Search Results (3,931)

Sustainable development views energy as a determinant of the interdependence between economic growth and ecosystem protection, which influences the specificity of energy-production relationships in the hospitality and catering sectors (HoReCa) and the Maintenance, Repair, and Operations (MRO) sector. The primary goal of this study is to identify and assess the structural relationships between environmental, fiscal, production, and energy factors in industrial enterprises and their impact on production and resource potential within the intersectoral network of the HoReCa and MRO sectors, taking into account emission burdens and fiscal instruments. The research procedure utilized partial least squares coherent structural equation modeling (PLSc-SEM). The model was built using Eurostat data from 2008 to 2020 for companies in 23 countries of the European Union. The analysis showed that the energy consumption of the hospitality and catering establishments (HoReCa) is the strongest predictor of MRO sector activity (β = 0.910), whereas the emission intensity of MROs exerts a comparatively minor effect. The results document the dominance of scale over emission intensity in shaping environmental burdens. Furthermore, they confirm the negative impact of environmental taxes on the remuneration fund of highly qualified specialists. The full mediation of operational scale was also demonstrated in the relationships between energy demand, emissions levels, and labor costs. The results of the study clearly indicate the need to integrate building energy policy with the decarbonization of technical services. From a macroeconomic perspective, this approach supports the achievement of sustainable development goals. Implementing predictive maintenance demonstrates a dual synergistic effect, combining maximized resource productivity with a simultaneous reduction in carbon footprint. Full article

Artificial Intelligence (AI) is increasingly reshaping customer relationship management (CRM) practices in service industries, yet its perceived effectiveness within emerging regional tourism economies remains underexplored. This study examined respondents’ perceptions of how AI-enabled capabilities influence CRM effectiveness within the tourism sector in Mthatha, in the Eastern Cape, South Africa. Existing AI–CRM research is largely concentrated in developed economies, limiting contextual understanding of its strategic value in resource-constrained and relational tourism environments. A positivist, quantitative explanatory design was adopted, and data were collected through a structured survey administered to managers and staff of tourism enterprises across the Eastern Cape (n = 121). Partial Least Squares Structural Equation Modelling was employed to assess the measurement model and test the hypothesized relationships. The model explained 63.2% of the variance in perceived CRM effectiveness. Sales forecasting and lead scoring exerted the strongest positive influence, followed by sentiment and feedback analysis, while personalization and automation showed positive but statistically insignificant effects. The findings suggest that tourism enterprises may achieve stronger relationship outcomes by prioritizing predictive and analytical AI tools while integrating automation within human-centered service strategies. The study extends AI–CRM theory to an emerging African tourism context and demonstrates that AI effectiveness is context dependent rather than universally transferable. Full article

Artificial feeds are considered a sustainable alternative to natural live feeds for mandarin fish (Siniperca chuatsi) aquaculture, but their impacts on hepatic metabolism and growth remain unclear. In this study, a total of 800 adult mandarin fish with an initial mean body weight of 152.4 ± 8.7 g were reared for 150 days, and we compared growth performance, liver histology and liver metabolomics of fish fed artificial (AF) or natural live feeds (NF). No significant differences were observed in body length, weight, or condition factor, but the hepatosomatic index (HSI) was significantly higher in the AF group (p < 0.01), accompanied by visible hepatomegaly, pale liver color and severe hepatic steatosis. Partial least squares-discriminant analysis (PLS-DA) showed clear separation of liver metabolomes between groups. Metabolic correlation network analysis revealed tightly connected functional modules of amino acids and lipids, and key metabolites demonstrated significant group-specific changes: energy metabolism intermediates (L-alanine, α-ketoglutarate, phosphoenolpyruvate) and stress-related indicators (cortisol, γ-aminobutyric acid) were significantly upregulated in the NF group, whereas lipid metabolites (cholesterol, phosphatidylcholine, ceramide) and progesterone were remarkably elevated in the AF group. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed upregulation of lipid-related pathways in the AF group and FoxO signaling pathway in the NF group. These findings confirm that artificial feeds drive hepatic lipid metabolism reprogramming without altering growth, but induce obvious hepatic steatosis in mandarin fish. Our findings provide a metabolic foundation for optimizing artificial feed formulations to improve hepatic health and sustainable culture of mandarin fish. Full article

This study examines the relationship between environmental, social, and governance (ESG) performance and corporate tax avoidance and investigates whether carbon performance serves as a transmission mechanism linking the two. Using an international panel of 15,840 firm-year observations from 1584 listed firms across 52 countries during the 2015–2023 period, the analysis employs random-effects generalized least squares (GLS), mediation analysis, instrumental variable (2SLS), and System Generalized Method of Moments (GMM) estimations. The results show that stronger ESG performance is associated with lower book–tax differences (BTD), indicating reduced corporate tax avoidance. Carbon performance is positively associated with ESG performance and partially mediates the ESG–tax avoidance relationship, explaining approximately 14% of the total effect. Additional analyses reveal that the Environmental pillar is the primary driver of this mediation mechanism, while the relationship is stronger among firms with higher governance quality. The findings remain robust to alternative measures of tax avoidance and sustainability performance, lagged specifications, instrumental variable estimation, and dynamic panel models. Overall, the study provides international evidence that environmental performance represents an important, though partial, pathway through which ESG engagement promotes more responsible corporate tax behavior, offering practical implications for policymakers, investors, and corporate managers seeking to strengthen sustainability, transparency, and fiscal accountability. Full article

Consumer resistance remains a major barrier to the effectiveness of AI-enabled social media marketing despite advances in content personalisation, influencer marketing, and intelligent recommendation systems. This study investigates how content personalisation, influencer trust, and platform interactivity influence consumer resistance, user engagement, and purchase intention by proposing a behaviourally informed Deep Reinforcement Learning (DRL) framework that integrates empirical behavioural modelling with adaptive optimisation. Survey data were collected from 619 higher education students in Saudi Arabia and analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM), Multi-Group Analysis (MGA), and a Deep Q-Network (DQN)-based optimisation framework. The results show that content personalisation, influencer trust, and platform interactivity significantly increase user engagement while reducing consumer resistance. User engagement positively influences purchase intention, whereas consumer resistance negatively affects purchasing behaviour. Multi-Group Analysis revealed that Generation Z responded more strongly to personalisation and platform interactivity, whereas Generation Y showed greater responsiveness to influencer trust. The proposed behaviourally informed DQN framework incorporated latent behavioural constructs and statistically validated structural relationships into the reinforcement learning environment to generate adaptive marketing policies. Compared with conventional static and rule-based strategies, the proposed framework achieved approximately 36% higher optimisation performance across repeated behavioural simulations. The study contributes by positioning consumer resistance as the central behavioural construct, introducing an integrated behavioural–computational framework that embeds empirical behavioural relationships into the DRL state representation, reward mechanism, and policy-learning process, and providing practical guidance for developing transparent, trust-sensitive, and adaptive social media marketing strategies that enhance user engagement, reduce consumer resistance, and improve purchase intention in digital commerce environments. Full article

Dendrobium crepidatum Lindl. et Paxt. is a medicinal Dendrobium species whose quality and market value may vary with geographic origin, making rapid origin traceability important for batch management, market supervision, and application promotion. This study used near-infrared spectroscopy (NIRS) combined with multivariate analysis and machine learning to discriminate the origin of D. crepidatum. Fifty batches of stem samples from Yunnan, Guangxi, and Guizhou, China, were analyzed after Savitzky-Golay smoothing, standard normal variate transformation, and first-derivative preprocessing. Principal component analysis (PCA) showed origin-related spectral variation, and a three-class partial least squares-discriminant analysis (PLS-DA) model achieved a mean cross-validated accuracy of 70.2% with a significant permutation-test result (p = 0.0020). Six machine learning algorithms, including KNN, CART, RF, NB, LDA, and ANN, were further compared using repeated nested cross-validation. KNN performed best, with an accuracy of 0.811 ± 0.029 and a macro F1-score of 0.813 ± 0.029, followed by RF (0.804 ± 0.038 and 0.805 ± 0.037, respectively). Key spectral variables were mainly located at 4231–4235 and 5523–5624 cm⁻¹ corresponding mainly to C-H-dominated overtone or combination absorptions with possible C-O/O-H-related contributions from carbohydrates, polysaccharides, phenolics, flavonoids, and other organic constituents. These results demonstrate the feasibility of NIRS combined with machine learning for preliminary origin traceability of D. crepidatum and provide spectral clues for future investigation of origin-related chemical variation and quality discrimination. Full article

This study examined the influence of social media content and online member group support on the environmental attitudes and green self-identity of organic food consumers in Ecuador, Peru, and Chile. Based on the Stimulus–Organism–Response (SOR) framework, data were collected from 766 consumers in Ecuador (n = 310), Peru (n = 259), and Chile (n = 197) through an online survey, the participants were adults from Ecuador, Peru, and Chile with different educational backgrounds who had purchased or consumed organic products during the month preceding the survey. The proposed model was assessed using partial least-squares structural equation modeling. The results revealed that both social media content and online member group support positively influenced environmental attitudes, while environmental attitudes significantly strengthened green self-identity, which in turn positively affected purchasing behavior of organic products. Although some relationships varied across countries, the mediating effects of environmental attitudes were consistently supported. Furthermore, the Measurement Invariance of Composite Models procedure established compositional invariance for all constructs across the three country pairs, and multigroup analysis did not identify significant differences in structural relationships between Ecuador and Peru or between Ecuador and Chile. These findings confirm the transnational robustness of the proposed framework, providing valuable insights into how digital social environments influence environmental attitudes, strengthen ecological self-identity, and promote the purchase of organic foods. Full article

The biodegradable plastic mulch (BDM) was advanced as a promising alternative to address the environmental and management issues associated with conventional polyethylene mulch. However, its uptake remains low, and empirical evidence on the sociopsychological drivers of BDM adoption among farmers in Egypt is limited. This study incorporates an extended theory of planned behavior (TPB) to predict farmers’ intention to adopt BDM. Three hundred and sixty farmers were selected in three governorates using a multistage sampling technique. Data analysis involved using partial least squares structural equation modeling (PLS-SEM). The findings indicated that the extended TPB model accounted for 51% of the total variation in predictive power. Three variables, including subjective norms, perceived behavioral control, and perceived self-identity, positively affected farmers’ intentions to adopt BDM, with the influence of attitudes being not statistically significant. The most critical barriers to adopting BDM from farmers’ perspectives encompassed limited local availability (71%), limited knowledge (56%), elevated cost (46%), field durability (39%), and limited use among other farmers (34%). These findings underscore the importance of broadening the focus beyond the technical benefits of BDM to urge and accelerate adoption. Accordingly, policymakers should focus on reducing adoption barriers and emphasize sociopsychological factors more through well-designed interventions to promote the adoption of BDM in agriculture. Full article

Cadmium (Cd) contamination in soil poses a serious threat to crop quality. Biochar is widely regarded as an effective amendment that can reduce Cd bioavailability and limit Cd uptake by crops. However, studies on the rapid and nondestructive evaluation of crop physiological responses under biochar-mediated alleviation of Cd stress remain insufficient. Spectral modeling methods can enable rapid and nondestructive monitoring of crop physiological status. In this preliminary experiment, Zhengdan 958 maize seedlings grown in Cd-contaminated soil were subjected to five biochar application rates: 0, 10, 30, 50, and 70 g/pot, designated as CK, A1, A3, A5, and A7, respectively. The study established a non-destructive spectral detection model for relative chlorophyll content expressed as SPAD values of maize leaves to achieve spectral inversion of leaf physiological information. The alleviating effect of biochar on Cd stress was evaluated by analyzing SPAD values and Cd accumulation in roots, stems, and leaves. The original spectral data underwent preprocessing steps including multivariate scattering correction, standard normal variable transformation, normalization, trend removal, first-order derivative transformation, and second-order derivative transformation. The effectiveness of different preprocessing methods was compared using partial least squares regression. Feature bands were identified via Pearson correlation analysis, and support vector regression models were established based on genetic algorithm (GA), particle swarm optimization (PSO), and grid search optimization. The results demonstrated that biochar application significantly increased the SPAD values of corn leaves (r = 0.879) and reduced the proportion of bioavailable Cd in soil, with the A7 treatment showing the most substantial decrease (30%). This indicates that biochar effectively mitigates Cd’s inhibitory effect on chlorophyll synthesis, with the alleviation effect enhancing as biochar application rates increased. Validation of the partial least squares regression model revealed that detrended spectra achieved optimal predictive performance (R²c = 0.94, RMSEC = 0.82, R²p = 0.88, RMSEP = 1.15), leading to the development of three optimized support vector regression models: GA-SVR, PSO-SVR, and GS-SVR. The GA-SVR model with a sigmoid kernel demonstrated the best internal validation performance for predicting SPAD values in maize leaves (R²c = 0.95, RMSEC = 0.24; R²p = 0.75, RMSEP = 1.63). This study provides preliminary theoretical support and technical reference for rapid spectral detection of the physiological status of maize under biochar-mediated mitigation of cadmium stress. Full article

Background/Objectives: Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy characterized by limited treatment options and poor prognosis. Gemcitabine is a commonly used chemotherapy; however, gemcitabine resistance in PDAC poses a critical barrier to effective treatment, as the underlying mechanisms are not yet fully understood. Methods: This study employs an exploratory untargeted metabolomics approach to investigate metabolic differences in PDAC cells in the presence and absence of gemcitabine treatment. HPAF-II, MIA PaCa-2, and BxPC-3 cell lines were used as models for gemcitabine-resistant, moderately responsive, and permissive PDAC cells, respectively. Results: MTT assay results revealed that BxPC-3 cells are highly sensitive to gemcitabine treatment, HPAF-II cells are the most resistant, and MIA PaCa-2 cells exhibit moderate sensitivity. Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) of the metabolomics data demonstrated clear differentiation of gemcitabine-treated and untreated (control) cells. When comparing the treated vs. control conditions, 170 metabolites matched to an in-house library of standards were significant (p < 0.05 or fold change ≥ 2 or VIP ≥ 1) differentiators in HPAF-II cells, whereas MIA PaCa-2 and BxPC-3 cells had 178 and 218 differentiating metabolites, respectively. HPAF-II cells treated with gemcitabine had significantly higher levels of N-acetylneuraminic acid and 7-dehydrocholesterol compared with the control group. In contrast, these metabolites were significantly lower or non-significant in BxPC-3 treated cells. Pathway analysis revealed that the steroid biosynthesis pathway was significantly perturbed in HPAF-II cells, whereas amino sugar and nucleotide sugar metabolism was predominantly altered in BxPC-3 cells. Conclusions: Overall, this exploratory study reveals metabolic differences between treated and untreated cells to derive targeted therapeutic strategies that could be used in the future to improve treatment outcomes for PDAC patients. Full article

Accurate measurement of coal calorific value is critical for efficient power generation. To address the limitations of conventional models on large-scale, heterogeneous datasets, this study proposes a novel deep learning framework, the Multi-View Transformer (MVFormer), utilizing fused Near-Infrared Spectroscopy (NIRS) and X-ray Fluorescence (XRF) data. The architecture employs a dual-pathway Transformer with a Masked Autoencoder pre-training strategy to enhance feature representation from over 20,000 coal samples. Furthermore, a multi-view fusion mechanism integrates diverse pre-processing perspectives to enhance generalization. Experimental results demonstrate that this approach significantly outperforms traditional Partial Least Squares (PLS) regression and Multilayer Perceptron (MLP) models. These findings validate the framework as a robust and precise solution for real-time industrial coal quality analysis, successfully achieving precise prediction of calorific value on large-scale coal datasets. Full article

Postharvest quality deterioration of Korla fragrant pear (Pyrus sinkiangensis Yu) severely constrains its market value, yet the regulatory role of preharvest soil management in shaping postharvest performance remains poorly understood. Although green manure is widely adopted to ameliorate orchard soil degradation, species-specific modulation of postharvest storage trajectories and the quantitative fidelity of soil-to-fruit nutrient transmission have rarely been resolved for climacteric pear species. This study investigated how green manure species modulate fruit quality at harvest and during postharvest storage life and their underlying soil–fruit linkages. Three preharvest treatments were imposed, as follows: control (CK), sweet clover (CM), and alfalfa (MX). Fruits were harvested and stored at 4 °C, with samplings at 1, 5, 10, 15, and 20 d. A critical quality transition was identified at 15 d, characterized by the concurrent peaking of soluble sugars, organic acids, vitamin C, and anthocyanins alongside an optimal sugar–acid ratio. Beyond this inflection point, CM and MX diverged markedly: CM enhanced soluble sugar accumulation, anthocyanin retention, and ester volatile production—most notably hexyl acetate, which increased over 14.4-fold—thereby generating a pronounced fruity aroma bouquet. Conversely, MX sustained higher amino acid and vitamin C levels and conferred superior late-storage stability, evidenced by a three-fold lower coefficient of variation in the sugar–acid ratio relative to CK. Partial-least-squares structural equation modeling (PLS–SEM) revealed soil fertility as the principal exploratory associative factor of fruit quality, but the fidelity of soil-to-fruit transmission was species-dependent. MX exhibited the highest observed associative strength (R² = 0.971), whereas CM exhibited attenuated transmission fidelity (R² = 0.777), with network analysis further indicating that CM exhibited divergent associative patterns of key soil–fruit correlations. These findings suggest that green manure identity is linked to postharvest quality through divergent soil–fruit coupling pathways: alfalfa shows nutrient transmission efficiency and stabilizes nutritional quality, whereas sweet clover promotes sugar-aroma accumulation at the cost of reduced soil–fruit conversion fidelity. Species-specific green manure selection thus offers a viable strategy for targeted modulation of postharvest traits in Korla fragrant pear. Full article

The literature pays little attention to the impact of gratifications on fake news sharing among Chinese social media users, and fuzzy-set qualitative comparative analysis (fsQCA) is rarely used in fake news research. This study investigated the relationship between gratifications and fake news sharing among Chinese users. Study 1 employed partial least squares structural equation modeling (PLS-SEM) and fsQCA on the questionnaire data from 315 participants. Study 2 analyzed predictions of self-reported sharing intentions in task scenarios using data from 98 new participants. The PLS-SEM revealed that instant news sharing was positively predicted by time-passing, entertainment, and socializing gratifications; and negatively predicted by information seeking. Fake news sharing is positively predicted by instant news sharing and negatively predicted by fact-checking. The fsQCA revealed three distinct antecedent configurations leading to high sharing among users, demonstrating that sharing is driven by diverse, equifinal pathways rather than a single set of common characteristics. Study 2 confirmed that self-reported sharing intention predicts sharing intention in task scenarios. Gratifications influence users’ fake news sharing through instant sharing and fact-checking, and three configurations prompt users to share fake news. These findings promote the cultural richness of the fake news-sharing research and offer practical implications. Full article

Atractylodis Rhizoma is an important traditional Chinese medicinal material derived from two botanical origins, Maocangzhu (MCZ) and Beicangzhu (BCZ), which are difficult to distinguish by conventional morphological identification because of their similar appearance. However, differences in botanical origin may lead to variations in odor, taste, volatile constituents, and non-volatile metabolites, thereby affecting quality evaluation and clinical application. This study aimed to systematically characterize the sensory and chemical differences between MCZ and BCZ and to identify potential markers for their discrimination. A multi-dimensional analytical strategy combining electronic nose, electronic tongue, headspace gas chromatography–ion mobility spectrometry (HS-GC-IMS), and ultra-high-performance liquid chromatography–Orbitrap high-resolution mass spectrometry (UPLC-Orbitrap MS) was established. Electronic nose and electronic tongue were used to digitize odor and taste characteristics, HS-GC-IMS was employed to profile volatile organic compounds, and UPLC-Orbitrap MS was applied to characterize non-volatile metabolites. Principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA), variable importance in projection (VIP) screening, permutation tests, and correlation analysis were further used to evaluate discrimination performance and screen characteristic markers. The electronic nose results showed that MCZ and BCZ exhibited distinct odor profiles, with W5S, W1W, and W1S identified as the main differential sensors, suggesting that nitrogen oxides, terpenoids, inorganic sulfides, and short-chain alkanes contributed to the odor differences between the two origins. Electronic tongue analysis further demonstrated clear taste discrimination, with sourness and richness identified as the key taste indicators. HS-GC-IMS detected 108 volatile organic compounds, and 24 volatile markers with VIP > 1.2 were screened as important contributors to the differentiation of MCZ and BCZ. Among them, propionic acid and 5-methyl-2-furancarboxaldehyde were mainly distributed in MCZ, whereas (E)-caryophyllene was present only or at higher levels in BCZ, indicating its potential as a characteristic volatile marker of BCZ. UPLC-Orbitrap MS detected 78 non-volatile constituents, and OPLS-DA screened 17 key non-volatile differential metabolites with VIP > 1.2. These results indicated that MCZ and BCZ could be clearly separated not only by sensory signals but also by volatile and non-volatile chemical profiles. This study revealed that the differences between MCZ and BCZ are mainly reflected in odor-active volatile compounds, key taste indicators, and non-volatile differential metabolites. The integration of electronic nose, electronic tongue, HS-GC-IMS, and UPLC-Orbitrap MS provides a comprehensive and reliable strategy for distinguishing the two botanical origins of Atractylodis Rhizoma. These findings provide valuable insights into the material basis underlying the sensory and chemical differences between MCZ and BCZ and offer scientific support for accurate authentication, quality evaluation, and rational clinical application of Atractylodis Rhizoma. Full article

Unraveling rhizosphere microbial assembly and plant–microbe co-adaptation is essential for understanding how fragile mountain ecosystems respond to environmental stress. This study investigated the rhizosphere bacterial communities of Carex enervis C. A. Mey, a dominant species in arid and semi-arid alpine meadows, along an altitudinal gradient from 1160 to 1860 m. By integrating high-throughput sequencing, iCAMP-based community assembly analysis, niche differentiation assessment, and partial least squares path modeling, we examined associations among macro-environmental gradients, rhizosphere soil conditions, bacterial community assembly, and ammonium nitrogen availability. The results revealed a dual-track assembly pattern. Macro-environmental heterogeneity, particularly in elevation and precipitation, was associated with rare microbial diversity primarily through heterogeneous selection. In contrast, abundance-weighted patterns suggested homogeneous selection of core dominant microbial groups in the rhizosphere. Within several dominant genera, closely related taxa showed divergent covariation patterns rather than uniform responses along the environmental gradient, suggesting potential fine-scale differentiation in environmental responses. Path analysis further indicated that enzyme-based rhizosphere activity proxies were associated with the relative abundance of microbial response groups and with the availability of ammonium nitrogen. These findings suggest that the rhizosphere conditions of Carex enervis are associated with bacterial assembly patterns, fine-scale taxon differentiation, and nutrient-related soil variables along the elevational gradient. This study provides new insight into plant–microbe co-adaptation in arid and semi-arid mountain ecosystems. Full article