ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
A fuzzy-stochastic multi-objective model for sustainable planning of a closed-loop supply chain considering mixed uncertainty and network flexibility

Closed-loop supply chain network design (CLSCND) has been increasingly spotlighted over the latest decade. The focus has been given to maximize the economic performance, resource utilization and sustainability through incorporating a holistic decision-making on both forward and reverse logistics. In this paper, a new fuzzy-stochastic multi-objective mathematical model is formulated for sustainable CLSCND. The model aims at balancing the trade-off between cost effectiveness and environmental performance under different types of uncertainty. The environmental performance of CLSCND is measured by carbon emission. Moreover, the network flexibility is modeled and incorporated in the decision-making so that customer demands can be fulfilled by different means. In order to solve the complex optimization problem, the model is first defuzzilized and converted into an equivalent crisp form. Then, a sample average approximation based weighting method (SAAWM) is developed to obtain a set of Pareto optimal solutions between cost and carbon emission under different uncertain environments. The model is validated through a set of numerical experiments. The computational results show, through the incorporation with network flexibility, the proposed mathematical model and solution approach can effectively generate consistent objective values and solutions over different scenario trees and obtain robust strategic decisions on facility locations. Meanwhile, the flexibility and rationality of the decision-making on transportation management, demand allocation and facility operations can be improved as well. (c) 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Collective correlations, dynamics, and behavioural inconsistencies of the cryptocurrency market over time

This paper introduces new methods to study behaviours among the 52 largest cryptocurrencies between 01-01-2019 and 30-06-2021. First, we explore evolutionary correlation behaviours and apply a recently proposed turning point algorithm to identify regimes in market correlation. Next, we inspect the relationship between collective dynamics and the cryptocurrency market size-revealing an inverse relationship between the size of the market and the strength of collective dynamics. We then explore the time-varying consistency of the relationships between cryptocurrencies' size and their returns and volatility. There, we demonstrate that there is greater consistency between size and volatility than size and returns. Finally, we study the spread of volatility behaviours across the market changing with time by examining the structure of Wasserstein distances between probability density functions of rolling volatility. We demonstrate a new phenomenon of increased uniformity in volatility during market crashes, which we term volatility dispersion.
Municipal solid waste management: Integrated analysis of environmental and economic indicators based on life cycle assessment

This paper develops a method to analyze municipal solid waste management systems (MSWMS) that integrates environmental and economic indicators using Life Cycle Assessment (LCA) and Life Cycle Costing (LCC). The method was tested in the city of Sorocaba, Brazil, a medium size municipality typical of many developing countries. Environmental impacts were analyzed considering system expansion, which combined the aspects of primary production and recycling processes with the impacts of MSWMS. The economic analysis included operating and investment costs to the costs of environmental externalities, thus enabling the analysis of total costs to society. An integrated analysis of environmental indicators revealed that the most significant reductions in environmental impacts occurred in the scenarios with higher rates of reuse of dry waste through recycling (70%), which lowered these impacts by up to 50% when compared to the current scenario. An analysis of economic performance indicated that the two scenarios that combined the highest recycling goals with greater transport efficiency and more composting yielded the best results, reducing the total social costs by 31% and 33%, respectively. Lastly, the integration of environmental and economic analyses revealed that the best results are obtained by a combination of composting, mechanical biological treatment and recycling, which would reduce the impacts of MSWMS by up to 33.7 points per invested dollar. The results supports the application of this proposed integrate approach to improve the current solid waste management system in Sorocaba and in other cities with a similar system and waste generation. (C) 2019 Elsevier Ltd. All rights reserved.
Impact Assessment of Additive Manufacturing on Sustainable Business Models in Industry 4.0 Context

Additive manufacturing has the potential to make a longstanding impact on the manufacturing world and is a core element of the Fourth Industrial Revolution. Additive manufacturing signifies a new disruptive path on how we will produce parts and products. Several studies suggest this technology could foster sustainability into manufacturing systems based on its potential of optimizing material consumption, creating new shapes, customizing designs and shortening production times that, all combined, will greatly transform some of the existing business models. Although it requires reaching a certain level of design maturity to completely insert this technology in an industrial setting, additive manufacturing has the potential to favorably impact the manufacturing sector by reducing costs in production, logistics, inventories, and in the development and industrialization of a new product. The transformation of the industry and the acceleration of the adopting rate of new technologies is driving organizational strategy. Thus, through the lenses of Industry 4.0 and its technological concepts, this paper aims to contribute to the knowledge about the impacts of additive manufacturing technology on sustainable business models. This aim is accomplished through a proposed framework, as well as the models and scales that can be used to determine these impacts. The effects are assessed by taking into account the social, environmental and economic impacts of additive manufacturing on business models and for all these three dimensions a balanced scorecard structure is proposed.
Diagnosis and Treatment of Renal Cell Carcinoma During Pregnancy

Renal cell carcinoma during pregnancy is extremely rare, but it is the commonest urological malignancy reported in pregnancy. Currently, no uniform domestic or international diagnostic or treatment criteria exist for these patients, so their diagnosis and treatment are challenging for urologists. The health and reproductive needs of these patients have improved in recent years because of the continuous development of medical technology. This article reviews the epidemiology, risk factors, diagnosis, treatment, and prognosis of renal cell carcinoma during pregnancy.
Morphological, physiological and molecular assessment of cotton for drought tolerance under field conditions

Climate change could be an existential threat to many crops. Drought and heat stress are becoming harder for cultivated crops. Cotton in Pakistan is grown under natural high temperature and low moisture, could be used as a source of heat and drought tolerance. Therefore, the study was conducted to morphological, physiological and molecular characterization of cotton genotypes under field conditions. A total of 25 cotton genotypes were selected from the gene pool of Pakistan based on tolerance to heat and drought stress. In field trail, the stress related traits like boll retention percentage, plant height, number of nodes and inter-nodal distance were recorded. In physiological assessment, traits such as photosynthesis rate, stomatal conductance, transpiration rate, leaf temperature, relative water content and excised leaf water loss were observed. At molecular level, a set of 19 important transcription factors, controlling drought/heat stress tolerance (HSPCB, GHSP26, HSFA2, HSP101, HSP3, DREB1A, DREB2A, TPS, GhNAC2, GbMYB5, GhWRKY41, GhMKK3, GhMPK17, GhMKK1, GhMPK2, APX1, HSC70, ANNAT8, and GhPP2A1) were analyzed from all genotypes. Data analyses depicted that boll retention percentage, photosynthesis, stomatal conductance, relative water content under the stress conditions were associated with the presence of important drought & heat TF/genes which depicts high genetic potential of Pakistani cotton varieties against abiotic stress. The variety MNH-886 appeared in medium plant height, high boll retention percentage, high relative water content, photosynthesis rate, stomatal conductance, transpiration rate and with maximum number transcription factors under study. The variety may be used as source material for heat and drought tolerant cotton breeding. The results of this study may be useful for the cotton breeders to develop genotype adoptable to environmental stresses under climate change scenario.
Proteogenomics for pediatric brain cancer

Pediatric central nervous system tumors are the most common tumors in children, it constitute 15%-20% of all malignancies in children and are the leading cause of cancer related deaths in children. Proteogenomics is an emerging field of biological research that utilizes a combination of proteomics, genomics, and transcriptomics to aid in the discovery and identification of biomarkers for diagnosis and therapeutic purposes. Integrative proteogenomics analysis of pediatric tumors identified underlying biological processes and potential treatments as well as the functional effects of somatic mutations and copy number variation driving tumorigenesis.
Feasibility study and economic analysis of stand-alone hybrid energy system for southern Ghana

Ghana is endowed with lot of potentials in the renewable energy sector which are yet to be fully exploited. This research evaluated the techno-economic potentials of PV-Wind-DG-Battery and Wind-DG-Battery hybrid power plants in the southern part of Ghana in a town call Mankwadze to ascertain the bankability of the two systems for large-scale commercial electricity generation. The levelized cost of energy (LCOE) and net present cost (NPC) metrics were used in the economic analysis of the modelled power plants. The LCOE and NPC of the two systems were estimated at 0.382 $/kWh and $8,649,054 for the PV-Wind-DG-Battery system, respectively, whiles the Wind-DG-Battery system also recorded 0.396 $/kWh and $8,966,700 for the LCOE and NPC, respectively. The base case (generator) recorded an LCOE of 0.412 $/kWh and an OC of $345,998 with an initial cost of $454,256. The obtained results are all relatively higher than the current cost of energy for household consumers in Ghana, but a sensitivity analysis showed that the LCOEs can be reduced when certain parameters such as cost of fuel, discount and inflation rates are varied. This suggests that such projects can be done when the necessary investment climate is created by those in authority.
Assessing the levelized cost of vanadium redox flow batteries with capacity fade and rebalancing

Redox flow batteries (RFBs) are an emerging technology suitable for grid electricity storage. The vanadium redox flow battery (VRFB) has been one of the most widely researched and commercialized RFB systems because of its ability to recover lost capacity via electrolyte rebalancing, a result of both the device configuration as well as the symmetry of the redox chemistry. Despite broad acknowledgement of the benefits of this differentiating feature to system resilience and longevity, assessments of its economic value to the VRFB system have thus far been limited. Here we develop a techno-economic framework that incorporates a physical model of capacity fade and recovery from rebalancing and other servicing methods into a levelized cost of storage (LCOS) metric. We then evaluate the impacts of different contributing factors to the LCOS of a VRFB and identify opportunities for cost reduction through operating strategies (e.g., rebalancing schedule), performance improvements (e.g., reducing fade rates), design decisions (e.g., battery sizing), and investment approaches (e.g., electrolyte leasing). We anticipate this analysis will provide new insights into the cost-drivers for VRFBs and motivate further research efforts in understudied yet important areas.
Levelised Cost of Storage (LCOS) analysis of liquid air energy storage system integrated with Organic Rankine Cycle

Liquid Air Energy Storage (LAES) is a unique decoupled grid-scale energy storage system that stores energy through air liquefaction process. In order to further increase the utilization ratio of the available waste heat discharged by the air compression and not effectively recovered during the discharge phase, the authors have previously investigated the thermodynamic feasibility of different integrated LAES systems and promising results have been shown for the LAES coupled with Organic Rankine Cycle (integrated system LAORC). In order to assess the economic feasibility of the integrated system, this paper presents a techno-economic comparative analysis of the stand-alone LAES and the integrated system LAORC. The LAES systems have been designed by means of the quasi non-dimensional maps developed by the authors and the Levelised Cost of Storage (LCOS) has been employed to evaluate the economic viability of the investment by also comparing the LAES with Li-ion batteries. The results show that the stand-alone LAES system is generally cost-competitive with Li-ion batteries and even more in LAORC integrated system with the advantage of additional power output from the ORC. When LAES is operated in cogenerative configuration, the LAORC integrated system produces the most significant results decreasing the LCOS by 10%. (C) 2020 Elsevier Ltd. All rights reserved.
The effect of FDI on environmental emissions: Evidence from a meta-analysis

One important and frequently-raised issue about foreign direct investment (FDI) is the potentially negative consequences for the environment. The potential environmental cost due to increased emissions may undermine the economic gains associated with increases in FDI inflow. Although the literature is dominated with this adverse view of FDI on the environment, there is also a possibility that FDI can contribute to a cleaner environment, especially, if FDI comes with green technologies and this creates spillovers for domestic industries. Theoretically, the effect of FDI on the environment can be negative or positive. To deal with the theoretical ambiguity about the FDI-environment nexus, many empirical studies have been conducted but their results only reinforce the controversy as they produce contrasting results. We conduct a meta-analysis of the effect of FDI on environmental emissions using 65 primary studies that produce 1006 elasticities. Our results show that the underlying effect of FDI on environmental emissions is close to zero, however, after accounting for heterogeneity in the studies, we find that FDI significantly reduces environmental emissions. Results remain robust after disaggregating the effect for countries at different levels of development as well as for different pollutants.
Experimental study of hydrogen enriched compressed natural gas (HCNG) engine and application of support vector machine (SVM) on prediction of engine performance at specific condition

The effect of excess air ratio (A) and ignition advance angle (Big) on the combustion and emission characteristics of hydrogen enriched compressed natural gas (HCNG) on a 6 cylinder compressed natural gas (CNG) engine has been experimental studied in an engine test bench, aiming at enriching the sophisticated calibration of HCNG fueled engine and increasing the prediction accuracy of the SVM method on automobile engines. Three different fuel blends were selected for the experiment: 0%, 20% and 40% volumetric hydrogen blend ratios. It is noted that combustion intensity varies with the excess air ratio and the ignition advance angle, so are the emissions. The optimal value of A or Big has been explored in the specific engine condition. Results show that blending hydrogen can enhance and advance the combustion and stability of CNG engine, and it also has some benefic influence on the emissions such as reducing the CO and CH4. Meanwhile, a simulation research on forecasting the engine performance by using the support vector machine (SVM) method was conducted in detail. The torque, brake specific fuel consumption and NOx emission have been selected to characterize the power, economic and emissions of the engine with various HCNG fuels, respectively. It can be seen that the optimal model built by the SVM method can highly describe the relationship of the engine properties and condition parameters, since the value of the complex correlation coefficient is larger than 0.97. Secondly, the prediction performance of the optimal model for torque or BSFC is much better than the case of NOx. Besides, the optimal model built by the PSO optimization method has the best prediction accuracy, and the accuracy of the model obtained based on the training group with 20% hydrogen blend ratio is the best compared with those of others. The upshots in this article provide experimental support and theoretical basis for the adoption of HCNG fuel on internal combustion engines as well as the application of intelligent algorithmic in the engine calibration technology field. (C) 2019 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
Entrepreneurial Leadership and Sustainable Performance of Manufacturing SMEs in Malaysia: The Contingent Role of Entrepreneurial Bricolage

The purpose of this research is to delve into the effect of entrepreneurial leadership (EL) on the economic, environmental, and social aspects of sustainable performance; the research also focuses on the contingency role of entrepreneurial bricolage (EB). The increasing concern focused on the triple bottom line (TBL) issues has triggered enthusiasm and inspiration in the field of sustainable entrepreneurship, especially amongst the small medium enterprise (SME) owners or leaders who are directly responsible for organizational sustainable performance. This research examines the relationships between EL and economically sustainable performance (ECSPF), environmentally sustainable performance (ENSPF), and social sustainable performance (SOSPF); these relationships are contingent upon EB through the lens of Upper Echelons Theory (UET) and Effectuation Theory (ET). A total of 146 responses from a cross-sectional survey from Malaysian manufacturing SMEs were investigated using Partial Least Squares-Structural Equation Modeling (PLS-SEM). The findings reveal that EL has significant effects on ENSPF and SOSPF, but has an insignificant effect on ECSPF. Meanwhile, EB does not play the contingent role in strengthening the relationships between EL and ECSPF, EL and ENSPF, and EL and SOSPF. This research highlights the importance of EL and shows that SME owners or leaders should embrace and develop their skills as a crucial step towards achieving sustainable performance for their companies. The implications and limitations of the research are discussed and recommendations for future research are also presented.
Bioremoval of PVP-coated silver nanoparticles using Aspergillus niger: the role of exopolysaccharides

Extensive use of engineered nanoparticles has led to their eventual release in the environment. The present work aims to study the removal of Polyvinylpyrrolidone-coated silver nanoparticles (PVP-Ag-NPs) using Aspergillus niger and depict the role of exopolysaccharides in the removal process. Our results show that the majority of PVP-Ag-NPs were attached to fungal pellets. About 74% and 88% of the PVP-Ag-NPs were removed when incubated with A. niger pellets and exopolysaccharide-induced A. niger pellets, respectively. Ionized Ag decreased by 553 and 1290-fold under the same conditions as compared to stock PVP-Ag-NP. PVP-Ag-PVP resulted in an increase in reactive oxygen species (ROS) in 24 h. Results show an increase in PVP-Ag-NPs size from 28.4 to 115.9 nm for A. niger pellets and 160.3 nm after removal by stress-induced A. niger pellets and further increased to 650.1 nm for in vitro EPS removal. The obtained findings show that EPS can be used for nanoparticle removal, by increasing the net size of nanoparticles in aqueous media. This will, in turn, facilitate its removal through conventional filtration techniques commonly used at wastewater treatment plants.
Deep Phenotypic Analysis for Transposition of the Great Arteries and Prognosis Implication

Background Transposition of the great arteries (TGA) consists of about 3% of all congenital heart diseases and 20% of cyanotic congenital heart diseases. It is always accompanied by a series of other cardiac malformations that affect the surgical intervention strategy as well as prognosis. In this study, we comprehensively analyzed the phenotypes of the patients who had TGA with concordant atrioventricular and discordant ventriculoarterial connections and explored their association with prognosis. Methods and Results We retrospectively reviewed 666 patients with a diagnosis of TGA with concordant atrioventricular and discordant ventriculoarterial connections in Fuwai Hospital from 1997 to 2019. Under the guidance of the Human Phenotype Ontology database, patients were classified into 3 clusters. The Kaplan-Meier method was used to analyze the prognosis, and the Cox proportional regression model was used to investigate the risk factors. In this 666-patient TGA cohort, the overall 5-year survival rate was 94.70% (92.95%-96.49%). Three clusters with distinct phenotypes were obtained by the Human Phenotype Ontology database. Kaplan-Meier analysis revealed a significant difference in freedom from reintervention among 3 clusters (P<0.001). To eliminate the effect of surgeries, we analyzed patients who only received an arterial switch operation and still found a significant difference in reintervention (P=0.019). Conclusions We delineated a big cardiovascular phenotypic profile of an unprecedentedly large TGA cohort and successfully risk stratified them to reveal prognostic significance. Also, we reported the outcomes of a large TGA population in China.
Does Emission Trading Boost Carbon Productivity? Evidence from China's Pilot Emission Trading Scheme

As the country with the largest carbon emissions globally, the effective operation of China's carbon emissions trading scheme (ETS) is of great importance to the global community in terms of mitigating climate change. This paper considers China's pilot ETS launched in 2013 as a quasi-natural experiment. Exploring provincial industrial-level data that are more in line with the ETS coverage, the difference-in-difference-in-difference (DDD) model is used to evaluate the impact of the ETS on carbon productivity. Considering different pilot regions and industries, we also analyze the heterogeneous effect of ETS. Moreover, the mediating effects of technical progress and capital investment are explored. We find that China's pilot ETS boosted carbon productivity. Among pilot regions, the best policy effectiveness appeared in Beijing, while the weakest effectiveness appeared in Chongqing. Among the pilot industries, the pilot ETS had better effectiveness in petrochemical and electric power industries and weaker effectiveness in building materials and transportation industries. Additionally, the pilot ETS promoted carbon productivity through both technological progress and capital investment, and the former contributed more. Our findings can provide empirical references and policy implications for nationwide implementation of ETS to further promote low-carbon economic transformation.