A Data-Driven Analysis on Sustainable Energy Security

This study provides a data-driven analysis that illustrates a clear renewable energy depiction in sustainable energy security and unveils the regional issues due to the literature solely occupies energy security concept in the descriptions view, and renewable energy differences related to regions are rarely discussed. A hybrid method is proposed to valid those indicators and shows the trend of future studies. This study enriches the challenges and opportunities by contributing to understand the fundamental knowledge of renewable energy in sustainable energy security frontier, conveyance directions for future study and investigation, and assessment on global renewable energy position and regional disparities. There are valid 19 indicators, in which energy demand, energy policy, renewable resources, smart grid, and uncertainty representing the future trends. World regional comparison includes 115 countries/territories and categorized into five geographical regions. The result shows that those indicators have addressed different issues in the world regional comparison.

Global Status of Bufavirus, Cosavirus, and Saffold Virus in Gastroenteritis: A Systematic Review and Meta-Analysis

Background: Bufavirus (BuV), Human Cosavirus (HCoSV), and Saffold (SAFV) virus are three newly discovered viruses and have been suggested as possible causes of gastroenteritis (GE) in some studies. The aim of the present study was to estimate the overall prevalence of viruses and their association with GE.Methods: A comprehensive systematic search was conducted in Scopus, Web of Science, PubMed, and Google scholar between 2007 and 2021 to find studies on the prevalence of BuV, HCoSV, and SAFV viruses.Result: Meta-analysis of the 46 included studies showed the low prevalence of BuV (1.%, 95% CI 0.6–1.5%), HCoSV (0.8%, 95% CI 0.4–1.5%), and SAFV (1.9%, 95% CI 1.1–3.1%) worldwide. Also, no significant association between these viruses and GE was observed. BuV was isolated from patients with GE in Africa, while SAFV was more common in Europe. BuV1 and BuV2 have the same prevalence between the three identified genotypes of BuV. HCoSV-C was the most prevalent genotype of HCoSV, and SAFV2 was the commonest genotype of SAFV. All of these viruses were more prevalent in children older than 5 years of age.Conclusion: This was the first meta-analysis on the prevalence and association of BuV, HCoSV, and SAFV with GE. While no significant association was found between infection with these viruses and GE, we suggest more studies, especially with case-control design and from different geographical regions in order to enhance our knowledge of these viruses.

Dual Burden of Malnutrition Among Adolescents With Hunger Aged 12–15 Years in 41 Countries: Findings From the Global School-Based Student Health Survey

Background: Hunger is a pandemic among adolescents, resulting in both underweight and obesity, and posing a substantial health challenge.Objective: To estimate the dual burden of malnutrition among adolescents with hunger.Design: Data were from the Global school-based Student Health Survey (GSHS). In total, data from 26,986 adolescents with hunger across 5 regions and 41 countries between 2010 and 2015 were analyzed in this study. Weighted prevalence and mean estimates of underweight, overweight, and obesity were calculated by gender, age, and country. Prevalence and 95% confidence intervals (CI) were calculated for regional and country-level income.Results: The total prevalence of underweight, overweight and obesity among young adolescents with hunger was 6.2% (95% CI: 4.4–8.0%), 25.1% (95% CI: 20.3–29.9%) and 8.9% (95% CI: 6.5–11.3%), respectively. Southeast Asia had the highest prevalence of underweight (17.2%; 95% CI: 7.3–27.0%). America had the highest regional prevalence of obesity (11.1%; 95% CI: 7.2–15.1%) and overweight (28.9%; 95% CI: 21.9–35.9%). Low income countries had relatively high prevalence of underweight (11.5%; 95% CI: 3.2–19.9%). High income countries had the highest prevalence of obesity (17.4%; 95% CI: 14.9–19.9%) and overweight (38.7%; 95% CI: 32.0–45.4%). The co-existence of underweight and overweight among adolescents with hunger was highest in the Eastern Mediterranean region, and in upper-middle and high-income countries.Conclusions: There is a dual burden of underweight and obesity among adolescents with hunger aged 12–15 years, which differs between geographical regions. The integration of targeted interventions and policies is required to simultaneously address both underweight and increasing rates of obesity among adolescents with hunger in different regions.

Outcomes and regional differences in practice in a worldwide coronary stent registry

ObjectiveThe primary objective was to assess the performance of a new generation thin-strut sirolimus-eluting coronary stent with abluminal biodegradable polymer in an all comer population. The secondary objective was to detail differences in contemporary percutaneous coronary intervention (PCI) practice worldwide.Methodse-Ultimaster was an all-comer, prospective, global registry (NCT02188355) with independent event adjudication enrolling patients undergoing PCI with the study stent. The primary outcome measure was target lesion failure (TLF) at 1 year, defined as the composite of cardiac death, target vessel myocardial infarction and clinically driven target lesion revascularisation. Data were stratified according to 4 geographical regions.ResultsA total of 37 198 patients were enrolled (Europe 69.2%, Asia 17.8%, Africa/Middle East 6.6% and South America/Mexico 6.5%) and 1-year follow-up was available for 35 389 patients (95.1%). One-year TLF occurred in 3.2% of the patients, ranging from 2% (Africa/Middle East) to 4.1% (South America/Mexico). In patients with acute coronary syndrome, potent P2Y12 inhibitors were prescribed in 48% of patients at discharge, while at 1 year 72% were on any dual antiplatelet therapy. Lipid-lowering treatment was administered in 80.9% and 75.5% of patients at discharge and 1 year, respectively. Regional differences in the profile of the treated patients as well as in PCI practice were reported.ConclusionsIn this investigation with worldwide representation, contemporary PCI using a new generation thin-strut sirolimus-eluting coronary stent with abluminal biodegradable polymer was associated with low 1-year TLF across clinical presentations and continents. Suboptimal adherence to current recommendations around antiplatelet and lipid lowering treatments was detected.

Multi-Mineral Element Profiles in Genuine and “Bathing” Cultured Chinese Mitten Crabs (Eriocheir sinensis) in Yangcheng Lake, China

The authentication of high-quality fishery products originating from specific geographical regions is urgently needed worldwide. Chinese mitten crabs (Eriocheir sinensis), originating from Yangcheng Lake, are prime counterfeiting targets for the same reasons. Foreign crabs that are cultured briefly in the lake, known as “bathed” crabs, are illegally marketed as natives, negatively impacting the product quality. To establish a method for distinguishing “bathed” and genuine crabs, in this paper we conduct a comparative investigation by an Agilent 7500ce ICP-MS on multi-mineral element profiling of the third pereiopod from genuine and one month deliberately “bathing” cultured crabs. The profiles of 11 elements were significantly different between the genuine and foreign crabs before and after bathing. The discriminant analysis reached 100% accuracy to separate the genuine and “bathed” crabs into different groups. Bathing culture was unable to converge element profiles between the genuine and foreign crabs. The biogeochemical profiles can be effective for distinguishing “bathed” crabs.

MARIDA: A benchmark for Marine Debris detection from Sentinel-2 remote sensing data

Currently, a significant amount of research is focused on detecting Marine Debris and assessing its spectral behaviour via remote sensing, ultimately aiming at new operational monitoring solutions. Here, we introduce a Marine Debris Archive (MARIDA), as a benchmark dataset for developing and evaluating Machine Learning (ML) algorithms capable of detecting Marine Debris. MARIDA is the first dataset based on the multispectral Sentinel-2 (S2) satellite data, which distinguishes Marine Debris from various marine features that co-exist, including Sargassum macroalgae, Ships, Natural Organic Material, Waves, Wakes, Foam, dissimilar water types (i.e., Clear, Turbid Water, Sediment-Laden Water, Shallow Water), and Clouds. We provide annotations (georeferenced polygons/ pixels) from verified plastic debris events in several geographical regions globally, during different seasons, years and sea state conditions. A detailed spectral and statistical analysis of the MARIDA dataset is presented along with well-established ML baselines for weakly supervised semantic segmentation and multi-label classification tasks. MARIDA is an open-access dataset which enables the research community to explore the spectral behaviour of certain floating materials, sea state features and water types, to develop and evaluate Marine Debris detection solutions based on artificial intelligence and deep learning architectures, as well as satellite pre-processing pipelines.

Assessment of factors associated with PSA level in prostate cancer cases and controls from three geographical regions

It is being debated whether prostate-specific antigen (PSA)-based screening effectively reduces prostate cancer mortality. Some of the uncertainty could be related to deficiencies in the age-based PSA cut-off thresholds used in screening. Current study considered 2779 men with prostate cancer and 1606 men without a cancer diagnosis, recruited for various studies in New Zealand, US, and Taiwan. Association of PSA with demographic, lifestyle, clinical characteristics (for cases), and the aldo–keto reductase 1C3 (AKR1C3) rs12529 genetic polymorphisms were analysed using multiple linear regression and univariate modelling. Pooled multivariable analysis of cases showed that PSA was significantly associated with demographic, lifestyle, and clinical data with an interaction between ethnicity and age further modifying the association. Pooled multivariable analysis of controls data also showed that demographic and lifestyle are significantly associated with PSA level. Independent case and control analyses indicated that factors associated with PSA were specific for each cohort. Univariate analyses showed a significant age and PSA correlation among all cases and controls except for the US-European cases while genetic stratification in cases showed variability of correlation. Data suggests that unique PSA cut-off thresholds factorized with demographics, lifestyle and genetics may be more appropriate for prostate cancer screening.

Survival analysis of neonatal mortality in Ghana using three population-based surveys

Background Neonatal mortality in many low-and middle-income countries (LMICs) remains high despite global efforts at addressing this challenge. Tackling neonatal death in LMICs is further complicated by lack of reliable data from individual countries in the region to inform effective context specific interventions. This study investigates the probability of neonatal survival and socio-demographic risk factors of neonatal mortality in Ghana. Methods Pooled data from three population-based surveys (N = 12,148) were analysed using multivariable Cox Proportional Hazards regression models. Results The risk of dying within the first 28 days of life was highest in the first week of life (early neonatal period), it then decreases sharply around the middle of the second week of life and remains low over the late neonatal period. Adjusted hazard ratios (HRs) showed that: rural residency (HR = 1.80, 95% CI: 1.15-2.75); birth order 2-3 (HR = 1.63, 95% CI: 1.10-2.42); birth order ≥7 (HR = 1.89, 95% CI: 1.07-3.33) increased the risk of neonatal death. Additionally, children born to women who were obese had higher risk of neonatal death (HR = 1.69, CI: 1.12-2.56) compared with those of women with optimal weight. Disparities in the risk of neonatal death by geographical regions were also found. Conclusion The risk of neonatal mortality is highest during the first week of life and it is socio-demographically patterned. The findings emphasise the need to tackle socio-demographic risk factors of neonatal mortality in order to achieve the Sustainable Development Goal 3, which is aimed at reducing neonatal mortality to 12 per 1000 live births by the year 2030.

Artificial intelligence predicts clinically relevant atrial high-rate episodes in patients with cardiac implantable electronic devices

To assess the utility of machine learning (ML) algorithms in predicting clinically relevant atrial high-rate episodes (AHREs), which can be recorded by a pacemaker. We aimed to develop ML-based models to predict clinically relevant AHREs based on the clinical parameters of patients with implanted pacemakers in comparison to logistic regression (LR). We included 721 patients without known atrial fibrillation or atrial flutter from a prospective multicenter (11 tertiary hospitals) registry comprising all geographical regions of Korea from September 2017 to July 2020. Predictive models of clinically relevant AHREs were developed using the random forest (RF) algorithm, support vector machine (SVM) algorithm, and extreme gradient boosting (XGB) algorithm. Model prediction training was conducted by seven hospitals, and model performance was evaluated using data from four hospitals. During a median follow-up of 18 months, clinically relevant AHREs were noted in 104 patients (14.4%). The three ML-based models improved the discrimination of the AHREs (area under the receiver operating characteristic curve: RF: 0.742, SVM: 0.675, and XGB: 0.745 vs. LR: 0.669). The XGB model had a greater resolution in the Brier score (RF: 0.008, SVM: 0.008, and XGB: 0.021 vs. LR: 0.013) than the other models. The use of the ML-based models in patient classification was associated with improved prediction of clinically relevant AHREs after pacemaker implantation.

Activity of vehicles in the bus rapid transit system Metrobús in Mexico City

In this paper, we analyze a massive dataset with registers of the movement of vehicles in the bus rapid transit system Metrobús in Mexico City from February 2020 to April 2021. With these records and a division of the system into 214 geographical regions (segments), we characterize the vehicles’ activity through the statistical analysis of speeds in each zone. We use the Kullback–Leibler distance to compare the movement of vehicles in each segment and its evolution. The results for the dynamics in different zones are represented as a network where nodes define segments of the system Metrobús and edges describe similarity in the activity of vehicles. Community detection algorithms in this network allow the identification of patterns considering different levels of similarity in the distribution of speeds providing a framework for unsupervised classification of the movement of vehicles. The methods developed in this research are general and can be implemented to describe the activity of different transportation systems with detailed records of the movement of users or vehicles.