Journal of Science & Technology
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Model-Driven Software Engineering with Low-Code and Generative AI for Enterprise-Grade Applications
No full textMDSE, low-code platforms, and generative AI are all changing the way enterprises create software. It examines at how model-driven abstractions and LLMs\u27 adaptive intelligence may make it faster to design, write, test, and deploy programs. A research concluded that AI-assisted modeling tools and low-code environments help firms be more productive, keep their design consistent, and follow the norms for corporate governance. You can automatically build code that is safe, scalable, and simple to maintain by using semantic model interpretation and AI-guided pattern discovery. Companies that have to follow rules should make sure that ERP-CRM connection, data integrity, and DevSecOps automation are at the top of their list of things to do. Theories, frameworks, and data are used in model-driven intelligent, AI-augmented commercial application development pipelines
Edge-Native Software Engineering Models for Ultra-Low Latency Enterprise Applications
No full textEdge-native software engineering reinvents business application design, deployment, and administration for ultra-low latency and resilience using distributed computing architectures and 5G networks. Edge-native business application engineering models and architectural paradigms using decentralized computing, containerized microservices, and event-driven orchestration are critiqued in this paper. Containerization, serverless edge frameworks, and dynamic orchestration pipelines enable near-real-time data source-service-consumer processing. Also investigated are heterogeneous edge and cloud node real-time data synchronization solutions for mission-critical industrial IoT automation, retail analytics, and latency-sensitive business operations. The project examines resilience architecture and adaptive workload allocation for 5G and SDN infrastructures to improve compute distribution and data proximity. Enterprise-grade systems with predictable performance under unanticipated network and resource constraints are created utilizing edge computing, DevOps automation, and distributed data management
Advancing Software Quality: A Comprehensive Exploration of Code Quality Metrics, Static Analysis Tools, and Best Practices
No full textIn the ever-evolving landscape of software development, maintaining high-quality code is crucial for the creation of robust, secure, and maintainable applications. This comprehensive exploration delves into the multifaceted aspects of code quality, static analysis tools, and best practices that significantly impact modern software development practices. Software quality assurance is a process for guesstimating and documenting the quality of the software products during each phase of the software development lifecycle [1]
The journey begins by unraveling the intricacies of code quality metrics, with a focus on widely-used tools such as SonarQube, ESLint, and Pylint. SonarQube, a versatile open-source platform, takes center stage with its ability to detect code smells, assess security vulnerabilities, and analyze code coverage. The examination of ESLint underscores its significance in JavaScript development, enforcing coding standards, preventing errors, and seamlessly integrating into development workflows. Pylint, tailored for Python, contributes to clean and maintainable code by conducting thorough code quality checks and error prevention. 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[2].
The study then extends to the impact of these tools on development workflows and the overall software development lifecycle (SDLC). Early issue detection, consistent code standards enforcement, and continuous improvement emerge as pivotal outcomes, shaping a culture of code quality excellence. The integration of these tools into Continuous Integration/Continuous Deployment (CI/CD) practices amplifies their influence, automating checks, preventing regressions, and ensuring that only code meeting predefined quality criteria progresses through the deployment pipeline.
The spotlight on ESLint delves into its role as a linchpin in JavaScript development, where it not only enforces coding styles but also prevents common errors and integrates seamlessly into development workflows. The article underscores how ESLint\u27s impact extends beyond the coding phase, enhancing code readability, fostering collaboration, and automating routine maintenance tasks. Software integration may not be as much of an issue on a one-person with few external system dependencies, but as the complexity of project increases there is a greater need to integrate and ensure that software components work together [3].
The synthesis of these insights forms a cohesive narrative, emphasizing the symbiotic relationship between code quality metrics, static analysis tools, and development practices. As the software development landscape continues to evolve, these tools stand as indispensable allies, contributing to the creation of high-quality, secure, and efficient software products. This exploration serves as a guide for developers, teams, and organizations striving to navigate the complexities of modern software development while adhering to the principles of code quality excellence
Exploring the Impact of Artificial Intelligence on Mental Health Interventions
No full textPurpose: To keep the early clinical improvements from mental health treatments, longer-term intervention programs may be necessary. Nevertheless, this may not be doable because of how intense early intervention programs that include face-to-face interactions are. To avoid the intervention\u27s advantages from eroding, it may be cost-effective and engaging to use internet-based treatments tailored to kids as an adjunct. Nevertheless, the delivery of therapeutic information in online interventions has traditionally been handled by human moderators. Customized online treatment cannot be informed without more advanced models that are sensitive to user data. Therefore, to reimagine online treatments for adolescent mental health, it is essential to combine user experience with advanced and innovative technology to provide information. The web application offers supervised social therapy. In this presentation, we will go over the key aspects of the system and talk about our ongoing projects including using AI and sophisticated computational approaches to make the system more user-friendly and better at finding and delivering therapeutic material.
Method/Findings: As a case study, They look at the ongoing Horizons site, which is a randomized controlled experiment that followed children as they recovered from early psychosis for five years. They are using MOST to power this experiment. They go over the background of the project, the main features, and how to utilize the web app. Along with highlighting some of the system\u27s shortcomings, we go over some of the advancements made to the system, such as the inclusion of relevant use patterns. As a result, we are now driven to improve the system with new mechanisms for treatment material distribution and to increase user engagement via the application of computational and artificial intelligence approaches. To customize interventions and scale the system, we focus on how we have used chatbot technology and natural language analysis.
Recommendations/ Results from the many clinical studies conducted so far have confirmed the practicality of the novel MOST system. An essential next step in the advancement of the software system is to include sophisticated and automated content delivery techniques. This will allow for more data-driven possibilities, better analysis of use trends, and the possibility of large-scale deployment (Boucher et al., 2021
Revolutionizing AI-driven Hypertension Care: A Review of Current Trends and Future Directions
No full textAlmost all countries have patients with hypertension as a standard but far-reaching medical concern, and this brings notable financial consequences. The combination of Artificial Intelligence and Machine Learning in controlling hypertension holds the potential for timely recognition, individualized management approaches, and adherence to medication monitoring. Nevertheless, healthcare faces hurdles in adopting such technologies due to data quality, system integration, ethical considerations, and regulatory barriers. This literature review mainly deals with the current state of AI and ML use in the management of hypertension. Particular attention is paid to their prediction, monitoring, and individualization of the therapeutic approaches. Key areas of interest include early detection, risk prediction, and developing individualized care plans. To promote the responsible and ethical use of AI in healthcare, future research in this field might include but not be limited to continuous monitoring, chronic disease management, and the integration of multi-modal data. Patient privacy, data security, algorithmic bias, and informed consent are the ethical issues to consider. Furthermore, the review discusses the ethical dilemmas surrounding patient privacy, data security, and programming biases in AI-driven healthcare solutions. To ensure that these technologies are effectively implemented in clinical practice, we need to address issues relating to data quality, system integration, ethics, and regulation. This may have potential results such as transforming hypertension management through sustained innovation efforts, thus improving quality care among hypertensive patients. Finally, the review highlights the future potential of AI to transform clinical practice, individualize treatment approaches, and mitigate the global impact of hypertension on public health
Solar Geoengineering: Assessing Whether Lack of Scientific Evidence Justifies Halting Solar Geoengineering Research
No full textThis article examines the relevance of the research on solar geo-engineering, which has become the new buzz-word in climate change mitigation. With current climate mitigation policies becoming evidently inadequate, one needs to look at science for the panacea. Solar geoengineering is a double-edged sword, capable of giving desirable results in the near future but equally capable of multiplying the complexities of the problem in long term future. There continue to exist certain deep-rooted concerns about the necessity and end-use consequences of this technology. The article attempts to examine both these facets. It also throws light on the concerns of indigenous communities and the principle of intergenerational justice with respect to the conduct of solar geoengineering research. The article ends with a conclusion which aims at giving a balanced solution to the research question
Assessing The Impact of Transparent AI Systems in Enhancing User Trust and Privacy
No full textThe study is about the impact of transparent systems to enhance users’ trust and privacy, and this consent is very important in the era of technology. Trust is a big factor when utilizing AI, and it is risky to develop trust as the privacy concern is there in the technology. In that factor, the study has focused on finding the impact of the transparent AI system in developing privacy and trust. Different kinds of literature pieces are also reviewed to gain knowledge about the subject matter. Moreover, a proper methodology is engaged to develop the study which has been followed by the result and discussion to meet the aim and objectives of the research
Enhancing Financial Analysis Through Artificial Intelligence: A Comprehensive Review
No full textFinancial analysis serves as the cornerstone of decision-making processes within various domains including businesses, investment firms, and regulatory bodies. As the financial landscape continues to evolve, the integration of artificial intelligence (AI) technologies has emerged as a transformative force, reshaping traditional approaches to financial analysis. This comprehensive review delves into the multifaceted realm of AI in financial analysis, aiming to elucidate its applications, benefits, challenges, and future trajectories.
The introduction outlines the foundational significance of financial analysis and delineates the pivotal role it plays in facilitating informed decisions across diverse sectors. With the advent of AI, particularly machine learning and deep learning techniques, there has been a paradigm shift in the methodologies employed for financial analysis, heralding a new era of data-driven decision-making.
The subsequent section navigates through the expansive spectrum of applications wherein AI augments financial analysis capabilities. From predictive analytics for forecasting market trends to sentiment analysis for gauging investor sentiment, AI facilitates a myriad of functionalities that enhance the accuracy, efficiency, and timeliness of financial insights. Moreover, the integration of AI in algorithmic trading, fraud detection, risk management, and customer behavior analysis underscores its versatility and utility across various facets of finance.
Highlighting the benefits of AI in financial analysis, the review delineates how AI-powered algorithms contribute to improved decision-making processes by harnessing vast amounts of data to generate actionable insights. The automation of repetitive tasks, coupled with real-time analytics capabilities, empowers financial professionals to make informed decisions swiftly, thereby enhancing operational efficiency and competitiveness.
However, amidst the transformative potential of AI in financial analysis, several challenges and limitations warrant consideration. Issues pertaining to data quality, ethical concerns, regulatory compliance, and interpretability of AI algorithms pose formidable obstacles that necessitate careful navigation. Moreover, the risk of overreliance on AI systems and susceptibility to cybersecurity threats underscore the importance of establishing robust governance frameworks and ethical guidelines.
Looking ahead, the review envisages a future brimming with opportunities for the continued evolution and integration of AI in financial analysis. Advancements in machine learning algorithms, coupled with the convergence of AI with emerging technologies such as blockchain, promise to unlock new frontiers in financial innovation. Moreover, the proliferation of AI applications in fintech and regtech domains heralds a seismic shift in how financial services are conceptualized, delivered, and regulated.
Drawing upon case studies and success stories, the review provides empirical evidence of the tangible impact of AI implementation on financial performance and strategic decision-making. By synthesizing existing literature and empirical insights, this review contributes to the discourse surrounding AI in financial analysis, offering valuable insights for researchers, practitioners, and policymakers navigating the complex interplay between technology and finance
New Computational Methods for Enhancing Reliability Testing of Interconnects in 3D ICs: Advanced Algorithms, Optimization Techniques, and Real-World Applications
No full textThe relentless scaling of transistor density in conventional two-dimensional (2D) integrated circuits (ICs) has reached its physical limitations. Three-dimensional (3D) ICs, with their stacked layers of active circuitry, have emerged as a promising solution to overcome these limitations and continue the miniaturization trend. However, the integration of these stacked layers introduces significant challenges, particularly regarding the reliability of interconnects – the pathways that carry electrical signals between various components on the chip. Due to the increased complexity and miniaturization of interconnects in 3D ICs, their susceptibility to various failure mechanisms, such as electromigration, thermal stress, and dielectric breakdown, is heightened. Ensuring the reliability of these interconnects is paramount for the functionality and robustness of 3D ICs.
This paper delves into novel computational methods designed to enhance the reliability testing of interconnects in 3D ICs. We focus on the development and implementation of advanced algorithms and optimization techniques to improve interconnect reliability. The paper comprehensively explores detailed methodologies, proposes innovative testing frameworks, and investigates real-world applications. By elucidating these advancements, we provide valuable insights into how these methods can be integrated into current industrial practices to effectively address the challenges of testing and ensuring reliability in 3D IC interconnects.
Detailed Methodologies
The paper commences by outlining the fundamental challenges associated with interconnect reliability in 3D ICs. It delves into the various failure mechanisms that threaten interconnect integrity, including electromigration, where the continuous flow of current can cause mass movement of atoms, leading to voids and opens in the interconnects. Additionally, thermal stress due to heat dissipation within the densely packed 3D structure can induce mechanical deformations and material degradation in the interconnects, ultimately resulting in failures. The paper further discusses the limitations of conventional testing methodologies employed for 2D ICs, highlighting their inadequacy in capturing the complexities of 3D interconnect structures.
To address these challenges, the paper proposes the development of advanced algorithms for comprehensive reliability testing. One such approach involves employing machine learning (ML) techniques for interconnect reliability assessment. Supervised learning algorithms can be trained on a vast dataset of 3D IC layouts, incorporating factors like material properties, interconnect dimensions, and operating conditions. This enables the algorithms to predict the susceptibility of specific interconnects to various failure mechanisms with high accuracy. Additionally, unsupervised learning techniques can be leveraged to identify hidden patterns and correlations within the data that might not be readily apparent through traditional methods. This facilitates the proactive identification of potential reliability risks in the design phase itself.
Furthermore, the paper explores the application of optimization techniques to enhance the reliability of 3D IC interconnects. Design space exploration (DSE) algorithms can be employed to systematically evaluate various design configurations and identify those that offer optimal reliability characteristics. These algorithms can consider factors like interconnect geometry, material selection, and routing strategies while adhering to design constraints such as power consumption and performance. By leveraging optimization techniques, designers can create 3D ICs with inherently more reliable interconnects, reducing the need for extensive post-fabrication testing.
Novel Testing Frameworks
The paper proposes the development of innovative testing frameworks specifically tailored for 3D IC interconnects. These frameworks encompass a comprehensive suite of techniques that go beyond traditional electrical testing methods. One such technique involves employing physical modeling tools to simulate the behavior of interconnects under various operating conditions. These simulations can provide valuable insights into the mechanical and electrical stresses experienced by the interconnects, enabling the identification of potential weak points before fabrication.
Furthermore, the paper explores the integration of advanced in-situ monitoring techniques within the testing frameworks. These techniques involve embedding sensors directly on the chip to monitor parameters such as temperature, current density, and strain in real-time. By analyzing the sensor data, engineers can gain valuable insights into the health and performance of the interconnects during operation. This facilitates the early detection of potential failures, allowing for corrective actions to be taken before catastrophic events occur.
The paper emphasizes the importance of incorporating statistical methods into the testing frameworks. Due to the inherent variability in fabrication processes and material properties, a certain degree of statistical variation is inevitable in the behavior of interconnects. Statistical methods, such as Monte Carlo simulations, can be employed to account for these variations and assess the overall reliability of the entire interconnect network. This probabilistic approach provides a more realistic picture of interconnect reliability compared to deterministic methods.
Real-World Applications
The paper underscores the practical significance of the proposed computational methods by exploring their application in real-world scenarios. One crucial application involves the design and development of high-performance computing (HPC) systems. HPC systems rely heavily on 3D ICs due to their ability to pack a large number of processing cores into a compact space. However, the reliability of interconnects in these systems is paramount, as any failure can lead to significant performance degradation and downtime. The advanced algorithms and testing frameworks proposed in this paper can be instrumental in ensuring the reliability of interconnects in HPC systems. By employing machine learning for early failure prediction and optimization techniques for designing inherently reliable interconnects, designers can create robust HPC systems that can withstand demanding workloads.
Another important real-world application lies in the field of neuromorphic computing. Neuromorphic computing aims to mimic the structure and function of the human brain, utilizing 3D ICs to create densely packed networks of artificial neurons. The reliability of interconnects in these systems is critical, as any disruptions can significantly impact the accuracy and performance of the neuromorphic computation. The proposed computational methods can play a crucial role in ensuring the reliability of interconnects in neuromorphic computing hardware. By leveraging in-situ monitoring techniques and statistical analysis, engineers can proactively identify and address potential reliability issues, paving the way for the development of reliable and high-performance neuromorphic systems.
Furthermore, the paper explores the application of these methods in the design of Internet-of-Things (IoT) devices. The proliferation of IoT devices necessitates the development of miniaturized, low-power, and reliable integrated circuits. 3D ICs provide a promising solution for achieving these goals. However, the reliability of interconnects in these resource-constrained devices is crucial for ensuring long-term functionality. The optimization techniques proposed in this paper can be employed to design 3D ICs for IoT devices with inherently reliable interconnects, even with limited power and area budgets. This paves the way for the development of dependable and long-lasting IoT devices.
This paper presents a comprehensive exploration of novel computational methods for enhancing the reliability testing of interconnects in 3D ICs. The paper delves into advanced algorithms, optimization techniques, and innovative testing frameworks, highlighting their potential to revolutionize the way 3D IC reliability is assessed and ensured. By integrating these methods into current design practices, the industry can create a new generation of highly reliable 3D ICs, unlocking their full potential for various real-world applications
Harnessing Artificial Intelligence for the Advancement of Mankind
No full textThe rapid progression of artificial intelligence (AI) over the past decade has ushered in an unprecedented era of technological advancement. From healthcare to environmental management, AI\u27s transformative potential promises to address some of the most pressing challenges facing humanity. This paper explores the multifaceted applications of AI, emphasizing its capacity to drive innovation, enhance quality of life, and foster sustainable development