Novel framework of retaining maximum data quality and energy efficiency in reconfigurable wireless sensor network

There are various unseen and unpredictable networking states in Wireless Sensor Network (WSN) that adversely affect the aggregated data quality. After reviewing the existing approaches of data quality in WSN, it was found that the solutions are quite symptomatic and they are applicable only in a static environment; however their successful applicability on dynamic and upcoming reconfigurable network is still a big question. Moreover, data quality directly affects energy conservation among the nodes. Therefore, the proposed system introduces a simple and novel framework that jointly addresses the data quality and energy efficiency using probability-based design approach. Using a simplified analytical methodology, the proposed system offers solution in the form of selection transmission of an aggergated data on the basis of message priority in order to offer higher data utilization factor. The study outcome shows proposed system offers a good balance between data quality and energy efficiency in contrast to existing system

Optimized architecture for SNOW 3G

SNOW 3G is a synchronous, word-oriented stream cipher used by the 3GPP standards as a confidentiality and integrity algorithms. It is used as first set in long term evolution (LTE) and as a second set in universal mobile telecommunications system (UMTS) networks. The cipher uses 128-bit key and 128 bit IV to produce 32-bit ciphertext. The paper presents two techniques for performance enhancement. The first technique uses novel CLA architecture to minimize the propagation delay of the 232 modulo adders. The second technique uses novel architecture for S-box to minimize the chip area. The presented work uses VHDL language for coding. The same is implemented on the FPGA device Virtex xc5vfx100e manufactured by Xilinx. The presented architecture achieved a maximum frequency of 254.9 MHz and throughput of 7.2235 Gbps

Chronic escitalopram treatment attenuated the accelerated rapid eye movement sleep transitions after selective rapid eye movement sleep deprivation: a model-based analysis using Markov chains

BackgroundShortened rapid eye movement (REM) sleep latency and increased REM sleep amount are presumed biological markers of depression. These sleep alterations are also observable in several animal models of depression as well as during the rebound sleep after selective REM sleep deprivation (RD). Furthermore, REM sleep fragmentation is typically associated with stress procedures and anxiety. The selective serotonin reuptake inhibitor (SSRI) antidepressants reduce REM sleep time and increase REM latency after acute dosing in normal condition and even during REM rebound following RD. However, their therapeutic outcome evolves only after weeks of treatment, and the effects of chronic treatment in REM-deprived animals have not been studied yet.ResultsChronic escitalopram- (10 mg/kg/day, osmotic minipump for 24 days) or vehicle-treated rats were subjected to a 3-day-long RD on day 21 using the flower pot procedure or kept in home cage. On day 24, fronto-parietal electroencephalogram, electromyogram and motility were recorded in the first 2 h of the passive phase. The observed sleep patterns were characterized applying standard sleep metrics, by modelling the transitions between sleep phases using Markov chains and by spectral analysis.Based on Markov chain analysis, chronic escitalopram treatment attenuated the REM sleep fragmentation [accelerated transition rates between REM and non-REM (NREM) stages, decreased REM sleep residence time between two transitions] during the rebound sleep. Additionally, the antidepressant avoided the frequent awakenings during the first 30 min of recovery period. The spectral analysis showed that the SSRI prevented the RD-caused elevation in theta (5 inverted question mark9 Hz) power during slow-wave sleep. Conversely, based on the aggregate sleep metrics, escitalopram had only moderate effects and it did not significantly attenuate the REM rebound after RD.ConclusionIn conclusion, chronic SSRI treatment is capable of reducing several effects on sleep which might be the consequence of the sub-chronic stress caused by the flower pot method. These data might support the antidepressant activity of SSRIs, and may allude that investigating the rebound period following the flower pot protocol could be useful to detect antidepressant drug response. Markov analysis is a suitable method to study the sleep pattern

Multiorgan MRI findings after hospitalisation with COVID-19 in the UK (C-MORE): a prospective, multicentre, observational cohort study

Introduction: The multiorgan impact of moderate to severe coronavirus infections in the post-acute phase is still poorly understood. We aimed to evaluate the excess burden of multiorgan abnormalities after hospitalisation with COVID-19, evaluate their determinants, and explore associations with patient-related outcome measures. Methods: In a prospective, UK-wide, multicentre MRI follow-up study (C-MORE), adults (aged ≥18 years) discharged from hospital following COVID-19 who were included in Tier 2 of the Post-hospitalisation COVID-19 study (PHOSP-COVID) and contemporary controls with no evidence of previous COVID-19 (SARS-CoV-2 nucleocapsid antibody negative) underwent multiorgan MRI (lungs, heart, brain, liver, and kidneys) with quantitative and qualitative assessment of images and clinical adjudication when relevant. Individuals with end-stage renal failure or contraindications to MRI were excluded. Participants also underwent detailed recording of symptoms, and physiological and biochemical tests. The primary outcome was the excess burden of multiorgan abnormalities (two or more organs) relative to controls, with further adjustments for potential confounders. The C-MORE study is ongoing and is registered with ClinicalTrials.gov, NCT04510025. Findings: Of 2710 participants in Tier 2 of PHOSP-COVID, 531 were recruited across 13 UK-wide C-MORE sites. After exclusions, 259 C-MORE patients (mean age 57 years [SD 12]; 158 [61%] male and 101 [39%] female) who were discharged from hospital with PCR-confirmed or clinically diagnosed COVID-19 between March 1, 2020, and Nov 1, 2021, and 52 non-COVID-19 controls from the community (mean age 49 years [SD 14]; 30 [58%] male and 22 [42%] female) were included in the analysis. Patients were assessed at a median of 5·0 months (IQR 4·2–6·3) after hospital discharge. Compared with non-COVID-19 controls, patients were older, living with more obesity, and had more comorbidities. Multiorgan abnormalities on MRI were more frequent in patients than in controls (157 [61%] of 259 vs 14 [27%] of 52; p<0·0001) and independently associated with COVID-19 status (odds ratio [OR] 2·9 [95% CI 1·5–5·8]; padjusted=0·0023) after adjusting for relevant confounders. Compared with controls, patients were more likely to have MRI evidence of lung abnormalities (p=0·0001; parenchymal abnormalities), brain abnormalities (p<0·0001; more white matter hyperintensities and regional brain volume reduction), and kidney abnormalities (p=0·014; lower medullary T1 and loss of corticomedullary differentiation), whereas cardiac and liver MRI abnormalities were similar between patients and controls. Patients with multiorgan abnormalities were older (difference in mean age 7 years [95% CI 4–10]; mean age of 59·8 years [SD 11·7] with multiorgan abnormalities vs mean age of 52·8 years [11·9] without multiorgan abnormalities; p<0·0001), more likely to have three or more comorbidities (OR 2·47 [1·32–4·82]; padjusted=0·0059), and more likely to have a more severe acute infection (acute CRP >5mg/L, OR 3·55 [1·23–11·88]; padjusted=0·025) than those without multiorgan abnormalities. Presence of lung MRI abnormalities was associated with a two-fold higher risk of chest tightness, and multiorgan MRI abnormalities were associated with severe and very severe persistent physical and mental health impairment (PHOSP-COVID symptom clusters) after hospitalisation. Interpretation: After hospitalisation for COVID-19, people are at risk of multiorgan abnormalities in the medium term. Our findings emphasise the need for proactive multidisciplinary care pathways, with the potential for imaging to guide surveillance frequency and therapeutic stratification

Newborn care: Effectiveness of simulation training for staff nurses

A neonate is also called a newborn. Aim: To assess the effectiveness of simulation training on knowledge and practice regarding newborn care among staff nurses. Research design: A quasi experimental non randomized control group design was used. Sampling and sampling technique: Sixty staff nurses each  in experimental and control group a were selected by non probability purposive sampling for the study in Rohilkhand Medical college hospital and Varunarjun Medical College Hospital. Knowledge and practice was assessed by using structured knowledge questionnaire and practice checklist.. The intervention included the simulation training of neonatal resuscitation and teaching on  immediate and routine newborn care. Results and findings: The study findings revealed that the  mean post-test knowledge score was higher i.e. (31.66±1.71) than the mean pretest knowledge score i.e. (20.68 ±4.68) in the experimental group. It revealed that the mean post-test practice score was higher i.e.( 24.71±0.45) than the mean pretest practice  score i.e. (21.03 ±1.30) in the experimental group. Data  revealed that the mean experimental group knowledge score was higher (31.66±1.17) than the mean control group knowledge score (26.03 ± 3.66). The difference was found to be statistically  significant at p=0.05 level of significance

Transforming Indian Sign Language into Text Using Leap Motion

Abstract: A sign language looks up the manual communication and body language to convey meaning, as opposed to acoustically conveyed sound patterns, which involve simultaneous combination of hand shapes, orientation and movement of hands, arms or body, and facial expressions to fluidly express a speaker's thought. The Leap device tracks the data like point, wave, reach, grab which is generated by a leap motion controller. The system implements DTW combined with IS algorithm for converting the hand gestures into an appropriate text, aided by leap device that consists of inbuilt camera and two IR sensor to capture hand signals. Neuro Linguistic Programming (NLP), a division of Artificial Intelligence which includes Natural Language Processing and Neural Networks. The IS invokes a trigger when the current environment changes dynamically, DTW handles gesture transformation mapped with similar patterns

Systemic Factors Affecting Diabetic Retinopathy

Diabetic retinopathy (DR) is one of the leading causes of preventable blindness in the world. Apart from local pathophysiological changes happening at the level of retina, various systemic factors also play a role in pathogenesis and progression of DR. In this article, we will discuss systemic factors affecting DR such as hyperglycaemia, hypertension, hyperlipidaemia, nephropathy, pregnancy, anaemia and cardiovascular disease, with their association to progression of DR

SDN-Assisted Safety Message Dissemination Framework for Vehicular Critical Energy Infrastructure

The proliferation of fifth-generation (5G) networks toward vehicle-to-everything (V2X) communication has paved the way for driverless autonomous vehicles (AVs) in vehicular critical energy infrastructures (CEI). Though technological advancements improve AVs, the safety-critical messages (SCMs) still play a vital role in reducing crashes, preventing injuries, and saving lives. AVs' high speed and complex network topology challenge disseminating SCMs with a highly successful delivery ratio and extremely low latency. Furthermore, the typical SCM dissemination schemes cause channel congestion and minimize the delivery ratio, making the systems incompatible with the AVs. Therefore, in this article, a software-defined-networking-assisted continuous clustering approach called migrating consignment region (MiCR) based on the federated K-means algorithm is proposed for disseminating SCMs to the AVs via 5G V2X communication. Unlike other methods that create clusters for every instance of SCM dissemination, MiCR continuously holds moving clusters for disseminating SCMs to AVs with ultrahigh reliability and low latency. The proposed MiCR approach has been simulated under real-time highway road maps and compared with other methods. The simulation results prove the superiority of MiCR in terms of network overload, SCM delivery ratio, latency, dissemination efficiency, and collision rate compared with the existing methods

Clinical profile of COVID-19-associated mucormycosis patients and the clinical suspects: a descriptive audit

Abstract Background India witnessed a massive surge of rhino orbital cerebral mucormycosis (ROCM) cases during the second wave of COVID-19, recording the highest number of cases in the world, indeed, an epidemic within the pandemic. Objectives To describe the clinical profile of patients with COVID-19-associated mucormycosis (CAM) and the clinical suspects for mucormycosis. Methods This single-center descriptive, observational study/audit was done at Indira Gandhi Medical College, Pondicherry, South India. This study is about the clinical profile of 7 CAM patients and 14 COVID-19 patients who were suspects of CAM, based on their risk factors and clinical symptoms, and were referred to the ENT department. Statistical analysis All the descriptive variables were summarized as mean, frequency, and percentages for qualitative data. Results All 7 CAM patients were COVID-19 positive and were not vaccinated against COVID-19, All 7 were known diabetic, all 7 had steroid therapy for their COVID status, and 5 out of 7 (71%) had uncontrolled diabetes mellitus at the time of diagnosis. Facial pain, nasal discharge, and eye swelling were the presenting symptoms of CAM. Maxillary and ethmoid sinuses were the most commonly involved para nasal sinuses. Four out of seven (57.1%) CAM patients survived after 16 months of follow-up, after surgical and medical treatment for CAM. Of the 14 clinical suspects who were negative for CAM, 2 were negative for COVID-19, their risk factors were brought under control, 3 expired due to COVID complications, and 9 patients are alive till date. Conclusion Uncontrolled diabetes is a risk factor for ROCM/CAM, another possible risk factor is steroid therapy, and we hypothesize that COVID infection could also be a possible risk factor that needs to be studied more extensively in a larger sample. Early clinical suspicion, withdrawal of steroids, rapid control of diabetes mellitus, appropriate investigations, and early surgical intervention combined with medical treatment offers better outcome