Search Results (122)

This study focuses on a profound paradox in eco-hotel evaluations: why do consumer ratings for location, a static asset, exhibit dynamic fluctuations? To solve this puzzle, we construct a two-stage signal-processing theoretical framework that integrates Signaling Theory and the Elaboration Likelihood Model (ELM). This framework posits that the dynamic trajectory of a hotel’s green attributes (encompassing eco-facilities, sustainable practices, and ecological experiences) constitutes a high-fidelity market signal about its underlying quality. We utilized natural language processing techniques (Word2Vec and LSA) to conduct a longitudinal analysis of over 60,000 real consumer reviews from Booking.com between 2020 and 2023. This study confirms that continuous improvements in green attributes result in significant positive spillovers to location scores, while any degradation triggers strong negative spillovers. More critically, consumer environmental involvement (CEI) acts as an amplifier in this process, with high-involvement consumers reacting more intensely to both types of signals. The research further uncovers complex non-linear threshold characteristics in the spillover effect, subverting traditional linear management thinking. These findings not only provide a dynamic and psychologically deep theoretical explanation for sustainable consumption behavior but also offer forward-thinking practical implications for hoteliers on how to strategically manage dynamic signals to maximize brand value. Full article

Clay minerals contain significant amounts of Fe in their alumosilicate framework, and this structural Fe can be reduced and re-oxidized, constituting a potentially renewable source of reduction equivalents in sedimentary environments. However, dissolution and/or clay mineral transformations during microbial Fe reduction contradict this concept. Here, we investigate how Fe reduction and re-oxidation affect the propensity of Fe to be released from the clay mineral structure and use selective sequential extractions in combination with Mössbauer spectroscopy. Negligible amounts of Fe were released in the sequential extraction of high Fe content clay minerals NAu-1 and NAu-2. Once aqueous Fe(II) was added as a reductant, the extraction procedure recovered the initially added Fe amount and up to 30% of the Fe from the clay mineral structure as both Fe(II) and Fe(III). Similar extents of Fe mobilization were found for clay minerals partly reduced (7%–20%) with dithionite, suggesting that mobilization was reduction-induced and independent of the source of reduction equivalents (Fe(II), dithionite). Although higher Fe reduction extents mobilized more structural Fe, i.e., >90% in fully reduced clay minerals, re-oxidation largely reverted the reduction-induced Fe mobilization in clay minerals. Our finding of reduction-driven Fe mobilization provides a plausible explanation for conflicting reports on Fe release from clay minerals and how extensive Fe atom exchange between aqueous and clay mineral Fe occurs. Full article

The increasing adoption of DNNs in critical domains such as healthcare, finance, and autonomous systems underscores the growing importance of explainable artificial intelligence (XAI). In these high-stakes applications, understanding the decision-making processes of models is essential for ensuring trust and safety. However, traditional DNNs often function as “black boxes,” delivering accurate predictions without providing insight into the factors driving their outputs. Expected gradients (EG) is a prominent method for making such explanations by calculating the contribution of each input feature to the final decision. Despite its effectiveness, conventional baselines used in state-of-the-art implementations of EG often lack a clear definition of what constitutes “missing” information. This study proposes DeepPrior-EG, a deep prior-guided EG framework for leveraging prior knowledge to more accurately align with the concept of missingness and enhance interpretive fidelity. It resolves the baseline misalignment by initiating gradient path integration from learned prior baselines, which are derived from the deep features of CNN layers. This approach not only mitigates feature absence artifacts but also amplifies critical feature contributions through adaptive gradient aggregation. This study further introduces two probabilistic prior modeling strategies: a multivariate Gaussian model (MGM) to capture high-dimensional feature interdependencies and a Bayesian nonparametric Gaussian mixture model (BGMM) that autonomously infers mixture complexity for heterogeneous feature distributions. An explanation-driven model retraining paradigm is also implemented to validate the robustness of the proposed framework. Comprehensive evaluations across various qualitative and quantitative metrics demonstrate its superior interpretability. The BGMM variant achieves competitive performance in attribution quality and faithfulness against existing methods. DeepPrior-EG advances the interpretability of complex models within the XAI landscape and unlocks their potential in safety-critical applications. Full article

The rational calculation of groundwater buoyancy directly impacts the safety of underground engineering. However, there is still no consensus on whether the reduction of groundwater buoyancy should be considered, and a theoretical explanation and quantification of buoyancy reduction in clayey soils is lacking. Based on laboratory engineering model tests, this study observed and analyzed the phenomenon of buoyancy reduction in saturated clayey soils. The contact area ratio of gravity water, calculated from geotechnical test data, was compared with the reduction slope. The experimental results indicated that the reduction slope of the fitted line between the static water head in the silty clay layer and the buoyancy water head was 0.8692. And theoretical analysis showed that the distribution of interparticle pore water pressure tends to attenuate from the pore center to the soil particle surface, suggesting a reduction in buoyancy head compared to the groundwater level. The reduction slope is theoretically equal to the contact area ratio of gravity water. Additionally, since limitations in current techniques for generalizing the soil–water constitutive models affect the reduction slope, this study proposes a method for determining the buoyancy reduction slope in saturated clayey soil based on the theory that interparticle pore water pressure distribution attenuates from the pore center to the soil particle surface. This method could potentially change the existing conceptual framework for buoyancy design in underground structures. Full article

The large-scale dynamics of the universe is generally described in terms of the time-dependent scale factor $a\left(t\right)$. To make contact with observational data, the $a\left(t\right)$ function needs to be related to the observable $z\left(r\right)$ function, redshift versus distance. Model fitting of data has shown that the equation that governs $z\left(r\right)$ needs to contain a constant term, which has been identified as Einstein’s cosmological constant. Here, it is shown that the required constant term is not a cosmological constant but is due to an overlooked geometric difference between proper time t and look-back time $t_{\mathrm{lb}}$ along lines of sight, which fan out isotropically in all directions of the 3D (3-dimensional) space that constitutes the observable universe. The constant term is needed to satisfy the requirement of spatial isotropy in the local limit. Its magnitude is independent of the epoch in which the observer lives and agrees with the value found by model fitting of observational data. Two of the observational consequences of this explanation are examined: an increase in the age of the universe from 13.8 Gyr to 15.4 Gyr, and a resolution of the $H_0$ tension, which restores consistency to cosmological theory. Full article

Absidia is the most species-rich genus within the family Cunninghamellaceae, with its members commonly isolated from diverse substrates, particularly rhizosphere soil. In this study, four novel Absidia species, A. irregularis sp. nov., A. multiformis sp. nov., A. ovoidospora sp. nov., and A. verticilliformis sp. nov., were discovered from soil samples collected in southern and southwestern China, using integrated morphological and molecular analyses. Phylogenetic analyses based on concatenated ITS, SSU, LSU, Act, and TEF1α sequence data reconstructed trees that strongly supported the monophyly of each of these four new taxa. Key diagnostic features include A. irregularis (closely related to A. oblongispora) exhibiting irregular colony morphology, A. multiformis (sister to A. heterospora) demonstrating polymorphic sporangiospores, A. ovoidospora (forming a clade with A. panacisoli and A. abundans) producing distinctive ovoid sporangiospores, and A. verticilliformis (next to A. edaphica) displaying verticillately branched sporangiophores. Each novel species is formally described with comprehensive documentation, including morphological descriptions, illustrations, Fungal Names registration identifiers, designated type specimens, etymological explanations, maximum growth temperatures, and taxonomic comparisons. This work constitutes the sixth instalment in a series investigating early-diverging fungal diversity in China aiming to enhance our understanding of the diversity of fungi in tropical and subtropical ecosystems in Asia. In this paper, the known species of Absidia are expanded to 71. Full article

Background: Depression constitutes a major public health issue, being one of the leading causes of the burden of disease worldwide. The risk of depression is determined by both genetic and environmental factors. While genetic factors cannot be altered, the identification of potentially reversible environmental factors is crucial in order to try and limit the prevalence of depression. Aim: A cross-sectional, questionnaire-based study on a sample from the multicultural region of Thrace in northeast Greece was designed to assess the potential association of depression with several sociodemographic characteristics, lifestyle, and health status. The study employed four machine learning (ML) methods to assess depression: logistic regression (LR), support vector machine (SVM), XGBoost, and neural networks (NNs). These models were compared to identify the best-performing approach. Additionally, a genetic algorithm (GA) was utilized for feature selection and SHAP (SHapley Additive exPlanations) for interpreting the contributions of each employed feature. Results: The XGBoost classifier demonstrated the highest performance on the test dataset to predict depression with excellent accuracy (97.83%), with NNs a close second (accuracy, 97.02%). The XGBoost classifier utilized the 15 most significant risk factors identified by the GA algorithm. Additionally, the SHAP analysis revealed that anxiety, education level, alcohol consumption, and body mass index were the most influential predictors of depression. Conclusions: These findings provide valuable insights for the development of personalized public health interventions and clinical strategies, ultimately promoting improved mental well-being for individuals. Future research should expand datasets to enhance model accuracy, enabling early detection and personalized mental healthcare systems for better intervention. Full article

Exposure to high surface ozone (O₃) concentrations, which is a major air pollutant and greenhouse gas, constitutes a significant public health concern, especially considering the potential adverse impact of climate change on future O₃ values. The implementation of increasingly effective methods to assess the factors determining the formation and variability of O₃ is, therefore, of great significance. In this study, a methodological approach combining both supervised and unsupervised machine learning algorithms (MLAs) with the Shapley additive explanations (SHAP) method was used to understand the key factors behind O₃ variability and to explore the nonlinear relationships linking O₃ to these factors. The SHAP analysis carried out at different event scales indicated (i) the dominant role of the meteorological variables in driving O₃ variability, mainly relative humidity, wind speed, and temperature throughout the study period; (ii) an increase in the contribution of temperature, nitrogen oxides, and carbon monoxide to high O₃ concentrations during a selected pollution event; (iii) the predominant effect of wind speed and relative humidity in shaping the O₃ daily patterns clustered using the k-means technique. The results obtained are expected to be useful for the definition of effective measures to prevent and/or mitigate the health damage associated with ozone exposure. Full article

Self-evaluation empowers students to progress independently and adapt their pace according to their unique circumstances. A critical facet of self-assessment and personalized learning lies in furnishing learners with formative feedback. This feedback, dispensed following their responses to self-assessment questions, constitutes a pivotal component of formative assessment systems. We hypothesize that it is possible to generate explanations that are useful as formative feedback using different techniques depending on the type of self-assessment question under consideration. This study focuses on a subject taught in a computer science program at a Spanish distance learning university. Specifically, it delves into advanced data structures and algorithmic frameworks, which serve as overarching principles for addressing complex problems. The generation of these explanatory resources hinges on the specific nature of the question at hand, whether theoretical, practical, related to computational cost, or focused on selecting optimal algorithmic approaches. Our work encompasses a thorough analysis of each question type, coupled with tailored solutions for each scenario. To automate this process as much as possible, we leverage natural language processing techniques, incorporating advanced methods of semantic similarity. The results of the assessment of the feedback generated for a subset of theoretical questions validate the effectiveness of the proposed methods, allowing us to seamlessly integrate this feedback into the self-assessment system. According to a survey, students found the resulting tool highly useful. Full article

Road horizontal curves, which significantly influence drivers’ visual search behavior and are closely linked to traffic safety, also constitute a crucial factor in sustainable road traffic development. This paper uses simulation driving experiments to explore the dynamic response characteristics of 27 typical subject drivers’ visual search behavior regarding road horizontal curve radius. Results show that in a monotonous, open road environment, the driver’s visual search is biased towards the inside of the curve; as the radius increases, the 85th percentile value of the longitudinal visual search length gradually increases, the 85th percentile value of the horizontal search angle gradually decreases, the 85th percentile value of vehicle speed gradually increases, and the dispersion and bias of the gaze points gradually decrease. The search length, horizontal angle, and speed approach the level of straight road sections (380 m, 10° and 115 km/h, respectively). When R ≥ 1200 m, a driver’s dynamic visual search range reaches a stable distribution state that is the same as that of a straight road. A dynamic visual search range distribution model for drivers on straight and horizontal curved road sections is constructed. Based on psychological knowledge such as attention resource theory and eye–mind theory, a human factor engineering explanation was provided for drivers’ attention distribution and speed selection mechanism on road horizontal curve sections. The research results can provide theoretical references for the optimization design of road traffic, decision support to improve the driver training system, and a theoretical basis for determining the visual search characteristics of human drivers in autonomous driving technology, thereby promoting the safe and sustainable development of road traffic. Full article

Background/Objectives: Working with young adults with T1D in outpatients clinic entails achieving a delicate balance between maintaining trust and improving diabetes management. By looking at the interactions between healthcare professionals and young adults with T1D as narrative emplotment, this article seeks to investigate how illness narratives are part of and actively worked on in consultations. Methods: Based on ethnographic observations of fourteen consultations with young adults 18–23 years of age, three narrative strategies to promote better diabetes management among the young adults were identified: (1) replacing sub-optimal practice with technology, (2) encouraging enhanced autonomy, and (3) setting realistic standards for diabetes care. Each strategy works to create a meaningful explanation for experienced challenges, formingas a basis for improved diabetes self-management. Results: Consultations were found to create a space where the meaning of living with an illness can be discussed between the healthcare professionals and the patients. Conclusions: Looking at how this meaning is negotiated in the consultations is an important aspect of understanding how daily diabetes management is made practicable, especially when working with young adults who are constituting their identities and often live with sub-optimal glycaemic control. Full article

The Android operating system has become increasingly popular, not only on mobile phones but also in various other platforms such as Internet-of-Things devices, tablet computers, and wearable devices. Due to its open-source nature and significant market share, Android poses an attractive target for malicious actors. One of the notable security challenges associated with this operating system is riskware. Riskware refers to applications that may pose a security threat due to their vulnerability and potential for misuse. Although riskware constitutes a considerable portion of Android’s ecosystem malware, it has not been studied as extensively as other types of malware such as ransomware and trojans. In this study, we employ machine learning techniques to analyze the behavior of different riskware families and identify similarities in their actions. Furthermore, our research identifies specific behaviors that can be used to distinguish these riskware families. To achieve these insights, we utilize various tools such as k-Means clustering, principal component analysis, extreme gradient boost classifiers, and Shapley additive explanation. Our findings can contribute significantly to the detection, identification, and forensic analysis of Android riskware. Full article

Background and Objectives: Laparoscopic cholecystectomy constitutes the current “gold standard” treatment of symptomatic gallstone disease. In order to avoid intraoperative vasculobiliary injuries, it is mandatory to establish the “critical view of safety”. In cases of poor identification of the cystic duct and artery leading to a missed intraoperative injury, patients present with elevated liver function tests (LFTs) or increased bilirubin postoperatively. The aim of this study is to present a series of patients of our institute with elevated liver enzymes and bilirubin after laparoscopic cholecystectomy in the absence of intraoperative injury or any other obvious etiology and to provide a possible explanation of this finding. Materials and Methods: From 2019 to 2023, 200 patients underwent elective laparoscopic cholecystectomy at the Papageorgiou General Hospital and at the European Interbalkan Medical Center of Thessaloniki utilizing the “critical view of safety” method. We retrospectively collected the intraoperative reports, and the pre- and postoperative imaging and laboratory studies of the patients included in this series. Postoperative LFTs and bilirubin levels were extracted and the reason for their transient elevation was examined. Results: From 200 cases of laparoscopic cholecystectomy, elevated LFTs and bilirubin were found in six patients on the first postoperative day, which is suggestive of a missed intraoperative injury. All patients were asymptomatic. During the investigatory workup, a triple-phase CT of the liver and/or an MRCP were ordered, but no pathological findings, such as biliary injury, hepatic artery injury or choledocholithiasis, were found. On postoperative day 3, LFTs and bilirubin levels decreased or normalized without any intervention. No postoperative complications were reported. Conclusions: In select cases, a transient increase in LFTs and/or bilirubin may be observed in the early postoperative period after elective laparoscopic cholecystectomy in the absence of an obvious etiology. A possible interpretation of these findings could involve the pneumoperitoneum or the anesthesia regimens used intra- or perioperatively. The specific cause, however, remains undetermined and yet to be examined by future studies. Full article

Public transit has been continuously developing because of advocacy for low-carbon living, and concerns about its safety have gained prominence. The various factors that constitute the bus operating environment are extremely complex. Although existing research on operational security is crucial, previous studies often fail to fully represent this complexity. In this study, a novel method was proposed to identify the risk factors for bus operations based on a Bayesian network. Our research was based on monitoring data from the public transit system. First, the Tabu Search algorithm was applied to identify the optimal structure of the Bayesian network with the Bayesian Information Criterion. Second, the network parameters were calculated using bus monitoring data based on Bayesian Parameter Estimation. Finally, reasoning was conducted through prediction and diagnosis in the network. Additionally, the most probable explanation of bus operation spatial risk was identified. The results indicated that factors such as speed, traffic volume, isolation measures, intersections, bus stops, and lanes had a significant effect on the spatial risk of bus operation. In conclusion, the study findings can help avert dangers and support decision-making for the operation and management of public transit in metropolitan areas to enhance daily public transit safety. Full article

The etiology of autism spectrum disorder (ASD) has not yet been completely elucidated. Through time, multiple attempts have been made to uncover the causes of ASD. Different theories have been proposed, such as being caused by alterations in the gut–brain axis with an emphasis on gut dysbiosis, post-vaccine complications, and genetic or even autoimmune causes. In this review, we present data covering the main streams that focus on ASD etiology. Data collection occurred in many countries covering ethnically diverse subjects. Moreover, we aimed to show how the progress in genetic techniques influences the explanation of medical White Papers in the ASD area. There is no single evidence-based pathway that results in symptoms of ASD. Patient management has constantly only been symptomatic, and there is no ASD screening apart from symptom-based diagnosis and parent-mediated interventions. Multigene sequencing or epigenetic alterations hold promise in solving the disjointed molecular puzzle. Further research is needed, especially in the field of biogenetics and metabolomic aspects, because young children constitute the patient group most affected by ASD. In summary, to date, molecular research has confirmed multigene dysfunction as the causative factor of ASD, the multigene model with metabolomic influence would explain the heterogeneity in ASD, and it is proposed that ion channel dysfunction could play a core role in ASD pathogenesis. Full article