Search Results (3,700)

Baihua Reservoir, a typical large waterbody in the karst region of southwestern China and an essential drinking water source, is characterized by a high carbonate buffering capacity that profoundly shapes the structure and function of its phytoplankton community. This study systematically elucidates the multi-dimensional driving mechanisms underlying seasonal phytoplankton community assembly in karst reservoirs by integrating multiple analytical models—including the Neutral Community Model, β-diversity decomposition, co-occurrence network analysis, XGBoost-SHAP machine learning, and Partial Least Squares Path Modeling—based on monthly sampling at five sites from 2020 to 2024. The results revealed that: (1) Stochastic processes dominated community assembly across all four seasons, while deterministic processes played a crucial role in local species turnover. (2) The co-occurrence network structure showed significant seasonal dynamics, with the composition of keystone species adaptively shifting in response to changing environmental conditions. (3) The key environmental factors influencing the phytoplankton community exhibited clear seasonal patterns, primarily pH, NH₃-N, and COD_Mn in spring; water temperature, COD_Mn, and NH₃-N in summer; TN, TP, and pH in autumn; and pH, water temperature, and DO in winter. To support the sustainable management of karst reservoirs, we propose seasonally differentiated strategies derived from our phytoplankton community analysis: target COD_Mn reduction in spring and summer, focus on TN and TP load control in autumn, prioritize water column stability in winter, and maintain hydrological connectivity and pH monitoring year-round. This approach enhances phytoplankton community stability, safeguards drinking water safety, and provides a targeted management model for similar reservoir ecosystems globally. Full article

Background: Reliable identification remains a cornerstone of forensic investigations, particularly when encountering degraded remains or suboptimal biological evidence. This study evaluates the potential of dentine metabolomics, utilizing proton nuclear magnetic resonance (¹H-NMR) spectroscopy, to detect cancer-associated metabolic signatures in dental tissues for forensic applications. Methods: Forty-four non-carious second molars were analyzed, comprising 22 samples from deceased individuals with a documented history of cancer and 22 age- and sex-matched controls. Metabolomic profiling was conducted using ¹H-NMR spectroscopy to identify and quantify dentine metabolites. Statistical evaluation included unsupervised principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), receiver operating characteristic (ROC) curve analysis, and exploratory binary logistic regression. Results: Among the 209 identified metabolites, inosinic acid and 2-ketobutyric acid were identified as the most robust discriminative biomarkers across both multivariate and univariate frameworks. The exploration within-sample predictive model achieved a Nagelkerke R² of 0.822 and an overall classification accuracy of 90.9%, with a specificity of 95.5% and a sensitivity of 86.4%. These key metabolites are fundamentally associated with purine metabolism and oxidative stress pathways frequently dysregulated in oncogenesis. Conclusions: This pilot study suggests that dentine may retain metabolomic information associated with cancer comorbidity under heterogeneous postmortem conditions. However, the findings remain exploratory and require validation in larger cohorts with standardized postmortem variables before practical forensic implementation. Full article

To address traditional beer detection limitations, this study proposes a rapid NIRS-based method for detecting key indicators and verifying authenticity. Designing Single-task (STL) and Multi-task learning (MTL) strategies, it employs Variable Importance in Projection for wavelength selection. Deep spectral features were extracted utilizing a Multi-Head Attention (MHA)-fused Convolutional Neural Network (CNN-MHA), Long Short-Term Memory (LSTM-MHA), and hybrid CNN-LSTM-MHA networks. To further enhance model performance, the Bayesian Optimization Algorithm globally optimized network hyperparameters in STL, alongside hyperparameters and multi-task loss weights in MTL. Partial least squares regression, support vector machine regression, and partial least squares discriminant analysis models were established using these features. Results indicate that the MTL-based CNN-LSTM-MHA network effectively learns shared features across multiple tasks, significantly improving model generalization. Specifically, the coefficients of determination ($R^2$) for alcohol content and original wort concentration in the validation set were 0.996 and 0.997, respectively, with relative root mean square errors (rRMSE) of 2.024% and 2.515%. In the independent test set, the $R^2$ were 0.995 and 0.991, with rRMSE of 2.515% and 2.087%, respectively. Furthermore, 100% classification accuracy was achieved across all datasets. This method provides an efficient technical solution for beer market regulation and real-time detection in production processes. Full article

Bisphenol A (BPA) is a widespread endocrine disruptor that interferes with metabolism in humans and animals by inducing oxidative stress, lipid peroxidation, and cell death. Probiotics, conversely, have shown potential in promoting host health and reducing the toxicity of endocrine-disrupting chemicals (EDCs). This study examined whether sub-chronic BPA exposure disrupts hepatic lipid metabolism in female zebrafish (Danio rerio), and whether co-administration of probiotics mitigates these effects. Adult females were exposed for 28 days to the following treatments: 10 µg/L BPA via water (BPA); 10⁹ CFU/g body weight/day of probiotic formulation (P); and both treatments (BPA+P). An untreated group served as a control (CTRL). Hepatic lipid composition was analyzed using UHPLC-QTOF-MS, while liver sections were investigated by Fourier Transform Infrared Imaging (FTIRI) spectroscopy. BPA exposure decreased 14 unsaturated triacylglycerols and lysophosphatidylcholine 18:0, suggesting steatosis onset and inflammation, while in the group exposed to BPA+P, the decrease was limited to 8 triacylglycerols and the reduction in lysophosphatidylcholine 18:0 was prevented. Analyses of pooled liver samples precluded modeling tank-level effects; thus, the results are interpreted as semi-quantitative. Partial least square discriminant analysis built on the comparison of all groups together confirmed an intermediate phenotype for BPA+P fish between BPA and P groups. The observed beneficial role of probiotics in counteracting BPA-related metabolic disturbances was also supported by FTIRI, evidencing the ability to mitigate the effects of BPA on lipid and glycosylated compound metabolism. These findings highlight the potential of probiotic supplementation as a practical and accessible strategy to mitigate BPA-induced metabolic disturbances, contributing to the development of mitigating approaches against environmental contaminant-related liver dysfunction. Full article

Small- and medium-sized enterprises (SMEs) increasingly rely on digital technologies to sustain innovation, yet limited empirical evidence explains how business intelligence capabilities translate into superior innovation outcomes, particularly in emerging economy contexts. Addressing this gap, this study examines the direct and indirect effects of business intelligence capabilities on innovation performance by unpacking the mediating role of knowledge management capability and the moderating role of data-driven decision making within an integrated Resource-Based View and Knowledge-Based View framework. Conceptually, the study advances prior research by clarifying the complementary roles of these theoretical perspectives: the Resource-Based View explains what strategic digital resources firms possess, the Knowledge-Based View explains how these resources are transformed into organizational knowledge through knowledge management capability, and data-driven decision making explains when these capabilities are effectively converted into innovation outcomes. Data were collected through a survey of 316 owners and senior managers of small- and medium-sized hotels operating in Amman, Jordan, and analyzed using partial least squares structural equation modeling (PLS-SEM) as the primary analytical technique. The results indicate that business intelligence capabilities exert a significant positive effect on innovation performance, with this relationship largely transmitted through knowledge management capability, demonstrating that the value of business intelligence lies in its integration into organizational knowledge processes rather than in data availability alone. Moreover, data-driven decision making strengthens the relationship between business intelligence capabilities and innovation performance, functioning as an execution-level capability that enhances the conversion of digital and knowledge-based resources into innovation outcomes. To further validate the robustness of the findings, a post-hoc moderated mediation analysis using Hayes’ PROCESS macro version 4.2 was conducted as a confirmatory analysis. By conceptualizing business intelligence, knowledge management, and data-driven decision making as an interconnected socio-technical capability system, this study advances digital innovation theory and offers actionable insights for SME managers seeking to orchestrate capabilities for innovation under resource constraints. Full article

Background/Objectives: Histologic remission has emerged as a key treatment target in inflammatory bowel disease (IBD), but routine assessment requires repeated endoscopy and biopsies. Blood-based indices reflecting inflammation, nutritional status and atherogenic risk are inexpensive and widely available, yet their integrated contribution to histologic activity remains unclear. This study addresses this gap by simultaneously analyzing a broad panel of 44 variables—including nutritional status indicators, CBC-derived inflammation indices, and atherogenic lipid indices—in IBD patients. Methods: In this retrospective study, 100 patients with IBD (50 Crohn’s disease [CD], 50 ulcerative colitis [UC]) without additional comorbidities and with concomitant histologic assessment were analyzed. Histologic activity was coded as active vs. remission. At the time of biopsy, the complete blood count, biochemistry and lipid profile were used to calculate immuno-nutritional indices (CONUT score, prognostic nutritional index), inflammatory indices (neutrophil-to-platelet ratio, platelet-to-lymphocyte ratio, lymphocyte-to-monocyte ratio [LMR], systemic immune-inflammation index, systemic immune-inflammation index, systemic inflammation response index [SIRI], aggregate index of systemic inflammation, C-reactive protein to albumin ratio) and atherogenic indices (atherogenic index of plasma [AIP], triglyceride-to-HDL cholesterol ratio). Variable selection was performed separately for CD and UC using least absolute shrinkage and selection operator (LASSO) regression and sparse partial least squares discriminant analysis (sPLS-DA). Independently associated predictors were then entered into multivariable logistic regression models, and their discriminative performance was evaluated using ROC analysis with bootstrap-derived 95% confidence intervals. Results: LASSO analysis identified a broadly similar systemic profile associated with histologic activity in CD and UC, dominated by the CONUT score, SIRI, AIP, LMR and red blood cell parameters, whereas demographic features and most routine biochemical markers were shrunk towards zero. Cross-validated AUCs for the LASSO models were 0.93 in CD and 0.87 in UC. sPLS-DA confirmed this pattern: CONUT, SIRI and AIP consistently showed the highest variable importance in projection scores and loadings on the first latent component. In multivariable regression, the CONUT score, SIRI and AIP remained independent predictors of histologic activity in CD, while hematocrit, CONUT score, SIRI and AIP were independently associated with histologic activity in UC. In ROC analysis, AUCs for CONUT, SIRI and AIP were 0.81, 0.89 and 0.87 in UC, and 0.72, 0.82 and 0.83 in CD, respectively. Conclusions: Histologic activity in IBD is characterized by a coupled systemic profile in which immuno-nutritional compromise (captured by CONUT) forms the core signal, supplemented by systemic inflammation (SIRI) and atherogenic dyslipidemia (AIP). These readily available blood-based indices may help to approximate histologic disease activity in clinical practice. However, considering that comorbid diseases may affect these indices, the strict exclusion criteria applied in this study may limit the generalizability of the findings among patients with IBD. Consequently, further validation in larger prospective cohorts is warranted. Full article

This study examines the intercorrelations that exist between brand equity, expectation confirmation, and sustainable repurchase intentions within Saudi Arabia’s burgeoning e-commerce sector, emphasizing its cultural and digital transformation context aligned with Vision 2030. The main objectives are to identify how brand perceptions influence customer satisfaction, and to explore the applicability of integrated theoretical frameworks, namely Brand Equity Theory and Expectation-Confirmation Theory in explaining sustainable consumer behavior in an emerging market. Utilizing a quantitative research approach, data was collected through an online self-reported questionnaire distributed via social media platforms targeted at active e-commerce consumers in the Hail region. Convenience sampling combined with snowballing yielded a sample size of 361 respondents, ensuring broader demographic representation. Data analysis was conducted using structural equation modeling with partial least squares (SEM-PLS), a technique suited for theory exploration and handling complex variable relationships. The findings demonstrate that brand awareness and brand image significantly positively influence customer satisfaction, which in turn positively impacts repurchase intentions in e-commerce platforms. Similarly, expectations and perceived performance also have significant positive effects on satisfaction, which in turn positively impacts repurchase intentions in e-commerce platforms. All hypotheses were supported, with significant relationships observed between the variables, with the model demonstrating robust validity and fit, evidenced by acceptable SRMR, d_ULS, and d_G values. The study’s originality lies in its culturally contextualized application of these theories to a less studied yet vital emerging market, providing novel insights into how cultural nuances influence digital consumer loyalty. These outcomes contribute to both academic theory and practical strategies for e-commerce firms aiming to build sustainable, trust-based relationships within culturally diverse digital environments, offering a valuable blueprint for similar markets undergoing digital transformation. Full article

This study investigates how innovative social media marketing activities influence consumer inspiration, food pleasure, and behavioral intentions in the context of hedonic food consumption and digital marketing innovation. Data collected from 425 consumers who had tried Dubai chocolate products in Türkiye were analyzed using the partial least squares structural equation modeling (PLS-SEM) method with SmartPLS 4 software. The results indicate that personalization, trendiness, and advertisement dimensions significantly enhance consumer inspiration, whereas entertainment and interaction dimensions show no significant effects. Consumer inspiration positively influences repurchase intention, recommendation intention, willingness to pay more, and food pleasure. Furthermore, food pleasure exerts a significant positive effect on recommendations and willingness to pay more but not on repurchase intention. Mediation analysis revealed that food pleasure partially mediates the relationships between consumer inspiration, recommendation intention, and willingness to pay more, whereas no mediating effect was found for repurchase intention. These findings contribute to innovation and knowledge literature by demonstrating how digital marketing activities foster emotional engagement, enhance consumer experiences, and promote sustainable behavioral intentions in the hedonic food sector. Full article

External defects considerably reduce the quality, consumer acceptance, and market value of citrus fruits. Therefore, a rapid and reliable, non-destructive inspection method is necessary for postharvest processing. In this study, a non-destructive approach for external defect classification of citrus fruits is developed by combining visible–near infrared hyperspectral imaging (HSI) with effective wavelength selection (EWS) algorithms. First, 1702 spectral samples of normal and defective regions on citrus fruit surfaces were collected. A partial least squares discriminant analysis (PLS-DA) model was developed using the full wavelength range (400–1000 nm), which achieved 99.02% prediction accuracy. Four EWS algorithms—weighted regression coefficients, variable importance in projection, sequential forward selection (SFS(5, 10, 15)), and random frog—were evaluated for optimal spectral dimensionality and computational efficiency. The SFS15-PLS-DA model, which selected 15 optimal variables out of the initial 300 and used maximum normalization preprocessing, achieved the highest prediction accuracy of 99.80%. This model demonstrated near-perfect classification while reducing the total number of wavelengths by 95.0% (from 300 to 15 wavelengths). Further, a pixel-wise image classification algorithm was implemented using the optimal model, which effectively detected physical damage, pest infestation, and fungal decay. These results demonstrate that combining HSI with EWS enables compact, interpretable, and high-performance models suitable for real-time postharvest sorting. This approach has strong potential to enhance automation, speed, and reliability in commercial citrus quality assessment. Full article

Traditional soil management in vineyards and olive groves of semi-arid regions relies on repeated tillage, which accelerates soil organic matter (SOM) oxidation and limits long-term carbon storage. In the context of carbon-neutral agricultural strategies, understanding how alternative practices influence SOM stocks, redistribution, and stabilization is essential. We sampled six paired sites in central Spain (three vineyards and three olive groves), each comprising adjacent plots under conventional tillage or continuous cover cropping, at 0–10 and 10–30 cm depths. We analyzed water-extractable organic carbon (WEOC), optical properties of water-extractable organic matter (WEOM; specific UV absorbance at 254 nm (SUVA₂₅₄) and the absorbance ratio E4/E6), β-glucosidase activity, and the SOC/clay ratio as a proxy for mineral-associated SOC stabilization. Depth was the main factor structuring SOC and biological activity, with higher values in the topsoil. Management effects on bulk SOC were limited although cover cropping increased aboveground biomass and influenced WEOC dynamics. Vertical contrasts (30–10 cm) showed a positive association between WEOC and SOC/clay, suggesting that increased WEOC at depth co-varies with stabilization potential. Partial least squares analysis for 10–30 cm showed that SOC/clay was associated with WEOC, E4/E6, and β-glucosidase activity. These results suggest that subsoil carbon stabilization in semi-arid conditions may be linked to DOC availability and microbial processing rather than directly to surface biomass inputs. Full article

Accurately tracing the geographical origin of Zanthoxylum schinifolium Sieb. et Zucc. is important for brand authentication, quality control, and food safety assurance. In this study, the stable isotope ratios (δ¹³C, δ¹⁵N, δ²H, δ¹⁸O) and the contents of 20 elements were analyzed in samples from three major production regions. Significant differences (p < 0.05) were observed in δ¹³C, δ²H, δ¹⁸O and most elemental profiles across origins. Chemometric methods—including principal component analysis (PCA), orthogonal partial least squares-discriminant analysis (OPLS-DA), and linear discriminant analysis (LDA)—were applied to classify samples by geographical origin. OPLS-DA identified key discriminators (VIP > 1) such as Ca, δ¹³C, Mg, δ²H, B, δ¹⁸O, Cr, Ni, Na, Pb, As, Co, Se, and Zn, achieving a classification accuracy of 96.8%. LDA based on the combined isotope and element datasets showed even higher performance, with an original discrimination rate of 98.4% and a cross-validated rate of 92.8%. The results demonstrate that integrating stable isotope and multi-element fingerprints with supervised classification models provides a reliable and effective approach for verifying the geographical origin of Zanthoxylum schinifolium, supporting its use in traceability systems and fair trade practices. Full article

Sustainable Supply Chain Management (SSCM) increasingly integrates environmental, social, and governance (ESG) criteria to address sustainability risks and performance across multi-tier supply networks. However, it remains unclear whether ESG practices directly enhance supply chain outcomes or primarily operate through the development of higher-order management capabilities. This study examines how ESG practices influence supply chain resilience, operational performance, and sustainability performance in the Mexican aerospace industry, emphasizing the mediating role of Sustainable Supply Chain Management Capability (SSCM Capability). Data were collected through a structured survey administered at the Mexico Aerospace Fair (FAMEX) in April 2025, yielding 217 valid responses from Tier 1–3 aerospace firms. The research adopts a hypothesis-driven design integrating Partial Least Squares Structural Equation Modeling (PLS-SEM) and Necessary Condition Analysis (NCA) to combine sufficiency- and necessity-based perspectives. The findings show that ESG practices primarily create value by enabling SSCM Capability, which is central to improving all performance dimensions. While ESG practices directly contribute to operational and sustainability performance, resilience improvements depend mainly on capability development. NCA results further indicate that ESG practices are foundational to SSCM Capability and high performance, whereas SSCM Capability constitutes a necessary condition for resilience. These findings underscore the critical role of capability building in translating ESG commitments into robust supply chain performance within compliance-intensive aerospace ecosystems in emerging economies. Full article

Artificial Intelligence (AI) is transforming audit practice by redefining traditional frameworks and enabling the automation of data analysis, risk assessment, substantive testing, and continuous monitoring. This study investigates the effect of AI adoption by audit firms on enterprise risk management (ERM). It further assesses the mediating role of Information Technology (IT) infrastructure flexibility and the moderating roles of technology competencies and organizational culture in this relationship. Data were collected from 355 top managers in Ghana using a judgmental sampling technique based on predefined inclusion and exclusion criteria. The analysis was conducted using Partial Least Squares Structural Equation Modelling (PLS-SEM) with SmartPLS 4.1.1.7. The findings indicate that AI adoption positively and significantly influences ERM and IT infrastructure flexibility. IT infrastructure flexibility also has a positive effect on ERM and partially mediates the relationship between AI adoption and ERM. In addition, technology competencies significantly strengthen the relationship between AI adoption and ERM. Organizational culture positively moderates the relationship between IT infrastructure flexibility and ERM. These insights underscore the need for strategic alignment between AI investments and organizational capabilities. The study contributes to the limited empirical literature on AI-driven ERM in emerging economies and offers insights for policymakers and regulators seeking to promote technology-aided ERM. Full article

Forest soil constitutes a critical reservoir within terrestrial carbon pools. Understanding the dynamics of soil organic carbon (SOC) in coniferous forests is crucial for enhancing ecosystem carbon sequestration capacity, yet systematic quantification of SOC characteristics and their driving factors remains limited across critical bioclimatic zones. This study examined SOC features in topsoil and driving factors across eight representative coniferous forest types in Longnan—an ecologically significant transition region of northwestern China. SOC concentrations ranged from 31.76 to 80.86 g·kg⁻¹, where Abies fargesii var. faxoniana exhibited significantly higher concentrations than other conifers. Fungal necromass dominated SOC formation (29%–45% contribution) versus minimal bacterial necromass inputs (3%–5%). Redundancy analysis identified that soil total nitrogen, C/N ratio, and tree evenness showed significant correlations with SOC concentrations and their fractions. Partial least squares path modeling revealed that tree species exerted a direct positive impact on soil total nitrogen, while having an adverse effect on soil pH. Lower soil pH and higher total nitrogen were associated with higher microbial-derived carbon and SOC concentrations. In contrast, plant-derived carbon exerted no direct influence on SOC concentrations, operating exclusively through microbial-derived carbon pathways. These results indicated that coniferous tree species-induced shifts in soil total nitrogen and pH facilitate the accumulation of microbial necromass carbon, rather than plant residues, and thus promote SOC sequestration. A. fargesii var. faxoniana can be regarded as a key strategic tree species for SOC sequestration and sustainable forest management, and its cultivation should be prioritized due to improvements in total nitrogen and microbial-derived carbon. Full article

Digital transformation has become important for SMEs that want better control, transparency, and coordinated operations. Yet, many studies treat digital tools in isolation and do not explain how AI and big data capabilities, together with process integration, drive governance outcomes. This gap limits a clear understanding of how digital transformation supports governance performance in SMEs. This study examines how digital transformation (DT) influences digital governance performance (DGP) in SMEs, with AI and big data analytical capability (AIBDAC) and process integration capability (PIC) as mediators. The research is grounded in the Resource-Based View, Dynamic Capabilities Theory, and the Technology Organization Environment framework. Data were collected from SMEs across five regions of Saudi Arabia using cluster and purposive sampling to target employees and managers involved in digital, analytical, and process integration work. A total of 396 valid responses were included in the analysis. Partial Least Squares Structural Equation Modelling (PLS SEM) was used to assess the measurement model, test the hypothesized paths, and evaluate mediation and moderation effects. The findings show that DT, AIBDAC, PIC, and top management support (TMS) have significant direct effects on DGP. AIBDAC and PIC act as key mediators, fully transmitting the effects of digital innovation capability and strategic readiness and partially mediating the effects of DT and TMS. Multi-group analysis shows that small and medium-large firms rely on different capability combinations. The study contributes by explaining how SMEs strengthen governance through capability development and offers practical guidance for improving governance through digital transformation. Full article