Abstract: Abstract Being able to comprehend informal arguments in scientific texts is important for scientific literacy in higher education. Successful intervention studies demonstrating that these skills can be trained in university students have not yet provided evidence that gains of explicit training can be maintained beyond immediate post-training assessment. In this study, we tested whether the gains in argument structure comprehension achieved using a self-directed, web-based training intervention could be maintained over several weeks as an indication of sustained improvement in scientific literacy. We also explored characteristics of students and their engagement with the training intervention that resulted in significant and sustained improvements of their argument structure comprehension skills. One hundred students took part in a voluntary supplement to their university courses, completing an online pretest, a 45-minute training session, a posttest ( n = 88), and a follow-up test ( n = 31). Training effects at posttest were compared with an active control group. The results suggest that the training group exhibited significant gains in argument structure comprehension. These gains were maintained across a four-week period. Students with low starting ability profited the most from the training and gains in argument comprehension were greatest for complex arguments. Training results were positively related to student motivation and this effect was fully mediated by their engagement with the training exercises. The results demonstrate that training gains can be maintained after immediate post-training assessment and suggest that training is particularly effective for low-performing students, for complex arguments, and when students are motivated and engage with the training exercises.

Abstract: <p><span>This article presents the design and implementation of a biomedical remote monitoring system intended for the real-time acquisition, processing, and transmission of physiological data. The system is based on a modular architecture structured in a star topology, integrating several biomedical sensors (electrocardiograph, stethoscope, thermometer, pulse oximeter, blood pressure monitor, and heart rate sensor). Data collected by these sensors is processed locally using an Arduino MEGA 2560 board and transmitted via an Ethernet Shield module configured as an HTTP server, enabling real-time display on a web interface hosted on a computer. This setup allows for reliable remote monitoring of vital signs, paving the way for applications in telemedicine, home care, and emergency response. The results highlight the relevance of this solution in resource-constrained settings, while emphasizing the importance of incorporating robust security features and advanced connectivity in future developments.</span></p>

Abstract: ABSTRACT Nowadays, there is an increasing demand for free software for broiler ration balancing, which is evident within relevant scientific and professional communities. While some available applications account for certain procurement costs, more affordable options usually come with limited functionality. Many users require customized solutions that meet their specific needs. Developing such projects requires collaboration between professionals in animal feed technology, animal science, and software development. Multidisciplinary projects could develop much faster if teams were not required to build solutions from scratch. The availability of open-source code, ready for modification and further development, could significantly enhance efficiency in these endeavors. The Web-Based Ration Balancing Tool (WBRB) is a server-side application designed to optimize the formulation of rations for chicken broilers. This tool streamlines the process of creating ration recipes, reducing the risk of errors associated with manual calculations or general spreadsheet-based solutions. With proper software integration, WBRB can even run offline. This free and open-source application is available to all interested users, and developers can modify the existing code to meet specific needs and requirements. WBRB is built using HTML and PHP, with MySQL as its relational database management system.

Abstract: ABSTRACT There is a large and growing number of trait databases in ecology and evolution. Structured and open‐access data repositories are important resources for allowing researchers to explore, visualize, and download such data for analysis. In this paper, we detail the design and structure of a simple Shiny web app intended to deploy trait databases as interactive web platforms. Using the Animal Culture Database (ACDB) as a case study, we highlight the web app's functionality and potential transversal applications for research and deploying other databases. The ACDB is a resource compiling socially transmitted traditions across the animal tree of life. It integrates multiple linked tables with explicit geospatial information, enabling data visualization and analysis of cultural behaviors across diverse taxa. The web app, accessible at https://datadiversitylab.github.io/ACDB/ , provides users with an intuitive interface to explore the latest version of the database, including population‐level data, behavioral descriptions, and geographic distributions. The code to deploy this web app, and a simplified version to deploy a basic template version, are available on the GitHub repositories https://github.com/datadiversitylab/ACDB and https://github.com/datadiversitylab/generic_shiny_data respectively.

Abstract: R script to reprocess the 'Peyrazet' archaeological dataset, which includes spatialised data about the excavated material at the Peyrazet (France) Paleolithic site, under Mathieu Langlais' direction (2008-2015). The reprocessed datset is intended for exploration using the archeoViz application.

Abstract: R script to reprocess the 'Peyrazet' archaeological dataset, which includes spatialised data about the excavated material at the Peyrazet (France) Paleolithic site, under Mathieu Langlais' direction (2008-2015). The reprocessed datset is intended for exploration using the archeoViz application.

Abstract: Structured Query Language Injection (SQLI) remains one of the most persistent and critical threats to web applications, as adversaries exploit insecure input handling to manipulate databases. Traditional defenses, such as rule-based filters and signature detection, struggle to adapt to novel or obfuscated attack patterns, leaving systems vulnerable to these threats. This work addresses this challenge by proposing a real-time SQL injection detection system powered by machine learning. We combined and balanced multiple public datasets to train models using the AutoGluon AutoML framework, ensuring improved robustness against class imbalance and diverse attack patterns. Experimental results show that ensemble classifiers, such as Random Forest and Extra Trees, achieved accuracies, precisions, recalls, and F1-Score exceeding 99.8%. The bestperforming model was integrated into a Flask-based web application via a REST API, enabling real-time classification of HTTP requests as legitimate or malicious. Tests demonstrated the system’s ability to block malicious queries while allowing normal authentication, highlighting its effectiveness and practical applicability compared to traditional rule-based methods.

Abstract: The advanced nature of web services creates security weaknesses such as SQL Injection (SQLi), Cross-Site Scripting (XSS) and API exploitation which threaten both data reliability and system stability. This research introduces the Automated Security Testing Framework (ASTF) to bring together different security testing methods within the DevSecOps development pipeline for web application security enhancement. Vulnerabilities get discovered in real time by Dynamic Application Security Testing (DAST), static Application Security Testing (SAST) which works alongside penetration testing and fuzz testing through their integration of OWASP ZAP, Burp Suite, Acunetix, SonarQube and Snyk tools. Application of AI security monitoring with continuous threat analysis optimizes security risk mitigation through reduced false positive incidents to 6% and it enhances security response efficiency. An evaluation of an e-commerce platform proves that its 90% decreased high-risk vulnerability exposure sustains development agility alongside ISO 27001 and GDPR compliance. The research showcases ASTF because it detects threats efficiently and handles automated patching as well as its easy CI/CD integration which protects modern web services actively.

Abstract: In this paper, we introduce Parallax, an automatic, application-agnostic, and resource-efficient web application honeypot generation and deployment framework. Parallax can generate honeypot clones of any live LAMP stack, without interfering with the availability of the web application, and deploys the clones alongside the original web application. In the Parallax-based network deployment, all attackers are seamlessly and covertly redirected to the honeypot clone, while benign visitors may continue their interaction with the original web application, same as before. Alongside Parallax, we introduce three independent sensitive data detection schemes, which we employ to isolate and replace the sensitive data of the original web application on the honeypot clone. As we allow attackers full interaction with all parts of the honeypot clone, we replace the sensitive data on the honeypot with realistic, context-aware, synthetic data using an LLM to ensure that none of the sensitive data of the original web application are compromised by attackers. To evaluate Parallax, we deploy it in the wild for five open-source web applications, and we examine the honeypot generation and deployment performance, as well as the interaction of attackers with the honeypot clones. Lastly, to evaluate the deceptive capability of the synthetically generated data, we conduct a large-scale user study and evaluate how well humans are able to differentiate between real and synthetic sensitive data.

Abstract: The article discusses the features of using the ASP.NET Core MVC platform on the example of developing a web application in the form of a pharmacy customer information system. The strengths and weaknesses of this platform are analyzed. Pharmacy customers are primarily interested in information about the availability of the necessary drugs, new drug arrivals, current promotional offers and much more. In addition, it is important for the client to be able to obtain the necessary information online and in a short time. And here you cannot do without a reliable, effective and fast web resource. However, in many cases, a web application can be quite simple and limited to providing several functions: user registration, payment processing, report generation. This is due to the fact that most pharmacies are small enterprises with limited financial capabilities. Therefore, it will be beyond the power of such an enterprise to develop and operate its own database. The question immediately arises of how to get around this problem. That is, what should be the platform for developing its own information system. In our opinion, a good solution is the ASP.NET Core MVC platform, on the basis of which the web application proposed in the article is developed. If only because it allows you to create a fully functional customer information system without using a database, while at the same time allowing you to store information in JSON file format

Abstract: Web agents are systems capable of navigating and interacting autonomously with the World Wide Web. While this concept is almost as old as the web itself, the recent success of large language models (LLMs) and their accompanying capabilities has accelerated the development of web agents.As the adoption of web agents continues, the need arises to compare actual performance and energy trade-offs, providing potential users and developers with insights on which web agent to select based on their preferences. However, concrete performance ratings and details on computational efficiency remain vague due to existing web agents and their respective benchmarks, which offer non-standardized performance ratings only. Further, the inside mechanisms of the agents obfuscate computational efficiency evaluations. In this survey, we systematically assess 79 web agents and their benchmarks (N=27), breaking down benchmark ratings and evaluating the computational efficiency of their crucial components. Our survey demonstrates two research gaps: (1) a lack of comparability among web agent performance ratings due to non-standardized metrics and (2) a lack of awareness of computational efficiency in the design of web agents, which puts their sustainability into question.We contribute step-by-step guides on developing web agents and selecting appropriate benchmarks for assuring computational efficiency. Our work enables further research and development of web agents with sustainability is a key factor.

Abstract: This study aims to develop a web-based sales and stock prediction system for Lazatto, a Food and Beverage (F&B) company, using the Double Moving Average (DMA) method. The background of this research is based on issues stock requirement planning is still done conventionally, where the head of the restaurant places stock orders solely based on personal experience and intuition, without utilizing past sales data as a basis for decision-making, which often result in overstocking or stockouts. By implementing a web-based forecasting information system, the company can obtain real-time and structured data. This study uses sales data from April 2024 to March 2025. The prediction results show a downward trend in sales for the "Kentang" (Potato) product, with a forecasted value of 107.33 for April 2025, compared to an actual value of 95. Model evaluation indicates an average MAPE of 21.19%, which is considered a "fair" level of forecasting accuracy. Additionally, the time required for weekly stock planning was reduced, and interviews with staff revealed increased user satisfaction and ease of use. The developed system has proven to support more accurate and efficient decision-making in inventory management.

Abstract: Web Applications provide wide range of services to its users in an easy and efficient manner. From the past few years web based attacks are increasing. Cross Site Scripting (XSS) is one of the major attacks found in web applications. In 2013, OWASP (Open Web Application Security Project) has ranked XSS third in the list of top 10 attacks found in web applications [11]. XSS attacks occur when an application takes insecure data and sends it to the browser without proper validation or escaping. This can result in hijacking of user sessions, defacing websites and redirecting the users to malicious sites. This paper presents a new XSS defense approach which is based on the OWASP guidelines available for prevention of XSS attacks. In this approach for XSS defense there is an XSS checker that will check for the unauthorized characters in each parameter in the input and block them on both client side and server side of a web application. Client side solutions reduces the run time overhead and server side solutions are more reliable as any attack occurring when request is going from client to server will be detected by server side solution only but it incurs runtime overhead. So a combination of both will be more robust as it can prevent most of the attacks and manage runtime overhead effectively. This approach is tested on a prototype. It is found that this approach covers major categories of XSS attacks i.e. reflected and stored and will require no additional frameworks.

Abstract: Plant diseases significantly impede agricultural pro- ductivity and global food security. Conventional diagnostic meth- ods are often slow, costly, and inaccessible, particularly for farmers in remote areas. This paper presents Agro-Doc, a web- based platform employing artificial intelligence (AI) for rapid and accessible plant disease identification. Agro-Doc utilizes a Convolutional Neural Network (CNN) based on a modified ResNet-50 architecture, enhanced with transfer learning, to analyze plant images submitted by users via a web interface. Key features include real-time disease classification, detailed disease information, treatment suggestions, and a community forum for knowledge exchange. A notable aspect is the deployment of the core AI model using TensorFlow.js, enabling efficient inference directly on the client-side (user’s browser), thereby enhancing speed, privacy, and offline potential. The system, built with Re- act.js, Node.js, and MongoDB, demonstrated an overall accuracy of 94.2% across 42 diseases and 15 crops in validation tests. Field condition accuracy was 88.5%. User evaluations confirmed high usability (average SUS score: 82.1). Agro-Doc showcases a practical approach to democratizing AI-driven agricultural diagnostics through accessible web technologies.

Abstract: In the modern digital era, maintaining a healthy lifestyle has become increasingly important due to the risingprevalence of obesity and lifestyle-related diseases. The Body Mass Index (BMI) is widely used as a simple andeffective tool to assess an individual’s health status based on height and weight. However, traditional BMIcalculators often lack interactivity, data storage, and personalized tracking, which limits their effectiveness inlong-term health monitoring. To address these limitations, this study presents the development of a Fitness Tracker(BMI Calculator) web application that combines BMI computation with secure login, personalized dashboards,and historical trend visualization. The system is built using HTML, CSS, and JavaScript for the frontend andPython Flask for the backend, with SQLite/MySQL databases for storing user data. Chart.js integration furtherenhances usability by providing interactive visualizations of BMI history. By enabling users to calculate, store,and track BMI over time, the application fosters continuous engagement and awareness of health status. Thisresearch highlights how digital platforms can support preventive healthcare and promote healthy lifestylepractices. The project also sets the foundation for future enhancements, including integration with wearabledevices, AI-driven health recommendations, and cloud-based fitness management systems.

Abstract: AI-based avatars embody human beings digitally through realistic 3D models or videos and can interact with learners in real-time. Thanks to new machine learning and LLM capabilities, they can perform numerous cognitive tasks and have advanced language capabilities. Available software solutions for creating AI-based avatars are often not grounded in educational theory and have yet to reach classrooms. In this technological contribution, we describe an authoring tool for creating AI-based avatars that builds on theories of sit-uated learning. It allows users to conveniently create AI-based avatars with a web interface, distribute them via the web browser, and gather log data. Moreover, we report on developing a language learning case for this tool and contemplate effective prompt engineering. We conclude with reflections on the characteristics of future AI-based avatars and discuss avenues for future research.

Abstract: This paper presents a comprehensive comparative study of Django and FastAPI, two widely used Python web frameworks, with a focus on their performance, scalability, and suitability for high-performance applications. Django, an established monolithic framework, is well known for its sturdy characteristics, security, and ease of development. FastAPI, a recently developed framework, is designed for high-speed, asynchronous API development, making it a strong contender for modern applications. This study analyzes key aspects such as request handling, database interactions, API development efficiency, and real-world use cases to highlight the strengths and limitations of both frameworks. Based on their project requirements, developers can choose between FastAPI and Django with the assistance of the research data.

Abstract: Tuberculosis (TB) remains one of the leading causes of infectious disease-related deaths worldwide. Model-informed precision dosing-based therapeutic drug monitoring (TDM) is a promising strategy to optimize anti-TB drugs doses based on pharmacokinetic (PK) profiles of patients. However, this approach requires significant time and trained personnel to interpret the results. To address this limitation, we developed and utilized an automated, web-based TDM platform that simplifies implementation and enhances accessibility, ultimately aiming to improve treatment outcomes. The system incorporates population PK models for both first- and second-line anti-TB drugs, integrating clinical data including demographics, NAT2 genotype and drug concentrations from limited sampling strategies. Bayesian forecasting is used to estimate individual PK parameters and simulate optimized dosing regimens. Clinicians can use the platform to automatically generate the individual concentration-time curve plot that compares a patient's exposure with population level references, along with a table displaying the estimated individual PK parameters. If the dose adjustment is needed, users may input alternative regimens and run the simulation to predict the corresponding PK metrics. These features enable users to visualize predicted outcomes, compare exposures against therapeutic targets, and support optimal dose selection. The system produces downloadable reports containing patient specific data, PK parameter values, graphical PK profiles, and pharmacogenomic interpretations with minimal user input. This automated web-based platform enhances the time-efficiency and accessibility of TDM, making it a practical tool for personalized TB therapy. It is especially valuable in resource-limited settings where expert support is limited, by supporting clinical decision making and improving patient outcomes.

Abstract: The rapid development of information technology demands digitalization across various public service sectors, including child health systems at Posyandu (Integrated Health Service Posts). Posyandu Kenanga Depok conducts monthly child health monitoring activities to ensure nutritional status and child development. However, the manual recording system for Child Health Cards (Kartu Sehat Balita/KSB) creates various operational problems. Posyandu cadres experience difficulties in data recording processes, information storage, and monthly report generation. Additionally, paper-based KSB held by parents are prone to loss and damage, resulting in poorly documented child development data. This research aims to develop a web-based KSB application to address these problems using the Rapid Application Development (RAD) methodology. The RAD method was chosen for its ability to accelerate system development processes by actively involving users in every development stage. The application was developed using PHP programming language, CodeIgniter 4 framework, MySQL database, and Visual Studio Code editor. The system is designed with three user levels: Posyandu cadres for managing child data and generating reports, parents for monitoring child development, and doctors for managing immunization and vitamin data. Development results demonstrate that the application successfully automates child data recording processes, facilitates monthly report generation, and enables parents to access child development information in real-time. The system also provides growth chart visualizations and immunization schedule reminders to support optimal child health monitoring.

Abstract: У статті описано та досліджено математичну модель похибок для оцінювання потенційної точності вимірювання зміни просторового положення вимірювальної мітки об’єкта автоматичного управління по сусідніх кадрах відеопотоку з відеокамери, що спостерігає мітку на об’єкті. Розглядається випадок, коли web-відеокамера встановлена на відстані порядку десятків сантиметрів від вимірювальної мітки. Міткою є лімб поворотного механізму наземної антенної системи космічної радіолінії зв’язку X-діапазону. За змінами положення зображення на сусідніх кадрах відеопотоку вимірюється зсув шкали лімба. Зсув розраховується шляхом обчислення положення максимуму взаємної кореляційної функції сигналу відповідних рядків сусідніх кадрів. Зсув шкали перераховується у зміну кутового положення антени. Наведено математичні залежності для оцінювання потенційно досяжної точності вимірювання зміни положення вимірювальної мітки залежності від умов спостереження. Розрахунок для дешевої web-відеокамери необхідних даних про максимально припустиму освітленість елементів матриці та СКВ шуму у напрузі сигналу, які не зазначаються у документах виробника, проведений за оригінальною методикою. У статті наведено міркування щодо використання міжрядкового накопичення відеосигналу від мітки, що являє собою вертикальні риски лімба. Зроблений висновок, що таке накопичення за умови гарного штучного освітлення не є доцільним з причини зростання математичного сподівання флуктуаційних шумів матриці. Наводяться приклади розрахунків для відеокамери, що спостерігає азимутальний лімб опорно-поворотного пристрою антенної системи наземної станції космічної радіолінії зв’язку. Для розглянутої конструкції безконтактного датчика на основі дешевої web-відеокамери розраховано оцінку потенційної похибки повороту антени, що складає трохи більше 0,2 кутових секунд. Вплив на оптичний сигнал шару повітря між камерою та міткою, вібрації, похибки механізму повороту та спектральні розбіжності відеосигналу не враховувалися. Стаття може стати в нагоді конструкторам оптико-електронних приладів для дистанційного безконтактного вимірювання просторового положення об’єктів.

Abstract: Examination systems through online evaluation tools are innovative digital platforms engineered to streamline and modernize the assessment process, especially for multiple-choice evaluations. Developed using web technologies such as HTML, CSS, and JavaScript, and powered by MySQL for robust data management, these systems offer a secure, user-friendly environment for examiners and candidates alike. Featuring functionalities like automated exam generation, randomized question distribution, time-bound assessments, and instant result processing, they eliminate manual workload and minimize human error. Secure logins, remote exam access, and built-in monitoring tools uphold exam integrity. With their centralized database and seamless design via Visual Studio Code, these systems provide real-time performance analytics and ensure fast, fair, and scalable evaluations, perfectly suited for educational institutions and organizations embracing digital transformation.

Abstract: Comparison of Svelte JS Framework with React JS on Javascript-Based Web Application Performance. Informatics and Computer Engineering Education Study Program, Informatics and Computer Engineering Department, Faculty of Engineering, Makassar State University, (supervised by Mustari Lamada and Abdul Wahid). Comparison of Svelte JS and React JS frameworks using GTMetrix and WebPageTest tools by taking performance parameters including first contentful paint, speed index, large contentful paint, time to interactive, total blocking time, and cumulative layout shift. Each framework was tested by creating a 3-page website that had different loads. The test results were converted into scores where the score range was 1-3 for each parameter tested. React JS gets a total score of 26, 25, and 28 for each page. Svelte JS gets a total score of 34, 34, and 33 for each page. Native JS gets a total score of 34, 33, and 33 for each page. From these scores, it can be concluded that the Svelte JS framework is faster than React JS.