Search Results (78,186)

Non-native fish invasions are important drivers of freshwater biodiversity and ecosystem function loss. In this study, we compared the trophic niches of four non-native (Rhinogobius giurinus, Hemiculter leucisculus, Hypomesus nipponensis, and Tachysurus fulvidraco) and one native fish species (Carassius auratus) from April 2022 to December 2023 in Erhai Lake, a typical plateau lake on the Yunnan–Guizhou Plateau, China. We analyzed δ¹³C and δ¹⁵N from 766 fish samples and calculated 103 SEA_b values across species, seasons, and lake regions. Stable isotope analyses revealed pronounced trophic niche differentiation between non-native and native fishes. Non-native species exhibited wider niche width (4.81 ± 0.48), lower overlap (24.43 ± 1.57), and higher within-group dispersion (2.69 ± 0.07), indicating greater trophic plasticity. In contrast, native fishes showed narrower niches (2.72 ± 0.32), higher overlap (37.32 ± 4.21), and lower plasticity (1.68 ± 0.08). Moreover, non-native and native fishes adopted contrasting trophic strategies: individual fitness increased with niche expansion in non-native fishes, whereas it declined in native fishes. Multiple linear analyses further indicated significant competitive effects of non-native fishes on native species’ niches, suggesting that niche expansion in native fishes represents a passive response to intensified competition rather than an adaptive strategy. Overall, the high trophic plasticity of non-native fishes and their asymmetric effects on native species imply a high risk of food web reorganization in Erhai Lake. These results provide guidance for the sustainable management of Erhai Lake, balancing invasive species control with native fish conservation. Our results underscored the importance of incorporating trophic interactions into invasion management and native fish conservation. Full article

This paper proposes a Bayesian estimation method for spatial lagged panel quantile models. The proposed model simultaneously considers spatial lag effects of the dependent variable and the quantile regression framework, enabling effective capture of spatial dependence and conditional distribution heterogeneity. The research constructs a Bayesian estimation framework based on the asymmetric Laplace distribution by decomposing the random disturbance term into a combination of normal and exponential distributions, successfully developing a probabilistic model with both thick tail robustness and computational efficiency. On this basis, the study derives the full conditional posterior probability distributions of model parameters and designs a hybrid Markov Chain Monte Carlo (MCMC) sampling algorithm integrating Gibbs sampling and Metropolis–Hastings algorithm for parameter estimation. Numerical simulation experiments demonstrate that, compared with traditional estimation methods, the proposed Bayesian estimation approach exhibits superior estimation accuracy and robustness across different quantiles, with particularly pronounced advantages in small sample and heavy-tailed distribution scenarios. This methodology provides a more reliable theoretical tool for analyzing panel data with spatial dependencies. This method can not only accurately quantify the spatial spillover effect, but also identify the different effects of the same influencing factor at different emission levels, which provides a strong methodological support for formulating differentiated and precise emission reduction policies. Full article

Environmental DNA (eDNA) metabarcoding has become a powerful, non-invasive method for detecting aquatic organisms. However, optimal sampling strategies for benthic taxa in lentic ecosystems remain unclear. This study evaluated the effectiveness of eDNA metabarcoding in detecting freshwater Unionidae mussels in lake water columns and examined their spatial and seasonal distribution patterns. We validated a mini-barcode primer targeting the mitochondrial 16S rDNA of unionid mussels through controlled laboratory experiments and field tests, confirming reliable amplification and accurate taxonomic assignment of freshwater bivalve DNA. Field surveys were conducted in four lakes within the Nakdong River basin, where eDNA samples were collected from littoral zones and from surface, mid-, and bottom layers of central lake areas during autumn and winter. Metabarcoding analysis identified 79 amplicon sequence variants (ASVs) representing four unionid taxa, with Cristaria plicata and Sinanodonta lauta comprising the majority of reads and ASVs. Overall, the number of Unionidae eDNA reads showed no significant seasonal differences, but there was notable spatial variation among sampling locations. Read numbers were significantly lower in littoral zones compared to central lake areas, with no significant differences detected among depth layers within the central zones. Species-specific analyses revealed contrasting spatial patterns: C. plicata had higher read abundance in mid- and bottom layers, while S. lauta was more frequently detected in surface and littoral samples. These findings suggest that the distribution of freshwater mussel eDNA in lakes is primarily influenced by spatial factors related to habitat preference and hydrodynamic mixing, rather than by seasonal variation during stable periods. This study offers practical insights for designing effective eDNA sampling strategies for benthic invertebrates in lentic ecosystems. Full article

Porcine epidemic diarrhea virus (PEDV) is a major pathogen responsible for severe diarrhea, dehydration, and high mortality in neonatal piglets, continually threatening global swine production. Rapid differentiation of its major genotypes (classical G1, variant G2, and recombinant S-INDEL) is vital for molecular epidemiology and effective disease control, yet existing approaches rely mainly on time-consuming sequencing and phylogenetic analysis of the S gene. To overcome this limitation, we developed a novel triplex TaqMan-based real-time PCR assay for rapid detection and differentiation of the three PEDV genotypes. The assay demonstrated high sensitivity, with the lowest detection limit of 10² copies/μL, and strong specificity, showing no cross-reactivity with six other common swine pathogens (TGEV, PDCoV, PoRV, PRRSV, CSFV, and PRV). It also exhibited excellent reproducibility, with both intra- and inter-assay coefficients of variation maintained below 1.5%. In clinical validation, the assay showed 100% concordance with results obtained from S gene sequencing and phylogenetic analysis. Furthermore, testing of 160 clinical samples revealed cases of co-infection involving G2 and S-INDEL strains. In conclusion, this rapid, specific, and reproducible assay provides a reliable tool for routine molecular diagnosis, facilitating large-scale epidemiological surveillance and enabling genotype-informed control strategies against PEDV. Full article

AISI 904L stainless steel (904L SS) is a promising material for nuclear power plant primary circuits due to its superior corrosion resistance, but its corrosion behavior under simulated high-temperature and high-pressure water environments with different microstructures remains poorly understood. In order to systematically investigate and clarify the electrochemical behavior and corrosion behavior under stress of 904L SS with three different microstructures (as-received, sensitized, and solution-treated) in a simulated primary circuit water environment of a nuclear power plant, experiments are conducted using dynamic polarization, electrochemical impedance spectroscopy (EIS), and U-bend immersion methods. The results show that temperature has a significant effect on corrosion resistance. As the temperature increases, the impedance of all microstructures decreases significantly, the passivation zone narrows, and the corrosion current density increases. Under high-temperature and high-pressure conditions, the corrosion resistance of the sensitized samples is the worst, while the samples treated with solution have the best overall performance. That is, microstructural optimization through solution treatment can effectively enhance the high-temperature and high-stress corrosion resistance of 904LSS in the primary circuit water environment. Full article

To address the lack of systematic comparison regarding rheological properties and the unclear structure–property relationships among three core fracturing fluid materials including synthetic polymers, vegetable gums, and microbial polysaccharides, this study selected acrylamide-based polymers, hydroxypropyl guar gum and xanthan gum as the representative systems. The steady-state viscosity, rheological curves, thixotropy, viscoelasticity, and temperature-shear resistance of the three samples were systematically characterized at concentrations ranging from 0.1 to 0.7 wt% using an MCR301 rotational rheometer. The outcomes indicate that the structural strength values of all three materials increase with rising concentration, but their rheological behaviors and stability differ significantly due to distinct molecular structures. The acrylamide-based copolymer forms a temporary network via weak hydrogen bonds (amide-carboxyl or amide-amide) and physical entanglements, exhibiting thixotropy and a stress pre-elastic response. The most significant effects occur at 0.7 wt%, with a thixotropic loop area of 2.874 Pa·s⁻¹ and a stress overshoot of 4.97 Pa.; hydroxypropyl guar gum has insufficient thermal stability and poor heat resistance. Its viscosity retention rate is as low as 31%, and it always exhibits a solution-type rheological property of G′ < G″; the xanthan gum exhibits elastic gel properties with tanδ < 1 due to its double-helix molecular structure. It has excellent temperature shear tolerance and the viscosity retention value can reach up to 98.6 mPa·s. Two mathematical models were established and demonstrated strong applicability: a modified Carreau model for flow curve fitting yielded a coefficient of determination (R²) greater than 0.95, enabling accurate description of fluid-type transitions; a four-parameter equation for temperature–shear resistance curves also achieved an R² above 0.95, effectively characterizing viscosity evolution with temperature. Full article

Background: Mesenchymal stem cells (MSCs) exhibit a high capacity for differentiation, possess anti-inflammatory and proangiogenic properties, and stimulate the growth and proliferation of neighboring cells. MSCs are a promising tool in regenerative medicine. However, the molecular mechanisms underlying the properties of these cells are not yet fully understood. Gene expression in MSCs influences their characteristics and differentiation potential. Therefore, it is essential to investigate factors affecting gene expression as well as those activating signaling pathways, which will enable more effective and individualized applications of MSCs. In this study, we aimed to identify signaling pathways involved in gene expression in umbilical cord-derived MSCs (UC-MSCs) that may be altered by maternal diabetes and hypothyroidism during pregnancy. Methods: The research material consisted of UC-MSCs. Samples obtained from nine participants were analyzed. UC-MSCs were isolated and cultured, and RNA was extracted. The isolated RNA was used for microarray-based gene expression analysis. Subsequently, pathway enrichment analysis was performed to identify the signaling pathways involved. Results: In the diabetes group, 340 genes (0.71%) were upregulated, while 268 genes (0.56%) were downregulated compared with UC-MSCs from the control group. In the diabetes group, the most compact module was composed of proteins associated with WNT/planar cell polarity (WNT/PCP) signaling. The second module included genes related to smooth muscle activity. In the hypothyroidism group, an association was identified between the extracellular matrix organization pathways (GO:0030198) and the extracellular structure organization (GO:0043062) pathways. Moreover, in this group, increased expression of MMP1, MMP10, and GREM1 was observed. Conclusions: In summary, our study demonstrated the impact of diabetes and hypothyroidism on gene expression in UC-MSCs. We also observed the activation of distinct signaling pathways depending on the presence of these conditions. However, this work represents a preliminary screening, and the results should be validated by PCR in a larger cohort. Full article

Conventional production methods of aragonite production utilize chemical inducers to promote the evolution of the calcite crystalline phase to the aragonite phase of calcium carbonate. The chemical inducers used require a considerable amount of magnesium chloride (MgCl₂) to induce crystallization, which is a major operational cost. Application of such materials in large amounts can be a deterrent to achieving a sustainable and economically feasible end-product derived from carbon dioxide (CO₂) molecules. A number of previous research works focused mainly on optimizing the usage of MgCl₂ or introducing alternative chemical inducers for aragonite production. In this work, we are proposing the usage of natural seawater as it is a naturally available and abundant resource to induce the synthesis and continuous production of aragonite compounds. Due to inconsistent quality and salinity of the natural seawater sampled, harvested, and dried, Red Sea Salt is utilized, blended at 33 g/L throughout the laboratory experiments for better statistical control, and is referred to as blended or artificial seawater. A methodology of utilizing seawater, which has a considerable concentration of MgCl₂ compound, can be utilized as a sustainable, natural, and economically feasible natural inducer to synthesize aragonite has been developed by utilizing artificial seawater for laboratory proof of concept. The main effects identified for the optimization of aragonite synthesis are lime (CaO) feedstock concentration in seawater, reaction temperature, and reaction duration. The experiment results indicated that only by increasing temperature and reaction duration, or both, can the aragonite yield be increased. It is suggested that the range of operation to obtain > 80% aragonite purity has been identified with the reaction temperature at 90 °C, reaction duration of 10 min, and CaO concentration in seawater at 1 g/L. The quality of the aragonite synthesized via seawater is characterized using XRD, ICP, FESEM, and TGA, and compared with aragonite particles synthesized using MgCl₂ inducers. In comparison, seawater aragonite has lower residual alkalinity compared to both calcite and aragonite via MgCl₂ and has a mixture of predominantly needle-shaped crystalline structure and remnants of cubic-shaped particles, presumably calcite, suitable for application in food, beverages, and pharmaceuticals (calcium antacids, nutritional supplements, chewable, lozenges). Full article

The risk of developing some types of head and neck squamous cell carcinoma (HNSCC) is seven times higher in males, and such disparities may not be associated only with tobacco and alcohol consumption or HPV infection. Therefore, the endocrine microenvironment is considered another risk factor, as epidemiologic studies have unequivocally shown the protective effect of estrogen in women. This research was focused on progesterone receptors (PRs), the least-studied sex hormone receptors in HNSCC. Our study included fresh tissue samples from 95 primary tumors, 25 metastatic lymph nodes and 40 healthy oral mucosa. Gene expression of nuclear (PGR) and seven membrane PRs (PAQR5, PAQR6, PAQR7, PAQR8, PAQR9, PGRMC1 and PGRMC2) was analyzed by qRT-PCR and associated with clinicopathological characteristics. The results showed that, compared to control tissue, PGR was increased in metastatic lymph nodes, while PAQR5, PAQR7, PAQR8 and PAQR9 were decreased in primary tumors (all p < 0.05). The expression of almost all PRs was greater in older patients and showed moderate to strong positive mutual correlations in both tumors and controls. PARQ8 and PAQR9 were increased in females and pT4 tumors (all p < 0.05). Survival analysis showed that higher PAQR5 (hazard ratio (HR) 2.8, 95% confidence interval (CI) 1.19–6.57, p = 0.019) and PAQR7 (HR 2.0, 95% CI 1.01–3.81, p = 0.048) were associated with worse overall survival, but their independence was not proven in multivariate analysis. Although most PRs were reduced in primary tumors, an increased PAQR5 expression, also associated with tumor invasion markers, could likely mark a specific aggressive, advanced stage of primary tumors and potentially serve as a negative prognostic biomarker for HNSCC. Full article

The increasing penetration of renewable energy and the frequent occurrence of extreme weather events have significantly heightened the uncertainty in power system operations. Simultaneously, the scarcity of renewable energy output samples under extreme meteorological conditions constrains the accurate assessment of extreme risks in system planning and dispatch. To bridge this gap, this work aims to propose a method for generating extreme wind power output scenarios that possess both diversity and statistical accuracy under limited sample conditions. To address this, this paper proposes a method for generating scenarios of extreme wind power output guided and constrained by statistical features. First, multidimensional statistical features are extracted from historical wind power output scenarios and combined, and a quantile threshold method is applied to screen out extreme wind power output scenarios. Subsequently, based on differentiated application requirements of the power system, extreme scenarios undergo preliminary classification followed by category-specific clustering analysis, achieving refined classification of the scenario set. Building on this, an improved generative adversarial network model is constructed, and the Wasserstein distance and gradient penalty mechanism are introduced to enhance training stability. Additionally, a statistical feature self-attention mechanism and feature loss function are designed to effectively constrain the consistency between generated scenarios and real scenarios in key statistical features. Results demonstrate that the proposed method can generate a set of extreme wind power output scenarios with both diversity and statistical accuracy under limited sample conditions, providing effective data support for system safety operation and risk prevention and control. Full article

This study investigates, with a particular focus on understanding how digital change shapes firm performance in an emerging economy context, first, the impact of digital disruptive innovation, conceptualized as an external condition characterized by technological, market, and competitive turbulence on firm performance within tech-intensive service sector companies, and second, the mediating influence of management skills, proxied by dynamic core managerial competence, and the moderating influence of modern management practices, proxied by innovative work practices, on this relationship. It also examines the moderating effect of innovative work practices on the relationship between digital disruptive innovation and dynamic core management competence, and the impact of COVID-19 on the link between dynamic core management competence and firm performance. This study applies structural equation modelling (SEM) (AMOS 26.0 software) to explore several hypotheses testing for target relationships. The sample was collected via a Qualtrics online survey from 730 senior executives working in digital telecom and banking firms in Pakistan. The study findings show that digital disruptive innovation has a negative effect on service sector performance, and this negative impact is also mediated by dynamic core management competence, as heightened digital disruption tends to weaken managerial competence, which subsequently affects firm performance. While innovative work practices exhibit a positive association with performance, they also positively moderate the negative effect of digital disruptive innovation on performance and mitigate the negative impact of dynamic core management competence on performance. The analysis also reveals that the COVID-19 pandemic positively moderates the effect of dynamic core management competence on performance, indicating that managerial adaptability becomes particularly important when firms operate under crisis conditions. Overall, this study highlights the significance of these phenomena on firm performance in an emerging economy context and provides practical insights for managers and policymakers operating in digitally disrupted service sectors. Full article

We aim to address the insufficient robustness of navigational line detection for rapeseed seed production sires in complex field scenarios and the challenges faced by existing models in balancing precision, real-time performance, and resource consumption. Taking YOLOv8n-seg as the baseline, we first introduced the ADown module to mitigate feature subsampling information loss and enhance computational efficiency. Subsequently, the DySample module was employed to strengthen target feature representation and improve object discrimination in complex scenarios. Finally, the c2f module was replaced with c2f_FB to optimise feature fusion and reinforce multi-scale feature integration. Performance was evaluated through comparative experiments, ablation studies, and scenario testing. The model achieves an average precision of 99.2%, mAP50-95 of 84.5%, a frame rate of 90.21 frames per second, and 2.6 million parameters, demonstrating superior segmentation performance in complex scenarios. SegNav-YOLOv8n balances performance and resource requirements, validating the effectiveness of the improvements and providing reliable technical support for navigating agricultural machinery in rapeseed seed production. Full article

As organizations attempt to retain a competitive advantage, it is becoming more important to understand how employees perform with respect to leadership styles. This paper examines the mediating effect of technical self-efficacy in the relationship between digital leadership and the employee performance of Egyptian service firms. The paper adopts a quantitative research approach, wherein 442 respondents were surveyed from two major Egyptian cities (Cairo and Alexandria). The study employed a purposive sampling technique to select respondents who were assumed to be familiar with the variables under study, like digital leadership practices. Partial Least Squares Structural Equation Modeling was used for the data analysis with the aid of SMART PLS software version 4. The findings reveal that there is a direct positive and statistically significant impact of adaptive leadership on employee performance. Similarly, there is also a positive and significant direct effect of innovative digital leadership on employee performance. This result validates the hypothesis that technical self-efficacy mediates the relationship between innovative digital leadership and employee performance, though partially. The study concludes that technical self-efficacy is one of the main psychological mechanisms that explain how digital-oriented leadership can be translated to better performance, and emphasizes the importance of the confidence and competence of employees in the usage of digital technologies. However, the absence of a significant mediation effect from technical self-efficacy between adaptive leadership and employee performance also shows that adaptive leadership has a direct behavioral and motivational channel of action, instead of an indirect channel of action like technology-based confidence. Thus, the study makes a clear contribution to the literature by advancing a comprehensive model that links digital leadership, employee performance, and technical self-efficacy. In conceptual terms, it builds on the digital leadership discourse by proposing technical self-efficacy as a mediating construct that explains the difference in how different employees respond to digital transformation initiatives. Organizations should, therefore, adopt an integrated leadership development approach that improves adaptive leadership competencies as well as digital leadership practices that facilitate technical self-efficacy to achieve improved long-term employee performance. Full article

Cardiovascular diseases and metabolic dysfunction-associated steatotic liver disease (MASLD) are increasing sharply worldwide and share overlapping pathophysiological pathways, including oxidative stress, inflammation, hyperglycemia, obesity, dyslipidemia, and hypertension. Dark chocolate, rich in cocoa flavanols such as epicatechin and catechin, exhibits antioxidant and anti-inflammatory effects. Based on these properties, this narrative review uniquely integrates evidence on chocolate’s effects on both cardiovascular and hepatic health, exploring shared mechanisms and clinical implications. Evidence from clinical studies suggests that chocolate modulates nitric oxide bioavailability and NADPH oxidase activity. Clinical findings demonstrate improvements in flow-mediated dilation, decreased NT-proBNP, reduced intestinal permeability and endotoxemia, improved lipid profile (increased HDL-c and reduced total cholesterol, LDL-c, and triglycerides), increased plasma polyphenols, improved platelet function, and attenuated hepatocyte apoptosis. These findings suggest a potential role for cocoa flavanol-rich dark chocolate in cardiometabolic health; however, the evidence remains preliminary and is limited by heterogeneous study designs, small sample sizes, and short intervention durations. Despite these limitations, current evidence supports the inclusion of moderate dark chocolate consumption as a possible adjunct strategy to mitigate cardiometabolic and hepatic metabolic risks. Further large-scale, long-term trials are needed to confirm these beneficial effects and to standardize the dosage and formulation of cocoa flavanols. Full article

In the context of global efforts to promote energy conservation and emission reduction, geopolitical conflicts have intensified the challenges of mitigating marine climate change, posing increasingly severe economic and climatic pressures on the shipping industry worldwide. Research on multi-objective route optimization is of great significance for addressing climate challenges and enhancing economic efficiencies. This field focuses on constructing multi-objective optimization models that aim to reduce voyage time, fuel consumption, navigational risks, and carbon emissions and solving them using various algorithms. However, determining the optimal route and sailing speed under complex and variable meteorological conditions remains a significant challenge owing to the presence of numerous unevenly distributed feasible solutions within a vast solution space, making it difficult for traditional intelligent algorithms to effectively explore this space. To address this issue, this study proposes a hybrid algorithm named NRRT by integrating the Rapidly exploring Random Tree (RRT) algorithm with the Non-dominated Sorting Genetic Algorithm III (NSGA-III). By improving the sampling logic of the RRT algorithm and combining the vessel’s voluntary speed loss with the sampling step size, the algorithm efficiently explored the feasible route set, enhancing the quality and diversity of the solutions. Subsequently, the NSGA-III algorithm treats sailing speed and heading as direct decision variables to perform multi-objective optimization on the explored routes and generate Pareto-optimal solutions. The optimization results demonstrate that the proposed method excels at generating route plans that effectively reduce costs, minimize emissions, and mitigate risks compared with the 3D Dijkstra algorithm and the improved NSGA-III algorithm. Full article