A Crossectional Study of Rajayakshma Symptoms in HIV Positive Patients

Background: According to Ayurveda, the HIV Symptoms may be correlated with Rajayakshama (Kshaya). The Symptoms, causative factors and treatment for the latter were found mentioned in many ancient Ayurvedic texts. Present study was aimed to assess the correlation of Rajayakshama symptoms described in Charak Samhita and symptoms seen in HIV positive patient. Material and Method: Present study was a Cross-sectional Survey Study, conducted in patientsof 25 - 50 years age group, irrespective of sex, religion, economic & marital status, diagnosed patients of HIV antibody test positive minimum 2 year ago.Results: Maximum number of HIV patient were from 31-40 years age group(67%). Majority of HIV patients were Female (81%).53 % patients’ were HIV Antibody Test Positive more than 2 year ago & 47% patients were more than 10 year ago.97 % patients were on ART Medication and only 3% patients were not given ART Medication.Ansatapa, Jwara and Parshwashulawere observed in all 100% patient. other symptoms Shirashula(99%); Aruchi(96%);Swarabheda(94%); Swasa(87%); Kasa(60%), Atisara(51 %); Raktasthivana(12%) and Raktavamana(4%). 69% cases were observed with 8-11 symptoms of Rajayakshma. 30% cases had 5-7 symptoms and only 1% cases had 1-4 symptoms of Rajayakshma.Conclusion: this disease is more related to mental status of the person. Following Dinacharya, Rutucharya, Sadvrittapalana, Aachararasayana etc. explained in Ayurveda will definitely help in treating AIDS and improving patient’s health.

THE STRUCTURAL COMPONENTS OF PROFESSIONAL SELF-CONSCIOUSNESS OF FUTURE SPECIALISTS

An important condition of the effective process of professional personal development is professional self-consciousness. It manifests in various forms related to the cognitive, emotional and volitional aspects of human mental activity.The educating process of professional self-consciousness is complicated and controversial. It involves many factors, the action of which can often lead to opposite consequences.The purpose of the article is a theoretical analysis of the structural components of professional self-consciousness, definition of the formational stages and evaluation criteria.Professional self-consciousness is a complex mental formation of a person, which is self-awareness of involvement in the profession, encourages professional self-realization and orients the person to active work in the professional field.We can distinguish three stages of professional self-consciousness formation: the stage of early professional self-consciousness that develops during the game (childhood), adolescent professional self-consciousness, characterized by prioritization in choosing future profession (middle and senior school age) and the stage of mature professional self-consciousness (university period, professional activity).The formation of professional self-consciousness at these stages is quite conditional because not every child has professional preferences. Every person following appropriate work can simultaneously form traits that reflect the characteristics of different stages of professional self-consciousness.We can distinguish three main criteria of different levels of professional students self-consciousness: cognitive, motivational-valued and activity-practical, which reveals the presence or absence of certain features that characterize professional self-consciousness.These criteria for assessing the levels of professional self-consciousness make possibility to establish the degree of presence of certain personality traits that are indicators of professional self-consciousness of each phenomenon component: a personʼs attitude to the profession, attitude to himself as a subject, to colleagues, to the object of labour. Keywords: self-consciousness; professional self-consciousness; structural components; formation system; stages of formation; self-awareness; evaluation criteria; levels of development; performance indicators.

ROBOGait: A Mobile Robotic Platform for Human Gait Analysis in Clinical Environments

Mobile robotic platforms have made inroads in the rehabilitation area as gait assistance devices. They have rarely been used for human gait monitoring and analysis. The integration of mobile robots in this field offers the potential to develop multiple medical applications and achieve new discoveries. This study proposes the use of a mobile robotic platform based on depth cameras to perform the analysis of human gait in practical scenarios. The aim is to prove the validity of this robot and its applicability in clinical settings. The mechanical and software design of the system is presented, as well as the design of the controllers of the lane-keeping, person-following, and servoing systems. The accuracy of the system for the evaluation of joint kinematics and the main gait descriptors was validated by comparison with a Vicon-certified system. Some tests were performed in practical scenarios, where the effectiveness of the lane-keeping algorithm was evaluated. Clinical tests with patients with multiple sclerosis gave an initial impression of the applicability of the instrument in patients with abnormal walking patterns. The results demonstrate that the system can perform gait analysis with high accuracy. In the curved sections of the paths, the knee joint is affected by occlusion and the deviation of the person in the camera reference system. This issue was greatly improved by adjusting the servoing system and the following distance. The control strategy of this robot was specifically designed for the analysis of human gait from the frontal part of the participant, which allows one to capture the gait properly and represents one of the major contributions of this study in clinical practice.

Multi-Templates Based Robust Tracking for Robot Person-Following Tasks

While the robotics techniques have not developed to full automation, robot following is common and crucial in robotic applications to reduce the need for dedicated teleoperation. To achieve this task, the target must first be robustly and consistently perceived. In this paper, a robust visual tracking approach is proposed. The approach adopts a scene analysis module (SAM) to identify the real target and similar distractors, leveraging statistical characteristics of cross-correlation responses. Positive templates are collected based on the tracking confidence constructed by the SAM, and negative templates are gathered by the recognized distractors. Based on the collected templates, response fusion is performed. As a result, the responses of the target are enhanced and the false responses are suppressed, leading to robust tracking results. The proposed approach is validated on an outdoor robot-person following dataset and a collection of public person tracking datasets. The results show that our approach achieved state-of-the-art tracking performance in terms of both the robustness and AUC score.

Brain-Adjusted Relational Leadership: A Social-Constructed Consciousness Approach to Leader-Follower Interaction

Relationship-based approaches to leadership represent one of the fastest-growing leadership fields and help us to understand better organizational leadership. Relation-based approaches emphasize the relationship and interaction between the leader and the follower. The emphasis is placed on the way that they interact and influence each other at attaining mutual goals. It is known that leaders are linked to followers and vice versa in a sense of responding to other's needs toward the achievement of mutual goals. Leaders and followers are an essential part of this social process implying that they are losing their traditional identity rooted in the formal organizational structure (manager-subordinate) and become inseparable actors of a co-constructing process of leadership. What is less known though is the way that leadership actors are linked to each other and in particular how they try to understand how to do that in the workplace. What is even less understood is the importance and role of consciousness in this relationship. Especially since consciousness appears to be both a fundamental and a very elusive element in human relations. Therefore, this paper conceptually explores the concept of consciousness within the context of the social brain theory to argue that leadership actors need to rethink their approach to individuality and focus on mutually dependent relations with each other. This paper contributes to the field of Neuro-management by introducing the concept of Homo Relationalis. In this respect, we suggest that leadership is not just a socially constructed element but also a social brain constructed phenomenon that requires an understanding of the human brain as a social organ. We further recommend a new approach of applying cognitive style analysis to capture the duality of leader/follower in the same person, following the self-illusion theory. Finally, we conclude that we need to further emphasize a social brain-adjusted relational leadership approach and we introduce two new cognitive styles that can help capture the essence of it.

Algorithm and Implementation of Human Following Co-bot using 2D LiDAR

Co-bots - collaborative robots are designed to interact directly with humans in close proximity. Some human-robot collaborative applications like surveillance, clinical services, media outlets, defense, and social associations, may require an autonomous robot to have the ability to distinguish and track a person and to follow him around. Smart suitcases and smart trolleys are practical applications of human detection and following co-bots. Initially seen as a special case of object tracking, the task of person following with autonomous robots before long turned into a challenging task of its own especially when using a short-range sensor like laser range scanner. Therefore, we have proposed and implemented an effective algorithm forintelligent detection, tracking, and following a specified target person. The decision is being made by the control unit based on the information obtained from 2D LIDAR mounted at knee height, hence, enabling the mobile co-bot to follow the target in a dynamic environment without any collision.

Target Recovery for Robust Deep Learning-Based Person Following in Mobile Robots: Online Trajectory Prediction

The ability to predict a person’s trajectory and recover a target person in the event the target moves out of the field of view of the robot’s camera is an important requirement for mobile robots designed to follow a specific person in the workspace. This paper describes an extended work of an online learning framework for trajectory prediction and recovery, integrated with a deep learning-based person-following system. The proposed framework first detects and tracks persons in real time using the single-shot multibox detector deep neural network. It then estimates the real-world positions of the persons by using a point cloud and identifies the target person to be followed by extracting the clothes color using the hue-saturation-value model. The framework allows the robot to learn online the target trajectory prediction according to the historical path of the target person. The global and local path planners create robot trajectories that follow the target while avoiding static and dynamic obstacles, all of which are elaborately designed in the state machine control. We conducted intensive experiments in a realistic environment with multiple people and sharp corners behind which the target person may quickly disappear. The experimental results demonstrated the effectiveness and practicability of the proposed framework in the given environment.