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A search for fourteen bands of the visibleand infrared systems of the beryllium hydride isotopomers, in addition to the previously identified bands A - X(0, 0; 1, 1; 2, 2) for BeH, A - X(0, 0; 1, 1; 2, 2) for BeD and A - X(0, 0; 1, 1; 2, 2) for BeT molecules was conducted. The equivalent widths were measured for the well isolated identified lines of the strongest band and the effective temperatures were estimated for the systems for which the presence of hints is confirmed.

Byline: R. Sangeetha, Bharath. Srinivasaiah, Venkataramaiah. Sudhir, Shweta. Naik Dear Editor, The occurrence of hiccups in a critically ill patient is a common encounter in an intensivist's practice. Though hiccup [...]

A claw is a star with three edges‎. ‎The Kneser graph $KG_{n,2}$ is the graph whose vertices are the ‎$‎2‎$‎-subsets of an $n$-set‎, ‎in which two vertices are adjacent if and only if their intersection is empty‎. ‎In this paper‎, ‎we prove that $KG_{n,2}$ is claw-decomposable‎, ‎for all $n \geq 6$‎.

In embedded applications and digital signal processing systems, multipliers are crucial components. In these applications, there is an increasing need for energy-efficient circuits. We use an approximate adder for error tolerance in the computational process to improve performance and reduce power consumption. Due to human perceptual constraints, computational errors do not significantly affect applications like image, audio, and video processing. Adiabatic logic (AL), which recycles energy, can also be used to build circuits that require less energy. In this work, we propose a carry save array multiplier employing an approximate adder based on CMOS logic and clocked CMOS adiabatic logic (CCAL) to conserve power. Additionally, using a precise full adder, multiplier parameters like average power and power delay product are calculated and compared with the multiplier. We performed simulations using 180 nm technology in Cadence Virtuoso. [ABSTRACT FROM AUTHOR]

Introduction: Diabetic neuropathy is a complication affecting almost 50% of the diabetic patients. Diabetic Peripheral Neuropathy (DPN) predominantly affects the hands and lower limbs. It leads to loss of protective sensation resulting in continuous injury to insensitive feet. The early detection of DPN using an objective screening test followed by its appropriate management is important as up to 50% of the patients may be asymptomatic. Objectives: To screen Diabetic patients attending an Urban Health and Training Centre of a medical college in Tamilnadu for Diabetic Peripheral Neuropathy. To assess the association between DPN and selected variables such as socio-demographic factors, glycaemic control, duration of diabetes, physical activity, body mass index, smoking and consumption of alcohol. Methods: The study was conducted among 204 diabetic patients attending an Urban Health and Training Centre. Participants were assessed using Michigan Neuropathy Screening Instrument (MNSI), which involves using a questionnaire followed by a physical examination. Results: Of the 204 patients, 58.8% were male. The mean age was 54.8 years (SD = 8.8 years). About 79.9% were employed of which 29.4% were skilled labourers. Mean duration of diabetes was 6.2 years (SD = 5.3 years). The proportion of diabetics who screened positive for Peripheral Neuropathy was 23% and 45.6% using MNSI questionnaire and examination, respectively. An age of 60 years and above was significantly associated with DPN (OR = 2.505, P value = 0.003). A duration of more than 4 years of diabetes was also significantly associated with DPN (OR = 1.872, P value = 0.02820). Conclusion: A high proportion of diabetics with peripheral neuropathy did not express symptoms specific for diabetics. Thus, a simple tool like MNSI would be useful in primary care settings to screen for peripheral neuropathy, and hence prevent disability”.

Kidney cancer is a significant health concern worldwide, with early detection crucial for successful treatment outcomes. Recently, there has been an increasing focus on investigating affordable and non-intrusive methods for detecting cancer at its early stages. This proposed approach delves into the utilization of antenna arrays to identify kidney cancer. Specifically, antenna array with circular polarization has been crafted to identify tumors in the human kidney. This array design has been implemented using CST software, operating at 2.4 GHz. The antenna array having a dimension of 150 mm x 150 mm. The array consists of four-square patches each having a length of 30 mm and the spacing between the patches is preferred to be 20 mm for ensuring the mutual coupling. FR4 substrate is used with the relative permittivity 4.3 and 1.6 mm thickness. The axial ratio of the circularly polarized antenna is 0.9 dB at 2.4 GHz. The reflection coefficient S 11 = –30dB. The renal phantom in humans has been built with the kidney, adrenal gland, ureter, skin, fat, and muscle components to allow for precise simulation. Following that, four stages of cancer tumors were built employing all of the necessary qualities. For every stage of the cancer tumor, the reflection coefficient growth and resonance frequency shift are calculated. For the initial stages, the resonance frequency shift is very low. Consequently, the primary determinant of detection is the rise in S 11 . For 3rd and 4th stages of the tumor, there is a significant change in resonance frequency and increase S 11 , which facilitates identification. The calculated specific absorption rate (SAR) is a smaller amount than the safety thresholds, indicating that using this method is safe. Overall, this research points to a novel, easy-to-use method for kidney cancer screening. This method has the following benefits: it is non-invasive, safe, quick, affordable, and comfortable, and it emits no ionizing radiation during the measurement process. Furthermore, a range of output parameters such as gain, directivity, and polar radiation have been accurately computed. [ABSTRACT FROM AUTHOR]

The Olympics are the world's biggest sporting event that is played in every four years in two different editions; summer edition and winter edition. Summer Olympics gets much attention around the world due to its global participation and larger number of sports coverage from across the globe. The Olympics are hosted every four year by different countries based on the winning hosting rights process. India which is the third largest economy in the world and recently has finished in the top 50 of the Tokyo Olympics medal tally list has shown its acute desire to host the Olympics edition of the year 2036. It will not be a cakewalk for India as it has to work a lot on the development of its sports infrastructure in the country. If India hosts the Olympics then it can be said that the sports infrastructure of the country will enhanced and it will help as a nation to host more sporting events in the future. Hence, understating the sports infrastructure development and changes due to the India's hosting Olympics will be crucial to study that has been discussed in this research article. [ABSTRACT FROM AUTHOR]

Information-Centric Networking (ICN) is promising system architecture for distributing popular information across the network. Its important feature is the node cache. ICN with caching is a very promising featured network structural design. There are numerous cache nodes distributed in the ICN. Excessive consumption of cache information on a tremendous number of nodes dissipates a large storage space and causes data redundancy, which reduces the cache hit rate. Therefore, an efficient caching deployment approach is required to improve the cache hit rate. A novel RAndom probit regressive Bucklin DEcision Forest classifier (RADEF) technique is introduced in Information-Centric Networking (ICN) for minimal network latency and higher cache hit rate. In RADEF Technique, probit regression and random forest classification processes are carried out for ICN with healthcare patient data. First, the patient information is collected from different IoT devices and registered. Every request to the router node is analyzed in the content storage (CS) by using a random probit regressive Bucklin decision forest classifier. The Bucklin decision forest classifier is an ensemble technique that includes a set of weak learners as decision trees (i.e., probit regression tree) and IoHT data are selected randomly for each decision tree. The Probit regression tree is constructed to analyze the patient healthcare data request search in the content storage (CS) of the router node in the ICN network by using a simple matching coefficient. If the copy is present in the content storage, the particular router nodes are chosen and deliver the content. If the copy is absent, the patient information is stored in the cache. Then, the weak learner's results are combined to make a strong output. Then the votes are generated for each decision tree. The votes of all decision trees are combined to identify the majority votes of data for classification by minimizing the error using the Bucklin voting method. In this way, content catching is effectively performed in ICN with minimum latency and a higher cache hit ratio. Experimental evaluation is carried out on factors such as cache hit rate, network latency, average request length, average response time, server traffic ratio, and hop reduction ratio regarding respect to the number of patient healthcare data. The analyzed results demonstrate the superior performance of our proposed RADEF technique when compared with existing methods. [ABSTRACT FROM AUTHOR]

The pandemic caused significant changes and consequences in all areas of daily life. The impact of the COVID-19 virus completely changed the way of living and working, and consequently, new working conditions were imposed on it. Daily exposure to new conditions causes certain stressful situations for all, and it has aggregated mental health suffering throughout the entire world. It affects not only working employees, migrant workers, and senior citizens but also students, who can become more agitated and restless. It is becoming very common among students in this world; many of them do not realize that they are affected by stress, but it is a very serious and emerging problem about stress among students, which affects students' learning, motivation, concentration, and social interaction. The study recognizes the pervasive nature of stress among students globally, with many remaining unaware of its profound effects. Stress emerges as a substantial and escalating problem affecting students' learning, motivation, concentration, and social interactions. This research aims to know the primary stress factors confronting students during crisis situations. Data synthesis involves a simple tabulation method, offering insights into the impact of stress on social work students across key parameters such as, academic, physical, psychological, and social stress. [ABSTRACT FROM AUTHOR]

Long-range dependence (LRD) in financial markets remains a key factor in determining whether there is market memory, herding traces, or a bubble in the economy. Usually referred to as ‘Long Memory’, LRD has remained a key parameter even today since the mid-1970s. In November 2016, a sudden and drastic demonetization measure took place in the Indian market, aimed at curbing money laundering and terrorist funding. This study is an attempt to identify market behavior using long-range dependence during those few days in demonetization. Besides, it tries to identify nascent traces of bubble and embedded herding during that time. Auto Regressive Fractionally Integrated Moving Average (ARFIMA) is used for three consecutive days around the event. Tick-by-tick data from CNX Nifty High Frequency Trading (CNX Nifty HFT) is used for three consecutive days around demonetization (approximately, 5000 data points from morning trading sessions on each of the three days). The results show a clear and profound presence of herd behavior in all three data sets. The herd intensity remained similar, indicating a unique mixture of both ‘Noah Effect’ and ‘Joseph Effect’, proving a clear regime switch. However, the results on the event day show stable and prominent herding. Mandelbrot’s specified effects were tested on an uncertain and sudden financial event in India and proved to function perfectly.

Human norovirus pathogenesis is incompletely understood due to a lack of appropriate animal disease models. Here, Green et al. show norovirus replication in chromogranin A-positive enteroendocrine cells and other epithelial cells in tissue from a pediatric intestinal transplant recipient with severe gastroenteritis.

Classifying VoIP (Voice over Internet Protocol) traffic is vital for optimizing network performance and Quality of Service (QoS). This study introduces the Multivariate Statistical-Based Classification (MVSC) system, designed to classify network traffic with high accuracy and efficiency. As traditional methods struggle in the diverse and complex landscape of today's network traffic, which includes voice, video, gaming, and data, the MVSC algorithm rises to the challenge. It employs Statistical Dissemination and leverages various statistical features such as Packet Size, Inter-Arrival Statistics, Packet and Data rates, Flow Length, and Five-tuple information to create nuanced profiles of network traffic packets. These packets are then grouped into distinct clusters based on their statistical attributes through Application Flow Cluster Grouping. A unique aspect of the MVSC system is its approach to representing each application flow as points in a two-dimensional space, where distances to predefined application profiles are calculated. The nearest profile then determines the type of VoIP traffic. Experimental results using university traffic data (KU-IDS) underscore the system's high accuracy, consistently around 98-99%. These findings affirm the system's suitability for real-time deployment. In summary, the MVSC system offers a robust and efficient solution for VoIP traffic classification, significantly boosting network performance and QoS, and proving to be an invaluable asset in contemporary network management. [ABSTRACT FROM AUTHOR]

Background and Aims: The autonomic nervous system (ANS) is cardinal for systemic homeostasis. Autonomic dysfunction is prevalent in as high as 65% of patients presenting for cardiac surgery in the Indian scenario. Pre-existing cardiac autonomic dysfunction (CAD) in surgical patients can accentuate perioperative haemodynamic fluctuations during stressful intraoperative events, predispose to adverse cardiac events, and contribute to morbidity and mortality. The prevalence and predictors of CAD in the elective neurosurgical population are unknown in the Indian scenario. The current study was conducted to bridge this knowledge gap. Methods: In this single-centre prospective observational study conducted at a tertiary care neurosciences centre, among 400 consenting adult patients of either gender, between 18 and 80 years of age, undergoing elective neurosurgery, the preoperative ANS function at the bedside was assessed as the primary outcome measure. The ANS status was evaluated using ANSiscope™-derived indices of heart rate variability. The diagnosis of CAD was made when the ANS index exceeded a threshold of 13.5. Data regarding predictors of CAD were collected from patient records as the secondary outcome measure. Statistical analysis was done using the R software. A P-value of <0.05 was considered statistically significant. Results: The prevalence of preoperative CAD in our study population was 79.7% (319/400 patients). None of the demographic and baseline clinical characteristics we studied predicted CAD in our study. Conclusion: We observed a significant prevalence of preoperative CAD among elective neurosurgical patients. None of the parameters we evaluated predicted CAD in our study. [ABSTRACT FROM AUTHOR]

Background and Aims: Anaesthesia information management system (AIMS) is increasingly implemented in many hospitals. Considering the capital cost involved in its installation and maintenance, it is important to evaluate its performance and adoptability by end users. This study assessed the completeness of manual data recording in the AIMS one year after its implementation and also evaluated potential predictors for completeness. Methods: In this retrospective audit of AIMS, 1000 electronic anaesthesia records of patients undergoing neurosurgical procedures over one year were assessed for completeness of 41 preidentified items, one year after its implementation. Parameters evaluated were patient identifiers, personnel identifiers, demographics, airway management parameters, anaesthesia management items and end-of-anaesthesia parameters. We hypothesised that completeness of anaesthesia record can be predicted by nature of surgeries, case sequence, seniority of anaesthesiologist and phase ( first or second) of the study period. Results: We observed higher completeness of manual data recording during phase 2 of AIMS use compared to phase 1. Higher grade of anaesthesiologist, second case of the day and emergency surgery led to reduction in completeness of data entry. Anaesthesiologist grade significantly predicted complete entry of 18 (44%) variables, case number predicted 8 (20%) variables and phase- and procedure-type predicted 6 (15%) and 5 (12%) variables, respectively. Conclusion: Completeness of manual data recording in the electronic AIMS is poor after one year of implementation. First case of the day, second phase of study period, elective cases and trainee anaesthesiologist are associated with better completeness of manual data recording in the AIMS.