Search Results (2,494)

Labor market forecasting relies heavily on economic time-series data, often overlooking the “health–wealth” gradient that links population health to workforce participation. This study develops a machine learning framework integrating non-traditional health and social metrics to predict state-level employment density. Methods: We constructed a multi-source longitudinal dataset (2014–2024) by aggregating county-level Quarterly Census of Employment and Wages (QCEW) data with County Health Rankings to the state level. Using a time-aware split to evaluate performance across the COVID-19 structural break, we compared LASSO, Random Forest, and regularized XGBoost models, employing SHAP values for interpretability. Results: The tuned, regularized XGBoost model achieved strong out-of-sample performance (Test $R^2$ = 0.800). A leakage-safe stacked Ridge ensemble yielded comparable performance (Test $R^2$ = 0.827), while preserving the interpretability of the underlying tree model used for SHAP analysis. Full article

We investigate how environmental, social, and governance (ESG) risk can spread between peers and its impact on long-term firm performance. Using data across six geographically diverse countries over a fourteen-year period, we find a significant spillover of ESG risks among multinational firms, which fails to yield a meaningful impact on the performance of affected firms. These findings place a spotlight on a critical gap in ESG risk management and echo an urgent signal for policy intervention, aligning with the United Nations’ faltering Sustainable Development Goals for 2030. This work is a clarion call for immediate academic and practical action in a world teetering on the brink of unsustainable practices. Our findings suggest that market-based mechanisms alone may be insufficient to discipline ESG risk, highlighting a potential role for regulatory oversight and policy attention. Full article

This study examines the impact of tax avoidance practices on investment efficiency in Egypt, with particular emphasis on the moderating role of earnings management by exploring whether these tactics reflect managerial opportunism or serve as a mechanism to ease financial constraints. We employ panel data regression to analyze a sample of 58 non-financial firms listed on the Egyptian Exchange (EGX) over the period 2017–2024, yielding 464 firm-year observations. Data are collected from official corporate websites, EGX, and Egypt for Information Dissemination (EGID). Grounded in agency theory, signaling theory, and pecking order theory, this study reveals how conflicts of interest and information asymmetry between managers and stakeholders lead to managerial opportunism. The findings show that tax avoidance undermines the investment efficiency in the Egyptian market. Earnings manipulation further intensified this effect due to the financial statements’ opacity. A closer examination reveals that earnings management exacerbates overinvestment by masking managerial decisions. Conversely, for financially constrained firms with a tendency to underinvest, tax avoidance and earnings management may contribute to improved efficiency by generating internal liquidity and alleviating external financing constraints. These results provide valuable insights for regulators, highlighting that policy should be directed against managerial opportunism and improving transparency, instead of focusing solely on curbing tax avoidance. From an investor perspective, they should closely monitor and understand the tax-planning strategies to ensure they enhance the firm’s value. Full article

This study addresses a pivotal strategic issue in hospitality e-commerce: how hotels can optimize cooperation with heterogeneous online travel agencies (OTAs). Moving beyond the conventional question of whether to cooperate, we investigate the interrelated decisions of which OTA type to partner with (quality-focused vs. price-focused) and which business model to adopt (merchant vs. agency). We develop a game-theoretic model that incorporates key e-commerce factors, including hotel capacity constraints, cross-channel spillover effects, and differential consumer acceptance of OTA types. Our analysis yields a contingent decision framework. We demonstrate that OTA cooperation becomes beneficial only when a hotel’s room capacity exceeds its direct-channel demand. The optimal strategy evolves with capacity: hotels with moderate capacity should partner with a single OTA type—predominantly the quality-focused one—while larger hotels should engage both types to maximize market coverage. In terms of business models, smaller hotels benefit from the risk-shifting merchant model, whereas larger hotels capture higher margins through the agency model. A key finding is the general superiority of a differentiated approach: applying the agency model to quality-focused OTAs and the merchant model to price-focused OTAs. This research provides a structured analytical framework to guide hotel managers in crafting e-commerce platform strategies and offers scholars a foundation for further inquiry into platform competition and contract design in digital marketplaces. Full article

Understanding how moral and emotional language operates in paid social advertising is essential for evaluating persuasion and its ethical contours. We provide a descriptive map of Moral Foundations Theory (MFT) language in Meta ad copy (Facebook/Instagram) drawn from seven global beverage brands across eight English-speaking markets. Using the moralstrength toolkit, we implement a two-channel pipeline that combines an unsupervised semantic estimator (SIMON) with supervised classifiers, enforces a strict cross-channel consensus rule, and adds a non-overriding purity diagnostic to reduce attribute-based false positives. The corpus comprises 758 text units, of which only 25 ads (3.3%) exhibit strong consensus, indicating that much of the copy is either non-moral or linguistically ambiguous. Within this high-consensus subset, the distribution of moral cues varies systematically by brand and category, with loyalty, fairness, and purity emerging as the most prominent frames. A valence pass (VADER) indicates that moralized copy tends toward negative valence, yet it may still yield a constructive overall tone when advertisers follow a crisis–resolution structure in which high-intensity moral cues set the stakes while surrounding copy positions the brand as the solution. We caution that text-only models undercapture multimodal signaling and that platform policies and algorithmic recombination shape which moral cues appear in copy. Overall, the study demonstrates both the promise and the limits of current text-based MFT estimators for advertising: they support transparent, reproducible mapping of moral rhetoric, but future progress requires multimodal, domain-sensitive pipelines, policy-aware sampling, and (where available) impression/spend weighting to contextualize descriptive labels. Full article

Hexahydrocannabinol (HHC), a hydrogenated derivative of Δ⁹-tetrahydrocannabinol (Δ⁹-THC), is a semi-synthetic cannabinoid marketed as an alternative to Δ⁹-THC. Its hydroxylated metabolite, 8-hydroxyhexahydrocannabinol (8-OH-HHC), exists as four stereoisomers: (6aR,8R,9R,10aR), (6aR,8S,9S,10aR), (6aR,8S,9R,10aR), and (6aR,8R,9S,10aR). However, the lack of reference standards has hindered pharmacokinetic and forensic studies. This work reports the first stereoselective synthesis and structural confirmation of all four 8-OH-HHC stereoisomers. Two strategies were employed: hydroboration–oxidation and epoxidation–reduction. Hydroboration of Δ⁸-THC with BH₃·THF followed by oxidation predominantly produced anti-isomers (6aR,8R,9R,10aR) and (6aR,8S,9S,10aR) in moderate yields, along with small amounts of syn-isomer (6aR,8S,9R,10aR), suggesting an atypical mechanistic pathway. In contrast, syn-isomers (6aR,8S,9R,10aR) and (6aR,8R,9S,10aR) were accessed via epoxidation of Δ⁸-THC acetate using mCPBA and subsequent reduction with NaBH₃CN/BF₃·OEt₂, affording the desired products with moderate selectivity. Absolute configurations were confirmed by nuclear Overhauser effect spectroscopy (NOESY). These methods will facilitate future pharmacokinetic and forensic research and support the development of improved detection strategies. Full article

This study evaluates whether an AI-built DeFi website case can match professionally developed DeFi platforms in digital marketing performance, innovation-related strategic behavior, and entrepreneurial impact. Using a multi-method design, we compare five established DeFi websites (Aave, Lido, Curve, MakerDAO, Uniswap) against one AI-built interface (Nexus Protocol). The analysis is designed as a five-platform benchmarking study of established professional DeFi websites, complemented by one AI-built case (Nexus Protocol) used as an illustrative comparison rather than a representative class of AI-built interface. The objectives are to (i) test differences in traffic composition and acquisition strategies, (ii) quantify how engagement signals predict authority and branded traffic, (iii) examine cognitive processing and trust-cue attention via eye tracking, and (iv) model emergent engagement and authority dynamics using agent-based simulation (ABM). Web analytics (March–October 2025) show significant variation in traffic composition across professional platforms (ANOVA F = 3.41, p = 0.0205), while regression models indicate that time on site and pages per visit positively predict Authority Score (R² = 0.61) and Branded Traffic (R² = 0.55), with bounce rate exerting an adverse effect. PCA and k-means clustering identify three strategic archetypes (innovation-driven, balanced-growth, efficiency-focused). Eye-tracking results show that professional interfaces generate tighter fixation clusters and shorter scan paths, indicating higher cognitive efficiency. In contrast, fixation on key UI elements and trust cues is comparable across interface types. ABM outputs further suggest that reduced engagement depth in the AI-built interface yields weaker long-run branded-traffic and authority trajectories. Overall, the study provides an integrated evaluation framework and evidence-based implications for AI-driven interface design in high-trust fintech environments. Full article

The stock market is of paramount importance to economic development. Investors who accurately predict stock price fluctuations based on its high volatility can effectively mitigate investment risks and achieve higher returns. Traditional time series models face limitations when dealing with long sequences and short-term volatility issues, often yielding unsatisfactory predictive outcomes. This paper proposes a novel algorithm, MSNet, which integrates a Multi-scale Channel Attention mechanism (MSCA) and Sparse Perturbation Greedy Optimization (SPGO) onto an xLSTM framework. The MSCA enhances the model’s spatio-temporal information modeling capabilities, effectively preserving key price features within stock data. Meanwhile, SPGO improves the exploration of optimal solutions during training, thereby strengthening the model’s generalization stability against short-term market fluctuations. Experimental results demonstrate that MSNet achieves an MSE of 0.0093 and an MAE of 0.0152 on our proprietary dataset. This approach effectively extracts temporal features from complex stock market data, providing empirical insights and guidance for time series forecasting. Full article

Pan-Homophonic events denote fluctuations in stock prices that are triggered by phonetic similarities between event keywords and stock tickers. As a relatively novel and under-researched phenomenon, they mirror a subtle yet influential behavioral deviation within financial markets. Centering on the case of Chuandazhisheng, this study delves into how such events produce dynamic and time-varying impacts on stock prices. A linguistic amplitude segmentation method is devised to discriminate between high- and low-intensity events based on information entropy. To separate pan-homophonic-driven price movements from broader market trends, the Relational Stock Ranking (RSR) model is integrated with a Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) framework to establish an adjusted price benchmark. The empirical analysis reveals a sequential price response: initial moderate fluctuations in the low-amplitude phase often yield to more prominent volatility in the high-amplitude phase. While price surges typically occur within one or two days of the event, they generally revert within approximately three weeks. Moreover, repeated exposures to homo- phonic stimuli seem to attenuate the response, indicating a decaying spillover pattern. These findings contribute to a more profound understanding of the intersection between linguistic cues and market behavior and provide practical insights for investor education, information filtering, and regulatory supervision. Full article

Background/Objectives: Digital transformation is increasingly essential for healthcare organizations to improve operational efficiency and service quality. However, in developing countries such as Indonesia, many lower-middle-class hospitals lag due to limited financial, human, and infrastructural resources. This study examines how environmental dynamism—comprising regulatory changes, market pressures, and technological shifts—affects the digital capabilities of these hospitals. Methods: A quantitative, cross-sectional survey was conducted in Class C and D hospitals across Indonesia. Respondents included hospital directors, deputy directors, and IT heads. Data were collected through structured questionnaires measuring environmental dynamism and digital capability using a six-point Likert scale. Reliability testing yielded Cronbach’s alpha values above 0.96 for both constructs. Correlation analysis was performed to examine the relationship between environmental dynamism and digital capability. Results: Findings reveal a weak positive correlation (r = 0.1816) between environmental dynamism and digital capability. Although external factors such as policy regulations and technological competition encourage digital adoption, hospitals with limited internal resources struggle to translate these pressures into sustainable transformation. Key challenges include low ICT budgets, inconsistent staff training, and insufficient infrastructure. Conclusions: The results suggest that environmental change alone cannot drive digital readiness without internal capacity development. To foster resilient digital healthcare ecosystems, policy interventions should integrate regulatory frameworks with practical support programs that strengthen resources, leadership, and human capital in lower-middle-class hospitals. Full article

Land consolidation requires reliable and objective land valuation to ensure transparency and fairness in the reallocation process. This study introduces a data-driven method for assessing agricultural site productivity based on vegetation indices derived from multispectral imagery, supported by Sentinel satellite data and validated using handheld chlorophyll meter measurements. Site productivity, defined as the land’s ability to generate yield and biological value, is determined by natural and environmental factors that directly influence economic worth. Vegetation indices (NDVI, SAVI) obtained from UAV imagery showed a strong correlation with chlorophyll content, confirming the reliability of this non-invasive assessment. The analysis, conducted in Poland and Slovakia, demonstrated the method’s applicability under two different land consolidation systems: a market-based model in Poland and an ecologically oriented approach in Slovakia. The proposed framework proved easy to implement and provided consistent results even without the use of ground control points. By reducing fieldwork time and costs while improving valuation accuracy, this method enhances the objectivity and transparency of land consolidation procedures. The findings confirm the potential of vegetation indices to support data-driven and environmentally informed land valuation across diverse consolidation contexts. Full article

Lexicographic preference trees (LP-Trees) provide a compact and expressive representation for modeling complex decision-making scenarios, yet measuring similarity between complete or partial structures remains a challenge. This study introduces PLPSim, a novel metric for quantifying alignment between partial lexicographic preference trees (PLP-Trees) and develops three coalition formation algorithms—HRECS1, HRECS2, and HRECS3—that leverage PLPSim to group agents with similar preferences. We further propose ContractLex and PriceLex protocols (comprising CLF, CFB, CFW, CFA, CFP) for coalition-based contract and pricing strategies, along with a new evaluation metric, F@Lex, which is designed to assess satisfaction under lexicographic preferences. To illustrate the framework, we generate a synthetic dataset (PLPGen) contextualized in a hybrid renewable energy market, where consumers’ PLP-Trees are aggregated and matched with suppliers’ tariff contracts. Experiments across 162 market scenarios, evaluated using Normalized Discounted Cumulative Gain (nDCG), Davies–Bouldin dispersion, and F@Lex, demonstrate that PLPSim-based coalitions outperform baseline approaches. The combination HRECS3 + CFP yields the highest consumer satisfaction, while HRECS3 + CFB achieves balanced satisfaction for both consumers and suppliers. While electricity tariffs and renewable energy contracts—static and dynamic—serve as the motivating example, the proposed framework generalizes to diverse multi-agent systems, offering a foundation for preference-driven coalition formation, adaptive policy design, and sustainable market optimization. Full article

Lavender is a perennial Mediterranean plant that has been cultivated throughout history for medicinal, aromatic, and cosmetic purposes. Due to its high economic and commercial value, it has become an important agricultural product worldwide. The low production cost, adaptability to environmental conditions, and demand for its versatile use in the global market make it a significant potential source of income for developing Mediterranean countries. This study aims to identify commercially suitable cultivation sites for Lavandula angustifolia Mill. using remote sensing (RS) and geographic information systems (GIS) technologies to support rural development. Within this scope, suitable cultivation habitat parameters for the species in open fields and natural conditions were determined; these parameters were weighted according to their importance using multi-criteria decision analysis (MCDA), and thematic maps were created for each parameter. The created maps were combined using weighted overlay analysis, and a final map was generated according to the suitability class. The results indicate that within the study area, 75,679.45 ha is mostly suitable, 388,832.71 ha is moderately suitable, 24,068.43 ha is marginally suitable, and 229,327.20 ha is not suitable. As a result, it has been observed that Lavandula angustifolia Mill., which is currently cultivated on approximately 4045 ha of land and contributes 429 tons of product to the regional economy, covers only a relatively small portion of the suitable cultivation sites identified in the study and is not utilized to its full potential. It is understood that the expansion of lavender cultivation in determined suitable sites has significant potential to substantially develop the region and its rural population in terms of both yield and production volume, and to involve women and youth entrepreneurs in agricultural employment. Full article

Extracts from the stem bark of Stryphnodendron adstringens (barbatimão) exhibit relevant medicinal properties, such as anti-inflammatory, antioxidant, antimicrobial, and wound-healing activities, which reinforce their potential for developing herbal medicines. The $550 billion plant bioactive market (by 2030) demands safer, green-chemistry-aligned extraction methods for responsible industrial scaling. In this study, dry extracts obtained from the stem bark of S. adstringens were obtained by ultrasound-assisted maceration in one- and two-step extraction systems. Parameters such as yield, solvent evaporation time, cost, acute toxicity, epigallocatechin gallate (EGCG) concentration, cell viability, antioxidant potential, and anti-inflammatory activity were evaluated. High-EGCG two-step organic extracts were industrially difficult, needing more raw material and toxic solvents. In contrast, the single-step extracts showed a better balance between yield, cost, safety, and biological efficacy. All extracts showed cell viability above 70% at safe concentrations and significantly reduced the production of inflammatory cytokines. Thus, the results confirm that optimizing single-step extraction, with lower environmental impact solvents, enables producing safe and effective polyphenol-rich extracts, consolidating water as the main candidate for industrial-scale phytotherapeutic formulations of barbatimão, in line with its traditional use in infusions. Full article

Coffee grounds can be used in agriculture as a bio-input to enhance soil fertility and biodiversity in the long term. Furthermore, the use of coffee grounds in agriculture is a sustainable practice because it reuses an organic waste product as natural fertilizer and minimizes the environmental impact resulting from the improper disposal of waste. This study aimed to identify the effects of coffee grounds on the growth and yield of iceberg lettuce plants. The experiment was conducted in a greenhouse using 4 kg of substrate in containers with a 5.356 dm³ capacity, following a completely randomized design in a 2 × 2 factorial arrangement. The primary treatment consisted of plants grown in two types of substrate: soil and sand (01) and soil, sand, and 10% coffee grounds (02). The secondary treatment corresponded to irrigation with water (01) and a 10% coffee ground extract solution (02). Coffee grounds incorporated into the soil increase soil fertility; however, they reduce lettuce growth due to the toxicity of the compounds present and should not be used without prior treatment. Processing coffee grounds into irrigation solutions shows promise due to its high potential for use as an agricultural bio-input in lettuce production. This solution enhances the growth and development of the species, resulting in vigorous plants with market value. Full article