Deployment Process Research Articles

GESTÃO ESCOLAR E AS TECNOLOGIAS

This article is part of the research line of the Master's program in Emerging Technologies in Education at MUST UNIVERSITY and addresses the importance of educational management for innovative projects and their implementation. The article aims to reason the processes of deployment and implementation and the authors involved. The methodology used for the work is qualitative research to gather important information about the significance of the project coordination team, strategies, and their utilization. Therefore, at the end of the research, it is clear that the educational coordination team is essential for the effectiveness of the projects.

Provably efficient security-aware service function tree composing and embedding in multi-vendor networks

Multicast greatly benefits many emerging applications such as federated learning, metaverse, and data warehouse. Recently, due to frequent cyber-attacks, multicast services have tended to request rigorous security agreements, which likely differ among the destinations. To meet such agreements, one can employ security-aware service functions (SFs) to construct the security-aware SF tree (S-SFT) for multicast services. A security-aware SF can be provided by various vendors with diverse configurations and implementation costs. The multi-configured SFs and the various security agreements will add significant complexity to the deployment process of the security-aware multicast request. In this work, for the first time, we study how to effectively compose and embed an S-SFT over the network with multiple vendors. We formulate the problem of security-aware SFT composing and embedding. We develop a new technique called cost-security-centrality (CSC) based on the pigeonhole’ s principle and propose a heuristic algorithm called CSC-based S-SFT deployment (CSC-SD). Via thorough mathematical proofs, we show that CSC-SD is logarithm approximate. Extensive simulations show that CSC-SD significantly outperforms the benchmarks and reveal that more function sharing facilitates saving implementation cost, but more routing sharing does not indicate saving routing cost.

Optimizing Cloud Computing Performance by Integrating the Novel PSBR Service Broker Policy and Load Balancing Algorithms

As cloud computing advances, organizations' IT infrastructure and application deployment processes are moving to the cloud because cloud computing provides everything as a service over the Internet. The performance of a cloud-based application is based on proper datacenter selection and workload distribution within the selected datacenter. Service broker policies are used for suitable datacenter selection, and load balancing algorithms(LBA) are applied to distribute workloads. This paper is to evaluate the effect of a proposed service broker policy (PSBR) on the performance of cloud-based applications with LBA. To achieve the objective, the behavior of the TikTok application was modeled using the worldwide users’ statistics on the cloud simulation framework, namely CloudAnalyst. As a result, the average response time and data center processing time are measured. Next, the PSBR provides better results than the existing service proximity-based policy. This paper supports cloud service providers' benefits, from coordination between data center configuration, data center selection, and workload distribution to cloud users' identification of the appropriate procedures for their organization or application. PSBR with Active Monitoring had the best average response time of 75.1 ms, while SPR consistently exhibited higher average times across all algorithms, with the highest being 84.5 ms for Round Robin. Under the PSBR policy, Throttled had the lowest average processing time (4.67), while Round Robin had the highest (5.72). Similarly, under the SPR policy, Throttled maintained its efficiency with the lowest average (4.8), while Round Robin showed the highest (5.79).

Kinematics and dynamics characteristics of a double-ring truss deployable antenna mechanism based on triangular prism deployable unit

In order to effectively improve the single-ring truss deployable antenna mechanism due to the large aperture caused by the problem with low structural strength and low-profile accuracy, a series of double-ring truss deployable antenna mechanisms (DRTDAM) are proposed with constant height during folding and deployment process. First, a variety of DRTDAMs are proposed based on tetrahedral units and their topologies are analyzed. Secondly, degree-of-freedom (DOF) characteristics of DRTDAM proposed in this paper are analyzed based on the screw theory and screw-constrained topological graphs and based on this, the kinematic characteristics of DRTDAM is investigated. Thirdly, the dynamics of the whole DRTDAM is built with Newton-Euler equations of multi-rigid body system. Finally, the correctness of above analysis is verified through dynamics analysis software ADAMS and numerical analysis software MATLAB, and the principle prototype is produced to verify the correctness of DOF analysis. The mechanism proposed in this paper enriches the configuration of DRTDAM, and the process of kinematic characterization method is clear and simple, which is meaningful for the research in space complex mechanism.

DevOps workflow optimization: Enhancing deployment and efficiency for cloud application

DevOps has quickly become an essential process for cloud software in the contemporary world of utilizing software. However, questions regarding the efficiency of the work flow and speed of application deployment still prevails especially when implemented under cloud environment. This paper focuses on the workflow optimization techniques of DevOps including automation of work, integration and deployment processes CI/CD, Infrastructure to code IaC, and monitoring. The aim is to give guidance on how to increase the velocity of deployment and operation when it comes to applications hosted in the cloud. The information presented from the evaluation of the main optimization strategies can help organizations to enhance the overall cycle time and improve the quality of the products at the same time.

CI/CD model for optimizing software deployment in SMEs

Small and Medium-sized Enterprises (SMEs) face unique challenges in software deployment, often dealing with limited resources, budget constraints, and the need for fast, reliable updates. This review outlines a Continuous Integration and Continuous Deployment (CI/CD) model specifically tailored to optimize software deployment in SME environments. The proposed CI/CD framework enhances operational efficiency by automating the integration, testing, and deployment processes, significantly reducing the risk of human error and deployment failures. The model leverages key CI/CD practices, including automated testing, version control, and continuous monitoring, to ensure seamless updates and minimal downtime. By integrating tools like Jenkins, GitLab, and Docker, SMEs can streamline code integration, enhance collaboration between development and operations teams, and enable faster, more frequent releases. Automated testing ensures that code changes are validated before deployment, identifying and addressing potential issues early in the development cycle. The framework also emphasizes rollback capabilities and monitoring systems to further reduce deployment risks. Rollbacks allow for quick recovery in case of failure, while continuous monitoring enables real-time detection of performance issues, helping SMEs maintain operational uptime and minimize service disruptions. Moreover, the model supports a microservices architecture, allowing SMEs to scale their applications efficiently without compromising stability. Incorporating CI/CD practices fosters agility, enabling SMEs to respond rapidly to market demands and customer feedback. This model not only improves software quality and deployment speed but also aligns with the budgetary and operational limitations of SMEs. It provides a cost-effective solution that enhances overall system reliability and reduces the frequency of software-related incidents. This CI/CD model serves as a practical approach to software deployment, empowering SMEs to optimize their software development lifecycle while ensuring continuous improvement and operational stability.

Introduction to the special issue on AI systems for the public interest

AI systems have been promised to reduce CO2 emissions, monitor biodiversity, support accessibility, or help analyse human rights violations. They are often seen as a crucial part of the solutions needed in our times ranging from addressing the climate crisis, public health, to improvement of social services, or urban planning. We find the reference to artificial intelligence (AI) in many documents and debates of the policy realm, assigning it a strong potential to contribute to all these domains. AI for the public interest, and its close relatives, AI for (common or social) good, have become a common theme not only for tech companies, but also for political actors in the EU, including for instance international NGO networks. However, most often the definition of the public interest in the best case is limited to references to AI ethics. Yet, the practical meaning of what a good use of AI and a purpose “for good” entails in its development and implementation is unclear. What is often missing is an understanding that spells out in practice what it means for the process of development and deployment of AI systems to serve the public interest, let alone a holistic view on the conditions for AI to best serve the collective well-being.

Cable length prediction for towing models of reverse towing systems based on the cable deployment process

Given the dynamic depth changes of the underwater support vessel in the reverse towing system, the vessel needs to continuously adjust the cable length by reeling in or out to ensure that the towed body is stably towed at the designated position. Therefore, this paper considers the impact of the cable deployment process on cable length prediction and proposes a model that can dynamically predict the required cable length for the towed body at the designated position. A floating tow experiment was conducted in a towing tank, and the predicted and experimental cable length data were compared to verify the accuracy of the dynamic cable length prediction algorithm. The lift height of the towed body was compared between the proposed model and the traditional model to verify the validity of the model. The relative error between the predicted cable lengths and the experimental data was 3.8%. The relative error in the lift height between the proposed model and the traditional model was 7.2%. The proposed model can continuously and accurately predict cable length, allowing the underwater support vessel to continuously adjust the cable length, thereby ensuring the stable operation of the towed body at the designated operational position.

Potato disease detection and control measure recommendation using CNN

Potato cultivation will continue to be a crucial agricultural endeavor, significantly enrich to global food supply. and economic prosperity. However, the ongoing vulnerability of potatoes to diseases, particularly named early Blight and late Blight, will persist as a substantial threat to crop yield and quality. Traditional disease identification methods, reliant on expert visual inspection, are expected to remain time-consuming, error-prone, and inadequate for timely intervention. This paper will outline to find Potato diseases and recommend effective control measures using convolutional Neural-Networks (CNN). The project will adopt an object-oriented methodology to ensure modularity and maintainability, leveraging TensorFlow for comprehensive dataset collection, data cleaning, and model training. TensorFlow Lite will be applied to optimize and quantify the developed model, enhancing the efficiency of the deployment process. The frontend will be crafted with React Native to facilitate user accessibility and seamless interaction. The resulting deep learning technique is anticipated to exhibit high accuracy in identifying early-blight and late-blight in potato plants, enabling users to receive prompt and reliable disease predictions and actionable control suggestions. This innovative approach aims to downgrade crop losses, improve productivity, and foster sustainable agricultural practices, thereby bolstering global food security in the future.

UAV Jamming Localization Against Random Eavesdropper Positions Using Cuckoo Search Algorithm

ABSTRACTUnmanned Aerial Vehicle (UAV) jammer localization refers to the process of determining the optimal location for a UAV jammer to be placed to interfere with an eavesdropper's communication with a target. In fact, the main goal is to maximize the jamming signal power at the eavesdropper's position while minimizing the jamming signal power at the target's position. This paper leverages Cuckoo Search algorithm for the secrecy rate optimization which refers to the amount of secure information transmitted from the target to the intended receiver. The model is composed with an UAV jammer, two ground nodes and an illegitimate user with unknown position. True uniform random deployment processes is used to model the randomness of the eavesdropper location. Further, a novel summed secrecy approach is proposed to determine the overall optimal UAV position. Simulations demonstrate the superiority of the novel approach versus other benchmark algorithms across various numbers of eavesdroppers.

Potential assessment and development obstacle analysis of CCUS layout in China: A combined interpretive model based on GIS-DEMATEL-ISM

Although carbon capture, utilization, and storage (CCUS) has enormous emission reduction potential and application prospects, it still faces many obstacles and challenges in the actual deployment and promotion process. This study aims to evaluate the spatial suitability of the CCUS layout comprehensively and deeply analyze key development obstacles to provide a scientific basis for the promotion and layout of CCUS. Therefore, this article constructs a combination of Geographic Information Systems (GIS), Decision-making trial and evaluation laboratory (DEMATEL) method, and Interpretive structure model (ISM). Firstly, a comprehensive evaluation is conducted on the suitability of the CCUS layout in China based on GIS technology, identifying high-potential areas. The results indicate that when the threshold is 80 % of the highest score for suitability, a total area of 1.46 million square kilometers is suitable for the CCUS project layout. When it dropped to 60 %, this data increased to 2.04 million square kilometers. These areas are mainly distributed in large basin areas with abundant resources. Secondly, the DEMATEL-ISM analysis framework is improved to systematically identify and analyze the key obstacles that constrain the promotion of CCUS in China. The results indicate that there is a very complex relationship between the obstacles that constrain the development of CCUS, with the fundamental reasons being the lagging technological level and imperfect policies and regulations. Finally, goal-oriented suggestions and response measures were proposed, and targeted analysis was conducted on high-suitability areas. This study constructed an interdisciplinary methodology interpretive model to provide decision support for the CCUS layout and targeted strategies to break through current development barriers and promote the rapid development of CCUS.

Web Application for Recommendation of Ayurvedic Drugs and Medicine using ML

This project aims to develop a comprehensive Ayurvedic drug recommendation website that seamlessly integrates traditional Ayurvedic principles with modern technology. The platform will cater to both students seeking educational resources and practitioners offering personalized healthcare guidance. The website's key features include drug prediction algorithms, herbal drug recommendations based on individualized assessments on symptoms, and a user-friendly interface. The initial phase involves meticulous planning, scoping, and research to define project objectives, target audiences, and select an appropriate technology stack. Subsequent weeks focus on design and prototyping, incorporating user feedback to ensure an intuitive and engaging interface. The front-end development phase emphasizes responsiveness and usability, while the back-end development integrates sophisticated algorithms for drug prediction and drug recommendation. User authentication and authorization mechanisms will be implemented to cater to both students and practitioners, ensuring a secure and personalized experience. The website's robustness and reliability will be validated through comprehensive testing, addressing any identified issues to guarantee a seamless user experience. The refinement and optimization phase involve continuous feedback collection, iterative improvements, and performance optimization. Beta testing will be conducted to gather user insights, refining the platform further. The deployment and launch process will involve hosting the website on a reliable platform, setting up monitoring tools, and officially releasing the Ayurvedic drug prediction and drug recommendation website to the public. This project combines the rich heritage of Ayurveda with cutting-edge technology, offering a valuable resource for education and healthcare in the Ayurvedic domain. The website aims to empower both students and practitioners with accurate drug predictions and personalized drug recommendations based on symptoms, fostering a holistic approach to healthcare that aligns with traditional Ayurvedic principle.

The Impact of an Automation System Built with Jenkins on the Efficiency of Container-Based System Deployment.

This paper evaluated deployment efficiency by comparing manual deployment with automated deployment through a CI/CD pipeline using Jenkins. This study involved moving from a manual deployment process to an automated system using Jenkins and experimenting with both deployment methods in a real-world environment. The results showed that the automated deployment system significantly reduced the deployment time compared to manual deployment and significantly reduced the error rate. Manual deployment required human intervention at each step, making it time-consuming and prone to mistakes, while automated deployment using Jenkins automated each step to ensure consistency and maximized time efficiency through parallel processing. Automated testing verified the stability of the code before deployment, minimizing errors. This study demonstrates the effectiveness of adopting a CI/CD pipeline and shows that automated systems can provide high efficiency in real-world production environments. It also highlights the importance of security measures to prevent sensitive information leakage during CI/CD, suggesting the use of secrecy management tools and environment variables and limiting access rights. This research will contribute to exploring the applicability of CI/CD pipelines in different environments and, in doing so, validate the universality of automated systems.

Transforming Hydrology Python Packages into Web Application Programming Interfaces: A Comprehensive Workflow Using Modern Web Technologies

The accessibility and deployment of complex hydrological models remain significant challenges in water resource management and research. This study presents a comprehensive workflow for converting Python-based hydrological models into web APIs, addressing the need for more accessible and interoperable modeling tools. The workflow leverages modern web technologies and containerization to streamline the deployment process. The workflow was applied to three distinct models: a GRACE downscaling model, a synthetic time series generator, and a MODFLOW groundwater model. The implementation process for each model was completed in approximately 15 min with a reliable internet connection, demonstrating the efficiency of the approach. The resulting APIs provide standardized interfaces for model execution, progress tracking, and result retrieval, facilitating integration with various applications. This workflow significantly reduces barriers to model deployment and usage, potentially broadening the user base for sophisticated hydrological tools. The approach aligns hydrological modeling with contemporary software development practices, opening new avenues for collaboration and innovation. While challenges such as performance scaling and security considerations remain, this work provides a blueprint for making complex hydrological models more accessible and operational, paving the way for enhanced research and practical applications in hydrology.

Identifying enablers for the successful deployment of prescriptive analytics – a multiple case study

Purpose Although it is commonly agreed that prescriptive analytics can benefit organizations by enabling better decision-making, the deployment of prescriptive analytics tools can be challenging. Previous studies have primarily focused on methodological issues rather than the organizational deployment of analytics. However, successful deployment is key to achieving the intended benefits of prescriptive analytics tools. Therefore, this study aims to identify the enablers of successful deployment of prescriptive analytics. Design/methodology/approach The authors examine the enablers for the successful deployment of prescriptive analytics through five organizational case studies. To provide a comprehensive view of the deployment process, each case includes interviews with users, managers and top management. Findings The findings suggest the key enablers for successful analytics deployment are strong leadership and management support, sufficient resources, user participation in development and a common dialogue between users, managers and top management. However, contrary to the existing literature, the authors found little evidence of external pressures to develop and deploy analytics. Importantly, the success of deployment in each case was related to the similarity with which different actors within the organization viewed the deployment process. Furthermore, end users tended to highlight user participation, skills and training, whereas managers and top management placed greater emphasis on the importance of organizational changes. Originality/value The results will help practitioners ensure that key enablers are in place to increase the likelihood of the successful deployment of prescriptive analytics.

Numerical Modeling and Analysis of Pendant Installation Method Dynamics Using Absolute Nodal Coordinate Formulation

Accurately simulating the deployment process of coupled systems in deep-sea environments remains a significant challenge. This study employs the Absolute Nodal Coordinate Formulation (ANCF) to dynamically model and analyze multi-body systems based on the Pendant Installation Method (PIM). Utilizing the principle of energy conversion, this study calculates the stiffness, generalized elastic forces, mass matrices, and Morison equation, formulating a motion equation for the dynamic coupling of nonlinear time-domain forces in cables during pendulum deployment, which is numerically solved using the implicit generalized-α method. By comparing the simulation results of this model with those from the catenary theory model, the advanced modeling capabilities of this model are validated. Lastly, the sensitivity of the multi-body system under various boundary conditions is analyzed. The results indicate that deployment operations are more effective in environments with strong ocean currents. Furthermore, upon comparing the impacts of structural mass and deployment depth on the system, it was found that deployment depth has a more significant effect. Consequently, the findings of this study provide a scientific basis for formulating subsequent optimization strategies.

RaDro: Indoor Drone Tracking Using Millimeter Wave Radar

Core to drone design is its ability to ascertain its location by utilizing onboard inertial sensors combined with GPS data. However, GPS is not always reachable, especially in challenging environments such as indoors. This paper proposes RaDro; a system that leverages millimeter-waves (mmWave) to precisely localize and track drones in indoor environments. Unlike commonly used alternative technologies, RaDro is cost-effective and can penetrate obstacles, a bonus in non-line-of-sight (NLoS) scenarios, which enhances its reliability for tracking objects in complex environments. It does this without the need for tags or anchors to be attached to the drone, achieving 3D tracking with just a single radar point, significantly streamlining the deployment process. Comprehensive experiments are conducted in different scenarios to evaluate RaDro's performance. These include employing different drone models with different sizes to execute a range of aerial manoeuvres across different flight arenas, each with its own settings and clutter, and encountering various LoS and NLoS scenarios in dynamic environments. The experiments aimed to assess the capabilities of the system to extract coarse-grained and fine-grained information for drone detection, motion recognition, and localization. The results showcase precise localization, achieving a 50% reduction in localization error compared to the conventional baseline. This localization accuracy remains resilient even when confronted with interference from other moving sources. The results also demonstrate the system's ability to accurately localize drones in NLoS scenarios where existing state-of-the-art optical technologies cannot work.

Enabling AI in Radiology: Evaluation of an AI Deployment Process.

Artificial intelligence (AI) is expected to transform healthcare systems and make them more sustainable. Despite the increased availability of AI tools for disease detection, evidence of their impact on healthcare organisations and patient care remains limited. Drawing on previous research underscoring the need for comprehensive evaluations of real-world AI deployments, this paper explores the challenges and opportunities encountered while procuring and implementing AI solutions for radiology. The paper aims to contribute to a better understanding of the complexities surrounding AI deployments in real-world clinical settings through a process evaluation study.

Deep learning-based computer vision in project management: Automating indoor construction progress monitoring

Progress monitoring is crucial for effective project management, particularly in construction projects. The adoption of computer vision with deep learning expedites automation, accuracy, and efficiency in construction progress monitoring by overcoming the challenges of laborious, and error prone manual methods. While there is growing attention on developing computer vision based deep learning models for construction progress monitoring, deployment platforms for project managers are lacking. Using computer vision, this study develops a Mask Recurrent Convolutional Neural Network deep learning model. It utilizes progress images of drywall construction from two indoor construction sites and tests the model on a third indoor site in Sydney, Australia. The model is capable of automated as-built visual detection and work-in-progress measurement. The study also provides an understanding on the deployment process of the deep learning model on a cloud-based platform called Streamlit. By developing a model tailored for automatically quantifying work-in-progress of indoor construction elements and detailing the process of deploying that model on a cloud-based platform, this study significantly advances digitalization of construction project management. Project managers, stand to benefit from these advancements by gaining access to more accurate and automated construction progress monitoring for better decision-making.

Deployment of A Data Consolidation System in an Aeronautical Administration: Application to the Rva/City of Kananga in the Democratic Republic of Congo

This article examines the specific challenges encountered by the aeronautical administration of the Airway Authority of the City of Kananga in the Democratic Republic of Congo in the management and consolidation of its aeronautical data, as well as possible solutions to remedy them. We present the different stages of the system deployment process, highlighting the technical, organizational and operational aspects to consider. Finally, the study provides an analysis of the results obtained after the implementation of the data consolidation system, highlighting the benefits and possible remaining challenges.