Journal of Science & Technology
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Revolutionizing Software Development Through Tools and Practices
No full textThe landscape of software development is undergoing a transformative revolution, propelled by a myriad of tools and practices that promise efficiency, reliability, and security in the creation of modern applications. This comprehensive exploration navigates through the key components shaping this evolution, focusing on automated testing tools, continuous integration practices, and the overall ecosystem that defines contemporary software development. Software quality is a critical factor in ensuring the success of software projects. Numerous software quality models have been proposed and developed to assess and improve the quality of software products [1].
Beginning with a meticulous examination of automated testing tools, the narrative unfolds to reveal the critical role played by unit testing, integration testing, performance testing, and security testing tools. We analyze and categorize research work mostly extracted from top-tier conferences and journals concerning energy efficiency across the software development lifecycle phases [2]. Each category serves as a crucial line of defense, ensuring the quality, robustness, and security of software systems. By delving into specific examples and exploring their features, strengths, and considerations, this examination provides valuable insights for developers and organizations seeking to fortify their codebases against an array of challenges.
The journey extends to the core of modern development practices – continuous integration. Tools like Jenkins, Travis CI, and GitLab CI/CD take center stage in streamlining development workflows, fostering collaboration, and maintaining code quality. In the face of global competition, businesses across various industries have increasingly turned to lean methodologies to enhance their production processes and remain competitive [3]. As the exploration unfolds, it becomes evident that these tools are not mere facilitators but catalysts for accelerated development cycles, providing developers with the means to deliver consistent and reliable software.
The narrative culminates in an overarching view, emphasizing the importance of aligning tools with organizational objectives, adopting best practices, and cultivating a culture of continuous improvement. In this ever-evolving technological landscape, the future of software development hinges not only on code but on the strategic utilization of tools and methodologies that drive innovation, enhance efficiency, and pave the way for a future where software development is synonymous with excellence and resilience. It investigates user personas, mental models, and usability studies to enhance the alignment of system requirements with user expectations and needs [4]
Kubernetes Networking: Challenges and Advances in Container Communication
No full textThe intricacies, developments, and potential paths of Kubernetes networking in containerized settings are examined in this review. This study\u27s primary goals were to explore the difficulties in Kubernetes networking architecture, look at new security and network policy developments, and discover ways to improve performance and Scalability. A thorough literature review of academic journals, technical reports, and industry publications was carried out to synthesize existing information and develop trends. Key findings show that fixing security flaws in multi-tenant settings, defining network policies across clusters, and guaranteeing compatibility with legacy systems are all challenging tasks. Promising answers to these problems can be found in the integration of service mesh technologies and improved encryption protocols, which are examples of advancements in network policies. The significance of standardized best practices for network security, real-time threat detection tools, and robust disaster recovery procedures is highlighted by policy implications. The present study enhances comprehension of the dynamic terrain of Kubernetes networking by emphasizing prospects for augmenting dependability, expandability, and safety within container communication frameworks
Reinforcement Learning for Training Autonomous LLM Coding Agents in Modular Software Development
No full textThe advent of large language models (LLMs) in software development has initiated a transformative paradigm in how code is generated, debugged, and optimized. This research paper delves into the application of reinforcement learning from human feedback (RLHF) methodologies to train LLMs as autonomous coding agents adept at handling modular software development. Modular programming, characterized by its decomposition of complex systems into smaller, manageable modules, presents unique challenges and opportunities for autonomous agents. The central focus of this study is to develop LLMs that can autonomously manage multi-step feedback loops and implement evaluation checkpoints for iterative optimization in modular software development projects.
The proposed methodology integrates RLHF strategies to enable LLMs to operate iteratively across modular software tasks, encompassing requirements interpretation, module generation, error identification, debugging, and integration. The iterative feedback mechanisms ensure that the LLM learns adaptively from simulated human inputs, enhancing its ability to produce optimized and error-free code over multiple cycles. By leveraging state-of-the-art reinforcement learning frameworks, the training process incorporates reward structures aligned with modular development principles, such as code reusability, functional coherence, and efficient debugging.
A notable application of this framework involves LLMs autonomously constructing web applications from minimal user inputs. These inputs, such as a simple project description or set of functional requirements, are incrementally parsed by the LLM, which generates corresponding modules, integrates them into a cohesive system, and validates their functionality. The study also emphasizes the role of automated evaluation checkpoints, enabling the LLM to assess code quality, scalability, and adherence to best practices at various stages of development. These checkpoints mimic the traditional iterative review cycles of human developers and ensure that the generated software meets predetermined performance benchmarks.
The implementation and results are demonstrated through several case studies, focusing on web application development, where the LLM autonomously constructs full-stack applications. Each case illustrates the LLM\u27s ability to handle challenges such as managing interdependencies between modules, resolving ambiguous requirements, and debugging complex errors without explicit human intervention. The findings highlight the potential of RLHF-trained LLMs in reducing development time, minimizing errors, and enabling scalable software development workflows.
Furthermore, the study explores the limitations and potential challenges of deploying such agents in real-world scenarios. These include computational constraints, scalability issues with reinforcement learning strategies, and the ethical implications of deploying autonomous coding agents in professional environments. The paper also discusses future research directions, such as integrating domain-specific knowledge into LLM training and enhancing the interpretability of reinforcement learning algorithms
Challenges And Opportunities in Scaling AI/ML Pipelines
No full textIn the ever-evolving landscape of technology, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative forces, reshaping industries and catalyzing innovation. As organizations increasingly recognize the potential of AI and ML to drive efficiency, enhance decision-making, and gain a competitive edge, the scalability of AI/ML pipelines becomes a paramount consideration. This abstract delves into the intricate web of challenges and promising opportunities that underpin the process of scaling AI/ML pipelines, shedding light on the multifaceted nature of this complex undertaking.
Scaling AI/ML pipelines is not merely a technical hurdle; it encompasses a spectrum of challenges that traverse data management, model complexity, deployment, monitoring, and cost management. At the core of these challenges lies the intricate dance with data—managing vast volumes, ensuring quality, and navigating the intricate balance between privacy and utility. As organizations grapple with diverse and ever-growing datasets, the need for robust data management strategies becomes imperative.
Model complexity amplifies the scaling challenge, demanding extensive computational resources and posing questions about interpretability and adaptability. Training intricate models at scale introduces concerns about resource allocation, bottlenecks, and the ever-elusive quest for model interpretability. Addressing these challenges necessitates a nuanced understanding of the interplay between the intricacy of models and the computational infrastructure supporting them.
The deployment of ML models at scale introduces its own set of challenges, encompassing issues such as version control, seamless integration with existing systems, and the need for scalable and flexible infrastructure. Monitoring and maintenance present ongoing challenges, requiring organizations to navigate the shifting landscape of model performance, detect anomalies, and adapt models to evolving data distributions—capturing the essence of the dynamic nature of real-world data.
Cost management emerges as a critical consideration, with organizations grappling with the financial implications of scaling AI/ML pipelines. Balancing the equation between computational resources, model training expenses, and the pursuit of optimal performance becomes a delicate exercise in efficient resource allocation and financial stewardship.
However, within these challenges lie promising opportunities that can propel organizations towards successful scaling of AI/ML pipelines. Automation and the integration of DevOps practices offer avenues for streamlining processes, reducing errors, and accelerating deployment cycles. Transfer learning and model optimization techniques present possibilities for enhancing scalability, allowing organizations to adapt pre-trained models to diverse tasks and datasets.
The advent of cloud and edge computing introduces a paradigm shift, providing organizations with the flexibility to scale infrastructure dynamically and deploy models closer to data sources. Collaboration and knowledge sharing emerge as powerful tools, fostering innovation and collective problem-solving in the face of scaling challenges.
This abstract also explores real-world case studies, offering tangible examples of organizations that have navigated the challenges and seized the opportunities in scaling their AI/ML pipelines. These case studies serve as beacons of insight, providing practical wisdom for organizations embarking on their own scaling journeys.
The challenges and opportunities in scaling AI/ML pipelines form a dynamic and evolving landscape. Organizations must navigate the complexities of data, model intricacy, deployment, monitoring, and cost management, while embracing opportunities presented by automation, transfer learning, cloud computing, and collaborative approaches. This abstract serves as a comprehensive exploration of this transformative journey, offering valuable insights for researchers, practitioners, and decision-makers alike
Smart Application using MQTT protocol for Industrial IoT and Retail
No full textThe advent of Industry 4.0 has propelled the integration of smart technologies into various sectors, with the Industrial Internet of Things (IIoT) playing a pivotal role in revolutionizing processes and operations. This paper introduces a smart application leveraging the Message Queuing Telemetry Transport (MQTT) protocol to enhance connectivity in both industrial and retail settings. The proposed solution capitalizes on MQTT\u27s lightweight and efficient communication model, facilitating seamless data exchange between devices, sensors, and control systems. In the industrial context, the application optimizes manufacturing processes, monitoring equipment performance, and facilitating predictive maintenance through real-time data analytics. In the retail sector, it enables intelligent inventory management, personalized customer experiences, and efficient supply chain coordination. The MQTT protocol ensures low-latency communication, minimizing data transfer delays and contributing to a responsive and agile system. Additionally, the application incorporates advanced security measures to safeguard sensitive data, ensuring the integrity and confidentiality of information exchanged. Case studies and performance evaluations illustrate the practical implementation and benefits of the smart application in real-world scenarios, validating its effectiveness in optimizing processes, reducing downtime, and improving overall operational efficiency
The Role of GABA in Anxiety Disorders
No full textGamma-aminobutyric acid (GABA) is the primary inhibitory transmitter in the central nervous system (CNS). One third of all CNS neurons are thought to be GABAergic. GABA is present in relatively high concen trations in the spinal cord and in all regions of the brain but does not exist in neurons outside the CNS.
The inhibitory action of GABA on neuronal activity in the CNS counterbalances the action of the excitatory neurotransmitter glutamate (Figure 1). The mutual homeo- stasis between glutamate and GABA works to modulate neuronal excitability and CNS arousal. This balance pre- vents excessive levels of neuronal hyperexcitability, which are known to occur in seizure disorders and pathologic anxiety and angiogenesis
Addressing Public Key Infrastructure (PKI) Challenges in V2X Networks: Strategies for Scalability, Certificate Management, and Trusted Authorities
No full textPublic Key Infrastructure (PKI) serves as a foundational element in the realm of securing Vehicle-to-Everything (V2X) communication networks. Its primary objective is to uphold the authenticity, confidentiality, and integrity of data exchanged within these networks. Despite its pivotal role, deploying and effectively managing PKI within V2X environments presents a host of formidable challenges. This article delves into the complexities surrounding PKI deployment specifically tailored to V2X networks, shedding light on the hurdles encountered and presenting innovative solutions to circumvent these obstacles.
One of the foremost challenges plaguing the implementation of PKI in V2X networks revolves around scalability. As the network expands to accommodate a burgeoning number of connected vehicles and infrastructure components, traditional PKI architectures often struggle to scale in tandem. This scalability conundrum necessitates a reevaluation of existing architectural paradigms to ensure that PKI infrastructures can seamlessly adapt to the ever-evolving demands of V2X environments.
Moreover, certificate management emerges as a significant stumbling block in the effective administration of PKI within V2X networks. The intricate web of certificates required to authenticate various entities, including vehicles, roadside units (RSUs), and traffic management systems, poses a formidable logistical challenge. The issuance, revocation, and renewal of certificates must be orchestrated with precision to maintain the integrity of the PKI ecosystem while simultaneously mitigating the risk of security breaches.
Furthermore, establishing trusted authorities within the V2X ecosystem presents yet another layer of complexity. The delineation of trust hierarchies and the designation of entities tasked with certificate issuance and validation necessitate meticulous planning and coordination. Without a cohesive framework governing the roles and responsibilities of these trusted authorities, the integrity of the entire PKI infrastructure may be compromised, leaving V2X networks vulnerable to exploitation.
In light of these challenges, this research article proposes a multifaceted approach aimed at alleviating the inherent complexities associated with PKI deployment in V2X networks. By exploring innovative solutions tailored to address scalability issues, certificate management complexities, and the establishment of trusted authorities, this article seeks to pave the way for the seamless integration of PKI within the burgeoning domain of V2X communication networks. Through collaborative efforts and a steadfast commitment to innovation, the V2X community can surmount these challenges and usher in a new era of secure and resilient V2X communication
Depression and Stress among Students in Faculty of Education of a Public University in Malaysia
No full textIntroduction: Psychological problems like anxiety, depression and stress significantly exacerbate the pressure on students to perform better. These factors collectively hamper their performance leading to low academic achievement especially among postgraduate students who majority of them are working and married. This study aims to identify the level of depression, anxiety and stress among postgraduate students, and to investigate the correlation between depression, anxiety and stress based on gender, marital status and age. Methods: The study was a quantitative survey and correlational research design using a clustered random sampling, selected from all postgraduate students (179) in Faculty of Education. The DASS-21 was used as measuring tools for depression, anxiety and stress among post- graduate students. Results: Findings of the study revealed that majority of the respondents are having moderate level of depression, anxiety and stress. Meanwhile, there were strong correlations between depression, anxiety and stress. However, there were no significant differences for level of depression, anxiety and stress based on demographic factors (gender, marital status and age). Conclusion: Based on the findings, it can be concluded that depression, anxiety and stress are strongly correlated. This study can give some insight to the educators, universities administration as well as students’ family to develop common intervention on how to curb depression, anxiety and stress among postgraduate students
The Level of Depression, Anxiety, and Stress Among Full-Time Housewives in Selangor, Malaysia
No full textPhysical activity was suggested to improve mental health. However, limited study was done to examine the relationship, especially among housewives. The aim of this study was to determine the relationship between physical activity level with the depression, anxiety, and stress among full-time housewives. A cross-sectional study was conducted among 297 respondents. The respondents were obtained by purposive sampling method. Socio-demographic, DASS-21 and SQUASH questionnaires were administered to the respondents. It was found that 50.5% of respondents were overweight. Level of MET/min of all of the respondents were reported low. The study indicates a high prevalence of housewives who have anxiety (45.1%) followed by stress (39.4%) and depression (34.7%). There is no relationship found between physical activity levels with mental health. As conclusion, effort to reduce sedentary behavior together with improving mental health among housewives is crucial
Navigating Sales Dynamics in Latin America: Insights from a Decade of Research
No full textThis systematic literature review examines the contributions of Latin authors to sales research from 2014 to 2024. The review incorporates a diverse array of publications, including journal articles, conference papers, and doctoral theses, focusing on technological advancements, sales strategies, and customer relationship management. Key findings highlight the significant influence of cultural, linguistic, and market-specific dynamics on sales practices in Latin America. The review reveals an emphasis on the adaptation of sales processes to local contexts, underscoring the importance of personal relationships, cultural nuances, and collective decision-making in shaping effective sales strategies. The insights gathered not only shed light on the current state of sales research in Latin America but also offer directions for future studies, emphasizing culturally informed sales strategies and the integration of technological tools to enhance market engagement