ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Investigation of an integrated system combining an Organic Rankine Cycle and absorption chiller driven by geothermal energy: Energy, exergy, and economic analyses and optimization

This study aims to investigate a novel hybrid system for combined cooling and power (CCP) driven by geothermal energy. This study is performed using energy, exergy, and economic analyses. The results of this study show that application of LiBr absorption chiller downstream of the Organic Rankine Cycle (ORC) cycle increases the energy efficiency of the system from 9.3% to 47.3%. But it decreases the exergy efficiency from 15.6% to 4.6%, mainly due to the increase in the exergy destruction of the system. By definition of a new parameter called the electricity and cooling cost for this hybrid system, the results show this parameter decreases from 0.0552 to 0.0028 $/kWh when the LiBr absorption chiller cycle is added. Moreover, a multi-objective optimization of this hybrid system is carried out by the MOPSO algorithm for two objective functions including cost of electricity and cooling (C-EC), and exergy efficiency (eta(ex)). The results show that the optimum electricity and cooling cost and exergy efficiency are 0.0033 $/kWh, and 6.8%, respectively. (C) 2020 Elsevier Ltd. All rights reserved.
Exploring the folding energy landscapes of heme proteins using a hybrid AWSEM-heme model

Heme is an active center in many proteins. Here we explore computationally the role of heme in protein folding and protein structure. We model heme proteins using a hybrid model employing the AWSEM Hamiltonian, a coarse-grained forcefield for the protein chain along with AMBER, an all-atom forcefield for the heme. We carefully designed transferable force fields that model the interactions between the protein and the heme. The types of protein-ligand interactions in the hybrid model include thioester covalent bonds, coordinated covalent bonds, hydrogen bonds, and electrostatics. We explore the influence of different types of hemes (heme b and heme c) on folding and structure prediction. Including both types of heme improves the quality of protein structure predictions. The free energy landscape shows that both types of heme can act as nucleation sites for protein folding and stabilize the protein folded state. In binding the heme, coordinated covalent bonds and thioester covalent bonds for heme c drive the heme toward the native pocket. The electrostatics also facilitates the search for the binding site.
A comparative analysis of novel combined flash-binary cycles for Sabalan geothermal wells: Thermodynamic and exergoeconomic viewpoints

Sabalan geothermal field is one of the most important geothermal fields in Iran. In this study, four novel configurations namely Single Flash-ORC (SF-ORC), Double Flash 1-ORC (DF1-ORC), Double Flash 2-ORC (DF2-ORC) and Triple Flash-ORC (TF-ORC) are proposed to produce power from two wells of the field. The configurations are assessed by thermodynamic and exergoeconomic viewpoints. The exergy and SPECO based exergoeconomic equations are developed for the components of the proposed cycles and thermodynamic as well as exergoeconomic performance parameters are calculated for the components and the cycles. A comprehensive parametric study is also carried out for the proposed configurations considering various working fluids in the ORCs. Moreover, an optimization is performed to maximize the produced power from the wells. The results depicted that the DF1-ORC produces 4.03%, 1.32% and 1.2% more power than the SF-ORC, DF2-ORC and TF-ORC, respectively, in the optimum condition. It can be concluded from the exergoeconomic analysis that the SF-ORC with R123 working fluid has the best exergoeconomic performance with the power specific cost of 3.62 $/GJ. The thermodynamic and exergoeconomic performance of the DF1-ORC is compared with previous similar studies which shows the superiority of the proposed cycle. (C) 2020 Elsevier Ltd. All rights reserved.
Do country risks influence carbon dioxide emissions? A non-linear perspective

This study explores the non-linear effects of real income, energy use, and country risks on carbon dioxide (CO2) emissions in a panel of 111 countries from 1985 to 2014. By applying the panel smooth transition regression model, we find that real income, energy use, and country risks have different impacts on CO2 emissions when using country risks as thresholds. As to the full sample, the results show an inverted U-shaped relationship between CO2 emissions and economic risk, and that the financial and political risk indices have monotonically increasing effects on CO2 emissions. As country risks decrease, the positive effects of real income and energy use on CO2 emissions are first large and then become small. Moreover, with country risks changing, low-income countries have larger sensitivities to CO2 emissions. (C) 2020 Elsevier Ltd. All rights reserved.
Impaired Metacognitive Differentiation, High Difficulty in Controlling Impulses and Non-acceptance of Emotions are Associated With the Severity of Gambling Disorder

The role of metacognition in gambling disorder (GD) is underexplored. To date, only two studies have investigated the role of metacognitive functioning, but among the adolescent population. The first aim of the current research was to assess and compare adult male gamblers with healthy controls (HCs) in relation to metacognition, impulsivity and emotional dysregulation. The second aim was to identify the variables among metacognition, impulsivity and emotional dysregulation associated with the severity of GD by means of linear regression.A total of 116 adult males (58 with GD and 58 HCs) completed self-report questionnaires on gambling severity, metacognition, emotional dysregulation and impulsivity. A linear regression analysis was run to assess the variables associated with gambling severity.Patients with GD exhibited more impaired scores than HCs in all the psychopathological dimensions investigated. More interestingly, gambling severity was significantly associated with metacognitive differentiation/decentration, difficulty in controlling impulses and non-acceptance of negative emotions.According to our results, the severity of gambling is associated with impaired metacognitive differentiation, high difficulty in controlling impulses and non-acceptance of negative emotions, and these findings can lead to new treatment implications. Interventions focused on metacognition and emotion regulation could help patients with GD to avoid maladaptive strategies such as behavioural addictions and, more specifically, to manage their own emotions. This type of treatment could help gamblers to become more aware of their internal state and learn strategies for adaptively managing emotions through functional metacognitive differentiation.
Energy production and trade openness: Assessing economic growth, CO2 emissions and the applicability of the cointegration analysis

The relationship between economic activities such as energy production, trade, and economic growth affects all areas of human life in terms of well-being, as well as a country's economic activities. In this study, we investigate these relationships using cointegration regression methods (FMOLS fully modified ordinary least square and DOLS dynamic ordinary least squares), we use the electricity production (hydro, natural gas and renewables), trade opening, GDP, and CO2 emissions to establish causality. We found that electricity generation, GDP and trade liberalization have both positive and negative effects on Brazil's economy. We also discovered a bidirectional causality between trade openness and all the energies produced in Brazil. Separately, we observed that GDP, hydropower, and renewables have negative effects on the CO2 emissions model, while only emissions of pollution and trade openness have positive effects on the economic growth model. These results have important policy implications for the Brazilian economy that does not support appropriate long-term sustainable development strategies. Consequently, policymakers should consider implementing appropriate management capacity to encourage the use of renewable energy and to benefit from the positive effects of economic growth and environmental policies to control the pollution rate through the potential of available natural resources. Our findings are not motivated by discrepancies or sample selection and survive multiple specifications, allowing to observe the relationship with great accuracy. Some diagnostic tests have been applied to show that it is not misleading.
Comparative assessment of engine performance and emissions fueled with three different biodiesel generations

Emissions from biodiesel combustion depend on types of feedstocks. This study investigated physicochemical properties of biodiesels in three various feedstocks generations and the effect of them on emissions and performance of the unmodified engine. Methyl esters from Eruca sativa (ES) which has crops with short production cycle and drought-tolerant capacity, waste cooking oil (WCO) as an economic feedstock, and microalgae Chlorella vulgaris (MCV) which have high growth rate and productivity without farmland requirement, were produced by transesterification process with methanol and KOH catalyst. Physicochemical properties of these biodiesels were determined by GC-MS and FTIR analysis and evaluated with ASTM D6751 standard. FAME components profile illustrated the high value of oleic acid (C18:1) in ES and WCO and long-chain fatty acids (C18:2 and higher) in MCV. The engine tests results indicated that MCV biodiesel produced slightly lower power and higher BSFC compared with others. There was a slight increase in NOx for MCV biodiesel compared with pure diesel. Conversely, ES and WCO biodiesels had a reduction in NOx. On the other hand, MCV biodiesel resulted in the greatest reduction in CO and HC emissions. Based on the results, MCV blends had the lowest emissions and the best function among all feedstocks. (C) 2019 Elsevier Ltd. All rights reserved.
Production of type-B trichothecenes by Fusarium meridionale, F. graminearum, and F. austroamericanum in wheat plants and rice medium

Food security goes beyond food being available; the food needs to be free of contaminants. Trichothecenes mycotoxins, produced by Fusarium fungus, are. among the most frequently found contaminants of wheat. In this study, we evaluated the production of trichothecenes Deoxynivalenol (DON), 3-acetyldeoxynivalenol (3-AcDON), 15-acetyldeoxynivalenol (15-AcDON), and nivalenol (NIV) by Fusarium meridionale, F. austroamericanum, and F. graminearum grown in wheat plants and rice medium. Fusarim meridionale was efficient only in the production of NIV (production range (pr) from 1340 to 2864 µg kg-1 in wheat plant), and F. austroamericanum in the production of 3-AcDON (pr from 50 to 192 µg kg-1 in wheat plant, and from 986 to 7045 µg kg-1 in rice medium) and DON (pr from 4076 to 13,701 µg kg-1 in wheat plant, and from 184 to 43,395 µg kg-1 in rice medium). Already, F. graminearum was efficient in the production of 3-AcDON only in rice medium (pr from 81 to 2342 µg kg-1), 15-AcDON in wheat plant (pr from 80 to 295 µg kg-1) and in rice medium (pr from 436 to 8597 µg kg-1), and DON also in wheat plant (pr from 7746 to 12,046 µg kg-1) and in rice medium (pr from 695 to 49,624 µg kg-1). The specificity of F. meridionale in the production of NIV but not the production of DON could generate a food security problem in regions where this species occurs and the amounts of NIV in grains and derivatives are not regulated in the food chain, as in Brazil.
Suitability of different precipitation data sources for hydrological analysis: a study from Western Ghats, India

For hydrological analysis, it is essential to have continuous and long-term precipitation data. However, the precipitation data from rain gauge stations are often insufficient and not continuous. At present, ground-based gridded data and satellite-based gridded data are often used as an alternative. However, these data sets have to be evaluated for their suitability in hydrological studies. The current study compared three different rainfall data sources with the observed station data for the Kallada River basin of Kerala, India. The ground-based gridded rainfall data from the India Meteorological Department (IMD), the high-resolution satellite product Tropical Rainfall Measuring Mission (TRMM 3B43, version 7), and the reanalysis data Modern-Era Retrospective Analysis for Research and Applications (MERRA) are used in the analysis. The correlation coefficient, normalized root mean square error, Nash-Sutcliffe efficiency, modified index of agreement, and volumetric efficiency are used as performance indicators. The performance indicator's weights are based on the entropy method. The multi-criteria decision-making techniques like compromise programming and Preference Ranking Organization Method (PROMETHEE II) are used for ranking the precipitation data sources. It is found that IMD ground-based gridded data is ranked 1 among the three data sets. The IMD ground-based gridded data are not homogeneous based on the absolute homogeneity test, even though they had the highest rank. The IMD gridded data are further corrected based on double mass curve analysis. The corrected data were analyzed using the precipitation concentration index (PCI) to assess the temporal variation in precipitation, and it was found that the location falls under a uniform distribution zone.
Hydroelectricity consumption forecast for Pakistan using ARIMA modeling and supply-demand analysis for the year 2030

The amount of electricity generation and its availability to the residents of a country reflects its level of development and economic condition. Water being one of the cheapest and renewable sources of energy, is being used to produce one-quarter of the total electricity production in Pakistan. This article presents a forecasting study of hydroelectricity consumption in Pakistan based on the historical data of past 53 years using Auto-Regressive Integrated Moving-Average (ARIMA) modeling. Based on the developed forecasting equation, the hydroelectricity consumption was predicted up to the year 2030. For validating the reliability of the forecasted data, the results were compared to the actual values which showed good fit with minimum deviation. The forecasted values of hydroelectricity consumption revealed an average annual increment of 1.65% with a cumulative increase of 23.4% up to the year 2030. The results were compared with the hydroelectricity generation plans of the Government of Pakistan for its effectiveness. A sensitivity analysis was also performed to study the relation of hydroelectricity consumption to the annual population and GDP growth rate of the country. The research shall significantly prove to be useful in better planning and management of water resources of Pakistan for future. (C) 2020 Elsevier Ltd. All rights reserved.
Plant cytoplasmic ribosomal proteins: an update on classification, nomenclature, evolution and resources

Standardized naming systems are essential to integrate and unify distinct research fields, and to link multi-species data within and across kingdoms. We conducted a comprehensive survey of cytoplasmic ribosomal proteins (CRPs) in the dicot model Arabidopsis thaliana and the monocot model rice, noting that the standardized naming system has not been widely adopted in the plant community. We generated a database linking the old classical names to their updated and compliant names. We also explored the sequences, molecular evolution, and structural and functional characteristics of all plant CRP families, emphasizing evolutionarily conserved and plant-specific features through cross-kingdom comparisons. Unlike fungal CRP paralogs that were mainly created by whole-genome duplication (WGD) or retroposition under a concerted evolution mode, plant CRP genes evolved primarily through both WGD and tandem duplications in a rapid birth-and-death process. We also provide a web-based resource (http://www.plantcrp.cn/) with the aim of sharing the latest knowledge on plant CRPs and facilitating the continued development of a standardized framework across the entire community.
Robust Atomic-Resolution Imaging of Lithium in Battery Materials by Center-of-Mass Scanning Transmission Electron Microscopy

The performance of energy storage materials is often governed by their structure at the atomic scale. Conventional electron microscopy can provide detailed information about materials at these length scales, but direct imaging of light elements such as lithium presents a challenge. While several recent techniques allow lithium columns to be distinguished, these typically either involve complex contrast mechanisms that make image interpretation difficult or require significant expertise to perform. Here, we demonstrate how center-of-mass scanning transmission electron microscopy (CoM-STEM) provides an enhanced ability for simultaneous imaging of lithium and heavier element columns in lithium ion conductors. Through a combination of experiments and multislice electron scattering calculations, we show that CoM-STEM is straightforward to perform and produces directly interpretable contrast for thin samples, while being more robust to variations in experimental parameters than previously demonstrated techniques. As a result, CoM-STEM is positioned to become a reliable and facile method for directly probing all elements within energy storage materials at the atomic scale.
Profiling office workers based on their self-reported preferences of indoor environmental quality and psychosocial comfort at their workplace during COVID-19

Due to the COVID-19 pandemic, a large number of office workers were required to conduct their work from home. Little is known about the indoor environmental quality (IEQ) preferences and psychosocial comfort preferences of staff working from home. Therefore this study aimed to cluster office workers working at home based on their self-reported preferences for IEQ and psychosocial comfort at their most used workspace and to identify these preferences and needs of workers during the COVID-19 pandemic. A questionnaire was administered to employees of ten offices in the Netherlands, and the 502 respondents were clustered with two models by using TwoStep cluster analysis. The first model was based on variables related to IEQ preferences, while the second was to psychosocial comfort preferences. The analysis revealed four IEQ clusters and six psychosocial comfort clusters. Comparison of these results with other similar studies proposed that the prevalence of anxiety, depression, migraine, and rhinitis, increased for this population during the work-from-home period of the pandemic. Further results suggest that both IEQ and psychosocial comfort preferences are situation- and gender-dependent.
Paths and strategies for sustainable urban renewal at the neighbourhood level: A framework for decision-making

With the rapid process of urbanisation in China, the challenges facing cities with regard to urban fabric, urban function, and human living conditions are enormous. Urban renewal provides opportunities to address these challenges and realise sustainable development. This study proposes a framework for integrating an indicator evaluation system and a decision-making matrix for urban renewal at the neighbourhood level in China; the level at which most development projects take place in a city. The indicator system covers subjective and objective evaluations on different aspects of neighbourhoods to shed light on specific problems and characteristics, namely, social aspects, economic aspects, environmental aspects, land use forms, building conditions, and facility conditions. The decision-making matrix is designed to provide implementation paths and corresponding strategies for urban renewal. The proposed framework was tested using a case study of eight neighbourhoods of Qixinggang, Yuzhong District, Chongqing in China. The results show the status quo and problems of the selected neighbourhoods and specific strategies to address these are subsequently proposed. Via in-depth investigations on various neighbourhoods within a city, decision-makers are capable of locating key areas and adjusting practical approaches from a 'one size fits all' approach to ones that are customised and offer small-scale improvements. The proposed framework can shed light on other empirical cities that possess similar local characteristics.
The long reach of COVID-19: multilateral policy priorities to limit persistent divergences

After a synchronized deep downturn in 2020, an uneven global recovery is underway. Differences in vaccine access and the size of policy support have shaped cross-country divergences in the rebound. The disparities in the strength of recovery are expected to persist into the medium term. A concerted multilateral effort is needed to limit these divergences, including through accelerating global access to vaccines, investing in climate change mitigation and adaptation, and ensuring financially vulnerable economies have access to international liquidity.
Oil Price, Energy Consumption, and CO2 Emissions in Turkey. New Evidence from a Bootstrap ARDL Test

The main objective of this research was to test the effect of oil prices, renewable and non-renewable energy consumption, and economic growth on Turkey's carbon emissions by using three co-integration tests, namely, the newly-developed bootstrap autoregressive distributed lag (ARDL) testing technique as proposed by (McNown et al., 2018); the new approach involving the Bayer-Hanck (2013) combined co-integration test; and the H-J (2008) co-integration technique, which induces two dates of structural breaks. The autoregressive distributed lag model (ARDL), dynamic ordinary least squares (DOLS), canonical cointegrating regression (CCR), and fully modified ordinary least square (FMOLS) approaches were utilized to test the long-run interaction between the examined variables. The Granger causality (GC) analysis was utilized to investigate the direction of causality among the variables. The long-run coefficients of ARDL, DOLS, CCR, and FMOLS showed that the oil prices had a negative influence on CO2 emissions in Turkey in the long run. Furthermore, the findings demonstrate that non-renewable energy, which includes oil, natural gas, and coal, increased CO2 emissions. In contrast, renewable energy can decrease the environmental pollution. These empirical findings can be attributed to the fact that Turkey is heavily dependent on imported oil; more than 50% of the energy requirement has been supplied by imports. Hence, oil price fluctuations have severe effects on the economic performance in Turkey, which in turn affects energy consumption and the level of carbon emissions. The study suggests that the rate of imported oil in Turkey must be decreased by finding more renewable energy sources for the energy supply formula to avoid any undesirable effects of oil price fluctuations on the CO2 emissions, and also to achieve sustainable development.