Search Results (43)

Assisted Reproductive Technology (ART), particularly In Vitro Fertilization (IVF), generates highly sensitive medical data classified as Protected Health Information (PHI) under international privacy and data protection laws. Ensuring the secure, transparent, and ethically governed management of this data is both essential and legally mandated. However, conventional Electronic Medical Record (EMR) systems often present significant challenges, including data-integrity risks, unauthorized access, and limited patient control—issues that become especially critical in contexts such as fertility preservation for cancer patients. EmbryoTrust introduces a blockchain-based framework designed to ensure the confidentiality, integrity, and availability of IVF-related information through a private, permissioned network integrated with role-based access control (RBAC). Smart contracts, implemented in Solidity on the Ethereum platform, verify spousal identities and enforce data immutability in compliance with religious legislation and ethical regulations. Off-chain data are stored in MongoDB for scalable, privacy-preserving management, while on-chain summaries provide tamper-evident traceability and verifiable auditability. The system was deployed and validated on the Ethereum Holešky testnet using Solidity 0.8.21 and Node.js 18.17, achieving an average transaction-confirmation time of 2.8 s, 99.9% uptime and a 95% user-satisfaction rate. Functional, integration, and usability testing confirmed secure and efficient data handling with minimal computational overhead. Comparative analysis demonstrated that the hybrid on-/off-chain architecture reduces latency and gas costs while maintaining automated compliance enforcement. The modular design enables adaptation to other jurisdictions by reconfiguring ethical and regulatory parameters within the smart-contract layer, ensuring flexibility for global deployment. Overall, the EmbryoTrust framework illustrates how blockchain logic can technically enforce medical and ethical rules in real time, providing a reproducible model for secure, culturally compliant, and privacy-preserving digital-health information management. Its alignment with Saudi Vision 2030 and the Wold Health Organization (WHO) Global Strategy on Digital Health 2020–2025 highlights its potential as a scalable solution for next-generation ART information systems. Full article

This paper addresses the need for accessible and interoperable supervision solutions within the Industry 4.0 paradigm, particularly for small-scale or resource-constrained environments. The proposed system integrates a web-based architecture using opensource technologies to enable real-time industrial monitoring and data acquisition. A hybrid setup was developed, combining a virtual glass manufacturing process in Factory IO with a physical three-phase induction motor controlled by a Modicon M580 PLC. The system architecture includes a local HMI developed in Control Expert and a remote interface built with React and Node.js, both synchronized through a MySQL 8.0 database populated via Python 3.13 using the Modbus TCP/IP protocol. Experimental results demonstrate consistent data synchronization, reliable multi-platform integration, and an average end-to-end latency of 156 ms, validating the feasibility of the approach for IIoTbased applications. The solution demonstrates how general-purpose web technologies can be effectively repurposed for industrial use, offering a cost-effective and scalable alternative to traditional SCADA systems. The proposed architecture is easily replicable, adaptable to various process configurations, and suitable for academic, prototyping, and SME environments. Full article

In this study, we used the hydrophobic-polar (HP) two-dimensional square and three-dimensional cubic lattice models for the problem of protein structure prediction (PSP). This kind of lattice reduces computational time and calculations, the conformational space from $9^n$ to $3^{n-2}$ for the 2D square lattice and $5^{n-2}$ for the 3D cubic lattice. Even within this context, it remains challenging for genetic algorithms or other metaheuristics to identify the optimal solutions. The contributions of the paper consist of: (1) implementation of a high-performing novel genetic algorithm (GA); instead of considering only the self-avoiding walk (SAW) conformations approached in other work, we decided to allow any conformation to appear in the population at all stages of the proposed all conformations biomimetic genetic algorithm (ACGA). This increases the probability of achieving good conformations (self avoiding walk ones), with the lowest energy. In addition to classical crossover and mutation operators, (2) we introduced specific translation operators for these two operations. We have proposed and implemented an HP Protein Visualizer tool which offers interpretability, a hybrid approach in that the visualizer gives some insight to the algorithm, that analyse and optimise protein structures HP model. The program resulted based on performed research, provides a molecular modeling tool for studying protein folding using technologies such as Node.js, Express and p5js for 3D rendering, and includes optimization algorithms to simulate protein folding. Full article

Universities face continuous challenges in ensuring rapid and efficient communication during emergencies due to outdated, fragmented, and manual notification systems. This research presents the design, development, and implementation of a multiplatform mobile application to optimize emergency notifications at the Escuela Superior Politécnica de Chimborazo (ESPOCH). The application, developed using the Flutter framework, offers real-time alert dispatch, geolocation services, and seamless integration with ESPOCH’s Security Unit through Application Programming Interfaces (APIs). A descriptive and applied research methodology was adopted, analyzing existing notification workflows and evaluating agile development methodologies. MOBILE-D was selected for its rapid iteration capabilities and alignment with small development teams. The application’s architecture incorporates a Node.js backend, Firebase Realtime Database, Google Maps API, and the ESPOCH Digital ID API for robust and scalable performance. Efficiency metrics were evaluated using ISO/IEC 25010 standards, focusing on temporal behavior. The results demonstrated a 53.92% reduction in response times compared to traditional notification processes, enhancing operational readiness and safety across the campus. This study underscores the importance of leveraging mobile technologies to streamline emergency communication and provides a scalable model for educational institutions seeking to modernize their security protocols. Full article

The Wi-SUN FAN (Wireless Smart Ubiquitous Network Field Area Network) standard facilitates large-scale connectivity among smart devices in utility networks and smart cities. Specifically designed for Low-Power and Lossy Networks (LLNs), Wi-SUN FAN supports the formation of multiple Personal Area Networks (PANs) and mesh topologies with multi-hop transmissions. However, the node association process, divided into five junction states, often results in prolonged connection times, particularly in multi-hop networks, thereby limiting network scalability and reliability. This study analyzes the factors affecting these delays, with a particular focus on Join State 1 (JS1), which relies on PAN Advertisement (PA) packets that use asynchronous communication and the trickle timer algorithm, frequently causing significant delays. To overcome this challenge in JS1, we propose the Parallel Rendezvous (PR) strategy, which forms synchronized clusters of unassociated nodes and leverages the standard’s PAN Advertisement Solicit (PAS) packets to rapidly disseminate network information. The proposed algorithm, PR Wi-SUN FAN, is evaluated through simulations in various network topologies, demonstrating notable improvements in linear, fully connected, and mesh scenarios. The most significant gains are observed in the linear topology, with reductions of up to 71.22% in association time and 59.56% in energy consumption during JS1. Full article

Databases are essential to modern information systems, ensuring reliable and structured data storage. PostgreSQL, a leading database management system, ranked fourth globally in 2022 and offers advantages such as free access and cross-platform compatibility. This research focuses on developing educational materials for learning PostgreSQL through a contemporary electronic resource. Utilizing technologies like React, Node.js, and Express, the project aims to create functional test databases and a server-side component for an interactive learning platform. Key tasks include studying relevant technologies, preparing learning materials, developing a student evaluation system, and designing test database structures. The project ultimately seeks to enhance database programming education and promote exploration of modern development technologies. Full article

Domain-Specific Languages with JSON grammar (JSON-DSLs) are specialized programming languages tailored to specific problem domains, offering higher abstraction levels and simplifying software implementation through the JSON standard. RhoArchitecture is an approach for designing and executing JSON-DSLs, incorporating a modular programming model, a JSON-based evaluation engine, and an integrated web development environment. This paper presents RestRho, a RESTful NodeJS server developed using two JSON-DSLs designed with RhoArchitecture: SQLRho and DBRestRho. These languages enable declarative specification of database operations and HTTP requests, respectively, supporting modularity, reuse, and template-based transformations. We validate the RestRho implementation through a dual approach. First, we apply software metrics to assess code quality, maintainability, and complexity. Second, we conduct an empirical study involving 39 final-year computer engineering students, who completed 18 structured tasks and provided feedback via questionnaires. The results demonstrate the tool’s usability, development efficiency, and potential for adoption in web application development. Full article

Hierarchical Federated Learning (HFL) for the Internet of Vehicles (IoV) leverages roadside units (RSU) to construct a low-latency, highly scalable multilayer cooperative training framework. However, with the rapid growth in the number of vehicle nodes, this framework faces two major challenges: (i) communication inefficiency under bandwidth-constrained conditions, where uplink congestion imposes significant burden on intra-framework communication; and (ii) interference from untrustworthy vehicle nodes, which disrupts model training and affects convergence. Therefore, in order to achieve secure aggregation while alleviating the communication bottleneck problem, we design a hierarchical three-layer federated learning framework with Gradient Quantization (GQ) and secure aggregation, called FedHSQA, which further integrates anomaly scoring to enhance robustness against untrustworthy vehicle nodes. Specifically, FedHSQA organizes IoV devices into three layers based on their respective roles: the cloud service layer, the RSU layer, and the vehicle node layer. During each non-initial communication round, the cloud server at the cloud layer computes anomaly scores for vehicle nodes using a Kullback–Leibler (KL) divergence-based multilayer perceptron (MLP) model. These anomaly scores are used to design a secure aggregation algorithm (ASA) that is robust to anomalous behavior. The anomaly scores and the aggregated global model are then transmitted to the RSU. To further reduce communication overhead and maintain model utility, FedHSQA introduces an adaptive GQ method based on the anomaly scores (ASQ). Unlike conventional vehicle node-side quantization, ASQ is performed at the RSU layer. It calculates the Jensen–Shannon (JS) distance between each vehicle node’s anomaly distribution and the target distribution, and adaptively adjusts the quantization level to minimize redundant gradient transmission. We validate the robustness of FedHSQA against anomalous nodes through extensive experiments on three real-world datasets. Compared to classical aggregation algorithms and GQ methods, FedHSQA reduced the average network traffic consumption by approximately 30 times while improving the average accuracy of the aggregation model by about 5.3%. Full article

Blockchain has several unique features: data integrity, security, privacy, and immutability. For this reason, it is considered one of the most promising new technologies for a wide range of applications. Initially prominent in cryptocurrencies such as Bitcoin, its applications have expanded into areas such as the Internet of Things. However, integrating blockchain into IoT systems is challenging due to the limited computing and storage capabilities of IoT devices. Efficient blockchain mining requires lightweight hash functions that balance computational complexity with resource constraints. In this study, we employed a structured methodology to evaluate hash functions for blockchain–IoT systems. Initially, a survey is conducted to identify the most commonly used hash functions in such environments. Also, this study identifies and evaluates a lightweight hash function, designated as HashLEA, for integration within blockchain-based IoT systems. Subsequently, these functions are implemented and evaluated using software coded in C and Node.js, thereby ensuring compatibility and practical applicability. Performance metrics, including software efficiency, hardware implementation, energy consumption, and security assessments, were conducted and analyzed. Ultimately, the most suitable hash functions, including HashLEA for blockchain–IoT applications, are discussed, striking a balance between computational efficiency and robust cryptographic properties. Also, the HashLEA hash function is implemented on a Raspberry Pi 4 with an ARM processor to assess its performance in a real-world blockchain–IoT environment. HashLEA successfully passes security tests, achieving a near-ideal avalanche effect, uniform hash distribution, and low standard deviation. It has been shown to demonstrate superior execution time performance, processing 100 KB messages in 0.157 ms and 10 MB messages in 15.48 ms, which represents a significant improvement in execution time over other alternatives such as Scrypt, X11, and Skein. Full article

This paper presents a fleet management system (FMS) for multiple robots, utilizing deep reinforcement learning (DRL) for dynamic task allocation and path planning. The proposed approach enables robots to autonomously optimize task execution, selecting the shortest and safest paths to target points. A deep Q-network (DQN)-based algorithm evaluates path efficiency and safety in complex environments, dynamically selecting the optimal robot to complete each task. Simulation results in a Gazebo environment demonstrate that Robot 2 achieved a path 20% shorter than other robots while successfully completing its task. Training results reveal that Robot 1 reduced its cost by 50% within the first 50 steps and stabilized near-optimal performance after 1000 steps, Robot 2 converged after 4000 steps with minor fluctuations, and Robot 3 exhibited steep cost reduction, converging after 10,000 steps. The FMS architecture includes a browser-based interface, Node.js server, rosbridge server, and ROS for robot control, providing intuitive monitoring and task assignment capabilities. This research demonstrates the system’s effectiveness in multi-robot coordination, task allocation, and adaptability to dynamic environments, contributing significantly to the field of robotics. Full article

Programming has gained significant importance worldwide as societies increasingly rely on computer application systems. To support novices in learning various programming languages, we have developed the Programming Learning Assistant System (PLAS). It offers several types of exercise problems with different learning goals and levels for step-by-step self-study. As a personal answer platform in PLAS, we have implemented a web application using Node.js and EJS for Java and Python programming. Recently, the Flutter framework with Dart programming has become popular, enabling developers to build applications for mobile, web, and desktop environments from a single codebase. Thus, we have extended PLAS by implementing the Flutter environment with Visual Studio Code to support it. Additionally, we have developed an image-based user interface (UI) testing tool to verify student source code by comparing its generated UI image with the standard one using the ORB and SIFT algorithms in OpenCV. For efficient distribution to students, we have generated Docker images of the answer platform, Flutter environment, and image-based UI testing tool. In this paper, we present the implementation of a web-based answer platform for the Flutter Programming Learning Assistant System (FPLAS) by integrating three Docker images using Docker Compose. Additionally, to capture UI images automatically, an Nginx web application server is adopted with its Docker image. For evaluations, we asked 10 graduate students at Okayama University, Japan, to install the answer platform on their PCs and solve five exercise problems. All the students successfully completed the problems, which confirms the validity and effectiveness of the proposed system. Full article

The “Study Buddy” project innovates by creating a dynamic web application that cultivates collaboration and support within the student community. This initiative harnesses the full potential of the MERN (MongoDB, Express.js, React, Node.js) stack to optimize student interactions, accelerate the learning process, and assist with a broad spectrum of academic and non-academic requirements. It revolutionizes how students engage, enabling them to share knowledge, pose queries, and offer assistance, ensuring robust security through authentication and authorization. Additionally, it empowers students to voice their concerns, propose groundbreaking ideas, and forge meaningful connections. This initiative fosters an inclusive and vibrant student environment, where information flows artlessly, challenges are met head-on, and students can thrive academically and beyond. Full article

Porcine reproductive and respiratory syndrome virus (PRRSV) is a pathogen that causes severe abortions in sows and high piglet mortality, resulting in huge economic losses to the pig industry worldwide. The emerging and novel PRRSV isolates are clinically and biologically important, as there are likely recombination and pathogenic differences among PRRSV genomes. Furthermore, the NADC34-like strain has become a major epidemic strain in some parts of China, but the characterization and pathogenicity of the latest strain in Inner Mongolia have not been reported in detail. In this study, an NADC34-like strain (CHNMGKL1-2304) from Tongliao City, Inner Mongolia was successfully isolated and characterized, and confirmed the pathogenicity in pigs. The phylogenetic tree showed that this strain belonged to sublineage 1.5 and had high homology with the strain JS2021NADC34. There is no recombination between CHNMGKL1-2304 and any other domestic strains. Animal experiments show that the CHNMGKL1-2304 strain is moderately virulent to piglets, which show persistent fever, weight loss and high morbidity but no mortality. The presence of PRRSV nucleic acids was detected in both blood, tissues, nasal and fecal swabs. In addition, obvious pathological changes and positive signals were observed in lung, lymph node, liver and spleen tissues when subjected to hematoxylin–eosin (HE) staining and immunohistochemistry (IHC). This report can provide a basis for epidemiological investigations and subsequent studies of PRRSV. Full article

Tracing serves as a key method for evaluating the performance of microservices-based architectures, which are renowned for their scalability, resource efficiency, and high availability. Despite their advantages, these architectures often pose unique debugging challenges that necessitate trade-offs, including the burden of instrumentation overhead. With Node.js emerging as a leading development environment recognized for its rapidly growing ecosystem, there is a pressing need for innovative performance debugging approaches that reduce the telemetry data collection efforts and the overhead incurred by the environment’s instrumentation. In response, we introduce a new approach designed for transparent tracing and performance debugging of microservices in cloud settings. This approach is centered around our newly developed Internal Transparent Tracing and Context Reconstruction (ITTCR) technique. ITTCR is adept at correlating internal metrics from various distributed trace files to reconstruct the intricate execution contexts of microservices operating in a Node.js environment. Our method achieves transparency by directly instrumenting the Node.js virtual machine, enabling the collection and analysis of trace events in a transparent manner. This process facilitates the creation of visualization tools, enhancing the understanding and analysis of microservice performance in cloud environments. Compared to other methods, our approach incurs an overhead of approximately 5% on the system for the trace collection infrastructure while exhibiting minimal utilization of system resources during analysis execution. Experiments demonstrate that our technique scales well with very large trace files containing huge numbers of events and performs analyses in very acceptable timeframes. Full article

Nowadays, people like to travel to alleviate the stress and depression they experience in their busy lives, and if they can do so at a low cost, it will be even more beneficial to them. A traveler can choose to book a travel ticket themselves or to contact a travel agency, who will book a ticket for them. A lack of vehicle management can cause travel agencies to lose profit. In this article, we have provided proficient solutions for vehicle optimization to minimize travel costs. Additionally, however, arranging an appropriate tour guide is sometimes expensive for travelers. Moreover, travelers often do not know about suitable travel packages and occasionally do not know where they may want to visit. In this paper, we have discussed how we used effective methods to reduce vehicle costs for tour purposes. Furthermore, an optimal bus seat occupancy solution has been developed, incorporating the package system and including a tour guide. Our system offers a remedy by providing access to cost-effective packages. We have also added a blog portal to our web-based software for an overview all tourist places and their information. As a result, we have found optimized routes and provide low-cost, high-value tours. We have also reduced carbon footprint emissions. Our proposed optimal vehicle usage system has been developed using Hypertext Markup Language (HTML), Cascading Style Sheets (CSS), the Tailwind Framework, React, and Node.js. Full article