Search Results (766)

The growing consumer interest in functional foods and healthy eating can unfortunately lead to the spread of misinformation and the belief in food-related myths. This study analyzed Brazilian consumers’ perceptions and beliefs about facts and myths regarding functional foods, focusing on attitudes, reference groups, and sociocultural factors affecting their perception. A theoretical model was developed, incorporating constructs such as attitudes (reward, trust, necessity, safety), beliefs, and reference groups. Data from 600 participants in the Southeast (n = 300) and Northeast (n = 300) of Brazil were collected through online questionnaires, with responses measured on a five-point Likert scale. Myths (widely held ideas lacking scientific basis) and ‘facts’ (evidence-based information) regarding functional food were selected via literature review and validated by nutrition experts. Structural equation modeling revealed that perceived necessity and reward were positively associated with myths, while safety perception was negatively associated with myths. Reference groups and beliefs were positively associated with facts. Cluster analysis identified two consumer profiles: (1) safety-conscious individuals, who prioritize food safety, and (2) engaged critics, influenced by reference groups and actively seeking information. These findings highlight the importance of culturally tailored communication strategies for countering myths and promoting functional foods in Brazil. Regulatory bodies in Brazil must enhance oversight of health claims to build consumer trust and encourage informed choices, fostering mindful consumption habits. Full article

Background: Hepatocellular carcinoma (HCC), a prevalent form of primary liver malignancy, arises from liver-specific hepatocytes. Senecio scandens Buch.-Ham(Climbing senecio) is a bitter-tasting plant of the Compositae family with anti-tumor properties. This study aims to identify the molecular targets and pathways through which Climbing senecio regulates HCC. Methods: Active ingredients of Climbing senecio were collected from four online databases and mapped to relevant target databases to obtain predicted targets. After recognizing the key pathways through which Climbing senecio acts in HCC. Gene expression data from GSE54238 Underwent differential expression and weighted gene correlation network analyses to identify HCC-related genes. The “Climbing senecio-Hepatocellular Carcinoma Targets” network was constructed using Cytoscape 3.10.1 software, followed by topology analysis to identify core genes. The expression and distribution of key targets were evaluated, and the differential expression of each key target between normal and diseased samples was calculated. Moreover, single-cell data from the Gene Expression Omnibus (GSE202642) were used to assess the distribution of Climbing senecio’s bioactive targets within major HCC clusters. An intersection analysis of these clusters with pharmacological targets and HCC-related genes identified Climbing senecio’s primary targets for this disease. Cell communication, receiver operating characteristic (ROC)analysis, survival analysis, immune filtration analysis, and molecular docking studies were conducted for detailed characterization. Results: Eleven components of Climbing senecio were identified, along with 520 relevant targets, 300 differentially expressed genes, and 3765 co-expression module genes associated with HCC. AKR1B1, CA2, FOS, CXCL2, SRC, ABCC1, and PLIN1 were identified within the intersection of HCC-related genes and Climbing senecio targets. TGFβ, IL-1, VEGF, and CXCL were identified as significant factors in the onset and progression of HCC. These findings underscore the anti-HCC potential and mode of action of Climbing senecio, providing insights into multi-targeted treatment approaches for HCC. Conclusions: This study revealed that Climbing senecio may target multiple pathways and genes in the process of regulating HCC and exert potential drug effects through a multi-target mechanism, which provides a new idea for the treatment of HCC. However, the research is predicated on network database analysis and bioinformatics, offering insights into HCC therapeutic potential while emphasizing the need for further validation. Full article

In the era of online accommodation reviews, understanding the consumer mind is essential for the hospitality industry. This study seeks to profile consumers based on their reservation decisions made after reviewing online feedback and to explore the complex relationship between consumer perceptions and their decision-making processes. To lay a solid foundation for this research, a thorough bibliometric analysis was conducted to map the existing literature and identify key trends in the field. Data were collected using a non-probability convenience sampling method through an online survey targeting Romanian residents. Performing a hierarchical cluster analysis, followed by a K-means cluster analysis, distinct consumer segments with varying levels of trust and responsiveness were identified. The four primary clusters are Young Risk-Averse Planners, Trust-Oriented Quality Seekers, Skeptical Detail Seekers and Independent Value Seekers. Each segment displayed unique preferences regarding the types of reviews they value and their influence on booking decisions. These findings highlight the need for hotel managers and marketers to develop tailored strategies that cater to the diverse needs of consumers, enhancing service delivery and promoting sustainable tourism practices. This research provides valuable insights into the dynamics of online reviews and stresses the importance of understanding consumer perceptions in navigating the complexities of today’s hospitality industry. Full article

In this paper, we analyse the attitudes and sentiments of Romanian smallholders towards mole infestations, as expressed in online contexts. A corpus of texts on the topic of ground moles and how to get rid of them was collected from social media and blog thread discussions. The texts were analysed using topic modelling, clustering, and sentiment analysis, revealing both negative and positive sentiments and attitudes. The methods used by farmers when dealing with ground moles involve both eco-friendly repellent solutions and toxic substances and pesticides. Even well-intentioned farmers are discouraged by crop and lawn damage, resorting to environmentally aggressive solutions. The study shows that the relationship between humans and moles could be improved by active education on effective ecological agricultural approaches. Full article

The rising interest in organic products aligns with a global push for sustainable development, notably through initiatives like the European Green Deal introduced by the European Commission. In Romania, although organic farming and product consumption are increasing, they remain well below the EU averages. This study explores Romanian consumers’ and non-consumers’ perceptions and attitudes toward organic products in a contemporary context shaped by post-COVID-19 adjustments and geopolitical tensions. By developing consumer profiles, the study provides insights to help manufacturers and sellers diversify their strategies such as to meet the EU Green Deal targets. Data were collected from 833 respondents using an online survey and then analyzed with SPSS 23.0. The sample is more representative of young, well-educated, urban residents and, therefore, not fully representative of the entire Romanian population. Descriptive statistics revealed socio-demographic profiles and means for variables reflecting consumer attitudes toward organic products. Exploratory factor analysis with Varimax rotation identified core dimensions among variables and cluster analysis was used to identify different consumer groups. Findings show that typical organic product consumers are younger, well educated, and value quality, reflecting a commitment to sustainable choices. However, high prices are the main barrier to market growth, deterring many potential consumers. Additionally, there is considerable skepticism about organic foods, with doubts about their advantages over conventional products, and a lack of information limits consumer understanding of organic product benefits. These obstacles hinder broader adoption of organic foods in Romania. Future public policies should better support organic market chains to promote the positive externalities of such products such as to meet the ambitious EU Green Deal targets. Full article

This study examined the differences in perceived stress, subjective well-being, psychosocial variables, and differences in parents’ parenting styles according to game use type among Korean adults. The study involved 300 participants in their 20s and 30s, a demographic typically associated with frequent gaming. Data were collected through an online survey company, and analyses were conducted using SPSS 25.0, including correlation, cluster, ANOVA, and correspondence analyses. The results showed that the general and adaptive game use groups had significantly lower levels of perceived stress than the maladaptive and risky game use groups. Additionally, the adaptive game use group exhibited significantly higher subjective well-being than the maladaptive game use group. In terms of psychosocial characteristics, except for the general game use group, none of the other groups considered online gaming as addictive. Parenting styles showed significant differences in relation to game use in adulthood. Notably, democratic parenting styles were associated with the general and adaptive game use groups, whereas neglectful parenting styles were linked to the risky game use group. These findings suggest that the risky game use group is as vulnerable to stress as the maladaptive game use group, emphasizing the need for targeted screening and social attention for the risky game use group. Full article

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

Deep learning approaches have been successfully applied to perform automatic classification of phase-resolved partial discharge (PRPD) diagrams. Under the supervised learning paradigm, however, the performance of classifiers strongly depends on the availability of large and previously labeled data sets. Labeling is an intensive and time-consuming labor, typically involving the manual annotation of a large number of data examples by an expert. In this work, we propose a label propagation algorithm applied to PRPD data sets, aiming to reduce the time necessary to manually label PRPDs. Our basic pipeline is composed of three phases: pre-processing, dimensionality reduction procedures, and clustering. Different configurations of the basic pipeline are tested by using PRPDs obtained from online measurements in hydrogenerators. The performance of each configuration is assessed by using the Silhouette, Caliński–Harabasz, and Davies–Bouldin scores. The clustering of the best three configurations is compared with annotated PRPDs by using the Fowlkes-Mallows index. Results suggest our strategy can substantially decrease the time for manual labeling. Full article

The strong wind environment causes the additional conductor of the overhead contact system (OCS) of the Lanzhou–Xinjiang high-speed railway to gallop, significantly impacting the safe operation of the train. This paper presents the design of an online monitoring system for the galloping of additional conductors in the OCS, utilizing video monitoring for accurate and real-time assessment. Initially, the dynamics of the OCS additional conductor and its operational environment are examined, leading to the selection of suitable data transmission and power supply methods to finalize the camera configuration. Secondly, a preprocessing method for enhancing images of galloping in OCS additional conductors is developed, effectively reducing noise in edge detection through a region chain code clustering analysis. The video monitoring system effectively extracts wire edges, addressing the issues of splitting, breakage, and edge overlap in edge detection, while accurately identifying wire targets in video images. In conclusion, a galloping monitoring test platform is established to extract galloping data from additional conductors through video monitoring. The analysis of the galloping frequency and amplitude facilitates the comprehensive monitoring and assessment of the galloping status of OCS additional conductors. The video monitoring system effectively extracts and analyzes galloping data of the OCS additional conductor, fulfilling the fundamental requirements for the online monitoring of additional conductor galloping, and possesses significant engineering application value. Full article

Energy efficiency constitutes a pivotal performance indicator for 5G New Radio (NR) networks and beyond, and achieving optimal efficiency necessitates the meticulous consideration of trade-offs against other performance parameters, including latency, throughput, connection densities, and reliability. Energy efficiency assumes it is of paramount importance for both User Equipment (UE) to achieve battery prologue and base stations to achieve savings in power and operation cost. This paper presents an exhaustive review of power-saving research conducted for 5G and beyond 5G networks in recent years, elucidating the advantages, disadvantages, and key characteristics of each technique. Reinforcement learning, heuristic algorithms, genetic algorithms, Markov Decision Processes, and the hybridization of various standard algorithms inherent to 5G and 5G NR represent a subset of the available solutions that shall undergo scrutiny. In the final chapters, this work identifies key limitations, namely, computational expense, deployment complexity, and scalability constraints, and proposes a future research direction by theoretically exploring online learning, the clustering of the network base station, and hard HO to lower the consumption of networks like 2G or 4G. In lowering carbon emissions and lowering OPEX, these three additional features could help mobile network operators achieve their targets. Full article

Transport Demand Management (TDM) is crucial in shaping travel behavior and enhancing urban mobility by promoting sustainable transport options. This study represents a comprehensive analysis of employee commuting behavior across seventy-seven cities in Italy, with a focus on Rome as a case study. It investigates some requirements of the workplace travel plan as a TDM strategy for promoting sustainable commuting. An online survey conducted in June 2022 yielded 2314 valid responses, including 1320 from private car drivers. K-means clustering was used to identify distinct behavioral patterns among commuters, revealing four clusters based on demographic factors and transport preferences, such as age, gender, family circumstances, vehicle ownership, willingness to walk, ride bicycles, or e-scooters, and reasons for mode choice. This study analyzed Rome’s public transport network, land use, and private car use. Results underscore the need for tailored transport policies that enhance inclusivity and accessibility, especially for employees with family members who cannot commute independently. A spatial analysis of Rome reveals significant infrastructure deficiencies, such as complicated transfers and inaccessible stations, which discourage PT use. Future research should explore the impact of remote work and psychological factors and conduct in-depth subgroup analyses to inform inclusive transport policy development. Full article

Alerts are an essential tool for the detection of anomalies and ensuring the smooth operation of online service systems by promptly notifying engineers of potential issues. However, the increasing scale and complexity of IT infrastructure often result in “alert storms” during system failures, overwhelming engineers with a deluge of often correlated alerts. Therefore, effective alert aggregation is crucial in isolating root causes and accelerating failure resolution. Existing approaches typically rely on either semantic similarity or statistical methods, both of which have significant limitations, such as ignoring causal relationships or struggling to handle infrequent alerts. To overcome these drawbacks, we propose a novel two-phase alert aggregation approach. We employ temporal–spatial clustering to group alerts based on their temporal proximity and spatial attributes. In the second phase, we utilize large language models to trace the cascading effects of service failures and aggregate alerts that share the same root cause. Experimental evaluations on datasets from real-world cloud platforms demonstrate the effectiveness of our method, achieving superior performance compared to traditional aggregation techniques. Full article

Financial technologies metamorphose economies with customer-focused innovation. In this way, financial inclusion is fostered and economic growth is increased. However, risks, trust issues, and ethical concerns stem from the faster advancement of digital technologies and expanding financial innovation. Thus, this paper aims to understand the risks and barriers associated with FinTech and consumer adoption, focussing on the impact of digitalization on financial products/services’ acceptance. The research investigates the impact of digitalization on financial services and the recognition of the role played in the global economy by FinTech. For this reason, the regression analysis was used to explore the influence and correlation of various variables on FinTech in Central and Eastern European (CEE) countries, such as Internet usage, online shopping, paying bills via the Internet, and making and receiving digital payments. The results show differences between three clusters of CEEs in terms of FinTech adoption. While several past studies have explored the advantages of FinTech, few studies have investigated the risks associated with its adoption, trust, and barriers to its usage in different country contexts. The present paper fills the gap by analysing the data on Internet usage, online shopping, paying bills via Internet, and sending or receiving digital payments in CEE countries. The study recommends that FinTech companies share information online not only to present their offerings to users, but also to promote financial education through clear and straightforward communication about the features of their services. This approach can indirectly benefit society by contributing to financial development, inclusion, social stability, and, consequently, sustainable development. Full article

Social networks, functioning as core platforms for modern information dissemination, manifest distinctive user clustering behaviors and state transition mechanisms, thereby presenting new challenges to traditional information propagation models. Based on hypergraph theory, this paper augments the traditional SEIR model by introducing a novel hypernetwork information dissemination SSEIR model specifically designed for online social networks. This model accurately represents complex, multi-user, high-order interactions. It transforms the traditional single susceptible state ($S$) into active ($S_a$) and inactive ($S_i$) states. Additionally, it enhances traditional information dissemination mechanisms through reaction process strategies (RP strategies) and formulates refined differential dynamical equations, effectively simulating the dissemination and diffusion processes in online social networks. Employing mean field theory, this paper conducts a comprehensive theoretical derivation of the dissemination mechanisms within the SSEIR model. The effectiveness of the model in various network structures was verified through simulation experiments, and its practicality was further validated by its application on real network datasets. The results show that the SSEIR model excels in data fitting and illustrating the internal mechanisms of information dissemination within hypernetwork structures, further clarifying the dynamic evolutionary patterns of information dissemination in online social hypernetworks. This study not only enriches the theoretical framework of information dissemination but also provides a scientific theoretical foundation for practical applications such as news dissemination, public opinion management, and rumor monitoring in online social networks. Full article

This study aims to bridge the gap between traditional consumer behavior analysis and modern techniques by integrating big data analysis, eye-tracking technology, and survey methods. The researchers considered that understanding consumer behavior is crucial for creating effective advertisements in the digital age. Initially, a big data analysis was performed to identify significant clusters of consumer sentiment from online reviews generated during a recent seasonal promotional campaign. The key factors were identified and grouped into the “Product”, “Model”, “Promo”, and “Effect” categories. Using these clusters as a foundation, an eye-tracking analysis measured visual attention metrics such as the fixation duration and count to understand how the participants engaged with the different advertisement content. Subsequently, a survey assessed the same participants’ purchase intentions and preferences related to the identified clusters. The results showed that the sentiment clusters related to products, promotions, and effects positively impacted the customer satisfaction. The eye-tracking data revealed that advertisements featuring products and models garnered the most visual attention, while the survey results indicated that promotional content significantly influenced the purchase intentions. This multi-step approach delivers an in-depth understanding of the factors that affect customer satisfaction and decision-making, providing valuable information for optimizing marketing strategies in the Korean skincare market. The findings emphasize the importance of integrating consumer sentiment analysis with visual engagement metrics to develop more effective and compelling marketing campaigns. Full article