Electromagnetic imaging and deep learning for transition to renewable energies: a technology review

Electromagnetic imaging is a technique that has been employed and perfected to investigate the Earth subsurface over the past three decades. Besides the traditional geophysical surveys (e.g., hydrocarbon exploration, geological mapping), several new applications have appeared (e.g., characterization of geothermal energy reservoirs, capture and storage of carbon dioxide, water prospecting, and monitoring of hazardous-waste deposits). The development of new numerical schemes, algorithms, and easy access to supercomputers have supported innovation throughout the geo-electromagnetic community. In particular, deep learning solutions have taken electromagnetic imaging technology to a different level. These emerging deep learning tools have significantly contributed to data processing for enhanced electromagnetic imaging of the Earth. Herein, we review innovative electromagnetic imaging technologies and deep learning solutions and their role in better understanding useful resources for the energy transition path. To better understand this landscape, we describe the physics behind electromagnetic imaging, current trends in its numerical modeling, development of computational tools (traditional approaches and emerging deep learning schemes), and discuss some key applications for the energy transition. We focus on the need to explore all the alternatives of technologies and expertise transfer to propel the energy landscape forward. We hope this review may be useful for the entire geo-electromagnetic community and inspire and drive the further development of innovative electromagnetic imaging technologies to power a safer future based on energy sources.This work was supported by the European Union’s Horizon 2020 research and innovation programme under grant agreements No. 955606 (DEEP-SEA) and No. 777778 (MATHROCKS). Furthermore, the research leading of this study has received funding from the Ministerio de Educación y Ciencia (Spain) under Project TED2021-131882B-C42.Peer ReviewedPostprint (published version

Analysis of the Willingness and Factors Influencing the Residents to Choose Between Chinese Medicine and Western Medicine under the New Coronavirus Pandemic: A Study in Zhejiang Province Community Health Service Center

Objective: To understand the willingness of Chinese residents to choose between Chinese and Western medicine in the face of sudden outbreak, this study aims to investigate and analyze the willingness and factors influencing Chinese residents (taking Zhejiang Province as an example) to choose between Chinese and Western medicine under the new coronavirus pandemic. Methods: The present study performed a large-scale cross-sectional online survey among 666 random residents in Zhejiang Province. We used questionnaires to investigate the feedback form from residents seeking medical care. In addition, a multivariate logistic regression model was used to analyze the influence of gender, education, medical reimbursement, and age on the choice of Chinese and Western medicine. Results: Among the patients with mild disease, 55.9% patients chose traditional Chinese medicine, while 44.1% chose Western medicine. Moreover, the proportion of patients with severe diseases who chose traditional Chinese medicine was 7.0%, while the rate of Western medicine was 93.0%. Among the patients suffering from mild diseases, the proportion of men who chose traditional Chinese medicine (46.2%) was lower than that of women (53.8%). The usage of Chinese medicine was preferred among residents of all ages, income levels, and educational backgrounds. A total of 93.0% of patients who chose Western medicine for treatment were severely ill, and the residents with severe diseases preferred Western medicine to Chinese medicine. People with high education and young were more inclined toward Western medicine for treatment compared with Chinese medicine. It was noted that people paid most attention to the medical insurance reimbursement ratio, followed by the distance between the medical institution and the place of residence. Conclusion: The acceptance of Chinese medicine among patients has generally increased; however, gender, educational background, and income still exert a great influence on the choice between Chinese and Western medicine

Electromagnetic imaging and deep learning for transition to renewable energies: a technology review

Electromagnetic imaging is a technique that has been employed and perfected to investigate the Earth subsurface over the past three decades. Besides the traditional geophysical surveys (e.g., hydrocarbon exploration, geological mapping), several new applications have appeared (e.g., characterization of geothermal energy reservoirs, capture and storage of carbon dioxide, water prospecting, and monitoring of hazardous-waste deposits). The development of new numerical schemes, algorithms, and easy access to supercomputers have supported innovation throughout the geo-electromagnetic community. In particular, deep learning solutions have taken electromagnetic imaging technology to a different level. These emerging deep learning tools have significantly contributed to data processing for enhanced electromagnetic imaging of the Earth. Herein, we review innovative electromagnetic imaging technologies and deep learning solutions and their role in better understanding useful resources for the energy transition path. To better understand this landscape, we describe the physics behind electromagnetic imaging, current trends in its numerical modeling, development of computational tools (traditional approaches and emerging deep learning schemes), and discuss some key applications for the energy transition. We focus on the need to explore all the alternatives of technologies and expertise transfer to propel the energy landscape forward. We hope this review may be useful for the entire geo-electromagnetic community and inspire and drive the further development of innovative electromagnetic imaging technologies to power a safer future based on energy sources

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

Ion Transport Behavior through Thermally Reduced Graphene Oxide Membrane for Precise Ion Separation

The cation transport behavior of thermally treated reduced graphene oxide membranes (GOMs) is reported. The GOMs were reduced by heat treatment at 25, 80, and 120 °C and then characterized by Fourier transform infrared spectroscopy, X-ray powder diffraction, and X-ray photoelectron spectroscopy to determine oxygen group content, C/O ratio, and layer spacing. The permeation rates of various cations with different sizes and charge numbers through these membranes were measured to understand the effect of the cations on transport behavior. The results indicated that the cation transport through the membranes depended on the layer spacing of the membrane and ion size and charge. Cations of the same valence permeating through the same GOM could be differentiated by their hydration radius, whereas the same type of cation passing through different GOMs could be determined by the spacing of the GOM layers. The cation valence strongly affected permeation behavior. The GOM that was prepared at 120 °C exhibited a narrow layer spacing and high separation factors for Mg/Ca, Mg/Sr, K/Ca, and K/Fe. The cations moving through the membrane could insert into the membrane lamellas, which neutralized the negative charge of the membrane, enlarged the layer spacing of the GOMs, and affected cation permeation