Search Results (157,539)

Against the backdrop of China’s dual carbon goals, investigating the impact of environmental taxes on carbon productivity is crucial for formulating effective regional environmental policies and achieving carbon peaking and carbon neutrality targets. This study employs panel data from 31 Chinese provinces spanning the period 2013–2023 and applies a spatial mediation model to empirically examine whether environmental taxes influence regional and neighboring carbon productivity through technological innovation. Furthermore, the study explores the underlying transmission mechanisms and regional heterogeneity of this effect. The analysis reveals that environmental taxation significantly enhances carbon productivity, accompanied by notable spatial spillover effects. Technological innovation serves as a partial mediator, indicating that such taxes indirectly foster productivity gains by stimulating the development of green technologies. From a regional perspective, the positive influence of environmental taxation is strongest in eastern China, produces moderate spillover effects in the central provinces, and demonstrates comparatively limited policy efficacy in the western regions. Full article

Background: The U.S. Food and Drug Administration (FDA) conducted a benefit–risk assessment for Moderna’s COVID vaccine mRNA-1273 prior to its full approval, announced 31 January 2022. The FDA’s assessment focused on males 18–64 years old because its risk analysis was limited to vaccine-attributable myocarditis/pericarditis (VAM/P), given the excess risk among males. The FDA’s analysis concluded that vaccine benefits outweighed risks, even for 18–25-year-old males (those at highest VAM/P risk). We reanalyze the FDA’s benefit–risk assessment using information available through the third week of January 2022 and focusing on 18–25-year-old males. Methods: We develop a benefit–risk model, extending the FDA’s, that can stratify benefits and risks of vaccination by prior-infection and comorbidity status. We use the FDA’s framework but apply our model to account for benefits derived from prior COVID infection, while also accounting for finer age stratification in COVID-hospitalization rates, incidental hospitalizations (those of patients who test positive for COVID but receive treatment for something else), more realistic projections of Omicron-infection rates, and more accurate VAM/P rates. Results: With hospitalizations as the principal endpoint of the analysis (those prevented by vaccination vs. those caused by VAM/P), our model finds vaccine risks outweighed benefits for 18–25-year-old males, except in scenarios projecting implausibly high Omicron-infection prevalence. Our assessment suggests that mRNA-1273 vaccination of 18–25-year-old males generated between 8% and 52% more hospitalizations for VAM/P compared to COVID hospitalizations prevented (over a five-month period of vaccine protection assumed by the FDA). The preceding assessment uses model inputs based on data available at the time of the FDA’s mRNA-1273 assessment. Moreover, these inputs as well as model outputs are validated by subsequently available data. Conclusions: The outcome of a vaccine benefit–risk assessment may be dramatically impacted by accounting for the benefits derived from prior infection by the vaccine-targeted disease. To increase public confidence in vaccines and thereby reduce vaccine hesitancy, public-health agencies should employ benefit–risk models capable of supporting stratification of vaccination recommendations not only based on age and sex but also on prior-infection and comorbidity status. Full article

Bamboo shoots represent a traditional food in China, with most varieties exhibiting a bitter taste; however, understanding of the compounds responsible for this bitterness remains limited. In this study, shoots of a special-germplasm bitter-tasting Dendrocalamus brandisii (Dbs) were investigated, using sweet-tasting Dendrocalamus brandisii (Db) shoots as a control. Electronic tongue analysis, broad-target metabolomics, targeted metabolomics, and transcriptomics were employed to identify the metabolites and key genes associated with bitterness in Dbs shoots. Electronic tongue measurements revealed a significant difference in bitterness between the two groups. Human sensory evaluation confirmed that Dbs was perceived as significantly more bitter and less sweet than Db (p < 0.01). Nontargeted metabolomics screening identified 43 differential metabolites, 19 of which were upregulated in Dbs. Targeted analysis of these differential metabolites, combined with the BitterDB database and previously reported bitter compounds, suggested that 4-Hydroxybenzoate, gallic acid, epicatechin, tryptophan, histidine, and apigenin may contribute to the bitterness of Dbs. Among these, 4-Hydroxybenzoate showed an approximately 92-fold higher content in Dbs compared to Db. The taste activity values (TAVs) of the identified bitter compounds were calculated; only 4-Hydroxybenzoate exhibited a TAV greater than 1 (14.581), while the TAV of the other compounds were all below 1. Integrating broad-target metabolomics, targeted metabolomics, and TAV analysis, 4-Hydroxybenzoate was inferred to be one of the primary bitter substances. Transcriptomic analysis indicated significant upregulation of key genes in the biosynthetic pathway of 4-Hydroxybenzoate, including PAL, 4CL, and C4H. Enzyme activity assays further demonstrated that phenylalanine ammonia-lyase, 4-coumarate-CoA ligase, and cinnamate 4-hydroxylase activities were markedly higher in Dbs than in Db. RT-qPCR validation confirmed that the expression levels of 4CL3, 4CL4, PAL1, PAL2, PAL3, PAL4, and PTAL were significantly elevated in Dbs, consistent with the transcriptomic data. In conclusion, 4-Hydroxybenzoate is proposed as the most likely key compound responsible for the bitterness in Dbs shoots. This study provides valuable insights into the bitterness formation mechanism in this Dbs and offers important information for the improvement of its edible quality. Full article

In recent years, the use of drones, also known as unmanned aerial vehicles (UAVs), has experienced a rapid increase due to their wide availability, compact size, low cost, and ease of operation. These devices have found applications in various areas, facilitating human work by covering large distances and operating in inaccessible or dangerous zones. However, their use has also been associated with malicious activities, such as property damage or threats to public security, which highlights the need to develop efficient and precise UAV detection systems. Although approaches based on neural networks have been proposed, they require large amounts of data for training and more computational resources for operation, which limits their applicability. In this study, we propose an alternative approach based on an analysis of audio features obtained through the fast Fourier transform (FFT) algorithm and classification using machine learning (ML) models. Our approach aims to detect the presence of drones using a minimal number of samples, meeting the requirements of efficiency, accuracy, robustness, low cost, and scalability necessary for a functional detection system. Full article

Sustainable performance in today’s digital world relies on understanding how technology supports sustainability through organisational processes and leadership. This study applies the Knowledge-Based View and Dynamic Capabilities Theory. It assesses how digital knowledge integration impacts sustainable performance in the hospitality sector. The study examines whether green knowledge sharing mediates the link between digital knowledge integration and sustainable performance. It also explores whether digital leadership strengthens this link. The research team collected data from 373 hotel and restaurant managers in Jordan and analysed the results using SmartPLS version 4. The analysis shows that digital knowledge integration enhances both sustainable performance and green knowledge sharing. Green knowledge sharing strongly associates with sustainable performance. Mediation analysis shows that green knowledge sharing partly explains the effect of digital knowledge integration on sustainable performance. Moderation analysis reveals that digital leadership amplifies the link between digital knowledge integration and sustainable performance. However, digital knowledge integration does not significantly affect the relationship between green knowledge sharing and sustainable performance. These findings clarify how digital knowledge integration, green knowledge sharing, and digital leadership interact to affect sustainable performance. The study provides practical and theoretical implications for hospitality managers aiming to leverage digital transformation and leadership to achieve sustainability goals. Full article

Against global challenges like climate change and biodiversity loss, sustainable development is the core orientation of engineering education transformation. Cultivating talents with interdisciplinary perspectives, systemic thinking and AI literacy is crucial for implementing the UN 2030 Sustainable Development Agenda. However, AI education focuses on seniors or graduates, with freshmen’s use of AI acting as “cognitive partners” for knowledge construction and complex problem-solving understudied, constraining AI’s potential in fostering early systemic thinking. We present a novel teaching practice integrating generative AI into an “AI-Environmental System Analysis” module, with Sousa chinensis habitat conservation as the case. Using a design-based research paradigm, we evaluated 24 student groups via system analysis briefs, AI usage reflections and course assessment data. Results show that the module effectively guided students to establish preliminary system analysis frameworks, with over 70% of groups identifying complex interactions among environmental factors. Students’ AI applications ranged from information retrieval to scenario simulation, initially forming systemic thinking and responsible AI literacy for sustainable development. This study provides a replicable paradigm for integrating AI and sustainable development education, clarifies the key role of structured instructional scaffolding, and enriches sustainable development-oriented engineering education pathways. Full article

The analysis of Synthetic Aperture Radar (SAR) imagery is essential to modern remote sensing, with applications in disaster management, agricultural monitoring, and military surveillance. A significant challenge is that the complex and noisy nature of SAR data severely limits the performance of traditional machine learning (TML) methods, leading to high error rates. In contrast, deep learning (DL) has recently proven highly effective at addressing these limitations. This study provides a comprehensive review of recent DL advances applied to SAR image despeckling, segmentation, classification, and detection. It evaluates widely adopted models, examines the potential of underutilized ones like GANs and GNNs, and compiles available datasets to support researchers. This review concludes by outlining key challenges and proposing future research directions to guide continued progress in SAR image analysis. Full article

This study explores how commercialization shapes gender representation and inequality within contemporary running culture. Situated within the broader context of sport and media consumption, it examines how bodies, identities, and spaces are disciplined by market-driven values. Using a critical ethnographic approach, 10 months of fieldwork were conducted across various running events in multiple urban locations. The primary researcher, a semi-professional female runner, participated as both insider and critical observer, supported by a research team in data collection, reflexive journaling, and thematic analysis. The findings reveal that promotional campaigns and commercial spaces reproduce gendered ideals: women are highlighted for beauty, charm, and body esthetics, while men are portrayed for endurance and performance. Female runners are frequently deployed as “marketing capital,” valued more for visual appeal than athletic ability. These dynamics transform public running spaces into gendered, semi-commercial arenas governed by capital, consumer culture, and the male gaze, reinforcing structural inequality under the guise of empowerment. Full article

Wind–solar power generation is inherently uncertain. These uncertainties bring considerable difficulties to the assessment of hosting capacity. To tackle these difficulties, it is essential to create typical scenarios that can precisely capture the statistical traits and interrelationships of wind–solar power. In this research, we systematically integrate various scenario generation techniques, resulting in the creation of a holistic framework grounded in kernel density estimation (KDE) and Copula functions. Our proposed approach represents the stochastic nature of wind–solar power output by constructing their respective probability density functions (PDFs). It comprehensively depicts the potential spatiotemporal complementarity between wind–solar power by utilizing Copula functions and establishing a joint probability distribution model. Through Monte Carlo simulation, we generated a large number of wind–solar output scenarios. Subsequently, we employed the K-means clustering algorithm to reduce the number of scenarios. The findings reveal that the integrated framework, which combines KDE and Copula theory, achieves higher fitting accuracy for the marginal distributions and correlation structures of wind–solar power generation. As a result, the generated scenarios are more representative and reliable, offering strong support for photovoltaic (PV) hosting capacity analysis (HCA) and the formulation of typical plans. We validate the proposed method using historical wind–solar data from several representative regions in China, such as Inner Mongolia, northern Hebei, the Beijing–Tianjin–Hebei region, and Hubei Province. This validation demonstrates the method’s applicability under various geographical and climatic conditions. Full article

Globally, downstream petroleum operations face increasing pressure to optimize energy use and reduce carbon emissions, with electrically driven pumping systems representing the dominant site-level electricity load. This study investigates why anticipated energy savings from a variable frequency drive (VFD) retrofit were not realized in a Kenyan petroleum depot. High-resolution PLC-SCADA data at 1 s intervals were analysed over a six-month period using a custom Pump-Arm Modelling framework, reconstructing pump operation and energy consumption as a function of loading arm demand, runtime, and control mode. Results show that, despite VFD installation, pump speeds remained effectively fixed, and energy consumption was governed by discrete loading arm demand rather than speed modulation. Eight months billing data confirmed that post-retrofit energy intensity increased, from an average of 0.493 to 0.555 kWh/m³, demonstrating that expected efficiency improvements were not achieved. Stratified analysis further showed that idle running due to manual overrides accounted for approximately 3% of total depot electricity consumption. To contextualize this performance gap, idealized affinity-law scaling was applied to observed partial-load intervals, revealing unrealized energy savings potential of approximately 92 MWh, equivalent to a depot-level savings potential of 28.4% ± 1.4%. The findings indicate that limited energy savings were attributable to open-loop control architecture, partial digital integration, and legacy equipment constraints rather than VFD hardware limitations, highlighting the need for demand-aware, closed-loop energy optimization in retrofitted petroleum depots. Full article

Online guided tours have become an essential form of non-contact tourism, yet the experiential attributes shaping participants’ digital tour experiences remain underexplored. This study aims to identify the core experiential dimensions of online guided tours by analyzing user-generated review data and interpreting the findings through the experience economy framework. A dataset of 1506 participant reviews was collected from major online guided tour platforms and analyzed using text mining techniques, including TF-IDF and Latent Dirichlet Allocation (LDA). The results reveal the following seven experiential attributes: entertainment, education, esthetics, escapism, presence, interactivity, and digital environment. These findings indicate that online guided tours extend beyond traditional 4E experience dimensions, incorporating digitally mediated elements such as real-time communication and platform-driven immersion. The proposed “4E + 3D Model” captures the hybrid nature of digital tourism experiences, combining classic experiential factors with technology-enabled components. This study contributes to tourism experience research by empirically validating an expanded experiential structure suitable for digital contexts. It also demonstrates the value of user-generated review analysis for deriving authentic experiential insights. The results provide practical implications for enhancing online guided tour design, emphasizing real-time interactivity, digital esthetics, and system stability to improve participant experiences in virtual tourism settings. Full article

Air transport networks function as strategic infrastructure whose structural evolution reflects broader geopolitical and economic forces. This study introduces a network-based interpretive framework for Geoeconomics in Air Transport by integrating complex network analysis with geoeconomic perspectives. It conceptualizes air transport networks as strategic economic infrastructure in which network topology encodes market access, power asymmetries, and resilience under geopolitical uncertainty. Using global civil aviation data, this paper constructs air transport networks at both the global level and across major regions—including the United States, Europe, the Middle East, ASEAN, China, and Japan—and compares passenger and cargo connectivity before (2019) and after (2023) the COVID-19 pandemic. Standard network metrics, such as centrality, topology, and connectivity, are used to quantify structural changes, which are subsequently interpreted through a geoeconomic lens. Global connectivity increased by approximately 8% in the post-pandemic period. In contrast, the United States—maintaining the most structurally resilient national air transport network—expanded by about 12%, while connectivity across Asian countries contracted, either domestically, internationally, or both. These patterns reflect a combination of intentional strategic responses and unintended structural adjustments. North American and European networks remain large-scale, meshed, and structurally resilient, whereas regions outside these core areas exhibit stronger hub-and-spoke dependence, both internally and in their connections with core regions. Such dependence signals persistent geoeconomic asymmetries and increased exposure to external shocks, despite higher traffic volumes per route. Betweenness centrality shifted markedly from European and North American hubs toward the Middle East, indicating the emergence of a geoeconomic intermediary region capable of sustaining connectivity across increasingly fragmented markets. The findings further demonstrate that, despite the United Kingdom’s withdrawal from the European Union, institutional and strategic realignments can enhance air transport network resilience in ways not anticipated by conventional geoeconomic interpretations of regional integration. By linking quantitative network outcomes with geoeconomic interpretation, this study provides reproducible insights into the strategic reconfiguration of global air transport systems under rising geopolitical uncertainty. Full article

Ginger (Zingiber officinale) is a widely recognized functional food, known for its anti-inflammatory, antioxidant, and digestive health benefits largely attributed to gingerol-related compounds. While traditional extraction-based methods have been used to characterize these metabolites, they often compromise the spatial context within tissues. This study represents the first application of matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) with ion mobility spectrometry (IMS) to map the detailed spatial distribution of key ginger metabolites (6-, 8-, and 10-gingerols and shogaols) in a complex matrix of an intact rhizome tissue. Rhizomes from five ginger accessions collected in Negros Occidental, Philippines, were cryosectioned at 20 μm, coated with 2,5-dihydroxybenzoic acid (DHB) matrix, and analyzed using MALDI MSI at 100 µm spatial resolution across an m/z range of 50–1200. The MALDI MSI revealed that 6-, 8-, and 10-gingerols were predominantly localized in the stele and cortex regions, while shogaols exhibited broader distribution, including the epidermis. Principal component analysis (PCA) on UPLC-ESI-QTOF-MS data of methanolic rhizome extracts revealed clustering patterns among the five ginger accessions. These findings provide a spatially resolved metabolomic profile of gingerols and shogaols, offering novel insights into the anatomical localization of bioactive compounds. This integrative approach establishes a foundation for future studies on ginger physiology, breeding, and quality control of ginger-derived natural products. Full article

For the hierarchical normal and normal-inverse-gamma model, we derive the Bayesian estimator of the variance parameter in the normal distribution under Stein’s loss function—a penalty function that treats gross overestimation and underestimation equally—and compute the associated Posterior Expected Stein’s Loss (PESL). Additionally, we determine the Bayesian estimator of the same variance parameter under the squared error loss function, along with its corresponding PESL. We further develop empirical Bayes estimators for the variance parameter using a conjugate normal-inverse-gamma prior, employing both the method of moments and Maximum Likelihood Estimation (MLE). Theoretical properties, including posterior and marginal distributions, two inequalities that relate two Bayes estimators and their corresponding PESLs, and consistencies of hyperparameter estimators and empirical Bayes estimators, are established. The simulation results demonstrate that MLEs outperform moment estimators in estimating hyperparameters, particularly with respect to consistency and model fit. Finally, we apply our methodology to real-world data on poverty levels—specifically, the percentage of individuals living below the poverty line—to validate and illustrate our theoretical findings. Full article

Climate-induced drought increasingly threatens water security in semi-arid regions, as rising temperatures become the primary driver of hydro-climatic variability. This study assessed long-term drought dynamics in the Nzhelele River Catchment (NRC), through Mann–Kendall (MK) trend analysis, Sen’s slope estimation, and the Standardized Precipitation Evapotranspiration Index (SPEI) for the period from October 1994 to September 2024. Aggregated in situ weather station data and 0.25° × 0.25° gridded climate node (GCN) datasets were used to quantify trends in mean annual temperature, potential evapotranspiration (PET), and precipitation. The results revealed a statistically significant warming trend of 0.037 °C/yr. and an increase in PET at an average of 6.343 mm/yr., while precipitation showed a weak, non-significant decline (–0.568 mm/yr.). SPEI analysis identified recurrent severe droughts between 2003 and 2009; 2010–2013; 2014–2016; and 2018–2020, with the 2014–2016 period as the most extreme climatic stress. Gridded SPEI aligns closely with station-derived SPEI across all accumulation scales (R² = 0.76–0.87; p-value < 0.001), supporting the use of ERA5-based climate products for drought monitoring in data-scarce regions. Due to the limited number of in situ stations and spatial averaging inherent in gridded datasets, the results provide an approximate representation of hydro-climatic conditions across the catchment. Overall, the findings indicate a shift toward temperature-driven drought regimes, growing climate risks to water availability, and the need for climate-resilient water resource planning. Full article