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Ultrafine-grained (UFG) high-Mn austenitic steel exhibited unusual discontinuous yielding on its stress-strain curve, characterized by a yield drop followed by a stress plateau, indicative of Lüders deformation. Utilizing the digital image correlation (DIC) technique, a strain-localized region known as a Lüders band was observed to propagate during Lüders deformation. Following microstructural observations using state-of-the-art techniques such as in-situ synchrotron XRD measurement during the tensile test and ex-situ S/TEM and SEM-ECCI, dislocation multiplication, rather than twinning, was theoretically identified as the primary deformation mechanism responsible for the unusual Lüders deformation in UFG high-Mn austenitic steel.

The Internet of Things (IoT) network infrastructures are becoming more susceptible to distributed denial of service (DDoS) attacks because of the proliferation of IoT devices. Detecting and predicting such attacks in this complex and dynamic environment requires specialized techniques. This study presents an approach to detecting and predicting DDoS attacks from a realistic multidimensional dataset specifically tailored to IoT network environments, named DDoSNet. At the beginning of the data preprocessing phase, the dataset must be cleaned up, missing values must be handled, and the data needs to be transformed into an acceptable format for analysis. Several preprocessing approaches, including data-cleaning algorithms and imputation methods, are used to improve the accuracy and dependability of the data. Following this, feature selection uses the African Buffalo Optimization with Decision Tree (ABO-DT) method. This nature-inspired metaheuristic algorithm imitates the behaviour of African buffalos to determine which traits are the most important. By integrating ABO with the decision tree, a subset of features is selected that maximizes the discrimination between regular network traffic and DDoS attacks. After feature selection, an echo-state network (ESN) classifier is employed for detection and prediction. A recurrent neural network (RNN) that has shown potential for managing time-series data is known as an ESN. The ESN classifier utilizes the selected features to learn the underlying patterns and dynamics of network traffic, enabling accurate identification of DDoS attacks. Based on the simulations, the proposed DDOSNet had an accuracy of 98.98 %, a sensitivity of 98.62 %, a specificity of 98.85 %, an F-measure of 98.86 %, a precision of 98.27 %, an MCC of 98.95 %, a Dice coefficient of 98.04 %, and a Jaccard coefficient of 98.09 %, which are better than the current best methods.

Background and Aims: Simulation is one of the important learning tools when it comes to skill acquisition and as a supplemental tool for training in high stake situations like COVID-19. The aim of this study is to meet the global requirements of knowledge on ventilatory management, prepare and to evaluate the effectiveness of the teaching module for non-anesthesiology trainees on COVID-19 patients. Methods: Quasi experimental cross sectional pilot study was conducted with a sample of twenty-six trainees. A teaching module was prepared and validated which consisted of lectures, audio-video sessions, demonstrations with hands-on training, debriefing, analytical-phase and reflection. Pre and Post evaluations from student t-test and direct observation of procedural skills (DOPS) were used for knowledge and skill assessment respectively and feedback obtained from Likert's score. Results: Pre- and Post-tests had a mean score of 7.42 ± 2.12 and 14.92 ± 2.9 respectively (P value 0.00001). DOPS included 16 point score, in which 23 trainees (88.4%) met the expectations and above expectations as per training objectives. A five-point Likert's score feedback revealed satisfactory and highly satisfactory scores of 100% (ABG), 96.1% (mechanical ventilation), and 84.6% (ventilation in COVID-19 patients). Overall satisfaction for the workshop among respondents was 100 per cent. Confidences of handling scores were 84.5% (interpreting ABG), 65.3% (maneuvering mechanical ventilation), and 96.15% (intubation in COVID-19 patients). Conclusion: A planned teaching module in ventilation management helps to train non-anaesthesiologists more effectively as a part of COVID-19 preparedness. Simulation with debriefing based training to the medical fraternity is the best alternative in the present pandemic and it will also ensure the safety of health care professionals.

Melioidosis which is known to be endemic in South-east Asia and Northern Australia is a treatable infection and is more common than currently appreciated. It is now expanding rapidly to other regions of the world predominantly in the Indian subcontinent. The key for successful management of this disease lies in facilitating rapid and accurate diagnosis by expert personnel and timely selection of adequate sensitive antibiotics. We present here a series of three cases of melioidosis involving lung and pleura along with clinico radiological and treatment details. [J Med Allied Sci 2019; 9(1.000): 36-39]

Sarcoidosis is a chronic granulomatous disease involving multiple organs. Most commonly affected organs are lungs, followed by skin, lymph nodes. Hematological manifestations of sarcoidosis are uncommon and may include hemolytic anemia, leukopenia, eosinophilia, peripheral lymphopenia but severe thrombocytopenia is very rare. Our patient presented with petechial rash, papular skin lesions and had severe thrombocytopenia. Skin biopsy showed granulomatous inflammation without necrosis with good clinical, radiological and hematological response to oral prednisolone. [J Med Allied Sci 2018; 8(2.000): 94-97]

Abstract Application of scanning transmission electron microscopy (STEM) to in situ observation will be essential in the current and emerging data-driven materials science by taking STEM’s high affinity with various analytical options into account. As is well known, STEM’s image acquisition time needs to be further shortened to capture a targeted phenomenon in real-time as STEM’s current temporal resolution is far below the conventional TEM’s. However, rapid image acquisition in the millisecond per frame or faster generally causes image distortion, poor electron signals, and unidirectional blurring, which are obstacles for realizing video-rate STEM observation. Here we show an image correction framework integrating deep learning (DL)-based denoising and image distortion correction schemes optimized for STEM rapid image acquisition. By comparing a series of distortion corrected rapid scan images with corresponding regular scan speed images, the trained DL network is shown to remove not only the statistical noise but also the unidirectional blurring. This result demonstrates that rapid as well as high-quality image acquisition by STEM without hardware modification can be established by the DL. The DL-based noise filter could be applied to in-situ observation, such as dislocation activities under external stimuli, with high spatio-temporal resolution.

Ultrafine-grained (UFG) high-Mn austenitic steel exhibited unusual discontinuous yielding on its stress-strain curve, characterized by a yield drop followed by a stress plateau, indicative of Lüders deformation. Utilizing the digital image correlation (DIC) technique, a strain-localized region known as a Lüders band was observed to propagate during Lüders deformation. Following microstructural observations using state-of-the-art techniques such as in-situ synchrotron XRD measurement during the tensile test and ex-situ S/TEM and SEM-ECCI, dislocation multiplication, rather than twinning, was theoretically identified as the primary deformation mechanism responsible for the unusual Lüders deformation in UFG high-Mn austenitic steel. [ABSTRACT FROM AUTHOR]

Background: Neurocognitive decline and depression are the most common unaddressed but noticeable adverse consequences linked with obstructive sleep apnea (OSA) which can affect work performance, social functioning, and quality of life. It is significant to explore both cognition and mood disorders in OSA, as there is adequate evidence in the literature supporting the efficacy of continuous positive airway pressure (CPAP) in the management. Aims and Objectives: The aim was to assess the neurocognitive profile and depression in patients with OSA in our center. Patients and Methods: This was a cross-sectional observational study conducted in a tertiary care center between January 2017 and January 2018. A total of 92 patients with an established diagnosis of OSA (by polysomnography) were included in the study. All patients were given questionnaires for the assessment of neurocognitive function and depression scoring. Results: Out of 92 OSA patients, 29 (31.5%) had normal cognition, 43 (46.7%) had mild cognitive impairment (CI), and 20 (21.7%) had major CI. Out of twenty patients with major CI, major attention deficit was observed in 17 (85%), memory impairment in 16 (80%), verbal fluency was affected in 13 (65%), deficit in language skills was observed in 7 (35%), and visuospatial abilities were affected in 17 patients (85%). Out of 92 OSA patients, 21 (22.8%) had depression and 71.4% of them were females. Conclusions: OSA is an independent risk factor for both depression and neurocognitive decline. It is highly recommended that patients with OSA should be screened for both, so as to provide better clinical outcomes.

Coxsackievirus group B (CVB) is a member of the genus Enterovirus in the family Picornaviridae. CVB infection has been implicated as a major etiologic agent of viral myocarditis, dilated cardiomyopathy, meningitis, and pancreatitis among children and young adults. Until date, no antiviral agent has been licensed for the treatment of Coxsackievirus infection. In an effort to identify antiviral agents against diseases caused by the CVB, we found that ethyl 3-hydroxyhexanoate (EHX), a volatile compound present in fruits and food additives, is a potent antiviral compound. In this study, we demonstrated that EHX treatment significantly inhibits CVB replication both in vivo and in vitro. Furthermore, EHX possesses antiviral activity at 50% effective concentration (EC50) of 1.2 μM and 50% cytotoxicity (CC50) of 25.6 μM, yielding a selective index (SI) value as high as 20.8. Insights into the mechanism of antiviral activity of EHX showed that it acts at the step of viral RNA replication. Since EHX has received approval as food additives, treatment of CVB-related infections with EHX might be a safe therapeutic option and may be a promising strategy for the development of semi-synthetic antiviral drugs for viral diseases.