A STUDY ON THE WORK-LIFE BALANCE OF WOMEN TEACHERS IN PRIVATE SCHOOLS OF BANDGAON BLOCK IN WEST SINGHBHUM DISTRICT OF JHARKHAND

The paper takes an in- depth look at work life balance considering in view of Balance in work and family life is an emerging challenge for women teachers of private School. The job of working women has changed throughout the world due to economic conditions and social demands. Although the significance of the ability to read and write can not be undermined, it's arguable that in order to effectively inform socio-economic development, literacy alone isn't enough. Quality education is essential for a state or a country to make strides in ground breaking exploration and other developments to ameliorate the quality of life of citizens and to ensure equitable and sustainable development develop a career. From this pressure most of rural women choose teaching profession. The present research categorizes selected variables as work and family related factors to study work life balance. This paper analyzes the causes of work and life imbalance with respect to Women teachers. An aggregate of 36 teachers responses from primary, secondary and high school are included in the study. Average literacy rate of Bandgaon Block in 2011 were 54.46 in which, man and women literacy were 69.02 and 39.95 respectively. Total literate in Bandgaon Block were 805 of which man and woman were 547 an 258 respectively. The study also brings out the relation between the various factors such as age, time spent, levels of stress, working overtime and balancing work and personal life of women teaching professionals. This study also identifies main problems and challenges which faced by unwedded and wedded working women. Both qualitative (interviews) and quantitative (questionnaires) instruments are taken to fulfill the objectives. Random sampling method was used for collection of primary data through questionnaires and interviews

Field Data-based Mathematical Simulation Of Manual Rebar Cutting

Construction process activities are very complex in nature and there have been attempts to simulate them via numerous methods. Manual work, which constitutes a large proportion of total construction in India and developing countries, requires emphasis. Field data-based mathematical simulations develop an empirical relation between inputs and outputs; once the model is developed and weaknesses have been identified, methods can be easily improved and optimised for output goals. This paper covers in detail the process of developing models for the rebar cutting subactivity of reinforced concrete construction in residential buildings. These models are evaluated using sensitivity analysis, optimisation techniques and reliability analysis and are validated using artificial neural networks

Near-zero environmental impact aircraft

The fundamental challenge facing today's aviation industry is to achieve net zero climate impacts while simultaneously sustaining growth and global connectivity. Aviation's impact on surface air quality, which is comparable to aviation's climate impact when monetized, further heightens this challenge. Prior studies have proposed solutions that aim to mitigate either aviation's climate or air quality impacts. No previous work has proposed an aircraft-energy system that simultaneously addresses both aviation's climate and air quality impacts. In this paper we (1) use a multi-disciplinary design approach to optimize aircraft and propulsion systems, (2) estimate lifecycle costs and emissions of producing sustainable fuels including the embodied emissions associated with electricity generation and fuel production, (3) use trajectory optimization to quantify the fuel penalty to avoid persistent contrail formation based on a full year of global flight operations (including, for the first time, contrail avoidance for a hydrogen burning aircraft), and (4) quantify climate and air quality benefits of the proposed solutions using a simplified climate model and sensitivities derived from a global chemistry transport model. We propagate uncertainties in environmental impacts using a Monte-Carlo approach. We use these models to propose and analyze near-zero environmental impact aircraft, which we define as having net zero climate warming and a greater than 95% reduction in air quality impacts relative to present day. We contrast the environmental impacts of today's aircraft-energy system against one built around either "drop-in" fuels or hydrogen. We find that a "zero-impact" aircraft is possible using either hydrogen or power-to-liquid "drop-in" fuels. The proposed aircraft-energy systems reduce combined climate and air quality impacts by 99%, with fuel costs increasing by 40% for hydrogen and 70% for power-to-liquid fueled aircraft relative to today's fleet (i.e., within the range of historical jet fuel price variation). Beyond the specific case presented here, this work presents a framework for holistic analysis of future aviation systems that considers both climate and air quality impacts.The fundamental challenge facing the aviation industry is to achieve near-zero environmental impacts while sustaining growth. We propose a near-zero impact aircraft, taking a lifecycle perspective across fuels, aircraft design, and operation

Machine-Learning-Based Radiomics for Classifying Glioma Grade from Magnetic Resonance Images of the Brain

Grading of gliomas is a piece of critical information related to prognosis and survival. Classifying glioma grade by semantic radiological features is subjective, requires multiple MRI sequences, is quite complex and clinically demanding, and can very often result in erroneous radiological diagnosis. We used a radiomics approach with machine learning classifiers to determine the grade of gliomas. Eighty-three patients with histopathologically proven gliomas underwent MRI of the brain. Whenever available, immunohistochemistry was additionally used to augment the histopathological diagnosis. Segmentation was performed manually on the T2W MR sequence using the TexRad texture analysis softwareTM, Version 3.10. Forty-two radiomics features, which included first-order features and shape features, were derived and compared between high-grade and low-grade gliomas. Features were selected by recursive feature elimination using a random forest algorithm method. The classification performance of the models was measured using accuracy, precision, recall, f1 score, and area under the curve (AUC) of the receiver operating characteristic curve. A 10-fold cross-validation was adopted to separate the training and the test data. The selected features were used to build five classifier models: support vector machine, random forest, gradient boost, naive Bayes, and AdaBoost classifiers. The random forest model performed the best, achieving an AUC of 0.81, an accuracy of 0.83, f1 score of 0.88, a recall of 0.93, and a precision of 0.85 for the test cohort. The results suggest that machine-learning-based radiomics features extracted from multiparametric MRI images can provide a non-invasive method for predicting glioma grades preoperatively. In the present study, we extracted the radiomics features from a single cross-sectional image of the T2W MRI sequence and utilized these features to build a fairly robust model to classify low-grade gliomas from high-grade gliomas (grade 4 gliomas)

Impact of Design Constraints on Noise and Emissions of Derivative Supersonic Engines

13-C-AJFE-MIT-052, 059Open Access, published by the American Institute of Aeronautics and Astronautics, Inc., with permission. Citation: Impact of Design Constraints on Noise and Emissions of Derivative Supersonic Engines Prakash Prashanth, Laurens J. A. Voet, Raymond L. Speth, Jayant S. Sabnis, Choon S. Tan, and Steven R. H. Barrett Journal of Propulsion and Power 2023 39:3, 454-463. https://doi.org/10.2514/1.B38918The propulsion systems used in commercial supersonic transport (SST) aircraft, such as the Concorde, have used repurposed engines or derivative engines based on cores from existing donor engines rather than purpose-designed clean-sheet engines. A similar approach is currently being adopted in the development of new SSTs. Turbomachinery components and cooling mass flow rates in derivative engines are sized by the design cycle of the donor engine and constrain the design of the derivative engine cycle. Here, we identify the constraints imposed by the donor engines and quantify their impact on the specific fuel consumption (SFC), certification noise, and NOx (oxides of nitrogen) emissions index [EI(NOx)] relative to purpose-designed clean-sheet engines. We design and optimize a clean-sheet and derivative engine for a notional 55 metric ton SST proposed by NASA. A clean-sheet engine optimized for SFC results in an approximately 4.5% reduction in SFC, an approximately 2.5-fold increase in EI(NOx), and a 1.2 EPNdB increase in certification noise relative to the derivative engine. Applying a constraint on EI(NOx) to the clean-sheet engine results in an approximately 0.5%reduction in SFC relative to the derivative engine. The work provides a quantitative comparison of clean-sheet purpose-built engines and derivative engines from an environmental perspective that can inform policy makers as they develop updated environmental standards for civil supersonic aircraft