To Study the Effect of Job Satisfaction on the Performance of Academic Faculties Working in Private Colleges and Private Universities in Indore

The objective of the existing study was to examine the impact of job satisfaction on the performance of employees working in private colleges and universities in Indore, India. To achieve this, questionnaires were distributed to a total of 60 employees, out of which 54 responses were received and considered as the sample from private colleges and universities in Indore. An equal number of employees (n = 54) were randomly selected from different types of organizations, including undergraduate and postgraduate colleges, as a comparison group. The study utilized a self-constructed questionnaire based on the Minnesota Satisfaction Questionnaire (MSQ-quick form) developed by Weiss et al. (1967), as well as a self-constructed Performance Evaluation Form (PRF). Initially, the reliability of both instruments was assessed to determine the significance of the scales. The study findings indicated a significant correlation between the type of occupation and job satisfaction. Moreover, a positive relationship between job satisfaction and employee performance was also observed. Therefore, the study concluded that satisfied employees performed better compared to dissatisfied employees, thus playing a significant role in the advancement of their organizations. Consequently, it is crucial for every organization to adopt specific strategies and methods to motivate and ensure employee satisfaction, thereby promoting high performance

Review of Reconfigurable Microstrip Patch antenna for Wireless Application

In recent time, world have seen a rapid growth in wireless communication. Development in antenna from single band to dual band and multi band had made the antenna system more compact. A frequency reconfigurable microstrip antenna using a PIN diode for multiband operation is using many application and hot research area. In this paper, reconfigurable microstrip patch antennas and their types like frequency, polarization, radiation pattern and gain are described

Community Detection over Social Media: A Compressive Survey

Social media mining is an emerging field with a lot of research areas such as, sentiment analysis, link prediction, spammer detection, and community detection. In today’s scenario, researchers are working in the area of community detection and sentiment analysis because the main component of social media is user. Users create different types of community in social world. The ideas and discussions in the community may be negative or positive. To detect the communities and their behavior researcher have done a lot of work, but still two major issues are presents per survey, Scalability and Quality of the community. These issues of community detection motivate to work in this area of social media mining. This paper gives a bird eye view over social media and community detection

Sperm penetration assay and its correlation with semen analysis parameters

Background: Aim of current study was to determine whether the Sperm Penetration Assay (SPA) can be used as a test to discriminate the infertile male from fertile one. We have also correlated the SPA with semen analysis.Methods: Sperm characteristics namely Semen analysis and the sperm penetration assay were tested in 44 infertile and 10 fertile men. Sperm penetration assay was determined by using zona free hamster eggs.Results: With decreasing spermatozoa concentration in the semen there was significant decrease in percentage penetration of zona free Hamster eggs (p0.05).  Conclusions: The Sperm penetration assay could discriminate the infertile group from fertile group significantly (p<0.001). The test appeared to be highly reproducible and probably identifies a truly infertile male.

Infra-red Spectroscopic Studies of GdBaCo2O5.5

This paper reports infrared spectroscopic studies on GdBaCo$_{2}$O$_{5.5}$ layered perovskite which exhibits successive magnetic transitions from paramagnetic to ferromagnetic to antiferromagnetic states as well as high temperature metal to insulator transition and a change in charge transport mechanism at low temperature. Infrared absorption spectra recorded at various temperatures in the range 80 K to 350 K reveal changes in the positions of Co-O stretching and bending frequencies which provide an explanation to the magnetic and transport behaviour of this compound.Comment: 5 figure

Characterization of Two Novel Variants of the Steroidogenic Acute Regulatory Protein Identified in a Girl with Classic Lipoid Congenital Adrenal Hyperplasia

Congenital adrenal hyperplasia (CAH) consists of several autosomal recessive disorders that inhibit steroid biosynthesis. We describe a case report diagnosed with adrenal insufficiency due to low adrenal steroids and adrenocorticotropic hormone excess due to lack of cortisol negative feedback signaling to the pituary gland. Genetic work up revealed two missense variants, p.Thr204Arg and p.Leu260Arg in the STAR gene, inherited by both parents (non-consanguineous). The StAR protein supports CYP11A1 enzyme to cleave the side chain of cholesterol and synthesize pregnenolone which is metabolized to all steroid hormones. We used bioinformatics to predict the impact of the variants on StAR activity and then we performed functional tests to characterize the two novel variants. In a cell system we tested the ability of variants to support cholesterol conversion to pregnenolone and measured their mRNA and protein expression. For both variants, we observed loss of StAR function, reduced protein expression and categorized them as pathogenic variants according to guidelines of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. These results fit the phenotype of the girl during diagnosis. This study characterizes two novel variants and expands the list of missense variants that cause CAH

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Accelerating Multiparametric MRI for Adaptive Radiotherapy

MR guided Radiotherapy (MRgRT) marks an important paradigm shift in the field of radiotherapy. Superior tissue contrast of MRI offers better visualization of the abnormal lesions, as a result precise radiation dose delivery is possible. In case of online treatment planning, MRgRT offers better control of intratumoral motion and quick adaptation to changes in the gross tumor volume. Nonetheless, the MRgRT process flow does suffer from some challenges that limit its clinical usability. The primary aspects of MRgRT workflow are MRI acquisition, tumor delineation, dose map prediction and administering treatment. It is estimated that the acquisition of MRI takes around 50% of the entire process. Further, delineating the tumor volumes and generating the dose map plans are labor-intensive and time-consuming yet necessary to prevent radio necrosis and associated toxicity. To this end, this dissertation focuses on the two important aspects of MRgRT. First, acceleration of reconstruction of multiparametric MRI (mpMRI). Second, prediction of precise dose maps from the pre-radiation therapy mpMRI sequences without the need of manual contouring. A joint reconstruction algorithm to accelerate the reconstruction of a series of complex T1w images, T1 and proton density maps simultaneously from the undersampled k-space data is presented. The ambiguity introduced by undersampling is resolved using model-based constraints, and structural information from a reference fully sampled image as the joint total variation prior. The algorithm is extended with minor modifications to accelerate the reconstruction of complex T2w, T2*w images and their parameter maps. Validation of the reconstructed images and parameter maps was carried out by computing tissue-type maps, as well as the maps of the Proton Density Fat Fraction (PDFF), Proton Density Water Fraction (PDwF), fat relaxation rate R_2f^* and water relaxation rate R_2w^* from the reconstructed data by comparing them with Ground Truth (GT) equivalents. It is demonstrated that using only 18% k-space data, it is possible to identify the tissue type maps like fluid, muscle, tumor and adipose with the same fidelity as that obtained using GT data. The mean T1 and T2 values in each tissue type were computed using only 18% k-space data, which were within 8%-10% of the GT values from fully sampled data. The PDFF and PDwF maps computed using 27% k-space data were within 3%-15% of GT values and showed good agreement with the expected values for the four tissue types. The next task focuses on directly predicting the optimum Radiation Therapy (RT) dose maps from the pre-RT mpMRI. It is now well established that the tumor volume comprises several different microenvironments. Hence, predicting a voxel-wise dose map from the pre-RT and prescribed/desirable post-RT mpMRI will yield better control of radionecrosis-related toxicity. Furthermore, it is also important for the radiation oncologist to simulate voxel-wise radiologic outcomes of specific RT dose map prescriptions on post-RT mpMRI. To accomplish these two tasks, end-to-end deep neural networks are trained. The forward model is used to predict post-RT changes on mpMRI using pre-RT mpMRI when administered with the radiation dose map. A variant of the pix2pix GAN network is trained to predict post-RT ADC maps, T1wCE, T2w, T1w, FLAIR MRI from pre-RT mpMRI and the radiation dose maps. The results of the forward model are validated by identifying the tissue type maps like blood volume, gray matter, white matter, edema, non-enhancing tumor, contrast enhancing tumor, hemorrhage, fluid and comparing them with the GT maps. Further, the quantitative validation is carried out by comparing the percentage of volumes of these tissue type maps from pre-RT, post-RT and predicted post-RT mpMRI. The results of the forward model are also tested with the simulated dose maps and comparing the changes on the predicted post-RT ADC maps that are mechanistically relatable to voxel-level tumor response to therapies. Next, a variant of pix2pix GAN is trained to predict the radiation dose maps from the pre-RT ADC maps and the prescribed post-RT ADC maps. This is called as the inverse model. It is determined from the simulated results that to achieve higher ADC values, higher RT dose maps are required. In summary, the results of the feasibility study showed that it is possible to identify various tissue type habitats from the reconstructed mpMRI scans using only 18% k-space data. This dissertation also highlights that it is possible to alleviate the manual aspects of Radiation Therapy planning by using pre-RT and post-RT mpMRIs to predict the Radiation dose maps

Accelerating Multiparametric MRI for Adaptive Radiotherapy

MR guided Radiotherapy (MRgRT) marks an important paradigm shift in the field of radiotherapy. Superior tissue contrast of MRI offers better visualization of the abnormal lesions, as a result precise radiation dose delivery is possible. In case of online treatment planning, MRgRT offers better control of intratumoral motion and quick adaptation to changes in the gross tumor volume. Nonetheless, the MRgRT process flow does suffer from some challenges that limit its clinical usability. The primary aspects of MRgRT workflow are MRI acquisition, tumor delineation, dose map prediction and administering treatment. It is estimated that the acquisition of MRI takes around 50% of the entire process. Further, delineating the tumor volumes and generating the dose map plans are labor-intensive and time-consuming yet necessary to prevent radio necrosis and associated toxicity. To this end, this dissertation focuses on the two important aspects of MRgRT. First, acceleration of reconstruction of multiparametric MRI (mpMRI). Second, prediction of precise dose maps from the pre-radiation therapy mpMRI sequences without the need of manual contouring. A joint reconstruction algorithm to accelerate the reconstruction of a series of complex T1w images, T1 and proton density maps simultaneously from the undersampled k-space data is presented. The ambiguity introduced by undersampling is resolved using model-based constraints, and structural information from a reference fully sampled image as the joint total variation prior. The algorithm is extended with minor modifications to accelerate the reconstruction of complex T2w, T2*w images and their parameter maps. Validation of the reconstructed images and parameter maps was carried out by computing tissue-type maps, as well as the maps of the Proton Density Fat Fraction (PDFF), Proton Density Water Fraction (PDwF), fat relaxation rate R_2f^* and water relaxation rate R_2w^* from the reconstructed data by comparing them with Ground Truth (GT) equivalents. It is demonstrated that using only 18% k-space data, it is possible to identify the tissue type maps like fluid, muscle, tumor and adipose with the same fidelity as that obtained using GT data. The mean T1 and T2 values in each tissue type were computed using only 18% k-space data, which were within 8%-10% of the GT values from fully sampled data. The PDFF and PDwF maps computed using 27% k-space data were within 3%-15% of GT values and showed good agreement with the expected values for the four tissue types. The next task focuses on directly predicting the optimum Radiation Therapy (RT) dose maps from the pre-RT mpMRI. It is now well established that the tumor volume comprises several different microenvironments. Hence, predicting a voxel-wise dose map from the pre-RT and prescribed/desirable post-RT mpMRI will yield better control of radionecrosis-related toxicity. Furthermore, it is also important for the radiation oncologist to simulate voxel-wise radiologic outcomes of specific RT dose map prescriptions on post-RT mpMRI. To accomplish these two tasks, end-to-end deep neural networks are trained. The forward model is used to predict post-RT changes on mpMRI using pre-RT mpMRI when administered with the radiation dose map. A variant of the pix2pix GAN network is trained to predict post-RT ADC maps, T1wCE, T2w, T1w, FLAIR MRI from pre-RT mpMRI and the radiation dose maps. The results of the forward model are validated by identifying the tissue type maps like blood volume, gray matter, white matter, edema, non-enhancing tumor, contrast enhancing tumor, hemorrhage, fluid and comparing them with the GT maps. Further, the quantitative validation is carried out by comparing the percentage of volumes of these tissue type maps from pre-RT, post-RT and predicted post-RT mpMRI. The results of the forward model are also tested with the simulated dose maps and comparing the changes on the predicted post-RT ADC maps that are mechanistically relatable to voxel-level tumor response to therapies. Next, a variant of pix2pix GAN is trained to predict the radiation dose maps from the pre-RT ADC maps and the prescribed post-RT ADC maps. This is called as the inverse model. It is determined from the simulated results that to achieve higher ADC values, higher RT dose maps are required. In summary, the results of the feasibility study showed that it is possible to identify various tissue type habitats from the reconstructed mpMRI scans using only 18% k-space data. This dissertation also highlights that it is possible to alleviate the manual aspects of Radiation Therapy planning by using pre-RT and post-RT mpMRIs to predict the Radiation dose maps