Intelligent vibration mitigation design for large sluice structures subject to flow-induced vibrations using machine learning and the ivy algorithm

Designing drilling fluids, spacers and cement slurries are all often done by trial and error in the laboratory. There are several test data that would not be used in this method and it is hard to digest a plethora of information for users and take intelligent and cost-effective decision to design a fluid with the desired properties. Therefore, trial and error method is considered to be time consuming, very costly and misleading. Today, there is a need for an intelligent system which uses all the available fluid design data stored in a database by which we can benefit from its insights for smart fluid designs. This predictive tool suggest a composition for drilling fluids, spacer fluid or cement slurry by implementing a machine learning algorithm on imported experimental data. This paper investigates and implements a data-driven predictive tool which uses Gaussian Process Regression (GPR). GPR as a novel machine learning method considerably reduces the costs of testing, optimizes the material use, integrates available experimental data and eliminates the user bias. This practical nonlinear regression method fosters an efficient and fast prediction analysis which do not require including complex physics of the underlying intricate chemical fluid behavior while integrating all available data from different databases. GPR has exceptional advantages over traditional regression methods since it does not require a known form for regression function. Also its capability of determining estimation error and confidence interval is unique. This machine learning based tool offers comprehensive insights for intelligent fluid design and considerably reduces the experiment cost. This study showcases an example through which of GPR predicts rheological properties and helped engineers to maintain the fluid rheological hierarchy for better cement jobs and well integrity. Zonal isolation has the utmost importance for carbon sequestration projects.

Abstract: Machine learning is transforming stock market prediction by leveraging vast datasets, advanced algorithms, and computational power to provide more accurate forecasts. While challenges remain, continuous advancements in artificial intelligence and deep learning are improving predictive models, making them an essential tool for traders and investors. Stock market prediction extremely challenging due to the dependence of stock prices on several financial, socio-economic and political parameters etc. For real life applications utilizing stock market data, it is necessary to predict stock market data with low errors and high accuracy. This needs design of appropriate artificial intelligence (AI) and machine learning (ML) based techniques which can analyze large and complex data sets pertaining to stock markets and forecast future prices and trends in stock prices with relatively high accuracy. This paper presents a comprehensive review on the various techniques used in recent contemporary papers for stock market forecasting. Keywords: Time Series Models, Stock Market Forecasting, Artificial Intelligence, Artificial Neural Networks, Forecasting accuracy.

Computational science in medicine is no longer a futuristic premise. The medical device industry has engineered Artificial Intelligence (AI) and Machine Learning (ML) tools to derive insights from large volumes...

Artificial intelligence (AI) has successfully made its way into contemporary industrial sectors such as automobiles, defense, industrial automation 4.0, healthcare technologies, agriculture, and many other domains because of its ability to act autonomously without continuous human interventions. However, this capability requires processing huge amounts of learning data to extract useful information in real time. The buzz around AI is not new, as this term has been widely known for the past half century. In the 1960s, scientists began to think about machines acting more like humans, which resulted in the development of the first natural language processing computers. It laid the foundation of AI, but there were only a handful of applications until the 1990s due to limitations in processing speed, memory, and computational power available. Since the 1990s, advancements in computer architecture and memory organization have enabled microprocessors to deliver much higher performance. Simultaneously, improvements in the understanding and mathematical representation of AI gave birth to its subset, referred to as machine learning (ML). ML includes different algorithms for independent learning, and the most promising ones are based on brain-inspired techniques classified as artificial neural networks (ANNs). ANNs have subsequently evolved to have deeper and larger structures and are often characterized as deep neural networks (DNN) and convolution neural networks (CNN). In tandem with the emergence of multicore processors, ML techniques started to be embedded in a range of scenarios and applications. Recently, application-specific instruction-set architecture for AI applications has also been supported in different microprocessors. Thus, continuous improvement in microprocessor capabilities has reached a stage where it is now possible to implement complex real-time intelligent applications like computer vision, object identification, speech recognition, data security, spectrum sensing, etc. This paper presents an overview on the evolution of AI and how the increasing capabilities of microprocessors have fueled the adoption of AI in a plethora of application domains. The paper also discusses the upcoming trends in microprocessor architectures and how they will further propel the assimilation of AI in our daily lives.

The high-paced development of wireless communication technologies brought about the growing need of smart, autonomous, and ultra-high-performance network structures, and the combination of Artificial Intelligence and Machine Learning in the 6G wireless communication networks is a major research issue. The conventional network management techniques cannot support the new demands that include ultra-low latency communications, intelligent spectrum management, mass connectivity, and real-time dynamic resource provision. This paper is a systematically-conducted literature review on the recent research on AI-driven networks, edge AI, federated learning, intelligent radio access network design, and AI-native architecture in next-generation networks based on PRISMA. The review focuses on the possibilities of self-organizing networks, digital twin networks, and smart connectivity that machine learning, deep learning, and autonomous wireless systems provide to the next generation of wireless networks working in terahertz communication ranges and integrated sensing and communication landscape. The findings indicate that AI based resource allocation, intelligent surfaces of reconfigurability, massive mimo optimization and network automation are all effective towards improving spectral efficiency, reliability and energy efficiency of 6G wireless communication. Moreover, the paper has also noted the increased importance of smart security mechanisms, distributed learning, and edge computing in enabling scalable and secure AI-native architectures. The results also indicate that the next generation wireless networks will be based upon intelligent spectrum management, autonomous control, and real-time data analytics to accommodate the new applications in holographic communication, smart cities, extended reality, and connected autonomous systems.

The integration of multimodal data and artificial intelligence (AI) in precision agriculture has the potential to significantly improve crop monitoring, management, and overall agricultural productivity. Precision agriculture is advancing rapidly by utilizing multimodal data and intelligent system design. These help to improve decision-making and increase farming efficiency. The main challenges are the fusion of data, introducing AI-powered analytics, and human-computer interaction (HCI) in the field of smart agricultural services. To address the challenges, we investigate how to merge various forms of data. Also, we studied innovative applications to use multimodal data and an intelligent system design to improve crop monitoring and management. This research shows a novel aspect to utilize and explains the difficulties of working with a variety of data sources, such as satellite images, sensor data, weather reports, and farmer inputs, to design an intelligent system. In addition, this research examines how advanced data fusion techniques bring these disparate data types together to provide accurate, real-time insights that help optimize farming practices. AI analytics enable intelligent decision-making and timely alerts, resulting in improved agricultural practices using an advanced predictive machine learning (ML) model. This study adds to the expanding body of knowledge on precision agriculture by introducing novel methodology and practical applications, opening the door for more sustainable and intelligent farming systems.

An intelligent design method for new binder material activated by solid wastes by integrating attention-based tree model and heuristic optimization algorithm

Artificial Intelligence (AI) systems that facilitate the automated layout design are a major breakthrough in design automation in a variety of areas, including architecture, industrial design, electronics, and digital interfaces. The conventional layout design procedures are based on human skills and manual corrections that are time consuming and can be subject to subjective judgements. The more recent advances of the AI, especially in machine learning and deep learning, have allowed systems to create, analyze, and optimize the layout configurations with little human involvement. The current paper gives a detailed structure of automated layout design incorporating generative adversarial networks (GANs), reinforcement learning (RL), and optimization algorithms. The suggested methodology entails data pre-processing methods, model training processes, and analysis metrics to determine the quality of designs, efficiency, and adaptability of the design. Implementation phase shows the interaction with the currently used design tools, which shows the ability of the system to come up with layouts that are aesthetically appealing, functional, and space conscious. Its applications in industry and architecture at the VLSI/PCB layout and dynamic web interface designs. Moreover, the paper examines the possibilities of using multimodal AI systems in real-time adaptive layouts, cross-domain generalization, and scalable implementation. The proposed system will fill the gap between computational intelligence and creative design concepts to increase productivity, consistency and innovativeness in the layout creation to provide a platform in the future research field of intelligent design automation.

Understanding how chemical degradation influences proton transport in Nafion membranes is critical for improving the reliability of proton exchange membrane fuel cells (PEMFCs). Here, we develop interpretable Machine Learning (ML) and Deep Learning (DL) frameworks to predict key transport properties, including vehicular and Grotthuss mechanism conductivities, and the tortuosity factor under varying water content, temperature, and degradation levels. Leveraging a high-fidelity, multiscale simulation data set, we trained a 3D convolutional neural network on voxelized membrane nanostructure data to predict proton conduction properties. Gradient-weighted class activation mapping is applied to highlight the spatial regions within the nanostructure that most strongly influence the predictions. We further develop random forest models based on extracted geometric features and macroscopic input conditions, with Shapley-value analysis used to quantify the impact of nanostructural and environmental factors. A fully coupled mechanism map is constructed to link hydration, temperature, and degradation with distinct conduction regimes. This visualization reveals how nanostructure aging reshapes proton conduction and provides a foundation for inverse design, which enables degradation-aware optimization of the membrane. Our study provides a multimodal AI framework capable of capturing both geometry and semantics in materials analysis and opens new avenues for predictive aging modeling and intelligent design in ion-conducting membranes.

As advanced amorphous materials with superior mechanical, physical, and chemical properties, metallic glasses (MGs) hold significant promise for a wide range of applications. However, their rational design and precise property control have long been impeded by notable challenges. These obstacles largely arise from the inherent compositional and structural complexity of MGs, which not only slows empirical trial-and-error experimentation but also limits the scalability of computationally intensive first-principles simulations. In recent years, machine learning has emerged as a transformative tool, offering unprecedented capabilities to decode these intricate relationships and overcome conventional research limitations. Here we provide a systematic overview of machine-learning-guided investigations of MGs and their associated data pipelines, centering on two key paradigms: the feature-driven ‘Keplerian’ data pipeline and a next-generation theory-experiment-aligned pipeline designed to close the gap between simulation and experiment. By breaking down both paradigms into modular workflow components, we underscore the essential roles of data standardization, interpretable feature engineering, and physics-informed validation in constructing a reliable research framework. We anticipate that a sufficiently robust, efficient, and generalizable data pipeline will not only unlock novel scientific insights and accelerate material discovery but also propel the field from a ‘trial-and-error’ approach toward an era of intelligent and principled design.

Artificial intelligence (AI) and machine learning (ML) have been growing at an incredible rate in recent years and they show no sign of stopping. Manufacturing, educational systems, transportation architecture, and genetic research are industries where artificial intelligence algorithms have been developed and found practical applications in which they can increase task efficiency and reduce cost through process optimization, pattern recognition, and automation. At NASA, one of the goals of the cognitive communications project has been to find applications for such algorithms to next-generation communication systems. The goal of this effort is to identify areas and approaches to intelligent system design and implementation which could allow NASA to support a larger space-and ground-based network while simultaneously reducing the operational costs involved with maintaining such a system This paper will evaluate the state of various approaches by searching for algorithms which are feasible to deploy directly onto future space systems with improved processing requirements. We begin by describing a set of heuristics through which algorithms may be compared, emphasizing memory and computational requirements, and heuristic bounds. We then evaluate general-purpose processing platforms onto which such algorithms may be deployed. We also evaluate how such systems may be packaged so as to offer a deterministic set of performance and decision metrics, to make the devices easier for system designers to include in present and future systems. We conclude the paper with a discussion of our findings, as well as where and how this study might continue in the future.

Metasurface is a revolutionary platform to achieve desired properties by artificially engineering meta‐atom's arrangements. However, the explosively expanding design space of advanced metasurfaces with multiple degrees of freedom (MDOF) has made the traditional human‐guided design methods increasingly ineffective, limiting the development of the metasurfaces. Intelligent design methods have been presented to tackle these challenges by introducing innovative computational models, but they are predominantly data‐driven and faced the issues of data scarcity, poor physical interpretability, and weak generalization capability. Here, a physics‐driven intelligent design (PDID) paradigm is proposed and demonstrates its application to design MDOF multiplexed metasurfaces. The PDID method integrates the physical prior knowledge into a deep neural network, thereby enhancing its physical interpretability and reducing its reliance on extensive databases. Compared to the traditional intelligent designs, this can reduce both design time and database size by two orders of magnitude. Through experimental validation of MDOF multiplexed metasurfaces, the versatility and computational efficiency of PDID are showed. This method not only presents a novel intelligent design tool but also exemplifies the integration of physical knowledge with machine learning to address the challenges. Its interdisciplinary insights offer significant potentials for innovative applications across the materials science, computational science, and information technology.

This paper provides a detailed evaluation of existing methods and techniques for optimizing 3D printing parameters across different additive manufacturing technologies. The optimization of printing parameters is essential to obtain superior mechanical properties, dimensional accuracy, surface quality, and production efficiency in 3D printed components. The review systematically examines methodological approaches, including statistical design of experiments (DOE), artificial intelligence and machine learning (AI/ML) applications, simulation-based optimization, and in-process monitoring and feedback control. The review focuses on material-specific optimization methods for polymers, metals, composites, and ceramics by analyzing process-specific parameters and material-dependent variables. The paper demonstrates real-world applications of optimization methodologies through case studies from aerospace, automotive, medical, and consumer goods manufacturing industries, which show substantial improvements in product performance, production speed, and cost-effectiveness.

The negotiation of stakeholder values as a collaborative process throughout technology development has been studied extensively within the fields of Computer Supported Cooperative Work and Human-Computer Interaction. Despite their increasing significance for cybersecurity incident response, there is a gap in research on values of importance to the design of open-source intelligence (OSINT) technologies for this purpose. In this paper, we investigate which values and value conflicts emerge due to the application and development of machine learning (ML) based OSINT technologies to assist cyber security incident response operators. For this purpose, we employ a triangulation of methods, consisting of a systematic survey of the technical literature on the development of OSINT artefacts for cybersecurity (N = 73) and an empirical value sensitive design case study, comprising semi-structured interviews with stakeholders (N = 9) as well as a focus group (N = 7) with developers. Based on our results, we identify implications relevant to the research on and design of OSINT artefacts for cybersecurity incident response.

Understanding the built environment is essential to the overall study of population dynamics, grid infrastructure, emergency response, among others. In the United States there are multiple classifications for buildings within the built environment such as residential, signifying family homes while commercial buildings consist of apartments or larger structures which are multi-purpose. While there is a high level of understanding of where these aforementioned structures are located, there is a third class of structures, mobile home parks (MHP) which have been under-represented in the literature despite there being an estimated 2.7 million of them within the United States. Research has shown that individuals who reside in MHP are at higher risk to extreme events due to their location and structural integrity of residence. Attention must now turn to identifying MHP at scale to help first responders and policy makers understand where these at risk populations reside. To address for this gap, we develop a novel methodology to infer MHP at scale based off morphologies derived at a building level. We show that across 3 million buildings in 6 states within the United States it is possible to identify MHP with 83% accuracy. This novel approach to identify MHP from other structures within the built environment using a machine learning approach provides a new tool to leverage in relation to helping at-risk populations.