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Abstract To address the challenges posed by imbalanced and limited battlefield data, which typically results in complex models prone to overfitting during training, we introduce a novel diffusion model grounded in Wasserstein distance (WDiffusion) tailored for the multi-categorical and multivariate characteristics inherent to intent recognition data. Subsequently, we propose a streamlined tactical intent recognition framework predicated on gate recurrent unit (GRU), designed to enhance model responsiveness, and train it on an innovative dataset. Comparative experimental analyses corroborate that the synthetic data generated via WDiffusion significantly outperforms other prevalent generation models. Furthermore, the WDiffusion-GRU model achieves a recognition accuracy of 97.09%, surpassing current aerial target intent recognition models by more than 1.07%. This advancement maintains high recognition precision while substantially curtailing model parameters, thereby amplifying the agility and reliability of battlefield commanders’ decision-making processes.

ObjectiveTo summarize typical situations of illegal addition， the illegal drug addition to ordinary food sold online that claimed to reduce weight was investigated， and its typical external characteristics analyzed.MethodsHigh throughput screening method and liquid chromatography tandem mass spectrometry method that could detect multiple types of compounds simultaneously and quantification accurately were developed. The packaging information， typical ingredients， gifts， production enterprises， and other external typical characteristics of the product were sorted out， and the correlation between detection results and external typical characteristics was analyzed.Results22.09% （19/86） of the weight loss claiming samples purchased online added drugs， and the drug addition behavior showed characteristics such as escape addition， combination addition， and main and auxiliary addition. There were certain correlations between drug addition and external typical features. Typical ingredients， gifts， trial products， products involving OEM， and products labeled with liability insurance had a higher probability of drug addition.ConclusionTypical situations analysis and summarization that are prone to illegal addition ， and we expected to give consumers moderate consumption advices and provide precise guidance for the regulation of illegal added products.

Based on the characteristics of the current new type of illegal addition in terms of substances， forms， carriers， and sales modes， the multiple ways of mining new unconventional illegal addition clues and hidden rules of the industry， the development status， integration， and application of corresponding reconnaissance technical means were discussed in this study. Targeted suggestions from the aspects of source control， inspection， and detection technology， information source channel， supervision mode， and network environment were put forward.

In order to accurately forecast the change of natural gas demand under the condition of multi-dimensional influencing factors, the adaptive inertia weight factor was introduced to improve the Particle Swarm Optimization (PSO) algorithm. Then, by combining the constructed Adaptive Particle Swarm Optimization (APSO) algorithm with the improved Tent mapping, hyper-parameter optimization was performed to the hidden layer nodes, the learning rounds and the initial learning rate in the Long Short-Time Memory Network (LSTM) model, so as to change the deficiency of the traditional LSTM model of setting the hyper-parameters empirically. Moreover, the calculation example was verified based on the subdivided data of 10 highly correlated influencing factors from 1999 to 2020, and the natural gas demand from 2021 to 2030 was forecasted. The results show that the improved Tent-APSO-LSTM model has the best optimization effect on the parameters and could be better applied to the gas demand forecasting in short and medium term.

A three-dimensional transient computational fluid dynamics model is developed to analyze the influence of rotational/non-rotational shoulder and pin characteristics on the thermal and material flow behavior in friction stir welded (FSW) aluminum alloy joints. Four tool designs with rotational/non-rotational shoulder and the pin with/without triple facets are considered. The heat cycle of the rotational and non-rotational shoulder friction stir welded joint (RS-FSW and NRS-FSW) is simulated and experimentally verified, and the welding temperature fields are analyzed. The welding peak temperature of the RS-FSWed joint is the highest, and the non-rotational shoulder significantly reduces the welding peak temperature and the range of the heat-affected zone (HAZ). The rotational shoulder promotes material flow and enhances flow velocity, whereas the non-rotational shoulder reduces both. An increase in the triple facets on the pin facilitates the material flow during NRS-FSW. Viscosity is a physical quantity associated with the welding temperature and strain rate, which is affected by the tool characteristics. The deformation zone caused by the tool is related to the viscosity of the material, the closer the tool, the lower the viscosity of the material. The viscosity of the material gradually increases with an increase in the distance from the tool surface.

As a typical deep unsupervised learning model, autoencoder can automatically learn effective abstract features from unlabeled samples.In recent years, autoencoder has been widely used in target recognition, intrusion detection, fault diagnosis and many other fields.Thus, the theoretical basis, improved methods, application fields and research directions of autoencoder were described and summarized comprehensively.At first, the network structure, theoretical derivation and algorithm flow of traditional autoencoder were introduced and analyzed, and the difference between autoencoder and other unsupervised learning algorithms was compared.Then, common improved autoencoders were discussed, and their innovation, improvement methods and relative merits were analyzed.Next, the practical application status of autoencoder in target recognition, intrusion detection and other fields were introduced.At last, the existing problems of autoencoder were summarized, and the possible research directions were prospected.

The efficiency and cognitive limitations of manual sample labeling result in a large number of unlabeled training samples in practical applications. Making full use of both labeled and unlabeled samples is the key to solving the semi-supervised problem. However, as a supervised algorithm, the stacked autoencoder (SAE) only considers labeled samples and is difficult to apply to semi-supervised problems. Thus, by introducing the pseudo-labeling method into the SAE, a novel pseudo label-based semi-supervised stacked autoencoder (PL-SSAE) is proposed to address the semi-supervised classification tasks. The PL-SSAE first utilizes the unsupervised pre-training on all samples by the autoencoder (AE) to initialize the network parameters. Then, by the iterative fine-tuning of the network parameters based on the labeled samples, the unlabeled samples are identified, and their pseudo labels are generated. Finally, the pseudo-labeled samples are used to construct the regularization term and fine-tune the network parameters to complete the training of the PL-SSAE. Different from the traditional SAE, the PL-SSAE requires all samples in pre-training and the unlabeled samples with pseudo labels in fine-tuning to fully exploit the feature and category information of the unlabeled samples. Empirical evaluations on various benchmark datasets show that the semi-supervised performance of the PL-SSAE is more competitive than that of the SAE, sparse stacked autoencoder (SSAE), semi-supervised stacked autoencoder (Semi-SAE) and semi-supervised stacked autoencoder (Semi-SSAE).

Abstract Background Autophagy is a highly conserved homeostatic process in the human body that is responsible for the elimination of aggregated proteins and damaged organelles. Several autophagy-related genes (ARGs) contribute to the process of tumorigenesis and metastasis of prostate cancer (PCa). Also, miRNAs have been proven to modulate autophagy by targeting some ARGs. However, their potential role in PCa still remains unclear. Methods An univariate Cox proportional regression model was used to identify 17 ARGs associated with the overall survival (OS) of PCa. Then, a multivariate Cox proportional regression model was used to construct a 6 autophagy-related prognostic genes signature. Patients were divided into low-risk group and high-risk group using the median risk score as a cutoff value. High-risk patients had shorter OS than low-risk patients. Furthermore, the signature was validated by ROC curves. Regarding mRNA and miRNA, 12 differentially expressed miRNAs (DEMs) and 1073 differentially expressed genes (DEGs) were detected via the GEO database. We found that miR-205, one of the DEMs, was negatively regulated the expression of ARG (NKX2–3). Based on STRING analysis results, we found that the NKX2–3 was moderately related to the part of genes among the 6 autophagy-related genes prognostic signature. Further, NKX 2–3 was significantly correlated with OS and some clinical parameters of PCa by cBioProtal. By gene set enrichment analysis (GSEA). Lastly, we demonstrated that the association between NKX2–3 and tumor mutation burden (TMB) and PDCD1 (programmed cell death 1) of PCa. Results We identified that the six ARGs expression patterns are independent predictors of OS in PCa patients. Furthermore, our results suggest that ARGs and miRNAs are inter-related. MiR-205 was negatively regulated the expression of ARG (NKX2–3). Further analysis demonstrated that NKX2–3 may be a potential biomarker for predicting the efficacy of anti-PD-1 therapy in PCa. Conclusions The current study may offer a novel autophagy-related prognostic signature and may identify a promising miRNA-ARG pathway for predicting the efficacy of anti-PD-1 therapy in PCa.

Linguistic term sets combining with different types of fuzzy sets to settle decision making problems have been a hot area of decision making research. Linguistic Pythagorean fuzzy sets (LPFSs) and soft sets have strong ability to model uncertainty of decision-making. However, there are still few studies about LPFSs at present and these studies have some deficiencies. Aiming at these problems, we do the following things. Firstly, we give the definition of linguistic Pythagorean fuzzy soft sets (LPFSSs), redefine the entropy for LPFSs and introduce a novel linguistic Pythagorean fuzzy entropy which is more simple and valid. Then we give another way to represent linguistic Pythagorean fuzzy numbers (LPFNs) which can better reflect the characteristics of LPFSs. Based on that, we give the definition of distance measures for LPFSs and propose a series of distance measures for LPFNs and LPFSs. Finally, we improve the TOPSIS method with the proposed entropy and distance measure under LPFSSs environment and then apply the method in two cases. Compared with other methods, it's shown that our method has better distinguishability in evaluation results and can deal with group decision-making problems under LPFSSs environment.

The theory of intuitionistic fuzzy set has attracted much attention because of its stronger ability in depicting and modeling uncertainty. Recent years, numbers of distance measures of intuitionistic fuzzy sets have been proposed to distinguish the information conveyed by intuitionistic fuzzy sets. However, many existing distance measures cannot meet the requirements of a metric distance. So defining new distance measure for intuitionistic fuzzy sets is important to provide more choice in application. A new method to measure the distance between intuitionistic fuzzy sets is proposed in this paper. The proposed method is developed based on the divergence measure between intuitionistic fuzzy sets, which was firstly introduced by Shannon based on the idea of entropy. The mathematical properties of the new distance measure are discussed, and it is shown that the proposed distance measure satisfies all axiomatic properties of intuitionistic fuzzy distance measure. Numerical examples are presented to verify the effectiveness and reasonability of the new distance measure. Finally, the new distance measure is applied in innovation management to solve the decision making problems. It is illustrated that the proposed distance measure performs better than most of existing measures.

BackgroundType III interferon, or interferon lambda (IFNλ) is a crucial antiviral cytokine induced by influenza infection. While IFNλ is important for anti-viral host defense, published data demonstrate that IFNλ is pathogenic during influenza/bacterial super-infection. It is known that polymorphisms in specific IFNλ genes affect influenza responses, but the effect of IFNλ subtypes on bacterial super-infection is unknown.MethodsUsing an established model of influenza, Staphylococcus aureus super-infection, we studied IFNλ3-/- and control mice to model a physiologically relevant reduction in IFNλ and to address its role in super-infection.ResultsSurprisingly, IFNλ3-/- mice did not have significantly lower total IFNλ than co-housed controls, and displayed no change in viral or bacterial clearance. Importantly, both control and IFNλ3-/- mice displayed a positive correlation between viral burden and total IFNλ in the bronchoalveolar lavage during influenza/bacterial super-infection, suggesting that higher influenza viral burden drives a similar total IFNλ response regardless of IFNλ3 gene integrity. Interestingly, total IFNλ levels positively correlated with bacterial burden, while viral burden and bronchoalveolar lavage cellularity did not.ConclusionsThese data suggest IFNλ2 can compensate for IFNλ3 to mount an effective antiviral and defense, revealing a functional redundancy in these highly similar IFNλ subtypes. Further, the IFNλ response to influenza, as opposed to changes in cellular inflammation or viral load, significantly correlates with susceptibility to bacterial super-infection. Moreover, the IFNλ response is regulated and involves redundant subtypes, suggesting it is of high importance to pulmonary pathogen defense.

Liver hepatocellular carcinoma (LIHC) is the most prevalent primary cancer of the liver, and immune-related genes (IRGs) regulate its development. So far, there is still no precise biomarker that predicts response to immunotherapy in LIHC. Therefore, this research seeks to identify immunogenic prognostic biomarkers and explore potential predictors for the efficacy of anti-PD-1/PD-L1 therapies in LIHC. The clinical data and gene expression profiles of patients diagnosed with LIHC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Moreover, IRGs were obtained from the ImmPort database. We discovered 35 IRGs that were differentially expressed between LIHC tissues and corresponding normal tissues. Through univariate Cox regression analysis, eight prognostic differentially expressed IRGs (PDEIRGs) were identified. Further, three optimal PDEIRGs (BIRC5, LPA, and ROBO1) were identified and used to construct a prognostic risk signature of LIHC patients via multivariate Cox regression analysis. The signature was validated by ROC curves. Subsequently, based on gene set enrichment analysis (GSEA) analysis, two out of the three optimal PDEIRGs (BIRC5 and LPA) were significantly enriched in the mismatch repair (MMR) pathway. Moreover, the two PDEIRGs (BIRC5 and LPA) were significantly correlated with the expression of genes related to mismatch repair (MLH1, MSH2, MSH6, and PMS2). Furthermore, correlations between the two PDEIRGs (BIRC5 and LPA) and immune checkpoints of cancer treatment (such as CTLA4, PD-1, and PD-L1) were demonstrated. Hyperprogressive disease (HPD) is a novel pattern of tumor progression which has a close relationship with immune checkpoint inhibitors (ICIs) utilization. MDM2 family amplification might promote the HPD phenomenon. Finally, we found a positive regulatory relationship between HPD related gene (MDM2) and BIRC5. Notably, MDM2 can either interact directly with BIRC5 or indirectly via downstream transcription factors of BIRC5. Overall, our study uncovered a novel 3-immune-related prognostic genes in LIHC.

Trailing intensive cooling with liquid nitrogen has successfully applied to friction stir welding of 2 mm thick 2060 Al-Li alloy. Welding temperature, plastic strain, residual stress and distortion of 2060 Al-Li alloy butt-joint are compared and discussed between conventional cooling and trailing intensive cooling using experimental and numerical simulation methods. The results reveal that trailing intensive cooling is beneficial to shrink high temperature area, reduce peak temperature and decrease plastic strain during friction stir welding process. In addition, the reduction degree of plastic strain outside weld is smaller than that inside weld. Welding distortion presents an anti-saddle shape. Compared with conventional cooling, the reductions of welding distortion and longitudinal residual stresses of welding joint under intense cooling reach 47.7 % and 23.8 %, respectively.

Among the many retained historical information, The CPC’s archives show their unique value because of their original and authentic nature. The CPC’s history archives are used to tell the red story, spread the red culture and highlight the unique advantages of retained historical information. In the one hundred birthday of CPC, test training base in the camp salute an archives exhibition tributing to CPC centennial. The archives exhibition expands the scope of publicity, restores historical information, and highlights the role of archives in education, communication and sharing.

In order to eliminate the flash, arc corrugation and concave in weld zone, level compensation friction stir welding (LCFSW) was put forward and successfully applied to weld 6061-T6 aluminum alloy with varied welding speed at a constant tool rotational speed of 1,800 rpm in the present study. The glossy joint with equal thickness of base material can be attained, and the shoulder affected zone (SAZ) was obviously reduced. The results of transverse tensile test indicate that the tensile strength and elongation reach the maximum values of 248 MPa and 7.1% when the welding speed is 600 mm/min. The microhardness of weld nugget (WN) is lower than that of base material. The tensile fracture position locates at the heat affected zone (HAZ) of the advancing side (AS), where the microhardness is the minimum. The fracture surface morphology represents the typical ductile fracture.

In order to improve the measurement accuracy of eccentric shaft,the eccentric shaft is tested and analyzed by secondary projection method. The measurement principle of the secondary projection method is analyzed. The projection data of the eccentric axes on the two planes perpendicular to each other are obtained by using the optical image measuring machine to perform the secondary projection measurement on the eccentric shaft. The projection data is processed to obtain the coordinate values of the center line end points of the eccentric axis in the spatial coordinate system,so as to further establish the true vector of the center line of each axis segment in space. According to the obtained vector data,the various parameters needed for the analysis of the eccentric shaft structure can be extracted,so that the eccentricity and concentricity of the eccentric shaft can be analyzed reasonably. The results show that the secondary projection method can greatly improve the measurement accuracy of the eccentric shaft.