection, making them a new research frontier. In this article, we focus on scorpion venom peptides as drugs, constructing an integrated knowledge framework from structural classification to clinical translation. First, scorpion venom peptides are systematically classified based on cysteine arrangement patterns and three-dimensional folding topology, and their structure–activity relationships are summarized. Based on this, the molecular mechanisms by which scorpion venom peptides regulate ion channels are systematically analyzed. We review the emerging pharmacological activities of scorpion venom peptides. Of particular note, the representative molecule SVHRSP has shown multi-target synergistic antioxidant and neuroprotective activity in models of Parkinson’s disease. We also systematically evaluate the application of engineering strategies, including cyclisation modification, nanodelivery, recombinant expression, and AI-assisted optimization, to overcome the translational bottlenecks in the development of scorpion venom peptides. However, it should be noted that most SVHRSP-related findings have been reported by a single research group; independent replication, pharmacokinetic characterization, and human efficacy data are still lacking. Its IND approval permits clinical investigation but does not yet constitute proven therapeutic benefit in patients. By integrating molecular structure, redox regulation mechanisms, and translational medicine perspectives, this review aims at providing a theoretical basis and practical pathways for scorpion venom peptides as precision therapeutic molecules for oxidative stress-related diseases. Full article

The ski-jump spillway is an energy-dissipating structure that discharges extra water beyond the dam’s capacity. The scour process occurs below spillways due to the collision of the water jet with high energy. It is critical to acquire information on scour holes to improve the dam’s safety and related components. Machine learning (ML) techniques have successfully demonstrated their effectiveness for modeling scour in hydraulic engineering. The present research considers novel approaches of ML models for estimating the scour hole geometries below ski-jump bucket spillways. This study investigates the capability of two novel feature-engineering approaches, namely Stronger Variable Creator Machine (SVCM) and High Correlated Variables Creator Machine (HCVCM), along with Gene Expression Programming (GEP) and their hybrid forms (SVCM+GEP and HCVCM+GEP), which were employed to predict normalized scour depth, scour length, and scour width below ski-jump spillways. Statistical metrics, graphical analyses, the Rank Mean (RM) method, the cross-validation approach, and $U_{95}$ index were used for the evaluation and reliability assessment of the proposed ML models. The results showed that hybrid ML models consistently outperformed individual algorithms. The results indicated that the SVCM+GEP method with $\mathrm{R}\mathrm{M}=1.83$ and $1.50$ had the highest performance compared to other methods for the prediction of ${\displaystyle \frac{Ds}{Dw}}$ and ${\displaystyle \frac{L_s}{D_w}}$, respectively. In addition, the HCVCM+GEP method with $\mathrm{R}\mathrm{M}=1.33$ was the best model for the prediction of ${\displaystyle \frac{Ws}{Dw}}$. In comparison with the conventional regression-based equations and previously reported ML methods, the proposed hybrid approaches improved the prediction results. In addition, the cross-validation method confirmed the robustness and generalization capability of the suggested hybrid ML models. The superior performance of the hybrid models is attributed to their ability to capture complex nonlinear interactions among hydraulic and geometric variables. The developed SVCM/HCVCM+GEP models provide accurate approaches for predicting scour parameters in hydraulic structures. Full article

Pipeline valves play a crucial role in oil and gas exploration, production, transportation, and storage, and a systematic understanding of patent technologies in this field can help identify innovation trends and formulate research and development (R&D) strategies. This study collected more than 5000 pipeline-valve-related patents worldwide from 2006 to 2025, including 2292 invention patents, and adopted a progressive patent analytics approach integrating statistical analysis, network analysis, text mining, and high-value invention patent analysis. The results show that innovation activity in this field has remained active over the past two decades, especially since 2016, when the number of patent publications exceeded 300 in almost every year. China, Russia, the United States, South Korea, and Canada are the major sources of patent activity, with Chinese enterprises and universities making important contributions in terms of patent quantity. However, the analysis of high-value invention patents indicates that representative patents from the United States, Canada, and Russia also have a strong influence. Core innovation directions cover multiple pipeline valve applications in oil and gas extraction, transportation, and storage, with valve control systems and mechanical structures constituting the dominant technologies. The ten identified technological themes and their evolution show that technological innovation in this field has gradually expanded from mechanical improvements in traditional valve bodies, sealing components, and pressure relief devices to diversified directions such as wellhead control, intelligentization, and low-carbon development. The analysis of high-value invention patents further confirms this trend, indicating that pipeline valve technology is being reshaped from a relatively mature mechanical technology field into an integrated technological system that combines mechanical reliability, intelligent control, and other dimensions. Full article

The development of digital photogrammetry techniques has revolutionized the study of marine ecosystems, enabling the generation of high-precision three-dimensional models from conventional imagery. Structure from Motion (SfM) algorithms have become effective tools for mapping and monitoring underwater habitats, offering a non-invasive and cost-effective alternative to traditional methods. This study presents a pilot methodological validation of SfM-based underwater photogrammetry for the non-invasive morphometric monitoring of vulnerable benthic species, using Pinna rudis. The research focused on refining photogrammetric methodologies for marine conservation, addressing technical challenges such as variations in light conditions, water turbidity, and image acquisition complexity. The study area, the Cabrera Archipelago Maritime-Terrestrial National Park, is a pristine marine environment in the western Mediterranean, hosting diverse benthic communities, including an abundant Pinna rudis population. Data acquisition comprises sampling by scuba diving techniques at depths ranging from 26 to 31 m, performed during the July 2022 field campaign within a permanent demographic plot established in 2013 and the methodology applied involved generating three-dimensional models using SfM, allowing for direct measurements of the seabed and extraction of morphometric parameters of sessile species. The characterization of the Pinna rudis aggregation was based on specimen density and size structure, determined using maximum shell width. The 3D model of the pilot plot covers 86.1 m², hosting 31 individuals. Morphometric measurements derived from SfM-based 3D models were validated against in situ diver measurements of maximum shell width. The results showed that the average maximum width obtained from 3D models (15.19 ± 3.23 cm) was consistent with in situ measurements (15.35 ± 3.48 cm). The mean difference between methods was −0.16 ± 0.82 cm, indicating a negligible systematic bias. The mean absolute error was 0.65 cm, corresponding to an average relative error of 4.34%, and a strong linear relationship was observed between both methods (r = 0.97). These results confirm that underwater photogrammetry is a reliable and non-invasive tool for monitoring vulnerable benthic species, providing high-resolution spatial and morphometric data to support conservation strategies in marine protected areas and allowing the collection of additional data compared to in situ surveys. Full article

This experimental study investigated the impact of embedding social and emotional learning (SEL) in science instruction on the academic and social–emotional outcomes of Arabic-speaking sixth graders, including those with reading difficulties (RD). Children from two schools in northern Israel (n = 101) were randomly assigned to either an intervention group, which received SEL-enriched science lessons featuring collaborative, reflective activities over 30 sessions, or a control group receiving traditional instruction. Pre- and post-tests assessed SEL competencies, motivation towards science, and academic achievements in science and mathematics. Results showed significantly greater gains in SEL skills, and in science motivation and science achievement in the intervention group compared to controls, whereas mathematics outcomes remained unchanged. Typically developing students and those with RD benefited similarly. Integration of SEL into science curricula thus enhances cognitive and social–emotional learning dimensions, particularly in linguistically and socio-economically marginalised populations. Implications for inclusive pedagogy and future research directions are discussed. Full article

In the strategic context of China’s efforts to promote agricultural power and modernization, the key to achieving sustainable development for agricultural enterprises lies in fostering breakthrough innovations and enhancing their market competitiveness. This paper uses Chinese agricultural enterprises listed on the A-share market from 2009 to 2024 as its research sample. From the perspective of breakthrough innovation in agriculture-related enterprises, it examines the association between agricultural new quality productivity and the sustainable development of agricultural enterprises. The regression results show that, first, agricultural new quality productivity is positively associated with breakthrough innovation in agricultural enterprises. After a series of robustness tests, these findings remain valid. Second, the bootstrap mediation results indicate that this relationship operates mainly through government policy orientation and enterprise knowledge creation capacity, while the indirect effects of government resource support and independent R&D capacity are weaker and not statistically robust. Furthermore, a heterogeneity test revealed that agricultural new quality productivity has a more pronounced positive association with breakthrough innovation in regions with strong intellectual property protection and high environmental regulations, as well as in samples where corporate executives demonstrate greater environmental awareness and companies achieve higher overall ESG scores. Finally, further analysis shows that as the level of corporate green transformation increases, the enabling effect of agricultural new quality productivity on breakthrough innovation in agricultural enterprises becomes more pronounced, providing evidence on how ANQP may support the sustainable development of agricultural enterprises. Full article

This paper examines whether total factor productivity (TFP) in Israeli agriculture has genuinely slowed or declined in recent years, or whether the reported trend is primarily driven by methodological choices, data limitations, and measurement error. We compare two widely used approaches to TFP measurement—those of the Bank of Israel and the U.S. Department of Agriculture (USDA)—which differ in their definitions of output, treatment of inputs, and assumptions regarding factor shares. We reconstruct and refine the underlying datasets, addressing important limitations in the existing measures, including the omission of foreign labor, inconsistencies in agricultural land measurement, and the application of non-representative input shares. Despite data improvements and methodological adjustments, both approaches yield similar qualitative conclusions. Following rapid increase in earlier decades, TFP growth in Israeli agriculture appears to have stagnated or declined since the early 2010s. A decomposition of output growth further indicates that recent production patterns have been driven primarily by greater input intensity per unit of land rather than by technological progress or efficiency gains. As a result, agricultural output has shown little or no net growth over the past decade. We discuss potential explanations for this slowdown, including climate change, the growing reliance on reclaimed and other marginal water sources, and the long-term decline in agricultural research and development (R&D) investment relative to sectoral output. Overall, the findings suggest that the productivity slowdown is real rather than an artifact of measurement and underscore the need for renewed investment in agricultural innovation and climate adaptation to sustain domestic production and strengthen food security. Full article

This article examines how smart cities can act as catalysts for enterprise development by integrating technological, infrastructural, governance and human capital dimensions into a coherent urban innovation ecosystem. Drawing on an extensive literature review, the study first conceptualizes smart cities as adaptive systems that combine physical infrastructure, digital data layers, and institutional frameworks, creating conditions for knowledge spillovers, entrepreneurial opportunities, and business model innovation. Empirically, the research is based on an expert survey conducted among 54 specialists from academia, business, and public administration, who assessed the importance of technological, infrastructural, governance, innovation ecosystem, and human capital factors for enterprise development in the context of smart cities. The results suggest that advanced digital technologies, smart infrastructure, open data, R&D support, startup programs and talent development are perceived by experts as key, mutually complementary drivers of firms’ innovation, efficiency, sustainable growth, and competitiveness, with notable differences between expert groups. On this basis, the study proposes a synthetic model of relationships and impact pathways linking smart city components with enterprise outcomes. The paper concludes with a discussion of the study’s limitations, related to the expert-based, country-specific, and perceptional character of the data, and outlines directions for further quantitative and qualitative research on the firm-level effects of smart city development. Full article

There is a need for research to support the transition away from single-use plastic packaging towards a circular economy. This research developed simulated wear processes and assessment techniques that emulate aspects of a reuse system in order to evaluate different plastic food packaging types that are typically used for single-use applications. Two thermoformed polyethylene terephthalate materials (rPET and heat-resistant PET) for food packaging trays were tested. Researchers subjected both thermoformed packs to a range of simulated wear processes including wash cycles, simulated damage, surface scratching, and artificial fouling. Assessment techniques included using adenosine triphosphate (ATP) swabs to indicate cleanliness of the pack surface and 3D scan data to measure physical change. The findings show that scratch damage applied to packs, following fouling and wash cycles, produced promising readings under 30 relative light units (RLUs) on ATP swabs. The heat-resistant PET packs exhibited minimal deformation throughout repeated wash cycles. The assessment techniques developed to evaluate plastic materials have provided valuable insight into the cleaning, damage, and deformation of plastic packaging. These insights can support more complex decision making in the design and production of circular food-to-go plastic packaging solutions. Full article

In the context of global market volatility and the pursuit of sustainable development, improving the financial resilience of manufacturing firms lays a critical foundation for high-quality development of the real economy. To explore the key channels through which ESG practices sustain financial stability amid external shocks, this study selects listed manufacturing enterprises in the Shanghai and Shenzhen A-share markets from 2015 to 2024 as the research sample based on the CSMAR database. It employs the entropy weight method to measure corporate financial resilience, uses a two-way fixed-effects model for benchmark regression, and conducts mechanism tests through mediation and moderation analyses to explore the underlying channels between ESG performance and financial resilience in manufacturing enterprises. The results indicate that improved ESG performance significantly enhances corporate financial resilience, and these findings remain robust after robustness tests and endogeneity treatments. ESG performance primarily enhances the financial resilience of manufacturing enterprises by alleviating financing constraints, increasing R&D investment intensity, and strengthening corporate environmental governance. Heterogeneity tests show that the positive impact of ESG performance on financial resilience is more pronounced in state-owned enterprises, manufacturing enterprises located in Central China, and those in the recession phase. Based on the above conclusions, this paper puts forward targeted suggestions for the government, manufacturing firms, and investors to promote ESG practices and boost financial resilience. Full article

Environmental research and development (R&D) support environmental improvement by advancing cleaner technologies, improving resource efficiency, reducing emissions, and helping firms meet sustainability goals and regulatory standards. This study examines the effect of environmental R&D on firms’ environmental performance and considers whether firm characteristics, specifically age and size, moderate this relationship. Using purposive sampling based on defined inclusion and exclusion criteria, the analysis draws on data for 303 non-financial firms listed on the Frankfurt Stock Exchange between 2007 and 2024, obtained from Refinitiv DataStream. Diagnostic tests revealed cross-sectional dependence, heterogeneity, and endogeneity in the dataset. To address these issues and ensure robust estimates, the Common Correlated Effects Mean Group (CCEMG), Feasible Generalized Least Squares (FGLS), and two-step difference Generalized Method of Moments (GMM) estimators were employed. The results show that environmental R&D has a positive and significant effect on environmental performance. Firm age and size further strengthen this relationship, indicating that older and larger firms benefit more from environmental R&D initiatives. The study recommends that firms increase investment in environmental R&D to stimulate innovation, enhance sustainable practices, and improve ecological outcomes. Policymakers should also encourage eco-innovation by developing supportive regulations, offering financial incentives for green technologies, and promoting sustainable technological advancement. Full article

The new energy vehicle (NEV) industry is a key sector for achieving dual carbon goals and advancing regional green transformation. Its sustainable development depends on the deep coupling of the industrial chain and the innovation chain. Drawing on data from Shaanxi’s NEV industry covering the period 2014–2023, this study employed kernel density estimation (KDE), the entropy weight method, the coupling coordination degree model, and the optimal parameter geographical detector. Specifically, we examine Shaanxi’s national positioning and spatial pattern within the NEV industry, the spatiotemporal evolution of the coupling coordination degree between its industrial and innovation chains, and the key driving factors along with their interaction mechanisms. The results indicate that Shaanxi is situated within the secondary core growth zone of central and western China. Within the province, the industry exhibits a pronounced spatial pattern characterized by single core concentration in Xi’an, contiguous support across the Guanzhong region, and point-like distribution in northern and southern Shaanxi. The dual-chain coupling coordination degree in Shaanxi’s NEV industry has improved steadily, resulting in a four-tier structure comprising core breakthrough, secondary catch-up, weak foundation, and lagging predicament categories. The dominant driving factors are Industrial Agglomeration Degree, Research and Development (R&D) Funding Input, and Resource Utilization Rate. The interaction between Resource Utilization Rate and Integration Degree exerts the strongest effect. Full article

The precision of 3D-printed food is dictated by the macroscopic textural stability of the dough system during extrusion. In this study, we investigated the nonlinear regulatory effects of Konjac Glucomannan (KGM) concentration and printing pressure on the textural properties of 3D-printed dough. Using a space-filling experimental design (n = 30), Support Vector Regression (SVR) and Gaussian Process Regression (GPR) models were developed to map the complex interactions between formulation and process variables. The results indicated that KGM concentration and printing pressure exhibit significant nonlinear coupling effects on hardness, cohesiveness, and chewiness. After 4-fold cross-validation and systematic hyperparameter optimization, the SVR model demonstrated satisfactory interpolative predictive performance within the investigated parameter space, achieving $R_p^2$ values of 0.990 for gumminess and 0.987 for chewiness, while the GPR model effectively characterized the predictive uncertainty. Furthermore, the model predicted a favorable processing region (0.5–0.8% KGM and 4.0–4.6 bar) within the investigated design space. This research provides a quantitative, data-driven framework for the formulation pre-optimization of 3D-printed dough under specific experimental settings. Full article

Background/Objectives: Transition metal complexes of imidazoles exhibit a variety of biological activities. This makes them promising metal-based drugs for use in medicine. The aim of this research is to investigate the complexes of zinc(II) with N-vinyl, N-allyl, and N-propargylimidazoles, represented by the formula [ZnL₂Cl₂], as potential drug candidates. Methods: Structural studies of the obtained complexes were performed using single-crystal X-ray diffraction analysis, IR and NMR spectroscopy. DFT calculations were used to determine structural, electronic and thermochemical parameters of the complexes. QSAR analysis was performed using PASS. The wound-healing and antihypoxic activities were studied in vivo using models of wounds and acute hypoxia of various origins. The antimicrobial activity of the complexes was evaluated against Staphylococcus aureus Wood 46, Escherichia coli M-17, and the yeast fungus Candida albicans 927. The cytotoxic activity was tested using several cell lines, including monkey kidney (Vero) cells, human cervical cancer cells (Hep2C and HeLa), human lung carcinoma (A549), and human embryonal rhabdomyosarcoma (RD). Results: New complexes of N-allylimidazole and N-allyl-2-methylimidazole with ZnCl₂ were synthesized and characterized. All the studied complexes possess diverse biological activities. While the antimicrobial activity was modest, a distinct antifungal activity was observed. The cytotoxicity of the complexes was found to be mainly in relation to Hep2c and RD cell lines. Conclusions: Based on the results of QSAR analysis and experimental findings, the diverse biological activities of the compounds indicate that they are promising lead structures for further optimization in drug development. Full article

Growth hormone-releasing hormone receptor 1a (GHS-R1a) is a key G protein-coupled receptor (GPCR) governing feeding and energy homeostasis. Accumulating evidence shows that GHS-R1a forms functional heterodimers with multiple metabolic-related GPCRs, including dopamine 2 receptor (D2R), melanocortin 3 receptor (MC3R), 5-hydroxytryptamine 2c receptor (5-HT2cR), orexin receptor 1 (OX1R) and cannabinoid receptor 1 (CB1R). These heterodimers undergo distinct signal transduction reprogramming, generating novel physiological effects that are not observed with individual receptors: for instance, GHS-R1a/D2R mediates an atypical calcium signaling pathway to regulate appetite, while GHS-R1a/5-HT2cR antagonizes ghrelin-induced orexigenic effects. Meanwhile, diverse detection techniques, including co-immunoprecipitation and fluorescence resonance energy transfer, have been developed to identify and validate GHS-R1a heterodimerization, laying a solid foundation for mechanistic research. This review systematically summarizes the molecular mechanisms of GHS-R1a heterodimer formation, the characteristic signal regulation patterns of different heterodimers, and their specific regulatory roles in feeding circuits. Furthermore, we discuss the existing research gaps in this field, such as the lack of in vivo detection methods for heterodimers and the unclear structural basis of dimerization. Finally, we highlight the potential of targeting specific GHS-R1a heterodimers as a novel therapeutic strategy for obesity and anorexia, providing new directions for future pharmaceutical development and clinical translation. Full article