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The present study unveils the service encounter barriers – interactional and instructional – faced by foreign consumers at food and beverage restaurants in China. It builds a conceptual framework and examines (1) how service encounter barriers create situational abnormality, (2) how situational abnormality engenders foreign consumers' felt discomfort that influences their revisit intentions and (3) how expectations disconfirmation moderates situational abnormality.

Convenience sampling using the survey method was employed to collect data from 517 foreign consumers – who stay in Beijing (China) – at food and beverage restaurants. The study used IBM SPSS 25.0 and Amos Graphics 24.0 to analyze the data and interpret results.

Findings reveal that interactional and instructional barriers positively create situational abnormality, which ultimately leads to foreign consumers' felt discomfort and their negative revisit intentions. Expectations disconfirmation significantly aggravates situational abnormality as a moderator.

This study investigates foreign consumers' behavior at food and beverage restaurants in China and cautions its generalizability. It suggests corroborating the foreign consumers' behavioral intentions in the context of other countries to generalize the findings and unleash other factors additive to comprehend their behavior in the wake of restaurant industry.

The extant literature has not examined the service encounter barriers faced by foreign consumers at food and beverage restaurants in China. The present study, responding to the previous calls, incorporated the service encounter barriers and their downstream effects on foreign consumers' behavioral responses. By doing so, it adds value to the domestic food and beverage restaurants and service firms in China, in particular, and paves the way to understand the interactional and instructional barriers in the global context, in general, by engaging the foreign consumers.

The prevalence of thyroid abnormalities among 831 sexual, violent, and non‐violent non‐sex offenders was found to be greater than found in the general population. Thyroid abnormalities were most common among violent offenders and among sex offenders who victimized children. Thyroid disorders were associated with psychotic diagnoses, delusions, mania, suicidal thoughts, and showed a trend to more suicide attempts. These disorders were undiagnosed in 49.1% of the cases prior to the present clinical assessment. Of these, 59.3% faced their first criminal charges, and the undiagnosed thyroid abnormalities may be important in the offenders’ treatment and may be possible legal mitigating factors in some offenses. Results indicate that a routine endocrine evaluation with blood tests would be a valuable addition to the assessment of violent and sexual offenders.

The purpose of this paper is to present and provide a critical review of most recent studies inquiring into brain abnormalities in psychopathy.

The authors provide an overview of the findings of neurobiological studies conducted in the last five years. Publications chosen for review were found using Web of Science, PsycINFO and Scopus search engines.

Data in the literature reveal that psychopathy is associated with brain abnormalities in frontal and temporo-limbic regions, i.e. regions responsible for moral decision making, emotional processing and learning. Additionally, interactions between the brain areas have been identified as crucial for the development of psychopathic personality traits. Research findings suggest that the flow of impulses between the frontal cortex and temporo-limbic structures in psychopaths is significantly hindered.

The current paper provides an in-depth review of most recent neurobiological studies inquiring into brain abnormalities associated with psychopathic personality traits. Moreover, a particular attention has been paid to identifying abnormalities in brain structures not previously studied in relation to psychopathy (e.g. mirror neuron system, white matter connections).

The purpose of this meta-analytic study is to investigate the possible influence of food shape abnormality on consumer’s willingness to buy fruits and vegetables. This research also investigates some possible moderators (methodological, cultural, socio-economic and contextual) that could influence the direct effects.

This study applied the meta-analysis approach to understand the effect of food shape abnormality on willingness to buy fruits and vegetables. In this research, 16 empirical articles were examined, with a total of 54 effect sizes.

The results showed consistent negative effects between food shape abnormality and consumers’ willingness to buy fruits and vegetables. This study also found significant effects related to culture (Hofstede’s cultural dimensions) and to socio-economic (Human Development Index) moderators. The findings demonstrated that cultures with higher power distance levels promoted stronger effects in the relationship between abnormally shaped food and willingness to buy. Additionally, related to social–economy aspects of a nation, the negative effects between abnormally shaped food and willingness to buy are stronger in countries with low human development rates.

Public policymakers can benefit from the main findings by implementing interventions strategies and education campaigns based on different cultural dimensions. In cultures characterized by high levels of aversion to uncertainty, social communication campaigns can build trust and provide the consumer more knowledge about abnormally shaped fruits and vegetables, whereas in cultures characterized by low levels of masculinity, related to higher levels of sustainability, local producers can benefit from the “local food” positioning to sell abnormally shaped fruits and vegetables.

This research advances studies about consumer behaviour in relation to food waste, highlighting factors beyond aesthetic issues, such as a nation’s culture and its economic context. These results open the way for new work in this area.

The purpose of this paper is to illustrate the application of neural network approach to analyze machine's behaviour quantitatively.

The model is developed based on real plant‐data from a variable speed drive centrifugal type pump. The best model settings are recorded and tested for another similar unit in the vicinity to check its generalization capabilities. Owing to the absence of faulty data, this model is tested against preventive maintenance data that show symptoms of abnormality that are seemingly undetected in existing monitoring and control systems. The paper systematically summarizes published literature and suggests suitable network architecture and its capabilities by illustrative example from oil export pumps from an oil and gas offshore production facility.

Artificial intelligent techniques provide a robust platform in providing useful information about system health and sub‐optimal performance.

In any industry, unexpected equipment downtime in principal questions the overall technical integrity of the platform raising major economical concerns. In the Oil & Gas sector, production platforms are in a 24/7 run mode, and thus undergoing major re‐engineering processes by improving existing surveillance and control techniques of their asset. Machine degradation and abnormalities gradually affect performance and in some cases these are not visible in existing condition monitoring (CM) schemes. Recently, there has been an increasing demand for testing and implementing intelligent techniques as a subsidiary to existing CM programs to monitor and assess system's health. Artificial neural networks have emerged as one of the most promising technique in this regard.

The proposed methodology highlights how healthy data from a system can be effectively modelled to identify significant abnormalities. This paper will be useful for experts working in the area of maintenance engineering to early identify state of the system performance.

The natural control, feedback, stimuli and protection of these subsequent principles founded this project. Via properly conducted experiments, a multilayer computer rehabilitation system was created that integrated natural interaction assisted by electroencephalogram (EEG), which enabled the movements in the virtual environment and real wheelchair. For blind wheelchair operator patients, this paper involved of expounding the proper methodology. For educating the value of life and independence of blind wheelchair users, outcomes have proven that virtual reality (VR) with EEG signals has that potential.

Individuals face numerous challenges with many disorders, particularly when multiple dysfunctions are diagnosed and especially for visually effected wheelchair users. This scenario, in reality, creates in a degree of incapacity on the part of the wheelchair user in terms of performing simple activities. Based on their specific medical needs, confined patients are treated in a modified method. Independent navigation is secured for individuals with vision and motor disabilities. There is a necessity for communication which justifies the use of VR in this navigation situation. For the effective integration of locomotion besides, it must be under natural guidance. EEG, which uses random brain impulses, has made significant progress in the field of health. The custom of an automated audio announcement system modified to have the help of VR and EEG for the training of locomotion and individualized interaction of wheelchair users with visual disability is demonstrated in this study through an experiment. Enabling the patients who were otherwise deemed incapacitated to participate in social activities, as the aim was to have efficient connections.

To protect their life straightaway and to report all these disputes, the military system should have high speed, more precise portable prototype device for nursing the soldier health, recognition of solider location and report about health sharing system to the concerned system. Field programmable gate array (FPGA)-based soldier’s health observing and position gratitude system is proposed in this paper. Reliant on heart rate which is centered on EEG signals, the soldier’s health is observed on systematic bases. By emerging Verilog hardware description language (HDL) programming language and executing on Artix-7 development FPGA board of part name XC7ACSG100t the whole work is approved in a Vivado Design Suite. Classification of different abnormalities and cloud storage of EEG along with the type of abnormalities, artifact elimination, abnormalities identification based on feature extraction, exist in the segment of suggested architecture. Irregularity circumstances are noticed through developed prototype system and alert the physically challenged (PHC) individual via an audio announcement. An actual method for eradicating motion artifacts from EEG signals that have anomalies in the PHC person’s brain has been established, and the established system is a portable device that can deliver differences in brain signal variation intensity. Primarily the EEG signals can be taken and the undesirable artifact can be detached, later structures can be mined by discrete wavelet transform these are the two stages through which artifact deletion can be completed. The anomalies in signal can be noticed and recognized by using machine learning algorithms known as multirate support vector machine classifiers when the features have been extracted using a combination of hidden Markov model (HMM) and Gaussian mixture model (GMM). Intended for capable declaration about action taken by a blind person, these result signals are protected in storage devices and conveyed to the controller. Pretending daily motion schedules allows the pretentious EEG signals to be caught. Aimed at the validation of planned system, the database can be used and continued with numerous recorded signals of EEG. The projected strategy executes better in terms of re-storing theta, delta, alpha and beta complexes of the original EEG with less alteration and a higher signal to noise ratio (SNR) value of the EEG signal, which illustrates in the quantitative analysis. The projected method used Verilog HDL and MATLAB software for both formation and authorization of results to yield improved results. Since from the achieved results, it is initiated that 32% enhancement in SNR, 14% in mean squared error (MSE) and 65% enhancement in recognition of anomalies, hence design is effectively certified and proved for standard EEG signals data sets on FPGA.

The proposed system can be used in military applications as it is high speed and excellent precise in terms of identification of abnormality, the developed system is portable and very precise. FPGA-based soldier’s health observing and position gratitude system is proposed in this paper. Reliant on heart rate which is centered on EEG signals the soldier health is observed in systematic bases. The proposed system is developed using Verilog HDL programming language and executing on Artix-7 development FPGA board of part name XC7ACSG100t and synthesised using in Vivado Design Suite software tool.

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.

We describe a 13 1/2‐year‐old boy with de novo inverted interstitial duplication 8q22.1‐q21.1 associated with mild phenotypic abnormalities, learning disabilities and autism. Psychometric and psychiatric evaluation was performed. Clinical genetic evaluation was supported by chromosome analysis of blood lymphocytes using GTG‐banding technique and Fluorescent In Situ Hybridization (FISH) with whole chromosome painting 8 probe. Clinical evaluation revealed mild phenotypic abnormalities, moderate learning disabilities and mild autistic disorder. The karyotype of the proband was interpreted as 46, XYqh+pat, 8q+.ish inv dup(8)(q22.1;q21.2)(wcp8+) de novo. Although partial trisomy for other segments of 8q, as well as mosaic trisomy 8, have been described in numerous cases, interstitial duplication of 8q21‐q22 seems extremely rare and the severity of the phenotypic abnormalities ranges from mild to profound.

The purpose of this paper is to describe the development of a novel mobile monitoring and control (MC) framework with active-push and plug-and-play capabilities. This proposed framework is particularly designed to addresses the shortcomings of the traditional factory MC systems in sharing information over the internet, protecting the system security, delivering warning messages, and deploying monitoring points.

By leveraging web service technology, mobile devices, and wireless communication, this paper describes the methodology and approach for designing a MC server, a wireless monitoring module (WMM), an intelligent v-Machine, two active-push mechanisms, a pocket PC application, and a smart phone application.

The designed WMM enables the monitoring points to be deployed in a mobile manner. The proposed mobile MC framework (MMCF) can timely detect abnormalities of appliances and equipment and turn off appliances in dangerous situations through WMM. It can also instantly deliver various warning contents to the mobile devices carried by the responsible persons. The v-Machine is built based on virtual metrology (VM) technology and can predict production precision of machined workpieces.

With the successful design and testing of the novel MMCF, this framework can obviously be used for many more applications and developments.

The authors' implement a factory MC system based on the proposed framework and conduct various integration tests on two electric appliances and a practical CNC machine tool in a factory. Testing results shows that the factory MC system works smoothly according the design goals and can overcome the shortcomings of traditional factory MC systems. The MC system also presents good performances, instantly delivering warning contents with a size ranging from 1K bytes to 10M bytes to the users within few seconds.

The proposed MMCF exploits various automation technologies to detect equipment's abnormalities, reduce the rate of product defects caused by human errors, reinforce security, prevent accidents, and ensure the safety of operations.

The proposed MMCF can effectively promote existing factory MC systems to achieve the merits of mobile MC, which is a unique contribution of this work, compared to previous studies. The results of this study can be applied to a variety of industrial automation applications, including factory automation and assembly automation.

The purpose of this study is to develop a deep learning framework for additive manufacturing (AM), that can detect different defect types without being trained on specific defect data sets and can be applied for real-time process control.

This study develops an explainable artificial intelligence (AI) framework, a zero-bias deep neural network (DNN) model for real-time defect detection during the AM process. In this method, the last dense layer of the DNN is replaced by two consecutive parts, a regular dense layer denoted (L1) for dimensional reduction, and a similarity matching layer (L2) for equal weight and non-biased cosine similarity matching. Grayscale images of 3D printed samples acquired during printing were used as the input to the zero-bias DNN.

This study demonstrates that the approach is capable of successfully detecting multiple types of defects such as cracks, stringing and warping with high accuracy without any prior training on defective data sets, with an accuracy of 99.5%.

Once the model is set up, the computational time for anomaly detection is lower than the speed of image acquisition indicating the potential for real-time process control. It can also be used to minimize manual processing in AI-enabled AM.

To the best of the authors’ knowledge, this is the first study to use zero-bias DNN, an explainable AI approach for defect detection in AM.