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An artificial immune system (AIS) for the detection and identification of abnormal operational conditions affecting an unmanned air vehicle (UAV) is developed using the partition of the universe approach. The performance of the proposed methodology is assessed through simulation within the West Virginia University (WVU) unmanned aerial system (UAS) simulation environment.

An AIS is designed and generated for a fixed wing UAV using data from the WVU UAS simulator. A novel partition of the universe approach augmented with the hierarchical multiself strategy is used to define the self, within the AIS paradigm. Several 2-dimensional and 3-dimensional commanded trajectories are simulated under normal and abnormal conditions affecting actuators and sensors. Data recorded are used to build the AIS and develop an abnormal condition detection and identification scheme for the two categories of subsystems. The performance of the methodology is evaluated in terms of detection and identification rates, false alarms and decision times.

The proposed methodology for UAV abnormal condition detection and identification has the potential to support a comprehensive and integrated solution to the problem of aircraft subsystem health management. The novel partition of the universe approach has been proven to be a promising alternative to the previously investigated clustering methods by providing similar or better performance for the cases investigated.

The promising results obtained within this research effort motivate further investigation and extension of the proposed methodology toward a complete system health management process, including abnormal condition evaluation and accommodation.

The use of the partition of the universe approach for AIS generation may potentially represent a valuable alternative to current clustering methods within the AIS paradigm. It can facilitate a simpler and faster implementation of abnormal condition detection and identification schemes.

In this paper, a novel method for AIS generation, the partition of the universe approach, is formulated and applied for the first time for the development of abnormal condition detection and identification schemes for UAVs. This approach is computationally less expensive and mitigates some of the issues related to the typical clustering approaches. The implementation of the proposed approach can potentially enhance the robustness of UAS for safety purposes.

This study examines the Gamestop (GME) short squeeze in early 2021. Using intraday data for the period 4/1/2021–5/2/2021, the author provides empirical evidence that the GME stock price exhibited abnormal behavior.

The author uses the popular Runs test to show that the GME returns were not randomly distributed, which is an indication of a violation of the Efficient Market Hypothesis (EMH). The main objective of the paper is to provide new quantitative evidence that stock returns are abnormal when short squeeze conditions emerge. The author employs the asymmetry Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) models (the Exponential GARCH (EGARCH) and the Threshold GARCH (TGARCH)) and provides evidence that an exceptional time series feature emerged during the examined period: the antileverage effect.

The results show that the GME returns were not randomly distributed during the examined period and the asymmetry GARCH models indicate that, in contrast to what the time series normally show, volatility increased when the GME prices increased.

This paper presents a new/alternative approach for the study of EMH and abnormal returns in financial markets. Further studies on market performance during similar short squeeze conditions should be carried out in order to obtain empirical evidence for the antileverage effect abnormality.

This paper could be useful for scholars who examine the EMH in financial markets because it suggests an additional method for testing abnormalities. It also presents a useful tool that allows practitioners to monitor for indications of abnormality in the stock market during a short squeeze, since the emergence of the antileverage abnormality could function as such an indication. Additionally, the outcome of this analysis could be useful for regulators because coordination among investors is easier than ever in the Internet era and such events may happen again in the future; even under normal (not short squeeze) conditions and lead to market instability.

This research differs from other studies that examine the GME case because it presents a new way to quantitatively present the abnormal performance of the stock markets for reasons that could be linked with the emergence of short squeeze conditions.

In treating both sewage and storm runoff, wastewater treatment plants are important to maintaining a healthy environment. If the plant operations managers do not respond correctly to plant conditions, environmental damage resulting in the deterioration of human health may be the result. Unfortunately, there are no formal models to help these managers; they rely upon their own intuition to manage the plants. The purpose of this paper is to investigate the effectiveness of various models, originally used for manufacturing, to detect process conditions in wastewater treatment facilities. We compare and contrast the performance of five statistical models and three neural network architectures. The data used in the research is 527 daily measurements of 38 sensor readings of the process state variables of an urban wastewater treatment plant.

This study aims to examine differences in regret between individuals with high vs low self-esteem that follows from negative appraisals for unsuccessful consumer decisions that are either congruent or not with perceived norms. This study also tested the mediating role of decision responsibility and the ability of psychological repair work in regulating regret.

Hypotheses were tested through four experimental studies using student and international panel samples across different consumer decision scenarios to generalize the findings of the study.

This study shows that high self-esteem individuals regret less a bad decision when it is congruent (normal) than when it is incongruent (abnormal) with the prevalent norms, while lower self-esteem individuals tend to regret equally both normal and abnormal decisions. This study further shows that this effect is driven by internal responsibility attributions. Finally, the results also suggest that high self-esteem people are more efficient than low self-esteem people in regulating regret, but only when the decision is abnormal.

The present research has important contributions to both regret and self-esteem literature. First, this study explored the role of self-esteem on regret, an individual variable that has been studied relatively little in regret literature. Second, this study has shown, consistent with recent findings, that decision congruence with the norms is a more suitable predictor of regret than whether the decision involves action or inaction. Finally, this study showed that stimulating individuals to self-enhance by engaging in psychological repair work led individuals to regulate regret, consistent with regret regulation theory.

The purpose of this paper is to design a unified operational design domain (ODD) monitoring framework for mitigating Safety of the Intended Functionality (SOTIF) risks triggered by vehicles exceeding ODD boundaries in complex traffic scenarios.

A unified model of ODD monitoring is constructed, which consists of three modules: weather condition monitoring for unusual weather conditions, such as rain, snow and fog; vehicle behavior monitoring for abnormal vehicle behavior, such as traffic rule violations; and road condition monitoring for abnormal road conditions, such as road defects, unexpected obstacles and slippery roads. Additionally, the applications of the proposed unified ODD monitoring framework are demonstrated. The practicability and effectiveness of the proposed unified ODD monitoring framework for mitigating SOTIF risk are verified in the applications.

First, the application of weather condition monitoring demonstrates that the autonomous vehicle can make a safe decision based on the performance degradation of Lidar on rainy days using the proposed monitoring framework. Second, the application of vehicle behavior monitoring demonstrates that the autonomous vehicle can properly adhere to traffic rules using the proposed monitoring framework. Third, the application of road condition monitoring demonstrates that the proposed unified ODD monitoring framework enables the ego vehicle to successfully monitor and avoid road defects.

The value of this paper is that the proposed unified ODD monitoring framework establishes a new foundation for monitoring and mitigating SOTIF risks in complex traffic environments.

Context and cultural condition given, cybernetic agency theory enables the anticipation of patterns of behaviour. However, this only occurs under “normal” conditions. Abnormal conditions occur when pathologies develop in the agency, especially within its Piagetian intelligences. An understanding of these pathologies, therefore, constitutes an appreciation of how abnormal behaviour develops. The paper aims to discuss these issues.

Different classifications of pathology are considered: autopathic and sociopathic, transitive and lateral pathologies, epistemological and ontological pathologies, within a system and outside system effects of pathologies. The effects of pathologies are inefficacy, loss of cohesion within a system, emerging neurosis, and not least corruption.

Within Agency Mindset Theory, four types of pathologies are identified: being detached from the cultural system, behaviour does not conform to extant values; an inhibited figurative intelligence is disturbing self-reference and resulting in incapability to learn cognitively; the operative system does not respond to strategic intentions: operative decision making is not anchored in ethical, ideological or strategic specifications of the social system; action and behaviour of the organisation are driven by outside interests.

This part of the research could only provide a framework for theoretically identifying the systemic roots of pathologies within social systems, but not provide an in-depth analysis of the shifts in values and practices, which accompany the emergence of pathologies.

The research is indicating that emergent pathologies and moves towards corruption could be either identified through underlying shifts in values and practices, but also through reduced functions (inefficacies) of the indispensable internal processes of an organisation (a social system), be it action-oriented or learning-oriented processes.

The paper draws on earlier work undertaken in the last few years by the same authors, who in a new way are pursuing new directions and extensions of that earlier research.

The safety and reliability of high-speed trains rely on the structural integrity of their components and the dynamic performance of the entire vehicle system. This paper aims to define and substantiate the assessment of the structural integrity and dynamical integrity of high-speed trains in both theory and practice. The key principles and approaches will be proposed, and their applications to high-speed trains in China will be presented.

First, the structural integrity and dynamical integrity of high-speed trains are defined, and their relationship is introduced. Then, the principles for assessing the structural integrity of structural and dynamical components are presented and practical examples of gearboxes and dampers are provided. Finally, the principles and approaches for assessing the dynamical integrity of high-speed trains are presented and a novel operational assessment method is further presented.

Vehicle system dynamics is the core of the proposed framework that provides the loads and vibrations on train components and the dynamic performance of the entire vehicle system. For assessing the structural integrity of structural components, an open-loop analysis considering both normal and abnormal vehicle conditions is needed. For assessing the structural integrity of dynamical components, a closed-loop analysis involving the influence of wear and degradation on vehicle system dynamics is needed. The analysis of vehicle system dynamics should follow the principles of complete objects, conditions and indices. Numerical, experimental and operational approaches should be combined to achieve effective assessments.

The practical applications demonstrate that assessing the structural integrity and dynamical integrity of high-speed trains can support better control of critical defects, better lifespan management of train components and better maintenance decision-making for high-speed trains.

The recent Covid-19 crisis has exposed the limitations of inventory leanness (i.e. keeping fewer inventories than expected), leading its followers to question whether it is the end of inventory leanness. This study aims to answer that question from a financial perspective.

This study considers 2019, 2020 and 2021 as the pre-, during- and post-Covid periods, respectively, and compares the financial performance and risks of firms that followed a lean inventory strategy (lean firms) to those that do not (non-lean firms). The sample is drawn from manufacturing firms in the USA, and the data are analyzed using univariate tools (such as a t-test) and multivariate regressions.

The results show that the financial performance of lean firms was better than that of non-lean firms under normal operating conditions in 2019, which continued to sustain during the crisis and post-crisis operating conditions in 2020 and 2021, respectively. Lean firms were also less risky than non-lean firms, except for in 2020, where they were equally risky.

A financial perspective suggests that managers of lean firms who might be thinking of changing over to a non-lean or more conservative strategy in the post-Covid era in relation to their firms' level of inventories do not need to do so unless otherwise required.

This is the very first study that shows the implications of inventory leanness for firms across three operating conditions: pre-crisis (normal business condition), crisis (abnormal business condition) and post-crisis (sub-normal business condition).

The purpose of this paper is to propose a few steps to enhance maintenance effectiveness in practice.

The author reviews the strengths and limitations of different approaches to maintenance management (MM). Some relevant practical observations of reliability engineering are discussed. Based on these, and drawing on the research on MM in relevant literature, the author suggests a few steps, suitable for implementation, for effective MM.

Review of available approaches to MM and some factors of reliability engineering points to some ways to improve maintenance effectiveness in the practical sense.

Implementation of the steps, as suggested in the paper, should enhance the effectiveness of MM leading to higher machine uptimes with less of maintenance costs. In such steps, the ease of implementation in practical situations has been given due importance.

In the steps, called as an “Actionable Program for Maintenance,” that the author suggests, he has highlighted some issues, and has made some suggestions, which are new.

The purpose of this paper is to provide a model for prediction of respiratory symptoms in the progression of COVID-19, social distancing, frequent hand washes, wearing of face mask in public are some of the potential measures of preventing the disease from further spreading. In spite of the effects and efforts taken by governments, the pandemic is still uncontrolled in major cities of the world. The proposed technique in this paper introduces a non-intrusive and major screening of vital symptoms and changes in the respiratory organs.

The novel coronavirus or Covid-19 has become a serious threat to social and economic growth of many nations worldwide. The pace of progression was significantly higher in the past two months. Identified by severe respiratory illness, fever and coughs, the disease has been threatening the lives of human society. Early detection and prognosis is absolutely necessary to isolate the potential spreaders of the disease and to control the rate of progression.

Recent studies have highlighted the changes observed in breathing characteristics of infected patients. Respiratory pattern of Covid-19 patients can be differentiated from the respiratory pattern of normal cold/flu affected patients. Tachypnoea is one among the vital signs identified to be distinguishing feature of Covid-19. The proposed respiratory data capture will commence with facial recognition, use of infrared sensors and machine-learning approaches to classify the respiratory patterns, which finally narrows down as a symptom of Covid-19.

Proposed system produced outcome of 94% accuracy, precision, recall and a F1-measure as an average in the conducted experiments. This method also proves to be a fruitful solution for large-scale monitoring and categorisation of people based on the symptoms.