Ergonomic Assessment and Musculoskeletal Health of the Underpri¬vileged School Children in Pune, India

Background: Musculoskeletal health in schoolchildren is a global health problem. The objective of the current study was to assess ergonomic be¬havior and muscu-loskeletal health in urban poor schoolchildren in Pune, India. Methods: Sixty-five (29 male students) slum dwelling schoolchildren were assessed for their ergonomics using a validated and reliable tool. Results: Average age was 13 years. Out of 65 students, 36 reported pres¬ence of musculoskeletal pain. In addition, 78.5% had bad ergonomics when lifting books. Sitting posture was poor in 67.7%, while 50.8% dem¬onstrated bad posture while using a computer. As far as the female stu¬dents were concerned, it was seen that out of 36 students, 55.6% com¬plained of presence of pain. Out of the 20 students who reported pain, 70% had a score lower than 7.5 on ergonomic score assessment (indicative of bad ergonomics). When the male students were considered, out of 29 students, 65.5% reported presence of pain. Out of the 19 students who reported pain, close to 79% demonstrated bad ergonomics. Conclusion: There is presence of musculoskeletal aches and pains in this under-privileged section of the society, including demonstration of bad er¬gonomics and postures while performing activities in school. Thus, mus¬culoskeletal health prob-lems exist in urban slum schoolchildren. It is im¬portant to identify health problems at an early stage in both boys and girls. Children are the future of the nation and hence, identification of health issues early in life and preventing them from becoming chronic is of utmost importance

Thermomagnetic convection in stratified ferrofluids permeated with dusty particles through a porous medium

In the present note, the stability problem of an incompressible dusty stratified ferromagnetic fluid is investigated through a porous medium when the fluid layer is subjected to vertical magnetic field intensity. The governing nonlinear equations are linearized using perturbation technique and the cases of exponentially varying stratifications for various physical parameters are discussed. The system is found to be stable for stable stratification in both the absence and presence of magnetic field. For unstable stratification, the system has both stabilizing and destabilizing effects in the presence of magnetic field under certain conditions, whereas in the absence of magnetic field, the system has only destabilizing effect. The variations in the growth rate with respect to kinematic viscosity, medium porosity, medium permeability, square of Alfvén velocity and suspended particle parameter are also shown analytically

Electrophoretic deposition of hydroxy-apatite on Ti6Al4V

In the present study, electrophoretic deposition of sintered and non sintered hydroxyapatite on Ti6Al4V has been carried out at a constant voltage of 30V for 5, 10 and 15 minute duration at different pH values of 1.5, 2.5 and 3.5 respectively for improving the property of coated layer. An uniform coating of hydroxyapatite (HAp) will results in a better cellular activity. As-received Ti-6Al-4V samples are polished by following the standard metallographic technique to prepare a scratch free surface. After coating under different processing conditions the surface are characterized by scanning electron microscopy (SEM) for morphological studies and x-ray diffraction technique for phase analysis. SEM images show that at lower pH value 1.5 and constant voltage 30V crack were not present at coating surface. Comparison between sintered (900˚ for 2 hrs) and non-sintered hydroxyapatite deposition and phase characterization analysis was done by XRD analysis. XRD analysis indicated that before sintering, some other compounds were present in HAp powder but after sintering they were not oxidized. The presence of chemical elements on the deposited surface was assessed by EDS. (Keywords: Ti6Al4V, Electrophoretic deposition, Hydroxyapatite, XRD, SEM, EDS

Gender Gating? Addressing Congestion to Improve Women\u27s Welfare on Online Matching Platforms

The problem of worsening gender skew is particularly damaging in the context of matching platforms since it affects the welfare and user experience of women participants. In this paper, we look at how a platform-level intervention could reduce the congestion for women and thus improve their overall user experience on the platform. We specifically look at a form of gender gating intervention in a leading matrimonial platform within one of their sub-domains. The intervention restricts the profile visibility of women users based on age, education, income, and marital status related to social norms. Our analysis shows that the platform-level intervention had the desired effect – women in the treatment group received fewer unwanted requests for contacts, experienced more matches, and initiated more contacts themselves, representing a better user experience. Our work extends the platform literature by studying how platform owners can improve Women\u27s welfare on matching platforms through market design

Skin Cancer Detection and Classification

Skin cancer is a term given to the uncontrolled growth of strange skin cells. It occurs whenever unrepaired DNA damages to skin cells trigger mutations, or any other genetic defects, that lead the skin cells to multiply readily and form malignant tumors. Image processing is a commonly used method for skin cancer detection from the appearance of the affected area on the skin. The input to the system is that the skin lesion image so by applying novel image process techniques, it analyses it to conclude about the presence of skin cancer. The Lesion Image analysis tools checks for the various Melanoma parameters Like Asymmetry, Border, Colour, Diameter, (ABCD rule), etc. by texture, size and form analysis for image segmentation and have stages. The extracted feature parameters are accustomed classify the image as traditional skin and malignant melanoma cancerlesion. Artificial Neural Network (ANN) is one of the important branches of Artificial Intelligence, which has been accepted as a brand-new technology in computer science for image processing. Neural Networks is currently the area of interest in medicine, particularly in the fields of radiology, urology, cardiology, oncology, etc. Neural Network plays a vital role in an exceedingly call network. It has been used to analyze Melanoma parameters Like Asymmetry, Border, Colour, Diameter, etc. which are calculated using MATLAB from skin cancer images intending to developing diagnostic algorithms that might improve triage practices in the emergency department. Using the ABCD rules for melanoma skin cancer, we use ANN in the classification stage. Initially, we train the network with known target values. The network is well trained with 96.9% accuracy, and then the unknown values are tested for the cancer classification. This classification method proves to be more efficient for skin cancer classification

PREDICTION OF ANTI-PARKINSON POTENTIAL OF PHYTOCONSTITUENTS USING PREDICTION OF ACTIVITY SPECTRA OF SUBSTANCES SOFTWARE

Objective: Neurodegenerative disorders are group of diseased conditions in which there is loss of neuron cells occur. The main objective of this study to find/search out the phytochemical with the help of prediction of activity spectra of substances (PASSs), those show maximum activity over the selected targets of the Parkinson's disease (PD).Methods: PASSs is a valuable software which is used in this study, to predict the anti-Parkinson activity of different compounds. Canonical simplified molecular-input line-entry system is used for the prediction of anti-Parkinson activity which is obtained from PubChem website. The predicted activity also compared with marketed compound like levodopa.Results: From the study, it was found that resveratrol was the only compound which has the activity on all the selected targets. On the other hand, stemazole and celastrol were found to have the least active compounds as both have the activity only on a single target.Conclusion: In this research work, we tried to compile the information regarding the PASS predicted anti-Parkinson activity of some important phytoconstituents. We found that resveratrol can be a target for further investigation in the development of drug therapy for PD

Improving Syntactic Relationships Between Language and Objects

This paper presents the integration of natural language processing and computer vision to improve the syntax of the language generated when describing objects in images. The goal was to not only understand the objects in an image, but the interactions and activities occurring between the objects. We implemented a multi-modal neural network combining convolutional and recurrent neural network architectures to create a model that can maximize the likelihood of word combinations given a training image. The outcome was an image captioning model that leveraged transfer learning techniques for architecture components. Our novelty was to quantify the effectiveness of transfer learning schemes for encoders and decoders to qualify which were the best for improving syntactic relationships. Our work found the combination of ResNet feature extraction and fine-tuned BERT word embeddings to be the best performing architecture across two datasets - a valuable discovery for those continuing this work considering the cost of compute for these complex models

W8 - Curvature Tensor in Generalized Sasakian Space Forms

The generalized Sasakian-space-forms and their properties have been examined by various researchers such as Alegre and Carriazo [2008], Prakasha [2012], Sarkar and Akbar [2014], Shanmukha et al. [2018], Sarkar and Sen [2012], Rajan and Singh [2020] and Sarkar and Sen [2012]. Motivated by the results of these works, we have proposed the idea of the W8−curvature tensor in generalized Sasakian-space- forms. The main goal of this paper is to investigate the curvature properties of generalized Sasakian-space-forms that satisfy the conditions ξ − W8− flatness, ϕ − W8−semi-symmetric, W8 · Q = 0, W8 · R = 0 and to prove some interesting results