Potential Anomalies Research Articles

Artificial Intelligence Driven Nuclear Reactor Instrumentation and Control Measurements In-core and Out-core in Nuclear Power Plants

The use of Artificial Intelligence (AI) in nuclear in-core instrumentation and control has the potential to improve the safety and efficiency of nuclear power plants. In-core instrumentation refers to the sensors and other measurement devices that are used to monitor the conditions inside the nuclear reactor core, such as temperature, pressure, and neutron flux. These measurements are used to control the operation of the reactor and ensure that it is operating within safe limits from Probabilistic Risk Assessment (PRA) point of view. One way that AI can be used in nuclear in-core and out-core Instrumentation and Control (I&C) as well as Instrumentation and Measurement (I&M) is by analyzing the data from these sensors in real-time and using machine learning algorithms to identify patterns and trends. This can help operators to detect potential problems or anomalies before they become critical, allowing them to take proactive measures to prevent accidents or malfunctions. AI can also be used to optimize the operation of the reactor by analyzing data from past operations and using this information to develop more efficient control strategies. For example, AI algorithms could be used to identify the most effective combination of control parameters to maintain a stable and safe reactor operation, while minimizing the use of fuel and other resources. Overall, the use of AI in nuclear In-core instrumentation and control has the potential to improve the safety, efficiency, and reliability of nuclear power plants. As AI technology continues to advance, we can expect to see more widespread adoption of these technologies in the nuclear industry

Unfavorable geology recognition in front of shallow tunnel face using machine learning

Subsoil profile mapping is typically based on spatially discrete borehole logs obtained from site geotechnical investigations. During the mapping, soil information between two boreholes is indeed unknown and must be interpolated. The interpolation is not always able to identify geological anomalies, posing additional risks to tunneling. To supplement this need, this study develops a time-efficient pattern recognition framework for detecting potential geological anomalies ahead of the tunnel face during the advancement of Tunnel Boring Machine (TBM). Three basic unfavorable geology scenarios are defined, including a soil type change with varying inclination, an interlayer, and a boulder. A series of three-dimensional finite element shallow tunneling models are built to generate synthetic spatial and temporal data of ground settlements and pore water pressures during tunneling with respect to the three basic scenarios. Three machine learning methods, i.e., random forest (RF), support vector machine (SVM), and artificial neural network (ANN) are developed to recognize the geological anomalies using inputs comprised of the synthetic data combined with Gaussian noise that is added to enhance regularization and model robustness. The result shows that the proposed pattern recognition approach exhibits a powerful capability in classifying the unfavorable geology as early as 4.5-8D (D represents tunnel diameter) ahead of the target anomalies. Finally, the efficiency and effectiveness of the proposed pattern recognition framework are demonstrated in a real tunneling project. This study is of value in the mitigation of tunneling risks.

Multiplicative Attacks with Essential Stealthiness in Sensor and Actuator Loops against Cyber-Physical Systems.

Stealthy attacks in sensor and actuator loops are the research priorities in the security of cyber-physical systems. Existing attacks define the stealthiness conditions against the Chi-square or Kullback-Leibler divergence detectors and parameterize the attack model based on additive signals. Such conditions ignore the potential anomalies of the vulnerable outputs in the control layer, and the attack sequences need to be generated online, increasing the hardware and software costs. This paper investigates a type of multiplicative attack with essential stealthiness where the employed model is a novel form. The advantage is that the parameters can be designed in a constant form without having to be generated online. An essential stealthiness condition is proposed for the first time and complements the existing ones. Two sufficient conditions for the existence of constant attack matrices are given in the form of theorems, where two methods for decoupling the unknown variables are particularly considered. A quadruple-tank process, an experimental platform for attack and defense, is developed to verify the theoretical results. The experiments indicate that the proposed attack strategy can fulfill both the attack performance and stealthiness conditions.

ECG Classification Using an Optimal Temporal Convolutional Network for Remote Health Monitoring.

Increased life expectancy in most countries is a result of continuous improvements at all levels, starting from medicine and public health services, environmental and personal hygiene to the use of the most advanced technologies by healthcare providers. Despite these significant improvements, especially at the technological level in the last few decades, the overall access to healthcare services and medical facilities worldwide is not equally distributed. Indeed, the end beneficiary of these most advanced healthcare services and technologies on a daily basis are mostly residents of big cities, whereas the residents of rural areas, even in developed countries, have major difficulties accessing even basic medical services. This may lead to huge deficiencies in timely medical advice and assistance and may even cause death in some cases. Remote healthcare is considered a serious candidate for facilitating access to health services for all; thus, by using the most advanced technologies, providing at the same time high quality diagnosis and ease of implementation and use. ECG analysis and related cardiac diagnosis techniques are the basic healthcare methods providing rapid insights in potential health issues through simple visualization and interpretation by clinicians or by automatic detection of potential cardiac anomalies. In this paper, we propose a novel machine learning (ML) architecture for the ECG classification regarding five heart diseases based on temporal convolution networks (TCN). The proposed design, which implements a dilated causal one-dimensional convolution on the input heartbeat signals, seems to be outperforming all existing ML methods with an accuracy of 96.12% and an F1 score of 84.13%, using a reduced number of parameters (10.2 K). Such results make the proposed TCN architecture a good candidate for low power consumption hardware platforms, and thus its potential use in low cost embedded devices for remote health monitoring.

Wavelet Entropy: A New Tool for Edge Detection of Potential Field Data

Subsurface source boundary identification is a major step in the interpretation of potential field anomalies in geophysical exploration. We investigated the behavior of wavelet space entropy over the boundaries of 2D potential field source edges. We tested the robustness of the method for complex source geometries with distinct source parameters of prismatic bodies. We further validated the behavior with two datasets by delineating the edges of (i) the magnetic anomalies due to the popular Bishop model and (ii) the gravity anomalies of the Delhi fold belt region, India. The results showed prominent signatures for the geological boundaries. Our findings indicate sharp changes in the wavelet space entropy values corresponding to the source edges. The effectiveness of wavelet space entropy was compared with the established edge detection techniques. The findings can help with a variety of geophysical source characterization problems.

New Guidance in South Africa on the Definition of “Associated Enterprises” in Connection with the Transfer Pricing Rules

This article deals with a draft interpretation note entitled Definition of “Associated Enterprise” issued by the South African Revenue Service on 14 October 2022. The draft note provides guidance on the interpretation and application of the definition of “associated enterprise” which is to be included in section 31(1) of the Income Tax Law. The purpose of this inclusion is to bring the legislation in line with international standards and to address a potential anomaly and reduction in the tax base created by the exclusion of “associated enterprises” from section 31. A de facto approach is adopted in regard to the concepts “manage”, “control” and “capital”. The potential consequences of this are briefly dealt with.

Scrutinizing and rooting the multiple anomalies of Nepal earthquake sequence in 2015 with the deviation–time–space criterion and homologous lithosphere–coversphere–atmosphere–ionosphere coupling physics

Abstract. The continuous increasing of Earth observations benefits geosciences and seismicity study but increases greatly the difficulties in understanding and discriminating multiple source data. Although the lithosphere–coversphere–atmosphere-ionosphere (LCAI) coupling paradigm and the deviation–time–space (DTS) criterion were presented for better searching for and understanding the potential seismic anomalies from multiple observations, the strict consistency of spatiotemporal characteristics and homologous physics of multiple-parameter seismic anomalies has not been investigated sufficiently. With the 2015 Nepal earthquake sequence being a typical case, the reported multi-parameter anomalies were systematically reviewed, and their space–time characteristics were summarized thoroughly in this study. Numerical simulation with refined geological structures in three-dimensional space revealed the inhomogeneous crustal stress field alteration (CSFA) along the faults and around the hypocenters of the 2015 Nepal earthquake sequence, which is expected to be the root of the seismic anomalies. The stress-activated positive charge carriers would have given rise to different responses near the ground surface (coversphere), including the microwave dielectric reduction, the additional infrared radiation, and the atmospheric ionization, which subsequently affected the physical properties of the atmosphere and the ionosphere and resulted in abnormal phenomena therein. Based on the DTS criterion and LCAI coupling paradigm, the seismic anomalies of the 2015 Nepal earthquakes were scrutinized strictly, and the retained anomalies were rooted carefully to the regional CSFA as well as its local blocking. Therefore, an integrated LCAI coupling framework with strict space–time correspondence and homologous physics in CSFA was proposed for the 2015 Nepal earthquake sequence. This research provides a definite philosophy as well as a practical solution for scrutinizing the rootable seismic anomalies from multi-parameter observations of earthquakes, which is of scientific meanings for searching earthquake precursors and reaching earthquake prediction.

West Africa's moist convective environment as observed by the Atmospheric InfraRed Sounder (AIRS)

AbstractKnowledge of the seasonal positioning of the Intertropical Discontinuity (ITD) is critical to understanding seasonal moist convective processes and associated rainfall over West Africa. This study constitutes a new analysis of the seasonality of moist convection over West Africa, relative to the ITD, based on NASA's Atmospheric Infrared Sounder (AIRS) measurements from 2003 to 2018. Results show that AIRS resolves the seasonal march of the ITD, including its inherent diurnal‐scale variations. AIRS captures the north–south daytime skin temperature dipole around the ITD, with greater relative temperatures to the north, especially during March–August. In the vicinity of the nighttime ITD, AIRS profiles indicate increased instability that is characteristic of nocturnal thunderstorm propagation. For seven Ghana weather stations, we show that AIRS positive moisture and equivalent potential temperature anomalies coincide with observed thunderstorm days. On these thunderstorm days, the mean latitude of the AIRS‐derived ITD is displaced 3°, 0.2°, and 2° north of its DJF, MAM, and SON climatological positions, respectively, and 1.2° south in JJA. Among four common thunderstorm initiation indices considered, the K‐index is determined to be most skillful. The findings of this study contribute to the Global Challenges Research Fund, African Science for Weather Information and Forecasting Techniques project's mission to build local tropical weather forecasting capacity and capabilities in West Africa.

The Impact of Tidal Straining and Advection on the Stratification in a Partially Mixed Estuary

Stratification and mixing of the water column is an important dynamic process in the estuary, which plays a significant role in the estuarine circulation, mass transport and energy exchange. Based on the multi-station synchronous observation data from 26 February to 6 March in 2011 during dry season in the North Channel of the Changjiang Estuary, the Richardson number, the Simpson number and the potential energy anomaly of water were calculated to analyze the tidal variation of the mixing and stratification processes. The roles of the depth-mean straining, longitudinal advection, non-mean straining and tidal stirring in the processes of mixing and stratification of the water column were analyzed by calculating the contribution terms of the time-derivative of potential energy anomaly. The results show that the mixing and stratification of the water column in the North Channel have significant spatiotemporal variation. Stability of the stratification gradually decreases from neap tide to spring tide. In the reaches of salt wedge migration, permanent stratification develops during neap and mean tide, with stability increasing on the flood and decreasing on the ebb, which is dominated by longitudinal advection. During spring tide, periodic stratification develops, with development of stratification on the flood and its breakdown on the ebb, which is dominated by longitudinal advection and tidal stirring. In the main reaches of saltwater intrusion, permanent stratification develops during neap tide, with stability increasing on the ebb and decreasing on the flood, which is dominated by depth-mean tidal straining. During mean and spring tide, periodic stratification occurs, with development of stratification on the ebb and its breakdown on the flood, which is controlled by depth-mean tidal straining and assisted by tidal stirring. In the North Channel, tidal advection is the main stratifying agent in the salt wedge migration reaches, and tidal straining is the main stratifying agent in the main reaches of saltwater intrusion.

CXray-EffDet: Chest Disease Detection and Classification from X-ray Images Using the EfficientDet Model.

The competence of machine learning approaches to carry out clinical expertise tasks has recently gained a lot of attention, particularly in the field of medical-imaging examination. Among the most frequently used clinical-imaging modalities in the healthcare profession is chest radiography, which calls for prompt reporting of the existence of potential anomalies and illness diagnostics in images. Automated frameworks for the recognition of chest abnormalities employing X-rays are being introduced in health departments. However, the reliable detection and classification of particular illnesses in chest X-ray samples is still a complicated issue because of the complex structure of radiographs, e.g., the large exposure dynamic range. Moreover, the incidence of various image artifacts and extensive inter- and intra-category resemblances further increases the difficulty of chest disease recognition procedures. The aim of this study was to resolve these existing problems. We propose a deep learning (DL) approach to the detection of chest abnormalities with the X-ray modality using the EfficientDet (CXray-EffDet) model. More clearly, we employed the EfficientNet-B0-based EfficientDet-D0 model to compute a reliable set of sample features and accomplish the detection and classification task by categorizing eight categories of chest abnormalities using X-ray images. The effective feature computation power of the CXray-EffDet model enhances the power of chest abnormality recognition due to its high recall rate, and it presents a lightweight and computationally robust approach. A large test of the model employing a standard database from the National Institutes of Health (NIH) was conducted to demonstrate the chest disease localization and categorization performance of the CXray-EffDet model. We attained an AUC score of 0.9080, along with an IOU of 0.834, which clearly determines the competency of the introduced model.

Listening to what the system tells us: Innovative auditing for distributed systems

IntroductionIn recent years, software ecosystems have become more complex with the proliferation of distributed systems such as blockchains and distributed ledgers. Effective management of these systems requires constant monitoring to identify any potential malfunctions, anomalies, vulnerabilities, or attacks. Traditional log auditing methods can effectively monitor the health of conventional systems. Yet, they run short of handling the higher levels of complexity of distributed systems. This study aims to propose an innovative architecture for system auditing that can effectively manage the complexity of distributed systems using advanced data analytics, natural language processing, and artificial intelligence.MethodsTo develop this architecture, we considered the unique characteristics of distributed systems and the various signals that may arise within them. We also felt the need for flexibility to capture these signals effectively. The resulting architecture utilizes advanced data analytics, natural language processing, and artificial intelligence to analyze and interpret the various signals emitted by the system.ResultsWe have implemented this architecture in the DELTA (Distributed Elastic Log Text Analyzer) auditing tool and applied it to the Hyperledger Fabric platform, a widely used implementation of private blockchains.DiscussionThe proposed architecture for system auditing can effectively handle the complexity of distributed systems, and the DELTA tool provides a practical implementation of this approach. Further research could explore this approach's potential applications and effectiveness in other distributed systems.

The security mindset: characteristics, development, and consequences

Abstract The world is facing a cybersecurity skills gap as cybercrime and cyberwarfare grow in importance. One often-discussed quality that is potentially relevant to cybersecurity recruitment and education is the so-called “security mindset”: a way of thinking characteristic of some security professionals that they believe to be especially advantageous in their work. Although some employers express a desire to hire people with a security mindset, and initiatives to cultivate the security mindset are being implemented, it has no common definition and little is known about its characteristics, its development, and its consequences. We interviewed 21 cybersecurity professionals who strongly identified as having a security mindset based on a minimal description drawn from existing literature. Thematic analysis of the interview data suggests that the security mindset can be conceptualized as consisting of three interconnected aspects—“monitoring” for potential security anomalies, “investigating” anomalies more deeply to identify security flaws, and “evaluating” the relevance of those flaws in a larger context. These three aspects develop in different ways and have different personal and professional consequences. Participants mostly spoke positively of the security mindset, but they also mentioned several disadvantages not mentioned by existing security-mindset literature, such as mental health pressures, workplace tensions, and negative effects on personal relationships. We discuss the implications of these findings for future study of the security mindset and suggest practical implications for cybersecurity management, education, and recruitment.

Institutionalization of Zakat and Waqf in Contemporary Economies

Zakat and Waqf are important social finance institutions, but their utilization and impact are less than optimal compared to the expectation and potential. Part of the reason is weak governance and enforcement. Another notable reason is the complexity of applying old jurisprudential axioms to new modes of production, income, and forms of wealth. Therefore, both jurisprudence related to the institutionalization of Zakat and Waqf and their current governance needs a fresh revisit given the changes in the type of production, the process of production, market structure, forms of business organization, income distribution, storable wealth forms and the functions of a state. The paper highlights several potential anomalies in applying the traditional understanding of institutionalizing Zakat in the context of modern economy, if the implications of choosing a particular way of interpretation among different sets of interpretations are not considered. Thus, serious deliberations are needed on challenges involved and how to overcome them and this paper adds to that dialogue.

Seabird abundances projected to decline in response to climate change in Britain and Ireland

Estimating species’ vulnerability to climate change is a key challenge in conservation. Many seabird species are particularly sensitive to the negative effects of warming and are thus threatened by climate change. We projected seabird abundance in 2050 in Britain and Ireland, an internationally important region for seabirds, under climate change scenario RCP8.5. Our projections were based on fitted relationships between abundance and both climatic (air temperature, precipitation) and oceanographic (sea surface temperature, potential energy anomaly) variables, estimated using a Bayesian hurdle model with spatial random effects. The most consistent responses to environmental variables were negative relationships between species presence or abundance and breeding season maximum terrestrial air temperature. Although data and modelling issues meant that future projections could be made for only 15 of the 25 species and with low predictive power for spatial abundance, predictive power was high for spatial presence and moderate for temporal abundance change. Projections suggest that seabirds will generally fare poorly under climate change in Britain and Ireland, with 10 species projected to decline in abundance by 2050. Variable model fit and projection uncertainty limited confidence, which was generally greater for declines than for increases. Fulmar, puffin and Arctic tern were each projected to decline by over 70% with higher confidence. For a few species, colonisation of new areas may reduce projected losses, but this is potentially limited by low availability of suitable habitat. Projected abundance change was more negative for marine specialists than for generalists. Our findings highlight the vulnerability and conservation needs of seabirds in a changing climate.

Ontology-based Solution for Handling Safety and Cybersecurity Interdependency in NFV Safety Architecture

For safety-critical systems based on Network Function Virtualization (NFV) technology both safety and cybersecurity need to be taken into account. In case these systems face an anomaly (either intentional or not), they can have an impact on humans and environment. Thus, it is understood that safety and cybersecurity affect each other and so they need to be considered as interdependent. For this reason, an ontology-based solution for safety is needed, through which it is possible to handle the safety and cybersecurity interdependency since ontologies describe the whole knowledge of a domain. Thus, we propose a new safety ontology for NFV framework which is able to cover reliability, availability, maintainability, and integrity-related breakdown types. This is because these properties interact and influence safety according to ENISA. Our ontology allows us to have a uniformized representation of the potential anomalies that a system and its elements can face. Based on this representation, it is also possible to have a decision-making process to avoid potential conflicts between safety and cybersecurity, in order to best handle their interdependency. According to the results of our implementation, it is shown that our ontology handles the safety and cybersecurity interdependency, and has little impact on decision-making time, which makes it an effective methodology for NFV framework.

Energy consumption mode identification and monitoring method of process industry system under unstable working conditions

Process industry systems under unstable working conditions are prone to potential anomalies, deviating from the original transition trajectory, and taking longer than expected to return to stability due to persistent disturbances from uncertainties and experience-based regulation errors. The energy waste caused by this situation has not received sufficient attention, and cannot be addressed by existing energy consumption monitoring methods. Herein, an energy consumption mode (ECM) identification and monitoring method under unstable working conditions is proposed, consisting of ECM identification model and multi-mode dynamic monitoring model, focusing on the variation rules of the correlation between energy consumption and other states of the system. In the ECM identification stage, the ECM correlation parameters that reflect the comprehensive production information are selected. Then, given the transfer characteristics of ECM, a Hidden Semi-Markov Model (HSMM) is constructed to fit the migration between modes and the duration within modes. The Variational Bayesian Gaussian Mixture Model is introduced to improve the HSMM, which solves the problem of lacking prior knowledge of ECM and achieves the automatic classification and online identification of ECM. In the dynamic monitoring stage of multi-ECMs, a series of dynamic kernel principle component analysis models are established, and the corresponding monitoring thresholds are set for each ECM. By calculating the maximum of the posteriori probability and the mode thresholds, the ECMs under unstable conditions can be accurately identified and automatically monitored. Compared with previous methods, the proposed method reduces the false detection rate and missed detection rate of abnormal ECM identification to 1.04% and 1.31% in the actual slag grinding production process, which proves its effectiveness.

Unveiling the Veiled: Leveraging Deep Learning and Network Analysis for De-Anonymization in Social Networks

Online anonymity allows individuals to safeguard their personal information. It provides the freedom for anyone to express themselves without being concerned about censorship, discrimination, or retaliation. This pseudonym also provides opportunities for individuals to promote open discourse and diverse viewpoints. However, in today's digital age, this online anonymity has become a growing concern. Although the safeguarding of people’s rights, the advancement of free speech, and the development of a more diverse and democratic online community all depend heavily on the anonymity of online users, there are risks as well, such as cyberbullying, harassment, and the propagation of false information. Because of their anonymity, predators, groomers, and other unscrupulous individuals may be able to take advantage of vulnerable individuals, especially children and adolescents. In order to trick victims into hazardous or abusive situations, adversaries can hide their identities. Deep learning and network analysis can be used to reveal the true identities of anonymous social media users in order to combat this. Deep learning algorithms are capable of analyzing a wide range of social network data, including user behavior, relationships, and content, in order to find patterns and correlations that can lead to the true identities of users that go anonymous. The proposed system includes examining network topology and group dynamics to identify potential anomalies and connections that could lead to de-anonymization. This paper proposes a novel module for de-anonymization integrating deep learning and network analysis in social networks.

Anomaly detection and community detection in networks

Anomaly detection is a relevant problem in the area of data analysis. In networked systems, where individual entities interact in pairs, anomalies are observed when pattern of interactions deviates from patterns considered regular. Properly defining what regular patterns entail relies on developing expressive models for describing the observed interactions. It is crucial to address anomaly detection in networks. Among the many well-known models for networks, latent variable models—a class of probabilistic models—offer promising tools to capture the intrinsic features of the data. In this work, we propose a probabilistic generative approach that incorporates domain knowledge, i.e., community membership, as a fundamental model for regular behavior, and thus flags potential anomalies deviating from this pattern. In fact, community membership serves as the building block of a null model to identify the regular interaction patterns. The structural information is included in the model through latent variables for community membership and anomaly parameter. The algorithm aims at inferring these latent parameters and then output the labels identifying anomalies on the network edges.

The Beta Anomaly and Mutual Fund Performance

We find evidence for the beta anomaly in mutual fund performance. This anomaly is not accounted for in the standard four-factor framework, nor by the addition of a betting-against-beta factor to the benchmark model. We identify the active component of alpha (active alpha) not attributable to the passive effects related to beta. Active alpha is persistent and associated with superior portfolio performance. We find that, although many investors use standard alpha to allocate capital, a subset of sophisticated investors allocate their money based on active alpha. Our procedure is useful across the commonly used benchmark models for measuring performance and can be extended to accommodate other potential factor beta anomalies. This paper was accepted by Karl Diether, finance. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022.4639 .

Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system

To support the reliable and resilient operation of modular reactors and microreactors, anticipatory control strategies have been proposed for achieving faster-than-real-time predictions and decision-making capabilities in anticipation of potential anomalies, including setpoint changes and cyber incidents. This work presents how anticipatory control strategies can be implemented via model predictive control (MPC) for a single heat pipe's temperature. Considering the uncertainty in developing and applying MPC, this work evaluates MPC performance given three different model forms: a linear response surface model, an artificial neural network (ANN), and an autoregressive model with exogenous input (ARX). This work also evaluates the impacts of different input biases and variance on MPC performance in order to account for potential sensor reading variations due to cyber incidents. We observe that the ANN and ARX result in more fluctuated control actions compared to the MPC with linear response surface model. However, when the cyber incidents are of large magnitudes, the linear response surface model produces smaller feasible regions than the ANN and ARX models under identical constraints.