Self consistent thermal wave model description of the transverse dynamics for relativistic charged particle beams in magnetoactive plasmas

Thermal Wave Model is used to study the strong self-consistent Plasma Wake Field interaction (transverse effects) between a strongly magnetized plasma and a relativistic electron/positron beam travelling along the external magnetic field, in the long beam limit, in terms of a nonlocal NLS equation and the virial equation. In the linear regime, vortices predicted in terms of Laguerre-Gauss beams characterized by non-zero orbital angular momentum (vortex charge). In the nonlinear regime, criteria for collapse and stable oscillations is established and the thin plasma lens mechanism is investigated, for beam size much greater than the plasma wavelength. The beam squeezing and the self-pinching equilibrium is predicted, for beam size much smaller than the plasma wavelength, taking the aberrationless solution of the nonlocal Nonlinear Schroeding equation.Comment: Poster presentation P5.006 at the 38th EPS Conference on Plasma Physics, Strasbourg, France, 26 June - 1 July, 201

Nuclear Shell Model Calculations on Positive and Negative Parity States in upper 0f7/2- Shell Nuclei

The excitation energies for both the positive and negative parities of  (90Sr,90Y,92Nb and 92Zr)  isotopes have been  calculated by employing modified surface delta interaction. A comparison between our results and the available experimental data to theoretical test  for shell model description in  isotopes  above. It was seen that the obtained theoretical results are in agreement with the experimental data for each of  the all isotopes under study . Keywords: Excitation energy , Modified surface delta interaction ,Shell model

Health Impact and Risk Factors Affecting South and Southeast Asian Women Following Natural Disasters: A Systematic Review.

(1) Background: Following natural disasters, women have a higher prevalence of adverse physical and mental health outcomes. Given that the South and Southeast Asia regions are highly disaster prone, a review was undertaken to identify the potential health impact and key risk factors affecting women after disasters in the countries located in South and Southeast Asia regions. (2) Methods: A systematic literature search of four databases yielded 16 studies meeting the inclusion criteria. The review was conducted according to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidance, between July 2008 and March 2021. (3) Results: The majority of studies reported women's negative/poor mental health, identifying a significant association of socio-demographics, during disaster exposure, post-disaster, and pre-existing risk factors. The six most-cited influences on women's mental health found in the reviewed literature were being female, adult age group, having no formal education, poverty or low economic status, poor physical health/physical injuries, and death of family members. Women's health during the post-disaster period was generally reported as poor among all the countries of the South and Southeast Asia regions. (4) Conclusions: Appropriate social support and the availability of free healthcare access for women are warranted in disaster-affected areas. This review offers a valuable contribution to the knowledge of women's health complications/challenges and associated risk factors related to disasters, essential for the development of strategies to help reduce this burden in the future. Further research is required on natural disasters to identify ways to reduce women's health impacts after natural disasters, especially in the context of low-income and lower-middle-income countries

Social Responsibility and Its Relation with Procrastination and Achievement Motivation among Students of Imam Abdulrahman Bin Faisal University

This study aimed at identifying the level of social responsibility, procrastination and achievement motivation among Imam Abdulrahman Bin Faisal University students in Dammam, Saudi Arabia. It also aimed to explore the correlational relations between social responsibility, procrastination and achievement motivation and to uncover the differences between male and female students on these three variables. The study was conducted on a sample of 1880 male and female students. Social responsibility, and procrastination scales, as well as the achievement motivation test were employed after verifying their psychometric properties. The results showed that while the university students practice both social responsibility and procrastination at a medium level, they practice achievement motivation at a high level. The study further showed a significant, negative correlation between social responsibility and procrastination, and significant positive correlation between responsibility and achievement motivation. Furthermore, the study showed statistically significant differences between males and females in social responsibility and procrastination in favour of male students; and statistically significant differences in the achievement motivation in favor of females

Efficient Thermal Image Segmentation through Integration of Nonlinear Enhancement with Unsupervised Active Contour Model

Thermal images are exploited in many areas of pattern recognition applications. Infrared thermal image segmentation can be used for object detection by extracting regions of abnormal temperatures. However, the lack of texture and color information, low signal-to-noise ratio, and blurring effect of thermal images make segmenting infrared heat patterns a challenging task. Furthermore, many segmentation methods that are used in visible imagery may not be suitable for segmenting thermal imagery mainly due to their dissimilar intensity distributions. Thus, a new method is proposed to improve the performance of image segmentation in thermal imagery. The proposed scheme efficiently utilizes nonlinear intensity enhancement technique and Unsupervised Active Contour Models (UACM). The nonlinear intensity enhancement improves visual quality by combining dynamic range compression and contrast enhancement, while the UACM incorporates active contour evolutional function and neural networks. The algorithm is tested on segmenting different objects in thermal images and it is observed that the nonlinear enhancement has significantly improved the segmentation performance

Automatic Building Change Detection in Wide Area Surveillance

We present an automated mechanism that can detect and characterize the building changes by analyzing airborne or satellite imagery. The proposed framework can be categorized into three stages: building detection, boundary extraction and change identification. To detect the buildings, we utilize local phase and local amplitude from monogenic signal to extract building features for addressing issues of varying illumination. Then a support vector machine with Radial basis kernel is used for classification. In the boundary extraction stage, a level-set function with self-organizing map based segmentation method is used to find the building boundary and compute physical area of the building segments. In the last stage, the change of the detected building is identified by computing the area differences of the same building that captured at different times. The experiments are conducted on a set of real-life aerial imagery to show the effectiveness of the proposed method

Towards an Energy-Aware Cloud Architecture for Smart Grids

Energy consumption in Cloud computing is a significant issue in regards to aspects such as the cost of energy, cooling in the data center and the environmental impact of cloud data centers. Smart grids offers the prospect of dynamic costs for a data center’s energy usage. These dynamic costs can be passed on to Cloud users providing incentives for users to moderate their load while also ensuring the Cloud providers are insulated from fluctuations in the cost of energy. The first step towards this is an architecture that focuses on energy monitoring and usage prediction. We provide such an architecture at both the PaaS and IaaS layers, resulting in energy metrics for applications, VMs and physical hosts, which is key to enabling active demand in cloud data centers. This architecture is demonstrated through our initial results utilising a generic use case, providing energy consumption information at the PaaS and IaaS layers. Such monitoring and prediction provides the groundwork for providers passing on energy consumption costs to end users. It is envisaged that the resulting varying price associated with energy consumption can help motivate the formation of methods and tools to support software developers aiming to optimise energy efficiency and minimise the carbon footprint of Cloud applications

Prolonged use of oral contraceptive pill, a co-factor for the development of cervical cancer

This study was carried out to assess the use of oral contraceptive pill as a co-factor for the development of cervical cancer. Among the 100 participants, 71% used oral contraceptives pill. Maximum (40%) used oral contraceptive pill for >5 years whereas 31% for <5 years. Histopathologically diagnosed invasive squamous cell carcinoma was 84% and adenocarcinoma was 16%

Prolonged use of oral contraceptive pill, a co-factor for the development of cervical cancer

This study was carried out to assess the use of oral contraceptive pill as a co-factor for the development of cervical cancer. Among the 100 participants, 71% used oral contraceptives pill. Maximum (40%) used oral contraceptive pill for >5 years whereas 31% for <5 years. Histopathologically diagnosed invasive squamous cell carcinoma was 84% and adenocarcinoma was 16%