Cultural Criminology and Gender Consciousness

Cultural criminology emerged in the mid-nineties with defining texts written by Jock Young, Keith Hayward, and Jeff Ferrell, among others. Since its inception, it has been criticized for its shallow connections with feminist theory. While in theory cultural criminology clearly acknowledges the influence of feminist scholarship, it has in practice often only superficially ‘added’ on gender and sexuality to its scholarly investigations. Yet, as we argue, research identified with cultural criminology has much to gain from feminist theory. This article reviews a range of cultural criminological scholarship, particularly studies of subcultures, edgework, and terrorism. We investigate three themes significant for feminist research: masculinities and femininities, sexual attraction and sexualities, and intersectionality. Such themes, if better incorporated, would strengthen cultural criminology by increasing the explanatory power of resulting analyses. We conclude by advocating that feminist ideas be routinely integrated into cultural criminological research. </jats:p

Liquid crystalline dendrimer: Sythesis and Chracterization

A new family of nematic liquid crystal dendrimers derived from 3,5-dihydroxybenzoic acid were synthesized. The synthesis of the dendrimers compounds shows the influence of the dendritic core on the mesomorphic properties. The liquid crystalline properties were studied by polarizing optical microscopy (POM) equipped with a hot stage, the structures of the synthesized compounds characterized using FTIR and 1HNMR spectroscopy

Culture-bound addictions among low income workers of Karachi, Pakistan

Background: The self-efficacy of individuals is influenced by experiences in the community, in the workplace, and in broader civil society, all of which exert a collective influence on attitudes and behaviors. The low-income population is more likely to engage in the use of culture bound addictive substances which include tobacco, gutkha, betel nut/areca nut, alcohol and caffeine. The objective of the study was to identify the type of culture-bound substances used by low-income workers and also to determine the prevalence of substance use among low-income workers, in Karachi, Pakistan.Methods: A cross sectional study was carried out in Karachi, Pakistan. Trained interviewers used a semi-structured questionnaire to interview 707 workers to collect information on socio-demographic characteristics, and addiction history. The data were analyzed using SPSS version 18.Results: Majority (26.4%) of the participants were aged between 26 and 30 years. More than one-third (35.1%) were educated up to secondary level only. Half of the sample (50.8) had 6 to 10 house hold members whereas only one member was employed among 34.8% of the respondents. 39.5% participants reported a household income between Rs11000 and Rs 20000 per month. Half of the sample (50.4%) reported some sort of substance use in their daily routine. A significant number (39.5%) of workers were found to be addicted to tobacco, gutkha or betel nut alone, while another 10.5% were using these substances in combination.Conclusions: Addiction to culture bound substances is prevalent among 50% of the low income workers of Karachi, Pakistan. The common culture bound addiction substances the workers were found to be using were tobacco, ghutka and betel nut. The findings of the present study can­not be generalized due to the limited sample. Still, the study provides evidence of this unhealthy behavior among workers that not only affects their productivity but plays a vicious role in poverty and poor health cycle. Future research should direct attention toward workers' health and working conditions to formulate effective public health interventions to reduce the risky behavior among low income workers. Moreover, there is a need to develop health education programs to create awareness and empowerment among low-income workers to prevent substance use.

FE analysis and experimental determination of a shaft deflection under three-point loading

Increasing industrial demand for new products including advanced production technology leads to substantial natural resources consumption. Furthermore, huge environmental pollution and emerging environmental legislation motivate the machine tools industry as one of the major resource consumers on a global scale to develop methods for more sustainable use of the Earth's resources. Machine tools re-engineering concerning design and failure analysis is an approach by which outdated machines are upgraded and restored to like-new machines. To evaluate the mechanical failure of the used machine components and to ensure their reliable future performance, it is essential to make material, design, and surface investigations. In this paper, an experimental approach based on the principle of a three-point bending test is presented to evaluate the shaft elastic behavior under loading. Moreover, finite element analysis and numerical integration method are used to determine the maximum linear deflection and bending stress of the shaft. Subsequently, a comparison between the results is made. In conclusion, it was found that the measured bending deflection and stress were well close to the admissible design values. Therefore, the shaft can be used again in the second life cycle. However, based on previous surface tests conducted, the shaft surface needs re-carburizing and refining treatments to ensure the reliable performance of the surface

Lottery Aware Sparsity Hunting: Enabling Federated Learning on Resource-Limited Edge

Edge devices can benefit remarkably from federated learning due to their distributed nature; however, their limited resource and computing power poses limitations in deployment. A possible solution to this problem is to utilize off-the-shelf sparse learning algorithms at the clients to meet their resource budget. However, such naive deployment in the clients causes significant accuracy degradation, especially for highly resource-constrained clients. In particular, our investigations reveal that the lack of consensus in the sparsity masks among the clients may potentially slow down the convergence of the global model and cause a substantial accuracy drop. With these observations, we present \textit{federated lottery aware sparsity hunting} (FLASH), a unified sparse learning framework for training a sparse sub-model that maintains the performance under ultra-low parameter density while yielding proportional communication benefits. Moreover, given that different clients may have different resource budgets, we present \textit{hetero-FLASH} where clients can take different density budgets based on their device resource limitations instead of supporting only one target parameter density. Experimental analysis on diverse models and datasets shows the superiority of FLASH in closing the gap with an unpruned baseline while yielding up to $\mathord{\sim}10.1\%$ improved accuracy with $\mathord{\sim}10.26\times$ fewer communication, compared to existing alternatives, at similar hyperparameter settings. Code is available at \url{https://github.com/SaraBabakN/flash_fl}.Comment: Accepted in TMLR, https://openreview.net/forum?id=iHyhdpsny

Don't Memorize; Mimic The Past: Federated Class Incremental Learning Without Episodic Memory

Deep learning models are prone to forgetting information learned in the past when trained on new data. This problem becomes even more pronounced in the context of federated learning (FL), where data is decentralized and subject to independent changes for each user. Continual Learning (CL) studies this so-called \textit{catastrophic forgetting} phenomenon primarily in centralized settings, where the learner has direct access to the complete training dataset. However, applying CL techniques to FL is not straightforward due to privacy concerns and resource limitations. This paper presents a framework for federated class incremental learning that utilizes a generative model to synthesize samples from past distributions instead of storing part of past data. Then, clients can leverage the generative model to mitigate catastrophic forgetting locally. The generative model is trained on the server using data-free methods at the end of each task without requesting data from clients. Therefore, it reduces the risk of data leakage as opposed to training it on the client's private data. We demonstrate significant improvements for the CIFAR-100 dataset compared to existing baselines

Coronavirus-induced Anxiety among Pregnant Women

Introduction: Anxiety about COVID-19 is common and seems to be mostly due to the unknown and confusing nature of the virus. Given the effects of stress on maternal and fetal health and the lack of a similar study on this issue and the importance of the issue, so we decided to assess the degree of anxiety in COVID-19 in pregnant women. Materials and Methods: The present study is a cross-sectional study (descriptive-analytical). The statistical population of this study includes pregnant women referred to Kashani Hospital in Jiroft from March to August 2020. A total of 182 pregnant women referred to the hospital were interred in the study. Results: The mean age of pregnant women was 27.2±7.2 years. Among the demographic factors of pregnant women, only a significant relationship was seen between maternal age and anxiety caused by COVID-19. Also, the mean anxiety of psychological symptoms was significantly higher than the physical symptoms (P&lt;0.001). Both factors indicated moderate anxiety. Conclusion: Given that there is currently limited information available to pregnant women with COVID-19 and its complications in pregnant mothers, it is necessary to pay more attention to corona prevention training programs and how to deal with stress and anxiety in pregnant women

Deep Learning Machine using Hierarchical Cluster Features

Deep learning of multi-layer computational models allowed processing to recognize data representation at multiple levels of abstraction. These techniques have greatly improved the latest ear recognition technology. PNN is a type of radiative basis for classification problems and is based on the Bayes decision-making base, which reduces the expected error of classification. In this paper, strong features of images are used to give a good result, therefore, SIFT method using these features after adding improvements and developments. This method was one of the powerful algorithms in matching that needed to find energy pixels. This method gives stronger feature on features and gives a large number of a strong pixel, which is considered a center and neglected the remainder of it in our work. Each pixel of which is constant for image translation, scaling, rotation, and embedded lighting changes in lighting or 3D projection. Therefore, the interpretation is developed by using a hierarchical cluster method; to assign a set of properties (find the approximation between pixels) were classified into one

Racial Inequality and the Implementation of Emergency Management Laws in Economically Distressed Urban Areas

This study examines the use of emergency management laws as a policy response to fiscal emergencies in urban areas. Focusing on one Midwestern Rust Belt state, we use a mixed methods approach – integrating chronology of legislative history, analysis of Census data, and an ethnographic case study – to examine the dynamics of emer- gency management laws from a social justice perspective. Analysis of Census data showed that emergency man- agement policies disproportionately affected African Americans and poor families. Analysis indicated that in one state, 51% of African American residents and 16.6% of Hispanic or Latinos residents had lived in cities that were under the governance of an emergency manager at some time during 2008–2013, whereas only 2.4% of the White population similarly had lived in cities under emergency management. An ethnographic case study high- lights the mechanisms by which an emergency manager hindered the ability of residents in one urban neighbor- hood, expected to host a large public works project, to obtain a Community Benefits Agreement intended to provide assistance to residents, most of whom were poor families with young children. We conclude with a dis- cussion of how emergency management laws may impact social service practice and policy practice in urban communities, framed from a social justice perspective. We argue that these are not race neutral policies, given clear evidence of race and ethnic disparities in their implementation

Relationship between Social Support and Mental Health of Novice Nurses during Coronavirus Epidemic

Abstract Background: Coronavirus has created a confusing and stressful situation around the world. In these circumstances, health care workers are most prone to vulnerability. The goal of this study was to investigate the relationship between social support and mental health of novice nurses during the outbreak of COVID-19 to provide basic information for intervention measures. Study design: cross-sectional study Methods: This study was performed in spring of 2020 in hospital affiliated with Ilam University of Medical Sciences. Data were collected using general information questionnaire, General Health Questionnaire (GHQ) and Phillip’s Social Support Questionnaire and analyzed by SPSS software, as well as descriptive and inferential statistics. Results: The total score of GHQ and social support was 24.58±12.063 and 70.77±9.761, respectively. There was a statistically significant inverse relationship between social support and mental health of participants. Among the demographic variables, there was a significant correlation between work experience, hospital, direct contact with COVID-19 patients and the number of working days in coronavirus ward with mental health and social support. Conclusion: The findings of the present study add to our knowledge obtained from previous studies by discovering the impact of social support on mental health of health care providers with special attention to novice nurses at the forefront. Ongoing monitoring of psychological consequences associated with COVID-19 outbreak and social support of them require further attention