The aim of this research was to explore the correlates of Turnover Intention. The correlates included Organization-based Self-esteem (OBSE), Job Stress (JS), Emotional Intelligence (EI), and Health Risk Behaviour (HRB) among Rescue 1122 Workers. The study also aimed to explore Emotional Intelligence (EI) as a predictor of Turnover Intention (TI); Organizationbased Self-esteem (OBSE), Job Stress (JS) and Health Risk Behaviour (HRB) among Rescue 1122 Workers. The sample of the study consisted of 200 male Rescue 1122 Workers of Lahore city. Systematic Random Sampling was employed to gather data from participants. Correlational Research Design was used. The major tools of this study included Turnover Intention Scale, Organization based Self-esteem Scale, The Subjective Job Stress Scale, Emotional Intelligence Scale and Health Risk Behaviour Questionnaire. These above mentioned tools were used after taking consent from the participants. Correlation and Regression Analysis were applied on the data. The results showed that there was significant positive relationship between Turnover Intention and Job Stress. Furthermore there was significant negative relationship between Turnover Intention and OBSE. Moreover Emotional Intelligence (EI) was a significant predictor of OBSE, Job Stress (JS), and Health Risk Behaviour (HRB).
Video and image data is the most important and widely used format of communication today. It is used as evidence and authenticated proof in different domains such as law enforcement, forensic studies, journalism, and others. With the increase of video applications and data, the problem of forgery in video and images has also originated. Although a lot of work has been done on image forgery, video forensic is still a challenging area. Videos are manipulated in many ways. Frame insertion, deletion, and frame duplication are a few of the major challenges. Moreover, in the perspective of duplicated frames, frame rate variation and loop detection are also key issues. Identification of forged duplication frames for large videos with variant frame rates in real-time is not applicable due to computational limitations, lack of generalization, and low-performance accuracy. This research has investigated the problem of frame duplication with varied frame rates using a deep learning approach. A novel deep learning framework consisting of Inflated 3D (I3D) and Siamese-based Recurrent Neural Network (RNN) is proposed to resolve the aforementioned issues. The first step in the proposed framework is to extract the features and convert videos into frames. I3D network receives an original and a forged video to detect frame-to-frame duplication. Then multiple frames are merged to create a sequence. This sequence is passed to Siamese-based RNN which is used for the sequence to sequence forgery detection in video. Media Forensic Challenge (MFC) is a relatively new dataset with various frame rates, and a huge volume of videos. MFC and Image Retrieval and Analysis Tool (VIRAT) datasets are used for training and validation of the proposed model. The accuracy of the proposed method with the VIRAT dataset is 86.6% and with the MFC dataset 93%. The comparative analysis with stateof-the-art approaches has shown the robustness of the proposed approach.
Chemotherapy is an essential part of a multimodal strategy in the treatment of many cancers. Chemotherapy-induced hair loss is believed to affect 65 percent of people. According to the study, chemotherapy-induced hair loss has been associated to anxiety, depression, a poor body image, low self-esteem, and a decreased sense of health. Objectives: To find out chemotherapy-induced alopecia distress levels among cancer patients' in Punjab's public and private hospitals. To find out the relationship between demographic variables and chemotherapy induced alopecia distress. Methods: A cross sectional study was conducted in public and private hospitals of Punjab, over the duration of 6 months, from October 2021 to March 2022. A sample of 323 respondents with the age range 19-54 was obtained. Data collection tool was adapted version of chemotherapy-induced alopecia distress scale (CASD). Frequencies and percentages of categorical variables were reported and Chi-square test was used to find out associations. Results: High distress level was 61% (n=196) while low distress level was 39% (n=127). Majority of the sample population consisted of participants belonging to age group 18-34 (n=146, 45.2%). Most of them were male 53% (n=173). Respondents diagnosed at stage2 had low distress level (54%) as compare to respondents who were diagnosed at stage3 and stage 4. Significant association (p-value ≤0.05) was found between Gender, family income, employment status, disease stage at diagnosis, number of chemotherapy cycles received and current active treatment. Conclusion: Chemotherapy-induced alopecia distress was associated with all of five domains i.e. physical, emotional, daily activities, relationships and treatment. To reduce the suffering caused by alopecia in cancer patients, appropriate therapies must be developed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.