scholarly journals Experimental Data of Fluid Phase Equilibria- Correlation and Prediction Models: A Review

Processes ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 277 ◽  
Author(s):  
Urszula Domańska
Keyword(s):  
Phase Equilibria ◽  
Binary Systems ◽  
Prediction Models ◽  
Research Work ◽  
Ternary Mixtures ◽  
Excess Gibbs Free Energy ◽  
Future Research ◽  
Published Data ◽  
Gas Liquid Chromatography ◽  
Excess Molar Enthalpy

The examples of phase equilibria in binary systems, solid/liquid (SLE), liquid/liquid (LLE), vapor/liquid (VLE), as well as liquid/liquid equilibria in ternary systems mainly containing ionic liquids (ILs), or the infragrance materials, or pharmaceuticals with molecular organic solvents, such as an alcohol, or water, or hydrocarbons, are presented. The most popular correlation methods of the experimental phase equilibrium data are presented, related to the excess Gibbs free energy models such as Wilson, universal-quasichemical, UNIQUAC and non-random two-liquid model, NRTL as well as several popular theories for the modeling of the phase equilibria and excess molar enthalpy, HE in binary or ternary mixtures are presented: the group contribution method (Mod. UNIFAC) and modified UNIFAC model for pharmaceuticals and lattice theory based on non-random hydrogen bonding (NRHB). The SLE, LLE, or VLE and HE of these systems may be described by the Perturbed-Chain Polar Statistical Associating Fluid Theory (PC-SAFT), or a Conductor-like Screening Model for Real Solvents (COSMO-RS). The examples of the application of ILs as extractants for the separation of aromatic hydrocarbons from alkanes, sulfur compounds from alkanes, alkenes from alkanes, ethylbenzene from styrene, butan-1-ol from water phase, or 2-phenylethanol (PEA) from water are discussed on the basis of previously published data. The first information about the selectivity of extrahent for separation can be obtained from the measurements of the limiting activity coefficient measurements by the gas–liquid chromatography technique. This review outlines the main research work carried out over the last few years on direct measurements of phase equilibria, or HE and limiting activity coefficients, the possibility of thermodynamic modeling with emphasis on recent research achievements and potential for future research.

scholarly journals Carbides and Nitrides of Zirconium and Hafnium

Materials ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 2728 ◽  
Author(s):  
Sergey V. Ushakov ◽  
Alexandra Navrotsky ◽  
Qi-Jun Hong ◽  
Axel van de Walle
Keyword(s):  
Experimental Data ◽  
Phase Equilibria ◽  
Ab Initio ◽  
Solid Solutions ◽  
Binary Systems ◽  
Experimental Studies ◽  
Published Data ◽  
Oxygen Solubility ◽  
Melting Temperatures ◽  
Ab Initio Computations

Among transition metal carbides and nitrides, zirconium, and hafnium compounds are the most stable and have the highest melting temperatures. Here we review published data on phases and phase equilibria in Hf-Zr-C-N-O system, from experiment and ab initio computations with focus on rocksalt Zr and Hf carbides and nitrides, their solid solutions and oxygen solubility limits. The systematic experimental studies on phase equilibria and thermodynamics were performed mainly 40–60 years ago, mostly for binary systems of Zr and Hf with C and N. Since then, synthesis of several oxynitrides was reported in the fluorite-derivative type of structures, of orthorhombic and cubic higher nitrides Zr3N4 and Hf3N4. An ever-increasing stream of data is provided by ab initio computations, and one of the testable predictions is that the rocksalt HfC0.75N0.22 phase would have the highest known melting temperature. Experimental data on melting temperatures of hafnium carbonitrides are absent, but minimum in heat capacity and maximum in hardness were reported for Hf(C,N) solid solutions. New methods, such as electrical pulse heating and laser melting, can fill the gaps in experimental data and validate ab initio predictions.

Dissolution of nuclear materials in aqueous acid solutions

2019 ◽  
Vol 35 (6) ◽  
pp. 707-734 ◽  
Author(s):  
Narasimhan Desigan ◽  
Nirav Bhatt ◽  
Madhuri A. Shetty ◽  
Gopala Krishna Pillai Sreekumar ◽  
Niranjan Kumar Pandey ◽  
...  
Keyword(s):  
Nuclear Fuel ◽  
Research Work ◽  
Future Research ◽  
Published Data ◽  
Nuclear Materials ◽  
Transfer Rates ◽  
Intrinsic Kinetics ◽  
Quantitative Understanding ◽  
Nuclear Fuel Reprocessing Plant ◽  
Rate Controlling Step

Abstract The quantitative understanding of the dissolution of nuclear fuel materials is essential for the process design and development of an industrial-scale nuclear fuel reprocessing plant. The main objective of this review article is to analyze the published data related to the dissolution of important nuclear materials, namely, urania, plutonia, thoria, and their oxides in the existing literature. The published results on rate-controlling step and reaction mechanism of dissolution processes are reconciled and reviewed in this work. Clear suggestions are made for future research work for the identification of rate-controlling step. Suggestions are also provided to overcome the shortfalls in the published data for the identification of intrinsic kinetics and mass-transfer rates.

scholarly journals Data-driven method for mobile game publishing revenue forecast

2021 ◽  
Author(s):  
Yanhui Su ◽  
Per Backlund ◽  
Henrik Engström
Keyword(s):  
Prediction Models ◽  
Time Series Prediction ◽  
Research Work ◽  
Data Driven ◽  
Future Research ◽  
Business Decisions ◽  
Mobile Game ◽  
Revenue Forecast ◽  
The Right ◽  
Game Developers

AbstractGames as a service is similar to software as a service, which provides players with game content on a continuous monetization model. Game revenue forecast is vital to game developers to make the right business decisions, such as determining the marketing budget, controlling the development cost, and setting up benchmarks for evaluating game publishing performance. How to make the revenue forecast and integrate it with the game publishing process is hard for small and medium-sized independent (indie) game developers. This includes all steps of the process, from forecasting to decision-making based on the results. This paper provides a data-driven method that uses the mobile game revenue forecast based on different time-series prediction models to drive the game publishing. We demonstrate how to use the data-driven method to guide an indie game studio to forecast revenue and then set the revenue forecast as the internal benchmark to drive game publishing. In practice, we involve a real game project from an indie game studio and provide guidance for one of their casual game projects. Then, based on the revenue forecast, we discuss how to set the revenue forecast as an internal benchmark and drive the actions for mobile game publishing. Finally, we make a conclusion on how our data-driven method can be used to drive mobile game publishing and also discuss future research work.

COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

Recent Advances on the Semi-Supervised Learning for Long Non-Coding RNA-Protein Interactions Prediction: A Review

2020 ◽  
Vol 27 (5) ◽  
pp. 385-391
Author(s):  
Lin Zhong ◽  
Zhong Ming ◽  
Guobo Xie ◽  
Chunlong Fan ◽  
Xue Piao
Keyword(s):  
Supervised Learning ◽  
Protein Interactions ◽  
Computational Models ◽  
Prediction Models ◽  
Chromatin Modification ◽  
Computational Prediction ◽  
Human Diseases ◽  
Future Research ◽  
Non Coding Rna ◽  
Long Non Coding Rna

: In recent years, more and more evidence indicates that long non-coding RNA (lncRNA) plays a significant role in the development of complex biological processes, especially in RNA progressing, chromatin modification, and cell differentiation, as well as many other processes. Surprisingly, lncRNA has an inseparable relationship with human diseases such as cancer. Therefore, only by knowing more about the function of lncRNA can we better solve the problems of human diseases. However, lncRNAs need to bind to proteins to perform their biomedical functions. So we can reveal the lncRNA function by studying the relationship between lncRNA and protein. But due to the limitations of traditional experiments, researchers often use computational prediction models to predict lncRNA protein interactions. In this review, we summarize several computational models of the lncRNA protein interactions prediction base on semi-supervised learning during the past two years, and introduce their advantages and shortcomings briefly. Finally, the future research directions of lncRNA protein interaction prediction are pointed out.

A Case Study: Analysis of Patents of Coronaviruses and Covid-19 for Technology Assesment and Future Research

2020 ◽  
Vol 26 ◽  
Author(s):  
Pankaj Musyuni ◽  
Geeta Aggarwal ◽  
Manju Nagpal ◽  
Ramesh K. Goyal
Keyword(s):  
Disease Management ◽  
Research And Development ◽  
Technological Development ◽  
Research Work ◽  
Large Family ◽  
Future Research ◽  
Biological Drugs ◽  
Ongoing Research ◽  
Patent Literature ◽  
Patent Documents

Background: Protecting intellectual property rights are important and particularly pertinent for inventions which are an outcome of rigorous research and development. While the grant of patents is subject to establishing novelty and inventive step, it further indicates the technological development and helpful for researchers working in the same technical domain. The aim of the present research work is to map the existing work through analysis of patent literature, in the field of Coronaviruses (CoV), particularly COVID-19 (2019-nCoV). CoV is a large family of viruses known to cause illness in human and animals, particularly known for causing respiratory infections as evidenced in earlier times such as in MERS i.e. Middle East Respiratory Syndrome; SRS i.e. Severe Acute Respiratory Syndrome. A recently identified novel-coronavirus has known as COVID-19 which has currently caused pandemic situation across the globe. Objective: To expand analysis of patents related to CoV and 2019-nCoV. Evaluation has been conducted by patenting trends of particular strains of identified CoV diseases by present legal status, main concerned countries via earliest priority years and its assignee types and inventors of identified relevant patents. We analyzed the global patent documents to check the scope of claims along with focuses and trends of the published patent documents for the entire CoV family including 2019- nCoV through the present landscape. Methods: To extract the results, Derwent Innovation database is used by a combination of different key-strings. Approximately 3800 patents were obtained and further scrutinized and analyzed. The present write-up also discusses the recent progress of patent applications in a period of the year 2010 to 2020 (present) along with the recent developments in India for the treatment options for CoV and 2019-nCoV. Results: Present analysis showed that key areas of the inventions have been focused on vaccines and diagnostic kits apart from the composition for treatment of CoV. We also observed that no specific vaccine treatments is available for treatment of 2019-nCov, however, developing novel chemical or biological drugs and kits for early diagnosis, prevention and disease management is the primarily governing topic among the patented inventions. The present study also indicates potential research opportunities for the future, particularly to combat 2019-nCoV. Conclusion: The present paper analyzes the existing patents in the field of Coronaviruses and 2019-nCoV and suggests a way forward for the effective contribution in this upcoming research area. From the trend analysis, it was observed an increase in filing of the overall trend of patent families for a period of 2010 to the current year. This multifaceted analysis of identified patent literature provides an understanding of the focuses on present ongoing research and grey area in terms of the trends of technological innovations in disease management in patients with CoV and 2019-nCoV. Further, the findings and outcome of the present study offer insights for the proposed research and innovation opportunities and provide actionable information in order to facilitate policymakers, academia, research driven institutes and also investors to make better decisions regarding programmed steps for research and development for the diagnosis, treatment and taking preventive measures for CoV and 2019-nCoV. The present article also emphasizes on the need for future development and the role of academia and collaboration with industry for speedy research with a rationale.

ANTECEDENTS AND CONSEQUENCES OF ORGANIZATIONAL JUSTICE

2016 ◽  
Vol 12 (02) ◽  
Author(s):  
Reeta Yadav
Keyword(s):  
Job Satisfaction ◽  
Organizational Justice ◽  
Organizational Citizenship ◽  
Organizational Citizenship Behavior ◽  
Turnover Intentions ◽  
Research Work ◽  
Citizenship Behavior ◽  
Future Research ◽  
Antecedents And Consequences ◽  
And Performance

Employee’s perception regarding fairness in the organization is termed as organizational justice. The objective of this paper is to study the antecedents and consequences of organizational justice on the basis of earlier relevant studies from the period ranging from 1964 to 2015. Previous research identified employee participation, communication, justice climate as the antecedents and trust, job satisfaction, commitment, turnover intentions, organizational citizenship behavior and performance as the consequences of organizational justice. Finding reveals the gaps existing in the literature and gives suggestions for future research work.

Application of the Redlich-Kwong-Soave equation of state to the solubility of water in compressed gases

1984 ◽  
Vol 49 (5) ◽  
pp. 1116-1121
Author(s):  
Josef P. Novák ◽  
Jaroslav Matouš ◽  
Petr Pick ◽  
Jiří Pick
Keyword(s):  
Equation Of State ◽  
Binary Systems ◽  
Equations Of State ◽  
Published Data ◽  
Interaction Coefficients

Published data on the solubility of water in compressed gases were employed for calculating the interaction coefficients kij in the Redlich-Kwong-Soave equations of state for binary systems of water with argon, nitrogen, CO2, N2O, CH4, C2H6, or C2H4. With these coefficients, the estimate of the solubility of water in these gases has been improved by more than one order.

scholarly journals Recommendation Systems for Education: Systematic Review

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1611
Author(s):  
María Cora Urdaneta-Ponte ◽  
Amaia Mendez-Zorrilla ◽  
Ibon Oleagordia-Ruiz
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Formal Education ◽  
Research Work ◽  
Hybrid Approach ◽  
Recommendation Systems ◽  
Educational Resources ◽  
Future Research ◽  
Collaborative Approach ◽  
Developmental Approach

Recommendation systems have emerged as a response to overload in terms of increased amounts of information online, which has become a problem for users regarding the time spent on their search and the amount of information retrieved by it. In the field of recommendation systems in education, the relevance of recommended educational resources will improve the student’s learning process, and hence the importance of being able to suitably and reliably ensure relevant, useful information. The purpose of this systematic review is to analyze the work undertaken on recommendation systems that support educational practices with a view to acquiring information related to the type of education and areas dealt with, the developmental approach used, and the elements recommended, as well as being able to detect any gaps in this area for future research work. A systematic review was carried out that included 98 articles from a total of 2937 found in main databases (IEEE, ACM, Scopus and WoS), about which it was able to be established that most are geared towards recommending educational resources for users of formal education, in which the main approaches used in recommendation systems are the collaborative approach, the content-based approach, and the hybrid approach, with a tendency to use machine learning in the last two years. Finally, possible future areas of research and development in this field are presented.

scholarly journals Assessing the indirect effects of COVID-19 on healthcare delivery, utilization and health outcomes: a scoping review

2021 ◽  
Author(s):  
Charlotte M Roy ◽  
E Brennan Bollman ◽  
Laura M Carson ◽  
Alexander J Northrop ◽  
Elizabeth F Jackson ◽  
...  
Keyword(s):  
Health Outcomes ◽  
Health Effects ◽  
Scoping Review ◽  
Cardiovascular Health ◽  
Healthcare Delivery ◽  
Final Analysis ◽  
High Income ◽  
Future Research ◽  
Published Data ◽  
Local Health

Abstract Background The COVID-19 pandemic and global efforts to contain its spread, such as stay-at-home orders and transportation shutdowns, have created new barriers to accessing healthcare, resulting in changes in service delivery and utilization globally. The purpose of this study is to provide an overview of the literature published thus far on the indirect health effects of COVID-19 and to explore the data sources and methodologies being used to assess indirect health effects. Methods A scoping review of peer-reviewed literature using three search engines was performed. Results One hundred and seventy studies were included in the final analysis. Nearly half (46.5%) of included studies focused on cardiovascular health outcomes. The main methodologies used were observational analytic and surveys. Data were drawn from individual health facilities, multicentre networks, regional registries, and national health information systems. Most studies were conducted in high-income countries with only 35.4% of studies representing low- and middle-income countries (LMICs). Conclusion Healthcare utilization for non-COVID-19 conditions has decreased almost universally, across both high- and lower-income countries. The pandemic’s impact on non-COVID-19 health outcomes, particularly for chronic diseases, may take years to fully manifest and should be a topic of ongoing study. Future research should be tied to system improvement and the promotion of health equity, with researchers identifying potentially actionable findings for national, regional and local health leadership. Public health professionals must also seek to address the disparity in published data from LMICs as compared with high-income countries.

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