ABSTRACT We have investigated current situations of web pages for reading support services for children in Japanese public library websites. Also, we have analyzed and considered the difference in contents of those web pages between two types of public library (prefectural libraries and municipal libraries), as well as overall tendency, based on the investigation and context of reading support for children in Japan. In addition, we have argued perspective on reading support services for children in Japanese public libraries.

Primary cardiovascular prevention was one of the main topics discussed during the 2025 ANMCO General States. The focus on this theme is due to the evidence that, although in high-income countries cardiovascular mortality has declined over the decades, the downward trend has slowed in the last years. Cardiovascular disease remains a leading cause of death worldwide, and a substantial proportion of cardiovascular events, including deaths, occurs in individuals with no previous history of disease. In this paper, the initiatives that ANMCO implements with the Heart Care Foundation to spread the culture of primary prevention are presented: from days dedicated to cardiovascular disease screening to training campaigns in schools and information and awareness campaigns through various digital tools (web pages, social media). Another aspect that ANMCO focuses on to foster cardiovascular prevention is the implementation of the One Health approach promoted by the World Health Organization. A healthy diet like the Mediterranean diet represents not only a lifestyle that promotes cardiovascular prevention but also an approach to health that respects and protects the environment. In addition, there are the "silent killers", environmental factors such as air pollution, noise and light pollution, and chemical pollution of land and water, all emerging risk factors that should be considered as targets of a One Health approach.

Reproducibility and standardization of methods are key approaches to microbiological research. However, laboratories differ widely in equipment, protocols, and operator expertise. These disparities are especially pronounced in fungal biology, since accurate spore counting and precise measurements of colony growth remain vulnerable to subjective bias and inconsistent technique. To standardize these foundational tasks, we developed two free, open-source image-analysis tools - SporeQuant and Orbisv2 - that automate hemocytometer spore counting and colony-area measurement, respectively. SporeQuant applies image thresholding and blob detection to identify individual spores and compute their concentration, while Orbisv2 segments colonies and calculates colony areas through thresholding and contour detection. Compared with manual analysis, SporeQuant and Orbis v2 markedly reduce analysis time by 63% and 76%, respectively, while achieving high measurement reliability, with MAE=0 for SporeQuant and R2>0.975 for Orbis v2. These tools reduce user-to-user variability, and achieve comparable or improved accuracy versus previously established methods. Furthermore, both tools can be accessed through a web page on both a computer or smartphone, requiring no specialized hardware, software, or prior knowledge. They provide accessible solutions for low-budget and high-throughput laboratories alike, enabling more consistent, scalable analysis of fungal growth and morphology in research and industrial settings.

Transitioning from the fossil-gasoline energy era to renewable electricity generation and electricity garage in remote places has the capacity to reduce both carbon emissions and costs. A techno-monetary evaluation for the implementation of a hybrid renewable energy device on the South Pole in Antarctica at the Turkish Antarctica Research Station, which currently hosts several high-electricity physics experiments with nontrivial electricity demands. RFR embedded GWO gives iterations in terms of sizing, placement, allocations, and cost. The IEEE 30 bus radial system network reduces power line losses. A tailored model for using sun photovoltaics, wind turbine generators, lithium-ion electricity storage, and long-duration energy garage at this web page is explored in extraordinary combinations with and without traditional diesel energy generation. We find that the least-price machine consists of all 3 strength technology resources and lithium-ion power storage. For instance, regular-country load of one hundred seventy kW, this hybrid device reduces diesel intake by way of 95% compared to an all-diesel configuration. Over the direction of a 15-12 months analysis length, the reduced diesel usage results in a net savings of $57M, with a time to pay back of approximately years. All the eventualities modeled display that the transition to renewables is fairly price-effective under the specific economics and constraints of this extremely far-off site.

In the digital era, social media platforms, online forums, and communication channels have become integral to human interaction. This project focuses on the design and development of an intelligent system capable of detecting and masking hate speech in online textual content using machine learning and natural language processing techniques. With the rapid growth of social media platforms and user-generated content, there has been a significant rise in the spread of offensive, abusive, and harmful language. Manual moderation is not only inefficient but also impractical due to the sheer volume of data generated every second. Therefore, this project aims to provide an automated, scalable, and efficient solution to identify and control hate speech in real time. The proposed system utilizes NLP techniques such as tokenization, stop-word removal, and vectorization (TF-IDF) to preprocess textual data. A supervised machine learning model is trained on labelled datasets to classify content into categories such as hate speech, offensive language, or neutral text. Once hate speech is detected, the system masks or replaces harmful words to ensure a safer user experience. The system is further integrated into a Chrome extension to provide real-time filtering of content on web pages. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score. This project contributes to building a more inclusive and secure digital environment.

In this paper, we propose a learning input assessment model based on multi-source data fusion, which analyzes students' expression features with VGG16 network, acquires mouse track data corresponding to students' expressions based on deep neural network, and then deeply fuses students' log data and interaction data based on the data conversion algorithm in the event window. The model is utilized to establish a smart classroom, based on which the multidimensional learning characteristics of 50 students are studied, thereby promoting the application of multi-source data fusion technology in the education sector. The multi-source data fusion model can effectively recognize the emotional changes of students in the smart classroom, and the accuracy of multi-emotion recognition is more than 95%. In the smart classroom, the students' mouse browsing trajectories focused on the web pages of “Teaching and Learning” and “Teaching Tools and Resources”, with a total click frequency of 1959 and 2005, respectively. Based on the above characteristics, the model classified the 50 students into five categories: focused students, persistent students, persistent students, occasional students and abandoned students, and the average learning input values of each category of students were 4.55, 3.57, 2.48, 1.48, and 0.54, respectively. The construction of the smart classroom is helpful for the wide dissemination of the model.

Enterprise organisation tackles large volumes of knowledge, which is not centralised but distributed across multiple heterogeneous sources. Such heterogeneous sources are structured datasets, web pages and documents. Traditional keyword- based systems failed to retrieve contextually relevant information, which produced incomplete or inaccurate responses. Even large language models (LLMs) generated hallucinated outputs. The reason for hallucinated outputs was nothing but operating without reliable knowledge grounding. Eventually, to overcome these challenges, this paper proposes a context-aware multi-source intelligent knowledge retrieval system based on the retrieval augmented generation (RAG) framework. This system integrates multiple data sources, including PDFs, websites, and structured data repositories.

Many non-protein-coding RNAs are being discovered each year. At first we know them only by their sequences in a few organisms, but to understand their function and interactions, we need to understand what 3D structures they may form, in whole or in part. Many hairpin and internal loops are known to form recurrent structured 3D motifs, for example, kink turn and sarcin-ricin internal loops, and GNRA and T-loop hairpin loops. A new non-protein-coding RNA may have one or more known structured 3D loop motifs. Here we introduce a tool which identifies loops in Rfam seed alignments that match known 3D loop motifs and makes those identifications easily accessible. JAR3D was developed to map sequences of hairpin and internal loops to known 3D motifs, and was extended for this work to three-way and four-way junction motifs. We applied JAR3D to 4166 Rfam seed alignments from Rfam 15.0 and made the results accessible on the JAR3D web page, making it easy to evaluate the possible matches for each loop in each Rfam family. We provide several examples which validate JAR3D's ability to identify the correct loop motif, using 3D structures of RNAs outside of the training set. We created a page to search for instances of a particular loop motif across all Rfam families, to study how widespread the occurrence of each motif is. We provide statistics on how many Rfam loops appear to match well to a known 3D motif. Match rates are much higher for internal loops than for hairpins or multihelix junctions.

The Website Accessibility Conformance Evaluation Methodology (WCAG-EM) directs an auditor to select samples of web pages for accessibility evaluation. However, the sampling method of WCAG-EM cannot statistically claim to represent pages outside of the selected sample. To generate an optimal representative sample, we previously proposed OPTIMAL-EM. In this article, we apply the framework to explore the relationships between two metrics in the framework: web page complexity and accessibility. We first cluster pages by their structure by employing t-distributed Stochastic Neighbour Embedding (t-SNE) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) by representing pages using two approaches: one including all HTML elements and another focusing on block-level HTML elements. We then compare the average complexity of a cluster and the variance of the complexity of a given cluster against the average accessibility of that cluster. To measure their correlations, we conducted a two-stage evaluation: an initial experiment with a website of 388 pages and a validation study with three additional random sites of 500 pages. Our experiments demonstrate that clusters characterised by lower variability in complexity tend to exhibit fewer accessibility barriers, suggesting that standardised and templated web design could be more beneficial to accessibility than more complex pages. Furthermore, the main contribution of this article is showing that by identifying and focusing on clusters with high complexity variance, auditors can target their efforts more effectively, prioritising areas more likely to present accessibility challenges. By providing a more systematic and scalable sampling method, our approach can optimise conformance evaluations of large websites by reducing the human effort involved, focusing on a more targeted assessment of more representative pages.

Abstract The accelerating digitization of local economies has fundamentally transformed the paradigm of business discovery and consumer decision-making. Traditional business directories, characterized by printed listings or static web pages, are increasingly inadequate for modern users who demand real-time information, personalized search experiences, geolocational intelligence, and interactive engagement with service providers. This paper presents ListPlace, a comprehensive web-based business discovery and listing platform engineered to bridge the gap between local enterprises and digital consumers. The platform is developed using a robust technology stack comprising HTML5, CSS3, JavaScript, Bootstrap 5, PHP (Laravel 10 framework), and MySQL 8.0. ListPlace facilitates efficient, bidirectional interaction between end-users and business owners by integrating advanced search functionalities, multi-criteria category-based filtering, dynamic location-aware result ranking, and a structured review and rating system designed to enhance trust and inform decision-making. The system follows a modular, layered architecture that ensures scalability, maintainability, and high performance under variable loads. A freemium monetization model is implemented, enabling businesses to amplify their visibility through premium subscription packages while preserving accessibility via free basic listings. A comprehensive evaluation of the system, including usability testing and performance benchmarking, demonstrates improved search result latency (average <200ms), high user satisfaction scores (System Usability Scale score of 82), and increased business profile engagement. The proposed platform offers a practical, scalable, and economically viable solution for modern digital directory ecosystems. Keywords: Business discovery platform, local search, Laravel, MySQL, review system, freemium model, responsive web design, usability engineering.

This project presents a Visual Rag System For Web Page Analysis designed to enhance the accuracy and relevance of AI-generated responses by grounding them in external data sources. Unlike traditional AI models that rely solely on pre-trained knowledge, this system dynamically retrieves relevant information from user-provided web content and generates context-aware answers. The system allows users to input a webpage URL, from which content is extracted, processed, and divided into smaller chunks. These chunks are converted into vector embeddings using a local embedding model, enabling efficient similarity-based retrieval. When a user submits a query, the system retrieves the most relevant content and feeds it into a powerful language model via the Groq API to generate precise and contextually accurate responses. Additionally, the project includes features such as quick summary generation, chat history, and multimodal support for images and text, making it more interactive and user-friendly. The system is implemented using Streamlit for the interface, Sentence Transformers for embeddings, and LLMs for response generation. This approach improves reliability, reduces hallucination, and ensures that responses are grounded in real-time data, making it highly useful for applications like research assistance, study tools, and intelligent document analysis.

This report describes and analyses the observations of the brighter or more interesting comets at perihelion during 2023, concentrating on those for which visual observations were obtained. Magnitude parameters are given for all comets with observations. Any evolution in the magnitude parameters of those periodic comets with multiple returns is discussed. Additional information on the comets discussed here, and on other comets seen or at perihelion during the year, may be found on the Section visual observations web pages.

Pneumonia, a leading cause of child morbidity and mortality in Pakistan needs to be studied beyond biomedical interventions to address structural inequities, language barriers, and the role of inclusive e-health communication in improving outcomes. Caregivers often access online health information for prevention and treatment of pneumonia, yet the translation of pneumonia related information ENG-UR received little attention. In this study, seven publicly available bilingual English–Urdu materials (three pamphlets and four web pages) published after 2015 by recognised health organisations were analysed and compared against content coverage, meaning consistency, terminology used, and actionability. Although core themes were maintained across languages, systematic differences were observed: English texts were generally more explanatory, whereas Urdu versions were more directive and condensed. Guidance on urgency and care-seeking was inconsistently presented, particularly in digital formats. Findings suggest that strengthening quality assurance of bilingual health communication may enhance clarity and support more timely care-seeking and equitable access to information.

A spy robot is a modern surveillance system used to monitor areas remotely without human presence, improving safety and security. This project develops a spy robot using ESP32 for robot movement control and ESP32-CAM for real-time video streaming. The ESP32 microcontroller controls the DC motors through a motor driver, allowing the robot to move forward, backward, left, and right. The robot is controlled wirelessly using Bluetooth communication from a mobile device, making it easy for the user to operate. The ESP32-CAM module captures live video and transmits it to a web page using Wi-Fi, enabling realtime monitoring of the surrounding environment. This allows the user to observe remote or dangerous areas safely and efficiently. The system is designed to provide continuous surveillance without the need for physical presence. The integration of wireless control and live video streaming improves the functionality and effectiveness of the robot. The project is low cost, portable, and easy to implement using IoT technology. This system can be used in security surveillance, military applications, industrial monitoring, and rescue operations. Overall, the project demonstrates an efficient and reliable solution for remote monitoring and surveillance using ESP32 and ESP32-CAM.

Objective: In recent years, injectable glucagon-like peptide-1 (GLP-1) receptor agonists have gained widespread attention due to their effectiveness in obesity management. However, these agents are frequently presented in media using simplified and misleading terminology such as “weight loss injections,” which may create unrealistic expectations and encourage inappropriate or off-label use. This study aimed to evaluate the content and characteristics of online information about weight-loss injections from a public health and ethical health communication perspective.Methods: This descriptive study employed qualitative content analysis of publicly accessible Turkish-language web pages. Data were collected between October and November 2025 using a Google search. Web pages published by private hospitals, private clinics, blogs, and news websites that provided informational or promotional content were included. Textual content was analyzed for the presence of medical indications, contraindications, complications, and side effects, prescription warnings, scientific references, emphasis on medical supervision, and recommendations for lifestyle modification. Visual content was assessed in terms of gender representation, depicted body type, and the use of symbolic elements such as pen needles. Quantitative findings were summarized descriptively, and inductive qualitative analysis was conducted to identify prominent themes.Results: A total of 44 web pages were analyzed. Scientific references and warnings that the product should be obtained by prescription were frequently absent. Although most pages emphasized medical supervision and lifestyle changes, information on indications and contraindications was often incomplete. Visual analysis showed a predominance of female and normal-weight body representations, potentially reinforcing narrow body ideals. Qualitative analysis identified themes including medical legitimacy, social media influence, psychological risks, and concerns regarding unauthorized access.Conclusion: Online content related to “weight loss injections” demonstrates significant scientific, ethical, and communicative shortcomings. Improving the quality of digital health communication and reframing these agents as medical treatments rather than cosmetic solutions is

ABSTRACT Neoliberalism commercializes entities based on economic value, promoting their commodification in a neoliberal market. This study aims to explore the commodification of English in the local linguistic market where neoliberal market‐driven policies commercialize English and amplify its status as a success, economic asset, and source of mobility. Drawing on Bourdieu's theory of linguistic capital, the study uncovers how English is perceived and portrayed as a commodity in the linguistic market. This study employed a qualitative research design involving interviews with 19 participants and carried out document analysis of university web pages and language center advertisements. Data were analyzed through constructivist grounded theory, content analysis, and multimodal analysis, and triangulated for trustworthiness. The findings demonstrate how English is pursued to ease business growth, economic mobility, and foster an entrepreneurial mindset in different neoliberal markets. This study reflects how neoliberal ideologies shape language perception in developing countries and suggests a critical (re)assessment of language policies that prefer economic value over linguistic diversity and cultural preservation.

ILLiad libraries are often faced with the task of customizing and updating their web pages, usually without extensive web development resources. This article presents some of the potential uses of the free versions of generative artificial intelligence chatbots to help provide bits of code to customize sections of ILLiad web pages. These chatbots can also be used to help troubleshoot or explain confusing parts of HTML, CSS and JavaScript to non-programmers, allowing interlibrary loan units using ILLiad web pages to exercise some control over the content and features of their web pages.

Abstract University websites serve as critical digital gateways to higher education and should be accessible to all users. Despite China possessing the world’s largest higher education system, systematic empirical research in this domain remains scarce. To bridge this gap, we utilized three automated tools to quantitatively evaluate the compliance of 993 key pages (e.g., homepages, undergraduate admissions) from 250 Chinese universities with the Web Content Accessibility Guidelines (WCAG 2.2) Level A and AA standards. It is noted that due to tool-specific technical limitations, effective sample sizes varied slightly: aXe (993 pages), Siteimprove (991 pages), and BrowserStack (957 pages). The findings uncover a stark reality that majority of evaluated pages failed to fully meet WCAG 2.2 Level A standards across all three tools used. For instance, aXe identified 36,283 issues (averaging 36.54 per page). A deeper analysis reveals that the majority of these issues were related to the Perceivable principle (69.49%) and contravene foundational Level A criteria (70.58%). Moreover, image-alt, link-name, and color-contrast were identified as the three most prevalent issues. Overall, the analysis indicates that accessibility was inadequately considered during the website development process. As a first step toward improvement, prioritizing the resolution of the three most prevalent issues would address the vast majority of current failures. Furthermore, universities should also invest in accessibility training and research for developers and institutionalize routine reviews of their web pages by both experts and users with disabilities.

The exploration of intervillage spatial patterns traditionally relies on direct survey and geographic data. The application of webometric techniques to extract intervillage relational networks, however, introduces a novel methodology that extends the analysis of rural spatial dynamics in a co-city region. This study constructs an intervillage network of 1,472 villages in a representative co-city region—Guangzhou and Foshan (Guangfo) in South China—using pairwise relational data collected from massive Internet Web pages via a webometrics approach. The research highlights methodological advancements by detailing the roles of villages based on metrics of “influence, attraction, and interaction,” thereby enhancing the granularity of urban network analysis down to the village level. Our findings demonstrate an uneven distribution pattern of village influence, particularly concentrated in the districts of Panyu, Baiyun, and Conghua in Guangzhou. Additionally, village attraction features a strong north–south and a weak east–west pattern, with a total radiation area of 3.5 million km2 for the sum of the top 1 percent linkage strengths per village and 13.4 million km2 for the top 10 percent. Moreover, our advanced network analysis delineates a hybrid polycentric interaction pattern, emphasized by significant interactions near administrative boundaries, and identifies five distinct community structures within the network. These methodological innovations provide pivotal insights for regional spatial rescaling and are instrumental for policymakers in planning and reconstructing rural villages within the Guangfo region.

BackgroundThe digitisation of herbarium collections is typically aimed at mobilising and distributing data about the specimens, while often omitting data about the individuals who contributed to the formation of these collections. Despite the obvious need to create databases of herbarium collectors, as well as to structure and standardise the data about them, there is currently no consolidated view on this regard. Many efforts have been made to develop various collector-centric databases, but there is no simple and quick way to present such data online by self-running herbaria or small initiatives. Therefore, here I introduce a framework that allows for easy configuration of WordPress to display data about herbarium collectors.New informationThis article describes the herbUA Collectors repo, allowing for the configuration of a standard WordPress installation for: (a) filling and online publishing the database of the herbarium collectors; (b) representing records about each collector as a single web page; (c) providing an interactive map search facility; (d) visualising the basic statistics on the database.