Driving green bond market through energy prices, gold prices and green energy stocks: evidence from a nonlinear approach

One of the most controversial concerns among the researchers is the expansion of the green bond markets, so as to reduce environmental pollution. The present study estimates the factors that help drive the global green bond markets, such as energy prices, gold prices, and green energy stocks. The study has applied Quantile Autoregressive Lagged Approach (QARDL) and Quantile Granger Causality test to estimate the causal relationship among the variables for January 2010 and June 2021. The QARDL findings reveal that for all the quantiles, the error correction term is statistically significant with the predicted negative sign. This confirms the existence of a strong long-run equilibrium relationship between the relevant variables and the green bonds market on a global level. The findings revealed that gold and energy prices have a lower effect on the green bonds market on every quantile, and also from the low to medium quantiles, respectively. While at the same time, the green energy stocks have an increasing effect on the green bonds market at higher quantiles. The results of the causal examination using Granger-causality in quantiles show a bi-directional causal relationship between the green bonds, energy prices, gold prices, and green energy stocks in the world econom

Photogrammetric Facial Analysis of Rhinoplasty Applicants in Shiraz

Background: Nose shape plays an important role in individuals’ facial appearance and its morphology depends on ethnicity, gender, and environmental conditions. Identifying nasal problems and measuring landmarks can lead to making a perfect surgery plan through preoperative image analysis. Methods: In this study, a photogrammetric analysis was performed on 120 female rhinoplasty applicants, aged 18-30 in Shiraz, Iran. Recorded parameters are nasal height and width, nasolabial and nasofrontal angle. Nasal indices were calculated according to heights and widths of noses. Also, facial asymmetry and nose hump checked for every patient. Results: Measurements showed that the average nasal index was 67.15 ± 4.72. Thus, the nose of rhinoplasty applicants was the leptorrhine type. Furthermore, the average nasofrontal and nasolabial angles were 145.22° ± 9.93°and 94.47° ± 14.25°. Among all applicants, 35 percent have an asymmetric nose and 31 percent have a nose hump. Conclusion:  An accurate facial analysis of rhinoplasty applicants was performed in this study, and the resultant facial profiles can be used in nose surgery planning and in further ethnic research

CO 2 -wettability of sandstones exposed to traces of organic acids: Implications for CO 2 geo-storage

Wettability of CO 2 -brine-mineral systems plays a vital role during geological CO 2 -storage. Residual trapping is lower in deep saline aquifers where the CO 2 is migrating through quartz rich reservoirs but CO 2 accumulation within a three-way structural closure would have a high storage volume due to higher CO 2 saturation in hydrophobic quartz rich reservoir rock. However, such wettability is only poorly understood at realistic subsurface conditions, which are anoxic or reducing. As a consequence of the reducing environment, the geological formations (i.e. deep saline aquifers) contain appreciable concentrations of various organic acids. We thus demonstrate here what impact traces of organic acids exposed to storage rock have on their wettability. Technically, we tested hexanoic acid, lauric acid, stearic acid and lignoceric acid and measured wettability as a function of organic acid concentration at realistic storage conditions (i.e. 25 MPa and 323 K (50 °C)). In addition, measurements were also conducted at ambient conditions in order to quantify the incremental pressure effect on wettability. Clearly, the quartz surface turned significantly less water-wet with increasing organic acid concentrations, even at trace concentrations. Importantly, we identified a threshold concentration at ˜10 −6 M organic acid, above which quartz wetting behaviour shifts from strongly water-wet to an intermediate-wet state. This wettability shift may have important consequences for CO 2 residual trapping capacities, which may be significantly lower than for traditionally assumed water-wet conditions where CO 2 is migrating through quartz rich reservoirs. © 2019 Elsevier Lt

Nanomaterial-based drilling fluids for exploitation of unconventional reservoirs: A review

© 2020 by the authors. The world\u27s energy demand is steadily increasing where it has now become difficult for conventional hydrocarbon reservoir to meet levels of demand. Therefore, oil and gas companies are seeking novel ways to exploit and unlock the potential of unconventional resources. These resources include tight gas reservoirs, tight sandstone oil, oil and gas shales reservoirs, and high pressure high temperature (HPHT) wells. Drilling of HPHT wells and shale reservoirs has become more widespread in the global petroleum and natural gas industry. There is a current need to extend robust techniques beyond costly drilling and completion jobs, with the potential for exponential expansion. Drilling fluids and their additives are being customized in order to cater for HPHT well drilling issues. Certain conventional additives, e.g., filtrate loss additives, viscosifier additives, shale inhibitor, and shale stabilizer additives are not suitable in the HPHT environment, where they are consequently inappropriate for shale drilling. A better understanding of the selection of drilling fluids and additives for hydrocarbon water-sensitive reservoirs within HPHT environments can be achieved by identifying the challenges in conventional drilling fluids technology and their replacement with eco-friendly, cheaper, and multi-functional valuable products. In this regard, several laboratory-scale literatures have reported that nanomaterial has improved the properties of drilling fluids in the HPHT environment. This review critically evaluates nanomaterial utilization for improvement of rheological properties, filtrate loss, viscosity, and clay- and shale-inhibition at increasing temperature and pressures during the exploitation of hydrocarbons. The performance and potential of nanomaterials, which influence the nature of drilling fluid and its multi-benefits, is rarely reviewed in technical literature of water-based drilling fluid systems. Moreover, this review presented case studies of two HPHT fields and one HPHT basin, and compared their drilling fluid program for optimum selection of drilling fluid in HPHT environment

Machine Learning Models for Predicting Breast Cancer Risk in Women Exposed to Blue Light from Digital Screens

Background: Nowadays, there is a growing global concern over rapidly increasing screen time (smartphones, tablets, and computers). An accumulating body of evidence indicates that prolonged exposure to short-wavelength visible light (blue component) emitted from digital screens may cause cancer. The application of machine learning (ML) methods has significantly improved the accuracy of predictions in fields such as cancer susceptibility, recurrence, and survival. Objective: To develop an ML model for predicting the risk of breast cancer in women via several parameters related to exposure to ionizing and non-ionizing radiation.Material and Methods: In this analytical study, three ML models Random Forest (RF), Support Vector Machine (SVM), and Multi-Layer Perceptron Neural Network (MLPNN) were used to analyze data collected from 603 cases, including 309 breast cancer cases and 294 gender and age-matched controls. Standard face-to-face interviews were performed using a standard questionnaire for data collection. Results: The examined models RF, SVM, and MLPNN performed well for correctly classifying cases with breast cancer and the healthy ones (mean sensitivity> 97.2%, mean specificity >96.4%, and average accuracy >97.1%).  Conclusion: Machine learning models can be used to effectively predict the risk of breast cancer via the history of exposure to ionizing and non-ionizing radiation (including blue light and screen time issues) parameters. The performance of the developed methods is encouraging; nevertheless, further investigation is required to confirm that machine learning techniques can diagnose breast cancer with relatively high accuracies automatically

Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BackgroundDisorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditions in 2015 and 2016, but these analyses did not include neurodevelopmental disorders, as defined by the International Classification of Diseases (ICD)-11, or a subset of cases of congenital, neonatal, and infectious conditions that cause neurological damage. Here, we estimate nervous system health loss caused by 37 unique conditions and their associated risk factors globally, regionally, and nationally from 1990 to 2021.MethodsWe estimated mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs), with corresponding 95% uncertainty intervals (UIs), by age and sex in 204 countries and territories, from 1990 to 2021. We included morbidity and deaths due to neurological conditions, for which health loss is directly due to damage to the CNS or peripheral nervous system. We also isolated neurological health loss from conditions for which nervous system morbidity is a consequence, but not the primary feature, including a subset of congenital conditions (ie, chromosomal anomalies and congenital birth defects), neonatal conditions (ie, jaundice, preterm birth, and sepsis), infectious diseases (ie, COVID-19, cystic echinococcosis, malaria, syphilis, and Zika virus disease), and diabetic neuropathy. By conducting a sequela-level analysis of the health outcomes for these conditions, only cases where nervous system damage occurred were included, and YLDs were recalculated to isolate the non-fatal burden directly attributable to nervous system health loss. A comorbidity correction was used to calculate total prevalence of all conditions that affect the nervous system combined.FindingsGlobally, the 37 conditions affecting the nervous system were collectively ranked as the leading group cause of DALYs in 2021 (443 million, 95% UI 378–521), affecting 3·40 billion (3·20–3·62) individuals (43·1%, 40·5–45·9 of the global population); global DALY counts attributed to these conditions increased by 18·2% (8·7–26·7) between 1990 and 2021. Age-standardised rates of deaths per 100 000 people attributed to these conditions decreased from 1990 to 2021 by 33·6% (27·6–38·8), and age-standardised rates of DALYs attributed to these conditions decreased by 27·0% (21·5–32·4). Age-standardised prevalence was almost stable, with a change of 1·5% (0·7–2·4). The ten conditions with the highest age-standardised DALYs in 2021 were stroke, neonatal encephalopathy, migraine, Alzheimer's disease and other dementias, diabetic neuropathy, meningitis, epilepsy, neurological complications due to preterm birth, autism spectrum disorder, and nervous system cancer.InterpretationAs the leading cause of overall disease burden in the world, with increasing global DALY counts, effective prevention, treatment, and rehabilitation strategies for disorders affecting the nervous system are needed

Global, regional, and national progress towards Sustainable Development Goal 3.2 for neonatal and child health: all-cause and cause-specific mortality findings from the Global Burden of Disease Study 2019

Background Sustainable Development Goal 3.2 has targeted elimination of preventable child mortality, reduction of neonatal death to less than 12 per 1000 livebirths, and reduction of death of children younger than 5 years to less than 25 per 1000 livebirths, for each country by 2030. To understand current rates, recent trends, and potential trajectories of child mortality for the next decade, we present the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 findings for all-cause mortality and cause-specific mortality in children younger than 5 years of age, with multiple scenarios for child mortality in 2030 that include the consideration of potential effects of COVID-19, and a novel framework for quantifying optimal child survival. Methods We completed all-cause mortality and cause-specific mortality analyses from 204 countries and territories for detailed age groups separately, with aggregated mortality probabilities per 1000 livebirths computed for neonatal mortality rate (NMR) and under-5 mortality rate (USMR). Scenarios for 2030 represent different potential trajectories, notably including potential effects of the COVID-19 pandemic and the potential impact of improvements preferentially targeting neonatal survival. Optimal child survival metrics were developed by age, sex, and cause of death across all GBD location-years. The first metric is a global optimum and is based on the lowest observed mortality, and the second is a survival potential frontier that is based on stochastic frontier analysis of observed mortality and Healthcare Access and Quality Index. Findings Global U5MR decreased from 71.2 deaths per 1000 livebirths (95% uncertainty interval WI] 68.3-74-0) in 2000 to 37.1 (33.2-41.7) in 2019 while global NMR correspondingly declined more slowly from 28.0 deaths per 1000 live births (26.8-29-5) in 2000 to 17.9 (16.3-19-8) in 2019. In 2019,136 (67%) of 204 countries had a USMR at or below the SDG 3.2 threshold and 133 (65%) had an NMR at or below the SDG 3.2 threshold, and the reference scenario suggests that by 2030,154 (75%) of all countries could meet the U5MR targets, and 139 (68%) could meet the NMR targets. Deaths of children younger than 5 years totalled 9.65 million (95% UI 9.05-10.30) in 2000 and 5.05 million (4.27-6.02) in 2019, with the neonatal fraction of these deaths increasing from 39% (3.76 million 95% UI 3.53-4.021) in 2000 to 48% (2.42 million; 2.06-2.86) in 2019. NMR and U5MR were generally higher in males than in females, although there was no statistically significant difference at the global level. Neonatal disorders remained the leading cause of death in children younger than 5 years in 2019, followed by lower respiratory infections, diarrhoeal diseases, congenital birth defects, and malaria. The global optimum analysis suggests NMR could be reduced to as low as 0.80 (95% UI 0.71-0.86) deaths per 1000 livebirths and U5MR to 1.44 (95% UI 1-27-1.58) deaths per 1000 livebirths, and in 2019, there were as many as 1.87 million (95% UI 1-35-2.58; 37% 95% UI 32-43]) of 5.05 million more deaths of children younger than 5 years than the survival potential frontier. Interpretation Global child mortality declined by almost half between 2000 and 2019, but progress remains slower in neonates and 65 (32%) of 204 countries, mostly in sub-Saharan Africa and south Asia, are not on track to meet either SDG 3.2 target by 2030. Focused improvements in perinatal and newborn care, continued and expanded delivery of essential interventions such as vaccination and infection prevention, an enhanced focus on equity, continued focus on poverty reduction and education, and investment in strengthening health systems across the development spectrum have the potential to substantially improve USMR. Given the widespread effects of COVID-19, considerable effort will be required to maintain and accelerate progress. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd