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In digital communication systems, channel encoders and interleavers play a crucial role in mitigating the random and burst errors introduced by noisy channels. While information about the encoder and interleaver is typically available in cooperative scenarios, non-cooperative military communication systems often lack such knowledge. This paper explores the application of deep learning to recognize four different interleavers such as block, convolutional, helical, and random especially in non-cooperative environments. By utilizing convolutional neural network and dilation residual network models, we perform the recognition task with input data for interleavers encoded using six different channel encoders such as block, convolutional, Bose-Chaudhuri-Hocquenghem, Reed-Solomon, low density parity check, and polar. Across all cases, both convolutional neural network and dilation residual network models consistently achieve classification accuracy exceeding 95% at varying signal-to-noise ratio values. In addition, we compare the classification accuracy and testing/training time of both our proposed models (i.e. convolutional neural network and dilation residual network) with the recurrent neural network, feedforward network, and autoencoder models available in the literature. Our findings reveal that the accuracy improves with high input sample length and yielding superior results for both the models. Notably, among the five models, dilation residual network outperforms other models in terms of classification accuracy and convolutional neural network outperforms dilation residual network in terms of training/testing time. Our results show that the convolutional neural network and dilation residual network models outperform the other methods. We also compare the classification accuracy of the proposed model with the existing rank-based approach proposed in the literature. Our findings indicate that at the same bit error rate values, our model demonstrates superior classification accuracy.

The first diagnosis of diabetic retinopathy (DR) must include lesion segmentation. As it takes a lot of time and effort to label lesions, automatic segmentation methods have to be created manually. The degree of the retina’s degenerative lesions determines how severe diabetic retinopathy is. A major influence is on the early detection of illness and treatment of DR. To reliably identify the sites of related lesions and identify various abnormalities in retinal fundus pictures, deep learning algorithms are crucial. Additionally, utilizing patch-based analysis, a deep convolutional neural network is constructed. In this study, encoder-decoder neural networks along with channel-wise spatial Attention Mechanisms are proposed. The IDRiD dataset, which includes hard exudate segmentations, is used to train and evaluate the architecture. In this method, image patches are created using the sliding window technique. To determine the effectiveness of the recommended strategy, a thorough experiment was conducted on IDRiD. In order to predict the various sorts of lesions, the trained network analyses the picture patches and creates a probability map. This technique’s efficacy and supremacy are confirmed by the expected accuracy of 99.94 %. The findings of this experiment show significantly enhanced performance in terms of accuracy when compared to prior research on comparable tasks.

Abstract Background The COVID-19 pandemic and its consequent “social distancing” has fueled the use of social media platforms for educational purposes. Since the start of the pandemic, a plethora of experts and self-proclaimed experts have been keenly delivering webinars on COVID. This begs the question “Do webinars on COVID-19 really help in the improvement of knowledge base or management skills?”. The questionnaire was designed to assess information regarding COVID-webinars and their usefulness from the end-user standpoint. The response to the questions was measured using a 4- or 5-point Likert scale. The survey was open for a 4-week period with the extension of 1 week. Results The response rate was 54% as 270 out of 500 participants responded to the questionnaire. The majority of the respondents were anesthesiologists in-training, post-graduates, fellows, and seniors belonging to tertiary care settings with fewer percentages belonging to physicians and others. Most of the doctors had attended an average of 2 webinars per week. The tests of model effects showed a significant negative correlation of webinar quality ratings for district hospital healthcare setting of the attendees (of p value of 0.013) and for the number of COVID-related webinars attended per week (p value of 0.009). Conclusions Most respondents had favorable perceptions of webinars happening during the pandemic. However, there is a need for improvisation in the volume of webinars, target-audience-based delivery, and participant interaction to add value to this new dimension of teaching-learning.

Catatonic patients may develop deep vein thrombosis (DVT) and pulmonary embolism (PE) due to prolonged periods of immobility. These life-threatening conditions demand prompt recognition and management. We describe the case of a patient with catatonia who presented to anesthesia for electroconvulsive therapy (ECT) at the outset of the current coronavirus disease 2019 pandemic. She complained of breathing difficulty and was suspected to have COVID-19 infection. On further evaluation, she was found to have DVT and PE and required oxygen therapy and intensive care management. The diagnostic delay in our patient would have probably not occurred, had it not been for the existing pandemic situation.

In this study, near-equiatomic NiTi thin films were deposited on silicon (1 0 0) substrates through direct current magnetron sputtering using Ni and Ti elemental targets without intentionally heating the substrate. Thin films were annealed at 600 °C for 1 h and then aged at 500 °C for 2, 4, and 6 h. X-ray diffraction studies revealed that the degree of crystallinity increased, and the dominant phase identified at room temperature was the R-phase. The surface roughness values were obtained through atomic force microscopy. As the aging time increased from 2 to 6 h, the hardness and elastic modulus values of the films increased for different load values. The high-resolution X-ray photoelectron spectroscopy results indicated that a TiO2 (metal oxide) layer was formed on the surface of the films aged for various durations. The layer was formed because of the enhanced thermodynamic nature caused by the Ni-rich residues and Si-C precipitates in the matrix occurring immediately adjacent to the metal oxide surface layer.

The purpose of this study was to assess the prevalence of frailty and nutritional status among older adults. This population-based cross-sectional study was conducted in 163 subjects aged 60–88 years, from Hyderabad City, South India. Data were obtained on sociodemographic details and anthropometry and biochemical parameters. Dietary intake was assessed by a three-day 24 h dietary recall, and the probability of adequacy (PA) was calculated using the estimated average requirements. Frailty indicators were as follows: handgrip strength was measured by using a Jamar dynamometer, gait speed was measured by a ten-meter length walk test, and low physical activity level, weight loss, and exhaustion were assessed using a questionnaire. Among the study population, 20% of the participants were frail and 80% were nonfrail. The prevalence of frailty is higher in older (30.1%) than the younger (12.2%) age groups, and it is more so in women (32.4%) than in men (10.1%). The lower educational status and income were associated with frailty. The PA of most of the nutrients was low in the frail group. Noticeably, the mean PA (MPA) across the fourteen micronutrients was significantly higher in nonfrail (38%) compared to the frail group (25%). The prevalence of frailty was higher in the lowest tertile of most of the food groups and nutrient intake compared to the highest tertile. The study revealed a 20% prevalence of frailty among urban older adults and provided evidence that inadequate intake of nutrients is independently associated with frailty.

Sugarcane bagasse (SB) is a lucrative feedstock for sustainable fuel production, but economical conversion into fermentable sugars and ethanol presents challenges from a biorefinery perspective. The present study aimed to screen and identify a robust cellulase-producing fungal strain to improve the saccharification of SB. Aspergillus fumigatus exhibited enhanced cellulase activity when untreated SB served as the substrate. The EMS mediated chemical mutagenesis of A. fumigatus with 200 mM EMS further enhanced the cellulase production by approximately 23.47%, relative to the wild-type strain. The process optimization method demonstrated peak cellulase activity on the 6th day of incubation, at 33 °C, and with an inoculum size of 5 × 107 spores. The optimization of the filter paper assay enhanced the maximum activity to 2.5 U/mL by maintaining 6 pH and 55 °C, along with the subsequent addition of MnCl2 ions to the reaction mixture. The Taguchi orthogonal array was employed to optimize the process parameters of enzymatic hydrolysis of alkali-pretreated SB, demonstrating highest efficiency when hydrolysis parameters were set to pH 4, 55 °C, 10 U enzyme, and 20 g/L substrate (SB) loading. The hydrolysate was utilized to evaluate bioethanol production employing Saccharomyces cerevisiae MTCC 824. The strain generated 4.2 g/L of ethanol with a total yield of 0.21 g/g. This study seeks to manage agricultural residues and wastes, generating a nutrient-rich hydrolysate that can be utilized by yeast strains for bioethanol production, thus rendering the entire process sustainable, environmentally friendly, and cost-effective. [ABSTRACT FROM AUTHOR]

Author(s): Ajay Singh [1]; Gautham Patel [1]; Venkata Ganesh (corresponding author) [1]; B. Naveen Naik [1]; Shiv L. Soni [1] Sir, With extensive vaccine outreach, the pandemic caused by the [...]

The emergence of the Internet of Things (IoT) has revolutionized healthcare by enabling remote patient monitoring systems. This system utilizes wearable sensors to collect vital signs such as heart rate, blood glucose level and body temperature. If the patient's temperature increases above 100 degrees Celsius then the alarm will be generated. The collected data is transmitted wirelessly to a secure cloud platform for real-time analysis and visualization. This allows Doctors to remotely monitor patients' health conditions, intervene promptly in case of emergencies, and improve overall care quality. [ABSTRACT FROM AUTHOR]