Public acceptability is crucial for the effectiveness of policy implementation. The carbon trading market is widely adopted by many countries and regions to achieve carbon neutrality and mitigate climate change. Our paper utilizes China's carbon trading market as a quasi-natural experiment, drawing on microdata from the China Residential Energy Consumption Survey to analyze the policy's impact on public acceptance of carbon pricing. We find that the carbon trading market significantly reduces the acceptability of carbon prices among households working in carbon-related industries in the pilot areas. This conclusion is still valid after a series of robustness checks. Regarding the mechanism of influence, the carbon trading market raises households' perceived costs, mainly reflected in the negative impact of rising product prices and increasing living costs. Finally, enhancing public perception of carbon, improving the distribution effect and decreasing the information asymmetry of the policy implementation can improve public acceptability of carbon prices.

The recently discovered social place cells and grid cells in hippocampal formation are believed to be the neural basis underlying relative navigation of conspecifics. In this paper, we propose a new brain-inspired relative navigation model in a large-scale 3D environment for collective UAVs that translates the neurodynamics of the social place cell–grid cell circuit to robotic relative navigation algorithm for the first time. Our approach comprises three key parts: (1) a 3D isotropic Gaussian function-based cube social place cell network (cube-SPCNet), (2) a 3D continuous attractor neural network-based cube grid cell network (cube-GCNet), and (3) a population vector-based neural decoding module. The resulting brain-inspired relative navigation model incorporates the good relative information abstraction capabilities of the cube-SPCNet with the powerful temporal filtering capabilities of the cube-GCNet, yielding robustness and accuracy performance improvement for relative navigation. Experimental results show the new method can provide more robust and precise relative navigation results than its conventional counterpart, displaying a possible brain-inspired solution for relative navigation enhancement for collective UAVs.

Bronze mou vessels appear in Shu tombs in south-west China during the Eastern Zhou period (c. 771–256 BC). Examination of these vessels reveals major changes in the supply of metal and alloying technology in the Shu State, throwing new light on the social impact of the Qin conquest and later unification of China.

The previous study indicated that ubiquitination is involved in the freezing tolerance of hydrated seeds. Parthenolide (PN), inducing the ubiquitination of MDM2, an E3 ring-finger ubiquitin ligase, adversely affects the freezing tolerance of hydrated lettuce seeds. Therefore, a proteomics analysis was conducted to identify PN's targets in hydrated seeds exposed to cooling conditions. Several pathways, including oxidative phosphorylation (KEGG00190), amino sugar and nucleotide sugar metabolism (KEGG00520), and biosynthesis of nucleotide sugars (KEGG01250), were enriched in the PN treatment under slow-cooling conditions (3°C h−1, P < 0.05). Among the proteins in oxidative phosphorylation, the expression of NADH dehydrogenases and ATP synthases (ATPsyn) decreased in PN treatment. In contrast, uncoupling proteins increased after PN treatment, which led to the dissociation of the electron transport chain from ATP synthesis. Treatments with rotenone, dicoumarol, and oligomycin (i.e., oxidative phosphorylation inhibitors) decreased the survival rate of hydrated seeds under freezing conditions, which indicated that energy metabolism was related to the freezing tolerance of hydrated seeds. The predicted interactions between PN and MDM2-like proteins of Lactuca indicated that LsMDM2-5 forms two potential hydrogen bonds with PN. Furthermore, based on AlphaFold predictions and yeast 2-hybrid results, MDM2-5 might interact directly with NADH2. The knockdown of MDM2-5 by RNAi caused a higher level of NADH2 and ATPsyn and a higher freezing tolerance of hydrated seeds. This indicated that MDM2 played negative roles in regulating ATP synthesis and freezing tolerance of hydrated seeds.

Rhopalosiphum padi is an important grain pest, causing severe losses during crop production. As a systemic insecticide, flonicamid can control piercing-sucking pests efficiently. In our study, the lethal effects of flonicamid on the biological traits of R. padi were investigated via a life table approach. Flonicamid is highly efficiently toxic to R. padi, with an LC50 of 9.068 mg L−1. The adult longevity and fecundity of the R. padi F0 generation were markedly reduced under the LC25 and LC50 concentrations of flonicamid exposure. In addition, negative transgenerational effects on R. padi were observed under exposure to lethal concentrations of flonicamid, with noticeable decreases in the reproductive period, adult longevity, total longevity, and total fecundity of the F1 generation under the LC25 concentration of flonicamid. Furthermore, the third nymph stage (N3), preadult stage, duration of the adult pre-reproductive period, duration of the total pre-reproductive period, reproductive period, adult longevity, total longevity, and total fecundity of the F1 generation were significantly lower under treatment with the LC50 concentration of flonicamid. The life table parameters were subsequently analysed, revealing that the intrinsic rate of increase (rm) and the net reproductive rate (R0) were significantly lower but that the finite rate of increase (λ) and the mean generation time (T) were not significantly different under the LC25 and LC50 concentrations of flonicamid. These data are beneficial for grain aphid control and are critical for exploring the role of flonicamid in the integrated management of this key pest.

Traditional bulky and complex control devices such as remote control and ground station cannot meet the requirement of fast and flexible control of unmanned aerial vehicles (UAVs) in complex environments. Therefore, a data glove based on multi-sensor fusion is designed in this paper. In order to achieve the goal of gesture control of UAVs, the method can accurately recognize various gestures and convert them into corresponding UAV control commands. First, the wireless data glove fuses flexible fiber optic sensors and inertial sensors to construct a gesture dataset. Then, the trained neural network model is deployed to the STM32 microcontroller-based data glove for real-time gesture recognition, in which the convolutional neural network-Attention mechanism (CNN-Attention) network is used for static gesture recognition, and the convolutional neural network-bidirectional long and short-term memory (CNN-Bi-LSTM) network is used for dynamic gesture recognition. Finally, the gestures are converted into control commands and sent to the vehicle terminal to control the UAV. Through the UAV simulation test on the simulation platform, the average recognition accuracy of 32 static gestures reaches 99.7%, and the average recognition accuracy of 13 dynamic gestures reaches 99.9%, which indicates that the system’s gesture recognition effect is perfect. The task test in the scene constructed in the real environment shows that the UAV can respond to the gestures quickly, and the method proposed in this paper can realize the real-time stable control of the UAV on the terminal side.

Reducing nitrogen (N) fertilizer application is a sustainable practice in rice production. The effects of reducing N fertilizer input on grain yield and rice quality of early- and late-season dual-use rice (ELDR) in South China remain uncertain. Therefore, a short-term field trial was conducted with a high-yielding ELDR cultivar (Yuehesimiao, YHSM) and a low-yielding ELDR cultivar (Meixiangzhan 2, MXZ). The rice was cultivated with a 20% reduced N application rate (RN2), a 10% reduced rate (RN1) and the conventional N application rate (CN). In the early season, compared to CN, RN2 reduced the grain yield of YHSM and MXZ by an average of 16.1 and 6.6%, respectively, while RN1 lowered YHSM grain yield by 11.2% on average and had no effect on MXZ yield. In addition, RN2 decreased the milling and eating qualities of the two cultivars in the early season, while RN1 did not alter their milling, appearance or eating qualities. In the late season, neither RN2 nor RN1 affected grain yield or rice quality for both cultivars. Therefore, short-term reductions of 10 and 20% in N application could maintain grain yield and rice quality at current N fertilizer application rates in the late season. However, the early-season results only recommended a 10% reduced N fertilizer application rate for low-yielding ELDR cultivars to sustain grain yield and rice quality. The findings of this study can provide a theoretical basis for N management of ELDR in South China.

Item-level response time (RT) data can be conveniently collected from computer-based test/survey delivery platforms and have been demonstrated to bear a close relation to a miscellany of cognitive processes and test-taking behaviors. Individual differences in general processing speed can be inferred from item-level RT data using factor analysis. Conventional linear normal factor models make strong parametric assumptions, which sacrifices modeling flexibility for interpretability, and thus are not ideal for describing complex associations between observed RT and the latent speed. In this paper, we propose a semiparametric factor model with minimal parametric assumptions. Specifically, we adopt a functional analysis of variance representation for the log conditional densities of the manifest variables, in which the main effect and interaction functions are approximated by cubic splines. Penalized maximum likelihood estimation of the spline coefficients can be performed by an Expectation-Maximization algorithm, and the penalty weight can be empirically determined by cross-validation. In a simulation study, we compare the semiparametric model with incorrectly and correctly specified parametric factor models with regard to the recovery of data generating mechanism. A real data example is also presented to demonstrate the advantages of the proposed method.

In item response theory (IRT), it is often necessary to perform restricted recalibration (RR) of the model: A set of (focal) parameters is estimated holding a set of (nuisance) parameters fixed. Typical applications of RR include expanding an existing item bank, linking multiple test forms, and associating constructs measured by separately calibrated tests. In the current work, we provide full statistical theory for RR of IRT models under the framework of pseudo-maximum likelihood estimation. We describe the standard error calculation for the focal parameters, the assessment of overall goodness-of-fit (GOF) of the model, and the identification of misfitting items. We report a simulation study to evaluate the performance of these methods in the scenario of adding a new item to an existing test. Parameter recovery for the focal parameters as well as Type I error and power of the proposed tests are examined. An empirical example is also included, in which we validate the pediatric fatigue short-form scale in the Patient-Reported Outcome Measurement Information System (PROMIS), compute global and local GOF statistics, and update parameters for the misfitting items.

In applications of item response theory (IRT), it is often of interest to compute confidence intervals (CIs) for person parameters with prescribed frequentist coverage. The ubiquitous use of short tests in social science research and practices calls for a refinement of standard interval estimation procedures based on asymptotic normality, such as the Wald and Bayesian CIs, which only maintain desirable coverage when the test is sufficiently long. In the current paper, we propose a simple construction of second-order probability matching priors for the person parameter in unidimensional IRT models, which in turn yields CIs with accurate coverage even when the test is composed of a few items. The probability matching property is established based on an expansion of the posterior distribution function and a shrinkage argument. CIs based on the proposed prior can be efficiently computed for a variety of unidimensional IRT models. A real data example with a mixed-format test and a simulation study are presented to compare the proposed method against several existing asymptotic CIs.

There has been regained interest in joint maximum likelihood (JML) estimation of item factor analysis (IFA) recently, primarily due to its efficiency in handling high-dimensional data and numerous latent factors. It has been established under mild assumptions that the JML estimator is consistent as both the numbers of respondents and items tend to infinity. The current work presents an efficient Riemannian optimization algorithm for JML estimation of exploratory IFA with dichotomous response data, which takes advantage of the differential geometry of the fixed-rank matrix manifold. The proposed algorithm takes substantially less time to converge than a benchmark method that alternates between gradient ascent steps for person and item parameters. The performance of the proposed algorithm in the recovery of latent dimensionality, response probabilities, item parameters, and factor scores is evaluated via simulations.

We generalize the power divergence (PD) family of statistics to the two-parameter logistic IRT model for the purpose of constructing hypothesis tests and confidence intervals of the person parameter. The well-known score test statistic is a special case of the proposed PD family. We also prove the proposed PD statistics are asymptotically equivalent and converge in distribution to ${\chi }_1^2$\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\chi _{1}^2$$\end{document}. In addition, a moment matching method is introduced to compare statistics and choose the optimal one within the PD family. Simulation results suggest that the coverage rate of the associated confidence interval is well controlled even under small sample sizes for some PD statistics. Compared to some other approaches, the associated confidence intervals exhibit smaller lengths while maintaining adequate coverage rates. The utilities of the proposed method are demonstrated by analyzing a real data set.

Samejima’s graded response model (GRM) has gained popularity in the analyses of ordinal response data in psychological, educational, and health-related assessment. Obtaining high-quality point and interval estimates for GRM parameters attracts a great deal of attention in the literature. In the current work, we derive generalized fiducial inference (GFI) for a family of multidimensional graded response model, implement a Gibbs sampler to perform fiducial estimation, and compare its finite-sample performance with several commonly used likelihood-based and Bayesian approaches via three simulation studies. It is found that the proposed method is able to yield reliable inference even in the presence of small sample size and extreme generating parameter values, outperforming the other candidate methods under investigation. The use of GFI as a convenient tool to quantify sampling variability in various inferential procedures is illustrated by an empirical data analysis using the patient-reported emotional distress data.