Investigation of structural, electronic and thermoelectric properties of XCUOTE (X: BI, CE, LE) with GGA-WC exchange correlation functional

Linearized augmented plane wave plus local orbitals (LAPW + lo) method designed within density functional theory (DFT) has been used in this study to calculate the structural, electronic and thermoelectric properties of XCuOTe (X=Bi, Ce, La). Generalized gradient approximation, Wu-Cohen (GGA-WC) parameterized exchange correlation functional, was used. The structural and electronic calculations have a good agreement with previous study. For thermoelectric calculation, semi empirical Boltzmann approach implemented in BoltzTraP package was used to calculate Seebeck coefficient, electronic conductivity as well as thermal conductivity. By referring to previous studies, the results have good agreement with them. In addition, the Seebeck coefficient of these materials was calculated as a function of the chemical potential at temperatures 300K, 600K, and 900K. Our calculations highlight suitability of these materials for applications in thermoelectric devices

Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions

Fake news and disinformation (FNaD) are increasingly being circulated through various online and social networking platforms, causing widespread disruptions and influencing decision-making perceptions. Despite the growing importance of detecting fake news in politics, relatively limited research efforts have been made to develop artificial intelligence (AI) and machine learning (ML) oriented FNaD detection models suited to minimize supply chain disruptions (SCDs). Using a combination of AI and ML, and case studies based on data collected from Indonesia, Malaysia, and Pakistan, we developed a FNaD detection model aimed at preventing SCDs. This model based on multiple data sources has shown evidence of its effectiveness in managerial decision-making. Our study further contributes to the supply chain and AI-ML literature, provides practical insights, and points to future research directions

Effects of Anacetrapib in Patients with Atherosclerotic Vascular Disease

BACKGROUND: Patients with atherosclerotic vascular disease remain at high risk for cardiovascular events despite effective statin-based treatment of low-density lipoprotein (LDL) cholesterol levels. The inhibition of cholesteryl ester transfer protein (CETP) by anacetrapib reduces LDL cholesterol levels and increases high-density lipoprotein (HDL) cholesterol levels. However, trials of other CETP inhibitors have shown neutral or adverse effects on cardiovascular outcomes. METHODS: We conducted a randomized, double-blind, placebo-controlled trial involving 30,449 adults with atherosclerotic vascular disease who were receiving intensive atorvastatin therapy and who had a mean LDL cholesterol level of 61 mg per deciliter (1.58 mmol per liter), a mean non-HDL cholesterol level of 92 mg per deciliter (2.38 mmol per liter), and a mean HDL cholesterol level of 40 mg per deciliter (1.03 mmol per liter). The patients were assigned to receive either 100 mg of anacetrapib once daily (15,225 patients) or matching placebo (15,224 patients). The primary outcome was the first major coronary event, a composite of coronary death, myocardial infarction, or coronary revascularization. RESULTS: During the median follow-up period of 4.1 years, the primary outcome occurred in significantly fewer patients in the anacetrapib group than in the placebo group (1640 of 15,225 patients [10.8%] vs. 1803 of 15,224 patients [11.8%]; rate ratio, 0.91; 95% confidence interval, 0.85 to 0.97; P=0.004). The relative difference in risk was similar across multiple prespecified subgroups. At the trial midpoint, the mean level of HDL cholesterol was higher by 43 mg per deciliter (1.12 mmol per liter) in the anacetrapib group than in the placebo group (a relative difference of 104%), and the mean level of non-HDL cholesterol was lower by 17 mg per deciliter (0.44 mmol per liter), a relative difference of -18%. There were no significant between-group differences in the risk of death, cancer, or other serious adverse events. CONCLUSIONS: Among patients with atherosclerotic vascular disease who were receiving intensive statin therapy, the use of anacetrapib resulted in a lower incidence of major coronary events than the use of placebo. (Funded by Merck and others; Current Controlled Trials number, ISRCTN48678192 ; ClinicalTrials.gov number, NCT01252953 ; and EudraCT number, 2010-023467-18 .)

Dimethyl fumarate in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial

Dimethyl fumarate (DMF) inhibits inflammasome-mediated inflammation and has been proposed as a treatment for patients hospitalised with COVID-19. This randomised, controlled, open-label platform trial (Randomised Evaluation of COVID-19 Therapy [RECOVERY]), is assessing multiple treatments in patients hospitalised for COVID-19 (NCT04381936, ISRCTN50189673). In this assessment of DMF performed at 27 UK hospitals, adults were randomly allocated (1:1) to either usual standard of care alone or usual standard of care plus DMF. The primary outcome was clinical status on day 5 measured on a seven-point ordinal scale. Secondary outcomes were time to sustained improvement in clinical status, time to discharge, day 5 peripheral blood oxygenation, day 5 C-reactive protein, and improvement in day 10 clinical status. Between 2 March 2021 and 18 November 2021, 713 patients were enroled in the DMF evaluation, of whom 356 were randomly allocated to receive usual care plus DMF, and 357 to usual care alone. 95% of patients received corticosteroids as part of routine care. There was no evidence of a beneficial effect of DMF on clinical status at day 5 (common odds ratio of unfavourable outcome 1.12; 95% CI 0.86-1.47; p = 0.40). There was no significant effect of DMF on any secondary outcome

Measurement of the photoionization cross-section of the 3p

The photoionization cross-section and number density of the 3p 2P1/2,3/2 excited levels of sodium have been measured as a function of the laser energy using two-step laser excitation in conjunction with a thermionic diode working in the space charge limited mode. Employing the saturation technique, the cross-sections for the 3p 2P1/2 and 3p 2P3/2 levels are determined as 2.16 (43) Mb and 3.74 (74) Mb respectively

An Artificial-Intelligence-Based omnichannel blood supply chain: A pathway for sustainable development

We formulated and tested an innovative omnichannel blood supply chain (OBSC) model based on artificial intelligence (AI) using inputs raised in semi structured interviews conducted with heath care practitioners in a blood supply chain. The proposed AI-based OBSC model addresses the supply and demand imbalance in crucial situations for blood supply chains. A resource dependence theory bottom-up approach was applied to underpin the OBSC model. This model consists of two parts: (a) helping to find the closest and fastest available blood supply (omnichannel perspective) and (b) raising a blood supply request among university students (with the help of the university’s IT system) through SMS messaging in case of emergencies or blood shortages (AI perspective). This OBSC model is significant because it contributes to the United Nations’ Sustainable Development Goals, specifically the goal 3 to “ensure healthy lives and promote well-being for all at all ages.

Using Smartphone Accelerometer for Human Physical Activity and Context Recognition in-the-Wild

Adaptation of smart devices is frequently rising, where a new generation of smartphones is growing into an emerging platform for personal computing, monitoring, and private data processing. Smartphone sensing allows collecting data from immediate environments and surroundings to recognize human daily living activities and behavioral contexts. Although smartphone-based activity recognition is universal; however, there is a need for coinciding recognition of in-the-wild human physical activities and the associated contexts. This research work proposes a two-level scheme for in-the-wild recognition of human physical activities and the corresponding contexts based on the smartphone accelerometer data. Different classifiers are used for experimentation purposes, and the achieved results validate the efficiency of the proposed scheme