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Introduction: Orthopaedic surgeons are among the most frequent users of radiation and are often exposed to X-Ray radiation regularly. There is an overall unsatisfactory level of knowledge, attitude, and practices regarding the same among surgeons. This study aimed to assess the practices among orthopaedic surgeons regarding radiation and its hazards in India. Methodology: This questionnaire-based survey was conducted between March and September 2022. The questionnaire broadly assessed the knowledge, awareness, and practices among orthopaedic surgeons regularly exposed to X-ray radiation. The questionnaire consisted of objective and Likert scale questions which were distributed to the respondents electronically using publicly available contact information. A qualitative review of interpretations obtained from the responses and available evidence in the literature was then performed. Results: The response rate for the survey was 36.4%. 65.9% reported using trunk protection all the time during procedures. 57.1% of respondents reported not using thyroid protection during procedures. 92.3% reported not using protective goggles and 100% did not use appropriate gloves. 76.9% were not issued dosimeters, and only 5.5% of those who had been issued one wore it during radiation procedures. 58.2% reported having a dedicated employee to operate the machine. 86.8% of respondents did not have formal training and 81.3% were not aware of institutes offering formal training. Conclusion: There is very high compliance with the use of protection aprons among the respondents however, the use of other apparel like thyroid shields, protection goggles and gloves was poor. The awareness regarding radiation exposure was also poor as most respondents were not issued dosimeters, however, those issued with one were well aware of their exposure. It is imperative to prioritize both surgeons' and patients' safety where radiation exposure is concerned. [ABSTRACT FROM AUTHOR]

X-rays are the primary tools in examining the suspected fractures in humans in this era. Manual examination of X rays is a time-consuming process, which requires expert radiologists or trained orthopaedic surgeons. The demanding workloads, unavailability of radiologists in small setups and at primary/community health centres results in the inability to diagnose and delay in the treatment due to unnecessary referrals by the primary care clinicians. Moreover, the shortage of expert radiologists and orthopaedic surgeons in medically under-resourced areas such as rural areas in India have motivated us to develop an automated fracture detection model. We have developed a deep neural network to detect, localize and divide the wrist region into segments to identify fractures around wrist joint in radiographs. The orthopaedic surgeon has manually annotated the fractures by drawing a bounding box and segmented mask. We have utilized two different domains of datasets for the better convergence of the model. There is a similarity in the crack patterns of the surface crack image dataset and the wrist fracture dataset, which consists of 3000 and 315 images. A part of the dataset is made publicly available for research purposes to overcome data collection and labelling barriers for identifying wrist fractures. The crack patterns in the wrist bone samples are learned by effectively transferring the knowledge or weights obtained from surface crack datasets. The proposed architecture utilizes Feature Pyramid Network as the backbone architecture where the last-level max pool layer of the architecture is replaced with the concatenation of AdaptiveConcatPool, AdaptiveAvgPool, AdaptiveMaxPool layers. Additionally, the concept of freezing and unfreezing the network during two phases of transfer learning is utilized for better model convergence. Every radiograph is assigned a ground truth label for evaluating the accuracy of the model. The performance measure for fracture detection and localization is evaluated using the Average precision value using the concept of Intersection over Union. The result of the proposed model is compared against the ground truth label annotated by the radiologists and related studies. The average precision of 92.278 on 50° and 79.003 on a strict scale of 75° was reported for fracture detection. Similarly, the average precision of 77.445 on 50° and 52.156 on a strict scale of 75° was reported for fracture segmentation. [ABSTRACT FROM AUTHOR]

The Solar X-ray Monitor (abbreviated as XSM) on board India's Chandrayaan-2 mission is designed to carry out broadband spectroscopy of the Sun from lunar orbit. It observes the Sun as a star and measures the spectrum every second in the soft X-ray band of 1 – 15 keV with an energy resolution better than 180 eV at 5.9 keV. The primary objective of the XSM is to provide the incident solar spectrum for the X-ray fluorescence spectroscopy experiment on the Chandrayaan-2 orbiter, which aims to generate elemental abundance maps of the lunar surface. However, observations with the XSM can independently be used to study the Sun as well. The Chandrayaan-2 mission was launched on 22 July 2019, and the XSM began nominal operations, in lunar orbit, from September 2019. The in-flight observations, so far, have shown that its spectral performance has been identical to that on the ground. Measurements of the effective area from ground calibration were found to require some refinement, which has been carried out using solar observations at different incident angles. It also has been shown that the XSM is sensitive enough to detect solar activity well below A class. This makes the investigations of microflares and the quiet solar corona feasible in addition to the study of the evolution of physical parameters during intense flares. This article presents the in-flight performance and calibration of the XSM instrument and discusses some specific science cases that can be addressed using observations with the XSM. [ABSTRACT FROM AUTHOR]

dvances in supramolecular chemistry and crystal engineering reported from India within the last decade are highlighted in the categories of new intermolecular interactions, designed supramolecular architectures, network structures, multi-component host-guest systems, cocrystals, and polymorphs. Understanding self-assembly and crystallization through X-ray crystal structures is illustrated by two important prototypes — the large unit cell of elusive saccharin hydrate, Na(sac) · 30HO, which contains regular and irregular domains in the same structure, and by the Aufbau build up of zinc phosphate framework structures, e.g. ladder motif in [CNH][Zn(HPO)] to layer structure in [CNH][Zn(HPO)] upon prolonged hydrothermal conditions. The pivotal role of accurate X-ray diffraction in supramolecular and structural studies is evident in many examples. Application of the bottomup approach to make powerful NLO and magnetic materials, design of efficient organogelators, and crystallization of novel pharmaceutical polymorphs and cocrystals show possible future directions for interdisciplinary research in chemistry with materials and pharmaceutical scientists. This article traces the evolution of supramolecular chemistry and crystal engineering starting from the early nineties and projects a center stage for chemistry in the natural sciences. [ABSTRACT FROM AUTHOR]

In recent years, Brain tumor detection and segmentation has created an interest on research areas. The process of identifying and segmenting brain tumor is a very tedious and time consuming task, since human physique has anatomical structure naturally. Magnetic Resonance Image (MRI) scan analysis is a powerful tool that makes effective detection of the abnormal tissues from the brain. Among different techniques, Magnetic Resonance Image (MRI) is a liable one which contains several modalities in scanning the images captured from interior structure of human brain. A novel hybrid energy-efficient method is proposed for automatic tumor detection and segmentation. The proposed system follows K-means clustering, integrated with Fuzzy C-Means (KMFCM) and active contour by level set for tumor segmentation. An effective segmentation, edge detection and intensity enhancement can detect brain tumor easily. For that, active contour with level set method has been utilized. The performance of the proposed approach has been evaluated in terms of white pixels, black pixels, tumor detected area, and the processing time. This technique can deal with a higher number of segmentation problem and minimum execution time by ensuring segmentation quality. Additionally, tumor area length in vertical and horizontal positions is determined to measure sensitivity, specificity, accuracy, and similarity index values. Further, tumor volume is computed. Knowledge of the information of tumor is helpful for the physicians for effective diagnosing in tumor for treatments. The entire experimentation was implemented in MATLAB environment and simulation results were compared with existing approaches. [ABSTRACT FROM AUTHOR]