Search Results (357)

Background/Objectives: Patients with an ileoanal pouch change their diet to manage their symptoms and will often resort to the internet for nutrition advice. Currently, no evidence-based dietary guidelines exist to inform online resources. Hence, this study aims to assess the quality of online nutrition information directed towards patients with an ileoanal pouch. Methods: A systematic Google search was conducted to identify consumer websites including information on nutrition for those with ileoanal pouches. Quality was assessed using the DISCERN instrument, and the readability of written content was assessed using the Flesch–Kincaid score. A summative content analysis was used to identify the frequency of particular topics. Websites were also assessed against standards from the National Institute for Health and Care Excellence (NICE) framework for shared decision-making support tools. Results: A total of 12 websites met the inclusion criteria. Mean total DISCERN scores across all websites are 33 out of 75, indicating that overall, the websites were of poor quality. The mean Flesch–Kincaid score was 57 out of 100, or “fairly difficult” in terms of readability. The main themes according to the content analysis were “general dietary advice for pouch”, “dietary strategies for symptom management”, “addressing risks associated with having a pouch”, and “optimisation of nutritional intake”. Overall, websites did not meet the standards for shared decision-making. Conclusions: Online nutrition information for patients with an ileoanal pouch is of poor quality and difficult to understand. There is a need for higher quality online resources for these patients, ideally co-produced with a multidisciplinary team and patient, to provide patients with good quality, understandable, and accessible nutrition information. Full article

Nowadays, accessibility to academic papers has been significantly improved with electric publications on the internet, where open access has become common. At the same time, it has increased workloads in literature surveys for researchers who usually manually download PDF files and check their contents. To solve this drawback, we have proposed a reference paper collection system using a web scraping technology and natural language models. However, our previous system often finds a limited number of relevant reference papers after taking long time, since it relies on one paper search website and runs on a single thread at a multi-core CPU. In this paper, we present an improved reference paper collection system with three enhancements to solve them: (1) integrating the APIs from multiple paper search web sites, namely, the bulk search endpoint in the Semantic Scholar API, the article search endpoint in the DOAJ API, and the search and fetch endpoint in the PubMed API to retrieve article metadata, (2) running the program on multiple threads for multi-core CPU, and (3) implementing Dynamic URL Redirection, Regex-based URL Parsing, and HTML Scraping with URL Extraction for fast checking of PDF file accessibility, along with sentence embedding to assess relevance based on semantic similarity. For evaluations, we compare the number of obtained reference papers and the response time between the proposal, our previous work, and common literature search tools in five reference paper queries. The results show that the proposal increases the number of relevant reference papers by 64.38% and reduces the time by 59.78% on average compared to our previous work, while outperforming common literature search tools in reference papers. Thus, the effectiveness of the proposed system has been demonstrated in our experiments. Full article

Informational divergence emerged as a significant phenomenon during the COVID-19 health crisis. This period was characterized by information overload and changes in the communication of public health recommendations and policies by authorities and media outlets. This study examines the impact of such divergence on the population’s mental health, focusing on primary emotions expressed in comments across digital ecosystems. A media EMIC approach was used to analyze digital ecosystems during March and April 2020. Data were collected from Twitter, YouTube, Instagram, official press websites, and internet forums, yielding 3,456,387 communications. These were filtered to extract emotion-expressing content, resulting in 106,261 communications. Communications were categorized into primary emotions (anger, disgust, joy, fear, and sadness) using an exclusionary emotion assignment procedure. Analysis techniques included polarity and term frequency calculation, content analysis using Natural Language Understanding, emotion intensity measurement using IBM Watson Analytics, and data reliability assessment using the ISMA-OWA operator. The findings suggest that exposure to informational divergence from governments, health organizations, and media negatively affected mental health, evidenced by sadness, fear, disgust, and anger, which are associated with elevated levels of stress, anxiety, and information fatigue. In contrast, information perceived as reflecting coordination, support, and solidarity elicited positive emotional responses, particularly joy. Full article

Introduction: The spread of health-related information across the internet necessitates an evaluation of public eHealth literacy, trust in different health information sources, including healthcare providers, and how eHealth literacy is related to trust in different sources. Methods: 407 individuals participated in a web-based survey in the Tabuk region of Saudi Arabia. Univariate analysis was used to evaluate the relationships between eHealth literacy and demographic variables, and multiple linear regression was used to measure the relationship between eHealth literacy and trust in health information sources after adjustment for demographic factors. Results: The average eHealth literacy of the respondents was 27.17 out of 40. eHealth literacy levels were higher among females, younger age groups, those in the higher-education category, and those with a chronic disease or currently on medication. For 51.9% of participants, physicians and healthcare workers were their main source of health information, while 40% considered the internet their main source. None of the study participants perceived physicians and healthcare workers as untrustworthy, and social media was the least trusted source. eHealth literacy was not related to trust in physicians and health workers but was positively associated with trust in specialized health websites and negatively associated with trust in social media. Conclusions: The findings suggest that the public tends to prefer and trust physicians and other healthcare workers as a primary source of health information, regardless of their eHealth literacy levels. A higher eHealth literacy level was associated with trust in specialized health websites and distrust in social media. Full article

Hand impairment affects millions of people. There are multiple factors that cause deficits, varying from physical injuries to neurological disorders. Upper-limb patients face significant difficulties in daily life. Rehabilitation aims at supporting them to regain functionality and increasing their independence and quality of life. Assessment is key to therapy, as it offers an evaluation of the condition of patients, leading to suitable treatments. Unfortunately, rehabilitation relies on clinical resources, making it expensive and time-consuming. Digital technology can provide solutions that make treatments more flexible and affordable. With the use of computer vision, we created an online platform that includes several exercises and serious games, based on movements and gestures performed in real-world treatments. Difficulty levels vary, and therapists can monitor these procedures remotely, while performance can be stored and tracked over time, identifying improvement. There is no need for any special equipment, as the platform can be accessed like a common website and all its applications require only a simple computer camera and stable Internet connection. In this article, we present our research approach, we analyze the development of the platform, and we provide a brief demonstration of its use in practice. Furthermore, we address some technical challenges and we share the results derived from preliminary test phases, concluding by outlining future plans. Full article

Background: Youth suicide remains a significant public health concern in Japan, driven by multifaceted factors such as academic pressures, social isolation, bullying, and family dysfunction. Recent societal changes, including the rise of internet addiction and subcultural influences from anime, manga, and gaming, have further shaped the psychological landscape of Japanese youth. The COVID-19 pandemic has exacerbated these challenges, intensifying feelings of loneliness and anxiety about the future. Methods: This study explores the impact of these factors on youth suicide risk through a systematic review of existing literature and statistical data, focusing on trends from 2000 to 2024. Results: In 2023, 513 school-aged youth in Japan died by suicide, marking persistently high rates. High school students accounted for the majority of cases, followed by middle and elementary school students. Key risk factors include intense academic expectations, cyberbullying, and internet addiction, which are often compounded by cultural stigmas surrounding mental health. Subcultures offer both solace and potential alienation, influencing youth emotions in complex ways. The COVID-19 pandemic has also worsened mental health issues and heightened suicide risks among this vulnerable group. Conclusions: The findings highlight the urgent need for comprehensive mental health support systems tailored to Japanese cultural contexts. Recommendations include enhancing access to school-based counseling, promoting family-based interventions, and implementing policies to regulate harmful online content. Additionally, efforts must address cultural attitudes that stigmatize mental health care. Collaborative societal and policy-level interventions are crucial for mitigating youth suicide and fostering a supportive environment for young people in Japan. Full article

This paper presents a review of the technologies and services associated with online grocery shopping. The progress in the field of online grocery shopping has been very rapid in recent years. Hence, there was a need to systematize knowledge about the latest various solutions used in this topic. The authors searched the internet, focusing on websites of different supermarkets, shops, and other services that offer online shopping, as well as reviewed scientific papers. Based on the collected material, the authors created four thematic parts, which include: (1) supermarket services; (2) dedicated grocery delivery services and farm-to-table; (3) shopping in Virtual Reality and the metaverse; smart devices and (4) AI in food ordering—the last part includes smart devices, such as smart refrigerators, ovens, their functionality, and the services connected with them. The authors refer to 243 sources. The research includes the three following objectives: (1) exploring and presenting the emerging applied ways of online grocery shopping, (2) exploring and presenting the latest technological advances related to the digitalization of grocery shopping, (3) discussing the upcoming technologies, services, and methods in online grocery shopping. This paper provides knowledge about a wide range of solutions offered by both supermarkets and stores (e.g., shopping applications, VR applications, metaverse shopping) and other companies (e.g., deliveries, product tracking), highlighting the numerous functions available thanks to smart devices (e.g., voice control, own shopping lists, control of products, their quantities and expiration dates, management of user preferences, and many more). This paper also discusses social issues related to the presented solutions, such as their influence on consumer behavior, barriers to adoption, and the associated challenges. Full article

Metal-containing compounds form a large and rapidly expanding group of high-energy materials. Many compounds in this class attract the attention of non-professionals, who may attempt the illegal production of explosives. Several of these substances have been commercially available and pose significant danger if used by terrorists or for criminal purposes. Others are experimental compounds, kinds of curiosities, often created by pyrotechnics enthusiasts, which can present serious risks to both the creators and their immediate surroundings. The internet hosts a vast amount of information, including recipes and discussions on forums, private websites, social media, and more. This paper aims to review the variety of metal-containing explosives and discuss their appeal and potential accessibility to unauthorized individuals. Full article

Today, there are many security problems at the technological level, especially in telecommunications. Cybercriminals invade and steal data from any system using vector attacks such as phishing through scam mail, fake websites and phone calls. This latter form of phishing is called vishing (phishing using voice). Through vishing and using social engineering techniques, attackers can impersonate family members or friends of potential victims and obtain information or money or a specific target objective. Traditionally, to carry out vishing attacks, attackers imitated the vocabulary, voice and tone of a person known to the victim. However, with current artificial intelligence (AI) tools, obtaining synthetic voices similar or identical to the person to be impersonated is more straightforward and precise. In this regard, this paper, using ChatGPT and three AI-enabled applications for voice synthesis presents a practical approach for deploying vishing attacks in an academic environment to identify the limitations, implications and possible countermeasures to mitigate the effects on Internet users. Results demonstrate the effectiveness of vishing attacks, and the maturity level of the employed AI tools. Full article

Phishing is one of the most widely observed types of internet cyber-attack, through which hundreds of clients using different internet services are targeted every day through different replicated websites. The phishing attacker spreads messages containing false URL links through emails, social media platforms, or messages, targeting people to steal sensitive data like credentials. Attackers generate phishing URLs that resemble those of legitimate websites to gain these confidential data. Hence, there is a need to prevent the siphoning of data through the duplication of trustworthy websites and raise public awareness of such practices. For this purpose, many machine learning and deep learning models have been employed to detect and prevent phishing attacks, but due to the ever-evolving nature of these attacks, many systems fail to provide accurate results. In this study, we propose a deep learning-based system using a 1D convolutional neural network to detect phishing URLs. The experimental work was performed using datasets from Phish-Tank, UNB, and Alexa, which successfully generated 200 thousand phishing URLs and 200 thousand legitimate URLs. The experimental results show that the proposed system achieved 99.7% accuracy, which was better than the traditional models proposed for URL-based phishing detection. Full article

In today’s digital era, the abundance of online services presents users with a daunting array of choices, spanning from streaming platforms to e-commerce websites, leading to decision fatigue. Recommendation algorithms play a pivotal role in aiding users in navigating this plethora of options, among which collaborative filtering (CF) stands out as a prevalent technique. However, CF encounters several challenges, including scalability issues, privacy implications, and the well-known cold start problem. This study endeavors to mitigate the cold start problem by harnessing the capabilities of natural language processing (NLP) applied to user-generated reviews. A unique methodology is introduced, integrating both supervised and unsupervised NLP approaches facilitated by sci-kit learn, utilizing benchmark datasets across diverse domains. This study offers scientific contributions through its novel approach, ensuring rigor, precision, scalability, and real-world relevance. It tackles the cold start problem in recommendation systems by combining natural language processing (NLP) with machine learning and collaborative filtering techniques, addressing data sparsity effectively. This study emphasizes reproducibility and accuracy while proposing an advanced solution that improves personalization in recommendation models. The proposed NLP-based strategy enhances the quality of user-generated content, consequently refining the accuracy of Collaborative Filtering-Based Recommender Systems (CFBRSs). The authors conducted experiments to test the performance of the proposed approach on benchmark datasets like MovieLens, Jester, Book-Crossing, Last.fm, Amazon Product Reviews, Yelp, Netflix Prize, Goodreads, IMDb (Internet movie Database) Data, CiteULike, Epinions, and Etsy to measure global accuracy, global loss, F-1 Score, and AUC (area under curve) values. Assessment through various techniques such as random forest, Naïve Bayes, and Logistic Regression on heterogeneous benchmark datasets indicates that random forest is the most effective method, achieving an accuracy rate exceeding 90%. Further, the proposed approach received a global accuracy above 95%, a global loss of 1.50%, an F-1 Score of 0.78, and an AUC value of 92%. Furthermore, the experiments conducted on distributed and global differential privacy (GDP) further optimize the system’s efficacy. Full article

The COVID-19 pandemic has not only brought a virus to the public, but also spawned a large number of rumors. The Internet has made it very convenient for media websites to record and spread rumors, while the official government, as the subject of rumor control, can release rumor-refutation information to reduce the harm of rumors. Therefore, this study took into account information-carrying variables, such as media websites and official governments, and expanded the classic ISR rumor propagation model into a five-dimensional, two-level rumor propagation model that interacts between the main body layer and the information layer. Based on the constructed model, the mean field equation was obtained. Through mathematical analysis, the equilibrium point and the basic reproduction number of rumors were calculated. At the same time, stability analysis was conducted using the Routh Hurwitz stability criterion. Finally, a numerical simulation verified that when the basic regeneration number was less than 1, rumors disappeared in the system; when the basic regeneration number was greater than 1, rumors continued to exist in the system and rumors erupted. The executive power of the official government to dispel rumors, that is, the effectiveness of the government, played a decisive role in suppressing the spread of rumors. Full article

During the COVID-19 epidemic, countries enacted autonomous measures to suspend long-distance travel. As a result, people used online platforms to share perspectives and disseminate their knowledge and skills. Internet learning content thus emerged as a primary solution. This study was conducted to assess the reactions of users to virtual tours. Participants were introduced to the 360-degree panoramic photography system of cultural monuments of the Taipei City Government and participated in an online cultural tour. A closed-ended questionnaire was distributed for their response. After compiling data from 31 participants, we analyzed the link between users’ demographic characteristics and their satisfaction levels with the online panoramic tour system. We discovered higher satisfaction rates of people with incomes exceeding that of the average participant. 83% of participants stated a willingness to explore scenic attractions virtually instead of physically traveling when unable to do so. The results of this study contribute to understanding the context of users’ post-visit satisfaction. The information gathered can be used to improve cultural heritage websites in terms of design, navigation, and cultural education, enabling virtual access to cultural sites and enriching users’ knowledge from home. Full article

Behind everyday websites, a hidden shadow world tracks the behavior of Internet users. Web tracking analyzes online activity based on collected data and delivers content tailored to users’ interests. It gathers vast amounts of information for various purposes, ranging from sensitive personal data to seemingly minor details such as IP addresses, devices, browsing histories, settings, and preferences. While Web tracking is largely a legitimate technology, the increase in illegal user tracking, data breaches, and the unlawful sale of data has become a growing concern. As a result, the demand for technologies that can detect and prevent Web trackers is more important than ever. This paper provides an overview of Web tracking technologies, relevant research, and website measurement tools designed to identify web-based tracking. It also explores technologies for preventing Web tracking and discusses potential directions for future research. Full article

The proportion of older adults is increasing globally, yet many of them experience food insecurity. Technological innovations, such as increased access to internet- and mobile-based food delivery apps (FDAs), may help mitigate food insecurity. However, this topic has been understudied. This scoping review searched for publications and online technical reports from around the world using interdisciplinary databases like ScienceDirect and internet sources like government websites, respectively. Eligible references were published recently (2019–present) and focused on general technology use, including apps, among older adults (≥50 years) or FDAs for food insecurity or nutritional health generally or specifically among older adults. The search identified 19 studies from 10 countries and extracted relevant information for summary tables. A limited number of studies supported the idea that FDAs can help address food insecurity, but there are important equity considerations for older adults living in rural areas or with constrained physical abilities. Consistently, customized app features and functions increased the intention to use FDAs. In addition, FDAs may have health and environmental impacts, such as food waste and increased access or promotion of ultraprocessed foods. Additional research is needed to elucidate the potential of FDAs to address food insecurity generally and specifically among older adults. Full article