China-India counterbalancing measures through international corridors and ports: the focus on Chabahar and Gwadar ports

Beijing and New Delhi, as new world emerging powers, despite border skirmishes, have not considered themselves arch-rivals. Still, the necessities of real politics have forced India to take counter-measures towards China's grand connectivity strategy in the framework of BRI and the Maritime Silk Road. This article assumes that China's grand connectivity strategy has not targeted India in particular, but unavoidably it has affected India's strategic interests in the Indian Ocean and Eurasia. In a qualitative and case study methodology, this research explains China's grand connectivity strategy and how it affects Indian strategic interests. It also elaborates on India's counter-measures vis-à-vis China policy. It concluded that the Chinese connectivity strategy has affected Indian strategic interests in the Indian Ocean and Eurasia. Therefore, Chabahar, Gwadar ports, and Malacca Strait are centers of gravity in these great connectivity rivalries

Assessing the Active Living Impacts of Urban Design Improvements: Brenham TX Main Street Program

Literature search indicates that Main Street Program (MSP) is considered to be successful in achieving the goals of ‘preferred place to live’ and ‘increased active living’. The four point approach of MSP, (organization, promotion, design and economic restructuring) reportedly improves businesses by improving the urban character while allegedly improving active living. This paper focuses on the urban improved areas under the MSP. This research links observable design changes incorporated in the MSP of Brenham TX with identified characteristics of active living. Time constraints prevented collecting sufficient data for a statistically significant study. However a framework is hereby established for future work that more rigorously links MSP and active living for statistical analysis. Preliminary conclusions are drawn based on analysis of the limited available data

Workplace Bullying and Intention to Leave: The Moderating Effect of the Organizational Commitment

The main reasoning of this study is to figure out the relations among workplace bullying and turnover intention of the employee treating organization commitment as a moderating variable. Workplace bullying have a positive impact on intention to leave which reveals the widespread impact that bullying can have on targets in that still less rigorous types of bullying are coupled with victims intention to leave the workplace (e.g. department), the organization or the job. Workplace bullying are more relevant to systematic flaws in the organization and less to employees’ performances while person-related bullying is related more to the personal characteristics of the victims, so irrespective of their commitment, exit from the organization in such circumstances happens to be the preferable alternative for the victim. This study provides an insight that the harmful effects of workplace bullying could be handled effectively through the moderating effects of organizational commitment on the relation between workplace bullying and intention to leave. It therefore, required conducting a further study on the join effects of organizational commitment and bullying in terms of some other variables such as in-role job performance, which are vital to the working of the organizations

Non-Contact Monitoring of Dehydration using RF Data Collected off the Chest and the Hand

We report a novel non-contact method for dehydration monitoring. We utilize a transmit software defined radio (SDR) that impinges a wideband radio frequency (RF) signal (of frequency 5.23 GHz) onto either the chest or the hand of a subject who sits nearby. Further, another SDR in the closed vicinity collects the RF signals reflected off the chest (or passed through the hand) of the subject. Note that the two SDRs exchange orthogonal frequency division multiplexing (OFDM) signal, whose individual subcarriers get modulated once it reflects off (passes through) the chest (the hand) of the subject. This way, the signal collected by the receive SDR consists of channel frequency response (CFR) that captures the variation in the blood osmolality due to dehydration. The received raw CFR data is then passed through a handful of machine learning (ML) classifiers which once trained, output the classification result (i.e., whether a subject is hydrated or dehydrated). For the purpose of training our ML classifiers, we have constructed our custom HCDDM-RF-5 dataset by collecting data from 5 Muslim subjects (before and after sunset) who were fasting during the month of Ramadan. Specifically, we have implemented and tested the following ML classifiers (and their variants): K-nearest neighbour (KNN), support vector machine (SVM), decision tree (DT), ensemble classifier, and neural network classifier. Among all the classifiers, the neural network classifier acheived the best classification accuracy, i.e., an accuracy of 93.8% for the proposed CBDM method, and an accuracy of 96.15% for the proposed HBDM method. Compared to prior work where the reported accuracy is 97.83%, our proposed non-contact method is slightly inferior (as we report a maximum accuracy of 96.15%); nevertheless, the advantages of our non-contact dehydration method speak for themselves.Comment: 8 pages, 9 figures, 2 table

Application of deep learning for livestock behaviour recognition: a systematic literature review.

Livestock health and welfare monitoring is a tedious and labour-intensive task previously performed manually by humans. However, with recent technological advancements, the livestock industry has adopted the latest AI and computer vision-based techniques empowered by deep learning (DL) models that, at the core, act as decision-making tools. These models have previously been used to address several issues, including individual animal identification, tracking animal movement, body part recognition, and species classification. However, over the past decade, there has been a growing interest in using these models to examine the relationship between livestock behaviour and associated health problems. Several DL-based methodologies have been developed for livestock behaviour recognition, necessitating surveying and synthesising state-of-the-art. Previously, review studies were conducted in a very generic manner and did not focus on a specific problem, such as behaviour recognition. To the best of our knowledge, there is currently no review study that focuses on the use of DL specifically for livestock behaviour recognition. As a result, this systematic literature review (SLR) is being carried out. The review was performed by initially searching several popular electronic databases, resulting in 1101 publications. Further assessed through the defined selection criteria, 126 publications were shortlisted. These publications were filtered using quality criteria that resulted in the selection of 44 high-quality primary studies, which were analysed to extract the data to answer the defined research questions. According to the results, DL solved 13 behaviour recognition problems involving 44 different behaviour classes. 23 DL models and 24 networks were employed, with CNN, Faster R-CNN, YOLOv5, and YOLOv4 being the most common models, and VGG16, CSPDarknet53, GoogLeNet, ResNet101, and ResNet50 being the most popular networks. Ten different matrices were utilised for performance evaluation, with precision and accuracy being the most commonly used. Occlusion and adhesion, data imbalance, and the complex livestock environment were the most prominent challenges reported by the primary studies. Finally, potential solutions and research directions were discussed in this SLR study to aid in developing autonomous livestock behaviour recognition systems

Weight patterns and perceptions among female university students of Karachi: A cross sectional study

Background: Body weight and its perception play an important role in the physical and mental well-being of a person. Weight perception is found to be a better predictor of weight management behaviour as compared to actual weight. In Pakistan, studies have been done on the prevalence of weight status but weight perception is still unexplored. The study was done to examine relationships between body weight perception, actual weight status, and weight control behaviour among the female university students of Karachi.Methods: A cross sectional study was carried out during Sep-Nov 2009 on female students in four universities of Karachi, Pakistan. Our final sample size included 338 female university students. Height and weight were measured on calibrated scales. A modified BMI criterion for Asian populations was used.Results: Based on measured BMI; the prevalence of underweight, normal weight and overweight females was 27.2%, 51.5% and 21.3% respectively. As a whole, just over one third (33.73%) of the sample misclassified their weight status. Among underweight (n=92), 45.70% thought they were of normal weight. No one who was truly underweight perceived them self as overweight. Among the normal weight (n= 174), 9.8% thought they were underweight and 23.6% considered themselves overweight. Among the overweight (n=72); 18.3% considered themselves normal. Only one female student thought she was underweight despite being truly overweight.Conclusions: Our study shows that among female university students in Karachi, the prevalence of being underweight is comparatively high. There is a significant misperception of weight, with one third of students misclassifying themselves. Underweight females are likely to perceive themselves as normal and be most satisfied with their weight. Health policy makers should implement these findings in future development of health interventions and prevention of depression, social anxiety and eating disorders associated with incorrect weight perception among young females. Studies that employ a longitudinal approach are needed to validate our findings

Allocation and migration of virtual machines using machine learning

Cloud computing promises the advent of a new era of service boosted by means of virtualization technology. The process of virtualization means creation of virtual infrastructure, devices, servers and computing resources needed to deploy an application smoothly. This extensively practiced technology involves selecting an efficient Virtual Machine (VM) to complete the task by transferring applications from Physical Machines (PM) to VM or from VM to VM. The whole process is very challenging not only in terms of computation but also in terms of energy and memory. This research paper presents an energy aware VM allocation and migration approach to meet the challenges faced by the growing number of cloud data centres. Machine Learning (ML) based Artificial Bee Colony (ABC) is used to rank the VM with respect to the load while considering the energy efficiency as a crucial parameter. The most efficient virtual machines are further selected and thus depending on the dynamics of the load and energy, applications are migrated from one VM to another. The simulation analysis is performed in Matlab and it shows that this research work results in more reduction in energy consumption as compared to existing studies

Investigation of MPPT Techniques Under Uniform and Non-Uniform Solar Irradiation Condition-A Retrospection

A significant growth in solar photovoltaic (PV) installation has observed during the last decade in standalone and grid-connected power generation systems. The solar PV system has a non-linear output characteristic because of weather intermittency, which tends to have a substantial effect on overall PV system output. Hence, to optimize the output of a PV system, different maximum power point tracking (MPPT) techniques have been used. But, the confusion lies while selecting an appropriate MPPT, as every method has its own merits and demerits. Therefore, a proper review of these techniques is essential. A “Google Scholar” survey of the last five years (2015-2020) was conducted. It has found that overall seventy-one review articles are published on different MPPT techniques; out of those seventy-one, only four are on uniform solar irradiance, seven on non-uniform and none on hybrid optimization MPPT techniques. Most of them have discussed the limited number of MPPT techniques, and none of them has discussed the online and offline under uniform and hybrid MPPT techniques under non-uniform solar irradiance conditions all together in one. Unfortunately, very few attempts have made in this regard. Therefore, a comprehensive review paper on this topic is need of time, in which almost all the well-known MPPT techniques should be encapsulated in one paper. This article focuses on classifications of online, offline, and hybrid optimization MPPT algorithms, under the uniform and non-uniform irradiance conditions. It summarizes various MPPT methods along with their mathematical expression, operating principle, and block diagram/flow charts. This research will provide a valuable pathway to researchers, energy engineers, and strategists for future research and implementation in the field of maximum power point tracking optimization