Mobile applications are an integral part of our daily life. From ordering food to online banking, they are used for a multitude of daily tasks. Currently, there are two main ways to build mobile applications — native apps and Progressive Web Apps (PWAs). Native apps are built separately for Android and iOS, while PWAs are web-based apps that function similarly to native apps within a browser environment. This paper compares these two technologies based on performance (speed, memory, battery), user experience (ease of use, satisfaction), and development efficiency (cost, maintenance). Real-world examples like Starbucks PWA, Twitter Lite, and Ma-Ease (a native Android app) are used to clearly illustrate these differences. The study found that native apps are superior in terms of performance and user experience, but PWAs are more cost-effective to develop and simpler to maintain. The System Usability Scale (SUS) scores for native apps were 83.2 (Excellent) and those for PWAs at 76.5 (Good). This paper assists developers and organizations in deciding which technology to choose based on their needs and budget. Keywords: Progressive Web Apps (PWA), Native Mobile Applications, Performance Benchmarking, User Experience (UX), System Usability Scale (SUS), Service Workers, Mobile Development, Cross-Platform.

PSLite Online is a modern, web-based application designed to facilitate the analysis of top-down mass spectrometry fragmentation data, particularly for targeted proteoform studies. As a successor to the Windows-only ProSight Lite, PSLite Online retains and expands upon its core functionalities while offering platform independence and enhanced usability through a responsive, browser-based interface. Users can load or manually define proteoforms, apply a wide range of modifications, and visualize fragmentation coverage in real time. Key new features include ProForma integration, correction for common mass deconvolution errors, SVG export, and cloud-based session sharing via persistent URLs. The application leverages modern web technologies and component libraries to deliver a lightweight, installable progressive web app (PWA) experience. Here, we show that PSLite Online can be used for the characterization of antibody subunits with glycan modifications. This solution broadens accessibility for proteomics researchers by eliminating installation barriers and enabling seamless collaboration across devices and platforms. PSLite Online is freely available at https://pslite.proteinaceous.net.

Gamers in Indonesia frequently encounter complex, non-transparent, and slow processes for game top-ups, compounded by security concerns on untrusted platforms. This project addresses these issues by proposing the development of "FOMOGame," a Progressive Web App (PWA). The main objective is to produce a functional Minimum Viable Product (MVP) that offers a fast, secure, and competitive e-commerce platform for popular game top-up services. The project utilizes the Agile methodology with iterative sprints and a User-Centered Design approach focused on enhancing usability and readability. The technical architecture is built on React.js and Tailwind CSS. The expected outcome is an MVP capable of efficiently processing simulated transactions, which will be validated through usability testing with active gamers.

Gendang Beleq is a traditional musical art of the Sasak people that continues to be preserved in Lombok. However, the process of renting Gendang Beleq instruments in East Lombok is still done manually, which makes it difficult for people to access clear and reliable information about rental locations, prices, and provider contacts. This study aims to develop a Progressive Web App (PWA)-based Geographic Information System that can provide accurate, fast, and easy-to-access information related to Gendang Beleq rentals. The research uses a Research and Development (R&D) approach with the ADDIE model, which includes the stages of analysis, design, development, implementation, and evaluation. The developed system displays an interactive map showing the locations of rental providers along with complete details such as prices, contacts, and descriptions of the instruments. The system also supports key PWA features, including app installation and offline access. Performance testing using GTMetrix resulted in scores of 98% for the admin application and 97% for the user application. Functional testing showed that the system is highly feasible, with alpha testing results of 94.5% for the admin system and 100% for the user interface, while beta testing achieved a 98% feasibility score. Overall, the PWA-based GIS developed in this study is considered effective, user-friendly, and capable of supporting the availability of Gendang Beleq rental information in East Lombok.

Children with dyslexia experience difficulties in recognizing characters, which lead to limitations in reading ability. This challenge is further compounded by their short attention span. Conventional therapy methods guided by child psychologists have been used for a long time but remain time-consuming and require intensive supervision. The growing trend of technological innovation has encouraged students to develop new approaches that enhance therapeutic experiences using modern technology. Therefore, this study introduces an innovation in the form of a multisensory therapy device based on the Internet of Things (IoT) and non-visual Augmented Reality (AR). The device is designed to improve therapeutic intervention for children with dyslexia. Dissolve integrates tactile puzzle elements equipped with NFC sensors and an ESP32 microcontroller, connected to a Progressive Web App (PWA) that provides real-time interactive feedback without using a camera. The development process was conducted by Information Systems students using the Design Thinking approach with the Double Diamond model (Discover–Define–Develop–Deliver). The tool was tested directly with children diagnosed with dyslexia. Experimental results show significant improvements in phonetic accuracy (from single-letter recognition to >95% sequential phoneme accuracy), decoding ability for two- to six-letter words and focus duration from approximately 30 minutes to one hour per session. Dissolve also demonstrated high usability, stable connectivity (BLE signal loss < 2%), and improved therapy efficiency compared to conventional methods. These findings affirm that Dissolve serves as an innovative multisensory therapy solution for children with dyslexia, making the therapeutic process more inclusive, efficient, and engaging.

Limited access to real-time information regarding the availability of camping equipment is one of the main obstacles for prospective renters in the rental process. In addition, conventional rental systems tend to be inefficient, provide incomplete information on available equipment, and lack digital features that support integrated booking and rental data management. This study aims to design and develop an online store application for camping equipment rental based on Progressive Web App (PWA) technology using the Laravel framework at Buton Adventure Store Baubau. The system development method employed is the System Development Life Cycle (SDLC) with a V-Model approach, which emphasizes a strong relationship between each design and testing phase to ensure system quality. The application was developed using the Laravel framework to support efficient development, system security, and ease of maintenance. The results show that the developed web-based application can present comprehensive camping equipment information, provide availability checking, online booking, and payment features, and offer a responsive and consistent user interface across multiple devices. Furthermore, the implementation of PWA technology allows the application to remain accessible under unstable network conditions. Overall, the developed application is expected to improve the efficiency of the camping equipment rental process, enhance access to real-time availability information, and deliver a faster, more practical, and modern user experience.

In India, land ownership still largely depends on paper- based documentation, which is susceptible to deterioration, forgery, and administrative inefficiencies. This research introduces LandLedger+, a secure and transparent digital land registry system designed to modernize property record management through cryptographic protection, cloud integration, and user-friendly accessibility. A systematic, phased methodology was employed to ensure structured development and reliable implementation. The process began with requirement analysis and research to identify user needs, functional requirements, and appropriate cryptographic algorithms. The finalized technology stack comprised Python, Django, Firebase/Supabase, and Progressive Web App (PWA) frameworks. The frontend was developed using HTML5, CSS/Tailwind, and JavaScript (React optional) to create responsive, cross-platform interfaces, while the backend managed authentication, document handling, and real-time data synchronization. Cryptographic algorithms were implemented for secure file encryption and decryption, complemented by role- based access control and metadata-based search. Rigorous testing and debugging ensured system security, performance, and functionality, followed by documentation and deployment on Vercel and Netlify. The results demonstrated that LandLedger+ effectively prevented duplication and unauthorized alterations, ensured data immutability through hashing, and enhanced accessibility through a user-friendly web interface. The discussion highlights the system’s potential to reduce fraud, improve administrative efficiency, and enable transparent property transfers. In conclusion, LandLedger+ presents a scalable and secure framework for transforming India’s land registry system, combining cryptographic security with cloud- based architecture to promote transparency, reduce fraud, and establish a citizen-centric approach to land governance.

Background: Indonesia’s aquaculture sector holds vast potential, yet many fish farmers, especially those in remote areas like offshore cages, face limited access to timely fish health services, leading to undetected disease outbreaks, mass fish mortality, and significant economic losses. Contribution: This study introduces Fish Doctor, a scalable platform integrating fish species detection, disease diagnosis, and expert consultation. It bridges the gap between AI-based detection and practical aquaculture needs in developing countries, supporting sustainable practices aligned with SDG 2 (Zero Hunger) and SDG 12 (Responsible Consumption and Production). Method: The application was built using Next.js, Express.js, MySQL, and integrates computer vision and expert systems. Its core features include image-based fish species detection using YOLOv11, rule-based disease diagnosis through forward chaining, and an online expert consultation module. Designed as a Progressive Web App (PWA), the system offers offline-first capabilities, enabling its use in low-connectivity environments. Results: The system was evaluated using test datasets of five fish species, achieving an average diagnostic accuracy above 80% and response times of less than 2 seconds per case. Conclusion: The developed platform demonstrates potential for improving early disease detection and reducing reliance on chemical treatments in aquaculture. Future research will involve usability testing with more than 100 fish farmers across multiple provinces to assess scalability and generalizability.

This project focuses on developing an IoT and AI-based Carbon Footprint Tracker for vehicle emission control. The system continuously monitors exhaust gases such as CO, CO₂, and hydrocarbons (HC) using gas sensors (MQ135, MQ7, MQ2) interfaced with an ESP32 microcontroller. The collected sensor data is transmitted to a cloud database for real-time analysis. Using AI algorithms, the system classifies vehicles as low or high emitters and provides instant alerts through a Progressive Web App (PWA) when emission levels exceed permissible limits.The application also offers maintenance recommendations and trend visualization, enabling users to track their vehicle’s environmental impact. This integrated system promotes predictive maintenance, fuel efficiency, and compliance with emission standards. Overall, it contributes to reducing pollution and supports sustainable transportation through smart, data-driven monitoring.

Modern retail environments are consistently challenged by operational inefficiencies that detract from the customer shopping experience, most notably the long queues and delays associated with traditional, manual billing systems. This paper introduces the Linea Smart Shopping Cart System, a web-based solution designed to directly address these challenges by bridging the gap between the convenience of e-commerce and the engagement of physical retail. The system's primary objective is to deliver a seamless, contactless shopping journey. It empowers customers to use their own mobile phone camera to scan products, which are then instantly added to a real-time virtual cart. Customers can then proceed to a secure self-checkout, completely bypassing the need for manual billing and significantly reducing checkout time. The Linea system is built upon scalable and modular three-tier architecture, comprising a frontend, backend, and add-on modules. The frontend is a responsive Progressive WebApp (PWA) developed using React, TypeScript, and Tailwind CSS. The entire backend is powered by Firebase, which provides a robust suite of servicesFirebase Authentication for secure, role-based login; Cloud Firestore as a NoSQL database for real-time cart synchronization and product management; Cloud Functions for server less logic, such as receipt generation and checkout timestamp calculations; and Cloud Storage for hosting digital receipts and product images. This cloud-based integration ensures low maintenance costs, high scalability, and seamless data synchronization between all users and administrators. A key differentiator for Linea is its focus on affordability and practical deployment, particularly for small and medium-sized retailers. While conceptually inspired by sensor-heavy systems like Amazon Go, Linea deliberately avoids the high infrastructure costs associated with computer vision and sensor hardware. Instead, it leverages the ubiquitous smartphone camera and open-source barcode scanninglibraries, making it a highly accessiblealternative. The system provides distinct role-based dashboardsCustomers can scan, add to their cart, and pay, while an Admin dashboard offers comprehensive tools for product CRUD operations, real-time inventory tracking, pricing updates, and viewing analytics. The implemented system successfully achieved its core objectives, resulting in a 100% contactless shopping experience and a 50% reduction in average checkout time, while also eliminating manual Billing errors. This scalable architecture not only modernizes the retail experience but also provides a solid foundation for future evolution. Planned future developments include the integration of an AI- driven recommendation engine, a Manager Dashboard with predictive sales analytics, and IoT-based RFID auto-detection to one day eliminate manual scanning entirely. Linea is thus positioned as a viable, affordable, and futuristic model for the next generation of retail transformation, especially within developing markets.

Paper-based prescriptions and reports constitute a major part of the medical health records across the globe. Accordingly, paper-based manual data recording is a common practice in the Community Health Centers (CHCs) in India. These records result in poor handling, fragmented information, inefficient data retrieval, sharing, and storage of clinical data. To address this gap, we present Intelligent Document Processing Application (IDPA), a low-cost, scalable data digitization pipeline combining Optical Character Recognition (OCR) and Vision-Language Models (VLMs) for converting bilingual (Hindi-English), handwritten, and numerical medical records into structured digital formats. IDPA comprises a two-stage pipeline, where Stage 1 employs table cell segmentation using OpenCV and Stage 2 uses OCR extraction with PaliGemma VLM. As a proof-of-concept, the application was tested using a dataset of 150 patient records of the Indian population, which exhibited prevalent data input issues including overwritten texts, obscured columns, and the application of whiteners. PaliGemma, refined using over 650 labelled table cell images, attained 74% accuracy and a 13% Character Error Rate (CER), outperforming other open-source VLM models in extracting the medical records. The extracted data is organized into structured dataframes, served through a FastAPI endpoint, and accessible through a Progressive Web App (PWA). The interface supports secure user authentication via Clerk API and enables real-time image upload, editable tabular outputs, and data export in CSV/PDF formats. Together, these digital tools offer an affordable, user-centric approach to improve healthcare data management in low-resource settings. They hold strong potential for integration with national health systems, improvement of continuity of care, enablement of longitudinal monitoring, and expansion into predictive analytics for clinical decision support.

ABSTRACT The problem of connecting users with verified local service providers quickly has been a significant issue in the era of urban digital transformation, particularly in emergencies. Conventional techniques of service discovery, for example, Directory or ad hoc mobile applications, cannot provide real‐time responsiveness, intelligent character prioritization, and accurate location. The AI‐Driven Smart Neighborhood Services is an affordable, highly evolved real‐time booking and online service and represents a complex geospatial intelligence, intelligent scheduling, and Progressive Web App (PWA) technology that can make it easy to discover and book professionals near their home or work areas. The technology incorporates the AI‐enhanced Haversine formula and the K‐Nearest Neighbor (KNN) algorithm to facilitate dynamic proximity matching, optimize booking scheduling using a Priority Queue, and employ a Bayesian average rating to ensure fair and impartial ranking of providers. The system is constructed on an expandable architecture (Next.js, Firebase Firestore, Google Maps API), which enables searching for emergency availability and provides a convenient user experience across all devices. The work provides a strong foundation for implementing geospatially aware, intelligent service platforms in a smart city ecosystem, where a gap exists between trusted local services and real‐time user demands.

The Batak Toba script is one of Indonesia’s cultural heritages that has become increasingly rare and less recognized among younger generations. This research aims to develop a handwriting recognition system for Batak Toba characters using the Convolutional Neural Network (CNN) method, capable of accurately recognizing characters, transliterating them into Latin script, and translating them into Indonesian. The dataset was self-generated using the Noto Sans Batak font and character combinations, totaling 113 labels, which were processed into 64×64 grayscale images. The CNN model was designed with several convolutional and pooling layers and compiled using the Adam optimizer and categorical cross-entropy loss function. Training results achieved a validation accuracy of 98.36% and a testing accuracy of 98.12%, with respective loss values of 0.0268 and 0.0295. The system was then integrated into a web-based application built as a Progressive Web App (PWA), supporting both online transliteration and translation features. These results demonstrate that the CNN approach is highly effective in recognizing Batak Toba characters. In the future, the system can be further developed into a full sentence-level OCR, integrated into a native Android application, and expanded with datasets from real handwritten samples.

Abstract- The author has implemented capturing the photo of the customer using angular web camera feature. We have worked in Extenda Project to provide simple and trusting lending solutions for micro, small and medium Enterprises(MSMEs) that lack collateral, helping them start or expand their businesses [1]. We have Personal details page, Employment details page, Income and Expense page, Additional details, Loan summary and Documents upload page. Keywords- progressive web apps, Firebase, micro services, web camera, document upload.

Online education has become an important channel for extensive, inclusive and flexible learning experiences. However, significant gaps persist in providing truly accessible, personalized and adaptable e-learning environments, especially for students with disabilities, varied language backgrounds, or limited bandwidth. This paper presents AccessiLearnAI, an AI-driven platform, which converges accessibility-first design, multi-format content delivery, advanced personalization, and Progressive Web App (PWA) offline capabilities. Our solution is compliant with semantic HTML5 and ARIA standards, and incorporates features such as automatic alt-text generation for images using Large Language Models (LLMs), real-time functionality for summarization, translation, and text-to-speech capabilities. The platform, built on top of a modular MVC and microservices-based architecture, also integrates robust security, GDPR-aligned data protection, and a human-in-the-loop to ensure the accuracy and reliability of AI-generated outputs. Early evaluations indicate that AccessiLearnAI improves engagement and learning outcomes across multiple ranges of users, suggesting that responsible AI and universal design can successfully coexist to bring equity through digital education.

BackgroundDespite the potential of digital adherence technologies to support patient-centered monitoring for tuberculosis (TB), there is limited research on incorporating indirect and direct adherence monitoring or assessing patients’ experiences with these technologies. The TB Treatment Support Tools (TB-TST) includes a comprehensive mobile app for patients and health care providers and a direct adherence metabolite test to report and monitor adherence.ObjectiveThis paper describes the iterative refinement process of the TB-TST intervention.MethodsTo refine the TB-TST intervention, we used an iterative approach involving multiple embedded mixed methods studies guided by the Information Systems Research framework and Design Thinking Process. Embedded studies included a randomized controlled pilot study, interviews, usability testing, and surveys with patients and experts to inform ongoing refinements. The project consisted of interface evaluation, high-level system design, and iterative redesign.ResultsThe TB-TST intervention was refined through 3 iterative phases. In Phase 1, based on feedback from pilot study participants and 4 experts in TB, improvements included an in-app discussion board, submission confirmations, and enhanced account recovery. Cultural adaptation was based on Hofstede’s dimensions. Phase 2 involved 4 Directed Research Groups and 19 stakeholders to redesign user flows, simplify reporting, and transition the app to a progressive web app, improving device compatibility. Phase 3 included usability testing cycles with 48 participants (26 patients and 22 health care professionals), yielding high satisfaction scores: patient app Mobile Health App Usability Questionnaire, mean 5.96 (SD 0.46); provider mobile dashboard IT Usability Evaluation Scale scores ranged from 5.83 to 6.23 out of 7, and optimization of interface and dashboard. Refinements included larger icons, streamlined onboarding, symptom summary enhancements, and a new cohort-level adherence graph. These modifications improved navigation, usability, and remote monitoring for patients with TB and providers in preparation for a multisite clinical trial.ConclusionsCombining multiple methods guided by the Information Systems Research framework and elements of the Design Thinking Process can help researchers and developers leverage the strengths of mixed methods iterative designs to create highly personalized and effective digital health interventions.

Abstrak. Efektivitas pengobatan, memiliki efek yang berbanding lurus dengan patuh tidaknya pengguna dalam melakukan konsumsi obat. Berbagai penelitian menunjukkan kurangnya sistem manajemen obat yang efektif dan mudah diakses, menjadi salah satu faktor penyebab ketidakpatuhan. Data yang ada mengenai masalah ini mendorong adanya solusi inovatif. Maka dari itu dikembangkanlah Medipulse, sebuah sistem manajemen obat berbasis web dengan penerapan desain responsif dan teknologi Progressive Web App (PWA). Bootstrap dipilih sebagai framework agar penerapan desain responsif bisa lebih mudah dilakukan dan aplikasi bisa berfungsi secara maksimal di berbagai perangkat. Sementara teknologi PWA diterapkan agar website bisa diakses dalam kondisi offline serta menyediakan notifikasi pengingat konsumsi obat. Hasil evaluasi yang dilakukan telah menunjukkan bahwa sistem ini mampu memudahkan pengguna dalam kepatuhan konsumsi obat. Dengan fitur-fitur yang dikembangkan, Medipulse diharapkan bisa menjadi solusi inovatif yang mampu meningkatkan efektivitas pengobatan. Penelitian ini juga diharapkan bisa menjadi referensi untuk mengembangkan sistem informasi kesehatan berbasis web di masa depan. Abstract. The effectiveness of medication has a directly proportional effect on whether or not users are compliant in taking their medication. Various studies have shown that the lack of an effective and accessible medication management system is one of the contributing factors to non-adherence. The existing data on this issue prompted an innovative solution. Therefore, Medipulse was developed, a web-based medication management system with responsive design and Progressive Web App (PWA) technology. Bootstrap was chosen as the framework so that the application of responsive design can be done more easily and the application can function optimally on various devices. While PWA technology is applied so that the website can be accessed in offline conditions and provide reminder notifications for drug consumption. The results of the evaluation conducted have shown that this system is able to facilitate users in drug consumption compliance. With the features developed, Medipulse is expected to be an innovative solution that can improve medication effectiveness. This research is also expected to be a reference for developing web-based health information systems in the future.

Abstrak. Perkembangan teknologi web telah membuka peluang baru dalam menghadirkan media edukasi yang fleksibel dan mudah diakses. Penelitian ini bertujuan untuk merancang dan mengembangkan aplikasi edukasi Keselamatan dan Kesehatan Kerja (K3) berbasis Progressive Web App (PWA) bernama K3POIN, menggunakan pendekatan model pengembangan ADDIE (Analysis, Design, Development, Implementation, Evaluation). Aplikasi dikembangkan dengan CodeIgniter 4, MySQL, dan Web Push API. Validasi dilakukan melalui uji performa dan kapabilitas PWA menggunakan Lighthouse dan PWABuilder. Hasil menunjukkan bahwa aplikasi telah memenuhi standar PWA modern, tampil responsif pada berbagai perangkat, dan mampu mengirim notifikasi otomatis berdasarkan jadwal praktik. Respons awal pengguna menunjukkan bahwa aplikasi K3POIN bermanfaat sebagai media belajar mandiri mengenai K3 di lingkungan laboratorium.

Parents are integral in shaping early childhood health behaviors, and mobile health (mHealth) interventions offer an accessible method of supporting them in this role. Optimizing participant engagement is key to mHealth effectiveness and impact; however, research examining personal predictors of engagement remains underexplored. We aimed to describe participant engagement with a novel parental mHealth intervention (Let's Grow) during the first 25 weeks of use and investigate whether engagement levels varied by family demographics and parental cognitions and behaviors relevant to the intervention. We used data from parents in the intervention group of the Let's Grow trial. The intervention targeted toddlers' movement behaviors, and the program (a purpose-designed progressive web app) was delivered via self-paced modules. The content was built around 3 main components (behavior change activities, information provision, and social support). Engagement data (web app analytics) collected across the first 25 weeks of the intervention were summarized as study-specific metrics (time using the app, proportion of accessed features and pages, clicks in the main parts of the app) and overall engagement measures (composite engagement index [EI], individual subindexes [click depth, loyalty, recency, and diversity]). The baseline measures included family demographics (main carer, child and family characteristics, and postcode) and parental cognitions and behaviors relevant to the intervention (coping, concern, and information seeking). Linear regression was used to assess associations between baseline and engagement measures. All parents allocated to the intervention group (n=682) were included. Most parents (609/682, 89.3%) logged in and used at least 1 app feature; those who never used the app were excluded from subsequent analyses. App access declined from 90.6% (552/609) in the first week to 31.2% (190/609) at 25 weeks. For users active during weeks 12 to 25, EI remained consistent and was nearly identical to the average EI (28%, range 3%-50%). More work hours, parents living together, having siblings in the family, and living in a regional or remote area were each associated with lower engagement on 10 out of 12 indicators (β=-31.37 to -0.01; all P≤.046). Higher education level was associated with higher engagement on 9 indicators (β=0.77-18.59; all P≤.02). Of the parental characteristics, only higher coping was positively associated with engagement (β=1.25; P=.003). Our findings indicate that time and sociodemographic factors might be the most relevant predictors of engagement and highlight the characteristics of parents who may benefit from more active strategies to support their engagement with digital interventions. The uptake and continued engagement with this app exceeded what is generally reported for apps, but it is unknown whether this is sufficient for behavior change. Individual and composite engagement measures yielded similar results, indicating that simpler, more feasible metrics can be useful for reporting engagement in digital interventions. RR2-10.1136/bmjopen-2021-057521.

Retail businesses, particularly hardware stores, often encounter challenges in order management such as delayed deliveries, inaccurate stock tracking, and limited information transparency factors that hinder operational efficiency and customer satisfaction. This study proposes a web-based order management system utilizing Progressive Web Apps (PWA) technology, developed with the Next.js framework. The Periodic Review System (PRS) method is implemented to calculate reorder points based on actual demand and safety stock levels. System development follows the Waterfall model, with data collected through observation, semi-structured interviews, and literature review. Testing confirms that the application enhances stock accuracy, minimizes delivery delays, supports offline access, and meets SEO performance standards. The implementation significantly improves operational efficiency and holds promise for boosting customer loyalty. The study concludes that PWA-based digital systems are practical, scalable solutions for the MSME sector, with future potential for integration of AI, CRM, and real-time analytics.