Conversion Model Research Articles

Study on calculation of transversely isotropic dynamic rock mechanical parameters and conversion model between dynamic parameters and static parameters

The shale oil reservoirs in the Jimsar Sag are strongly heterogeneous. Therefore, it is of practical significance to relate the dynamic elastic stiffness to the static elastic stiffness under the assumption of transverse isotropy. The acquisition of stiffness parameters depends on lab measurements or Sonic Scanner logging. Aiming at the problem that the lab measurements results are limited and discontinuous, the acquisition of special logging data is difficult in the well site, a method using only the conventional well-log data to predict the dynamic elastic stiffnesses of shale is proposed. Inspired by ultrasonic experimental test method, the study simplified the types of tested waves and complemented missing datas. According to the experimental proof, we concluded that: (1) Under the assumption of transverse isotropy, using simplified Thmosen model and velocity conversion relationship can accurately calculate the P-wave and S-wave velocities in the 45° angle direction. (2) Compared with the conventional isotropic algorithm, specify this algorithm can better reflect real mechanical conditions of the formation, and better characterize the variation of mechanical parameters with depth. (3) Effective conversion models between dynamic elastic modulus and static elastic modulus in vertical and horizontal directions were obtained in some areas of Jimsar.

The background adaptation of the skin color in the loach Paramisgurnus dabryanus

Body color change is one of the most important manifestations of fish adaptation to the environment. In this study, the body color change of two kinds of loach (Paramisgurnus dabryanus) with different body colors under five different background colors was first studied. It was found that the color of dorsal skin of gray-black loach changed more than that in the abdominal skin, and the L* value of the dorsal skin on the red, yellow and white backgrounds changed significantly (P < 0.05). Compared with the gray-black loach, the body color of golden loach changed less, and the L* value of the back skin and the abdomen skin did not change significantly (P > 0.05). Furthermore, the dynamic change characteristics and adaptive mechanism of body color in different background colors was studied in a black-white background conversion model. The results showed that the greyscale color decreased from 72.33 ± 2.55–38.22 ± 2.33, the melanocytes changed from dendritic to mass, and the L* value of the back skin increased significantly (P < 0.05) after transferring the loach from a black background to a white background. The concentration of plasma ATCH decreased significantly (P < 0.05), and that of MCH increased significantly (P < 0.05). When transferring the loach from a white background to a black background, these indicators showed an opposite trend. This study provides a scientific basis for healthy farming of loach and understanding the background color adaptation mechanism of fish.

Enhancing Conversational Model With Deep Reinforcement Learning and Adversarial Learning

Enhancing Conversational Model With Deep Reinforcement Learning and Adversarial Learning

Experimental Study on Flow Characteristics of Large-scale Liquid Oxygen Delivery System

To deeply explore the flow law of the large-scale liquid oxygen delivery system, several liquid oxygen delivery tests have been conducted and measured. On this basis, the model of the pipeline characteristic of the liquid oxygen delivery system is constructed according to the hydrodynamic theory to obtain the relationship equation between the gas cushion pressure and liquid oxygen flow, and the frequency conversion model of the liquid oxygen pump is constructed according to the pump affinity laws to obtain the relationship equation between the liquid oxygen pump frequency, flow, and head. Under the working pressure limitation of the storage tank, liquid oxygen pump, and pipeline, the ultimate capacity of the liquid oxygen delivery system is calculated using the above models. This provides a database and theoretical basis for further optimization of liquid oxygen delivery process parameters and control strategies, as well as evaluation and exploitation of system performance potentials.

Narratological Analysis of English as a Second Language Students’ Success Stories

This article attempted to analyze the success stories of students at the tertiary level in an ESL context during the COVID-19 remote learning mode. This paper provided a meta-analysis of the narrative analysis using Polanyi’s framework (Polanyi, 1981). I argued that these success stories provided data to evaluate the conversational narrative framework on structural components and this meta-analysis highlighted the problems as well as the potentials of Polanyi’s conversational narrative model of analysis in the field of narratology. Data revealed that such evaluation of a conversational narrative’s non-Story world clauses offered critical insights as Polanyi delineated in her model.

Optimization Scheduling Method of Solar Photovoltaic and Fuel Cell Combined Power Generation System

The performance of the battery used in the traditional solar photovoltaic power generation system is poor, and the solar energy has a certain volatility, which makes the performance of the solar photovoltaic power generation system decline significantly. In order to improve the performance of the solar photovoltaic power generation system, a solar photovoltaic fuel cell combined power generation system has been developed. However, there are some problems in the process of traditional combined generation system optimal scheduling, such as high investment cost and total cost, and short generation time of optimal scheduling scheme. This paper takes solving the problems of traditional system optimal scheduling as the research goal, a new optimization scheduling method of solar photovoltaic and fuel cell combined power generation system was designed. The composition and topological structure of the solar photovoltaic fuel cell combined power generation system are analyzed. The system is composed of photovoltaic array, fuel cell, electrolytic cell, short-term energy storage unit and energy control unit. It can mainly convert sunlight radiation into electric energy, and convert it into DC or AC used by people through multiple links, so as to ensure the stability and security of power supply in our country. A scheduling model is established according to the photovoltaic cell power generation model, the fuel cell power generation model, the electrolytic hydrogen production model, the battery model, the power conversion model and the combined power generation system model. And the multi-objective cuckoo algorithm is used to solve the model, the optimization scheduling results of solar photovoltaic fuel cell combined power generation system are obtained. The experimental results show that the total investment cost of this method is reduced by 123678.4 yuan and 175858.7 yuan compared with the experimental comparison method, and the total cost is reduced by 301195.5 yuan and 414991.8 yuan compared with the experimental comparison method. It shows that compared with the experimental comparison method, the total investment cost and total cost of this method are lower, and the generation time of the optimal scheduling scheme is between 0.19s and 0.25s, and the practical application effect is good. It fully solves the problems existing in the traditional methods and has certain application significance.

Analysis of the accuracy of in-situ concrete characteristic compressive strength assessment in real structures using destructive and non-destructive testing methods

Estimation of in-situ concrete compressive strength in existing structures is considered as a crucial step in assessing structural capacity. Destructive testing (DT) by coring in conjunction with non-destructive testing (NDT) offers an interesting method for concrete strength assessment in existing constructions. The non-destructive assessment of in-situ strength requires a correlation between NDT measurements and the core compressive strengths. Therefore, the accurate quality of the in-situ strength assessment is a fundamental point. The NDT techniques such as Rebound Hammer (RH) and Ultrasonic Pulse Velocity (UPV) are widely used separately or in combination to estimate the in-situ strength. A series of on-site data (205 data triplets) of non-destructive testing (RH and UPV) and coring have been carried out on structural elements (columns and beams) in an existing building to assess the in-situ concrete strength according to the new standard version procedures (EN 13791-2019) with a comparison to its old version 2007. The analysis highlighted in a real on-site situation the positive effect of the new procedures in the recent version (2019) of EN 13791 on strength assessment, namely: the selection process of coring positions, the required number of cores, and the identification method of the conversion model. However, the statistical procedures proposed in the new version for calculating the in-situ characteristic strength appear to underestimate the results and seem to be excessively conservative as compared to the 2007 version.

Kinetic Modeling of Glycerol Hydrogenolysis: A Short Review

Glycerol hydrogenolysis represents one of the most promising technologies for future bio-refineries. In this context, kinetic modeling provides key quantitative assessment of the significance of various reactions for process development. However, as of present, there are only limited studies on detailed kinetic modeling of glycerol conversion to 1,2-propanediol, ethylene glycol and other alcoholic products. In this work, a comprehensive summary on kinetic modeling of glycerol hydrogenolysis has been conducted to reveal the possible mechanism involved in the activation of the C-H and C-O bond in glycerol molecules. In particular, power law and Langmuir–Hinshelwood model types have been critically discussed with mechanistic insights. The outcome of this review article will offer alternative views on the scale-up design of glycerol hydrogenolysis to glycols, as well as hydrogenolysis of various other bio-derived compounds to value-added chemicals.

Combined mathematical and artificial intelligence modeling of catalytic bio-methanol conversion to dimethyl ether

This study is devoted to developing a combined mathematical and artificial intelligence modeling for simulating the conversion of bio-methanol into dimethyl ether. This methodology couples a precise mathematical model with single-layer perceptron artificial neural networks formulated by several combinations of training and transfer functions, to constitute a machine learning that overcomes the computational tools with the capability of learning without being explicitly programmed, so that it may be used to teach these tools to handle the data with high efficiency in order to predict the upcoming unseen ones precisely. Hence, the novelty of this study deals with the development of a machine learning modeling combined with artificial neural networks, benefitting from an extended mathematical-model-driven dataset, applied for the first time in the conversion of bio-methanol into dimethyl ether in large-scale catalytic dehydration reactors, assessing two process schemes, namely adiabatic and water-cooled operations. To this scope, datasets have been generated by an accurate mathematical model validated experimentally by reference pilot data, adopting the best equilibrium and kinetic expressions, and the simulated numerical data have been then employed as the source of the machine learning models. Extended databanks for the sensitivity analyses of the two process schemes have been then generated by the model (2688 data points in each case), 70% of which has been employed for the artificial neural networks training with mean squared error as the performance function. A single-layer perceptron network yielded the best performances in the adiabatic and water-cooled process schemes, revealing less than 0.5% deviations for the input variables (feed temperature, pressure, flow rate, and concentration) and outputs (temperature, pressure, and flow rates of methanol, dimethyl ether, and water). The simulations revealed that, as the best result of this work, methanol may be converted into dimethyl ether up to ∼80% at 498 K, depending on the validated equilibrium constant adopted, with a maximum error of 0.11%. As a further relevant result, this study demonstrated that analyzing the methanol conversion into dimethyl ether reaction as a function of several variables such as feed flow rate, feed methanol concentration, and input temperature, the accuracy of the neural network with a small number of neurons (5) did not provide accurate output results, whereas 15 neurons in the hidden layer intensify their precision, with the interesting feature that a single-layer perceptron network may detect even small changes to the operating variables.

Learning to Ask: Conversational Product Search via Representation Learning

Online shopping platforms, such as Amazon and AliExpress, are increasingly prevalent in society, helping customers purchase products conveniently. With recent progress in natural language processing, researchers and practitioners shift their focus from traditional product search to conversational product search. Conversational product search enables user-machine conversations and through them collects explicit user feedback that allows to actively clarify the users’ product preferences. Therefore, prospective research on an intelligent shopping assistant via conversations is indispensable. Existing publications on conversational product search either model conversations independently from users, queries, and products or lead to a vocabulary mismatch. In this work, we propose a new conversational product search model, ConvPS, to assist users in locating desirable items. The model is first trained to jointly learn the semantic representations of user, query, item, and conversation via a unified generative framework. After learning these representations, they are integrated to retrieve the target items in the latent semantic space. Meanwhile, we propose a set of greedy and explore-exploit strategies to learn to ask the user a sequence of high-performance questions for conversations. Our proposed ConvPS model can naturally integrate the representation learning of the user, query, item, and conversation into a unified generative framework, which provides a promising avenue for constructing accurate and robust conversational product search systems that are flexible and adaptive. Experimental results demonstrate that our ConvPS model significantly outperforms state-of-the-art baselines.

Detecting Mentions of Green Practices in Social Media Based on Text Classification

The paper is devoted to the task of searching for mentions of green practices in social media texts. The relevance of this task is dictated by the need to expand existing knowledge about the use of green practices in society and the spread of existing green practices. This paper uses a text corpus consisting of the texts published on the environmental communities of the VKontakte social network. The corpus is equipped with an expert markup of the mention of nine types of green practices. As part of this work, a semi-automatic approach is proposed to the collection of additional texts to reduce the class imbalance in the corpus. The approach includes the following steps: detecting the most frequent words for each practice type; automatic collecting texts in social media that contain the detected frequent words; expert verification and filtering of collected texts. The four machine learning models are compared to find the mentions of green practices on the two variants of the corpus: original and augmented using the proposed approach. Among the listed models, the highest averaged F1-score (81.32%) was achieved by Conversational RuBERT fine-tuned on the augmented corpus. Conversational RuBERT model was chosen for the implementation of the application prototype. The main function of the prototype is to detect the presence of the mention of nine types of green practices in the text. The prototype is implemented in the form of the Telegram chatbot.

Dynamic performance of the combined stirling thermoelectric conversion technology for a lunar surface nuclear power system

The space nuclear reactor power system is one of the most potential energy and power supplies for future lunar science exploration. Unlike the devices on Earth, the devices operating on the lunar surface require a higher level of intelligent and automatic system control. Thus, it is necessary to establish a comprehensive and accurate dynamic system model to improve the system's automatic control. In the present study, a dynamic system model of a lunar surface nuclear power system combined with a Stirling cycle was proposed. The dynamic system model consists of a reactor core model, a heat rejection model, and a Stirling thermoelectric conversion model (while the reactor core is simplified as a point heat source). MATLAB and Simulink platforms are applied to implement the system model. To verify the system model and analyze the dynamic performance, simulations of the several reactor startup processes, different reactor thermal power (reactivity disturbances), and different lunar surface temperature conditions were carried out. The results show that the effects of thermal power variation on the dynamic system model have a nonlinear delay. Furthermore, the lunar surface temperature significantly impacts the dynamic system's operation and response. The present study provides a practical tool for the transient analysis of the lunar surface nuclear reactor power system and theoretical support for the design of lunar surface nuclear reactors.

Understanding conversational interaction in multiparty conversations: the EVA Corpus

This paper focuses on gaining new knowledge through observation, qualitative analytics, and cross-modal fusion of rich multi-layered conversational features expressed during multiparty discourse. The outlined research stems from the theory that speech and co-speech gestures originate from the same representation; however, the representation is not solely limited to the speech production process. Thus, the nature of how information is conveyed by synchronously fusing speech and gestures must be investigated in detail. Therefore, this paper introduces an integrated annotation scheme and methodology which opens the opportunity to study verbal (i.e., speech) and non-verbal (i.e., visual cues with a communicative intent) components independently, however, still interconnected over a common timeline. To analyse this interaction between linguistic, paralinguistic, and non-verbal components in multiparty discourse and to help improve natural language generation in embodied conversational agents, a high-quality multimodal corpus, consisting of several annotation layers spanning syntax, POS, dialogue acts, discourse markers, sentiment, emotions, non-verbal behaviour, and gesture units was built and is represented in detail. It is the first of its kind for the Slovenian language. Moreover, detailed case studies show the tendency of metadiscourse to coincide with non-verbal behaviour of non-propositional origin. The case analysis further highlights how the newly created conversational model and the corresponding information-rich consistent corpus can be exploited to deepen the understanding of multiparty discourse.

The gamma dose rate analysis method for the maintenance of China initiative accelerator driven subcritical system based on the voxelized algorithm

Gamma dose rate analysis is the important issue for the maintenance of China initiative accelerator driven subcritical system (CiADS), which has direct impact on the nuclear radiation protection and the safe working environment for operators. However, the gamma radiation assessment in CiADS facility is the complex and challenge task, since there are a lot of complicated geometries with large amount of high order curve surfaces that are difficult to describe in the simulation process. Additionally, the shielding design in maintenance scenario has the requirement of high efficient calculation with acceptable accuracy. In this context, the point kernel method and the model simplification procedure by means of the voxelized model description have been adopted in the dose rate analysis for the purpose of obtaining the required flexible and diverse features, where the complex three dimensional geometries constructed by computer aided design (CAD) tools can be automatically converted to the approximate voxelized structure, and the radiation sources with arbitrary shapes are sampled based on the basic units in the conversion model. Furthermore, the CAD geometry optimization method has been verified in considering the volume deviation between the original CAD structure and the voxelized model. The feasibility and applicability of the dose rate analysis method have been tested in comparison with the FLUKA code, where all the numerical simulation results demonstrate that the proposed assessment method has the potential in meeting the design requirements during the maintenance of CiADS facility.

Transient multiphysics characteristics of a space thermionic reactor based on a coupling analysis

With the development of deep space exploration, the energy requirements of spacecraft are increasing. How to provide more reliable and lasting energy is a complex problem. The space reactor offers outstanding advantages, such as good environmental adaptability, wide power coverage, compact structure, and strong endurance. Therefore, space reactors have broad application prospects in space exploration.The work of this paper has developed a transient coupling code for the space thermionic reactor TOPAZ-II based on OpenFOAM. The reactor neutron physics model, transient heat transfer model, thermoelectric conversion model, and electromagnetic pump model are included in the developed code, considering the Doppler effect. The thermoelectric conversion model and the transient coupling code were validated by comparing them with the experimental values. The results show that the calculated results agree well with the experimental values.The start-up and shutdown of TOPAZ-II were simulated. The results show that the Doppler effect has a more significant influence at low temperatures. The change in fuel temperature relative to power is more gradual when the reactor is started than during shutdown. This is because thermal resistance impedes heat transfer between regions, and changes in thermal resistance between regions affect the rate of change in fuel temperature. A sharp variation in electrical power can be observed during both start-up and shutdown because of the thermoelectric conversion characteristics in the work region. The emitter temperature is more sensitive to electric power. Thermionic fuel starts supplying power to the electromagnetic pump at 2250 s during start-up, and the power supply to the electromagnetic pump stops at 1200 s during shutdown. In the later stages of start-up and shutdown, a peak in coolant temperature appears, which is caused by the relative change between mass flow rate and thermal power. The calculation results prove that TOPAZ-II has a sufficient safety margin during start-up and shutdown.

Process modeling of syngas conversion to ethanol and acetic acid via the production of dimethyl ether and its carbonylation

Process modeling of syngas conversion to ethanol and acetic acid via the production of dimethyl ether and its carbonylation

How macrostructures enhance droplet coalescence jumping: A mechanism study

Macrostructures exhibit excellent performance in coalescence-induced droplet jumping enhancement. Previous velocity prediction models were mainly established for flat plain surface, which usually set up the balance relationship between excess surface energy, kinetic energy and dissipation. But these models are no longer appropriate in macrostructure cases due to great variation of oscillational kinetic energy and gravitational potential energy caused by textures. In this work, coalescence-induced droplet jumping on macrostructures was investigated numerically on flat, sidewall, string, ridge and egg shape substrates. An energy conversion model based on energy efficiency rather than energy balance was established for departure velocity by proposing three sub-efficiencies to evaluate jumping enhancement. In this model, oscillational kinetic energy was distinguished from dissipation, gravitational potential energy induced by macrostructures were considered, and the real droplet morphology were used to calculate surface energy. By dividing the whole droplet jumping process into merge and contact stage, the influence of macrostructures on duration of each stage was closely connected to the macrostructure effects on dissipation and kinetic energy conversion. Pressure and velocity distributions near the macrostructure were also discussed to further explore the mechanism for droplet jumping enhancement. Further, the significant increase of initial surface energy for egg case points out a possible new approach to enhance jumping velocity. This model helps us understand the determinants of jumping enhancement with macrostructures and it also applies to flat plain.

Praying Truthfully: Sincerity and the Inducing of Belief

ABSTRACTIn a Jewish context, it seems, it is a naïve consensus view that in praying liturgically one aims to express to God, in the manner of ordinary, interpersonal conversation, those thoughts stated by the text. But on this ordinary conversation model (OCM), a problem of insincerity arises when, as commonly happens, the text states a claim the practitioner does not believe. The idea of redeeming one's prayer by reinterpretation is, I argue, incompatible with OCM. Another strategy, which finds some encouragement in Jewish tradition, is to try inducing the missing belief. I further argue, however, that for one's expression of a belief to be proper, in the sense of being authentic, this belief must be corroborated by the evolving, diachronically largely‐coherent understanding distinctive of one's person—a requirement which an induced, otherwise‐missing belief cannot fulfill. This, I suggest, provides some reason to seek a model of liturgical prayer different from OCM.

Deriving Ionospheric Sporadic E Intensity From FORMOSAT‐3/COSMIC and FY‐3C Radio Occultation Measurements

AbstractThe total electron content (TEC) profiles from the FORMOsa SATellite mission‐3/Constellation Observing System for the Meteorology, Ionosphere, and Climate (COSMIC) and the Fengyun‐3C (FY‐3C) radio occultation (RO) missions during 2006 and 2019 are used to establish the models for deriving the ionospheric sporadic E (Es) critical frequency (foEs). Smax is derived as the maximum vertical gradient of TEC disturbance, which is extracted from a RO TEC profile using the singular spectrum analysis method. Three candidate conversion models are obtained by fitting Smax with foEs observed by collocated ionosondes, and the one with the best performance, the COSMIC&amp;FY‐3C (CF) model, is applied for deriving foEs (foEsCF) from the TEC data of the two missions jointly. For comparison, the C model, which is of similar form as the CF model while being established without using the FY‐3C data, is applied for deriving foEs (foEsC) from the COSMIC TEC data only. The validations over four independent ionosonde stations demonstrate that mean improvements of 0.11 MHz (32%) in the absolute bias and 0.24 MHz (17%) in the root mean square error are obtained by foEsCF compared with foEsC. The spatiotemporal distributions of Es intensity during 2015 and 2019, presented respectively by foEsCF and foEsC, are further compared. The CF model provides more abundant and more detailed information about Es intensity distribution, which is most distinct at high latitudes where the contribution of FY‐3C RO TEC data is significant. Our results reveal that with the aging of the COSMIC satellites, FY‐3C TEC data is a valuable supplementary RO data source for Es‐related studies.

A STUDY OF SOCIAL SUCCESS FACTORS OF KM FOR CREATION OF NEW KNOWLEDGE IN HIGHER EDUCATION

Dearing report by Higher Education Funding Council for England, 2006, suggested that in the times of intensified global competition, high- level skills and knowledge would become pertinent and indispensable for the country’s economic success. The SECI model of knowledge conversion given by Nonaka and his colleagues, is extensively used across industries and nations to explore the knowledge creation dimension of knowledge management. Areas of KM such as creation of novel knowledge and knowledge as intellectual capital require a lot of work and consideration from both theoretical as well as applied perspectives. With this in mind, the present study seeks to explore the social enablers or success factors contributing to knowledge creation in the higher education institutions in India. The findings suggest that the social success factors in the study significantly impact the process of knowledge creation. Moreover, different social enablers contribute significantly to different modes of the process of conversion and creation of new knowledge. The pragmatic implication of the present study could guide institutions to assess the prevalence of knowledge creation practices and success factors to be emphasized more to increase knowledge conversion and creation with respect to their organization.