Oscillator-based grid-forming (GFM) methods are getting focused for the global synchronization potential. However, existing methods like dispatchable virtual oscillator control (dVOC) are two-state oscillators, which cannot smoothly handle large disturbances from the primary source or the power grid. This paper presents a full-state virtual oscillator control (fVOC) strategy with frequency and voltage magnitude as extended oscillator states from the dc voltage. Both the theoretical analysis and experimental tests show that the GFM photovoltaic (PV) with fVOC can robustly withstand large disturbances and provide elastic grid support during the fluctuations of solar irradiance, load change or short-circuit fault that result in dc-link power imbalances. The nonlinear model considering primary dynamics is derived to illustrate the enhanced large-signal dc voltage stability of fVOC based PVs. The validity of the proposed method is verified by both numerical simulations and hardware tests.

Global warming is a prominent problem in the world, and a lot of research has been done on how to stop or slow it down. The main cause of global warming is pollution, especially the four main pollutants–carbon monoxide, ozone, nitrogen dioxide, and sulfur dioxide.1 With the use of AI, models can be built that provide insight into pollutant levels across regions of the US. In this project, linear regression models were built that predicted these four pollutant levels across four regions of the US. Pollution levels were studied and used to determine the impact of each pollutant in each region. Results showed that ozone in the west was predicted to have the worst pollutant levels, suggesting resources can be allocated to this region over others to lessen the harmful effects of pollution.

Mixed Integer Programming (MIP) is a foundational problem in combinatorial optimization, with wide-ranging applications in industries like logistics, scheduling, and resource allocation. Traditional methods for solving MIP, such as branch-and-bound and branch-and-cut, face scalability issues, particularly for large and complex problem instances. Recently, hybrid approaches combining machine learning (ML) with classical optimization methods have shown significant promise in improving solver efficiency and solution quality. In this paper, we review the integration of ML into MIP solvers, focusing on three key areas: branch-and-bound enhancements, cutting plane generation, and heuristic optimization for approximate solutions. ML models, including supervised learning, reinforcement learning, and graph neural networks, are used to inform critical decision-making processes within the solver, leading to faster convergence and better performance. We highlight how ML-enhanced solvers reduce computation time, improve scalability, and adapt dynamically to different problem structures. Finally, we discuss challenges such as generalization across diverse MIP instances and seamless integration with existing solvers. The future of MIP solving lies in further refining these hybrid methods, making ML-driven solvers a powerful tool for solving complex optimization problems.

Code generation by Llama 3.1 models, such as Meta's Llama 3.1 405B, represents a significant advancement in the field of artificial intelligence, particularly in natural language processing and programming automation. This paper explores the capabilities and applications of Llama-driven code generation, highlighting its ability to translate natural language prompts into executable code across multiple programming languages. Key features include contextual awareness, multi-language support, and enhanced debugging and optimization functionalities. By examining these aspects, we illustrate how Llama can serve as a versatile tool for developers of all skill levels, improving productivity and efficiency in software development. The potential implications for education, industry, and the future of coding practices are also discussed, underscoring the transformative impact of AI in programming. Experimentation shows that while Llama 3.1 405B performs well with simple algorithmic and data structure based problems, it still struggles with problems on Quantum Computing, Bioinformatics, and Artificial Intelligence.

Microscopic images of cells often contain multiple objects with similar properties. In this study, we collected videos of blood microcirculation in nailfold capillaries to identify correlations between video data and indicators of complete blood count tests. The specific features of these videos include a high number of capillaries and a high variance in their visual quality. Therefore, we proposed a novel loss function and a video processing framework that combines metric learning and classification with uncertainty estimation. We also applied test-time augmentation to enhance the confidence of the predictions. During simulation experiments, we used shallow and deep learning models to compare a set of classifiers for the collected metadata. Both types of models demonstrated good results, with an accuracy of up to 80% in gender classification. Deep learning models outperformed shallow models and also classified the age of the volunteers with an accuracy of 74%. Subsequently, we addressed a series of binary classification tasks to identify conditionally high or low levels of the main complete blood count indicators. The obtained accuracy of the models reached 63%. Although the accuracy of the blood test classifiers is not yet sufficient for clinical purposes, we have demonstrated that video data correlates with some of the indicators.

With the growing integration of converter-interfaced generating units in power systems, interactions between converters and grids have been observed across both low and highfrequency ranges, resulting from the diverse dynamics of these converters. Moreover, incorporating the complex dynamic model of converters, which involves a large number of state variables, increases the computational burden when using conventional methods to identify unstable and interaction modes. These modes are also influenced by the parameters of both converters and networks, necessitating numerous time-consuming re-computations of all eigenvalues for parametric variation analysis in large power systems. To address these challenges, this paper proposes a method that utilizes closely located open-loop modes and their displacement from open-loop to closed-loop modes to selectively identify interaction modes within power systems. The proposed method leverages the open-loop subsystem eigenvalues and the interconnections between state variables of different subsystems to determine the closed-loop eigenvalue using a second-order eigenvalue approximation, thereby avoiding the need for the calculation of all eigenvalues of the entire system. The interaction information is then used to obtain a reduced-order eigenvector approximation to improve the computation time as compared to the full-order system. The identified interaction modes and corresponding computation time using the proposed method are compared with the traditional modal participation method. The proposed method provides an efficient approach to identifying interactions between converters and the power system.

Healthcare sector enhances the integration of artificial intelligence, natural language processing and machine learning in data governance for developing automation procedures. Data management offered AI-powered ETL testing with the adoption of an automation framework for resolving data governance issues [1]. This insight promotes operational efficiency with assurance of data quality that empowers data security by ML, AI and NLP frameworks. The framework allows healthcare institutions to arrange unstructured data in effective insights into patient care and treatment procedures. Advanced analytics leads to the development of predictions on patient outcomes in treatment procedures. The automated process of ETL improves efficient information collection by NPL with the creation of predictive models in monitoring data management by ML [2].

Quantum computing represents a monumental leap in computational technology, leveraging the principles of quantum mechanics to solve complex problems at unprecedented speeds. Unlike classical computers, which rely on bits as the smallest unit of information, quantum computers utilize qubits that can exist in multiple states simultaneously, enabling them to perform many calculations at once. This paper delves into the foundational concepts of quantum computing, including superposition, entanglement, and quantum gates, and explores its potential applications across various domains, such as cryptography, drug discovery, optimization problems, and artificial intelligence. We will discuss current advancements in quantum technology, highlighting key breakthroughs and the progress made by leading research institutions and companies. Furthermore, this paper addresses the significant challenges that quantum computing faces, including error rates, qubit coherence, and scalability, which hinder its widespread adoption. By examining these facets, we aim to provide a comprehensive overview of how quantum computing could transform various fields and the hurdles that must be overcome to realize its full potential, ultimately suggesting future directions for research and development in this revolutionary area of technology.

In the context of paralleled Silicon Carbide (SiC) dies at low stray inductance, the current imbalance proves to be exceptionally sensitive to the static parameters of the devices. Even under conditions of full layout symmetry, significant current mismatch persists. This issue becomes prominent in emerging technologies utilizing standard cell design and die embedding, characterized by low stray power loop inductance. Traditional packages with relatively high stray inductance do not exhibit this particular problem. Addressing the challenges posed by these low-inductance technologies, this paper introduces a passive current-sharing method employing AC-coupled inductor. The proposed method is demonstrated through the implementation of a highly integrated converter with power loop inductance less than 1 nH. Notably, the presented approach is straightforward to implement and achieves near-perfect current sharing among paralleled dies, thereby facilitating the realization of high-current rating converters.

The importance of continuously emerging new distribution is a mandate to understand the world and environment surrounding us. In this paper, the author will discuss a new distribution defined on the interval (0,1) as regards the methodology of deducing its PDF, some of its properties and related functions. A simulation and real data analysis will be highlighted.

The increasing reliance on machine-generated text across various applications has highlighted the importance of ensuring high-quality outputs that are both coherent and contextually accurate. Conventional approaches to prompt engineering often involve manual tuning, which introduces limitations in scalability and consistency, particularly for tasks that demand real-time responsiveness. A novel token-level-guided automatic prompt optimization (TAPO) framework has been developed to address these challenges, offering an adaptive mechanism that refines prompts through real-time feedback at the token level. Through its integration with the Mistral model, the framework significantly improves fluency, coherence, and factual accuracy across a range of text generation tasks. The results demonstrate that the TAPO framework outperforms human-designed prompts by dynamically adjusting token probabilities, providing a more efficient and scalable solution for high-quality text generation. This contribution opens new possibilities for fully automated prompt optimization in LLMs, reducing human intervention while maintaining the adaptability required for complex language tasks.

Dimensionality reduction problems in deep learning architectures have become a crucial task in the field of data science due to increasing burdens in computational costs or complexity issues generated in processing massive big data. To handle this issue, methodologies such as neural network(NN) weight matrix compression based on clustering or network weight pruning based on information criterions were introduced to reduce memory issues or inefficiencies within fitted neural networks. However, though network compression methods itself have been suggested in various research, there still lies certain limitations in optimizing compressed NN to avoid over-fitting issues and achieve robustness in unobserved data. To solve this limitation, this research constructed a novel weight clipping method called Bayesian Weight Clipping based Neural Network Compression Optimization(BaWcNN) that can guide existing compression methods to find optimal solutions by providing information boundaries within fitted weight matrix prior to compression. Specifically, by implementing a double-changepoint model to probe for weight information bounds under MCMC sampling, and clipping controversial weights to information boundaries based on Euclidean distance, BaWcNN was found to substantially increase neural network compression results in classification performance or regression analysis for unobserved data under UCI Repository real datasets: Heart Disease Data, Parkinson's Disease Telemonitoring Data. Through BaWcNN weight clipping in weight matrices, it is expected that network compression for deep learning based models can leap towards high efficiency, which can avoid generations of non-relevant weight centroids in compression results deduced through weight sharing via clustering, or increase model robustness by eliminating controversial weights in parameter space.

Client Relationship Management (CRM) solutions are essential for managing client contacts, increasing sales, and boosting customer retention in today's cutthroat corporate market. These procedures might be completely transformed by the incorporation of Artificial Intelligence (AI) into CRM, giving companies the ability to better comprehend and serve their customers' demands. Predictive analytics, automated customer support, and targeted marketing are just a few of the features that AI-powered CRM systems provide. These features improve customer experiences, maximize sales tactics, and boost operational effectiveness. However, there are substantial expenses associated with integrating AI into CRM, such as those related to hiring new staff, integrating the system, training them, and continuing maintenance. An extensive cost-benefit analysis of integrating AI into CRM systems is done in this paper. It examines the possible return on investment (ROI) and offers insights into the financial, operational, and strategic ramifications of using AI-enhanced CRM. The article attempts to evaluate how the advantages-such as higher sales, customer retention, and data-driven decision-making-balance against the initial and ongoing expenses using qualitative research, case studies, and a comparative analysis between traditional and AI-driven CRM systems. It also highlights the difficulties and unstated costs that come with using AI, such as regulatory compliance and data protection issues. The study comes to the conclusion that while if AI-powered CRM requires a significant upfront investment, in the long run, the benefits-such as better customer insights, automated chores, and increased marketing efficiencygenerally exceed the drawbacks. Businesses thinking about incorporating AI into their CRM strategy are given advice, which includes taking a staged approach and keeping an eye on system performance to optimize return on investment. This study advances knowledge about artificial intelligence's function in customer relationship management and offers a road map for companies looking to use AI to adopt a more efficient and customer-focused strategy.

Predicting the occurrence of solar activities have been a major concern over time. Solar flare which is a predominant solar activity affects communication and power systems here on earth. In this research paper, various machine learning and visualization techniques were deployed to discover hidden pattern within dataset and develop machine learning models for predicting the class of solar flares. The used datasets were provided by NGDC, SILSO and SWPC space bodies. These datasets contain Solar flare datasets, sunspot datasets and Active region magnetic properties data. These datasets were associated using python programming language with Active region number and occurrence data been the important features for association of flares, sunspots and Active region magnetic properties. A robust single dataset was generated. Visualization was then performed on the dataset using various visualization techniques such as bar plots, scatter plots, box plots and bubble plots. For model development, 4 machine learning models were deployed which are Random forest, Decision Tree, Bagging Classifier and Support Vector Machine (SVM). Random forest outperformed other models on predicting the class of an associated flare by producing an accuracy of 79% while SVM performed poorly on the used dataset.

Ransomware attacks have increasingly threatened the integrity of digital infrastructures, compromising sensitive data and disrupting business operations worldwide. The complexity and rapid evolution of ransomware strains render traditional detection methods insufficient, prompting the need for more proactive and adaptive solutions. A novel machine learning-based method for automated file trap selection is introduced, offering early detection of ransomware through strategically deployed decoy files. This approach leverages machine learning models, such as Random Forest, SVM, and Neural Networks, to identify and respond to ransomware behaviors with high accuracy and low false positive rates. Additionally, reinforcement learning is employed to dynamically optimize the placement of file traps, ensuring that the system adapts to emerging threats in real-time. Comprehensive experiments across various ransomware families reveal that the system consistently achieves robust detection, minimal latency, and scalability across different environments. The findings emphasize the effectiveness of combining advanced machine learning algorithms with real-time feature extraction to significantly enhance ransomware detection capabilities.

This paper applies Maximum Likelihood Estimation (MLE) to the identification of a state-space model for inertial sensor drift. The discrete-time scalar state considered is either a first-order Gauss-Markov process or a Wiener process, both of which are common drift terms in inertial sensor noise models. The measurement model includes an additive white measurement noise. In setting up the MLE, the likelihood function (LF) is derived within the steady-state Kalman filter framework. The resulting log-likelihood function (LLF) can be expressed as a quadratic function of the measurements. This allows for an explicit expression of the LLF, facilitating the evaluation of the Cramér-Rao Lower Bound (CRLB) and thence testing and ultimately confirming the statistical efficiency, i.e., the optimality, of the ML estimators. Simulations demonstrate the optimal performance of the estimators, and applications to real sensor data indicate advantages over the Allan variance method for drift modeling.

This paper explores various methods for analyzing, manipulating, and evaluating survey data. Using a Jupyter notebook, we examine trends such as the increase in the number of published survey articles over the years. Additionally, we categorize each article into different taxonomy topics. Through data manipulation, we create a TF-IDF matrix, enabling us to predict an article’s taxonomy category based on key words from its title, summary, and associated categories

Spasticity is painful and disabling condition that has substantial impact on people with long-term neurological conditions. Assessing spasticity and determining the efficacy of current treatments is limited by the measurement tools available in clinical practice. We convened an expert panel of clinicians and engineers to identify a solution to this urgent clinical need. We established that a reliable ambulatory spasticity monitoring system that collates clinically meaningful data remotely would be useful in the management of this complex condition. This paper provides an overview of current practices in managing and monitoring spasticity. Then, the paper describes how a remote monitoring system can help in managing spasticity and identifies challenges in development of such a system. Finally the paper proposes a monitoring system solution that exploits recent advancements in low-energy wearable systems comprising of printable electronics, a personal area network (PAN) to low power wide area networks (LPWAN) alongside back-end cloud infrastructure. The proposed technology will make an important contribution to patient care by allowing, for the first time, longitudinal monitoring of spasticity between clinical follow-up, and thus has life altering and cost-saving implications.