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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).

This chapter discusses the practical challenges and opportunities involved in merging the two fields of cognitive neurosciences and strategic management, starting from the premise that the need to marry them is justified by their complementarities, as opposed to the level of analysis on which they both focus. We discuss the potential benefits and drawbacks of using methods borrowed from cognitive neurosciences for management research. First, we argue that there are clear advantages in deploying techniques that enable researchers to observe processes and variables that are central to management research, with the caveat that neuroscientific methods and techniques are not general-purpose technologies. Second, we identify three core issues that specify the boundaries within which management scholars can usefully deploy such methods. Third, we propose a possible research agenda with various areas of synergy between the complementary capabilities of management and neuroscience scholars, aiming to generate valuable knowledge and insight for both disciplines and also for society as a whole.

This chapter is structured for teaching young learners with other health impairments in special education. Under the Individuals with Disabilities Education Improvement Act (IDEA, 2004), other health impairments represent chronic or acute health problems such as asthma, attention deficit disorder or attention deficit hyperactivity disorder (ADHD), diabetes, epilepsy, a heart condition, hemophilia, lead poisoning, leukemia, nephritis, rheumatic fever, and sickle cell anemia and adversely affect a child’s educational performance. The chapter is organized around definitions, prevalence, etiologies, intervention strategies, and teaching considerations for selected disabling conditions in this disability category.

If we want to improve managerial cognition, we need to capture the full spectrum of cognitive functions and the complex processes through which they unfold. I propose two very different methods (one older and low-tech, one newer and high-tech) that allow us to observe cognitive functions and processes directly in real time.

Prader-Willi syndrome (PWS) is a genetic disorder characterised by excessive appetite and progressive obesity. The causes of hyperphagia are unknown and this has implications on treatment limiting the options available. The purpose of this paper is to systematically synthesise the latest evidence regarding all causes as well as management of hyperphagia in people with PWS.

A computer-based literature search, citation tracking and related articles search was undertaken. Primary research studies using genetically confirmed Prader-Willi participants specifically focused on the causes and treatment of overeating was included. Studies were described, critically analysed, presented and discussed in the review.

In total, 18 relevant studies were identified. Nine studies were included in the causes of hyperphagia and focused largely on the roles of secretory hormones, and reward-related areas of the brain. Nine studies were included in the management of hyperphagia and related to physical exercise and food control, antidiabetic agents and weight loss surgery.

The causes and the management of hyperphagia are still to be determined despite promising findings. The small number and heterogeneity of the included studies and participants limits conclusions. There is the need for future research to use larger systematic studies to validate findings so far.

Students with physical and health impairments represent a small but growing group of individuals with diverse educational needs. They are those students whose physical limitations or health problems interfere with school attendance or learning to such an extent that special services, training, equipment, materials, or facilities are required. Therefore, the purpose of this chapter is to discuss some of these impairments and acquaint both general and special educators with interventions for helping students with physical and health impairments succeed.

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.

Pervasive health-care computing applications in medical field provide better diagnosis of various organs such as brain, spinal card, heart, lungs and so on. The purpose of this study is to find brain tumor diagnosis using Machine learning (ML) and Deep Learning(DL) techniques. The brain diagnosis process is an important task to medical research which is the most prominent step for providing the treatment to patient. Therefore, it is important to have high accuracy of diagnosis rate so that patients easily get treatment from medical consult. There are many earlier investigations on this research work to diagnose brain diseases. Moreover, it is necessary to improve the performance measures using deep and ML approaches.

In this paper, various brain disorders diagnosis applications are differentiated through following implemented techniques. These techniques are computed through segment and classify the brain magnetic resonance imaging or computerized tomography images clearly. The adaptive median, convolution neural network, gradient boosting machine learning (GBML) and improved support vector machine health-care applications are the advance methods used to extract the hidden features and providing the medical information for diagnosis. The proposed design is implemented on Python 3.7.8 software for simulation analysis.

This research is getting more help for investigators, diagnosis centers and doctors. In each and every model, performance measures are to be taken for estimating the application performance. The measures such as accuracy, sensitivity, recall, F1 score, peak-to-signal noise ratio and correlation coefficient have been estimated using proposed methodology. moreover these metrics are providing high improvement compared to earlier models.

The implemented deep and ML designs get outperformance the methodologies and proving good application successive score.

As one of the most dynamic and consequential areas of biomedical research, neuroscience must be analyzed in a broader political context. Research initiatives, individual use, and aggregate social consequences of unfolding knowledge about the brain and the accompanying applications require particularly close scrutiny because of the centrality of the brain itself to human behavior and thoughts. As one of the last frontiers of medicine, neuroscience has strong support because it promises to benefit many patients suffering from an array of behavioral, neurological, and mental disorders and injuries. Given the inevitability of expanded strategies for exploration and therapy of the brain, it is important that the political issues surrounding their application be clarified and debated before such techniques fall into routine use.