ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Hybrid decision tree-based machine learning models for short-term water quality prediction

Water resources are the foundation of people's life and economic development, and are closely related to health and the environment. Accurate prediction of water quality is the key to improving water management and pollution control. In this paper, two novel hybrid decision tree-based machine learning models are proposed to obtain more accurate short-term water quality prediction results. The basic models of the two hybrid models are extreme gradient boosting (XGBoost) and random forest (RF), which respectively introduce an advanced data denoising technique - complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Taking the water resources of Gales Creek site in Tualatin River (one of the most polluted rivers in the world) Basin as an example, a total of 1875 data (hourly data) from May 1, 2019 to July 20, 2019 are collected. Two hybrid models are used to predict six water quality indicators, including water temperature, dissolved oxygen, pH value, specific conductance, turbidity, and fluorescent dissolved organic matter. Six error metrics are introduced as the basis of performance evaluation, and the results of the two models are compared with the other four conventional models. The results reveal that: (1) CEEMDAN-RF performs best in the prediction of temperature, dissolved oxygen and specific conductance, the mean absolute percentage errors (MAPEs) are 0.69%, 1.05%, and 0.90%, respectively. CEEMDAN-XGBoost performs best in the prediction of pH value, turbidity, and fluorescent dissolved organic matter, the MAPEs are 0.27%, 14.94%, and 1.59%, respectively. (2) The average MAPEs of CEEMDAN-RF and CEEMMDAN-XGBoost models are the smallest, which are 3.90% and 3.71% respectively, indicating that their overall prediction performance is the best. In addition, the stability of the prediction model is also discussed in this paper. The analysis shows that the prediction stability of CEEMDAN-RF and CEEMDAN-XGBoost is higher than other benchmark models. (C) 2020 Elsevier Ltd. All rights reserved.
Spatial-temporal pattern evolution and driving factors of China's energy efficiency under low-carbon economy

Improving energy efficiency and building a low-carbon economy are the important ways to resolve the current contradiction between economic growth and the environment in China. In this paper, we use the super efficiency Slack-Based Measure model (super-efficiency SBM model) to measure the energy efficiency of 30 provinces in China, and then conduct Empirical Orthogonal Function (EOF) to analyze its spatial-temporal evolution. Moreover, we use the Geographically and Temporally Weighted Regression (GTWR) to analyze the spatial-temporal heterogeneity of its driving factors. The results showthat: (i) during the sample period, China's energy efficiency shows a rapidly upward trend, accompanied by the gradually strengthening spatial pattern of the "eastern>central>western"; (ii) the spatial pattern of the "southern>northern" exhibited by the annual growth rate of energy efficiency experienced a process of weakening first and then gradually strengthening; (iii) the influencing effects of market openness, relative energy price and industry structure on energy efficiency have no significant heterogeneity as a whole; (iv) the effects of environmental regulation intensity, the marketization level, the technical level, energy consumption structure and economic development level have significant spatial heterogeneity, and the effects of energy conservation and emission reduction policies has significant temporal heterogeneity. (C) 2020 Published by Elsevier B.V.
Life cycle assessment and techno-economic analysis of biomass-to-hydrogen production with methane tri-reforming

In this study, the techno-economic, energy consumption (EC), and environmental performances of biomass-to-hydrogen (BTH) production with/without methane tri-reforming (MTR) from corn straw were studied. The techno-economic analysis includes an assessment of the energy efficiency, MTR operating conditions, total capital investment (TCI), production cost (PC), and payback period. The BTH energy efficiency with MTR is 17.08% higher than that without MTR. The Claus unit is the largest contribution unit of energy loss. The reaction temperature and operating pressure of one MTR unit are 800 degrees C and 0.1 MPa, respectively. Compared with BTH without MTR, the TCI of BTH with MTR increased by 10.97%, and the PC decreased by 10.12%. The static payback period of BTH with MTR is approximately 4.72 yr. BTH without MTR exhibits 3.09% less EC and 7.85% greenhouse gas emissions than that of BTH with MTR. The life cycle analysis of the BTH process illustrates that the BTH with MTR can realize negative carbon dioxide (CO2) emissions when considering the natural carbon cycle. The evaluation method combines a thermodynamic analysis with a life cycle assessment, which is significant for the development of clean hydrogen production technology with low carbon and high energy savings. (C) 2020 Elsevier Ltd. All rights reserved.
Industrial demand-side flexibility: A key element of a just energy transition and industrial development

In many countries, industry is one of the largest consumers of electricity. Given the special importance of electricity for industry, a reliable electricity supply is a basic prerequisite for further industrial development and associated economic growth. As countries worldwide transition to a low-carbon economy (in particular, by the development of renewable energy sources), the increasing fluctuation in renewable energy production requires new flexibility options within the electricity system in order to guarantee security of supply. It is advanced in this paper that such a flexibility transition with an active participation of industry in general has unique potential: It will not only promote green industrial development, but also become an engine for inclusive industrial development and growth as well as delivering a just transition to a low-carbon economy. Given the high potential of industrial demand-side flexibility, a first monitoring approach for such a flexibility transition is illustrated, which bases on a flexibility index. Our flexibility index allows for an indication of mis-developments and supports an appropriate implementation of countermeasures together with relevant stakeholders. Hence, it holds various insights for both policy-makers and practice with respect to how industrial demand-side flexibility can ensure advances towards an inclusive, just, and sustainable industrial development.
Digital Business Model, Digital Transformation, Digital Entrepreneurship: Is There A Sustainable "Digital"?

Digitalization plays a major role in contributing towards the United Nations Sustainable Development Goals. Without transformation of existing businesses, both economic and environmental challenges of the future cannot be solved sustainably. However, there is much confusion on interrelationships and terms dealing with digitization or digitalization: Digital business model, digital transformation, digital entrepreneurship. How do these terms interrelate with and to digitalization, and how do they support firms to grow sustainably? To answer this question, we identified seven core digital-related terms based on a structured literature search within the management and economics domain, namely: Digital, Business Model, Digital Business Model, Digital Technology, Digital Innovation, Digital Transformation, and Digital Entrepreneurship. Thereafter, we analyzed prior literature for deriving a common understanding and definition as a basis for interrelations within a conceptual framework. Definitions were presented in a case study setup with twelve innovation and research and development (R&D) managers from various business units of a German high-tech company. Based on these insights, we propose a conceptual framework on how Digital Readiness, Digital Technology, and Digital Business Models might sustainably relate to Innovation, moderated by a Digital Transformation Process. With this approach, we aim to equip practitioners and researchers alike in handling and addressing change through digitalization sustainably.
Balloons, tails and bubbles: depicting speech and thought out of the brain and into the clinic

Illustrations of the internal workings of the brain often depict arrows. In contrast, many illustrations which depict the link between certain brain functions and the outside world harness a graphic technique more usually associated with forms of popular culture such as comics. This technique comprises a balloon containing an image or message linked either by a tail emanating from the mouth when representing speech, or by a stream of bubbles emanating from the head when representing thought. Although a pictorial speech device first appeared over two millennia ago, balloons with their linkages now have various important practical clinical applications, notably in autism spectrum disorders, profound deafness in children without neurocognitive impairment, and sexual health education.
Successional adaptive strategies revealed by correlating arbuscular mycorrhizal fungal abundance with host plant gene expression

The shifts in adaptive strategies revealed by ecological succession and the mechanisms that facilitate these shifts are fundamental to ecology. These adaptive strategies could be particularly important in communities of arbuscular mycorrhizal fungi (AMF) mutualistic with sorghum, where strong AMF succession replaces initially ruderal species with competitive ones and where the strongest plant response to drought is to manage these AMF. Although most studies of agriculturally important fungi focus on parasites, the mutualistic symbionts, AMF, constitute a research system of human-associated fungi whose relative simplicity and synchrony are conducive to experimental ecology. First, we hypothesize that, when irrigation is stopped to mimic drought, competitive AMF species should be replaced by AMF species tolerant to drought stress. We then, for the first time, correlate AMF abundance and host plant transcription to test two novel hypotheses about the mechanisms behind the shift from ruderal to competitive AMF. Surprisingly, despite imposing drought stress, we found no stress-tolerant AMF, probably due to our agricultural system having been irrigated for nearly six decades. Remarkably, we found strong and differential correlation between the successional shift from ruderal to competitive AMF and sorghum genes whose products (i) produce and release strigolactone signals, (ii) perceive mycorrhizal-lipochitinoligosaccharide (Myc-LCO) signals, (iii) provide plant lipid and sugar to AMF, and (iv) import minerals and water provided by AMF. These novel insights frame new hypotheses about AMF adaptive evolution and suggest a rationale for selecting AMF to reduce inputs and maximize yields in commercial agriculture.
Levelised cost of energy, A challenge for offshore wind

Long-term economic viability of offshore wind power is not only reliant on reducing installation, commissioning, and operations and maintenance costs, but also on the elimination of subsidies and grants. Current economic analyses use historical price and cost data to predict the levelised cost, net present value, payback period and internal rate of return from offshore wind. These analyses use parameters such as water depth at site, number and size of turbines, grid connection costs, equipment costs, revenue from the wholesale market price of electricity, operations and maintenance costs, revenue from subsidies and the cost of finance amongst others to build a model to determine the viability of the array. Here we review the challenges of accurately estimating levelised cost of energy (LCOE) for offshore wind outlining differing approaches to calculating LCOE, the factors influencing this, and the impact of variation in LCOE calculation. Current costs for the production of offshore wind energy are summarised based on publicly available datasets. (C) 2020 Elsevier Ltd. All rights reserved.
Dynamic relationship among agriculture-energy-forestry and carbon dioxide (CO2) emissions: empirical evidence from China

This study aims to explore the dynamic association among crop production, livestock production, power consumption in agriculture, forest area, and carbon dioxide (CO2) emissions. Based on the annual data of China, spanning the period 1990 to 2016, the study applied the auto-regressive distributed lag (ARDL) bounds testing approach. In addition, the fully modified ordinary least squares (FMOLS) canonical cointegration regression (CCR) and the Granger causality tests are employed to check the robustness of the ARDL estimations. The ARDL-bounds testing approach indicated that all variables share a long-run connection. The long- and short-run ARDL estimations confirmed that crop production, as well as livestock production, has a significant positive effect on CO(2)emissions in both cases. However, power consumption in agriculture and forest area has a negative effect on it, indicating that both variables reduce CO(2)emissions in the long and short run. These results stood robust under various regression estimators and confirmed the findings of the ARDL method. Additionally, the results of the causality approach specified that a unidirectional causality is running from crop production, power consumption in agriculture, and forest area to CO(2)emissions. The causality between livestock production and CO(2)emissions is bidirectional. Therefore, the directions of this connection also validate the outcomes under various techniques used for robustness. These findings suggest that the government must reconsider its policies related to agricultural and livestock production and adopt environment-friendly practices in the agriculture sector that may reduce the carbon footprints in the long run.
Aligned and Divergent Perceptions of Support Persons' Role in Triadic Gynecologic Cancer Communication

Health care providers routinely advise cancer patients to involve support persons in oncology care to fulfill critical support roles. This qualitative descriptive study explored alignment of triadic perceptions of support person involvement in oncology treatment visits and cancer-related care from the perspectives of patients with gynecologic cancer (n = 18), regular visit-attending support people (n = 16), and health care providers (n = 10), including oncologists, nurses, and medical assistants. Semi-structured interviews (N = 44) captured perceptions of facilitation and interference of support persons' roles within and outside appointments with oncology providers. Thematic analyses revealed alignment and divergence regarding support persons' instrumental, informational, and emotional support behaviors. Perspectives aligned regarding what support functions companions provide. However, patients and support persons emphasized the significance of instrumental followed by informational and emotional support, whereas oncology providers highlighted informational, followed by emotional and instrumental support. Discussion provides insight into each role's perspective in the triad.
Use of carbide slag from acetylene industry for activation of ground granulated blast-furnace slag

This study investigates the use of carbide slag (CS), an industrial by-product generated in acetylene production, as a potential alternative to hydrated lime (HL) for activation of ground granulated blast-furnace slag (GGBS), another industrial by-product generated during the process of iron production. Specimens of CS-GGBS and HL-GGBS pastes with different CS or HL contents were prepared and cured for different periods, and then tested for compressive strength. X-ray diffraction (XRD), thermogravimetric analysis (TGA), Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM) were also used to investigate the hydration characteristics of CS-GGBS. The results indicated that CS-GGBS yielded similar compressive strength as HL-GGBS with the same CS/HL content at the same curing periods of 28 and 56 days, i.e. CS could replace HL to activate GGBS, which would result in both environmental and economic benefits. The Ca(OH)(2) in CS accelerated the hydration of GGBS, and hence more hydration products were produced. However, excessive CS addition would decrease the GGBS content and increase crystal calcium hydroxide in the matrix, causing strength decrease. Hence, there was an optimum CS/HL content to achieve the highest compressive strength, which was 10% for 7 days and 5% for 28 and 56 days. (C) 2019 Elsevier Ltd. All rights reserved.
Artificial intelligence approaches to the determinants of women's vaginal dryness using general hospital data

The aim of this study is to analyse the determinants of women's vaginal dryness using machine learning. Data came from Korea University Anam Hospital in Seoul, Republic of Korea, with 3298 women, aged 40-80 years, who attended their general health check from January 2010 to December 2012. Five machine learning methods were applied and compared for the prediction of vaginal dryness, measured by a Menopause Rating Scale. Random forest variable importance, a performance gap between a complete model and a model excluding a certain variable, was adopted for identifying major determinants of vaginal dryness. In terms of the mean squared error, the random forest (1.0597) was much better than linear regression (17.9043) and artificial neural networks with one, two and three hidden layers (1.7452, 1.7148 and 1.7736, respectively). Based on random forest variable importance, the top-10 determinants of vaginal dryness were menopause age, age, menopause, height, thyroid stimulating hormone, neutrophils, years since menopause, lymphocytes, alkaline phosphatase and blood urea nitrogen. In addition, its top-20 determinants were peak expiratory flow rate, low-density lipoprotein cholesterol, white blood cells, monocytes, cancer antigen 19-9, creatinine, eosinophils, total cholesterol, triglyceride and amylase. Machine learning presents a great decision support system for the prediction of vaginal dryness. For preventing vaginal dryness, preventive measures would be needed regarding early menopause, the thyroid function and systematic inflammation.Impact StatementWhat is already known on this subject? Only a few studies have investigated the risk factors of vaginal dryness in middle-aged women. More research is to be done for finding its various risk factors, identifying its major risk groups and drawing its effective clinical implications.What do the results of this study add? This study is the first machine-learning study to predict women's vaginal dryness and analyse their determinants. The random forest could discuss which factors are more important for the prediction of vaginal dryness. Based on random forest variable importance, menopause age was the most important determinant of vaginal dryness and their association was discovered to be negative in this study. Vaginal dryness was closely associated with the height, rather than the body weight or body mass index. The importance rankings of blood conditions related to systematic inflammation were within the top-20 in this study: neutrophils, lymphocytes, white blood cells, monocytes and eosinophils.What are the implications of these findings for clinical practice and/or further research? Machine learning presents a great decision support system for the prediction of vaginal dryness. For preventing vaginal dryness, preventive measures would be needed regarding early menopause and systematic inflammation.
Clinical applications of point-of-care ultrasound in brain injury: a narrative review

The use of point-of-care ultrasound has increased considerably over the last two decades. It has become a readily available, non-invasive bed-side modality for the rapid diagnosis and management of patients in various settings. Point-of-care ultrasound for assessing the heart, lung, abdomen, airway and vascular systems is now well established. Transcranial Doppler ultrasound for assessing cerebral blood flow has been in use for many years. In recent years, brain ultrasonography has been shown to be a powerful tool in the rapid bed-side assessment of cerebral haemodynamics and structural pathology. Brain ultrasound has been shown to be useful in both the emergency department and neurocritical care setting for the non-invasive assessment of raised intracranial pressure, cerebral autoregulation and diagnosis of intracranial haematoma and other space-occupying lesions. Point-of-care ultrasound is an important alternative technique to routine imaging and invasive monitoring techniques, and results are comparable. Competence is easily attainable without formal radiology training. With technological advancement, applications for the use of brain ultrasound continue to grow. This review discusses the clinical use of point-of-care ultrasound in the diagnosis and management of brain injury.
School-related Promotive Factors Related to Cannabis Use Among American Indian Adolescents

Reservation-dwelling American Indian adolescents are at exceedingly high risk for cannabis use. Prevention initiatives to delay onset and escalation of use are needed. School engagement and student's positive experiences at school have been identified as key promotive factors against cannabis use in the general population of adolescents, but little work has examined these factors among American Indian youth. Seven school-related promotive factors were examined as predictors of past 30-day cannabis use (measured 6 months later), controlling for previous onset of cannabis use as well as a set of relevant potential confounders. Models were tested using 280 adolescents in 6th or 7th grade at the start of the study from two reservation-based schools in the US. Students were surveyed three times, with 6 months in between each survey. The average age at the start of the study was 11.99 years (SD = 0.87) and 54% of participants were female. Using a cumulative logit model to predict past 30-day use, American Indian youth who reported greater school bonding, academic aspirations, proclivity to endeavor in their studies, and interest in school at Wave 2 reported less 30-day cannabis use at Wave 3 (controlling for onset of cannabis at Wave 1 and several other control variables). No evidence of an effect of self-reported grades, perceived safety, or participation in school-related extracurricular activities was found. Given substantial deterioration of these school-related promotive factors over time, and the effect of the school-related promotive factors on subsequent cannabis use, efforts to design and test interventions to promote school engagement as a protective measure against cannabis use is warranted.
Gamification in software engineering: the mediating role of developer engagement and job satisfaction

Gamification seeks to encourage behavior of participants by borrowing elements of games, such as scoring points. Few rigorous studies exist of gamification in software organizations, and several questions have remained unanswered, for example, what might drive developers to partake, and what are the consequences of developer engagement. This article seeks to provide some answers through a rigorous empirical study at one organization that created an internal gamification platform. We develop a theoretical model that seeks to explain why developers may participate, and develop the concept of developer engagement, which we link to job satisfaction. We collected data from two sources that were linked together: developer opinion data collected through a survey, and data from the organization's version control system. We test our theoretical model using structural equation modeling and moderation analysis, and find support for our model. These findings suggest that gamification can be an effective mechanism to engage developers within the organization, and that developer engagement is positively associated with job satisfaction, which is a key outcome that is of great interest to software organizations.