Testing and evaluation of reinforced concrete beam-column-slab joint

Eksperimentalno istraživanje seizmičkog ponašanja identičnih podsklopova greda-stup obavljeno je ispitivanjem dvaju modela spoja u mjerilu 1:2, pri čemu jedan model ima ploču, a drugi nema. Kvalitativni model kojim se simulira sudjelovanje međukatne ploče razvijen je uspostavljanjem obrazaca pojave pukotina u ploči (linija popuštanja) i stanja deformacije u armaturi ploče. Na bazi kontrolnih rezultata zaključeno je da model spoja dovodi do boljeg predviđanja seizmičkog ponašanja te da taj model omogućava eksplicitno ocjenjivanje djelovanja ploče na spoj.An experimental investigation of seismic behaviour of identical beam-column subassemblies was conducted by testing two half-scale joint models: one without a slab and the other with a slab. A qualitative model simulating participation of the floor slab was developed by establishing the slab crack patterns (yield lines) and the state of strain in slab bars. Based on the verification results, it was concluded that the joint model improves prediction of seismic behaviour, and that this model allows explicit evaluation of the slab effect on the joint

Willingness to vaccinate against COVID-19 among healthcare workers: an online survey in 10 countries in the eastern Mediterranean region

BACKGROUND: Willingness of healthcare workers to be vaccinated is an important factor to be considered for a successful COVID-19 vaccination programme. This study aims to understand the willingness of health workers to receive a COVID-19 vaccine and its associated concerns across 10 countries in the Eastern Mediterranean Region (EMR). METHOD: A cross-sectional study was conducted in January 2021 among healthcare workers in EMR using an online survey. Data were analyzed using IBM SPSS software package version 20.0. RESULTS: A total of 2806 health workers (physicians, nurses and pharmacists) completed and returned the informed consent along with the questionnaire electronically. More than half of the respondents (58.0%) were willing to receive a COVID-19 vaccine, even if the vaccination is not mandatory for them. On the other hand, 25.7% of respondents were not willing to take COVID-19 vaccine while 16.3 % were undecided. The top three reasons for not willing to be vaccinated were unreliability of COVID-19 vaccine clinical trials (62.0%), fear of the side effects of the vaccine (45.3%), and that COVID-19 vaccine will not give immunity for a long period of time (23.1%). CONCLUSION: Overall, the study revealed suboptimal acceptance of COVID-19 vaccine among the respondents in the EMR. Significant refusal of COVID-19 vaccine among healthcare professionals can reverse hard-won progress in building public trust in vaccination program. The findings suggest the need to develop tailored strategies to address concerns identified in the study in order to ensure optimal vaccine acceptance among healthcare workers in the EMR

Sediment Delivery to Sustain the Ganges-Brahmaputra Delta Under Climate Change and Anthropogenic Impacts

The principal nature-based solution for offsetting relative sea-level rise in the Ganges-Brahmaputra delta is the unabated delivery, dispersal, and deposition of the rivers’ ~1 billion-tonne annual sediment load. Recent hydrological transport modeling suggests that strengthening monsoon precipitation in the 21st century could increase this sediment delivery 34-60%; yet other studies demonstrate that sediment could decline 15-80% if planned dams and river diversions are fully implemented. We validate these modeled ranges by developing a comprehensive field-based sediment budget that quantifies the supply of Ganges-Brahmaputra river sediment under varying Holocene climate conditions. Our data reveal natural responses in sediment supply comparable to previously modeled results and suggest that increased sediment delivery may be capable of offsetting accelerated sea-level rise. This prospect for a naturally sustained Ganges-Brahmaputra delta presents possibilities beyond the dystopian future often posed for this system, but the implementation of currently proposed dams and diversions would preclude such opportunities

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study

Summary Background Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally. Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income countries globally, and identified factors associated with mortality. Methods We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis, exomphalos, anorectal malformation, and Hirschsprung’s disease. Recruitment was of consecutive patients for a minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause, in-hospital mortality for all conditions combined and each condition individually, stratified by country income status. We did a complete case analysis. Findings We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal malformation, and 517 with Hirschsprung’s disease) from 264 hospitals (89 in high-income countries, 166 in middleincome countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male. Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3). Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups). Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in lowincome countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries; p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11], p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20 [1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention (ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed (ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65 [0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality. Interpretation Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between lowincome, middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger than 5 years by 2030

Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats

In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development’s (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security

Self-optimization of Handover Control Parameters for Mobility Management in 4G/5G Heterogeneous Networks

A large number of small cells in the next-generation mobile networks is expected to be deployed to satisfy 5G requirements. Mobility management is one of the important issues that require considerable attention in heterogeneous networks, where 5G ultra-dense small cells coexist with the current 4G networks. An efficient handover (HO) mechanism is introduced to address this issue and improve mobility management by adjusting HO control parameters (HCPs), namely, time-to-trigger and HO margin. Dynamic HCPs (D-HCPs), which explores user experiences to adjust HCPs and make an HO decision in a self-optimizing manner, is proposed in this paper. D-HCPs classify HO failure (HOF) into three categories, namely, too late, too early and wrong cell HO, and simultaneously adjust HCPs according to the dominant HOF. The algorithm is evaluated using different performance metrics, such as HO ping-pong, radio link failure and interruption time, with different mobile speed scenarios. Simulation results show that the proposed D-HCPs algorithm adaptively optimizes the HCPs and outperforms other algorithms from the literature

Identifying Insomnia From Social Media Posts: Psycholinguistic Analyses of User Tweets

BackgroundMany people suffer from insomnia, a sleep disorder characterized by difficulty falling and staying asleep during the night. As social media have become a ubiquitous platform to share users’ thoughts, opinions, activities, and preferences with their friends and acquaintances, the shared content across these platforms can be used to diagnose different health problems, including insomnia. Only a few recent studies have examined the prediction of insomnia from Twitter data, and we found research gaps in predicting insomnia from word usage patterns and correlations between users’ insomnia and their Big 5 personality traits as derived from social media interactions. ObjectiveThe purpose of this study is to build an insomnia prediction model from users’ psycholinguistic patterns, including the elements of word usage, semantics, and their Big 5 personality traits as derived from tweets. MethodsIn this paper, we exploited both psycholinguistic and personality traits derived from tweets to identify insomnia patients. First, we built psycholinguistic profiles of the users from their word choices and the semantic relationships between the words of their tweets. We then determined the relationship between a users’ personality traits and insomnia. Finally, we built a double-weighted ensemble classification model to predict insomnia from both psycholinguistic and personality traits as derived from user tweets. ResultsOur classification model showed strong prediction potential (78.8%) to predict insomnia from tweets. As insomniacs are generally ill-tempered and feel more stress and mental exhaustion, we observed significant correlations of certain word usage patterns among them. They tend to use negative words (eg, “no,” “not,” “never”). Some people frequently use swear words (eg, “damn,” “piss,” “fuck”) with strong temperament. They also use anxious (eg, “worried,” “fearful,” “nervous”) and sad (eg, “crying,” “grief,” “sad”) words in their tweets. We also found that the users with high neuroticism and conscientiousness scores for the Big 5 personality traits likely have strong correlations with insomnia. Additionally, we observed that users with high conscientiousness scores have strong correlations with insomnia patterns, while negative correlation between extraversion and insomnia was also found. ConclusionsOur model can help predict insomnia from users’ social media interactions. Thus, incorporating our model into a software system can help family members detect insomnia problems in individuals before they become worse. The software system can also help doctors to diagnose possible insomnia in patients