Predictions Of Theory Research Articles

Energy-saving and CO2 reduction strategies for new energy vehicles based on the integration approach of voluntary advocacy and system dynamics.

The low-carbon development of new energy vehicles (NEVs) is critical to achieving the goals of carbon peaking and carbon neutrality. As such, combining gray model theory with system dynamics (SD-GM) and based on the bidirectional-cycle prediction theory, we propose a NEV annual average mileage algorithm considering the impact of the epidemic in China, taking private cars as an example. Then, combining a voluntary advocacy strategy (VA) with the SD-GM theory (VA-SD-GM integration), we establish an energy-saving and carbon-reduction management model. To evaluate the proposed algorithm, we performed a dynamic simulation. The results indicate that the enhanced green scenario enabled significant energy-saving and CO2 reduction performance but would cause side effects in the long term. Compared with the enhanced green scenario, the linkage mode reduced the impact of parking space tension, the number of NEV trips, and the intensification of traffic congestion by approximately 33%, 50%, and 34%, respectively. It effectively suppressed the continuous increase in side effects and had a synergistic effect of carbon reduction, energy conservation, congestion alleviation, and side-effect reduction. The study provides a theoretical basis for optimizing the energy-saving and CO2 reduction path of NEVs.

Religious Environments, Governments, and the Density of Nonprofit Organizations

This study examines the impact of religious environments on the jurisdictional density of nonprofit organizations. It has been argued that religiosity can affect nonprofit activities by promoting prosocial attitudes, collectivism, collaborating and bonding, and business ethics. While prior research has investigated the effects of religiosity on various aspects associated with nonprofit activities, such as volunteering, generosity, nonprofit management, and prosocial attitudes, there remains a dearth of studies exploring the direct relationship between religious environment and the size of nonprofit sectors. Existing research yields mixed results with certain limitations. This research addresses these limitations and finds that a more vibrant religious environment contributes to a higher density of both religious and nonreligious nonprofit organizations. The study also finds that the effect is more pronounced in areas with a higher government presence. This finding is consistent with the prediction of interdependence theory but not government failure theory.

Optimized combined spectral calibration model based on single-constant Kubelka–Munk theory for color prediction of pre-colored cotton fiber blends

There is a complex interaction between primary fibers in pre-colored fiber blends, which leads to the impracticality of Duncan’s additive theory in the single-constant Kubelka–Munk (KM-1) model, resulting in poor accuracy for color prediction. This paper builds an optimized combined spectral calibration model based on the KM-1 function to solve this issue. This proposed model involves colored and non-colored parts with trained spectral coefficients, and was established to achieve a good matching between the predicted and actual ratio of the absorption coefficient K to the scattering coefficient S, K/ S of samples for color prediction from the perspective of the spectrum. Experimentally, five primary cotton fibers were selected and prepared into fiber blends to demonstrate its validation, and its optimal number of training samples was found to be only 14, which was much less than the general training sampling method using half of all samples directly. Finally, a remarkable average color difference of only 0.63 CIEDE2000 units for 74 test specimens was achieved on this optimized model, which was significantly lower than that of the KM-1 model (∼1.16), two-constant KM model (∼1.03), Stearns–Noechel model (∼1.16) and Friele model (∼1.11). The results indicate that the optimized model behaves well and could be applied in the color prediction of pre-colored fiber blends.

Personalized treatment selection via product partition models with covariates.

Precision medicine is an approach for disease treatment that defines treatment strategies based on the individual characteristics of the patients. Motivated by an open problem in cancer genomics, we develop a novel model that flexibly clusters patients with similar predictive characteristics and similar treatment responses; this approach identifies, via predictive inference, which one among a set of treatments is better suited for a new patient. The proposed method is fully model based, avoiding uncertainty underestimation attained when treatment assignment is performed by adopting heuristic clustering procedures, and belongs to the class of product partition models with covariates, here extended to include the cohesion induced by the normalized generalized gamma process. The method performs particularly well in scenarios characterized by considerable heterogeneity of the predictive covariates in simulation studies. A cancer genomics case study illustrates the potential benefits in terms of treatment response yielded by the proposed approach. Finally, being model based, the approach allows estimating clusters' specific response probabilities and then identifying patients more likely to benefit from personalized treatment.

The effects of individual heterogeneity on social contagions

Abstract Despite having significant effects on social contagions, individual heterogeneity has frequently been overlooked in earlier studies. To better understand the complexity of social contagions, a non-Markovian model incorporating heterogeneous social influence and adoption thresholds is introduced. For theoretical analysis, a generalized edge-based compartmental theory which considers the heterogeneities of social influence and adoption thresholds are developed. Focusing on the final adoption size, critical propagation probability and the phase transition type, social contagions for adoption thresholds follow normal distributions with various standard deviations, follow various distributions, and correlate with degrees are investigated. When thresholds follow normal distributions, a larger standard deviation results in a larger the final adoption size when the information propagation probability is relatively low. However, when the information propagation probability is relatively high, a larger standard deviation result in a smaller final adoption size. When thresholds follow various distributions, crossover phenomena in phase transition are observed in investigating the relationship of the final adoption size versus the average adoption threshold for some threshold distributions. When thresholds are correlated with degrees, similar crossover phenomena occur in investigating the relationship of the final adoption size versus the degree correlation index. Additionally, we find that increasing the heterogeneity of social influence suppresses the effects of adoption threshold heterogeneity on social contagions in the three cases. Our theory predictions agree well with the simulation results.

Study of the Long-Range Exchange Coupling in Nd-Fe-B/Ti/Fe Multilayered Structure

The exchange coupling between two ferromagnetic thin films, one with magnetically hard and the other with soft phases, separated by a thin non-magnetic layer, is studied. Nd-Fe-B/Ti/Fe thin film heterostructures were fabricated using DC magnetron sputtering on Si substrates, which were heated in situ at 650 °C using a house-built vacuum-compatible heater. The effect of the thickness of the Ti buffer layer and the annealing temperature on the formation of various phases of Nd-Fe-B was investigated. The effect of the thickness of the non-magnetic Ti spacer layer on the exchange coupling strength between the hard phase Nd-Fe-B ferromagnetic thin layer and the soft phase transition metal Fe layer was experimentally investigated. Hysteresis loops of multilayer thin films indicate an antiferromagnetic coupling was observed when the thickness of the spacer layer was 2 nm. This is within the range of an antiferromagnetic coupling calculation based on RKKY theory predictions.

Higher-order shear deformation theory for accurate prediction of vibration behavior of thick piezoelectric disks and design of efficient surface electrodes

A modified higher-order shear deformation plate theory (MHSDT) is proposed to predict the resonance frequencies and related mode shapes of piezoelectric disks of various thicknesses. The theory explicitly accounts for the effects of the electrical potential on the displacement of the disk. The equations of motion for the disk are derived using Hamilton's principle in conjunction with a variational approach. It is shown analytically that the distribution of the electrical field on the surface of the disk is quantitatively similar to the distribution of the sum of the total stresses within the disk. Thus, the deformation of the disk can be approximated directly from the stress distribution on the disk, thereby greatly simplifying the solution process. The validity of the proposed MHSDT is confirmed by comparing the solutions for the natural frequencies, displacement fields, and electric field distributions of the disk for various radius-to-thickness ratios and boundary conditions with those obtained using Reddy’s third-order plate theory and COMSOL finite element simulations, respectively. The results show that the solutions obtained from the MHSDT method are consistently closer to the exact solutions than those determined using Reddy’s third-order method. Finally, the linear relationship between the electric field and the flexural displacement of the disk is leveraged to design surface electrodes capable of exciting specific flexural mode shapes of the disk under a free boundary condition. The efficiency of the designed electrodes is experimentally demonstrated using an amplitude-fluctuation electronic speckle pattern interferometer (AF-ESPI).

An implicit leadership theory examination of cultural values as moderators of the relationship between destructive leadership and followers’ task performance

Destructive leadership and its effects on followers all over the world have been emphasized in cross-cultural research in recent years. Despite continued scholarly and practitioner interest in both destructive leadership and job performance due to its implications for cross-cultural research, we have an incomplete understanding of the impact of cultural context on the relationship between destructive leadership and followers’ task performance. We advance international business research by building and testing implicit leadership theory predictions about the impact of country-level cultural values as moderators of the relationship between destructive leadership and followers’ task performance. We test our hypotheses and research question with a meta-analytic dataset that includes respondents from 12 countries (k = 72, N = 20,878). Our results demonstrate that several of the Global Leadership and Organizational Behavior Effectiveness (GLOBE) framework’s cultural value dimensions moderate the relationship between destructive leadership and followers’ task performance across countries. The findings are driven by the strong predictive validity of the performance orientation, in-group collectivism, gender egalitarianism, and humane orientation cultural value dimensions. Our contributions are important because we generate nuanced knowledge about the independent, relative, and collective predictive validity of cultural values in explaining the strength of the relationship between destructive leadership and followers’ task performance across countries.

Behavioral biases of cryptocurrency investors: a prospect theory model to explain cryptocurrency returns

PurposeThe study aims to test prospect theory (PT) predictions in the cryptocurrency (CC) market. It proposes a new asset pricing model that explores the potential of prospect theory value (PTV) as a significant predictor of CC returns.Design/methodology/approachThe study comprehensively analyses a large sample set of 1,629 CCs, representing more than 95% of the CC market. The study uses a portfolio analysis approach, employing univariate and bivariate sorting techniques with equal-weighted and value-weighted portfolios. The study also employs ordinary least squares (OLS) regression, panel data methods and quantile regression (QR) to estimate the models.FindingsThis study demonstrates an average inverse relationship between PTV and CC returns. However, this relationship exhibits asymmetry across different quantiles, indicating that investor reactions vary based on market conditions. Moreover, PTV provides more robust predictions for smaller CCs characterized by high volatility and illiquidity. Notably, the findings highlight the dominant role of the probability weighting (PW) component in PT for predicting CC behaviors, suggesting a preference for lottery-like characteristics among CC investors.Originality/valueThe study is one of the early studies on CC price dynamics from the PT perspective. The study is the first to apply a QR approach to analyze the cross-section of CCs using a PT-based asset pricing model. The results shed light on CC investors' decision-making processes and risk perception, offering valuable insights to regulators, policymakers and market participants. From a practical perspective, a trading strategy centered around the PTV effect can be implemented.

Viscous dissipation and dynamics in simulations of rotating, stratified plane-layer convection

ABSTRACT Convection in stars and planets must be maintained against viscous and Ohmic dissipation. Here, we present the first systematic investigation of viscous dissipation in simulations of rotating, density-stratified plane layers of convection. Our simulations consider an anelastic ideal gas, and employ the open-source code Dedalus. We demonstrate that when the convection is sufficiently vigorous, the integrated dissipative heating tends towards a value that is independent of viscosity or thermal diffusivity, but depends on the imposed luminosity and the stratification. We show that knowledge of the dissipation provides a bound on the magnitude of the kinetic energy flux in the convection zone. In our non-rotating cases with simple flow fields, much of the dissipation occurs near the highest possible temperatures, and the kinetic energy flux approaches this bound. In the rotating cases, although the total integrated dissipation is similar, it is much more uniformly distributed (and locally balanced by work against the stratification), with a consequently smaller kinetic energy flux. The heat transport in our rotating simulations is in good agreement with results previously obtained for 3D Boussinesq convection, and approaches the predictions of diffusion-free theory.

Individual differences in internal models explain idiosyncrasies in scene perception

According to predictive processing theories, vision is facilitated by predictions derived from our internal models of what the world should look like. However, the contents of these models and how they vary across people remains unclear. Here, we use drawing as a behavioral readout of the contents of the internal models in individual participants. Participants were first asked to draw typical versions of scene categories, as descriptors of their internal models. These drawings were converted into standardized 3d renders, which we used as stimuli in subsequent scene categorization experiments. Across two experiments, participants' scene categorization was more accurate for renders tailored to their own drawings compared to renders based on others' drawings or copies of scene photographs, suggesting that scene perception is determined by a match with idiosyncratic internal models. Using a deep neural network to computationally evaluate similarities between scene renders, we further demonstrate that graded similarity to the render based on participants' own typical drawings (and thus to their internal model) predicts categorization performance across a range of candidate scenes. Together, our results showcase the potential of a new method for understanding individual differences – starting from participants' personal expectations about the structure of real-world scenes.

Carbon price interval prediction method based on probability density recurrence network and interval multi-layer perceptron

Low-carbon development is an inevitable choice under global climate change, and carbon markets have emerged accordingly. Carbon price forecasting is a crucial aspect of research on carbon-reduction strategies. Traditional forecasting methods are mostly focused on point-valued time series and contain limited information, which causes point forecasting to lose volatility information. Such limitations highlight the importance of proposing a precise interval-valued time series prediction system. To address this issue, we propose a carbon price interval forecasting framework based on a probability density recurrence network and interval multi-layer perceptron (MLPI) from the perspective of uncertainty in the presence of carbon price fluctuations. First, we convert the original data into a probability density sequence using kernel density estimation and then build the network. To extract valid information and exclude abnormal data in the carbon price sample data, we clean the data using network topology and link prediction theory; then, the MLPI model is used for interval forecasting. To verify the forecasting performance of this method, we conduct an empirical analysis using real interval-valued carbon futures price data from the EU carbon market and compare its forecasting precision with other models under the same conditions. The results show that the proposed carbon price interval prediction framework is superior to traditional methods in terms of forecasting precision.

The Impact of Capital Structure on the Performance of Serbian Manufacturing Companies: Application of Agency Cost Theory

This paper examines the impact of debt in the capital structure on agency costs and therefore on the performance of a company. The efficiency of companies was estimated using two parametric techniques: Ordinary Least Squares (OLS) methods and a Stochastic Frontier Analysis (SFA). The estimated efficiency represents a measure of (inverse) agency costs. Agency costs cause a lower level of efficiency compared to companies that have minimized these costs, and companies that reach the efficiency frontier, in the observed context of this research, are viewed as those that have minimized agency costs. A panel regression model was applied in order to determine the direction and intensity of the influence of leverage and control variables on the initially estimated efficiency of the company. The results of this research on Serbian manufacturing companies show the expected positive effect of capital structure (leverage) on the efficiency of the company, which is in accordance with the predictions of the agency cost theory. The contribution of this research is reflected in the application of efficiency as a performance indicator in the observed context of examining the theory of agency costs, bearing in mind that the measure of efficiency is closer to the theoretical view of these costs.

Field-view model for triboelectric nanogenerator motion superposition analysis

The precise value of the triboelectric nanogenerators’ (TENGs) energy output is hard to predict under coupled three-dimensional spatial motions in practical situations. In addition, motion monitoring based on TENGs in engineering applications requires in-depth support from basic theories. Herein, we introduce the applied field-view motion superposition output model (MSOM) of TENGs to address these challenges. Firstly, a comprehensive model for the field portrait and output prediction of the TENG with spatial motions based on basic physical and electrical theory is established through superposed motion decoupling. Secondly, a triboelectric signal spectrum model is provided to reveal the mechanisms of the TENG’s self-powered spatial motion frequency monitoring. The model validation experiments indicate a high degree of uniformity in practical measurements of the proposed MSOM model predictions. Likewise, the spectrum analysis of the TENG’s signals is verified to be an effective method for frequency monitoring and confirms a reliable observing error, which is less than 1.8%. When the TENG is equipped with three-dimensional motion traits, the MSOM model dramatically expands its physical theories for output prediction, conversion efficiency calculation, and motion monitoring.

ChatGPT on the Gravitational Redshift

This is my first attempt to communicate with the ChatGPT on the gravitational redshift. Chat GPT during our half-hour conversation reacted promptly to explain to me the basic information about the gravitational redshift. Chat GPT confirmed the unique position of Einstein’s gravitation theory which is very well confirmed by numerous experiments. It will be extremely difficult to propose a new gravitational theory for the Euclidean space. Such a theory should explain the existing data for Euclidean space. Moreover, it should define the limitations of the general theory of relativity and propose some experiments that will enable to compare predictions of both theories.

Modeling of Machine Learning-Based Extreme Value Theory in Stock Investment Risk Prediction: A Systematic Literature Review.

The stock market is heavily influenced by global sentiment, which is full of uncertainty and is characterized by extreme values and linear and nonlinear variables. High-frequency data generally refer to data that are collected at a very fast rate based on days, hours, minutes, and even seconds. Stock prices fluctuate rapidly and even at extremes along with changes in the variables that affect stock fluctuations. Research on investment risk estimation in the stock market that can identify extreme values is nonlinear, reliable in multivariate cases, and uses high-frequency data that are very important. The extreme value theory (EVT) approach can detect extreme values. This method is reliable in univariate cases and very complicated in multivariate cases. The purpose of this research was to collect, characterize, and analyze the investment risk estimation literature to identify research gaps. The literature used was selected by applying the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and sourced from Sciencedirect.com and Scopus databases. A total of 1107 articles were produced from the search at the identification stage, reduced to 236 in the eligibility stage, and 90 articles in the included studies set. The bibliometric networks were visualized using the VOSviewer software, and the main keyword used as the search criteria is "VaR." The visualization showed that EVT, the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models, and historical simulation are models often used to estimate the investment risk; the application of the machine learning (ML)-based investment risk estimation model is low. There has been no research using a combination of EVT and ML to estimate the investment risk. The results showed that the hybrid model produced better Value-at-Risk (VaR) accuracy under uncertainty and nonlinear conditions. Generally, models only use daily return data as model input. Based on research gaps, a hybrid model framework for estimating risk measures is proposed using a combination of EVT and ML, using multivariable and high-frequency data to identify extreme values in the distribution of data. The goal is to produce an accurate and flexible estimated risk value against extreme changes and shocks in the stock market. Mathematics Subject Classification: 60G25; 62M20; 6245; 62P05; 91G70.

Attention influences the effects of the previous form orientation on the current motion direction estimation

Recent studies have found that the estimates of motion directions are biased toward the previous form orientations, showing serial dependence, and the serial dependence does not involve cognitive abilities. In the current study, we conducted two experiments to investigate whether and how attention—a cognitive ability—affected the serial dependence. The results showed that serial dependence was present in the current study, reproducing the previous findings. Importantly, when the attentional load reduced the reliability (i.e., estimation accuracy and precision) of previous form orientations (Experiment 1), the serial dependence decreased, meaning that the biases of motion direction estimates toward previous form orientations were reduced; in contrast, when the attentional load reduced the reliability of current motion directions (Experiment 2), the serial dependence increased, meaning that the biases of motion direction estimates toward previous form orientations were increased. These trends were well consistent with the prediction of the Bayesian inference theory. Therefore, the current study revealed the involvement of attention in the serial dependence of current motion direction estimation on the previous form orientation, demonstrating that the serial dependence was cognitive and the attentional effect can be a Bayesian inference process, initially revealing its computational mechanism.

Research on Trajectory Tracking Control of a Semi-Trailer Train Based on Differential Braking

How to improve the driving performance of the vehicle while carrying out path tracking control has become a hot issue in current research. In this paper, an MPC (Model predictive control) path tracking control algorithm incorporating differential braking control is proposed. By establishing a vehicle dynamics model of a semi-trailer train, the model predictive control theory is adopted for path tracking. Then, the vehicle dynamics model, considering the additional yaw moment, is established to design the differential braking control strategy. Under low-speed working conditions, the PID (Proportional Integral Derivative) algorithm is used to solve the additional yaw moment with the yaw rate of the tractor traveling alone as the desired value. Under high-speed working conditions, the Fuzzy PID algorithm is used to solve the additional yaw moment with the control objective of reducing the articulation angle. Simulation models are built using MATLAB/Simulink, and TruckSim for numerical experimental validation. The numerical experimental results show that the differential braking control method proposed in this paper can improve the maneuverability of vehicles driving in low-speed conditions and the stability of vehicles driving in high-speed conditions without decreasing the precision of path tracking control.

Effective field theory bootstrap, large-N χPT and holographic QCD

We review the effective field theory (EFT) bootstrap by formulating it as an infinite-dimensional semidefinite program (SDP), built from the crossing symmetric sum rules and the S-matrix primal ansatz. We apply the program to study the large-N chiral perturbation theory (χPT) and observe excellent convergence of EFT bounds between the dual (rule-out) and primal (rule-in) methods. This convergence aligns with the predictions of duality theory in SDP, enabling us to analyze the bound states and resonances in the ultra-violet (UV) spectrum. Furthermore, we incorporate the upper bound of unitarity to uniformly constrain the EFT space from the UV scale M using the primal method, thereby confirming the consistency of the large-N expansion. In the end, we translate the large-N χPT bounds to constrain the higher derivative corrections of holographic QCD models.

CrackYOLO: Rural Pavement Distress Detection Model with Complex Scenarios

The maintenance level of rural roads is relatively low, and the automated detection of pavement distress is easily affected by the shadows of rows of trees, weeds, soil, and distress object scale disparities; this makes it difficult to accurately evaluate the distress conditions of the pavement. To solve the above problems, this study specifically designed a target detection network called Crack Convolution (CrackYOLO) for pavement crack extraction on rural roads. CrackYOLO is based on an improved YOLOv5. The shadow created by rows of trees leads to the loss of crack features in the feature extraction and downsampling stages of the network; therefore, CrackConv and Adapt-weight Down Sample (ADSample) were introduced to strengthen the ability to locate and identify cracks. Due to disturbances such as soil and weeds, which cause the extraction of more redundant features, the Channel And Spatial mixed attention mechanism (CAS) was introduced to enhance crack weight. To address the issue of missed detections of fine cracks due to significant scale variations in crack objects in the same image, Multi Scale Convolution (MSConv) and Multi Scale Head (MSHead) were incorporated during the feature fusion and prediction inference stages of the network, thereby improving the multi-scale detection performance. In order to verify the effectiveness of the proposed method, the detection accuracy of CrackYOLO when used on the LNTU_RDD_NC dataset was determined to be 9.99%, 12.79%, and 4.61% higher than that of the current pavement crack detection models YOLO-LWNet, Faster R-CNN, and YOLOv7. At the same time, we compare the above model on public datasets of different scenarios, and the experimental results show that CrackYOLO has the same strong performance in urban roads and other scenarios.