Search Results (3,654)

The rapid growth of e-commerce has transformed customer behaviors, demanding deeper insights into how demographic factors shape online user preferences. This study performed a threefold analysis to understand the impact of these changes. Firstly, this study investigated how demographic factors (e.g., age, gender, education) influence e-customer preferences in Serbia. From a sample of n = 906 respondents, conditional dependencies between demographics and user preferences were tested. From a hypothetical framework of 24 tested hypotheses, this study successfully rejected 8/24 (with p < 0.05), suggesting a high association between demographics with purchase frequency and reasons for quitting the purchase. However, although the reported test statistics suggested an association, understanding how interactions between categories shape e-customer profiles was still required. Therefore, the second part of this study considers an MCA-HCPC (Multiple Correspondence Analysis with Hierarchical Clustering on Principal Components) to identify user profiles. The analysis revealed three main clusters: (1) young, female, unemployed e-customers driven mainly by customer reviews; (2) retirees and older adults with infrequent purchases, hesitant to buy without experiencing the product in person; and (3) employed, highly educated, male, middle-aged adults who prioritize fast and accurate delivery over price. In the third stage, the clusters are used as labels for Machine Learning (ML) classification tasks. Particularly, Gradient Boosting Machine (GBM), Decision Tree (DT), k-Nearest Neighbors (kNN), Gaussian Naïve Bayes (GNB), Random Forest (RF), and Support Vector Machine (SVM) were used. The results suggested that GBM, RF, and SVM had high classification performance in identifying user profiles. Lastly, after performing Permutation Feature Importance (PFI), the findings suggested that age, work status, education, and income are the main determinants of shaping e-customer profiles and developing marketing strategies. Full article

Tropical cyclones (TCs) significantly impact the Caribbean Lesser Antilles, often causing severe wind and water damage. Traditional flood hazard assessments simplify TC rainfall as single-peak, short-duration events tied to specific return periods, overlooking the spatial–temporal variability in rainfall that TCs introduce. To address this limitation, a new user-friendly tool incorporates spatial–temporal rainfall variability into TC-related flood hazard assessments. The tool utilizes satellite precipitation data to break down TC-associated rainfall into distinct pathways/scenarios, mapping them to ground locations and linking them to specific sections of the storm’s rainfall footprint. This approach demonstrates how different areas can be affected differently by the same TC. In this study, we apply the tool to evaluate rainfall patterns and flood hazards in St. George’s, Grenada, during Hurricane Beryl in 2024. The scenario representing the 75th quantile in Spatial Region 2 (S2-Q_0.75) closely matched the actual rainfall observed in the study area. By generating multiple hazard maps based on various rainfall scenarios, the tool provides decision-makers with valuable insights into the multifaced flood hazard risks posed by a single TC. Ultimately, island communities can enhance their early warning and mitigation strategies for TC impacts. Full article

Physical Trading Card Games (TCGs) face inherent challenges, including vulnerability to theft, damage, and counterfeiting. Trading systems primarily depend on third-party marketplaces that collect fees from each trade, without benefiting the game developers. Players also deal with problems associated with shipping, such as high prices, long shipping times, and the risk of counterfeit goods. This paper introduces a blockchain-based solution that decouples card ownership from the physical media, which represents ownership with secure and verifiable digital tokens. The system leverages Ethereum Virtual Machine (EVM), the ERC-1155 standard, and InterPlanetary File System (IPFS) storage, ensuring flexibility, scalability, and cost-efficiency. The adoption of the lazy minting technique minimizes upfront costs for game developers by creating tokens only when acquired by end users. Physical representations of such digital goods can be printed on demand as they remain only a game accessory. The system also provides low-cost exchanges, significantly reducing the financial and logistical burdens associated with the trading of physical assets. Finally, the protocol empowers developers to monetize secondary markets through transaction fees. This approach addresses the limitations of physical card systems and also unlocks new opportunities for innovation and revenue in the TCG ecosystem. Full article

Drivers with physical and/or mental impairments face many driving challenges. However, not many studies have been carried out to understand the factors that contribute to crashes involving these drivers and how these factors influence their crash outcomes. This study aims to address this gap in the road safety literature. The study uses historical crash data from the State of Alabama for at-fault physically impaired drivers and utilizes a random parameter with heterogeneity in a mean modeling approach to account for unobserved heterogeneity. The model estimation results reveal that in rural areas, driving over the speed limit, the time of crash being between 6.00 p.m. and 11.59 p.m., younger drivers, employed and distracted drivers were associated with severe injuries. Minor injury crashes are found to be associated with female drivers, state roads and residential areas. Finally, property-damage-only crashes are more associated with weekdays, driving under the influence of alcohol or drugs, a road with left curvature, driving too fast for the road conditions and intersections. The results obtained provide a foundation for the adoption of targeted countermeasures to improve highway safety for physically impaired drivers and all road users in general. Full article

Introduction: Patients with rheumatoid arthritis (RA) and juvenile idiopathic arthritis (JIA) are at a twice-higher risk of developing adverse pregnancy outcomes, such as preterm births and infants with a low birth weight. We aimed to evaluate fetal growth among patients with and without rheumatoid arthritis and juvenile idiopathic arthritis (RA and JIA). Materials and Methods: We conducted a population-based cohort study in Denmark from 2008–2018, which included 503,491 singleton pregnancies. Among them, 2206 were pregnancies of patients with RA and JIA. We linked several nationwide databases and clinical registries in Denmark to achieve our aim. First, we used the International Classification of Diseases-10 codes to identify pregnant patients with RA and JIA from the National Patient Registry. Next, we obtained fetal biometric measurements gathered from second-trimester fetal ultrasound scans and birthweights through the Fetal Medicine Database. Finally, we computed a fetal growth gradient between the second trimester and birth, using the mean difference in the Z-score distances for each fetal growth indicator. We also calculated the risk of small for gestational age (SGA). All outcomes were compared between pregnant individuals with and without RA and JIA, adjusted for confounders. Results: Maternal RA and JIA were not associated with a reduction in the estimated fetal weight (EFW) at 18 to 22 weeks of gestational age [adjusted mean EFW Z-score difference of 0.05 (95% CI 0.01, 0.10)]. We observed reduced mean Z-score differences in the weight gradient from the second trimester to birth among offspring of patients with RA and JIA who used corticosteroids [−0.26 (95% CI −0.11, −0.41)] or sulfasalazine [−0.61 (95% CI −0.45, −0.77)] during pregnancy. Maternal RA and JIA were also associated with SGA [aOR of 1.47 (95% CI 1.16, 1.83)] and the risk estimates were higher among corticosteroid [aOR 3.44 (95% CI 2.14, 5.25)] or sulfasalazine [(aOR 2.28 (95% CI 1.22, 3.88)] users. Conclusions: Among pregnant patients with RA and JIA, fetal growth restriction seemed to occur after 18 to 22 weeks of gestational age. The second half of pregnancy may be a vulnerable period for optimal fetal growth in this population. Full article

This research aims to assess consumer attitudes towards purchasing pre-loved luxury fashion items and explore how these attitudes influence their intention to buy such products. Luxury goods consumption is evolving into a multifaceted proposition where customers actively take on new responsibilities. In addition to being purchasers and users, they occasionally turn into luxury brand product dealers. Luxury fashion, which includes more expensive materials, apparel, and frequently new and limited-edition items, is unquestionably stylish. Luxury brands could draw clients and the attention of many audiences, becoming quite prominent, even though luxury fashion only makes up a small portion of the economy compared to other significant businesses. Using a convenience sampling technique, data were collected from 282 individuals in Cairo. The analysis was conducted through SPSS software v2023. Our findings show that consumers’ concerns about the environment have a big influence on their perceived value (PI) of used luxury fashion items, both directly and indirectly through the mediation of their desire for sustainability. Nonetheless, attitude strength has a moderating effect on this association. It is interesting to note that the relationship between environmental concern and sustainability is weakened under the influence of attitude strength. Furthermore, our findings indicate that modest levels of attitude strength make it easy to change how customers’ environmental concerns affect their previously owned luxury fashion items. High-end stores can also fight off counterfeit marketplaces by providing authentication services to consumers of pre-loved luxury clothing. This study emphasizes the role of consumer attitude as a mediator in shaping purchase intentions for pre-loved luxury fashion. However, its focus on one region and cross-sectional data collection presents limitations. Future studies should explore other markets and use longitudinal data for a deeper understanding. This research contributes to the existing literature by offering insights for consumers, marketers, and sellers promoting pre-loved luxury fashion. Full article

Background: Diseases in humans caused by amphizoic amoebae that can result in visual impairment and even blindness, have recently been identified more frequently worldwide. Etiologically complex incidents of keratitis, including those connected with Acanthamoeba strains detected in Poland, were evaluated in this study. Methods: Corneal samples from cases resistant to antimicrobial therapy assessed for epidemiological, microbiological and parasitological aspects were investigated by phase-contrast microscope, slit lamp and by confocal microscopy. In vitro techniques were applied for detection of bacteria and fungi, and corneal isolates cultured under axenic condition using BSC medium—for detection of Acanthamoeba spp.; molecular techniques were applied for amoeba species identification. Results: Most etiologically complicated keratitis cases, detected in ~84% of incidents, was due to exposure of contact lenses to tap water or pool water; trophozoites and cysts of Acanthamoeba, concomitant bacteriae, e.g., Pseudomonas aeruginosa, fungi and microfilariae were identified in contact lens users. Conclusions: In samples from contact lens wearers where microbial keratitis is identified along with some connection with the patient’s exposure to contaminated water environments, a risk of Acanthamoeba spp. infections should be considered. Understanding the complicated relationship between Acanthamoeba spp., co-occurring pathogens including associated endosymbionts is needed. In vivo confocal microscopy and in vitro cultivation were necessary to identify potentially contagious concomitant factors affecting the complex course of the keratitis. Full article

Chatbots are widely used in customer services contexts today. People using chatbots have their pragmatic reasons, like checking delivery status and refund policies. The purpose of the paper is to investigate what are those factors that affect user experience and a chatbot’s service quality which influence user satisfaction and electronic word-of-mouth. A survey was conducted in July 2024 to collect responses in Hong Kong about users’ perceptions of chatbots. Contrary to previous literature, entertainment and warmth perception were not associated with user experience and service quality. Social presence was associated with user experience, but not service quality. Competence was relevant to user experience and service quality, which reveals important implications for digital marketers and brands of adopting chatbots to enhance their service quality. Full article

With the rapid growth of the electric vehicle market, the importance of the user experience and product sustainability requirements for intelligent charging stations has become increasingly significant. However, accurately capturing the complex associations between design features and sustainability elements remains challenging. Therefore, this study aims to balance user needs and environmental standards in designing smart charging piles, ensuring adherence to symmetry principles. This balance addresses the growing demand for personalization and ensures sustainability. In this paper, the semiotic approach to product construction (SAPAD) model is introduced to analyze the user behavioral process in depth and clarify the core needs of users. Subsequently, these core needs are translated into specific technical requirements for products, and a correlation matrix linking user needs with product technical requirements is constructed using fuzzy quality function deployment (FQFD) to identify design features that fulfill the user requirements. The sustainability factors are then comprehensively evaluated and prioritized based on three dimensions: economic, environmental, and social, i.e., the triple bottom line (TBL). Furthermore, a mapping matrix is developed to connect the design features and sustainability factors, which is combined with the particle swarm optimization–random forest (PSO-RF) algorithm to predict the sustainability factors associated with design features that meet users’ needs. The number of branches m and the maximum depth d of the random forest (RF) algorithm are optimized using the particle swarm optimization (PSO) method. The results indicate that the SAPAD-FQFD model effectively identifies the user needs and relevant product design features. In contrast, the PSO-RF model adeptly manages the nonlinear relationships between charging pile design features and various sustainability factors, e.g., aesthetics and material selection, ensuring that the intelligent charging pile meets users’ core needs in terms of form and function, while embodying the principles of design symmetry. This integrated approach effectively bridges the gap between user needs analysis and product functional design, ensuring the sustainability of the design solution. This study contributes a sustainable framework for the development and design of smart charging piles and related products, further promoting the adoption of green design principles and symmetry design concepts within the supporting infrastructure of new energy vehicles. Full article

In transportation systems, road users have diverse preferences when planning their trips and responding to traffic conditions in a large city. Connected vehicles can capture the preferences of individual users for trip planning, leading to improved road performance. However, managing a large number of connected vehicles with differing user preferences in a large city is a daunting task. This paper develops an integrated traffic management system with the consideration of user preferences to optimize the performance of each user. In the system, connected vehicles are introduced to estimate traffic conditions and costs associated with different user preferences. The system will utilize the information to search for multi-layer vehicle control instructions that account for user preferences in mobility, energy consumption, and driving comfort. Microscopic simulations were carried out to assess the system’s efficacy in mitigating road congestion, reducing fuel consumption, and restricting turns. The results reveal that implementing the system can reduce vehicle delay by up to 32%, fuel consumption by 4%, and left and right turns by 24%. Additionally, the paper evaluates the impact of market shares of connected vehicles with different preferences to analyze their performance at different stages of connected vehicle development. The work can contribute to the development of advanced transportation services in future cities and enhance urban mobility and energy sustainability. Full article

The rapid increase in data volume and variety within the field of machine learning necessitates ethical data utilization and adherence to strict privacy protection standards. Fair federated learning (FFL) has emerged as a pivotal solution for ensuring fairness and privacy protection within distributed learning environments. FFL not only enhances privacy safeguards but also addresses inherent limitations of existing federated learning (FL) systems by fostering equitable model training across diverse participant groups, mitigating the exclusion of individual users or minorities, and improving overall model fairness. This study examines the causes of bias and fairness within existing FL systems and categorizes solutions according to data partitioning strategies, privacy mechanisms, applicable machine learning models, communication architectures, and technologies designed to manage heterogeneity. To mitigate bias, enhance fairness, and strengthen privacy protections in FL, this study also explores fairness evaluation metrics, relevant applications, and associated challenges of FFL. Addressing bias, fairness, and privacy concerns across all mechanisms serves as a valuable resource for practitioners aiming to develop efficient FL solutions. Full article

Background: Gestational diabetes mellitus (GDM) occurs in approximately 9% of pregnancies, and proper glycemic control is of utmost importance in the prevention of GDM-associated obstetric complications. Flash glucose monitoring (FGM), a subtype of continuous glucose monitoring (CGM), offers intermittent blood glucose scanning and is considered a propitious alternative to the standard method of self-monitoring of blood glucose (SMBG). Aim: The aim of this review was to systematically assess the efficacy and acceptability of FGM in in pregnancies complicated by GDM. Methods: A systematic literature search was performed in the PubMed, MEDLINE, Scopus, and Cochrane databases. The review was conducted following the PRISMA guidelines, and the study protocol has been registered in the PROSPERO database with the registration number CRD42024545874. Results: A total of 872 articles were initially identified, 141 publications underwent an in-depth full-text analysis, resulting in 133 studies being excluded from further assessment. Eventually, eight studies were included in the analysis. Conclusions: The analysis revealed that FGM is a safe and efficient method of glycemic control in GDM. The majority of the studies consider its accuracy comparable to SMBG. Furthermore, FGM is well accepted by patients with numerous advantages in user-friendliness over SMBG. Full article

Background and Objectives: For persons with rheumatoid arthritis (RA), the accompanying systemic inflammatory conditions often insidiously damage extra-glandular organs, causing poor outcomes. Despite evidence manifesting the application of rehabilitation services (RSs), the association between RSs use and changes in the inflammatory response among persons with RA has not yet been established. With that in mind, this study aimed to evaluate changes in C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) before and after long-term RSs use. Materials and Methods: For this two-group pre and posttest study, medical data of 4505 persons with RA aged 20–70 years between 2012 and 2017 were retrieved from an electronic medical record database held by a hospital in Taiwan. Of them, 1387 subjects were categorized as RSs users, who received RSs at least six times within the first year of RA onset. Generalized estimating equations analysis was then employed to compare the changes in ESR and CRP at baseline, and at 12, 18, 24, 30, and 36 months after RA onset. Results: After adjusting for inherent differences and mature impact, those receiving standard care plus RSs were found to have a lower CRP level than those without receiving RSs. This benefit was maintained within a 3-year follow-up period. Additionally, a slight but nonsignificant reduction in ESR existed over the same timeframe. Conclusions: Integrating RSs into conventional care may be helpful to modulate the inflammation for RA patients, but further research via randomized controlled trials is needed to validate the application of RSs. Full article

The maturation of internet usage environments has elevated User Experience (UX) to a critical factor in system success. However, traditional manual UX testing methods are hampered by subjectivity and lack of standardization, resulting in time-consuming and costly processes. This study explores the potential of Large Language Models (LLMs) to address these challenges by developing an automated UX testing tool. Our innovative approach integrates the Rapi web recording tool to capture user interaction data with the analytical capabilities of LLMs, utilizing Nielsen’s usability heuristics as evaluation criteria. This methodology aims to significantly reduce the initial costs associated with UX testing while maintaining assessment quality. To validate the tool’s efficacy, we conducted a case study featuring a tennis-themed course reservation system. The system incorporated multiple scenarios per page, allowing users to perform tasks based on predefined goals. We employed our automated UX testing tool to evaluate screenshots and interaction logs from user sessions. Concurrently, we invited participants to test the system and complete UX questionnaires based on their experiences. Comparative analysis revealed that varying prompts in the automated UX testing tool yielded different outcomes, particularly in detecting interface elements. Notably, our tool demonstrated superior capability in identifying issues aligned with Nielsen’s usability principles compared to participant evaluations. This research contributes to the field of UX evaluation by leveraging advanced language models and established usability heuristics. Our findings suggest that LLM-based automated UX testing tools can offer more consistent and comprehensive assessments. Full article

Traditional recommendation systems, which rely on static user profiles and historical interaction data, frequently face difficulties in adapting to the rapid changes in user preferences that are typical of dynamic environments. In contrast, recommendation algorithms based on deep reinforcement learning are capable of dynamically adjusting their strategies to accommodate real-time fluctuations in user preferences. However, current deep reinforcement learning recommendation algorithms encounter several challenges, including the oversight of item features associated with high long-term rewards that reflect users’ enduring interests, as well as a lack of significant relevance between user attributes and item characteristics. This leads to an inadequate extraction of personalized information. To address these issues, this study presents a novel recommendation system known as the Multi-Level Hierarchical Attention Mechanism Deep Reinforcement Recommendation (MHDRR), which is fundamentally grounded in a multi-layer attention mechanism. This mechanism consists of a local attention layer, a global attention layer, and a Transformer layer, allowing for a detailed analysis of individual attributes and interactions within short-term preferred items, while also exploring users’ long-term interests. This methodology promotes a comprehensive understanding of users’ immediate and enduring preferences, thereby improving the overall effectiveness of the system over time. Experimental results obtained from three publicly available datasets validate the effectiveness of the proposed model. Full article