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The primary challenge facing the electricity transmission system in the aftermath of the disruption is reestablishing stability. Through adjustment of the transmission parameters, the Flexible AC Transmission System (FACTS) devices have the potential to bring the system back into stable operation. It has been shown that using many FACTS devices together is more effective than using any one device alone. In this paper, the concept of coordination control between a Static Var Compensator (SVC) and a Static Synchronous Series Compensator (SSSC) is suggested in order to make the system more stable. The equivalent impedance of the line may be reduced by using the SSSC, which will also result in improved active power transfer capabilities for the line. A static var compensator, often known as an SVC, is used for shunt compensation in order to keep the bus voltage magnitude stable. When compared to the use of a single device, the use of SVC and SSSC coordinated control results in a much quicker arrival to the steady state response of the system. Both of these devices are controlled by using the same signal input tracking. It has also been seen that the system has resumed its previous synchronized state.

After a disruption in the power transmission system, the most crucial challenge is restoring the system’s stability. The FACTS devices can manipulate the transmission parameters to bring the system back into stable operation. This paper discusses the operational concepts and applications of FACTS in power systems such as the Static Var Compensator (SVC) and the Static Synchronous Series Compensator (SSSC). When combined with the other devices, the effectiveness of each individual device is increased. This paper proposes the coordinated control of SVC and SSSC to improve system stability. The SSSC can be utilized to lower the line’s equivalent impedance and improve its operational power transfer capabilities. The SVC is used for shunt compensation to maintain bus voltage magnitude. Furthermore, the steady-state response of the system is faster when SVC and SSSC coordinated control is employed compared to a single device. The resultant variable signal of the SSSC controller is tracked to control the SVC controller. A significant fault is introduced in the tie-line of the inter-area system to analyse and validate the proposed coordinated control. MATLAB/Simulation results are presented for the proposed approach, and also a real-time simulator is used to validate the simulation results.

The monsoon on the Indian subcontinent remains a seasonal occurrence that all inhabitants of the subcontinent desire. Modern scientific knowledge of methods of rain forecasting has originated recently. However, traditional indigenous wisdom is peculiar to our country. In the past, India had a magnificent scientific and technical legacy. Even today, it is common that village astrologers (pandits) are right in a surprisingly high percentage of their rain predictions. Scientists and local traditional farmers have a long history of astronomical research and treatises that predict rainfall. They use different methods to forecast rainfall conditions based on numerous panchangs, almanac bio-indicators (Bhoum method), non-bio-indicators (Antariksh method), and predict the likely behavior of climate in the planting season. Rainfall forecasting also aids in the planning of operations by agriculturists, builders, water supply engineers, and others. All mortals from the subcontinent have looked at it from their own perspective, and it continues to be the subject of intense multi-dimensional engagement. The monsoon has provided a means of life for numerous civilizations while also shaping the drainages and palaeo-geography of the subcontinent. The objective of this article is to document some indigenous knowledge for forecasting climate and environmental dynamics towards community resilience.

A variant of neural network for processing with images is a convolutional neural network (CNN). This type of neural network receives input from an image and extracts features from the image while also providing learnable parameters to effectively do the classification, detection, and many other tasks. In the present work, U-Net convolutional neural network is implemented on Jupyter platform by using Python programming for fracture surface image segmentation in Oil Hardening Non-Shrinking (OHNS) die steel after the machining process. The results showed that the fracture cracks can be validated by testing with higher accuracy. The plot of accuracy vs. number of epochs showed the obtained accuracy score 0f 1.0 which means that 100 % of points were correctly labeled by our implemented algorithm.

Ghosal hematodiaphyseal dysplasia (GHDD) is an autosomal recessive disorder characterized by diaphyseal dysplasia of long bones, bone marrow fibrosis, and steroid-responsive anemia. Patients with this disease have a mutation in the thromboxane-AS1 (TBXAS1) gene located on chromosome 7q33.34. They present with short stature, varying grades of myelofibrosis, and, hence cytopenias. Patients with the above presentation were evaluated through clinical presentation, X-ray of long bones, bone marrow examinations, and confirmed by genetic testing. In this article, we present two cases: The first case is a 3-year-old boy who presented with progressive pallor and ecchymotic patches for a year. On investigation, he had bicytopenia and bone marrow fibrosis. His anemia was steroid responsive and was finally diagnosed as GHDD. The second case is a 20-month-old girl who presented with blood in stools, developmental delay, anemia, and increased intensity of long bones on X-ray. Since other investigations were normal, suspicion of GHDD was raised, and a genetic workup was conducted which suggested mutation in TBXAS1 gene, confirming the diagnosis of GHDD. Children with refractory anemia and cortical thickening on skeletogram should always be evaluated for dysplasias. Timely treatment with steroids reduces transfusion requirements and halts bone damage, thus leading to better growth and improved quality of life. [ABSTRACT FROM AUTHOR]

A variant of neural network for processing with images is a convolutional neural network (CNN). This type of neural network receives input from an image and extracts features from the image while also providing learnable parameters to effectively do the classification, detection, and many other tasks. In the present work, U-Net convolutional neural network is implemented on Jupyter platform by using Python programming for fracture surface image segmentation in Oil Hardening Non-Shrinking (OHNS) die steel after the machining process. The results showed that the fracture cracks can be validated by testing with higher accuracy.

This paper presents a new optimized fractional slot concentrated winding (FSCW) design for torque ripple minimization in permanent magnet motors through space harmonic reduction. The proposed design offers the minimum total harmonics distortion in the magnetomotive force (MMF) produced by the winding whereas winding factor of the working harmonic component is set to the maximum value. A detailed mathematical derivation of the winding function is presented for general symmetrical FSCW using Fourier series expansion. The optimization methodology presented for the new optimized FSCW design results in optimal number of conductors at optimal slot positions while keeping slot pitch equal in the core design. The harmonic analysis of various examples of FSCW is presented which shows a significant reduction in the non-working harmonics in the proposed optimized design. A 24-slot 22-pole permanent magnet synchronous motor is designed and analyzed using the two-dimensional finite element method. The magnetic analysis of motors shows that the non-working harmonics in the air-gap flux distribution are minimized in the case of proposed design as obtained from the analysis of winding functions. The dynamic analysis shows a substantial improvement in the performance of proposed FSCW machine over the conventional machine.

The advancement in technology has attracted researchers to electric discharge machining (EDM) for providing a practical solution for overcoming the limitations of conventional machining. The current study focused on predicting the Material Removal Rate (MRR) using machine learning (ML) approaches. The process parameters considered are namely, workpiece electrical conductivity, gap current, gap voltage, pulse on time and pulse off time. Cryo-treated workpiece viz, Nickel-Titanium (NiTi) alloys, Nickel Copper (NiCu) alloys, and Beryllium copper (BCu) alloys and cryo-treated pure copper as tool electrode was considered. In the present research work, four supervised machine learning regression and three supervised machine learning classification-based algorithms are used for predicting the MRR. Machine learning result showed that gap current, gap voltage and pulse on time are most significant parameters that effected MRR. It is observed from the results that the Gradient boosting regression-based algorithm resulted in the highest coefficient of determination value for predicting MRR while Random Forest classification based resulted in the highest F1-Score for obtaining MRR.

This article presents the design of a reduced device count and enhanced compensation capability 5-level transistor clamped H-bridge (TCHB) inverter topology based dynamic voltage restorer (DVR) in a three phase system. TCHB-based DVR is designed for the prototype distribution system. The improved performance of the DVR is observed due to the implementation of the TCHB inverter. The unit vector template based PI controller is used for DVR implementation. The developed DVR is used to eliminate the various power quality disturbances (PQD) such as the sag, voltage imbalance, voltage harmonics, and voltage swell for the three phase system. Compensation capability of the developed DVR is demonstrated through simulation and experimentation. The developed DVR is working effectively by mitigating power quality disturbances with reduced filter size and switching frequency in comparison with existing 2-level topologies. This topology requires only one DC source as compared to CHB MLI. The grid is operated at 110 V/phase and 1.5 kVA/phase rating and DVR is designed for 55 VA/phase rating. [ABSTRACT FROM AUTHOR]

Background: Psychiatric advance directives have been incorporated in the Mental Health Care Act 2017 despite strong concerns about their feasibility and utility in the Indian patient population. Data on its utility in India is very scarce. Aims: To determine the possible treatment options our clients make as a part of psychiatric advance directives. Materials and Methods: Fifty consecutive individuals with severe mental illness were interviewed using a self-designed semi-structured tool to find out the possible choices they make as part of advance directives and the factors affecting their choices. Results: About 10% of the participants failed to understand the concept of advance directives. Of those who understood, 89% were willing to make advance directives, 15% refused future hospitalizations, 47% refused future electroconvulsive therapies (ECTs), and 62% refused physical restraints in future. Conclusion: The majority of the participants agreed to make advance directives. The majority of those who agreed to make advance directives refused to undergo ECTs and physical restraints in future episodes of illness. Approximately 10% of the patients could not understand the concept of advance directives.

The monsoon on the Indian subcontinent remains a seasonal occurrence that all inhabitants of the subcontinent desire. Modern scientific knowledge of methods of rain forecasting has originated recently. However, traditional indigenous wisdom is peculiar to our country. In the past, India had a magnificent scientific and technical legacy. Even today, it is common that village astrologers (pandits) are right in a surprisingly high percentage of their rain predictions. Scientists and local traditional farmers have a long history of astronomical research and treatises that predict rainfall. They use different methods to forecast rainfall conditions based on numerous panchangs, almanac bio-indicators (Bhoum method), non-bio-indicators (Antariksh method), and predict the likely behavior of climate in the planting season. Rainfall forecasting also aids in the planning of operations by agriculturists, builders, water supply engineers, and others. All mortals from the subcontinent have looked at it from their own perspective, and it continues to be the subject of intense multi-dimensional engagement. The monsoon has provided a means of life for numerous civilizations while also shaping the drainages and palaeo-geography of the subcontinent. The objective of this article is to document some indigenous knowledge for forecasting climate and environmental dynamics towards community resilience. [ABSTRACT FROM AUTHOR]

Introduction: Fine Needle Aspiration Cytology (FNAC) has its definite value in the diagnosis of various mass lesions including breast lesions. Pattern is a regular and intelligible form or sequence discernible in the way which suggests a particular lesion. Patterns observed on cellular morphology in the aspirate are limited and need categorisation, as the risk of malignancy differs with the patterns. Aim: To study and analyse patterns of various breast lesions on FNAC. To categorise various breast lesions according to patterns. Also, to compare sensitivity and specificity of different patterns with histopathology as a gold standard and to assess risk of malignancy in individual patterns. Materials and Methods: This was a retrospective and prospective study in which patterns of various breast lesions on FNAC were studied. The data of 800 patients attending the OPD and IPD of Indira Gandhi Government Medical college Nagpur, over a period of 3 years (2015-2017), was analysed. The patterns evaluated were biphasic, inflammatory, fluid rich, epithelial cell rich, spindle cell rich, small round cell rich and pleomorphic. Results: The lesions were classified as per patterns followed by evaluation of accuracy and risk of malignancy associated with each pattern. We found that the risk of malignancy varies with patterns. Conclusion: Systematic pattern analysis and systematic categorisation of the breast lesions as per patterns, assists the cytopathologists to reach the final impression. Each pattern has its own implications for management and has a variable risk of malignancy