Identifying multidisciplinary problems from scientific publications based on a text generation method

Purpose:A text generation based multidisciplinary problem identification method is proposed,which does not rely on a large amount of data annotation.Design/methodology/approach:The proposed method first identifies the research objective types and disciplinary labels of papers using a text classification technique;second,it generates abstractive titles for each paper based on abstract and research objective types using a generative pre-trained language model;third,it extracts problem phrases from generated titles according to regular expression rules;fourth,it creates problem relation networks and identifies the same problems by exploiting a weighted community detection algorithm;finally,it identifies multidisciplinary problems based on the disciplinary labels of papers.Findings:Experiments in the“Carbon Peaking and Carbon Neutrality”field show that the proposed method can effectively identify multidisciplinary research problems.The disciplinary distribution of the identified problems is consistent with our understanding of multidisciplinary collaboration in the field.Research limitations:It is necessary to use the proposed method in other multidisciplinary fields to validate its effectiveness.Practical implications:Multidisciplinary problem identification helps to gather multidisciplinary forces to solve complex real-world problems for the governments,fund valuable multidisciplinary problems for research management authorities,and borrow ideas from other disciplines for researchers.Originality/value:This approach proposes a novel multidisciplinary problem identification method based on text generation,which identifies multidisciplinary problems based on generative abstractive titles of papers without data annotation required by standard sequence labeling techniques.

REGULARITY PROPERTIES OF AN IDENTIFICATION PROBLEM FOR GEOTHERMAL RESERVIOR EXPLOITATION

The problem of determining the pass on heat coefficient of the water-bearing stratum in geothermal reservior exploitation is investigated using the regularised output-least-square formulation. The regularity properties of the coefficient is obtained.

Finding the Time-dependent Term in 2D Heat Equation from Nonlocal Integral Conditions

The aim of this paper is to find the time-dependent term numerically in a two-dimensional heat equation using initial and Neumann boundary conditions and nonlocal integrals as over-determination conditions.This is a very interesting and challenging nonlinear inverse coefficient problem with important applications in various fields ranging from radioactive decay,melting or cooling processes,electronic chips,acoustics and geophysics to medicine.Unique solvability theo-rems of these inverse problems are supplied.However,since the problems are still ill-posed(a small modification in the input data can lead to bigger impact on the ultimate result in the output solution)the solution needs to be regularized.Therefore,in order to obtain a stable solution,a regularized objective function is minimized in order to retrieve the unknown coefficient.The two-dimensional inverse problem is discretized using the forward time central space(FTCS)finite-difference method(FDM),which is conditionally stable and recast as a non-linear least-squares minimization of the Tikhonov regularization function.Numerically,this is effectively solved using the MATLAB subroutine lsqnonlin.Both exact and noisy data are inverted.Numerical results for a few benchmark test examples are presented,discussed and assessed with respect to the FTCS-FDM mesh size discretisation,the level of noise with which the input data is contaminated,and the choice of the regularization parameter is discussed based on the trial and error technique.

MULTIPLE PARAMETERS IDENTIFICATION PROBLEMS IN RESISTIVITY WELL-LOGGING

In petroleum exploitation, the main aim of resistivity well-logging is to determine the resistivity of the layers by measuring the potential on the electrodes. This mathematical problem can be described as an inverse problem for the elliptic equivalued surface boundary value problem. In this paper, the author gets the expression of the derivative functions of the potential on the electrodes with respect to the resistivity of the layers. This allows us to solve the identification problem of the resistivity of the layers.

Volterra filter modeling of a nonlinear discrete-time system based on a ranked differential evolution algorithm

This paper presents a ranked differential evolution(RDE) algorithm for solving the identification problem of nonlinear discrete-time systems based on a Volterra filter model. In the improved method, a scale factor, generated by combining a sine function and randomness, effectively keeps a balance between the global search and the local search. Also, the mutation operation is modified after ranking all candidate solutions of the population to help avoid the occurrence of premature convergence. Finally, two examples including a highly nonlinear discrete-time rational system and a real heat exchanger are used to evaluate the performance of the RDE algorithm and five other approaches. Numerical experiments and comparisons demonstrate that the RDE algorithm performs better than the other approaches in most cases.