Volume 10 | Issue 6 |

 Abstract

Shvahs means breathing. Normally human being breathes fifteen times in an average in a minute including both - inhalation and exhalation. However, during the outburst of emotions or anxiety, the breathing rate increases to twenty to twenty five breaths per minute and may increase further in the intensity of the emotions. It is believed that rapid breathing is often harmful to the health and may disturb the blood circulation. Meditation (dhyana) becomes more effective in the above cases if it is built upon asana (physical posture), pranayama (breath control), pratyahara (control of the senses, moving the focus to the inside), and dharana (concentration). We know that during inhalation, the lungs get filled with the air and the diaphragm is lowered and in turn expands the abdomen. In contrast, during exhalation the lungs contract along with the abdominal muscles. If we add our attention with the breathing, we can see the breath as well as perceive our consciousness. Regular practice of perception of breathing imparts physical, psychological, and spiritual benefits to the practitioner.

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Shvash-Preksha, Perception of Breathing and Preksha Meditation

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Online purchase behaviour, Rural and urban consumers in Ranga Reddy and Karimnagar districts of Telangana State, E-tailers, Online shopping, Electronic commerce, Consumer expectations.

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Nowadays, brain signals are employed in various scientific and practical fields such as Medical Science, Cognitive Science, Neuroscience, and Brain Computer Interfaces. The brain signal analysis is faced with complex challenges including small sample size, high dimensionality and noisy signals. The detection of EEG brain signal in patient to distinguish between normal and abnormal which are tumor and epilepsy, therefore, a very demanding process that requires a detailed analysis of the entire length of the EEG data. This paper describes an automated classification of EEG signals for the detection of normal and abnormal using NSCT and ANN Classifier. Feature extracted from input EEG signals. The performance of the proposed algorithm has been evaluated using in EEG signals. The results showed that the proposed classifier has the ability of classifying EEG signals efficiently.

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EEG Signal, Artificial neural networks, non subsampled contourlet transform

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Now-a-days with the growing industries large amount of waste products are produced. Disposal of these waste materials are required as they cause harm to human and other life. For its disposal large area is covered. As the production of waste is increasing day by day, we need to find a substitute to overcome this problem. Fly ash is a waste product produced due to burning of coal in power generating plants. Concrete consists of cement, sand, fine and coarse aggregate. Production of cement emits large quantity of greenhouse gases. Due to increase in construction work, demand for cement will also increase. So, to avoid production of greenhouse gases and to produce environment friendly concrete, cement is replaced with certain percentage of fly ash. Replacement of cement with certain percentage of fly ash is to improve the strength of pavement concrete. The fly ash used in this study is from NTPC, Badarpur, Delhi and is of Class B. In the experimental study the replacement of OPC (Ordinary Portland Cement) with fly ash is done and to get the desired workability and strength. Polymer based superplasticizer is used for all grades of concrete. The compressive and flexural strength of PQC (Pavement Quality Concrete) with different percentage of fly ash has been studied for 3, 14 and 28 days. From the experimental results it has been noted that without superplasticizer the compressive strength of Pavement Concrete is less for both 7days and 28 days. With the use of superplasticizer, the desired compressive strength is achieved. But in case of flexural strength even without superplasticizer concrete attains the required strength and with superplasticizer the flexural strength of PQC achieve higher strength.

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EFFECT OF FLY ASH ON STRENGTH OF PAVEMENT QUALITY CONCRETE

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ON THE PAPER

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BHOOJAL,JALSTAR,KRISHI,SHINCHAI,JALAPURTI,KISAN,KHETI

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Recent studies show the potential of artificial intelligence (AI) as a screening tool to detect COVID-19 pneumonia based on chest x-ray (CXR) images. However, issues on the datasets and study designs from medical and technical perspectives, as well as questions on the vulnerability and robustness of AI algorithms have emerged. In this study, we address these issues with a more realistic development of AI-driven COVID-19 pneumonia detection models by generating our own data through a retrospective clinical study to augment the dataset aggregated from external sources. We optimized five deep learning architectures, implemented development strategies by manipulating data distribution to quantitatively compare study designs, and introduced several detection scenarios to evaluate the robustness and diagnostic performance of the models. At the current level of data availability, the performance of the detection model depends on the hyperparameter tuning and has less dependency on the quantity of data. InceptionV3 attained the highest performance in distinguishing pneumonia from normal CXR in two-class detection scenario with sensitivity (Sn), specificity (Sp), and positive predictive value (PPV) of 96%. The models attained higher general performance of 91-96% Sn, 94-98% Sp, and 90-96% PPV in three-class compared to four-class detection scenario. InceptionV3 has the highest general performance with accuracy, F1-score, and g-mean of 96% in the three-class detection scenario. For COVID-19 pneumonia detection, InceptionV3 attained the highest performance with 86% Sn, 99% Sp, and 91% PPV with an AUC of 0.99 in distinguishing pneumonia from normal CXR. Its capability of differentiating COVID-19 pneumonia from normal and non-COVID-19 pneumonia attained 0.98 AUC and a micro-average of 0.99 for other classes.

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To develop a novel method for COVID-19 pneumonia in lungs with generation of appointment

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Cardiovascular disease, aging.

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The paper tries to elucidate the relationship between spirituality and psychological well-being in young adult professionals of Chhattisgarh. The role of spirituality in enhancing mental health and well-being of an individual. It was hypothesized that spiritual working professionals will show better psychological well-being than non-spiritual working professionals. The sample includes 50 participants, 25 from spiritual community(performing meditation, journaling, and gratitude practices on regular basis), and 25 from non-spiritual community. The statistical analysis was done using independent t-test and a significant difference was found between two groups. The paper concluded that there is a difference between well-being of people who are involved in the spiritual activities than the people who are not. And that promoting spiritual practices can be beneficial in enhancing mental health of working people and it can be a cure to their psychological challenges

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SPIRITUALITY, NON-SPIRITUALITY, PSYCHOLOGICAL WELLBEING, PROFESSIONALS

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High-Rise Building are considered between 23 m to 150 m high. Buildings giant than 150 m are considered as Skyscrapers. Hence, the utmost critical issue for the structural engineer is the height of the building. As the high-rise structures has many technical issues but the most susceptible issues are considered to be seismic and wind forces acting in lateral directions. This project work symbolizes the study of behavior and identification of the existence of stiffness irregularity if swimming pool is positioned at different height of Building Using ETABS. The main target is to achieve the effective position of swimming pool which can be applied in the high-rise building.

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High-Rise, Swimming Pool, Stiffness, Dynamic

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