ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Moving to economic growth without water demand growth - a decomposition analysis of decoupling from economic growth and water use in 31 provinces of China

Increasing water consumption from various economic activities has posed increasing challenges for the sustainability of developing countries. In particular, China is facing a sharp conflict between rapid economic development and water shortage. Evaluating the decoupling state between economic growth and water consumption and exploring the driving factors behind this could serve to develop strategy to moving to economic growth without water use growth. To this end, this work uses the Tapio decoupling and LMDI decomposition methods to evaluate the decoupling performance between China's water consumption and economic growth at the national and provincial levels, and six driving factors are decomposed, namely water consumption intensity, industrial structure, economic development, water resource utilization rate, water resource endowment and population size. Results show that: (1) Only two decoupling states, strong decoupling and weak decoupling, occurred at national level, and the decoupling index shows a decreasing trend. (2) 31 provinces showed only two states of strong decoupling and weak decoupling. More than 60% of the provinces showed strong decoupling after 2011, and the decoupling effect was significantly better than that of 2004-2011. (3) The effects of water consumption intensity and industrial structure drive the occurrence of decoupling. Economic level and population size have a positive incremental effect on water consumption. Finally, we propose policy recommendations such as developing water-saving technologies and optimizing industrial structure to promote water sustainability. The comprehensive methodology in this paper provides a theoretical reference for research in other countries or other environmental issues. Governments in various regions can formulate effective measures to achieve sustainable use of water resources, responding to the 2030 Agenda for Sustainable Development. (C) 2020 Elsevier B.V. All rights reserved.
A coupling methodology of the analytic hierarchy process and entropy weight theory for assessing coastal water quality

Rapid economic development in coastal areas has gradually increased the risk of coastal water quality deterioration. The assessment methods of coastal water quality are multifarious, but many depend on either subjective judgment or objective calculation. We proposed a weighted sum methodology by integrating the subjective analytic hierarchy process and objective entropy theory (AHP-entropy weight methodology) to obtain an overall evaluation of coastal water quality. The mathematical models to transform the biochemical and physical parameter values and soluble substance concentrations into index scores have been formulated in comparison to the national water quality classification scheme. The application of the AHP-entropy weight methodology was demonstrated in the nearshore area of Yangjiang city, China, based on 23 seawater sampling stations in autumn 2017 and spring 2018. Datasets including biochemical and physical parameters, nutrients, and heavy metals have been converted into water quality index scores based on the proposed mathematical model. Results revealed that the overall water quality fell into the "good" class in both sampling seasons. The spatial distribution of the water quality index scores demonstrated that the relatively worse water quality occurred in estuarine and nearshore areas, signifying the negative effect of coastal anthropogenic activities. The statistical analyses like the hierarchical cluster analysis interpreted that the river input acted as a main source of pollutants in the study area. The AHP-entropy weight methodology could be a preferred way to assist decision-makers in properly evaluating the current state of coastal water quality in an unbiased, objective manner.
Revisiting the Dynamics of Tourism, Economic Growth, and Environmental Pollutants in the Emerging Economies-Sustainable Tourism Policy Implications

Tourism contributions to economic growth and well-being have been widely acknowledged; however, its impacts on the environment demand an integrated approach to policy improvement across institutions in the emerging economies for the development of sustainable tourism practices. This study investigates the causal relationship between tourism, economic growth (GDP, capital investment), energy consumption, and environmental pollutants in developing economies, explicitly focusing on the case of Pakistan. Various econometric procedures and techniques were applied to test the proposed hypotheses. The findings suggest that economic growth support tourism development. Tourists' arrivals have a significant positive impact on energy consumption, capital investment, and CO2 emissions; besides, environmental pollutant (CO2) causes negative effects on tourism. The results suggest that a 1 unit increase in tourism increases CO2 emissions metric tons per capita by 0.26 units in the long-run. A 1 unit increase in capital investment increases CO2 emissions metric tons per capita by 0.21 units, and a 1 unit increase in energy consumption increases CO2 emissions metric tons per capita by 0.51 units in the long-run. In the short-run, a 1 unit increase in tourism, capital investment, and energy consumption rises CO2 emissions metric tons per capita by 0.045, 0.04, and 0.08 units, respectively. Sustainable tourism remains a sole option in developing economies to enhance the competitiveness of tourism as a tool for friendly developments. Thus, tourism policies are needed to be integrated with overall economic, environmental, and energy policies to encourage the shift towards sustainable tourism development to minimize environmental pollution.
Shrinking ageing population and other drivers of energy consumption and CO2 emission in the residential sector: A case from Eastern Europe

Energy use and the resulting environmental impacts are interrelated with socioeconomic development of a certain region. What is more, the residential sector plays an important role as a major consumer of the energy. Thus, it is important to identify the underlying patterns of energy consumption and the resulting CO2 emission amidst the external shocks. This paper focuses on the case of residential energy consumption and CO2 emission in Lithuania over 2004-2016. This example is important given the context of shifts in the size and structure of population, economy and energy transformation in Lithuania that occurred over the period covered. The analysis relies on the index decomposition analysis and factorizes the changes in CO2 emission into the effects of population size, household size, dwelling area, energy intensity and carbon factor. The results indicate that decline in energy intensity and population played the most important role in pushing the CO2 emission down, yet these effects were offset by the changes in the lifestyle of the population (i.e. household size and dwelling area) along with increasing carbon factor. These findings are contrasted to the corresponding findings form the earlier studies around the world and the resulting policy implications are delivered.
Multi-step ahead forecasting of regional air quality using spatial-temporal deep neural networks: A case study of Huaihai Economic Zone

Air pollution has endangered both ecological environment and human health. Long-term prediction of air quality index (AQI) is an effective approach to early warning of, and prompt response to, pollution events to support cleaner industrial production. However, existing approaches to forecasting long-term air quality need further improvement. In this paper, we proposed a novel spatial-temporal deep learning algorithm based on bidirectional gated recurrent unit integrated with attention mechanism (BiAGRU), for more accurate air quality forecasting. The historical air quality measurements and meteorological monitoring data were constructed as a spatial-temporal matrix suitable for model input. The performance of the proposed BiAGRU model was evaluated by a series of metrics. The RMSE, MAE, R-2 and Fractional Bias (FB) values of the proposed BiAGRU model are 31.10, 23.06, 0.60, and 0.015, respectively, for 24 h multi-step ahead prediction assignments using Huaihai Economic Zone dataset. Quantitative comparison between models indicates the developed BiAGRU model outperformed various traditional machine learning algorithms and advanced deep neural network methods in term of lower error bias and higher accuracy and regression scores. This work is of importance to strengthen regional prevention and control of air pollution. (C) 2020 Elsevier Ltd. All rights reserved.
Robust stochastic optimal dispatching method of multi-energy virtual power plant considering multiple uncertainties

In recent years, with the rapid development of the energy Internet and the deepening of the complementary coupling of various energy sources, the concept of multi-energy virtual power plant comes into being. At the same time, insufficient research on optimal scheduling of multi-energy virtual power plants under multiple uncertainties. Here we propose a robust stochastic optimal dispatching method to solve the scheduling problem under multiple uncertainties. For the source side uncertainties, the uncertain set of cardinalities with a robust adjustable coefficient is adopted to describe the output of wind turbines and photovoltaics. For the load side uncertainties, the Wasserstein generative adversarial network with gradient penalty is used to generate electric, thermal, cooling, and natural gas load scenarios, and the K-medoids clustering is used to get typical scenes. A two-stage robust stochastic optimal model of the min-max-min structure was established. Based on the dual transformation theory and the column constraint generation algorithm, the original model was solved alternately. Finally, the effectiveness of the proposed model and algorithm is verified by simulation analysis. The proposed method can get the scheduling scheme with the lowest operating cost in the worst scenario and is conducive to reducing the overall scheduling cost of the system.
Hormone replacement therapy - Current recommendations

Menopause is a major life event that can affect women in several ways. Its onset marks the end of the reproductive life cycle, and its impact can be both short and long term. Menopause is often a gradual process, preceded by a transitional period known as perimenopause. The average age of menopause in the UK is 51. The clinical manifestations of menopause result from the eventual exhaustion of oocytes within the ovaries. This leads to a chronic hypo-estrogenic state, which in the short term causes menopausal symptoms and over a long term, has an impact on bone and cardiovascular health. There has been a steep drop in the prescription of hormone replacement therapy (HRT) following the publication of the Women's Health Initiative Study and the Million Women Study. It is currently estimated that approximately a million women in the UK are taking HRT for control of their menopausal symptoms. This review summarises the current recommendations for HRT use in menopausal women. The benefits of HRT in improving the symptoms of menopause are discussed as well as the potential role of HRT in managing long-term sequelae is covered. Evidence pertaining to the potential risks associated with HRT is also be reviewed.
Change Point Inference in the Presence of Missing Covariates for Principal Surrogate Evaluation in Vaccine Trials

We consider the use of threshold-based regression models for evaluating immune response biomarkers as principal surrogate markers of a vaccine's protective effect. Threshold-based regression models, which allow the relationship between a clinical outcome and a covariate to change dramatically across a threshold value in the covariate, have been studied by various authors under fully observed data. Limited research, however, has examined these models in the presence of missing covariates, such as the counterfactual potential immune responses of a participant in the placebo arm of a standard vaccine trial had s/he been assigned to the vaccine arm instead. Based on a hinge model for a threshold effect of the principal surrogate on vaccine efficacy, we develop a regression methodology that consists of two components: (1) The estimated likelihood method is employed to handle missing potential outcomes, and (2) a penalty is imposed on the estimated likelihood to ensure satisfactory finite sample performance. We develop a method that allows joint estimation of all model parameters as well as a two-step method that separates the estimation of the threshold parameter from the rest of the parameters. Stable iterative algorithms are developed to implement the two methods and the asymptotic properties of the proposed estimators are established. In simulation studies, the proposed estimators are shown to have satisfactory finite sample performance. The proposed methods are applied to analyze a real dataset collected from dengue vaccine efficacy trials to predict how vaccine efficacy varies with an individual's potential immune response if receiving vaccine.
The Marine Plastic Litter Issue: A Social-Economic Analysis

The issue of marine plastic litter pollution is multifaceted, cross-sectoral, and ongoing in the absence of appropriate management measures. This study analysed the issue of marine plastic litter pollution in the context of the Descriptor 10 of the Marine Strategy Framework Directive and Good Environmental Status of the oceans and seas. The Driver-Pressure-State-Impact-Response (DPSIR) framework was used to assess the causes, effects, and management measures to changes in the marine environment resulting from marine plastics pollution. We noted that less than 10 peer-reviewed publications have applied the Driver-Pressure-State-Impact-Response (DPSIR) model to the issue of marine plastics pollution. Some basic needs such as food security, movement of goods and services, and shelter are also some of the major drivers of marine plastic pollution. The use of plastics is linked to multiple economic sectors (fisheries, agriculture, transport, packaging, construction) and other human activities. A significant amount of the resulting pressures came from the economic sectors for packaging and construction. State changes occurred at the environmental (contamination and bioaccumulation), ecosystem (ingestion of plastics, ghost fishing) and ecosystem service levels (supply of sea food, salt and cultural benefits), with possible loss of jobs and income being some of the observed impacts on human welfare. Responses as management measures, which are tailored to meet each component of the DPSIR framework, were identified. These included policies, regulations, technological advancement and behavioural change. The research acknowledges the issue of marine plastics pollution as a global environmental problem and recommends a trans-disciplinary approach, involving all types of stakeholders. Future research and analysis applying the DPSIR framework will be useful to provide the information necessary for the effective, adaptive management of litter pollution by marine plastics.
Targeted PET/MRI Imaging Super Probes: A Critical Review of Opportunities and Challenges

Recently, the demand for hybrid PET/MRI imaging techniques has increased significantly, which has sparked the investigation into new ways to simultaneously track multiple molecular targets and improve the localization and expression of biochemical markers. Multimodal imaging probes have recently emerged as powerful tools for improving the detection sensitivity and accuracy-both important factors in disease diagnosis and treatment; however, only a limited number of bimodal probes have been investigated in preclinical models. Herein, we briefly describe the strengths and limitations of PET and MRI modalities and highlight the need for the development of multimodal molecularly-targeted agents. We have tried to thoroughly summarize data on bimodal probes available on PubMed. Emphasis was placed on their design, safety profiles, pharmacokinetics, and clearance properties. The challenges in PET/MR probe development using a number of illustrative examples are also discussed, along with future research directions for these novel conjugates.
Does communicable diseases (including COVID-19) may increase global poverty risk? A cloud on the horizon

Coronavirus epidemic can push millions of people in poverty. The shortage of healthcare resources, lack of sanitation, and population compactness leads to an increase in communicable diseases, which may increase millions of people add in a vicious cycle of poverty. The study used the number of factors that affect poverty incidence in a panel of 76 countries for a period of 2010-2019. The dynamic panel GMM estimates show that the causes of death by communicable diseases, chemical-induced carbon and fossil fuel combustion, and lack of access to basic hand washing facilities menace to increase poverty headcounts, whereas, an increase in healthcare expenditures substantially decreases poverty headcounts across countries. Further, the results show the U-shaped relationship between economic growth and poverty headcounts, as economic growth first decreases and later increase poverty headcount due to rising healthcare disparities among nations. The causality estimates show that lack of access to basic amenities lead to increase of communicable diseases including COVID-19 whereas chemical-induced carbon and fossil fuel emissions continue to increase healthcare expenditures and economic growth in a panel of selected countries. The rising healthcare disparities, regional conflicts, and public debt burden further 'hold in the hand' of communicable diseases that push millions of people in the poverty trap.
Levels and sources of hourly PM2.5-related elements during the control period of the COVID-19 pandemic at a rural site between Beijing and Tianjin

To control the spread of the novel coronavirus disease 2019 (COVID-19) in China, many anthropogenic activities were reduced and even closed on the national scale. To study the impact of this reduction and closing down, hourly concentrations of PMis-related elements were measured at a rural site before (12-25 January 2020), during (26 January-9 February 2020) and after (22 March-2 April 2020) the control period when all people remained socially isolated in their homes and could not return to economic zones for work. Nine major sources were identified by the positive matrix factorization model, including fireworks burning, coal combustion, vehicle emissions, dust, Cr industry, oil combustion, Se industry, Zn smelter, and iron and steel industry. Before the control period, K, Fe, Ca, Zn, Ba and Cu were the main elements, and fireworks burning, Zn smelter and vehicle emissions provided the highest contributions to the total element mass with 55%, 12.1% and 10.3%, respectively. During the control period, K, Fe, Ba, Cu and Zn were the dominating elements, and fireworks burning and vehicle emissions contributed 55% and 27% of the total element mass. After the control period, Fe, K, Ca, Zn and Ba were the main elements, and dust and iron and steel industry were responsible for 56% and 21% of the total element mass. The increased contribution from vehicle emissions during the control period could be attributed to our sampling site being near a town hospital and the fact that the vehicle activities were not restricted. The source apportionment results were also related to air mass backward trajectories. The largest reductions of dust, coal combustion, and the industrial sources (Cr industry, Zn smelter, Se industry, iron and steel industry) were distinctly seen for northwest transport (Ulanqab) and were least significant for northeast transport (Tangshan and Tianjin). (C) 2020 Elsevier B.V. All rights reserved.
A spatial econometric empirical research on the impact of industrial agglomeration on haze pollution in China

Based on statistical data on 30 provincial administrative regions in China from 2000 to 2016, this paper conducts an empirical study of the impact of industrial agglomeration on haze pollution using the spatial Dubin model (SDM), spatial lag model (SLM), and spatial error model (SEM). The findings are as follows: (1) Industrial agglomeration can effectively reduce the degree of haze pollution. (2) Haze pollution has an inverted U-shaped relationship with economic development and population agglomeration. (3) The secondary industry has a positive correlation with haze pollution, while the tertiary industry can reduce haze pollution but not in an obvious manner. (4) The level of innovation and urbanization can help to reduce haze pollution, and the level of economic opening up and carbon dioxide emissions can exacerbate haze pollution. (5) Due to the insufficient commercialization of scientific and technological achievements, investment in science and technology is not obviously effective in preventing and controlling haze pollution. The relationship between environmental regulation and haze pollution is still unclear due to regional differences and the varied effectiveness of law enforcement. The study suggests that the government should guide industrial agglomeration in a reasonable manner, improve joint prevention and control across regions, and strengthen environmental regulation to prevent and control haze pollution.
Evaluation of seismic performance factors for tension-only braced frames

The tension-only braced frames (TOBFs) are widely used as a lateral force resisting system (LFRS) in low-rise steel buildings due to their simplicity and economic advantage. However, the system has poor seismic energy dissipation capacity and pinched hysteresis behavior caused by early buckling of slender bracing members. The main concern in utilizing the TOBF system is the determination of appropriate performance factors for seismic design. A formalized approach to quantify the seismic performance factor (SPF) based on determining an acceptable margin of safety against collapse is introduced by FNMA P695. The methodology is applied in this paper to assess the SPFs of the TOBF systems. For this purpose, a trial value of the R factor was first employed to design and model a set of TOBF archetype structures. Afterwards, the level of safety against collapse provided by the assumed R factor was investigated by using the non-linear analysis procedure of FNMA P695 comprising incremental dynamic analysis (IDA) under a set of prescribed ground motions. It was found that the R factor of 3.0 is appropriate for safe design of TOBFs. Also, the system overstrength factor (Omega(0)) was estimated as 2.0 by performing non-linear static analyses.