Search Results (10,237)

Rice seed vigor is one of the critical factors determining rice yield and quality. Identifying substances related to seed vigor and rapidly assessing seed vigor by non-destructive methods are of great significance for increasing rice production. This study employed near-infrared diffuse reflectance spectroscopy (NIR-DRS) and transmission spectroscopy (NIR-TS) to evaluate the vigor of naturally aged rice seeds. The NIR-DRS failed to establish a reliable relationship between spectral data and seed vigor, proving ineffective in distinguishing seed vigor. After enhancing the spectral differences between viable and non-viable seeds, the NIR-TS successfully identified high-vigor and non-viable seeds, with a partial least squares discriminant analysis (PLS-DA) model achieving accuracy and germination rates of 84.52% and 88.57% on the test set, respectively. Furthermore, three algorithms, including interval partial least squares (iPLS), genetic algorithm (GA), and competitive adaptive reweighted sampling (CARS), were applied to extract characteristic spectral wavelengths associated with seed vigor. Among these, the CARS algorithm performed the best, identifying 38 characteristic wavelengths. Wavelength analysis indicated that rice seed vigor is primarily influenced by molecules such as starch, protein, moisture, and lipids. Using the characteristic wavelengths selected by the CARS algorithm, a PLS-DA prediction model for rice seed vigor was constructed, achieving high accuracy and germination rates of 90.47% and 95.38% on the test set, respectively. This study demonstrates that NIR-TS outperforms NIR-DRS in assessing rice seed vigor. Moreover, wavelength selection techniques can effectively identify characteristic spectral features related to seed vigor and significantly enhance the prediction accuracy of the model. Full article

Rhodiola species are traditionally used to mitigate hypoxia-related symptoms, but comparative evidence on their chemical bases and active constituents is limited. We implemented an integrated “qualitative analysis–pharmacological exploration–correlation analysis–molecular validation” workflow to compare Rhodiola crenulata, R. kirilowii, and R. rosea. Ultra-high-performance liquid chromatography–Q Exactive mass spectrometry (UPLC-QE-MS) profiling identified 175 metabolites across the three species, of which 161 were shared; multivariate analyses (principal component analysis, PCA; partial least squares–discriminant analysis, PLS-DA) revealed 30 differential compounds. In a normobaric hypoxia mouse model using herbal powder solutions, all three species significantly increased survival time versus control (p < 0.05), with mean survival times of 48.16 min (RR), 47.07 min (RC), and 44.82 min (RK) compared with 44.34 min for the positive control. Chemometric correlation (partial least squares regression, PLSR) combined with grey relational analysis (GRA) prioritized 14 compounds consistently associated with anti-hypoxia efficacy; six representative metabolites—epicatechin, 3-O-galloylquinic acid, salidroside, p-coumaric acid-4-O-glucoside, citric acid, and geraniol—were selected for in silico assessment. Molecular docking against hypoxia-inducible factor-1α (HIF-1α) yielded favorable binding poses (docking scores < −4.0), providing preliminary molecular-level plausibility without claiming mechanistic proof. This multi-level approach clarifies chemical–pharmacological relationships among Rhodiola species and provides prioritized candidate compounds for targeted isolation and mechanistic validation. Full article

This study addresses a critical gap in understanding how Lean Manufacturing (LM) practices, particularly people-centered approaches, can enhance operational performance within the unique industrial context of Saudi Arabia’s Vision 2030 economic transformation. The concept of Lean Manufacturing involves a systematic approach and principles aimed at enhancing efficiency, minimizing inefficiencies, and boosting output in manufacturing operations. While LM principles are well-established globally, their application in Gulf Cooperation Council (GCC) economies remains understudied, particularly regarding the central role of workforce engagement in successful implementation. The main objective of the study is to investigate the implications of LM on the productivity of the industry sector. Specifically, this research examines how the integration of people-centered practices with traditional LM constructs (Just-in-Time, Jidoka, Stability and Standardization) influences operational outcomes in Saudi manufacturing firms. A survey was conducted among specific private and public enterprises to collect data, yielding a 55.8% response rate and 67 valuable responses from a pool of 120 contacted companies. The sample encompassed small, medium, and large enterprises across seven manufacturing sectors. SmartPLS 3 and SPSS were used to assess the structural and measurement models. Common method bias was evaluated using Harman’s single-factor test. The findings demonstrate that implementing the recommended LM structural model significantly enhances operational performance. Notably, people integration exhibited the strongest influence on operational performance (β = 0.361), suggesting that human-centered approaches may be particularly salient in the Saudi context. These findings offer practical guidance for manufacturing firms seeking to align lean initiatives with Vision 2030 objectives. Full article

In this study, in situ P-doping of Ge-based layers has been studied and compared with implanted layer profiles acting as absorbent top layer in PIN photodetectors. Several structures containing multilayers of n⁺-Ge/i-Ge, n⁺-GeSi/i-Ge, and n⁺-Ge/i-GeSi, were designed to regulate dopant out-diffusion and interface quality. The purpose of this study is to make an optimized n-type doping layer for PIN photodetectors with low dark current, high responsivity, and high quantum efficiency operating in short wavelength infrared (SWIR) region. The Ge-based structure on Si substrate was transferred to oxidized Si substrate and was finally back-etched from Si to form Ge-on-insulator (GOI) substrate. Comprehensive characterization using high-resolution X-ray diffraction (HR-XRD), secondary ion mass spectrometry (SIMS), scanning electron microscopy (SEM), transmission electron microscopy (TEM), atomic force microscopy (AFM), and photoluminescence (PL) have been applied at the first stage of our work. The initial Ge layer contains tensile strain of 0.15–0.17%. PL measurements further indicate a redshift of the Γ-LH transition and carrier-concentration-induced quenching at high doping levels, highlighting the competing effects of band filling and non-radiative recombination in heavily n-doped Ge structures. To circumvent this fundamental trade-off, we devised a decoupled device strategy in which the active absorption region employs an intrinsic Ge/GeSi nanoscale multilayer structure to preserve crystal and interface quality. Although, the epitaxial growth parameters were on the optimized conditions, still out-diffusion (in form of segregation and auto-doping) of P could not be impeded. Our final n-type layer in PIN structure was formed by implantation. This approach yields high-performance photodetectors with a peak responsivity of 0.99 A/W at 1550 nm, a corresponding external quantum efficiency of 79%, and low specific contact resistivities of 2.66 × 10⁻⁶ Ω·cm² (n-type) and 1.38 × 10⁻⁸ Ω·cm² (p-type). This work demonstrates that the strategic combination of multilayer/interface engineering and ion-implantation-based doping is a highly effective strategy for tailoring the optoelectronic properties of Ge-based nanomaterials for high-performance SWIR photodetection. Full article

Background: The aim of the study was to examine the relationship between body weight (weight category groups) and the ability to maintain balance in conditions of restricted field of vision and support area in female wrestlers. Methods: The study involved female wrestlers (n = 34), who were divided into three weight categories. The Romberg test was performer using a freeSTEP STANDARD stabilometric platform. The analysed stabilometric parameters measuring the range of centre-of-pressure (CoP) deviation included total path length (PL) of foot pressure on the ground. Results: The postural stability of female wrestlers was determined by their position, the quality of visual information, the limitation of the support base, and their weight category. As visual information and the support base were limited (when standing on one leg), the deviation of the centre of gravity increased (right leg: f² = 0.293, p < 0.005). Lightweight competitors showed the biggest body deviations in the ‘standing with both feet and eyes open’ position compared to the middleweight and heavyweight categories (eyes open EO f² = 0.233, p < 0.05; eyes closed EC f² = 0.217, p < 0.05). The differences between the tests with eyes open and closed were statistically significant only in the lightweight category. Conclusions: Balance training on the right leg with eyes open can be a sensitive diagnostic test, highlighting differences in stability levels between weight groups. It indicates the need to develop stability while eliminating visual compensation in order to compensate for these differences in competitive sports. Full article

This research explores the multifaceted dimensions of individual entrepreneurship and the factors that influence entrepreneurial activity in Morocco. In line with the country’s orientation and governmental initiatives toward entrepreneurship, this paper highlights how financial and digital literacies, as well as entrepreneurial competency, influence entrepreneurs’ decisions to sustain and build resilient businesses. PLS-SEM was employed to analyze data collected through an online questionnaire from 155 individual entrepreneurs. The findings suggest that financial literacy and digital literacy have a positive and significant impact on entrepreneurial competency. Entrepreneurial competency has a statistically significant and positive impact on both sustainable performance and resilience. Furthermore, entrepreneurial competency has been proven to act as a mediator between financial literacy and resilience, as well as between financial literacy and sustainable performance. Similarly, entrepreneurial competency bridges the relationships between digital literacy and resilience, as well as between digital literacy and sustainable performance. Despite limitations regarding the population frame, the strong connections between constructs suggest that Moroccan authorities should reconsider existing support policies and develop more cohesive programs that assist entrepreneurs in improving their performance and resilience. These findings provide practitioners with actionable insights for shaping technical tools and adopting professional practices. For researchers, the results offer a foundation for future in-depth studies to evaluate the effectiveness of government initiatives and to support entrepreneurs through robust, data-driven analysis. Full article

The complexity of internal control in commercial banks continues to increase, and relevant reports exhibit notable lag and template issues. In response to the demand to transform unstructured disclosures into actionable insights, this study proposes an “augmented Business Intelligence (BI) framework” that integrates a text-based internal control quality assessment system, a dual-validation process, and the resulting Intelligent Internal Control Decision Support System (IIC-DSS). By combining large language models and neural-symbolic models of regulatory prototypes, a quality evaluation system for internal control based on complex text is constructed using a mixed probability mechanism to reduce interference from defensive disclosures. A dual validation process is designed with Partial Least Squares Structural Equation Modeling (PLS-SEM). PLS-SEM verification confirms the construct validity of this evaluation system, while XGBoost verification indicates that internal control quality has incremental predictive ability for asset quality deterioration. The IIC-DSS uses SHapley Additive exPlanations (SHAP) to explain XGBoost outputs, quantifying the marginal contribution of each control factor to the predicted risk. Overall, this study advances internal-control measurement by establishing a neural-symbolic, text-to-indicator representation within an augmented BI architecture and empirically demonstrating its utility in improving predictive power for bank asset quality deterioration and in enhancing decision transparency via explainable AI. Full article

Background/Objectives: Pregnancy loss is a common and multifactorial complication of human reproduction, traditionally attributed to fetal chromosomal abnormalities, maternal anatomical and endocrine disorders, and immune dysfunction. Growing evidence now indicates that the maternal microbiome, particularly within the reproductive tract, plays a critical role in implantation, placental development, and the maintenance of immune tolerance during early pregnancy. Importantly, the influence of the microbiome on miscarriage appears to be strongly modulated by host genetic background and immune regulation. Methods: This narrative review summarizes current evidence linking alterations in the vaginal, endometrial, placental, and gut microbiomes to miscarriage, with a specific focus on host genetics and immune–microbial interactions. Results: We discuss how genetic variation in innate and adaptive immune pathways, inflammatory signaling, and mucosal barrier function may shape host responses to microbial communities, thereby influencing susceptibility to PL. In addition, we highlight emerging data on microbiome-driven regulation of gene expression and epigenetic modifications in the endometrium and decidua, emphasizing the role of microbial metabolites in immune tolerance and placental function. Conclusions: By integrating findings from microbiome research, host genomics, immunology, and epigenetics, this review proposes a framework in which miscarriage is viewed as a consequence of disrupted host–microbe crosstalk rather than isolated pathology. Finally, we address key methodological challenges and outline future research directions aimed at advancing mechanistic understanding and translational applications. Full article

Background: Early diagnosis of rheumatoid arthritis (RA) remains challenging due to the limited performance of existing serum biomarkers. This exploratory study aimed to identify novel serum metabolite and lipoprotein biomarkers for RA and to develop interpretable machine learning models for screening. Methods: This study employed ¹H-NMR metabolomics to analyze serum from 77 RA patients and 70 healthy controls, quantifying 38 endogenous metabolites and 112 lipoprotein parameters. Seven key biomarkers were identified using multiple criteria and Least Absolute Shrinkage and Selection Operator (LASSO) regression. The dataset was split into training and testing sets (7:3 ratio), and four machine learning models were constructed. The Random Forest (RF) model was further interpreted using the SHapley Additive exPlanations (SHAP) method. Results: The selected biomarkers, including formic acid and High-density lipoprotein 4 phospholipids (H4PL), showed significant associations with RA. In the internal test set, the RF model demonstrated promising discriminatory ability. Additionally, a proof-of-concept regression model for predicting the Disease Activity Score in 28 joints (DAS-28) score was developed, explaining a portion of its variance (R² = 0.548) in this cohort. Conclusions: This exploratory, single-center study identifies a novel panel of potential biomarkers for RA and provides a preliminary, interpretable predictive tool. The findings, particularly the internally validated high performance of certain markers, are hypothesis-generating and underscore the need for validation in larger, multi-center cohorts. The DAS-28 prediction model also warrants further investigation. Full article

Digital literacy has become central to educational participation and social inclusion worldwide. Yet significant disparities persist in low-resource schooling contexts, where unequal access to digital infrastructure may shape learners’ confidence in engaging with technology. This study examines the relationship between socioeconomic access and digital self-efficacy among underserved high school students in South Africa through the lens of the UNESCO Digital Literacy Global Framework (DLGF). Data were collected from 286 learners in three Eastern Cape secondary schools in South Africa using a structured questionnaire measuring constructs derived from the DLGF. Focusing on two core constructs, socioeconomic access and digital self-efficacy, a partial PLS-SEM model was estimated with SmartPLS 4. The reflective measurement model showed acceptable reliability and convergent validity (CR = 0.83, AVE = 0.51 for socioeconomic access; CR = 0.79, AVE = 0.65 for digital self-efficacy). Socioeconomic access was positively associated with digital self-efficacy ($\beta$ = 0.38, t = 7.04, p < 0.001), explaining 15% of its variance ($R^2$ = 0.15). These findings suggest that policies aimed at improving device availability and reliable connectivity in underserved schools may strengthen learners’ confidence in performing foundational digital literacy tasks, particularly information search and source evaluation. Full article

As urban heritage tourism faces the challenge of balancing conservation and development, understanding the non-linear drivers of tourist loyalty becomes crucial for sustainable development of urban heritage tourism. This study delves into how perceived authenticity and cultural involvement affect tourist loyalty in an urban heritage district and tests whether these effects vary across different experience levels. Data was collected using a structured survey questionnaire, which was administered through on-site convenience sampling. To ensure the reliability and validity of the data, a face-to-face recruitment approach was employed, with participants invited to complete the survey immediately after their visit. The sample consisted of 469 domestic tourists in the Pingjiang Historic Block. The study applied quadratic modeling within Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze both linear and non-linear relationships among the key factors. The results indicate that loyalty is driven primarily by experiential and involvement-related processes, with cultural motivation and object-based authenticity influencing loyalty mainly through existential authenticity and cultural involvement, rather than through direct effects. The findings showed that the non-linear relationships were observed in the process of loyalty formation, whereas the relationships among antecedents and cultural involvement remain largely linear. Specifically, both object-based authenticity and existential authenticity have a minimum credibility threshold that must be met before loyalty increases meaningfully, while cultural involvement follows an inverted U-shaped pattern, implying diminishing returns beyond an optimal level. This study contributes to a further understanding of the complex relationship between authenticity and tourist loyalty. The findings provide a novel perspective for policymakers, offering insights into how the non-linear relationships of loyalty can be leveraged to promote the sustainable development of urban heritage tourism. Full article

The SARS-CoV-2 papain-like protease (PL^pro) is an essential viral enzyme that promotes viral polyprotein processing while simultaneously suppressing the host innate immune response, which makes it a primary target for developing antiviral drugs. The present study employs a comprehensive approach integrating untargeted metabolomic profiling, in silico molecular docking and dynamics simulations, Molecular Mechanics Generalized Born Surface Area (MM-GBSA) energetic assessments, and biochemical enzyme assays. This integrated method aims to discover natural PL^pro inhibitors from two ethnomedicinal plants, Lippia javanica and Acorus calamus, which have long been utilized in African traditional medicine to treat respiratory diseases. Comprehensive metabolite profiling using untargeted Ultra-Performance Liquid Chromatography–Tandem Mass Spectrometry (UPLC-MS/MS) and Global Natural Products Social (GNPS) molecular networking revealed flavonoid glucuronides and phenylpropanoid derivatives as the major constituents in both plant species. In situ histochemical staining further offered spatial validation of phenolic- and lignin-associated tissues, supporting the phenolic-dominated molecular families detected by GNPS molecular networking. In silico evaluation of six selected compounds demonstrated spontaneous and thermodynamically favorable binding to PL^pro, with ΔG_bind values ranging from −5.63 to −6.43 kcal/mol. Catechin-7-glucoside emerged as the lead compound, establishing multiple hydrogen bond networks with Asp164, Gln269, Tyr264, and Asn267, supplemented by hydrophobic engagement with Pro247 and Pro248, and π-π stacking with the blocking loop 2 (BL2 loop). Molecular dynamics simulations confirmed the stability of the protein–ligand complexes. Biochemical enzyme assays confirmed concentration-dependent inhibition of PL^pro proteolytic and deubiquitinating activity by both crude plant extracts and isolated bioactive compounds. However, S-adenosyl-methionine showed comparatively high PL^pro proteolytic activity (IC₅₀ 5.872 µM) compared to catechin-7-glucoside, with an IC₅₀ of 7.493 µM, exhibiting efficacy similar to the reference inhibitor GRL0617. Both the extracts of L. javanica and A. calamus have shown significant inhibitory activity while maintaining cell viability in Human embryonic kidney 293T cell (HEK293T) culture models, indicating a favorable safety profile of the tested concentrations. Based on these results, catechin-based polyphenols and phenylpropanoid derivatives appear as promising lead compounds for the development of PL^pro inhibitors. To progress toward therapeutic use, further work is necessary in pharmacokinetics, structural optimization, and antiviral validation in cell models. Full article

Artisanal fishing, understood as a form of rural entrepreneurship, extends beyond purely economic performance; however, the quantitative influence of informal institutions on sustainable value creation remains insufficiently examined. This study analyzes how Indigenous governance, cooperation, and ancestral knowledge shape the economic, social, and environmental dimensions of sustainability in artisanal fisheries. A quantitative, non-experimental, cross-sectional design was applied using face-to-face surveys administered to 148 fishers in Taganga, Colombia. Data were analyzed using partial least squares structural equation modeling (PLS-SEM) to assess both measurement and structural relationships. The results indicate that Indigenous governance is strongly associated with sustainable value creation, significantly enhancing economic, social, and environmental outcomes through collective participation, cooperative arrangements, and community-based environmental practices. Ancestral knowledge shows a specific but meaningful relationship with social value by reinforcing cultural identity and community cohesion; this construct was measured through fishers’ perceptions of intergenerational knowledge transmission, traditions, and cultural values shared by elder fishers. Cooperation exhibits a positive association with economic value by strengthening productive resilience, although its effects do not extend to broader social outcomes. Overall, the model explains moderate proportions of economic and social value and a low proportion of environmental value. These findings demonstrate that informal institutions operate as differentiated yet essential mechanisms of sustainability, highlighting the importance of strengthening Indigenous governance, consolidating cooperative practices, and integrating ancestral knowledge into governance strategies for small-scale fisheries. Full article

This study investigates the psychological mechanisms underlying tourists’ carbon offset behavior in air travel by distinguishing between offset choice (OC) and offset spending (OS). Grounded in the Value–Belief–Norm (VBN) framework, the model integrates Environmental Value and Literacy (EVL), Green Identity and Social Motives (GISM), Trust and Risk Perception (TRP), Personal Norm Activation (PNA), and Perceived Effectiveness (PEF). Data were collected onsite from 500 international and domestic tourists at Suvarnabhumi International Airport, Thailand, between June and July 2025, and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that EVL and GISM significantly enhance both PNA and PEF, which in turn exert strong positive effects on OC and OS. PNA emerges as the strongest predictor of both participation and financial commitment, highlighting the central role of moral obligation in motivating carbon offset behavior. While TRP significantly strengthens personal moral norms, its direct effect on Perceived Effectiveness is not significant, suggesting that trust primarily operates through ethical pathways rather than cognitive evaluations of program effectiveness. By distinguishing between participation decisions and spending behavior, this study extends VBN theory to the context of carbon offsets in aviation and demonstrates the mediating roles of moral norms and Perceived Effectiveness in translating environmental values and social identity into compensatory climate action. The findings offer practical implications for airlines and policymakers, emphasizing the importance of moral framing, transparency, and social identity engagement to promote voluntary carbon offset adoption in emerging carbon markets. Full article

This study investigates the relationship between green technology, company green commitment, and the internationalization performance of small and medium-sized enterprises (SMEs), with a particular focus on the mediating role of eco-innovation and the moderating effect of government support programs. Based on a resource-based view, we develop and test a comprehensive model using the partial least squares structural equation modeling (PLS-SEM) method to determine the variables that significantly affected the mechanisms (i.e., green technology, company green commitment) and internationalization performance. The results revealed that both green technology and company green commitment positively affect the internationalization performance of SME with eco-innovation mediating the relationships. Furthermore, the government support programs moderate the relationship between SMEs’ green technology/company green commitments and internationalization performance by strengthening this relationship under higher levels of institutional support. This suggests that policymakers need to focus on the intensity and effectiveness of government support programs, and enterprises should actively integrate high-quality policy resources to maximize international competitiveness. The findings contribute to sustainable international business by elucidating the mechanisms through which sustainable development translates into competitive advantages. Practical implications suggest that SME managers should strategically align technological and commitment-based green elements while actively leveraging governmental support mechanisms to optimize international expansion. Full article