This paper presents the design, development, and evaluation of BrainQuest, an interactive gamified educational web application for children aged 5–12 years, built using the MERN stack (MongoDB, Express.js, React.js, Node.js). BrainQuest addresses the growing need for engaging, curriculum-aligned digital learning tools by applying gamification principles — including XP-based progression, badge rewards, level systems, and daily missions — to four core learning modules: Math Challenges, Jigsaw Puzzles, a Drawing Board, and Science Quizzes. The platform employs a three-tier client-server architecture with JWT-based authentication, RESTful API design, and a document-based MongoDB schema optimized for real-time progress tracking. A Parent Dashboard provides parents with monitoring tools, playtime controls, and weekly progress reports. Performance testing across 107 test cases achieved a 100% pass rate, with page load times under 1.4 seconds and API response times averaging 210ms. Results confirm that the MERN stack is highly effective for building scalable, interactive EdTech applications, and that gamification significantly enhances learner engagement. Future extensions include AI-powered adaptive learning, multiplayer modes, and React Native mobile deployment. Keywords—MERN Stack, Educational Technology, Gamification, React.js, MongoDB, Node.js, Children's Learning, Parent Dashboard, Web Application Development, XP System

With the escalating need to derive and evaluate subjective information from social media platforms such as Twitter, where millions of Twitter users update their tweets in the form of textual data every day, the application of Sentiment Analysis becomes even more relevant. This paper proposes an integrated system by integrating the applications of NLP and ML techniques to classify and derive the sentiment and psychological indicators of users instantaneously and with higher accuracy and explainability. For the instant derivation of sentiment polarity and psychological indicators with better accuracy, the proposed system uses TF-IDF feature extraction with Logistic Regression classification. In addition, it uses VADER sentiment analyzer that gives an instant rule-based classification of emotional tone of given text. The proposed system includes a number of preprocessing stages of data such as microtext normalization, tokenization, removal of stopwords and lemmatization. The proposed system is implemented and the results indicate that the proposed lightweight and interpretable approach obtains performance close to the state-of-the-art while maintaining the lowest computational cost. The proposed system is showcased via the development of a Streamlit-based web application to make the system user-friendly for researchers, practitioners as well as for the public. The derived system can instantaneously classify any text to positive, negative, neutral sentiments and also derive the psychological indicators of users related to mental health such as anxiety, sadness and also depression. Hence, the proposed system bridges the existing gap between research prototypes and practical applications with respect to social media sentiment analysis.

BackgroundYoung adults face rising wellness challenges, including prediabetes risk, requiring sustained engagement with preventive health interventions. Digital wellness applications offer promise for promoting healthy lifestyle behaviors, yet high dropout rates and inadequate personalization limit their effectiveness. This paper outlines the technical implementation and formative usability evaluation of MiCARE, a theory-driven progressive web application (PWA) designed to support sustained wellness engagement among young adults through user-centered design.ObjectiveThis study aims to systematically implement theory-driven design specifications into a functional web application, the MiCARE platform, and to conduct a formative usability evaluation with a convenience sample of 20 university-affiliated young adults aged 18 to 34 years in Victoria, Australia, in both rural and urban areas using the task-technology fit and unified theory of acceptance and use of technology frameworks as organizing lenses to assess usability, usefulness, and satisfaction.MethodsThis is an embedded mixed methods study conducted across 2 phases: phase 3 and phase 4. Phase 3 involves the technical implementation of 6 theory-driven features (ie, empathetic chatbot, learning hub, dynamic goal setting, gamification, personalized reminders, and progress dashboard) using HTML5, CSS3, JavaScript, Google Dialogflow ES, and Firebase services, following the Agile methodology over 6 months with biweekly self-managed sprints and clinical verification. Phase 4 is a 3-month formative usability feasibility evaluation with 20 young adults recruited from La Trobe University (Bundoora and Bendigo campuses). Participants will complete screening and initial, midpoint, and final surveys assessing usability, usefulness, and satisfaction, while real-time use analytics captures engagement patterns. Data analysis will use the task-technology fit and unified theory of acceptance and use of technology frameworks as interpretive guides, with quantitative data analyzed using descriptive statistics (R Studio) and qualitative feedback analyzed through thematic analysis (NVivo). Use analytics will provide descriptive contextual information only. The study has received ethics approval from the La Trobe University Human Research Ethics Committee (HEC24507).ResultsThe study will take place between 2025 and 2026. Phase 3 (technical implementation) commenced in October 2025 and is currently ongoing, with core features under active development and verification. Phase 4 (formative usability and feasibility evaluation) is scheduled to commence following completion of phase 3. Evaluation results will be disseminated in academic forums and peer-reviewed publications in early 2027. The findings will enable us to evaluate the feasibility, acceptability, and usability of a theory-driven PWA in this university-affiliated sample, informing refinements and future larger-scale studies.ConclusionsThis study will contribute to the technical implementation and formative usability evaluation of a multitheoretical, user-centered PWA for wellness engagement in preventive health, bridging the gap between conceptual frameworks and deployed interventions.

Air pollution is a major concern for human health, so accurate air quality prediction is essential for effective pollution management. This study proposes two hybrid models, ARIMA-ANN-REG and ARIMA-ANN-QREG, for forecasting daily air quality index (AQI) in Hat Yai, Songkhla, Thailand, and evaluates their performance against traditional models such as ARIMA and ANN, as well as the simpler hybrid ARIMA-ANN. The research utilized daily AQI data from January 2, 2013, to June 30, 2023, with 80% of the 3,013 observations used for training and the remaining 603 for testing. Six evaluation metrics were employed: root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), coefficient of determination ($$r^2$$), mean fractional bias (MFB), and mean fractional error (MFE). Results indicated that the ARIMA(1,1,1) model had strong performance with low RMSE (7.708), MAE (5.578), and MAPE (19.006%), along with a solid $$r^2$$ of 65% and minimal bias (MFB = 0.017, MFE = 0.181). While the ANN model achieved a slightly higher $$r^2$$ (65.71%), it also exhibited higher MAPE (21.553%) and MFB (0.077), suggesting a tendency to overpredict. Among the hybrids, ARIMA-ANN-REG performed better than ARIMA-ANN by incorporating linear regression, enhancing accuracy, and reducing bias. Notably, the ARIMA-ANN-QREG model achieved the lowest MAPE (18.647%), MFB (0.001), and MFE (0.179), demonstrating superior accuracy and minimal bias. To support practical application, a user-friendly web application was also developed to facilitate fitting these models to users’ time series data, making advanced forecasting tools accessible for environmental monitoring and decision-making.

As the development of web applications continues to expand, impacting various aspects of users’ lives, from social interactions and healthcare to business and education, there is an increasing need to understand and implement web accessibility guidelines. These guidelines enable developers to create more effective, user-friendly applications that align with the standards set by the Web Content Accessibility Guidelines (WCAG), and enhancing the experiences of millions of users with disabilities. Although several research papers provide tools for measuring web accessibility issues, there remains a significant gap in studies examining how these issues affect the quality of web applications. In this work, we experimentally investigate the diffuseness of web accessibility defects in real-world web applications and assess how these defects impact the specific domains of the web apps included in our study. Furthermore, we explore the relationship between structural elements, web app features, and the prevalence of accessibility issues within these applications.

The prediction of crop yields is influenced by various factors such as weather conditions, agronomic practices, and management strategies. Accurately predicting oil palm yield is crucial for sustainable production, as it plays a significant role in global food security. Challenges such as climate change and nutrient deficiencies have adversely affected yields, highlighting the necessity for a specialized web application tailored to the oil palm industry. This study presents a machine-learning-based web application that utilizes a deep learning model to estimate oil palm yields by integrating key parameters, including weather, agronomy, and satellite data. The application features a user-friendly interface and a dashboard for comparing predicted and actual yields, enhancing user engagement and facilitating collaboration among stakeholders. By deploying this tool on the cloud, plantation managers can make informed decisions early in the yield prediction process, ultimately improving plantation management and profitability. This web application is designed to provide valuable insights to stakeholders, contributing to effective decision-making in the oil palm sector.

Authentication security is a crucial aspect of web application development, particularly in login systems that function as the primary gateway to an application or web service. The authentication process plays a vital role in ensuring that only authorized users can access the system, thereby preventing data breaches, information misuse, and potential system damage. Traditional login methods that store passwords in plain text or rely solely on session IDs are vulnerable to theft and session hijacking. This research proposes the implementation of JSON Web Tokens(JWT) with Base64 encoding, integrated through Web Service technology, to enhance login system security. JWT is a JSON-based token standard used for secure information exchange between clients and servers. The token consists of three parts—header, payload, signature—each encoded using Base64URL. Base64 is an encoding method that converts binary data into ASCII text, making it safe for transmission over HTTP protocols. In this implementation, the bcrypt algorithm is utilized for password hashing, ensuring that the original password is never stored directly in the database. The Web Service acts as an intermediary for communication between the client and server without the need to store session data on the server. During login, the system verifies credentials, generates a JWT token, and sends it to the client for subsequent authenticated requests. Testing results demonstrate improved security, as tokens can be verified without maintaining server-side sessions, while Base64 encoding ensures safe data transmission. This implementation is expected to serve as a practical solution and reference for future advancements in web application security.

Template engines play a pivotal role in modern web application development by enabling the dynamic rendering of content, products, and user interfaces. Today, they are essential for any website that handles dynamic data, from e-commerce to social media. However, their widespread adoption also makes them attractive targets for attackers seeking to exploit vulnerabilities and gain unauthorized access to web servers. This paper presents a comprehensive assessment of the risks associated with template engines, with a particular focus on the consequences of Server-Side Template Injection (SSTI) and the ease with which such vulnerabilities can escalate to Remote Code Execution (RCE), a critical security concern in web application development.

This research paper focuses on the development and analysis of a web-based system which is designed to study road traffic conditions and their routing behaviour in a dynamic environment. Nowadays Traffic congestion has become a major issue in urban areas due to the increasing number of vehicles and frequent changes in road conditions such as congestion, accidents, and peak-hour delays. Efficient route planning during those conditions requires routing approaches that can calculate traffic variations in real time. In this thesis, the road network is modelled as a weighted graph, where intersections are represented as nodes and road segments are represented as edges with weights corresponding to traffic conditions. The proposed web application allows users to interact with the road network by selecting source and destination points. They can also modify traffic conditions, and routing techniques and observe how routes change dynamically. Both static shortest path computation and dynamic routing approaches are implemented to study their behaviour and performance under varying traffic scenarios. The study provides visual representation of road networks and routing paths and also their performance metrics such as computation time and route cost. Thus it helps understand the clear comparison between static and dynamic routing methods and the impact of traffic changes depending on routing decisions. The developed web application serves as an analytical and educational tool for studying road traffic analysis and routing algorithms. The study shows the usefulness of web-based platforms in visualizing traffic behaviour and evaluating routing performance. But the current approach is limited to simulated data. This leaves room for future work that could improve the system by integrating real-world traffic data and explore more advanced routing strategies.

To develop a web-based application that uses multiple regression analysis to predict the risk of silicosis among stone carvers in Nakhon Ratchasima, Thailand. Data from 243 stone carvers were used to construct a multiple regression model incorporating key associated variables key associated variables: concentration of respirable silica dust, daily working hours, presence of underlying diseases, and residential proximity to the workplace. Model performance was assessed via R², adjusted R², RMSE, and the significance of associated variables. The model was integrated into a user-friendly web application and deployed for real-time risk assessment among 362 stone carvers. Silicosis risk scores were categorized into five levels to facilitate interpretation and targeted interventions. The Mann‒Whitney U test was applied to compare silicosis risk scores before and after application. The regression model explained 66.2% of the variance in silicosis risk scores (adjusted R² = 0.662), with strong predictive accuracy (RMSE = 2.59). All predictor variables were statistically significant (p < 0.05). The web application assigned silicosis risk scores ranging from 12 to 25, with 77.1% of participants classified as “very high risk.” However, no statistically significant difference was observed between the model and web application silicosis risk scores (p = 0.155); nonetheless, the observed trend suggests potential benefits in enhancing worker awareness and promoting protective behaviors. The developed multiple regression model and web application provide an effective tool for real-time silicosis risk prediction and stratification in stone carving communities. This digital health tool shows promise for early risk detection and prevention of silicosis in workers.

In recent years, excessive tillering caused by high temperatures during early growth has contributed to rice quality deterioration in warm regions of Japan. Accurate determination of midseason drainage timing is essential but remains difficult due to year- and cultivar-dependent variability. In this study, we developed a smartphone-based web application that estimates rice tiller number from canopy images and diagnoses the optimal timing of midseason drainage by comparing estimated tiller numbers with cultivar-specific target values. The system operates entirely on a smartphone using HTML5 canvas-based pixel extraction, JavaScript computation, and Google Apps Script-based backend processing. Field experiments conducted in Chiba Prefecture using three rice cultivars showed a strong linear relationship between estimated and observed tiller numbers (R2 = 0.9439). The root mean square error (RMSE) was 42.6 tillers m−2, with a consistent negative bias (−34.6 tillers m−2), indicating systematic underestimation. Considering typical tiller increase rates near midseason drainage (12.0–24.3 tillers m−2 day−1), these errors correspond to approximately 1–3 days of growth progression, which is acceptable for timing-based decision-making. Although the system does not aim to provide precise absolute tiller counts, it reliably captures relative growth-stage dynamics and supports threshold-based diagnosis. The proposed approach enables rapid, on-site decision support using only a smartphone, contributing to labor-saving and improved water management in rice production.

ABSTRACT As software applications get increasingly connected and complex, cybersecurity becomes more and more important to consider during development and evaluation. Software engineers need to be aware of various security threats and the countermeasures that can be taken to mitigate them. Currently, there is a lack of guidance for software engineers aiming to develop secure web applications. We conducted a design science research study, resulting in a set of guidelines to aid software engineers in developing secure web applications. The set of guidelines was constructed based on interview data with 10 industry practitioners. These guidelines were then evaluated using a survey with 28 respondents. Additionally, we conducted experiments in which we provided a large language model with our guidelines and vulnerability reports as input. The large language model should extend the given vulnerability reports by recommending which of our guidelines can help prevent the given vulnerability in the future. The extended reports were evaluated by two external researchers experienced in cyber security and one author. Our results indicate that developers consider using these proposed guidelines for the development and assessment of secure web applications in different stages of the software development lifecycle. Our results also show that it is possible to automatically enhance vulnerability reports to support developers meaningfully and that the guidelines recommended by the large language model are useful to prevent the respective vulnerabilities in the future.

This paper presents a detailed exploration of the architectural development of an offline-first Progressive Web Application (PWA) prototype, specifically designed for gamified learning systems. The core research involves the technical examination of the PWA prototype, emphasizing its architectural design and crucial offline capabilities in the context of gamification elements. The study highlights the critical architectural components necessary for offline-first PWAs in educational settings, including the usage of PWA service workers and caching mechanisms. Key gamification features were identified and integrated, differentiating between those suitable for offline settings (like Points Systems, Badges/Achievements, Progress Tracking) and those requiring online connectivity (like Global Leaderboards and Social Interactions). The prototype, built using the React frontend framework and Supabase BAAS for the backend, demonstrates the potential of offline-first strategies in educational technology. It provides practical insights into the challenges and opportunities of maintaining engaging, uninterrupted learning experiences, particularly in low-connectivity environments. In conclusion, while offline-first PWAs effectively support learning activities with intermittent internet access, the research suggests that a careful balance must be managed between robust offline functionality and the richness of dynamic online interactivity to fully maximize the benefits of gamification.

The article presents methods and key aspects of security issues related to the development of web applications using ASP.NET Core technology. In general, these issues are considered from different points of view, namely access and trust management, protection against injections and cross-site attacks, architectural solutions and isolation. In modern distributed systems, security is based on the principle of least privilege, which is ensured by the Claims-Based Authorisation method, which is scientifically more flexible and accurate than traditional role-based authorisation. Access is granted not on the basis of role definition, but on the basis of a set of requirements or assertions, which allows for the implementation of so-called fine-grained access control. In the context of an application programming interface, the user authentication key acts as a trust encapsulation mechanism. The token contains cryptographically signed assertions, allowing applications to be stateless. However, the scientific problem here is revoking tokens and ensuring a short lifetime for access tokens to minimise the risk of their compromise. Combating OWASP Top 10 vulnerabilities is built into the framework through secure coding practices. ASP.NET Core uses context-sensitive encoding mechanisms, such as in Razor Pages, to automatically context-encode output (HTML encoding). This is the primary line of defence against XSS attacks by changing how the browser interprets data. The mechanism for protecting against CSRF attacks is based on the use of synchronised tokens, which are a cryptographic way of confirming that a request comes from a trusted source. It also uses request parameterisation through object-relational mapping, such as Entity Framework Core, and implements a secure data access pattern that minimises the attack surface for SQL injections by separating commands from data. From a system architecture perspective, ASP.NET Core addresses security issues at the configuration and deployment level. The middleware system allows for the centralised implementation of security policies such as HTTPS redirection and CORS (Cross-Origin Resource Sharing) management, providing aggregated security control at the network level. Secret management also plays a role in ensuring security, as the modern paradigm requires complete separation of key secrets and passwords from code and configuration files. ASP.NET Core supports integration with Key Vaults (secret storage), which is an implementation of the ‘do not store secrets in code’ principle, and this increases security by isolating sensitive information.

Noncommunicable diseases are prevalent in the Philippines. Filipinos increasingly suffer from undernutrition or overnutrition. This can be caused by a lack of budget and nutritional awareness. Tolerating improper macronutrient intake is related to a higher risk of chronic diseases. Although varying solutions have been attempted, there is potential for discoveries. Specifically, with food choice technologies, motivation is an under-researched topic. Thus, this study explores the topic through the development of a meal planner web application that enables users to plan meals based on recipe selections tailored to their recommended macronutrient intake and available ingredients. Considering price, convenience, and experience, a proposed motivational concept based on nudging was to enable users to find recipes by scanning available ingredients. For this, a machine learning object detection model was trained to detect ingredients and find recipes. The model had a mean Average Precision (mAP) of 0.526 and an Average Recall (AR) of 0.539. Still, it could detect ingredients even when set at an accuracy of 90% and above. It was then integrated into a web application. Once accessible, the deployed web application was evaluated by nutritionist dietitians and other respondents. An average System Usability Scale (SUS) score of 79/100 was obtained. Moreover, the computed scores for the Intrinsic Motivation Inventory (IMI) subscales were all higher than 5 out of 7, with a Cronbach’s Alpha value of 0.7. Responses to open-ended questions also showed that users are motivated to eat healthier if they have increased nutritional awareness and pre-existing ingredients in suggested recipes.

Challenges with donors, health screening process, retrieval of donor information, and managing donation schedules and inventory. This Capstone Project aims to address these gaps by developing a web-based application to support the Human Milk Bank and Lactation Support Center (HMBLSC) at J.R. Borja General Hospital. The system is a web-based application. The system chose the Agile Methodology, an SDLC that allows continuous iterations based on feedback. Laravel for the web framework, PHP language, MySQL for database management. Figma for UI design, Draw.io for diagrams, Trello as project management tool, and SUS for usability. Intended users of the system are the HMBLSC staff, breastmilk donors, mothers, and infants in need. The key features includes online health screening, donation and request scheduling, home collection with location sharing, along with inventory monitoring and tracking, validation of records, and report generations. The testing indicates the good performance is good across modules, with all of the test cases passed. The system is effective with a SUS score of 77.9(excellent), showing higher user satisfaction. The web application improves breastmilk donation and request processes, offering reliability, usability, and efficient workflow. Future enhancements include a module for admins to publish announcements, and adding sms notifications for scalability.

User authentication security remains a major challenge in web application development, particularly in facing threats such as brute force attacks, credential theft, and data breaches. Conventional login systems that rely solely on static hashing methods are considered insufficiently adaptive to evolving security threats. This study aims to develop an adaptive login system based on the Laravel framework by combining the Advanced Encryption Standard (AES) and Bcrypt algorithms to enhance authentication data security. AES is utilized for symmetric encryption of sensitive data, while Bcrypt is used for password hashing due to its strength and resistance to brute force attacks. The adaptive login system is also equipped with logic to detect suspicious login patterns, such as repeated login attempts, triggering special handling like additional verification or delay mechanisms. The implementation is carried out using Laravel due to its robust security management features and efficient development support. Test results show that the developed system provides dual protection for login data, increases resistance to attacks, and maintains good performance. Therefore, the combination of AES and Bcrypt in an adaptive login system on Laravel proves to be effective in enhancing user authentication security in modern web applications.

This paper presents a multi-client web platform for telemetry of mechanical property testing, built around an existing tensile testing machine as an approach to its modernization. Extension and force measurements are performed using potentiometers with analog outputs connected to a 24-bit analog-to-digital converter (ADS1256) integrated with a Raspberry Pi 5 microcomputer. The communication module enables data acquisition and timing, the organization of a wireless Wi-Fi network, the operation of web and DNS servers, and full-duplex information exchange via the WebSocket protocol. A specially developed web application with a graphical user interface allows researchers to observe the "force-extension" curve and numerical values in real time on personal digital devices without installing specialized software. The platform supports simultaneous multi-client access, which increases the accessibility and convenience of experimental research. Validation experiments on reinforcing steel confirmed stable platform performance at a data acquisition frequency of 15Hz with up to 15 simultaneous client connections. The measurement accuracy was within±1% for both force and extension, meeting ISO 6892-1 requirements. The proposed solution provides an affordable and flexible tool for scientific and educational purposes, enhancing the informativeness and accuracy of experimental tests while offering a practical pathway for upgrading conventional tensile testing machines.

Colorectal cancer is the third most diagnosed cancer worldwide, and immune checkpoint inhibitors have shown promising therapeutic outcomes in selected patient groups. This study performed a comprehensive analysis of multi-omi... | Find, read and cite all the research you need on Tech Science Press

Collaboration between designers and developers in modern web application development is often fragmented, leading to inefficiencies, delays, and repetitive manual implementation. This paper presents a Collaborative Web Designing Platform using Liveblocks, a web-based system that enables real-time multi-user UI design combined with AI-assisted frontend code generation. The platform allows multiple users to collaboratively design interfaces on a shared canvas with instant synchronization, presence awareness, and conflict-free updates. Visual designs and textual prompts are automatically converted into production-ready frontend code using modern frameworks such as Next.js. A live sandbox preview enables immediate validation and iterative refinement of generated applications. Experimental evaluation demonstrates improved productivity, reduced development time, and enhanced collaboration compared to traditional design-to-development workflows. The proposed system provides a scalable and efficient solution for modern collaborative web development environments.