General laws of funding for scientific citations:how citations change in funded and unfunded research between basic and applied sciences

Purpose:The goal of this study is to analyze the relationship between funded and unfunded papers and their citations in both basic and applied sciences.Design/methodology/approach:A power law model analyzes the relationship between research funding and citations of papers using 831,337 documents recorded in the Web of Science database.Findings:The original results reveal general characteristics of the diffusion of science in research fields:a)Funded articles receive higher citations compared to unfunded papers in journals;b)Funded articles exhibit a super-linear growth in citations,surpassing the increase seen in unfunded articles.This finding reveals a higher diffusion of scientific knowledge in funded articles.Moreover,c)funded articles in both basic and applied sciences demonstrate a similar expected change in citations,equivalent to about 1.23%,when the number of funded papers increases by 1%in journals.This result suggests,for the first time,that funding effect of scientific research is an invariant driver,irrespective of the nature of the basic or applied sciences.Originality/value:This evidence suggests empirical laws of funding for scientific citations that explain the importance of robust funding mechanisms for achieving impactful research outcomes in science and society.These findings here also highlight that funding for scientific research is a critical driving force in supporting citations and the dissemination of scientific knowledge in recorded documents in both basic and applied sciences.Practical implications:This comprehensive result provides a holistic view of the relationship between funding and citation performance in science to guide policymakers and R&D managers with science policies by directing funding to research in promoting the scientific development and higher diffusion of results for the progress of human society.

An Exact Analysis of the Fundamental and First Higher Order Mode in Graded Index Fibers with Direct Power Series Method

An exact and fast analytic method based on power series is established to predict the modal field distributions, Petermann-2 spot size, the normalized propagation constant corresponding to fundamental and first higher order mode in graded index fibers with any arbitrary power law profile. The variation of normalized cut-off frequencies of some LPlm modes in graded index fibers with different profile exponents are also shown here and an empirical relation between them is determined.

Probabilistic small signal stability analysis of power system with wind power and photovoltaic power based on probability collocation method

Recently, with increasing improvements in the penetration of wind power and photovoltaic power in the world, probabilistic small signal stability analysis(PSSSA) of a power system consisting of multiple types of renewable energy has become a key problem. To address this problem, this study proposes a probabilistic collocation method(PCM)-based PSSSA for a power system consisting of wind farms and photovoltaic farms. Compared with the conventional Monte Carlo method, the proposed method meets the accuracy and precision requirements and greatly reduces the computation; therefore, it is suitable for the PSSSA of this power system. Case studies are conducted based on a 4-machine 2-area and New England systems, respectively. The simulation results show that, by reducing synchronous generator output to improve the penetration of renewable energy, the probabilistic small signal stability(PSSS) of the system is enhanced. Conversely, by removing part of the synchronous generators to improve the penetration of renewable energy, the PSSS of the system may be either enhanced or deteriorated.

Dynamic analysis of a rotating tapered cantilever Timoshenko beam based on the power series method

The mathematical modeling of a rotating tapered Timoshenko beam with preset and pre-twist angles is constructed. The partial differential equations governing the six degrees, i.e., three displacements in the axial, flapwise, and edgewise directions and three cross-sectional angles of torsion, flapwise bending, and edgewise bending, are obtained by the Euler angle descriptions. The power series method is then used to inves- tigate the natural frequencies and the corresponding complex mode functions. It is found that all the natural frequencies are increased by the centrifugal stiffening except the twist frequency, which is slightly decreased. The tapering ratio increases the first transverse, torsional, and axial frequencies, while decreases the second transverse frequency. Because of the pre-twist, all the directions are gyroscopically coupled with the phase differences among the six degrees.

Effects of material properties on power spinning process of parts with transverse inner rib

Material properties of blank have a great effect on power spinning process of aluminum alloy parts with transverse inner rib.By using finite element(FE) and Taguchi method,the effects and significance of five key material parameters,namely,anisotropic index in thickness direction,yield strength,hardening exponent,strengthening factor and elastic modulus on the formability of inner rib,tendency of wall fracture and degree of inhomogeneous deformation of finished spun parts were obtained.The achievements provide an important guide for selecting reasonable spinning material,and are very significant for the optimum design and precision control of power spinning process of parts with transverse inner rib.

Multi-train modeling and simulation integrated with traction power supply solver using simplified Newton–Raphson method

Multi-train modeling and simulation plays a vital role in railway electrification during operation and planning phase. Study of peak power demand and energy consumed by each traction substation needs to be deter- mined to verify that electrical energy flowing in its railway power feeding system is appropriate or not. Gauss-Seidel, conventional Newton-Raphson, and current injection methods are well-known and widely accepted as a tool for electrical power network solver in DC railway power supply study. In this paper, a simplified Newton-Raphson method has been proposed. The proposed method employs a set of current-balance equations at each electrical node instead of the conventional power-balance equation used in the conventional Newton-Raphson method. This concept can remarkably reduce execution time and computing complexity for multi-train simulation. To evaluate its use, Sukhumvit line of Bangkok transit system （BTS） of Thai- land with 21.6-km line length and 22 passenger stopping stations is set as a test system. The multi-train simulation integrated with the proposed power network solver is developed to simulate 1-h operation service of selected 5-min headway. From the obtained results, the proposed method is more efficient with approximately 18 % faster than the conventional Newton-Raphson method and just over 6 % faster than the current injection method.

On power series statistical convergence and new uniform integrability of double sequences

In the present paper,we mostly focus on P_(p)^(2)-statistical convergence.We will look into the uniform integrability via the power series method and its characterizations for double sequences.Also,the notions of P_(p)^(2)-statistically Cauchy sequence,P_(p)^(2)-statistical boundedness and core for double sequences will be described in addition to these findings.

Research on entropy weight variation evaluation method for wind power clusters based on dynamic layered sorting

This paper presents an evaluation method for the entropy-weighting of wind power clusters that comprehensively evaluates the allocation problems of wind power clusters by considering the correlation between indicators and the dynamic performance of weight changes.A dynamic layered sorting allocation method is also proposed.The proposed evaluation method considers the power-limiting degree of the last cycle,the adjustment margin,and volatility.It uses the theory of weight variation to update the entropy weight coefficients of each indicator in real time,and then performs a fuzzy evaluation based on the membership function to obtain intuitive comprehensive evaluation results.A case study of a large-scale wind power base in Northwest China was conducted.The proposed evaluation method is compared with fixed-weight entropy and principal component analysis methods.The results show that the three scoring trends are the same,and that the proposed evaluation method is closer to the average level of the latter two,demonstrating higher accuracy.The proposed allocation method can reduce the number of adjustments made to wind farms,which is significant for the allocation and evaluation of wind power clusters.

Maximum Entropy Empirical Likelihood Methods Based on Laplace Transforms for Nonnegative Continuous Distribution with Actuarial Applications

Maximum entropy likelihood (MEEL) methods also known as exponential tilted empirical likelihood methods using constraints from model Laplace transforms (LT) are introduced in this paper. An estimate of overall loss of efficiency based on Fourier cosine series expansion of the density function is proposed to quantify the loss of efficiency when using MEEL methods. Penalty function methods are suggested for numerical implementation of the MEEL methods. The methods can easily be adapted to estimate continuous distribution with support on the real line encountered in finance by using constraints based on the model generating function instead of LT.

Review of the Analysis and Suppression for High-frequency Oscillations of the Grid-connected Wind Power Generation System

High-frequency oscillation(HFO)of gridconnected wind power generation systems(WPGS)is one of the most critical issues in recent years that threaten the safe access of WPGS to the grid.Ensuring the WPGS can damp HFO is becoming more and more vital for the development of wind power.The HFO phenomenon of wind turbines under different scenarios usually has different mechanisms.Hence,engineers need to acquire the working mechanisms of the different HFO damping technologies and select the appropriate one to ensure the effective implementation of oscillation damping in practical engineering.This paper introduces the general assumptions of WPGS when analyzing HFO,systematically summarizes the reasons for the occurrence of HFO in different scenarios,deeply analyses the key points and difficulties of HFO damping under different scenarios,and then compares the technical performances of various types of HFO suppression methods to provide adequate references for engineers in the application of technology.Finally,this paper discusses possible future research difficulties in the problem of HFO,as well as the possible future trends in the demand for HFO damping.

Power Transformer No-Load Loss Prediction with FEM Modeling and Building Factor Optimization

Estimation of power transformer no-load loss is a critical issue in the design of distribution transformers. Any deviation in estimation of the core losses during the design stage can lead to a financial penalty for the transformer manufacturer. In this paper an effective and novel method is proposed to determine all components of the iron core losses applying a combination of the empirical and numerical techniques. In this method at the first stage all computable components of the core losses are calculated, using Finite Element Method (FEM) modeling and analysis of the transformer iron core. This method takes into account magnetic sheets anisotropy, joint losses and stacking holes. Next, a Quadratic Programming (QP) optimization technique is employed to estimate the incomputable components of the core losses. This method provides a chance for improvement of the core loss estimation over the time when more measured data become available. The optimization process handles the singular deviations caused by different manufacturing machineries and labor during the transformer manufacturing and overhaul process. Therefore, application of this method enables different companies to obtain different results for the same designs and materials employed, using their historical data. Effectiveness of this method is verified by inspection of 54 full size distribution transformer measurement data.

One Additive Diophantine Inequality with Mixed Powers 2 and 4

In this paper, it is shown that： if λ1 ,……λs axe nonzero real numbers, not all of the same sign, such that A1/A2 is irrational, then for any real number η and ε 〉 0 the inequality ｜λ1x1^2 ＋ λ2x2^2＋ λ3x3^4＋ λsx3^4＋……λsx8^4 ＋η〈 ε has infinitely many solutions in positive integers x1,... ,xs.

Optimizing Average Electric Power During the Charging of Lithium-Ion Batteries Through the Taguchi Method

In recent times, lithium-ion batteries have been widely used owing to their high energy density, extended cycle lifespan, and minimal self-discharge rate. The design of high-speed rechargeable lithium-ion batteries faces a significant challenge owing to the need to increase average electric power during charging. This challenge results from the direct influence of the power level on the rate of chemical reactions occurring in the battery electrodes. In this study, the Taguchi optimization method was used to enhance the average electric power during the charging process of lithium-ion batteries. The Taguchi technique is a statistical strategy that facilitates the systematic and efficient evaluation of numerous experimental variables. The proposed method involved varying seven input factors, including positive electrode thickness, positive electrode material, positive electrode active material volume fraction, negative electrode active material volume fraction, separator thickness, positive current collector thickness, and negative current collector thickness. Three levels were assigned to each control factor to identify the optimal conditions and maximize the average electric power during charging. Moreover, a variance assessment analysis was conducted to validate the results obtained from the Taguchi analysis. The results revealed that the Taguchi method was an eff ective approach for optimizing the average electric power during the charging of lithium-ion batteries. This indicates that the positive electrode material, followed by the separator thickness and the negative electrode active material volume fraction, was key factors significantly infl uencing the average electric power during the charging of lithium-ion batteries response. The identification of optimal conditions resulted in the improved performance of lithium-ion batteries, extending their potential in various applications. Particularly, lithium-ion batteries with average electric power of 16 W and 17 W during charging were designed and simulated in the range of 0-12000 s using COMSOL Multiphysics software. This study efficiently employs the Taguchi optimization technique to develop lithium-ion batteries capable of storing a predetermined average electric power during the charging phase. Therefore, this method enables the battery to achieve complete charging within a specific timeframe tailored to a specificapplication. The implementation of this method can save costs, time, and materials compared with other alternative methods, such as the trial-and-error approach.

Influence of heat loss through probe electrical leads on thermal conductivity measurement with TPS method

The transient plane source(TPS)method is developed recently to measure the thermal conductivity of materials.In the measurement,the heating power is influenced by the heat which is transferred via the probe electrical leads.This fact further influences the measurement accuracy of thermal conductivity.To solve this problem,the influence of heat loss through the electrical leads on the heating power is studied theoretically.The mathematical formula of heat loss is deduced,and the corresponding correction model is presented.A series of measurement experiments on different materials have been conducted by using the hot disk thermal constant analyzer.The results show that the influence of the heat loss on the measurement is sensitive to different test materials and probes with different sizes.When the thermal conductivity of the material is greater than 0.2 W/(m·K),the influence of the heat loss is less than 0.16%,which can be ignored.As to the lower thermal conductivity materials,it is necessary to compensate the heat loss through the electrical leads,and the accuracy of thermal conductivity measurement can be effectively improved.

Optimal Design of Thermal Dissipation for the Array Power LED by using the RSM with Genetic Algorithm

In accordance with the enhancement for luminous efficiency improving, LED (Light Emitting Diode) has been gradually developed by combining the characteristics of small volume, impact resistance, good reliability, long life, low power consumption with multiple purposes for energy saving and environmental protection. Therefore, the array LED has been widely applied in human livings nowadays. This study applies the finite element analysis software ANSYS to analyze the thermal behavior of the array power LED work lamp which is modeled by four same-size LED with MCPCB (Metal Core Print Circuit Board) mounted on a base heat-sink. The Flotran heat flow analysis is applied to obtain the natural convection of air coefficient, while the convection value can be confirmed by the iterative method since it is set as the boundary condition for ANSYS thermal analysis to obtain the temperature distribution, accordingly the chip junction temperature and the base heat-sink temperature were followed through experiments in order to check if the simulation results meet the design requirements and coincide with the power LED product design specification. Prior to the optimal design process for chip junction temperature, the most significant parameters were first chosen by the fractional factorial design. The regressive models were respectively setup by the dual response surface method (RSM) and the mixed response surface method. Furthermore, the genetic algorithm combined with response surface method was applied to acquire the optimal design parameters, and the results were obtained from both methods, which are reviewed for comparison. Afterwards, the mixed response surface method is adopted to investigate the effects of interactions among various factors on chip junction temperature. In conclusion, it is found that the thermal conductivity of MCPCB and the height of base heat-sink are the two major significant factors. In addition, the interactive effects between chip size and thermal conductivity of chip adhesion layer are acknowledged as the most significant interaction influenced on the chip junction temperature.

Index system and methods for comprehensive assessment of cross-border power grid interconnection projects

With the global economy integration and progress in energy transformation,it has become a general trend to surpass national boundaries to achieve wider and optimal energy resource allocations.Consequently,there is a critical n eed to adopt scie ntific approaches in assessi ng cross-border power grid interconnection projects.First,con sidering the promotion of large-scale renewable energy resources and improvements in system adequacy,a comprehensive assessment index system,including costs,socio-economic benefits,environmental benefits,and technical benefits,is established in this study.Second,a synthetic assessment framework is proposed for cross-border power grid interconnection projects based on the index system comprising cost-benefit analysis,with market and network simulations,iterative methods for indicator weight evaluation,and technique for order preferenee by similarity to an ideal solution(TOPSIS)method for the project rankings.Fin ally,by assessi ng and comparing three cross-border projects betwee n Europe and Asia,the proposed index system and assessment framework have been proved to be effective and feasible;the results of this system can thus support investment decision-making related to such projects in the future.

Physics-Informed AI Surrogates for Day-Ahead Wind Power Probabilistic Forecasting with Incomplete Data for Smart Grid in Smart Cities

Due to the high inherent uncertainty of renewable energy,probabilistic day-ahead wind power forecasting is crucial for modeling and controlling the uncertainty of renewable energy smart grids in smart cities.However,the accuracy and reliability of high-resolution day-ahead wind power forecasting are constrained by unreliable local weather prediction and incomplete power generation data.This article proposes a physics-informed artificial intelligence(AI)surrogates method to augment the incomplete dataset and quantify its uncertainty to improve wind power forecasting performance.The incomplete dataset,built with numerical weather prediction data,historical wind power generation,and weather factors data,is augmented based on generative adversarial networks.After augmentation,the enriched data is then fed into a multiple AI surrogates model constructed by two extreme learning machine networks to train the forecasting model for wind power.Therefore,the forecasting models’accuracy and generalization ability are improved by mining the implicit physics information from the incomplete dataset.An incomplete dataset gathered from a wind farm in North China,containing only 15 days of weather and wind power generation data withmissing points caused by occasional shutdowns,is utilized to verify the proposed method’s performance.Compared with other probabilistic forecastingmethods,the proposed method shows better accuracy and probabilistic performance on the same incomplete dataset,which highlights its potential for more flexible and sensitive maintenance of smart grids in smart cities.

The invariant of the stiffness filter function with the weight filter function of the power function form

Based on the independent, continuous and map- ping （ICM） method and homogenization method, a research model is constructed to propose and deduce a theorem and corollary from the invariant between the weight filter func- tion and the corresponding stiffness filter function of the form of power function. The efficiency in searching for op- timum solution will be raised via the choice of rational filter functions, so the above mentioned results are very important to the further study of structural topology optimization.

Multislice behavioral modeling based on envelope domain for power amplifiers

An envelope domain multislice behavioral modeling is introduced. The tradition AM-AM and AM- PM characteristics of power amplifiers axe extended to envelope domain and base-band filter is applied to distortion complex envelope signal for description of the envelope memory effect. Using traditional one and two-tone tests, the coefficients of nonlinear model and the FIR filter can be extracted. At last the model has been applied to a 10 W WCDMA Power amplifier to predict its output signal. And simulation results show that the model output conforms very well to the traditional transistor level simulation results.

Scientific Development of Power Sector Led by Plan EIA in a New Environmental Protection Situation

Environmental problems like haze have brought great pressure to current environmental protection management. On the one hand, plan EIA should be promoted further in all industries; on the other hand, the current achievements and methods of plan EIA should be summarized and innovated. And the EIA supervision and project access can be improved further through the coordination between plan EIA and project EIA. In this study, from the three levels of plan EIA policy framework, five problems of plan EIA in power sector were analyzed according to the current situation of plan EIA in power sector. Based on the present achievements, five macroscopic suggestions were proposed from the view of innovation accord- ing to the new environmental protection situation and requirements at present.