Managing student academic progress across multiple dimensions remains a significant operational challenge for educational institutions. Traditional record-keeping approaches based on spreadsheets and paper registers are inefficient, errorprone, and unable to provide timely analytical insights. This paper presents Academic Growth Tracker Pro, a desktop-based application developed using Python with a Tkinter graphical user interface, SQLite database backend, and integrated modules for webcam-based student identification, analytics visualization, PDF report generation, and attendance tracking. The system consolidates student management, faculty oversight, academic performance recording, and administrative analytics into a unified platform. Real-time chart rendering via Matplotlib and automated PDF generation via ReportLab allow stakeholders to derive actionable insights without requiring external tools. Experimental deployment demonstrates measurable gains in data accessibility, reporting efficiency, and institutional oversight. The system achieves a 94.6% database query accuracy under concurrent load and reduces report generation time by approximately 78% compared to manual methods. Future scope includes cloud synchronization, predictive performance modeling, and biometric authentication integration.

In the twenty-first century, the increasing prevalence of chronic diseases and structural barriers in accessing healthcare services have necessitated a more active role for individuals in managing their own health. This transformation has shifted healthcare from a hospital-centered model toward daily life practices, emphasizing the concept of self-care. Although mobile health (mHealth) technologies offer significant potential in this context, the fragmentation of health data across multiple platforms and privacy concerns associated with cloud-based solutions hinder widespread user adoption. This study presents the design and development of a Mobile Health Tracking System that prioritizes user data privacy through an offline-first architecture and local data storage. The application was developed using the Flutter framework and the Dart programming language to support both Android and iOS platforms. Data persistence is ensured through an on-device SQLite database, preventing the sharing of sensitive health information with third-party servers by default. The system includes essential modules such as Body Mass Index (BMI) calculation, customizable user profiles, medication reminder mechanisms, blood pressure monitoring, heart rate tracking, sleep data management, and interactive health visualizations implemented using the fl_chart library. System performance was evaluated based on user interface responsiveness, database query execution times, and notification service stability. The results indicate that local database–based mHealth applications provide significant advantages in terms of data privacy, offline accessibility, and user trust. Furthermore, the findings demonstrate that Flutter’s single-codebase architecture offers a cost-effective and high-performance solution for developing cross-platform healthcare applications. Additionally, the application supports the generation of personalized PDF health reports from user data. To mitigate the risk of data loss during device changes, an optional Firebase-based cloud synchronization feature has been integrated, allowing users to back up and restore their data at their discretion.

The increasing use of artificial intelligence (AI) in smart environments, industrial automation, healthcare monitoring, and Internet of Things (IoT) networks has increased the computational requirements and power consumption of cloud computing infrastructure. The use of cloud computing for AI processing faces challenges such as high latency, high bandwidth consumption, privacy concerns, and environmental emissions due to the large number of data centers. This work introduces a sustainable and low-power AI model based on the smart edge architecture․ It includes energy-efficient machine learning algorithms deployed at the edge for making smart decisions․ This model achieves optimal performance using lightweight model deployment, dynamic resource allocation, and hardware-aware optimizations such as model quantization and hardware pruning techniques․ A modular architecture is proposed that increases efficiency by focusing on data acquisition, edge processing, AI inference, energy management, and cloud synchronization․ Performance metrics include latency, energy consumption, compute efficiency, and inference quality․ The latter, in particular, is important for providing efficient AI capabilities on devices with limited resources․ Results of experiments show that local processing reduces network and energy use compared to processing in the cloud․ The proposed framework enables scalable and efficient deployment of AI while minimizing the environmental impact and maintaining performance to support sustainable computing․

Mobile cloud backup applications are widely used to safeguard user data. This paper found that current cloud backup is inefficient on resource-limited mobile devices because it consumes excessive CPU resources for delta synchronization that requires intensive hash computation to identify the modified ranges of file data. To address this issue, this paper presents SolFS, an operation log versioning file system to optimize mobile cloud backup efficiency. The core idea is that if the cloud backup application knows the modified offset and length of each write since the last backup, it will be able to identify the new modified data and upload them only, avoiding data hashing throughout the entire file. SolFS proposes a series of designs to achieve this design goal. First, SolFS introduces per-file mergeable operation logging that allows each file to manage its write operation logs (i.e., offset and length) in an extent tree and merge operation logs with contiguous or overlapping modified ranges of file data. Then, SolFS proposes the operation log persistence and versioning mechanism that allows different cloud backup applications to manage their own file versions without interfering with each other. In addition, SolFS incorporates techniques such as compact log and dynamic granularity, to optimize the memory and storage overhead to the system. Finally, SolFS achieves hash-free file difference identification with the minimum extra overhead and extends the ability of cloud backup applications. In addition to the APP side, we extend the SolFS design to the cloud backup server to reduce the server-side computation overhead. Furthermore, the SolFS-enabled server integrates a new shadow file mechanism to significantly mitigate the write overhead caused by file reconstruction. The experimental results show that SolFS can significantly reduce the computational overhead of server-side by over 90% on average and the total cloud synchronization time by over 88.8% when files are updated. On the server side, the SolFS-enabled server can reduce up to 78% of file reconstruction latency.

Stroke continues to impose an enormous morbidity and mortality burden worldwide. Stroke survivors often incur debilitating consequences that impair motor function, independence in activities of daily living and quality of life. Rehabilitation is a pivotal intervention to minimize disability and promote functional recovery following a stroke. The Internet of Medical Things, a network of connected medical devices, software and health systems that collect, store and analyze health data over the internet, is an emerging resource in neurorehabilitation for stroke survivors. Technologies such as asynchronous transmission to handle intermittent connectivity, edge computing to conserve bandwidth and lengthen device life, functional interoperability across platforms, security mechanisms scalable to resource constraints, and hybrid architectures that combine local processing with cloud synchronization help bridge the digital divide and infrastructure limitations in low-resource environments. This manuscript reviews emerging rehabilitation technologies such as robotic devices, virtual reality, brain-computer interfaces and telerehabilitation in the setting of neurorehabilitation for stroke patients.

The convergence of blockchain, IoT, and cloud technologies has created new opportunities for secure, transparent, and autonomous data management in smart manufacturing systems. In this study, we developed a Cloud-Blockchain Integrated Fermentation Device (CBIFD) that enables real-time monitoring and distributed logging of environmental data during fermentation. The system employs a modular sensor configuration to measure pressure, temperature, humidity, internal temperature, CO2, and pH, and is integrated with an Arduino Nano 33 IoT. Its cloud-native, self-contained architecture eliminates the need for a local server, ensuring high mobility and scalability. System performance was validated in a laboratory setting, demonstrating seamless data acquisition, reliable cloud synchronization, and consistent execution of blockchain transactions. Moreover, comparative analysis with existing IoT-blockchain architectures highlights the superior autonomy and infrastructure flexibility of the CBIFD. This work provides a robust foundation for future research aimed at enhancing process transparency and data-driven control in intelligent fermentation environments.

The digitalization of business processes has become an urgent need for Micro, Small, and Medium Enterprises (MSMEs) in Indonesia. However, technology implementation at the MSME level still faces limitations related to infrastructure, cost, and digital literacy. This research develops an on-premise (Windows-based) sales and purchase information system at Ibu Siti's Store in Cicalengka as a pragmatic solution to connectivity and reliability constraints. The system was designed using the Agile methodology, enabling iterative and adaptive development tailored to user needs. The on-premise platform was selected to support offline operations, integration with local cashier hardware, low implementation costs, and high stability within a small business environment. The system is equipped with role-based access control, automated database backups, and features for transaction and inventory reporting. Test results indicate an improvement in transaction efficiency and data accuracy. This research also outlines future development opportunities, including barcode scanner integration, minimum stock notifications, and cloud synchronization, to align with the trend toward smarter, more connected MSME digitalization.

Purpose- The main objective of the proposed paper is to create and implement a real-time wearable health monitoring system based on IoT, i.e., Oxy Sense-Wear, that will enable the constant control of the main physiological parameters, such as ECG, EMG, SpO2, body temperature, and physical activity. The system is aimed at long-term surveillance of the elderly, bedridden, and long-term chronic disease patients, and this allows the patient to identify abnormal health conditions in time and provide proper medical care. Design/methodology/approach-The given device is a soft wearable chest strap with built-in biomedical sensors and powered by an ESP32 microcontroller. Live information is collected, analyzed, and sent through Wi-Fi to a cloud-based server and Android smartphone application. Physiological alerts will activate the buzzer and instant mobile notification when the physiological thresholds are surpassed. The software was used to design and simulate the hardware that was being developed with Proteus and create firmware in the Arduino IDE and the mobile application on Android Studio. Findings- There is a reliable real-time performance as experimental assessment shows heart rate changes with a deviation of +/-2 BPM, SpO 2 values were always in the range of 96-98, and body temperature was monitored accurately between 36.0 o C and 38.8 o C. Fall events were identified with great success at acceleration levels more than 2.5 g, and low false positives. The system recorded a mean alert latency of less than 500 ms and could operate continuously (8 to 12 hours, depending on charge) and thus proved to be viable in the case of personal and clinical remote healthcare monitoring. Originality/value-The proposed Oxy Sense-Wear platform is the first to offer a single multi-parameter sensing, real-time alerting, cloud synchronization, mobile connectivity, and OTA-enhanced platform in a small and wearable size. The work done in the future will be on the implementation of more sophisticated machine-learning algorithms for predictive health analytics, improving the security of the collected data by using encrypted authentication to provide more connection options with the use of the BLE and 5G technology to support the large-scale implementation and integration with the hospital information system. In an effort to be more concise and clearer, this manuscript lays emphasis on system-level insights, comparative appraisal, and quantitative performance assessment rather than a description of the components at a much more detailed level.

Abstract Cloud storage services such as Microsoft OneDrive are widely used in modern computing environments, making them an important source of digital evidence during forensic investigations. However, the proprietary nature of OneDrive synchronization mechanisms and the absence of explicit user-action logs pose significant challenges for accurate forensic analysis. This study examines OneDrive synchronization artifacts generated on Windows operating systems by performing controlled user actions and analyzing the resulting client-side forensic artifacts. A structured experimental methodology was adopted to correlate local file system activities with OneDrive client logs to reconstruct synchronization behavior. The analysis demonstrates that OneDrive synchronization activity can be reliably reconstructed by identifying and correlating distinct synchronization cycles, even in the absence of explicit file-level logs. The findings highlight the forensic significance of OneDrive client logs and provide practical insights for investigators analyzing cloud synchronization activity on Windows systems. Keywords: Digital Forensics, OneDrive, Cloud Storage, Windows Operating System, Synchronization Artifacts

Background: Mobile technologies have also revolutionized the information access and management practices in the academic libraries because of their rapid development. University librarians are also becoming expected to have a very high level of mobile personal information management (MPIM) in order to assist in research, teaching, and digital library services. This paper analyses the MPIM capabilities of librarians in the twin cities, emphasizing on the most important dimensions of efficient mobile-based information handling. Purpose: The research focuses on determining the skills in mobile information searching, organization, retrieval, communication and collaboration, security and privacy management, as well as information sharing and dissemination. It also aims at determining the differences and disparities in competencies between demographic and institutional groups. Methodology: The quantitative survey design was used to collect the structured information on 250 librarians who work at the public and private universities in Rawalpindi and Islamabad. The MPIM skills were measured with a validated questionnaire which has Likert scale items in six domains. Descriptive statistics, including frequencies and percentages, were applied to analyze the data and interpret the respondents’ competency levels. Results: The results show that overall MPIM competencies of the respondents are relatively high, with high levels of skills in mobile information searching, communication, collaboration, and mobile information authentication based security practices. The average level of skill was identified in such aspects as backup routines, app management, cloud synchronization, and regular data preservation. The disparity in competency patterns was determined by differences in experience, designation, and institutional support. Conclusion: The paper concludes by finding out that the university librarians have a solid foundation of MPIM skills, which makes them well-positioned to facilitate mobile-enabled academic contexts. Nevertheless, there are significant weaknesses in the areas of progressive organization, security monitoring, and cross-platform management, which point to the necessity of professional development. The development of such competencies will make librarians better able to perform the tasks of supporting the changing digital library service and increasing the efficiency of the workflow, as well as addressing the demands of the dynamic information of academic users.

I-Canopy: A resilient IoT platform for adaptive environmental monitoring at the edge

Cardiovascular diseases are among the leading causes of sudden death worldwide, often due to the lack of timely diagnosis and continuous monitoring. Conventional ECG monitoring systems require patients to stay in hospitals or clinics for observation, which limits accessibility and continuity of care. This study presents the development of a cost-effective Internet of Things (IoT)-based ECG monitoring system designed to provide real-time cardiac health supervision from any location. The proposed system employs an AD8232 ECG sensor for signal acquisition and a NodeMCU ESP8266 microcontroller for wireless data transmission. The collected ECG data are uploaded to the Ubidots cloud platform, where authorized users can visualize and analyse the cardiac waveform using a web interface or mobile application. In case of abnormal signal patterns, the system automatically sends alert notifications to medical professionals and caregivers, enabling immediate response. Experimental validation on multiple users demonstrated reliable ECG signal capture and accurate cloud synchronization, confirming the system’s effectiveness in remote cardiac monitoring. This IoT-integrated framework offers a practical approach to early detection of heart irregularities and supports continuous patient observation, thereby reducing the risk of critical cardiac events and improving the accessibility of preventive healthcare.

Background: Mobile technologies have also revolutionized the information access and management practices in the academic libraries because of their rapid development. University librarians are also becoming expected to have a very high level of mobile personal information management (MPIM) in order to assist in research, teaching, and digital library services. This paper analyses the MPIM capabilities of librarians in the twin cities, emphasizing on the most important dimensions of efficient mobile-based information handling. Purpose: The research focuses on determining the skills in mobile information searching, organization, retrieval, communication and collaboration, security and privacy management, as well as information sharing and dissemination. It also aims at determining the differences and disparities in competencies between demographic and institutional groups. Methodology: The quantitative survey design was used to collect the structured information on 250 librarians who work at the public and private universities in Rawalpindi and Islamabad. The MPIM skills were measured with a validated questionnaire which has Likert scale items in six domains. Descriptive statistics, including frequencies and percentages, were applied to analyze the data and interpret the respondents’ competency levels. Results: The results show that overall MPIM competencies of the respondents are relatively high, with high levels of skills in mobile information searching, communication, collaboration, and mobile information authentication based security practices. The average level of skill was identified in such aspects as backup routines, app management, cloud synchronization, and regular data preservation. The disparity in competency patterns was determined by differences in experience, designation, and institutional support. Conclusion: The paper concludes by finding out that the university librarians have a solid foundation of MPIM skills, which makes them well-positioned to facilitate mobile-enabled academic contexts. Nevertheless, there are significant weaknesses in the areas of progressive organization, security monitoring, and cross-platform management, which point to the necessity of professional development. The development of such competencies will make librarians better able to perform the tasks of supporting the changing digital library service and increasing the efficiency of the workflow, as well as addressing the demands of the dynamic information of academic users.

This paper introduces a software-defined architectural model for modernizing upstream transport in hybrid fiber-coaxial networks through user-space UDP termination using the VPP HostStack. The approach aims to overcome performance limitations inherent in DOCSIS-based systems by relocating transport-layer processing from centralized cores to multi-access edge computing nodes co-located with Remote PHY Devices. Unlike centralized vCMTS deployments or full-fiber upgrades, this model enables enhanced throughput and session-level concurrency without modifying coaxial infrastructure, DOCSIS PHY signaling, or customer premises equipment. The system is built on FD.io’s Vector Packet Processing framework and validated through CI-integrated benchmarking using the CSIT testbed and iperf3 traffic generator. Test executions were performed on a two-node Intel Ice Lake setup interconnected via 100GE Intel E810CQ interfaces. The benchmarking workflow used iperf3 dynamically linked with libvcl_ldpreload.so, enabling transparent redirection of UDP socket operations to the HostStack without modifying the application code. Two traffic scenarios were evaluated: a baseline with one UDP stream achieving 15.5 Gbps, and a scaled case with one client transmitting ten concurrent streams reaching 28.1 Gbps. The results demonstrate an 81% throughput gain with minimal variance, confirming the HostStack’s efficiency under concurrent flow conditions. Integration via LD_PRELOAD proved stable, transparent, and production-compatible. These findings validate the scalability, reproducibility, and operational viability of edge-based UDP termination for HFC modernization. The proposed model offers a low-disruption upgrade path for cable operators, enabling phased deployment of user-space transport enhancements with CI/CD compatibility, improved upstream utilization, and support for multi-session, latency-sensitive applications such as telemetry, video streaming, and cloud synchronization. In addition, this architecture enables measurable performance improvements without hardware changes, costly rewiring, or disruptions to existing customer services.

Small and medium-sized enterprises (SMEs) form the backbone of many economies, yet they often face significant challenges in managing their operations effectively due to limited resources and technical expertise. This paper presents the design and development of the "Small-Medium Business Enterprises Manager App," a robust, cross-platform solution tailored for SMEs. The application integrates essential business management functionalities, including invoicing, expense management, and financial reporting, to provide a unified tool for business owners. With its intuitive interface, the app empowers retailers, wholesalers, and distributors to streamline sales and purchase processes, monitor product details, and generate insightful reports for better decision-making. By incorporating cloud synchronization, the app enables users to securely access and manage their business data from mobile and desktop platforms, ensuring flexibility and real-time collaboration. The study delves into the technical architecture, user experience design, and implementation challenges encountered during the app’s development. It also evaluates the app’s impact on operational efficiency and financial compliance through case studies and performance metrics. This research highlights the potential of technology-driven solutions in overcoming traditional business hurdles, fostering growth, and enabling SMEs to compete effectively in a dynamic marketplace. The findings underscore the importance of accessible and scalable digital tools in driving the digital transformation of small businesses.

Modern airport supply chains demand real-time visibility, security, and transparency across all cargo-handling operations.However, current centralized systems are often prone to inefficiencies, data tampering, and limited traceability.This paper presents a hybrid architecture that integrates blockchain and cloud technologies to streamline airport cargo tracking.Leveraging Ethereum-based smart contracts deployed via Ganache and real-time cloud synchronization using Firebase Firestore, the proposed system ensures both immutability and accessibility.A React.js-based frontend provides an intuitive user interface for cargo management, while Python scripts automate bulk data processing from CSV files.The architecture aims to eliminate manual errors, enhance security, and offer scalable deployment across airport logistics.The system was tested with thousands of cargo records, demonstrating seamless synchronization between the blockchain and cloud layers.

The purpose of the article is to develop and validate a hybrid biomedical signal monitoring system that integrates edge-computing capabilities with Microsoft Azure cloud services. The article describes a new edge-and-cloud architecture based on the EmotiBit wearable sensor and a Raspberry Pi gateway, enabling continuous acquisition, local buffering, and scalable cloud synchronization of multi-modal biometric signals. Using hardware evaluation (signal accuracy, power consumption, wireless connectivity), Azure Blob Storage, Azure SQL Database, Power BI dashboards, and Azure Stream Analytics, the authors demonstrate a seamless pipeline for real-time visualization and post-hoc analysis of electrocardiogram (ECG), photoplethysmogram (PPG), galvanic skin response (GSR), and additional vital signs. We illustrate the proposed system by conducting a rigorous battery performance analysis under three workloads continuous sensing alone, sensing with local storage, and combined local plus cloud upload and comparing operational endurance across battery capacities ranging from 1,200 mAh (≈12 h runtime) to 6,000 mAh (≈45 h runtime). Our proposal allows improvement in data delivery reliability to ≥99.5 % under network latencies up to 200 ms and reduces baseline clinical monitoring load by up to 30 %. The new method for performance evaluation is confirmed by quantitative runtime and reliability calculations. New research results supplement existing telemedicine paradigms by demonstrating enhanced patient mobility, near-real-time anomaly detection, and scalable data management, and can be used for remote patient monitoring, clinical diagnostics, and large-scale health studies. This paper is novel because it jointly optimizes edge-device design, battery endurance, and cloud analytics within a unified, deployable Azure framework.

This project develops an advanced video processing system capable of detecting and annotating objects in video footage. By leveraging deep learning-based object detection models, the system identifies, tracks, and labels objects across video frames, ensuring accurate analysis over time. The implementation uses Flask as a web framework, OpenCV for video handling, and the YOLO model for efficient object detection. Users can upload videos, specify target objects, and receive annotated outputs with bounding boxes and confidence levels. The system supports real-time processing, enabling automated monitoring applications such as surveillance, traffic analysis, and security enforcement. A Firebase backend facilitates cloud storage and retrieval of processed videos and detection results. Optimized frame sampling ensures efficient computation, reducing processing time without compromising accuracy. The application enhances decision-making by providing structured data insights extracted from video content. Additional features include detection result saving, cloud synchronization, and historical data access for further analysis. The system's flexible architecture allows future enhancements, including integration with live camera feeds and expanded detection capabilities. Key Words:Video Processing, Object Detection, Annotation, Computer Vision, Automated Monitoring, Surveillance, Deep Learning.

Gesture recognition technology has emerged as a transformative solution for natural and intuitive human–computer interaction (HCI), offering touch-free operation across diverse fields such as healthcare, gaming, and smart home systems. In mobile contexts, where hygiene, convenience, and the ability to operate under resource constraints are critical, hand gesture recognition provides a compelling alternative to traditional touch-based interfaces. However, implementing effective gesture recognition in real-world mobile settings involves challenges such as limited computational power, varying environmental conditions, and the requirement for robust offline–online data management. In this study, we introduce ThumbsUp, which is a gesture-driven system, and employ a partially systematic literature review approach (inspired by core PRISMA guidelines) to identify the key research gaps in mobile gesture recognition. By incorporating insights from deep learning–based methods (e.g., CNNs and Transformers) while focusing on low resource consumption, we leverage Google’s MediaPipe in our framework for real-time detection of 21 hand landmarks and adaptive lighting pre-processing, enabling accurate recognition of a “thumbs-up” gesture. The system features a secure queue-based offline–cloud synchronization model, which ensures that the captured images and metadata (encrypted with AES-GCM) remain consistent and accessible even with intermittent connectivity. Experimental results under dynamic lighting, distance variations, and partially cluttered environments confirm the system’s superior low-light performance and decreased resource consumption compared to baseline camera applications. Additionally, we highlight the feasibility of extending ThumbsUp to incorporate AI-driven enhancements for abrupt lighting changes and, in the future, electromyographic (EMG) signals for users with motor impairments. Our comprehensive evaluation demonstrates that ThumbsUp maintains robust performance on typical mobile hardware, showing resilience to unstable network conditions and minimal reliance on high-end GPUs. These findings offer new perspectives for deploying gesture-based interfaces in the broader IoT ecosystem, thus paving the way toward secure, efficient, and inclusive mobile HCI solutions.

ABSTRACT: The proliferation of digital technologies in higher education has created both opportunities and challenges for academic resource sharing. While students increasingly seek peer-driven learning experiences, they simultaneously face critical trust issues with informal note-sharing channels such as WhatsApp, Telegram, and generic cloud platforms. This paper presents NoteHub, an Android-based mobile application designed to address documented gaps in academic resource sharing by integrating three core innovations: (1) an administrator-mediated verification system ensuring content authenticity, (2) real-time cloud synchronization via Firebase infrastructure, and (3) gamification mechanisms promoting sustained user engagement. Through Design Science Research methodology, the system was developed over 18 weeks using Agile practices and evaluated through mixed-methods evaluation involving 150 undergraduate students across a 12-week deployment period. Quantitative findings demonstrate significant improvements: user confidence in note authenticity increased from 42% (informal channels) to 88% (NoteHub platform, p<0.001), mean System Usability Scale score reached 78.9 (indicating "good" usability), and academic performance of active contributors exceeded non-users by 6.7 percentage points (82.4% vs. 75.7%, p=0.039). Qualitative analysis of 18,342 chat messages revealed 68% academically-focused discourse, validating the platforms educational orientation