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Abstract The controllable fabrication of patterned p-type and n-type channels with precise doping control presents a significant challenge, impeding the realization of complementary metal-oxide-semiconductor (CMOS) logic using a single van der Waals material. However, such an achievement could offer substantial benefits by enabling continued transistor scaling and unprecedented interlayer interconnect technologies. In this study, we devise a precise method for two-dimensional (2D) semiconductor substitutional doping, which allows for the production of wafer-scale 2H-MoTe2 thin films with specific p-type or n-type doping. Notably, we extend this approach to the synthesis of spatially selective doped 2H-MoTe2 thin films via a one-step growth method, facilitating the monolithic integration of p-type and n-type semiconductor channels. Leveraging this advancement, we successfully fabricate a chip-sized 2D CMOS inverter array that demonstrates excellent device performance and yield. Collectively, these findings represent a significant stride towards the practical incorporation of 2D semiconductors in very large-scale integration technology.

In multichannel adaptive radar target detection, diverse nonhomogeneous background factors can cause considerable outlier interference, making it challenging to meet the requirements of independent and identically distributed training data. Current methods for screening training data rely on prior knowledge of the number of outliers, often leading to poor performance in real-world scenarios where this number is usually unknown. This paper addresses these issues by focusing on adaptive training data screening when the number of outliers is unknown. First, the outlier set is estimated using maximum likelihood estimation, assuming known covariance matrices of clutter and noise. In particular, the training data is initially ranked based on the generalized inner product of each range cell data, approximately transforming the maximum likelihood estimation of the outlier set to the estimation of the number of outliers. Second, a fast maximum likelihood estimation algorithm is employed to calculate the unknown covariance matrix, and an adaptive screening approach is designed for scenarios with an unspecified number of outliers. Furthermore, to address the adverse effects of outliers on ranking performance, a normalized generalized inner product form is devised utilizing the normalized sampling covariance matrix. This form is subsequently incorporated into an iterative estimation procedure to improve the adaptive screening accuracy of training data. Simulation results demonstrate that the screening accuracy of the normalized generalized inner product exceeds that of the generalized inner product. Moreover, through even a small number of reiterations, maintaining a consistent enhancement in terms of the Normalized Signal-to-Interference Ratio (NSIR) is still possible. Compared with existing methods, the proposed algorithm considerably improves screening performance, especially when the number of outliers is unknown.

Based on the click chemical reaction between hybrid rhodamine and thiol compounds and the declick chemical reaction caused by the coordinative interaction between thiol compounds and mercury ions, a ternary system of coumarin hybrid rhodamine probe, 1,3,5-benzenetrithiol and Hg2+ for fluorescence detection of Hg2+ was constructed. In this system, the probe underwent an addition reaction with the thiol group in 1,3,5-benzenetrithiol, destroying the conjugate structure of the probe and thus resulting in quenching of its red fluorescence. Hg2+ could remove 1,3,5-benzenetrithiol from the probe’s structure by coordinative binding to the compound, so the conjugate structure and red fluorescence of the probe were restored. The fluorescence intensity of the probe had a good linear relationship with the concentration of Hg2+ in the range of 5 × 10-9-1 × 10-8 and 1 × 10-8–1.1 × 10-7 mol/L, and the detection limit was 2 × 10-9 mol/L. Common heavy metal ions had no significant interference on the specific recognition of Hg2+. The recoveries of Hg2+ from spiked drinking water samples ranged from 86.7% to 110.2%.

ObjectiveIn order to accurately quantify the proportion of bovine-derived ingredients in meat products， the Mitochondrial Cytb gene of bovine for quantitatively detection of bovine ingredients by the digital PCR （Dpcr） technology was developed in this paper.MethodsThis duplex dignal PCR was developed and optimized by designing bovine-derived specific， duck-derived specific， porcine-derived specific and animal-derived universal primers and probes according to standards.ResultsGood linear relationships between the raw beef meat weight and DNA content and between the DNA content and DNA copy number. By using the dPCR method， we found good linear relationships between the raw meat weight and DNA content and between the bovine -derived DNA content and DNA copy number. Using the DNA content as an intermediate value， we established the following formulae for calculating the weight of the original raw beef meat from the specific DNA copy number： Mbeef=0.020 9C+0.676. The mixed samples of known bovine quality were tested， and the results showed that the method can accurately quantify bovine-derive dingredients， which has litter interference by foreign species. Using this method to test 10 samples of the bovine-derived products on the market， by using the ratio of universal and specific amplified copy number， it could determine whether other animal-derived components were present in the beef meat product. it was found that the content of some samples was not up to standard.ConclusionThis method can realize the quantitative detection of bovine origin components， which can be used as a basis to distinguish intentional addition from unintentional pollution and provide a strong technical guarantee for law enforcement and supervision departments.

The new generation information technology, such as big data, artificial intelligence, has an important role in promoting the digital transformation of the aviation manufacturing industry. In view of the characteristics of multi-source and heterogeneity, few samples, and strong correlation of data in the aviation manufacturing industry, utilizing a new generation of knowledge engineering technologies such as knowledge graph with the ability of structured description and efficient management of data, a technical system and process are established in this paper for aviation data intelligence based on the event graph. The research focuses on the technical methods such as ontology modeling, event relationship recognition, event extraction, and event disambiguation for aviation data. With a selection of aviation product quality data for data intelligence technology validation, a series of work are carried out pertinent to application and prototype system developments, including classification of quality problem causes, construction of quality event graph, logic knowledge push, etc., to assist the identification of quality problem and the rapid response to quality issues. The results indicate that the technical system and path of using data and knowledge to carry out digital intelligent quality management are feasible, and the quality knowledge extraction algorithm based on event graph has strong practicality. It provides support for promoting the application of digital intelligence in the whole life cycle of aviation manufacturing industry.

Abstract This study considers the problem of detecting range‐spread targets embedded in subspace interference plus Gaussian clutter with an unknown covariance matrix. The target and interference signals are modeled in terms of deterministic signals belonging to two known subspaces, respectively. Based on the Gradient test criterion, two adaptive detectors are devised for rejecting subspace interference in homogeneous and partially homogeneous environments, respectively. Both of the proposed detectors theoretically exhibit a desirable property of a constant false alarm rate with respect to the clutter covariance matrix as well as the power level. Furthermore, the numerical results show that, compared with their existing counterparts, the proposed detectors exhibit better detection performance and satisfactory suppression performance for the interference.

Abstract In the military and civilian surveillance domain, it is of great significance to mine regular behaviours of targets for situation awareness and command decision support. Most of the existing trajectory clustering algorithms only consider the similarity of spatial position of the trajectory, without sufficient multi‐dimensional information such as time, course and velocity. Some approaches based on information fusion take these multi‐dimensional information into account, but the features with different dimensions fused by weight coefficients are not robust and universal for different scenarios. In this paper, a regular behaviour mining method based on spatiotemporal trajectory multi‐dimensional features and density clustering is proposed. Firstly, multi‐dimensional Hausdorff similarity is defined to measure spatiotemporal trajectory from different feature dimensionalities. Different from methods based on information fusion, the proposed method defines trajectory density in feature similarity of different dimensions and adaptively determines parameters according to feature distribution in different dimensions. Experimental results in simulated and radar measured trajectory data show that the proposed method can be accurate and robust in clustering evaluation indexes such as Purity, Precision, Recall and Rand Index from different scenarios, which has a good application prospect in intelligent surveillance tasks.

Loess is widely deposited in arid and semi-arid areas and is characterized by low dry density, developed pore space, and loose structure, which is not commensurate with high structural strength and shear strength in the dry state. Many natural phenomena and experimental results show that intact loess is very sensitive to the change of water content, and a slight increase in water content can cause a rapid reduction in strength. Abundant information is available in the literature for the collapsibility of loess. However, research on the evolution of loess compressibility during wetting, which is very helpful in understanding loess collapsible deformation caused by long-term irrigation, remains minimal. To this end, in this article, the evolution of the compressibility of intact loess at different wetting stages is investigated by oedometer tests, and microstructure and pore size distribution (PSD) were characterized on intact loess specimens at different water contents before and after oedometer tests by scanning electron microscope (SEM) and mercury intrusion porosimetry (MIP) methods. The results show that the compression index (Cc) and secondary compression index (Cα) of intact loess depend on water content and vertical stress and change abruptly when vertical stress exceeds yield stress. Cα/Cc values of intact loess are not constant, which increases with vertical stress to a peak and then gradually decreases to 0.025. Wetting and loading can cause damage to the microstructure of intact loess; specifically, loading leads to the collapse of the overhead structure and the transformation from a bimodal PSD into a unimodal PSD, and wetting intensifies the collapse of the microstructure to form a compacted interlocking structure and promotes the transformation of medium pores into small pores.

In order to improve the accuracy and robustness of radar target recognition under low SNR conditions, a novel radar high range resolution profile (HRRP) target recognition method based on feature pyramid fusion lightweight CNN is proposed in this paper. The proposed method combines the multi-scale space theory with a deep convolutional neural network. Because of the local connection characteristic of convolutional kernel, feature extracted by CNN mainly focus on the local information of the target. To make full use of both the local and global information in the target HRRP, multi-scale representation of the HRRP with different Gaussian kernels is introduced to construct the multi-channel input of the model. The generalization performance is improved by reducing the parameters of the proposed model with a depthwise separable convolution feature extraction block. Simultaneously, feature pyramid fusion is adapted to take full advantage of the features extracted by each block, which effectively improves the stability of the model and the training efficiency. The experimental results show that the multi-scale representation of the HRRP contributes to robust feature extraction. Meanwhile, the proposed feature pyramid fusion lightweight CNN can effectively prevent over-fitting and improve the stability of the model.

A novel radar target recognition method based on the deep one-dimensional residual-inception network is proposed for a high-resolution range profile (HRRP). The traditional methods based on shallow models can hardly extract the complete information of the targets HRRP from different angles. The deep models, such as sparse autoencoder, have been adopted to solve this problem. However, these deep models with a huge amount of parameters require more training samples to guarantee the generalization performance. To solve the above-mentioned problem, a model based on the one-dimensional convolutional kernel and a pooling layer is proposed. It is worth noting that the one-dimensional convolutional kernel and pooling operation have the potential to overcome the translation sensitivity and target aspect sensitivity of the HRRP, and both of them can greatly reduce the parameters and improve the generalization performance of the model. In addition, a new loss function is proposed to further enhance the separability of features. The experimental results show that compared with other four deep models, the proposed model can achieve a good performance in recognition accuracy and robustness.

Arabidopsis is used as a model species in numerous physiological and genetic studies. Most of them employ parafilm-wrapped sterile culture. Here we demonstrate that this method is prone to potential artifacts and can lead to erroneous conclusions. We compared the effect of different sealing methods including air-permeable paper tape and traditional parafilm on Arabidopsis seedling growth, root development and gene expression network. Although seedlings grown in Petri dishes after 1 week sealed with paper tape showed a similar growth phenotype to that of parafilm-sealed seedlings, more than 700 differentially expressed genes (DEG) were found, including stress and nutrition-responsive genes. In addition, more H2O2 was accumulated in the tissues of parafilm-sealed plants. After 14 days of growth, paper tape-sealed plants grew much better than parafilm-sealed ones and accumulated higher chlorophyll content, with 490 DEGs found. After 3 weeks of growth, paper tape-sealed plants had higher chlorophyll and better growth compared to parafilm-sealed ones; and only 10 DEGs were found at this stage. Thus, the obvious phenotype observed at the latter stage was a result of differential gene expression at earlier time points, mostly of defense, abiotic stress, nutrition, and phytohormone-responsive genes. More O2 content was detected inside paper tape-sealed Petri dishes at early growth stage (7 days), and distinct difference in the CO2 content was observed between parafilm-sealed and paper tape-sealed Petri dishes. Furthermore, the carbon source also influenced seedlings growth with different sealing methods. In conclusion, conventional sealing using parafilm was not the optimal choice, most likely because of the limited gas exchange and a consequent stress caused to plants.

Three cyst-forming Sarcocystis species have been identified in horsemeat; however, there exists considerable confusion concerning their relationships. Here, 74% (34/46) of the examined tissue samples from horses contained sarcocysts based on examination by light microscopy (LM), and the organism was identified as Sarcocystis bertrami based on cyst structure. The S. bertrami cysts were microscopic (up to 6750 μm in length) and exhibited a striated wall with 2.0–5.1 μm villar protrusions (vps) under LM. Transmission electron microscopy (TEM) observations showed that the vps were tightly packed, similar to “type 11c”. Four genetic markers (18S, 28S, ITS1 and the mitochondrial cox1 gene) of S. bertrami were sequenced and analyzed. The 28S and ITS1 sequences are the first records for Sarcocystis in horses. The newly obtained sequences of the 18S and cox1 genes both shared the highest similarities with those of S. bertrami and S. fayeri obtained from horses. Phylogenetic analysis based on the 18S, 28S and cox1 sequences revealed that S. bertrami and S. fayeri formed an independent clade within a group comprising Sarcocystis spp. from ruminants and pigs. Therefore, S. bertrami and S. fayeri are considered to represent the same species of Sarcocystis in horses, and S. fayeri is a junior synonym of Sarcocystis bertrami.

This paper mainly focused on the problems of low loading accuracy in electric linear loading system, Firstly, the mathematical model is done on loading motor, loading motor driver and ball screw in the system. Then, the current loop proportional control is introduced, which improves the response speed of the load motor; In order to improve the loading accuracy and restrain excess force, a parallel algorithm based on fuzzy PID and repetitive control is designed in the force loop. The fuzzy controller improves the dynamic performance and anti-interference ability of the system. The repetitive controller periodically adjusts the deviation, which reduces the steady-state error of the system. Combination of the two controller results in good dynamic and static characteristics. The simulation results show that the proposed control algorithm is feasible, which has a certain engineering reference value.

Model selection is important in any statistical analysis, and the primary goal is to find the preferred (or most parsimonious) model, based on certain criteria, from a set of candidate models given data. Several recent publications have employed the deviance information criterion (DIC) to do model selection among different forms of multilevel item response theory models (MLIRT). The majority of the practitioners use WinBUGS for implementing MCMC algorithms for MLIRT models, and the default version of DIC provided by WinBUGS focused on the measurement-level parameters only. The results herein show that this version of DIC is inappropriate. This study introduces five variants of DIC as a model selection index for MLIRT models with dichotomous outcomes. Considering a multilevel IRT model with three levels, five forms of DIC are formed: first-level conditional DIC computed from the measurement model only, which is the index given by many software packages such as WinBUGS; second-level marginalized DIC and second-level joint DIC computed from the second-level model; and top-level marginalized DIC and top-level joint DIC computed from the entire model. We evaluate the performance of the five model selection indices via simulation studies. The manipulated factors include the number of groups, the number of second-level covariates, the number of top-level covariates, and the types of measurement models (one-parameter vs. two-parameter). Considering the computational viability and interpretability, the second-level joint DIC is recommended for MLIRT models under our simulated conditions. [This article was published in "Journal of Educational Measurement" (EJ1208645).]

Intercultural communication education (ICE) is occurring in different departments and faculties of higher education globally. Yet, there is a lack of research on how this complex field is perceived by both teachers and students. The same goes for the perceptions of the ideal teacher of the subject. This article proposes to fill these gaps by problematizing current discussions about ICE. First, the article presents an overview of the field and shows how polysemic, ideologically and politically oriented ICE is. Based on focus groups with Chinese students of Business English at a Chinese university, this case study examines how they discuss, construct and perceive, on the one hand, the idea of intercultural communication as a subject, and on the other, those who teach it. ICE is multifaceted in Chinese higher education and we do not claim to generalize for the field as a whole in this paper. Based on a dialogical approach to students' discourses, the results show that they do not seem to have a uniform and coherent way of representing "intercultural communication." As far as teachers of the subject are concerned, native/non-native and Chinese/non-Chinese dichotomies seem to dominate. Implications are discussed for the future of ICE in higher education.

Testing item-level fit is important in scale development to guide item revision/deletion. Many item-level fit indices have been proposed in literature, yet none of them were directly applicable to an important family of models, namely, the higher order item response theory (HO-IRT) models. In this study, chi-square-based fit indices (i.e., Yen's Q[subscript 1], McKinley and Mill's G[superscript 2], Orlando and Thissen's S-X[superscript 2], and S-G[superscript 2]) were extended to HO-IRT models. Their performances are evaluated via simulation studies in terms of false positive rates and correct detection rates. The manipulated factors include test structure (i.e., test length and number of dimensions), sample size, level of correlations among dimensions, and the proportion of misfitting items. For misfitting items, the sources of misfit, including the misfitting item response functions, and misspecifying factor structures were also manipulated. The results from simulation studies demonstrate that the S-G[superscript 2] is promising for higher order items.