ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Optimized sizing of a standalone PV-wind-hydropower station with pumped-storage installation hybrid energy system

The development and utilization of renewable energy sources can not only effectively reduce carbon dioxide emissions, but also provide access to electricity to more parts of the world. A standalone hybrid system based on renewable sources is a promising way to supply reliable and continuous power in remote areas to which the grid has not extended. This paper designs and investigates a photovoltaics (PV)-wind-hydropower station with pumped-storage installation (HSPSI) hybrid energy system in Xiaojin, Sichuan, China as case of study. HSPSI can use the available flow of the river and store surplus energy generated from wind and PV by pumping water from the lower reservoir to the upper one. From the perspective of the investors, the techno-economic index is usually used to design the PV-wind-HSPSI hybrid energy system which aims to find the optimal configure with maximum power supply reliability and minimum investment cost. The trade-off analysis between the two objectives is based on Pareto optimality theory by means of Multi-Objective Particle Swarm Optimization (MOPSO). Besides, this paper takes the curtailment rate (CR) of the wind and PV power into consideration due to policy requirements. The relationship between the two objectives under various CR are analyzed and compared. Several results can be obtained as follows: 1) Comparing with the PV-HSPSI and wind-HSPSI hybrid energy system, the levelized cost of energy (LCOE) of the PV-wind-HSPSI hybrid energy system can reduce by 32.8% and 45.0% respectively. 2) For the PV-wind-HSPSI hybrid energy system, the LCOE can be as low as 0.091 $/kWh when 5% LPSP can be acceptable. 3) The policy of CR is unfavorable for the investors which leads to a higher investment cost. 4) Particle Swarm Optimization (PSO) performs better than genetic algorithm (GA) and Simulated Annealing method (SA) with a least LCOE. 5) both MOPSO and weighted sum approach (WSA) have a good performance to find the Pareto fronts and its hypervolume indicator (HV) is calculated. (c) 2019 Elsevier Ltd. All rights reserved.
Fiscal policy and CO2 emissions from heterogeneous fuel sources in Thailand: Evidence from multiple structural breaks cointegration test

This study investigated the dynamic linkage between fiscal policy, energy and CO2 emissions from heterogeneous fossil fuel sources in the context of the environmental Kuznets curve (EKC) framework for Thailand. With annual data from 1972 to 2014 while incorporating structural breaks, the study employed a Maki cointegration test and the dynamic ordinary least squares estimation approach. The results found that a 1% increase in fiscal policy brought about a 6.5% (p < 0.05) increase in the low CO2 emitting gaseous fuel sources (natural gas), a 0.2% (p < 0.01) reduction in the intermediate CO2 emitting liquid fuel sources (crude oil derivatives), and an insignificant increase 0.2% (p > 0.05) in the high CO2 emitting solid fuel sources (coal derivatives). While a 1% increase in fiscal policy abates aggregated CO2 emissions by 0.2% (p < 0.05), the existence of the EKC hypothesis was validated in all models. The causality test revealed a bi-directional causal relationship between fiscal policy and CO2 emissions and unidirectional flow from fiscal policy to energy consumption. This confirms that fiscal policy initiatives towards energy consumption have long-run implications for environmental quality. Our findings support the energy-led growth hypothesis for the Thai economy. The implication of the finding is that increasing the share of clean and renewable energy sources should be encouraged-rather than energy conservation policies, which obstruct energy supply and utilization. This highlights a more efficient way of harnessing energy sources through the instrumentality of fiscal policy. (C) 2019 The Author(s). Published by Elsevier B.V.
Health insurance, medical debt, and financial well-being

We study the financial protection provided by health insurance through two natural experiments-the Affordable Care Act's under 26 provision and Medicare eligibility. In both cases, the coverage expansion sharply reduces medical debt in collections for consumers within the affected ages but does not systematically improve credit outcomes not directly related to medical care. This is consistent with the infrequent repayment rate and lack of persistence on credit reports that we document for medical collections, which mute a key channel through which reductions in medical collections could directly affect the other financial outcomes studied here. These results help clarify the role of health insurance in broader financial health and suggest that, at least among the populations studied here, medical debts in collection may often be a symptom rather than a cause of wider financial distress as measured on credit reports.
Emergency preparedness during the COVID-19 pandemic: Modelling the roles of social media with fuzzy DEMATEL and analytic network process

While the utility of social media has been widely recognized in the current literature, minimal effort has been made to further the analysis of their roles on disruptive events, such as the COVID-19 pandemic. To address this gap, this work comprehensively identifies the 16 prevalent social media roles in disaster preparedness during the COVID-19 pandemic. Furthermore, an integrated fuzzy decision-making trial and evaluation laboratory (FDEMATEL) and analytic network process (ANP), hereby termed the FDANP methodology, is used to perform the causal analysis of social media roles and to systemically measure the priority of these roles in emergency preparedness. Among the identified roles, those considered top priority are social media roles concerned with the facilitation of public health policy development, prevention of misinformation, and management of public behavior and response. These results were found to be robust, as evidenced by the sensitivity analysis. The implications of these findings were also detailed in this work in the context of a developing country.
Repulsion-Based p-Dispersion with Distance Constraints in Non-Convex Polygons

Motivated by the question of optimal facility placement, the classical p-dispersion problem seeks to place a fixed number of equally sized non-overlapping circles of maximal possible radius into a subset of the plane. While exact solutions to this problem may be found for placement into particular sets, the problem is provably NP-complete for general sets, and existing work is largely restricted to geometrically simple sets. This paper makes two contributions to the theory of p-dispersion. First, we propose a computationally feasible suboptimal approach to the p-dispersion problem for all non-convex polygons. The proposed method, motivated by the mechanics of the p-body problem, considers circle centers as continuously moving objects in the plane and assigns repulsive forces between different circles, as well as circles and polygon boundaries, with magnitudes inversely proportional to the corresponding distances. Additionally, following the motivating application of optimal facility placement, we consider existence of additional hard upper or lower distance bounds on pairs of circle centers, and adapt the proposed method to provide a p-dispersion solution that provably respects such constraints. We validate our proposed method by comparing it with previous exact and approximate methods for p-dispersion. The method quickly produces near-optimal results for a number of containers.
An MCDM-based social network analysis of water governance to determine actors' power in water-food-energy nexus

A major water governance concern is how to coordinate the complex relationships of the water, food, and energy sectors and the resulting economic, social, and environmental consequences. Focusing on the challenges in one sector (e.g. water shortage in the water sector) and making decisions without considering other sectors will not solve these problems; rather, it will create a new problem (the decline of food production) in the food sectors. The nexus approach is a novel method to represent the interrelated challenges of the water, food, and energy sectors by considering the sectors' policies to achieve sustainable development. The present study analyzes the social network of nexus actors in the Yazd-Ardakan aquifer, Yazd province, Iran. For this purpose, 54 partners in the nexus network were first identified in various public, private, semi-private and non-governmental organizations. Three types of interactions (knowledge and information exchange, budget transfer, and collaboration) were analyzed among the actors. The actor-network was, then, assessed at the levels of the entire network and individual actors. Then, a coherence analysis was performed by the density index at the entire network level, and the power analysis was carried out using in-degree, out-degree, betweenness, closeness, and eigenvector centrality indices at the individual actor level. Social network analysis criteria were, then, incorporated with the multi-criteria decision-making model ELECTRE I to select the key and powerful actors in the nexus network. Next, the powerful actors were identified and introduced with respect to 15 criteria employed in the social network analysis. According to the results, the power structure in the nexus network of the research area is not at equilibrium. Most of the power lies with the public sector. The water, food, and energy sectors act alone and they do not exploit their maximum organizational capacities to establish relationships with one another.
The Advantages of Industry 4.0 Applications for Sustainability: Results from a Sample of Manufacturing Companies

Far from being exclusively related to economic considerations, the advantages of Industry 4.0 applications also include environmental and social concerns. An increasing amount of scientific publications relate the implementation of the fourth industrial revolution paradigm to sustainability. Several studies reported opportunities of Industry 4.0 implementation particularly to the environmental dimension of sustainability, e.g., through improved logistics streams and lowered waste from production. The present research aims at providing evidence on whether manufacturing companies consider Industry 4.0 implementation as an advantage contributing to environmental and social sustainability in terms of lower environmental impact of production, as well as higher physical relief for workers and flexibility of work organisation. The results were an attempt to study such relations with company sizes, industry sectors, turnover and self-assessed levels of digitalization varying. The sample encompasses 65 companies located in the Marche region (Italy). The results show that overall the perception of economic opportunities prevail, while the association of a beneficial impact of Industry 4.0 on environmental sustainability is rather low across companies, regardless of their size, turnover and digital level. As for the statistically significant variables, the results suggest a strong association of the size and the digital level to specific Industry 4.0 related advantages, referring to the social and economic dimension of sustainability, respectively.
Does the popularization of agricultural mechanization improve energy-environment performance in China's agricultural sector?

Environmental problems caused by the rapid popularization of agricultural mechanization and the increase of energy input in China's agricultural production have caused increasing concern. Based on the study sample of China's 30 provinces from 2000 to 2017, this paper estimates the energy-environment performance of agricultural sector using the global DEA method. Furthermore, it applied a panel TOBIT model based on censored data to empirically analyze the impact of popularization of mechanization on energy-environment performance in China's agricultural sector. The main findings are as follows: Firstly, the mean value of energy-environment performance is 0.1842, and the values of national and regional performance showed a downward trend in the sample period, indicating that most regions in China's agricultural sector did not perform well in terms of energy saving and pollution control. Secondly, the development of agricultural mechanization has a significant negative effect on energy-environment performance. This result confirms the fact that the regions with high penetration rates of agricultural mechanization usually have a low energy-environment performance. Finally, in regard to the control variables, energy price, reservoir infrastructure construction and agricultural industrial structure negatively affect energy-environment performance, while the agricultural labor force and agricultural mechanization service organizations have a positive effect on China's energy-environment performance. (C) 2020 Elsevier Ltd. All rights reserved.
Trend in Extreme Precipitation Indices Based on Long Term In Situ Precipitation Records over Pakistan

Assessing the long-term precipitation changes is of utmost importance for understanding the impact of climate change. This study investigated the variability of extreme precipitation events over Pakistan on the basis of daily precipitation data from 51 weather stations from 1980-2016. The non-parametric Mann-Kendall, Sen's slope estimator, least squares method, and two-tailed simple t-test methods were used to assess the trend in eight precipitation extreme indices. These indices were wet days (R1 >= 1 mm), heavy precipitation days (R10 >= 10 mm), very heavy precipitation days (R20 >= 20 mm), severe precipitation (R50 >= 50 mm), very wet days (R95p) defining daily precipitation >= 95 percentile, extremely wet days (R99p) defining daily precipitation >= 99 percentile, annual total precipitation in wet days (PRCPTOT), and mean precipitation amount on wet days as simple daily intensity index (SDII). The study is unique in terms of using high stations' density, extended temporal coverage, advanced statistical techniques, and additional extreme indices. Furthermore, this study is the first of its kind to detect abrupt changes in the temporal trend of precipitation extremes over Pakistan. The results showed that the spatial distribution of trends in different precipitation extreme indices over the study region increased as a whole; however, the monsoon and westerlies humid regions experienced a decreasing trend of extreme precipitation indices during the study period. The results of the sequential Mann-Kendall (SqMK) test showed that all precipitation extremes exhibited abrupt dynamic changes in temporal trend during the study period; however, the most frequent mutation points with increasing tendency were observed during 2011 and onward. The results further illustrated that the linear trend of all extreme indices showed an increasing tendency from 1980- 2016. Similarly, for elevation, most of the precipitation extremes showed an inverse relationship, suggesting a decrease of precipitation along the latitudinal extent of the country. The spatiotemporal variations in precipitation extremes give a possible indication of the ongoing phenomena of climate change and variability that modified the precipitation regime of Pakistan. On the basis of the current findings, the study recommends that future studies focus on underlying physical and natural drivers of precipitation variability over the study region.
Stir bar sorptive-dispersive microextraction by a poly(methacrylic acid-co-ethylene glycol dimethacrylate)-based magnetic sorbent for the determination of tricyclic antidepressants and their main active metabolites in human urine

A poly(methacrylic acid-co-ethylene glycol dimethacrylate)-based magnetic sorbent was used for the rapid and sensitive determination of tricyclic antidepressants and their main active metabolites in human urine. This material was characterized by magnetism measurements, zeta potential, scanning electron microscopy, nitrogen adsorption-desorption isotherms, and thermogravimetric analysis. The proposed analytical method is based on stir bar sorptive-dispersive microextraction (SBSDME) followed by liquid chromatography-tandem mass spectrometry. The main parameters involved in the extraction step were optimized by using the response surface methodology as a multivariate optimization method, whereas a univariate approach was employed to study the desorption parameters. Under the optimized conditions, the proposed method was properly validated showing good linearity (at least up to 50 ng mL-1) and enrichment factors (13-22), limits of detection and quantification in the low ng L-1 range (1.4-7.0 ng L-1), and good intra- and inter-day repeatability (relative standard deviations below 15%). Matrix effects were observed for the direct analysis of urine samples, but they were negligible when a 1:1 v/v dilution with deionized water was performed. Finally, the method was successfully applied to human urine samples from three volunteers, one of them consuming a prescribed drug for depression that tested positive for clomipramine and its main active metabolite. Quantitative relative recoveries (80-113%) were obtained by external calibration. The present work expands the applicability of the SBSDME to new analytes and new types of magnetic sorbents.
Prediction short-term photovoltaic power using improved chicken swarm optimizer - Extreme learning machine model

Photovoltaic power generation is greatly affected by weather conditions while the photovoltaic power has a certain negative impact on the power grid. The power sector takes certain measures to abandon photovoltaic power generation, thus limiting the development of clean energy power generation. This study is to propose an accurate short-term photovoltaic power prediction method. A new short-term photovoltaic power output prediction model is proposed Based on extreme learning machine and intelligent optimizer. Firstly, the input of the model is determined by correlation coefficient method. Then the chicken swarm optimizer is improved to strengthen the convergence. Secondly, the improved chicken swarm optimizer is used to optimize the weights and the extreme learning machine thresholds to improve the prediction effect. Finally, the improved chicken swarm optimizer extreme learning machine model is used to predict the photovoltaic power under different weather conditions. The testing results show that the average mean absolute percentage error and root mean square error of improved chicken swarm optimizer - extreme learning machine model are 5.54% and 3.08%. The proposed method is of great significance for the economic dispatch of power systems and the development of clean energy. (c) 2019 Elsevier Ltd. All rights reserved.
Global assessment of environment, health and economic impact of the novel coronavirus (COVID-19)

The institution of social distancing and punitive measures to contain the spread of COVID-19 through human-to-human transmission has environmental, health and economic impact. While the global pandemic has led to the enhancement of the health system and decline of emissions, economic development appears deteriorating. Here, we present the global environmental, health and economic dimension of the effect of COVID-19 using qualitative and empirical assessments. We report the health system policies, environmental sustainability issues, and fiscal, monetary and exchange rate measures introduced during lockdown across countries. While air pollution is reported to have declined, municipal and medical waste is increasing. The COVID-19 global pandemic uncertainty ranks the UK as the country with the highest uncertainty level among 143 countries. The USA has introduced 100% of pre-COVID-19 crisis level GDP, the highest policy cut-rate among 162 countries. Science, innovation, research and development underpin COVID-19 containment measures implemented across countries. Our study demonstrates the need for future research to focus on environment-health-economic nexus-a trilemma that has a potential trade-off.
Neuropathology of Vascular Brain Health: Insights From Ex Vivo Magnetic Resonance Imaging-Histopathology Studies in Cerebral Small Vessel Disease

Sporadic cerebral small vessel disease (SVD) is a major contributor to vascular cognitive impairment and dementia in the aging human brain. On neuropathology, sporadic SVD is characterized by abnormalities to the small vessels of the brain predominantly in the form of cerebral amyloid angiopathy and arteriolosclerosis. These pathologies frequently coexist with Alzheimer disease changes, such as plaques and tangles, in a single brain. Conversely, during life, magnetic resonance imaging (MRI) only captures the larger manifestations of SVD in the form of parenchymal brain abnormalities. There appears to be a major knowledge gap regarding the underlying neuropathology of individual MRI-detectable SVD abnormalities. Ex vivo MRI in postmortem human brain tissue is a powerful tool to bridge this gap. This review summarizes current insights into the histopathologic correlations of MRI manifestations of SVD, their underlying cause, presumed pathophysiology, and associated secondary tissue injury. Moreover, we discuss the advantages and limitations of ex vivo MRI-guided histopathologic investigations and make recommendations for future studies.
Effect of aluminum balls on the productivity of solar distillate

Drinking water has become a real global problem, despite the abundance of unsaturated saltwater and colorless, which do not meet the requirements of the World Health Organization. The El Oued, in the southeastern part of Algeria, contains vast quantities of salt water and non-potable water. Solar distillation can be used as a solution to resolve this trouble. To provide drinking water, we use free sunlight to treat salt water. The traditional solar distillation device, which is one mile long (0.5 x 0.5 m), is designed to confirm the effectiveness of this solution. The traditional single slope solar distillation, its dimensions (0.5 x 0.5 m), is designed to confirm the effectiveness of this solution. Solar distillation is an environmental, economic and simple technique, but it needs some modifications to increase the distilled water production. In this work, we added aluminum balls inside the solar distillate box. In this work, we added 42 aluminum balls with a diameter of 2 cm inside a solar distillates box; positioned as follows (6 balls x 7 balls). The results showed that aluminum balls increased the distillation of distilled water by about 27.16 %.