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Cross-domain Consultation Systems have become essential in numerous critical applications, for instance, an online citizen complaint system. However, addressing complaints with distinct orality characteristics often necessitates retrieving and integrating knowledge from diverse professional domains. This scenario represents a typical cross-domain problem. Nevertheless, the prevailing approach of utilizing generative large language models to tackle this problem presents challenges including model scale and drawbacks like hallucination and limited interpretability. To address these challenges, we proposed a novel approach called the Core-View Contrastive Learning (CVCL) network. Leveraging contrastive learning techniques with an integrated core-adaptive augmentation module, the CVCL network achieves accuracy in cross-domain information matching. Our objective is to construct a lightweight, precise, and interpretable cross-domain consultation system, overcoming the limitations encountered with large language models in addressing such challenges. Empirical validation of our proposed method using real-world datasets demonstrates its effectiveness. Our experiments show that the proposed method achieves comparable performance to large language models in terms of accuracy in text-matching tasks and surpasses the best baseline model by over 24 percentage points in F1-score for classification tasks. Additionally, our lightweight model achieved a performance level of 96% compared to the full model, while utilizing only 6% of the parameters.

In order to explore the formation path of unsafe behaviors of coal miners in the perspective of informal organizations, and to find out the factors influencing unsafe behaviors of coal miners, based on the existing research, the hypothesis that the relationship between organizational environment and social network is a guide to the unsafe behavior of coal miners is put forward, by using SPSS analysis software and AMOS modeling tool to process data and model fitting, the formation path of unsafe behavior of coal miners was determined. The results show that the organizational environment and social network directly or indirectly affect the unsafe behavior of coal miners, and there are two main paths for the formation of unsafe behavior, they are organizational influence → unsafe supervision → precondition of unsafe behavior → unsafe behavior of coal miners, degree of association → dependence → unsafe behavior of coal miners. It can effectively reduce the occurrence of unsafe behavior of coal miners by monitoring social network relationship and promoting informal organization atmosphere of coal miners through improving organizational environment and behavior, to provide new ideas for coal mine enterprise management decision-making.

Background: H1N1 is one of the major subtypes of influenza A virus (IAV) that causes seasonal influenza, posing a serious threat to human health. A traditional Chinese medicine combination called Qingxing granules (QX) is utilized clinically to treat epidemic influenza. However, its chemical components are complex, and the potential pharmacological mechanisms are still unknown. Methods: QX’s effective components were gathered from the TCMSP database based on two criteria: drug-likeness (DL ≥ 0.18) and oral bioavailability (OB ≥ 30%). SwissADME was used to predict potential targets of effective components, and Cytoscape was used to create a “Herb-Component-Target” network for QX. In addition, targets associated with H1N1 were gathered from the databases GeneCards, OMIM, and GEO. Targets associated with autophagy were retrieved from the KEGG, HAMdb, and HADb databases. Intersection targets for QX, H1N1 influenza, and autophagy were identified using Venn diagrams. Afterward, key targets were screened using Cytoscape’s protein–protein interaction networks built using the database STRING. Biological functions and signaling pathways of overlapping targets were observed through GO analysis and KEGG enrichment analysis. The main chemical components of QX were determined by high-performance liquid chromatography (HPLC), followed by molecular docking. Finally, the mechanism of QX in treating H1N1 was validated through animal experiments. Results: A total of 786 potential targets and 91 effective components of QX were identified. There were 5420 targets related to H1N1 and 821 autophagy-related targets. The intersection of all targets of QX, H1N1, and autophagy yielded 75 intersecting targets. Ultimately, 10 core targets were selected: BCL2, CASP3, NFKB1, MTOR, JUN, TNF, HSP90AA1, EGFR, HIF1A, and MAPK3. Identification of the main chemical components of QX by HPLC resulted in the separation of seven marker ingredients within 195 min, which are amygdalin, puerarin, baicalin, phillyrin, wogonoside, baicalein, and wogonin. Molecular docking results showed that BCL2, CASP3, NFKB1, and MTOR could bind well with the compounds. In animal studies, QX reduced the degenerative alterations in the lung tissue of H1N1-infected mice by upregulating the expression of p-mTOR/mTOR and p62 and downregulating the expression of LC3, which inhibited autophagy. Conclusions: According to this study’s network pharmacology analysis and experimental confirmation, QX may be able to treat H1N1 infection by regulating autophagy, lowering the expression of LC3, and increasing the expression of p62 and p-mTOR/mTOR.

With the demand for healthy life and the great advancement of flexible electronics, flexible sensors are playing an irreplaceably important role in healthcare monitoring, wearable devices, clinic treatment, and so on. In particular, the design and application of polyimide (PI)-based sensors are emerging swiftly. However, the tremendous potential of PI in sensors is not deeply understood. This review focuses on recent studies in advanced applications of PI in flexible sensors, including PI nanofibers prepared by electrospinning as flexible substrates, PI aerogels as friction layers in triboelectric nanogenerator (TENG), PI films as sensitive layers based on fiber Bragg grating (FBG) in relative humidity (RH) sensors, photosensitive PI (PSPI) as sacrificial layers, and more. The simple laser-induced graphene (LIG) technique is also introduced in the application of PI graphitization to graphene. Finally, the prospect of PIs in the field of electronics is proposed in the review.

The problem of goal recognition involves inferring the high-level task goals of an agent based on observations of its behavior in an environment. Current methods for achieving this task rely on offline comparison inference of observed behavior in discrete environments, which presents several challenges. First, accurately modeling the behavior of the observed agent requires significant computational resources. Second, continuous simulation environments cannot be accurately recognized using existing methods. Finally, real-time computing power is required to infer the likelihood of each potential goal. In this paper, we propose an advanced and efficient real-time online goal recognition algorithm based on deep reinforcement learning in continuous domains. By leveraging the offline modeling of the observed agent’s behavior with deep reinforcement learning, our algorithm achieves real-time goal recognition. We evaluate the algorithm’s online goal recognition accuracy and stability in continuous simulation environments under communication constraints.

Abstract CD19 chimeric antigen receptor (CAR)-T cells have been used to treat patients with refractory chronic lymphocytic leukemia (CLL). However, approximately 50% of patients do not respond to this therapy. To improve the clinical outcome of these patients, it is necessary to develop strategies with other optimal targets to enable secondary or combinational CAR-T cell therapy. By screening a panel of surface antigens, we found that CD32b (FcγRIIb) was homogeneously expressed at high site density on tumor cells from CLL patients. We then developed a second-generation CAR construct targeting CD32b, and T cells transduced with the CD32 CAR efficiently eliminated the CD32b+ Raji leukemic cell line in vitro and in a mouse xenograft model. Furthermore, CD32b CAR-T cells showed cytotoxicity against primary human CLL cells that were cultured in vitro or transplanted into immunodeficient mice. The efficacy of CD32b CAR T cells correlated with the CD32b density on CLL cells. CD32b is not significantly expressed by non-B hematopoietic cells. Our study thus identifies CD32b as a potential target of CAR-T cell therapy for CLL, although further modification of the CAR construct with a safety mechanism may be required to minimize off-target toxicity.

Most coal mine accidents are caused by the unsafe behavior of employees. Previous studies have shown that there is a significant connection among the working environment, the psychological state of employees, and unsafe behaviors. However, the internal biological mechanism has not been revealed. To explore the physiological and psychological alterations of coal mine workers and the underlying mechanisms that cause unsafe behaviors, the current study established a novel coal mine environment biological simulation (CEBS) model in mice. This model recreated the underground workplace environment facts in coal mines such as temperature, humidity, and noise, and mice were employed to receive these conditioning stresses according to the 8-h work. Animal behavior tests were performed to evaluate the evolution of the mental state including anxiety and depression, as well as the abilities of learning and memory during the 4-week environmental simulation. CEBS mice showed the adaptation process of anxiety from occurrence to stability in the process of environmental simulation, and also suffered from severe depression compared to the control mice. In addition, impaired spatial memory was also implicated in mice after 4-week CEBS. The behavior results of CEBS mice were consistent with the previous psychological investigation of coal workers. In summary, a novel mouse model was established in this study to depict the occurrence of negative emotions and impaired cognition in coal miners by simulating the underground workplace environment, which provided a basis for further exploring the biological mechanism of miners’ unsafe behavior.

As one of the types of coal mine disasters and accidents, gas explosion is an important resistance affecting coal mine safety production. How to effectively identify gas explosion, find out the causes of gas explosion accidents, and make timely prediction and rectification is an urgent problem in coal mine industry. In this paper, the cause model of coal mine gas explosion is constructed based on the Bayesian network, and the cause chain of gas explosion accident is established by SPSS correlation analysis. The main factors leading to gas explosion accidents are found by parameter learning, reverse reasoning, sensitivity analysis, and key cause path. The results show that the main causes of mine gas explosion are gas accumulation and fire source explosion. The main causes of gas accumulation are no wind or breeze caused by local ventilator problems. The generation of electric spark is the main cause of fire source explosion. Cable damage, short circuit, mechanical and electrical equipment explosion, illegal operations, and lack of safety skills are also important factors leading to gas explosion.

Tabanidae suck the blood of humans and animals, are important biological vectors for the transmission of diseases, and are of considerable economic and medical significance. However, current knowledge about the mitochondrial genome of this family is limited. More complete mitochondrial genomes of Tabanidae are essential for the identification and phylogeny. Therefore, this study sequenced and analyzed six complete mitochondrial (mt) genome sequences of four genera of Tabanidae for the first time. The complete mt genomes of the six new sequences are circular molecules ranging from 15,851 to 16,107 base pairs (bp) in size, with AT content ranging from 75.64 to 77.91%. The six complete mitochondrial genomes all consist of 13 protein-coding genes (PCGs), 2 ribosomal RNA genes (RRNA), 22 transfer RNA genes (tRNAs), and a control region, making a total of 37 functional subunits. ATT/ATG was the most common start codon, and the stop codon was TAA of all PCGS. All tRNA except tRNA Ser1 had a typical clover structure. Phylogeny was inferred by analyzing the 13 concatenated amino acid sequences of the 22 mt genomes. Bayesian inference, maximum-likelihood trees, and maximum-parsimony inference analyses all showed consistent results. This study supports the concept of monophyly of all genus, ratifies the current taxonomic classification, and provides effective genetic markers for molecular classification, systematics, and genetic studies of Tabanidae.

Advances in automotive technology require networks to support a variety of communication requirements, such as reliability, real-time performance, low jitter, and strict delay limits. Time-Sensitive Network (TSN) is a keyframe transmission delay-guaranteed solution based on the IEEE 802 architecture of the automotive Ethernet. However, most of the existing studies on automotive TSN performance are based on a single mechanism, lacking a complete and systematic research tool. At the same time, the design method should be considered from a global perspective when designing an automotive TSN system, rather than only considering a single mechanism that TSN applies to. This paper discusses the correspondence between traffic types and automotive scenarios and proposes a methodology to target the delay constraint of traffic types as the design goal of automotive TSN networks. To study the performance of automotive TSN under different mechanisms such as time-aware shaper (TAS), credit-based shaper (CBS), cyclic queuing and forwarding (CQF), etc., this paper also develops a systematic automotive TSN simulation system based on OMNeT++. The simulation system plays a crucial role in the whole methodology, including all applicable TSN standards for the automotive field. Lastly, a complex automotive scenario based on zonal architecture provided by a major motor company in Shanghai is analyzed in the simulated system; verifying TSN can guarantee real-time performance and reliability of the in-vehicle network.

Determination of bamboo age is an important task for bamboo forest management and bamboo utilization. However, the bamboo age is usually manually determined in the field, which is time-consuming and labor-intensive. Due to the ability to generate very high-density point clouds, terrestrial laser scanning (TLS) has been applied in forestry to acquire forest parameters. This study evaluated the potential of using the laser echo intensity data generated by TLS technology to determine the Moso bamboo age represented by “du.” The intensity data were first corrected for the distance and incidence angle effects using an intensity correction method that constructed an empirical correction model by fitting piecewise polynomials to the intensity data collected based on a reference target. Then the models expressing the relationship between intensity and bamboo culm section number were constructed for different bamboo du by fitting polynomials to the intensity data of individual bamboo culms through least-squares adjustment. For a bamboo plant whose age is determined, the bamboo du could be determined based on the constructed intensity-culm section models. The proposed bamboo age determination method was tested at a site in a managed Moso bamboo forest in Lin’an District, Hangzhou City, Zhejiang Province, China. From the test site, 56 and 120 bamboo plants with known bamboo ages were selected to construct the intensity-culm section models and to validate the bamboo age determination method, respectively. The bamboo age determination accuracies for each bamboo du were all above 90%. The result indicates a great potential for automatic determination of bamboo age in practice using TLS technology.