Deployment Of Applications Research Articles

Deployment of a Web Application for Fitting Experimental Data at the JINR Cloud Infrastructure

Deployment of a Web Application for Fitting Experimental Data at the JINR Cloud Infrastructure

Rare-earth praseodymium-substituted Bi5Ti3FeO15 exhibiting enhanced piezoelectric properties for high-temperature application

Owing to their exceptional piezoelectric effects, piezoelectric materials play a crucial role in high-end technologies and contribute significantly to the national economy. Bismuth layer-structured ferroelectrics (BLSFs) possess high Curie temperatures, making them a focal point of research in high-temperature piezoelectric sensor devices. However, their poor piezoelectric performance and low direct-current (DC) electrical resistivity hinder their effective deployment in high-temperature applications. To overcome these shortcomings, we employed composition optimization by partially substituting bismuth ions with rare-earth praseodymium ions. This approach enhances the piezoelectric performance and improves the DC electrical resistivity by preventing the loss of volatile bismuth ions and stabilizing the bismuth oxide layer (Bi2O2)2+, thereby reducing the concentration of oxygen vacancies. Consequently, we achieved a large piezoelectric constant d33 of 23.5 pC/N in praseodymium-substituted Bi5Ti3FeO15, which is three times higher than that of pure Bi5Ti3FeO15 (7.1 pC/N), along with a high Curie temperature TC of 778 °C. Additionally, the optimal composition of 4 mol% praseodymium-substituted Bi5Ti3FeO15 exhibits good thermal stability of electromechanical coupling characteristics up to 300 °C. This study holds promise for a wide array of high-temperature piezoelectric applications and has the potential to accelerate the development of high-temperature piezoelectric sensor technologies.

Real-Time Video Streaming Applications in Military 5G Networks: A Technical Review

Abstract: The integration of advanced telecommunications technologies into military operations marks a pivotal evolution in modern warfare strategies. With the advent of military-grade 5G networks, there arises an unprecedented capacity for real-time data streaming and enhanced operational transparency on the battlefield. This literature review critically examines the deployment of real-time streaming applications within such networks, focusing on a technology stack comprising React Native, Node.js, MongoDB, Docker, and WebRTC. These technologies collectively form the backbone of a system designed to leverage the full capabilities of 5G’s high throughput and ultra-low latency, essential for the demands of contemporary military engagements.

Energy-Aware Microservice-Based Application Deployment in UAV-Based Networks for Rural Scenarios

Yearly, the rates of Internet penetration are on the rise, surpassing 80% in developed nations. Despite this progress, over two billion individuals in rural and low-income regions face a complete absence of Internet access. This lack of connectivity hinders the implementation of vital services like remote healthcare, emergency assistance, distance learning, and personal communications. To bridge this gap and bring essential services to rural populations, this paper leverages Unmanned Aerial Vehicles (UAVs). The proposal introduces a UAV-based network architecture and an energy-efficient algorithm to deploy Internet of Things (IoT) applications. These applications are broken down into microservices, strategically distributed among a subset of UAVs. This approach addresses the limitations associated with running an entire IoT application on a single UAV, which could lead to suboptimal outcomes due to battery and computational constraints. Simulation results conducted in a realistic scenario underscore the effectiveness of the proposed solution. The evaluation includes assessing the percentage of IoT requests successfully served to users in the designated area and reducing the energy consumption required by UAVs during the handling of such requests.

Mining for cost awareness in the infrastructure as code artifacts of cloud-based applications: An exploratory study

Context:Cloud computing’s rise as the primary platform for software development and delivery is largely driven by the potential cost savings. However, it is surprising that no empirical evidence has been collected to determine whether cost awareness permeates the development process and how it manifests in practice. Objective:This study aims to provide empirical evidence of cost awareness by mining open source repositories of cloud-based applications. The focus is on Infrastructure-as-Code artifacts that automate software (re)deployment on the cloud. Methods:A systematic examination of 152735 repositories yielded 2010 relevant hits. We then analyzed 538 relevant commits and 208 relevant issues using inductive and deductive coding and corroborated findings with discussions from Stack Overflow. Results:The findings indicate that developers are not only concerned with the cost of their application deployments but also take actions to reduce these costs beyond selecting cheaper cloud services. We also identify research areas for future consideration. Conclusion:Although we focus on a particular Infrastructure-as-Code technology (Terraform), the findings can be applicable to cloud-based application development in general. The provided empirical grounding can serve developers seeking to reduce costs through service selection, resource allocation, deployment optimization, and other techniques.

Thermophysical properties of equiatomic CrMnFeCoNi, CrFeCoNi, CrCoNi, and CrFeNi high- and medium-entropy alloys

High- and medium-entropy alloys have emerged as promising materials with significant potential for diverse technical applications. Notably, their use in the field of energy technology has attracted particular interest, where thermophysical properties play a pivotal role. In this context, this study aims to investigate the thermophysical properties, namely thermal expansion behavior, thermal diffusivity, specific heat capacity, thermal conductivity, and the electric resistivity of single-phase face-centered cubic alloys between 300 K and 773 K. In addition to thermal conductivity, the electric resistivity was measured. Based on the Wiedemann-Franz law, electronic and phononic contributions to the overall thermal conductivity were evaluated. Complementary insights into the electronic state of the investigated materials were obtained by electron spin resonance measurements, revealing the presence of short-range ordering in all alloys. The specific heat capacity is found to dominate the thermal conductivity. Furthermore, the study shows the decisive influence of chemical complexity on the thermophysical properties. Notably, the observed influences extend to the electronic thermal conductivity, pointing out the correlation between composition and heat transport properties. This comprehensive investigation provides a foundation for understanding and tailoring the thermophysical behavior of the studied alloys, offering valuable insights for their targeted deployment in advanced energy applications.

Data Interoperability for Ambulatory Monitoring of Cardiovascular Disease: A Scientific Statement From the American Heart Association.

Wearable devices are increasingly used by a growing portion of the population to track health and illnesses. The data emerging from these devices can potentially transform health care. This requires an interoperability framework that enables the deployment of platforms, sensors, devices, and software applications within diverse health systems, aiming to facilitate innovation in preventing and treating cardiovascular disease. However, the current data ecosystem includes several noninteroperable systems that inhibit such objectives. The design of clinically meaningful systems for accessing and incorporating these data into clinical workflows requires strategies to ensure the quality of data and clinical content and patient and caregiver accessibility. This scientific statement aims to address the best practices, gaps, and challenges pertaining to data interoperability in this area, with considerations for (1) data integration and the scope of measures, (2) application of these data into clinical approaches/strategies, and (3) regulatory/ethical/legal issues.

Containerized Microservices for Mobile Applications Deployed on Cloud Systems

This study explores the transformative role of containerized microservices in the sphere of mobile application development, especially within public cloud ecosystems. It focuses on how technologies such as Docker and Kubernetes contribute to improving deployment, scalability, and overall management of mobile applications, with an emphasis on containerizing backend services. We analyze their efficiency in streamlining deployment processes, focusing on how they improve the application’s performance and reliability. Additionally, we examine various alternative deployment strategies, such as blue-green, rolling, and canary releases, to emphasize their effectiveness in minimizing risks and facilitating smooth transitions in dynamic cloud environments. The study takes a comprehensive approach to achieve this goal, which includes a systematic review of existing literature, a thorough examination of relevant use cases, and an assessment of open-source technologies. Our findings reveal not only the practical benefits of these strategies but also their strategic application, offering important insights for software engineers and decision-makers. This study emphasizes the significance of integrating and optimizing containerized microservices in mobile app development to achieve more efficient, scalable, and manageable application lifecycles on cloud-based platforms.

Human Emotion Detection Using CNN and Transfer Learning

Facial emotion detection is a critical component in human-computer interaction, mental health assessment, and security systems. In this project, we propose a robust facial emotion detection system leveraging state-of-the-art deep learning techniques. Our system utilizes Convolutional Neural Networks (CNNs) to extract meaningful features from facial images and classify them into five distinct emotional categories: neutral, surprise, sad, happy, and angry. We conducted extensive experiments on a diverse dataset consisting of over 10,000 annotated facial images collected from various sources. Through data augmentation techniques such as rotation, translation, and flipping, we expanded the dataset to enhance model training. Additionally, we employed transfer learning by fine-tuning a pre-trained CNN model, ResNet50, on our dataset to leverage its learned features. This project presents a system for real-time emotion monitoring using computer vision techniques. The system utilizes the Haar Cascade Classifier for face detection in live webcam video streams. Furthermore, we evaluated the system's performance across different lighting conditions, poses, and facial occlusions to assess its robustness in real-world scenarios. Our results indicate that the system maintains consistent performance across diverse conditions, making it suitable for deployment in applications requiring real-time facial emotion recognition.

Meta Heuristic Technique with Reinforcement Learning for Node Deployment in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are vital in applications like environmental monitoring, smart homes, and battlefield surveillance. Comprising small devices with limited resources, WSNs require efficient node deployment for power optimization and prolonged network lifetime, ensuring sufficient coverage and connectivity. This study introduces an Intelligent Satin Bower Bird Optimizer augmented with reinforcement learning (ISBO-RL), enhancing coverage and connectivity. ISBO-RL focuses on optimal sensor placement for improved coverage and connectivity, using an Optimum Position Finding (OPF) method to identify key sensor node locations. Reinforcement learning is integrated into the ISBO algorithm, allowing nodes to adapt based on performance and changing conditions. Experimental results on diverse platforms highlight ISBO-RL’s efficacy and its superior coverage and connectivity performance as compared to other algorithms. ISBO-RL represents a significant advancement in the field of Wireless Sensor Networks, offering a promising solution to address the challenges of efficient node deployment and network optimization in various critical applications.

Long Duration Energy Storage Using Hydrogen in Metal-Organic Frameworks: Opportunities and Challenges.

Materials-based H2 storage plays a critical role in facilitating H2 as a low-carbon energy carrier, but there remains limited guidance on the technical performance necessary for specific applications. Metal-organic framework (MOF) adsorbents have shown potential in power applications, but need to demonstrate economic promises against incumbent compressed H2 storage. Herein, we evaluate the potential impact of material properties, charge/discharge patterns, and propose targets for MOFs' deployment in long-duration energy storage applications including backup, load optimization, and hybrid power. We find that state-of-the-art MOF could outperform cryogenic storage and 350 bar compressed storage in applications requiring ≤8 cycles per year, but need ≥5 g/L increase in uptake to be cost-competitive for applications that require ≥30 cycles per year. Existing challenges include manufacturing at scale and quantifying the economic value of lower-pressure storage. Lastly, future research needs are identified including integrating thermodynamic effects and degradation mechanisms.

Flexible zinc-ion hybrid micro-supercapacitors with polymeric current collector for integrated energy storage in wearable devices

The ubiquity of conventional micro-fabrication techniques has encountered impediments in constructing cost-effective micro-devices, thereby constraining their broad deployment. Concurrently, the suboptimal energy density inherent to supercapacitors exacerbates this challenge. This study presents a straightforward assembly methodology for the fabrication of a mechano-electrochemically efficient micro-zinc ion hybrid supercapacitor (M−ZSC), alongside the introduction of an innovative polymeric current collector. The M−ZSC is fabricated through a process involving masked spray deposition of a novel adhesive current collector and the electrode materials, and electroplating of zinc nanosheets onto the anode finger, creating an electrochemically performant and mechanically robust device. The resulting M−ZSC exhibits a notable areal capacitance of 52.2 mF/cm2 and an energy density of 18.5 µWh/cm2 for the normal mass-loading of 3.6 mg/cm2, which is amongst the highest reported values for energy storage. The masked spray deposition/hot pressing technique enabled us to fabricate free-standing thick electrodes with a loading density of up to 56.1 mg/cm2, facilitating the achievement of an ultrahigh areal capacitance of 227 mF/cm2 and an energy density of 80.5 µWh/cm2. Furthermore, the M−ZSC exhibits a robust retention of 95 % capacitance at 1 mA/cm2. Critical to its flexibility, the device is constructed using purely additive deposition methods on flexible substrates and is therefore well-tailored for deployment in flexible electronics and on-chip applications. These micro-devices are well-poised for seamless integration within a singular electronics package or chip architecture.

A Regeneration Model for Mitigation Against Attacks on HTTP Servers for Mobile Wireless Networks

The widespread expansion of the internet has fueled a global surge in the utilization of various online transactions, with a significant portion of such services operating on mobile web platforms. Simultaneously, the deployment of innovative Mobile Ad Hoc Network (MANET) technologies and mobile applications has grown as solutions for diverse tasks. Unfortunately, this progress has attracted the attention of hackers, who continually devise new strategies to exploit the vulnerabilities inherent in mobile networks. This study aims to address the escalating challenges posed by cyber threats in the era of widespread internet expansion, particularly focusing on securing mobile web platforms against sophisticated attacks such as Structured Query Language Injection (SQLi) for web-based database solutions and Denial of Service (DoS/DDoS) for various applications. In response to the identified vulnerabilities, this paper proposes an HTTP regeneration (HReg) model that not only detects various cyber-attacks but also ensures the uninterrupted provision of critical services during such incidents. The model introduces an innovative regeneration algorithm capable of scanning both the connection channel and web application to detect attacks, creating survivable connections within the underlying TCP engine to replace compromised ones during an ongoing attack. In simulated environments using OMNeT++, where the server is subjected to attacks, the experimental results demonstrate the efficacy of the model. The response and performance metrics, including throughput (73%), delivery ratio (68.8%), delay (3s), and network load, showcase the model's ability to detect and neutralize attacks. A comparison with state-of-the-art approaches highlights the superior performance of the regeneration model, attributed to its additional survivability layer. While the regeneration model proves robust in simulated environments, its real-world application may encounter limitations. Future research should explore these limitations to enhance the practical applicability of the proposed model. The proposed HReg model's resilient performance under attack conditions ensures the survivability of web-based applications. This innovative approach offers practical implications for securing mobile web platforms, providing continuous delivery of critical services even in the face of persistent and evolving cyber threats. This research addresses a significant gap in existing efforts by not only focusing on attack detection but also emphasizing the development of a survivable TCP connection for HTTP servers during attacks. The introduced regeneration model stands out for its unique ability to maintain service continuity, showcasing originality in the approach to cybersecurity in the context of web-based applications.

Machine Learning Approach to Predict the Dengue Cases Based on Climate Factors

Dengue is a global health issue threatening public health, particularly in developing countries. Effective disease surveillance is critical to anticipate impending outbreaks and implement appropriate control responses. However, delays in dengue case reporting are frequent due to human resource shortfalls. Improved outbreak predictive capacity also requires additional input on vector presence and abundance, which is currently not captured in the surveillance platform. Thus, we developed a prototype AI application, “Dengue Forecasting", that leverages machine learning methods in filing the dengue case report and incorporates dengue vector and climatic parameters. This application simplifies the recording of dengue cases, vector abundance (Angka Bebas Jentik/ABJ), and selected climatic variables (sun exposure, temperature, humidity, wind speed, and precipitation) in Bandung City. The relevant data were extracted from Indonesia’s Ministry of Health and the Meteorological, Climatological, and Geophysical Agency. The entire process, from developing the model to deployment, was conducted under R programming language version 4.2.2 using packages (caret, shiny.io). The linear regression model demonstrated the highest precision (RMSE= 268.32 and MAE= 164.1) in predicting the dengue cases and outbreaks. We also applied this to the application deployment. “Dengue Forecasting” has the potential to assist policymakers at the district level, complementing Dengue EWARS, in anticipating and mitigating dengue outbreaks, especially in Bandung City.

First-principles thermodynamics of precipitation in aluminum-containing refractory alloys

Materials for high-temperature environments are actively being investigated for deployment in aerospace and nuclear applications. This study uses computational approaches to unravel the crystallography and thermodynamics of a promising class of refractory alloys containing aluminum. Accurate first-principles calculations, cluster expansion models, and statistical mechanics techniques are employed to rigorously analyze precipitation in a prototypical senary Al–Nb–Ta–Ti–V–Zr alloy. Finite-temperature calculations reveal a strong tendency for aluminum to segregate to a single sublattice at elevated temperatures. Precipitate and matrix compositions computed with our ab-initio model are in excellent agreement with previous experimental measurements (Soni et al., 2020). Surprisingly, conventional B2-like orderings are found to be both thermodynamically and mechanically unstable in this alloy system. Complex anti-site defects are essential to forming a stable ordered precipitate. Our calculations reveal that the instability of B2 compounds can be related to a simple electron counting rule across all binary alloys formed by elements in groups 4,5, and 6. The results of this study provide viable routes toward designing high-temperature materials for deployment in extreme environments.

Gas Level and Leakage Detection with Automatic Booking

Gas Level and Leakage Detection with Automatic Booking

Design and implementation of duty cycle-based futuristic clustering technique in WSN

In recent times, wireless sensor networks (WSNs) and their applications have exhibited a remarkable surge. These networks strive to devise and implement strategies that optimize network energy utilization, thereby extending their operational lifespan. An energy efficient network can be achieved using renewable source of energy and by controlling the duty cycle of nodes. The pivotal role of duty cycle in curtailing energy consumption in WSNs cannot be overstated. In this work, we introduce a novel duty cycle based futuristic clustering technique (DCBFCT) employing a nearest neighbor approach. This technique selectively induces sleep and awake modes in nodes, effectively minimizing the network’s overall energy consumption and, consequently, prolonging its lifespan. It calculates optimal node duty cycle values based on distance. Results demonstrate a substantial reduction in energy consumption, exhibiting an improved network lifetime. Empirical results presented in this study not only affirm the effectiveness of DCBFCT but also contribute valuable insights toward the development of sustainable and resilient WSNs in the era of burgeoning sensor network applications. The experimentation is conducted using the MATLAB/Simulink tool, considering diverse cases. The scalability and versatility of DCBFCT make it suitable for deployment in real-world applications, ranging from environmental monitoring to industrial automation.

Service Coalition Based Joint Application Deployment and Task Assignment for Mobile Edge Computing

Service Coalition Based Joint Application Deployment and Task Assignment for Mobile Edge Computing

Evaluation of Student Success Across the Subjects using MERN Stack

bstract: The Evaluation of Student Success across the Subjects is an essential tool in educational institutions, facilitating the efficient management and dissemination of student performance data. This paper presents the design and implementation of a SRMS using the MERN stack, an innovative technology stack comprising MongoDB, Express.js, React.js, and Node.js. The proposed system leverages the full-stack JavaScript environment to enhance the interactivity, real-time data processing, and user experience of the SRMS.React.js is employed on the frontend to provide an intuitive and dynamic user interface, making it easier for users to interact with the system effectively. We discuss the development process, focusing on the integration of these technologies, and demonstrate how the MERN stack facilitates rapid development and deployment of a web-based application. The system's performance is evaluated based on criteria such as response time, data integrity, and user satisfaction. Initial results indicate that the SRMS is not only faster and more reliable but also provides enhanced accessibility and user engagement compared to traditional systems. This paper aims to contribute to the academic community by providing insights into the application of the MERN stack in developing educational tools, thereby suggesting a pathway for future research and development in educational technology systems

An Enhanced Hidden Markov Model for Map-Matching in Pedestrian Navigation

Map-matching is a core functionality of pedestrian navigation applications. The localization errors of the global positioning systems (GPSs) in smartphones are one of the most critical factors that limit the large-scale deployment of pedestrian navigation applications, especially in dense urban areas where multiple road segments exist within the range of GPS errors, which can be increased by tall buildings neighboring each other. In this paper, we address two issues of practical importance for map-matching based on the Hidden Markov Model (HMM) in pedestrian navigation systems: large localization error in the initial phase of map-matching and HMM breaks in open field traversals. A heuristic method to determine the probability of initial states of the HMM based on a small number of GPS data received during the short warm-up period is proposed to improve the accuracy of initial map-matching. A simple but highly practical method based on a heuristic evaluation of near-future locations is proposed to prevent the malfunction of the Viterbi algorithm within the area of open fields. The results of field experiments indicate that the enhanced HMM constructed via the proposed methods achieves significantly higher map-matching accuracy compared to that of state of the art.