Perspectives on Women\u27s Studies from India: Strengths, Struggles and Implications for Programs in the U.S.

An important goal of Women’s Studies (WS) is the advancement of women’s rights not just locally, but on a global scale. How well this goal is accomplished will ultimately depend on the current WS curricula adapting to include international and transnational perspectives. This paper investigates how Indian-WS programs, with some comparisons to WS programs in the US, are meeting this challenge. It begins by tracing the development of WS and examines its curricula by conducting a content analysis of ten syllabi from Indian universities and offers reflections from WS practitioners in India. The research yields important insights on institutionalization of WS programs, its interdisciplinarity, pedagogy, theories, methods, and effects of globalization on societies on the curricula. It also reveals strengths and struggles of India-WS programs, which are compared directly to those of US-WS programs. Such a comparison of the programs will prove fruitful in developing effective transnational theories that truly address women’s issues on both a global and local scale and impact the long-term advancement of WS programs

Spirituality and Mental Health; an Unbreakable Interface

For downloading the full-text of this article please click here.Spirituality is broadly defined as something which everyone can experience, that helps us to find meaning and purpose in the things we value, brings hope and healing in times of suffering and loss and encourages us to seek a better relationship with ourselves For downloading the full-text of this article please click here.Please cite this article as: Bhatia SM. Spirituality and Mental Health; an Unbreakable Interface.Journal of Pizhūhish dar dīn va salāmat. 2020; 5(4): 1-6. https://doi.org/10.22037/jrrh.v5i4.2730

Types of coping as a determinant of quality of life of mothers of children with intellectual disability and autism

Background: It is well established that caregivers of children with developmental disabilities experience greater stress than caregivers of children without developmental disabilities. However, the experience of stress is dependent on the types of coping strategies that are used to manage stress. Such stress also affects the quality of life (QoL) of parents. Thus, parents of children with disabilities have specific mental health needs which play an important role in affecting their own and their child's QoL. Aim: To study coping as a determinant of QoL of mothers of children with intellectual disability (ID) and children with autism. Methodology: The sample consisted of 100 mothers of children (between 5-12 years of age) selected with purposive sampling, having an ID (n=50) or autism (n=50), diagnosed as per ICD-10 DCR criteria. In addition, the diagnostic assessment was also based on psychometric testing. Types of coping were assessed using Ways of Coping Questionnaires. QoL of caregivers was assessed by WHOQoL-BREF. Result: Multiple regression analysis revealed that seeking social support and planful problem solving were significant determinants of all four domains of QoL. Escape avoidance was a significant determinant of physical, psychological and social domains of QoL of mothers. Positive reappraisal significantly predicted the psychological and social domains of QoL in mothers. Conclusion: Types of coping are the determinant of  QoL of mothers of children with ID and autism. Keywords: Quality of life, coping, autis

Semantic segmentation of landcover for cropland mapping and area estimation using Machine Learning techniques

ABSTRACTThe paper has focussed on the global landcover for the identification of cropland areas. Population growth and rapid industrialization are somehow disturbing the agricultural lands and eventually the food production needed for human survival. Appropriate agricultural land monitoring requires proper management of land resources. The paper has proposed a method for cropland mapping by semantic segmentation of landcover to identify the cropland boundaries and estimate the cropland areas using machine learning techniques. The process has initially applied various filters to identify the features responsible for detecting the land boundaries through the edge detection process. The images are masked or annotated to produce the ground truth for the label identification of croplands, rivers, buildings, and backgrounds. The selected features are transferred to a machine learning model for the semantic segmentation process. The methodology has applied Random Forest, which has compared to two other techniques, Support Vector Machine and Multilayer perceptron, for the semantic segmentation process. Our dataset is composed of satellite images collected from the QGIS application. The paper has derived the conclusion that Random forest has given the best result for segmenting the image into different regions with 99% training accuracy and 90% test accuracy. The results are cross-validated by computing the Mean IoU and kappa coefficient that shows 93% and 69% score value respectively for Random Forest, found maximum among all. The paper has also calculated the area covered under the different segmented regions. Overall, Random Forest has produced promising results for semantic segmentation of landcover for cropland mapping

A rare case of ruptured caesarean scar pregnancy

Caesarean scar pregnancy (CSP) is a rare form of ectopic pregnancy. The incidence is approximately 1:2000 pregnancies and has potentially life-threatening complications. Ours is a rare case of scar ectopic pregnancy who had taken medical termination of pregnancy (MTP) kit while being unaware of her pregnancy location and presented with uterine rupture and hemoperitoneum. A 24-year-old female, P2L2A1, with previous two caesarean section (CS), presented with the complaint of bleeding per vaginum with acute pain abdomen and history of MTP kit intake at 7 weeks’ period of gestation (POG). She received symptomatic treatment at local hospital without any diagnosis being made but brought an ultrasound showing anterior myometrium defect with scar site hematoma and free fluid. She presented with moderate pallor, tachycardia and suprapubic tenderness. She was subsequently taken up for laparotomy in view of probable ruptured CSP. Intra-operatively, actively bleeding scar ectopic was seen with hemoperitoneum. The contents were scooped out and repair done with bilateral tubal ligation. She was resuscitated with adequate blood products. Embryo implantation in the region of a previous CS scar is rare and a delay in either diagnosis or treatment can have catastrophic complications like haemorrhage, rupture and significant maternal morbidity as seen in our case. Therefore, we should have a high index of suspicion of scar pregnancy especially in cases of previous CS so that timely intervention can be done preventing maternal morbidity. Unwarranted use of misoprostol can be deleterious when site of implantation is unknown, particularly in CSP

Assessment of nodal target definition and dosimetry using three different techniques: implications for re-defining the optimal pelvic field in endometrial cancer

1. To determine the optimal pelvic nodal clinical target volume for post-operative treatment of endometrial cancer. 2. To compare the DVH of different treatment planning techniques applied to this new CTV and the surrounding tissues. Based on the literature, we selected a methodology to delineate nodal target volume to define a NEW-CTV and NEW-PTV. Conventional 2D fields, 3D fields based on anatomic guidelines per RTOG 0418, 3D fields based on our guidelines, and IMRT based on our guidelines were assessed for coverage of NEW-CTV, NEW-PTV, and surrounding structures. CT scans of 10 patients with gynecologic malignancies after TAH/BSO were used. DVHs were compared. For NEW-PTV, mean V45Gy were 50% and 69% for 2D and RTOG 0418-3DCRT vs. 98% and 97% for NEW-3DCRT and NEW-IMRT (p < 0.0009). Mean V45Gy small bowel were 24% and 20% for 2D and RTOG 0418-3DCRT, increased to 32% with NEW-3DCRT, and decreased to 14% with IMRT (p = 0.005, 0.138, 0.002). Mean V45Gy rectum were 26%, 35%, and 52% for 2D, RTOG 0418-3DCRT, and NEW-3DCRT, and decreased to 26% with NEW-IMRT (p < 0.05). Mean V45Gy bladder were 83%, 51%, and 73% for 2D, RTOG 0418-3DCRT, and NEW-3DCRT, and decreased to 30% with NEW-IMRT (p < 0.002). Conventional 2D and RTOG 0418-based 3DCRT plans cover only a fraction of our comprehensive PTV. A 3DCRT plan covers this PTV with high doses to normal tissues, whereas IMRT covers the PTV while delivering lower normal tissue doses. Re-consideration of what specifically the pelvic target encompasses is warranted

Gaussian Process Regression (GPR) Method for the Prediction of Rate Coefficients of Gas-phase Reactions in Chemical Ionization Mass Spectrometry

Reaction kinetics of chemical ionization mass spectrometry (CI-MS) based ion-molecule reactions is an important component in the quantification of trace-level volatile organic compounds (VOCs). The rate coefficients of such CI-MS reactions are predicted using the Gaussian process regression (GPR) machine learning method from the dipole moment, polarizability, and molecular weight of the molecules, mitigating experimental complexity in CI-MS rate coefficient estimation. GPR can make predictions combining prior knowledge (kernel function) which is considered the heart of the GPR model and provide uncertainty measures over predictions. A suitable kernel combination with proper tuning of parameters can make the Gaussian process more robust and powerful. Various kernel combinations, such as kernel addition and multiplication, are tested in the GPR prediction of rates. A blend of radial basis function (RBF), white noise, and squared exponential kernel performs better, and the predicted rates are in close agreement with the experimental rates. GPR provides an alternative to the capture collision rates and can be useful when there are no experimental data available and/or the available data contain large uncertainty in the rate coefficients

A Gaussian process regression (GPR) quest to predict HOMO-LUMO energy

Machine learning methods employ statistical algorithms and pattern recognition techniques to learn patterns and make predictions based on statistical patterns. Global reactivity descriptors, such as HOMO-LUMO energy, chemical potential (µ), chemical hardness (η), softness (σ) and electrophilic index (ω) are predicted using Gaussian process regression (GPR) machine learning method. GPR predicted values are in close agreement with the values obtained via ab initio methods. Over 85% prediction accuracy in HOMO energies and reactivity parameters is observed, while LUMO energies were in good range with the DFT evaluations. An appropriate kernel combination with proper tuning of parameters and the selection of quality correlated data can make the GPR model robust and powerful. Machine learning models like GPR could play a pivotal role in assisting and accelerating ab initio calculations and providing insights for highly complex molecular systems