Multidisciplinary Digital Publishing Institute (Switzerland)
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Determinants of Success in Online Travel: Examining the Effect of a Comprehensive Higher-Order Model on e-Service Quality on Loyalty and Customers’ Citizenship Behavior
No full textThis study proposes and validates a comprehensive model of the determinants of online travel company success by establishing the relationships between a properly conceptualized higher-order e-service quality construct, perceived value, and satisfaction on customer loyalty and customers’ citizenship behavior. The model was tested using structural equation modeling and data collected on 257 US travelers. Results reveal that e-service quality positively influences customers’ loyalty and citizenship behavior both directly and indirectly (through perceived value and satisfaction). Perceived value also exerts a direct positive influence on satisfaction. The results provide theoretical and practical implications by helping to demystify the relationships between the tested variables, as well as by increasing our understanding of the determinants of success in online travel websites
Caffeine Mitigates Adenosine-Mediated Angiogenic Properties of Choroidal Endothelial Cells Through Antagonism of A1 Adenosine Receptor and PI3K-AKT Axis
No full textAging reduces the tissue regenerative capacity, promotes chronic inflammation, and contributes to neurodegenerative diseases, including age-related macular degeneration (AMD). AMD is a leading cause of vision loss in older adults and manifests as dry (atrophic) or wet (neovascular) disease. Although dry AMD is more prevalent, neovascular AMD (nAMD) causes the most severe vision impairment and remains a major public health burden. Oxidative stress-mediated inflammation and dysfunction of retinal pigment epithelium (RPE) cells and choriocapillaris drive early AMD. Neovascular AMD is marked by pathologic choroidal neovascularization (CNV), driven largely by dysregulated VEGF signaling. Anti-VEGF therapies are the current standard of care for nAMD but require frequent intravitreal injections, carry procedure-related risks, and are ineffective in a substantial subset of patients, underscoring the need for new therapeutic approaches. Caffeine, a widely consumed and well-tolerated adenosine receptor antagonist, has emerging relevance in vascular regulation and inflammatory signaling. Extracellular ATP and its metabolites, including adenosine, accumulate under stress and act through purinergic receptors to influence angioinflammatory processes. We recently showed that systemic caffeine administration suppressed CNV in vivo, an effect partly reproduced by the adenosine receptor A2A antagonist Istradefylline. Here, we investigated the cell-autonomous effects of caffeine on mouse choroidal endothelial cells, focusing on its role as an adenosine receptor antagonist and its potential to inhibit pathological neovascularization
Data Mining for Early Fault Detection in Artificial Satellites: A Review
No full textEarly detection of faults in artificial satellites is crucial for the success of missions, but it is hindered by the scarcity of data on faults and the limitations of traditional monitoring methods. As an alternative, technological trends such as data mining have emerged. This study presents a literature review to provide an in-depth examination of the landscape of data mining applications for early fault detection in satellites. Following the PRISMA protocol, the available scientific corpus from seven scientific databases was reviewed, and 52 primary studies were selected from an initial set of 2726 records published between 2011 and 2024. The results indicate that this is a rapidly expanding field, with an annual growth rate of 35.72%, characterized by a significant geopolitical concentration of research and funding led by China. From a methodological point of view, unsupervised approaches (~40%) predominate, a response to the lack of labeled in-flight data. However, supervised and hybrid models demonstrate superior performance, achieving F1 scores above 97% when selected or simulated data are available. A misalignment was identified in the domain, although research clearly favors the EPS due to the availability of data. Operational statistics, however, confirm that the ADCS system is the primary cause of mission failure. It is essential to note that the limited availability of public datasets and models, with less than 15% of studies providing access, is the main obstacle to reproducibility and progress. The primary conclusion of this work is that the field is expanding, and all stakeholders must contribute to its continued growth. Key actions include establishing public benchmarks that enable transparent evaluation, exploring physics-based models that account for uncertainty to address data scarcity, and concerted efforts to bridge the transfer gap from academic satellite operations to the real world
A Reliable Control Strategy for Dual Induction Motor Drive System Consisting of Five-Leg Inverter
No full textThis paper proposes a reliable control strategy for dual induction motor drives using a five-leg inverter (FLI). Since the FLI has the structural characteristic where both motors share a common leg, the current of the common leg can flow at twice the magnitude of the other leg currents. To prevent this overcurrent, this paper proposes a reliable integrated control strategy for both normal and open-circuit fault conditions in the FLI. Under normal conditions, overcurrent can occur when the phase and frequency of the current for both motors are distinct; therefore, the angle controller and current limitation prevent overcurrent. In contrast, an open-circuit fault in the FLI can cause overcurrent due to altered current paths. To ensure a safe shutdown, identifying the specific location of the faulty switch is essential. Therefore, fault diagnosis is required using the stator currents. Once the fault is located, a fault-tolerant method is applied to safely stop the motors, considering both the fault location and the rated current of the common leg. Consequently, the proposed system enables reliable operation of dual induction motor drives under various conditions. The experimental results verify the effectiveness of the proposed system
Boosted NH3 Selective Catalytic Oxidation Activity over V-Pt-Ti Catalysts: Insight into Preparation Method Effects
No full textIn this work, V-Pt-Ti catalysts were synthesized employing impregnation (IP), precipitation (PC), sol-gel (SG), thermal decomposition (TD), and hydrothermal (HD) methods. A systematic study has been carried out to investigate impacts of various preparation methods on the performance of NH3 selective catalytic oxidation (SCO) at temperatures from 150 °C to 450 °C. N2 adsorption/desorption, XPS, XRD, H2-TPR, NH3-TPD, O2-TPD, SEM, TEM, and in situ DRIFTS were adopted to characterize the physico-chemical property of V-Pt-Ti catalysts. The results suggested that V-Pt-Ti catalysts synthesized by precipitation methods (denoted as VPT-PC) exhibited notably better SCO performance across the 150–450 °C temperature range compared with those produced by impregnation (IP), sol-gel (SG), thermal decomposition (TD), and hydrothermal (HD) methods. The outstanding performance of the VPT-PC catalyst could be ascribed to its larger surface area, higher relative contents of Pt0, V5+, and Oα, more abundant surface acid sites, and better redox property. In situ DRIFTS results suggested that NO2 species could participate in NH3 oxidation reaction on the surface of the VPT-PC catalyst, which was beneficial for improving the SCO activity
Effectiveness of a Mobile-Based Self-Regulation Training on Youths’ Affect
No full textBackground: The rising prevalence and enduring impact of mental health problems in youth have intensified the call for population-level prevention. Low positive and high negative affect in childhood are vulnerability factors for mental health problems in adolescence. Supporting youth in managing affect during early adolescence may foster mental health preventively. Self-regulation training has shown promise in this regard. Moreover, its parallels with Behavioral Activation (BA) align with the recommendation to adapt evidence-based clinical interventions into scalable, accessible formats for prevention. Methods: This study examined whether a 12-day mobile-based self-regulation training, consistent with BA principles and delivered in an innovative digital format, could increase positive and decrease negative affect in a sample of 156 youths (Mage = 10.0). Results: No significant group differences emerged in affect change over time, and neither baseline levels of self-control nor emotion regulation strategies moderated the effects. Conclusions: The findings suggest that low-intensity mobile-based interventions may be insufficient to produce meaningful affect change in youth. The potential need to shift from universal prevention strategies to more selective approaches targeting at-risk youth is discussed
HFSA-Net: A 3D Object Detection Network with Structural Encoding and Attention Enhancement for LiDAR Point Clouds
No full textThe inherent sparsity of LiDAR point cloud data presents a fundamental challenge for 3D object detection. During the feature encoding stage, especially in voxelization, existing methods find it difficult to effectively retain the critical geometric structural information contained in these sparse point clouds, resulting in decreased detection performance. To address this problem, this paper proposes an enhanced 3D object detection framework. It first designs a Structured Voxel Feature Encoder that significantly enhances the initial feature representation through intra-voxel feature refinement and multi-scale neighborhood context aggregation. Second, it constructs a Hybrid-Domain Attention-Guided Sparse Backbone, which introduces a decoupled hybrid attention mechanism and a hierarchical integration strategy to realize dynamic weighting and focusing on key semantic and geometric features. Finally, a Scale-Aggregation Head is proposed to improve the model’s perception and localization capabilities for different-sized objects via multi-level feature pyramid fusion and cross-layer information interaction. Experimental results on the KITTI dataset show that the proposed algorithm increases the mean Average Precision (mAP) by 3.34% compared to the baseline model. Moreover, experiments on a vehicle platform with a lower-resolution LiDAR verify the effectiveness of the proposed method in improving 3D detection accuracy and its generalization ability
Genetic Variants in Liver Cirrhosis: Classifications, Mechanisms, and Implications for Clinical Practice
No full textBackground: Cirrhosis represents the final common pathway of chronic liver injury, arising from diverse etiologies such as metabolic, viral, autoimmune, and alcohol-related liver diseases. Despite similar exposures, disease progression varies considerably among individuals, suggesting a genetic contribution to susceptibility and outcome. Objective: This narrative review examines how specific genetic variants influence the risk, progression, and phenotypic expression of cirrhosis. It provides a structured synthesis of established and emerging gene associations, emphasizing their biological mechanisms and potential clinical relevance. Methods: This narrative review synthesizes evidence from all major biomedical and scientific databases, including PubMed, Scopus, Web of Science, and Google Scholar, as well as reference lists of relevant articles, covering literature published between 2005 and 2025 on genetic polymorphisms associated with cirrhosis and its etiological subtypes. Content: Variants are categorized into four mechanistic domains—metabolic regulation, immune modulation, liver enzyme activity, and ancestry-linked expression patterns—representing a novel integrative framework for understanding genetic risk in cirrhosis. Well-characterized variants such as PNPLA3, TM6SF2, HSD17B13, and MBOAT7, along with less commonly studied loci and chromosomal alterations, are discussed in relation to major etiologies, including MASLD/MASH, viral hepatitis, alcohol-related liver disease, and autoimmune conditions. Conclusions: Genetic insights into cirrhosis offer pathways toward early risk stratification and personalized disease management. While polygenic risk scores and multi-omic integration show promise, their clinical translation remains exploratory and requires further validation through large-scale prospective studies
A Framework for Mitigating Greenwashing in Sustainability Reporting
No full textGreenwashing in environmental, social, and governance reporting poses a significant threat to corporate accountability and stakeholder trust. This article provides a comprehensive synthesis of existing research to evaluate the role and effectiveness of sustainability assurance as a primary mechanism to combat greenwashing and proposes a framework for it. Based on a systematic literature review, this paper consolidates empirical findings indicating that sustainability assurance has a significant inhibitory effect on corporate greenwashing and is positively valued by capital markets, as evidenced by lower equity capital costs. However, the analysis also reveals that the effectiveness of assurance is not uniform; it is moderated by contextual factors such as the strength of the national legal environment and, in particular, regulatory environments, which can be exploited to legitimize overstated disclosures. This paper proposes a conceptual framework for anti-greenwashing assurance that integrates five interconnected pillars (regulatory, stakeholder engagement, third-party verification, corporate culture and internal controls, and technologies), forming a synergistic ecosystem of deterrents which collectively shape the integrity and credibility of sustainability reporting practices. To enhance the effectiveness of greenwashing mitigation, the proposed framework must be further strengthened by integrating the core principles of transparency, materiality, and verifiability across all its pillars
Bayesian Optimisation and Adaptive Evolutionary Algorithms for Higher-Order Fuzzy Models with Application on Wind Speed Prediction
No full textTo cope with the highly stochastic nature of wind speed, we explored the development of a predictive methodology. Considering an absence of studies pertaining to wind speed prediction that utilise state-of-the-art fuzzy models, the proposed approach adopted a novel higher-order Takagi–Sugeno–Kang fuzzy model intermixed with variational mode decomposition. The novelty of the predictive fuzzy model arises from the enhancement of rule consequents to include generalised terms and the incorporation of model complexity into the training scheme. To optimise the model, two approaches are considered: an adaptive differential evolution and a surrogate-based optimisation algorithm. The evolutionary approach employed two populations and a dual mutation scheme. The surrogate-based optimisation employed a Bayesian framework by fitting a Gaussian process model to the objective function. The latter approach yielded accurate predictive results while rapidly reducing the training time of the fuzzy model. A sequential wrapper-based algorithm was developed to effectively determine the feature space. The variational mode decomposed wind speed data were predicted individually, using an associated optimised fuzzy model. The proposed method was applied to a real-world wind speed dataset with exceptional approximation results. Comparisons with several artificial intelligence models highlighted the effectiveness and statistical significance of the methodology