Nuclear infrastructure systems play an important role in national security. The functions and missions of nuclear infrastructure systems are vital to government, businesses, society and citizen's lives. It is crucial to design nuclear infrastructure for scalability, reliability and robustness. To do this, we can use machine learning, which is a state of the art technology used in various fields ranging from voice recognition, Internet of Things (IoT) device management and autonomous vehicles. In this paper, we propose to design and develop a machine learning algorithm to perform predictive maintenance of nuclear infrastructure. Support vector machine and logistic regression algorithms will be used to perform the prediction. These machine learning techniques have been used to explore and compare rare events that could occur in nuclear infrastructure. As per our literature review, support vector machines provide better performance metrics. In this paper, we have performed parameter optimization for both algorithms mentioned. Existing research has been done in conditions with a great volume of data, but this paper presents a novel approach to correlate nuclear infrastructure data samples where the density of probability is very low. This paper also identifies the respective motivations and distinguishes between benefits and drawbacks of the selected machine learning algorithms.

Predicting the intraday stock jumps is a significant but challenging problem in finance. Due to the instantaneity and imperceptibility characteristics of intraday stock jumps, relevant studies on their predictability remain limited. This paper proposes a data-driven approach to predict intraday stock jumps using the information embedded in liquidity measures and technical indicators. Specifically, a trading day is divided into a series of 5-minute intervals, and at the end of each interval, the candidate attributes defined by liquidity measures and technical indicators are input into machine learning algorithms to predict the arrival of a stock jump as well as its direction in the following 5-minute interval. Empirical study is conducted on the level-2 high-frequency data of 1271 stocks in the Shenzhen Stock Exchange of China to validate our approach. The result provides initial evidence of the predictability of jump arrivals and jump directions using level-2 stock data as well as the effectiveness of using a combination of liquidity measures and technical indicators in this prediction. We also reveal the superiority of using random forest compared to other machine learning algorithms in building prediction models. Importantly, our study provides a portable data-driven approach that exploits liquidity and technical information from level-2 stock data to predict intraday price jumps of individual stocks.

Artificial intelligence (AI) for the purpose of this review is an umbrella term for technologies emulating a nephropathologist’s ability to extract information on diagnosis, prognosis, and therapy responsiveness from native or transplant kidney biopsies. Although AI can be used to analyze a wide variety of biopsy-related data, this review focuses on whole slide images traditionally used in nephropathology. AI applications in nephropathology have recently become available through several advancing technologies, including (i) widespread introduction of glass slide scanners, (ii) data servers in pathology departments worldwide, and (iii) through greatly improved computer hardware to enable AI training. In this review, we explain how AI can enhance the reproducibility of nephropathology results for certain parameters in the context of precision medicine using advanced architectures, such as convolutional neural networks, that are currently the state of the art in machine learning software for this task. Because AI applications in nephropathology are still in their infancy, we show the power and potential of AI applications mostly in the example of oncopathology. Moreover, we discuss the technological obstacles as well as the current stakeholder and regulatory concerns about developing AI applications in nephropathology from the perspective of nephropathologists and the wider nephrology community. We expect the gradual introduction of these technologies into routine diagnostics and research for selective tasks, suggesting that this technology will enhance the performance of nephropathologists rather than making them redundant.

We introduce an ensemble learning scheme for community detection in complex networks. The scheme uses a Machine Learning algorithmic paradigm we call Extremal Ensemble Learning. It uses iterative extremal updating of an ensemble of network partitions, which can be found by a conventional base algorithm, to find a node partition that maximizes modularity. At each iteration, core groups of nodes that are in the same community in every ensemble partition are identified and used to form a reduced network. Partitions of the reduced network are then found and used to update the ensemble. The smaller size of the reduced network makes the scheme efficient. We use the scheme to analyze the community structure in a set of commonly studied benchmark networks and find that it outperforms all other known methods for finding the partition with maximum modularity.

Understanding neural activity patterns in the developing brain remains one of the grand challenges in neuroscience. Developing neural networks are likely to be endowed with functionally important variability associated with the environmental context, age, gender, and other variables. Therefore, we conducted experiments with typically developing children in a stimulating museum setting and tested the feasibility of using deep learning techniques to help identify patterns of brain activity associated with different conditions. Approach. A four-channel dry EEG-based Mobile brain-body imaging data of children at rest and during videogame play (VGP) was acquired at the Children's Museum of Houston. A data-driven approach based on convolutional neural networks (CNN) was used to describe underlying feature representations in the EEG and their ability to discern task and gender. The variability of the spectral features of EEG during the rest condition as a function of age was also analyzed. Main results. Alpha power (7–13 Hz) was higher during rest whereas theta power (4–7 Hz) was higher during VGP. Beta (13–18 Hz) power was the most significant feature, higher in females, when differentiating between males and females. Using data from both temporoparietal channels to classify between VGP and rest condition, leave-one-subject-out cross-validation accuracy of 67% was obtained. Age-related changes in EEG spectral content during rest were consistent with previous developmental studies conducted in laboratory settings showing an inverse relationship between age and EEG power. Significance. These findings are the first to acquire, quantify and explain brain patterns observed during VGP and rest in freely behaving children in a museum setting using a deep learning framework. The study shows how deep learning can be used as a data driven approach to identify patterns in the data and explores the issues and the potential of conducting experiments involving children in a natural and engaging environment.

This interdisciplinary book is written for government and industry professionals who need a comprehensive, accessible guide to modern energy security. Introducing the ten predominant energy types, both renewable and non-renewable, the book illustrates the modern energy landscape from a geopolitical, commercial, economic and technological perspective. Energy is presented as the powerhouse of global economic activities.

Industrial Control System (ICS) networks face novel challenges in risk management, feature agility, and deployment flexibility. Essential hardware control systems may have a lifetime of decades while the need for business features and the network security landscape evolve on a daily basis. Even the mix of common protocols for network connectivity is likely to undergo significant market disruption over the 50+ year lifetime of a large industrial complex. Given this reality, the University of Houston Networking Lab [4] has embarked upon an effort, facilitated by the Department of Energy CREDC[2] program, to decouple the long development cycles of hardened industrial equipment from the ever-changing realities of both the local and wide-area networks they must use to transport essential sensor data and control messages.

Accurate and cost-effective seizure severity trackingis an important step towards limiting the negative effects ofseizures in epileptic patients. Electroencephalography (EEG)is employed as a means to track seizures due to its hightemporal resolution. In this research, seizure state detection wasperformed using a mixed-filter approach to reduce the numberof channels. We first found two optimized EEG features (onebinary, one continuous) using wrapper feature selection. Thisfeature selection process reduces the number of required EEGchannels to two, making the process more practical and cost-effective. These continuous and binary observations were used ina state-space framework which allows us to model the continuoushidden seizure severity state. Expectation maximization wasemployed offline on the training and validation data-sets toestimate unknown parameters. The estimated model parameterswere used for real-time seizure state tracking. A classifier wasthen used to binarize the continuous seizure state. Our resultson the experimental data (CHB-MIT EEG database) validate theaccuracy of our proposed method and illustrate that the averageaccuracy, sensitivity, and false positive rate are85.8%,91.5%,and14.3%respectively. This type of seizure state modeling couldbe used in further implementation of adaptive closed-loop vagusnerve stimulation applications.

Examining sea, land, and air transportation systems and linkages, Logistics and Transportation Security: A Strategic, Tactical, and Operational Guide to Resilience provides thorough coverage of transportation security. Its topics include hazardous material handling, securing transportation networks, logistics essentials, supply chain security, risk assessment, the regulatory framework, strategic planning, and innovation through technology.

With 80 percent of the world’s commodities being transported by water, ports are the pillars of the global economy. Port Management and Operations offers readers the opportunity to enhance their strategic thinking and problem-solving skills, while developing market foresight. It examines global port management practices at the regulatory, commercial, technological, operational, financial, and sociopolitical levels.