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The standard algorithms of decision trees and their derived methods are usually constructed on the basis of the frequency information. However, they still suffer from a dilemma or multichotomous question for continuous attributes when two or more candidate cut points have the same or similar splitting performance with the optimal value, such as the maximal information gain ratio or the minimal Gini index. In this paper, we propose a unified framework model to deal with this question. We then design two algorithms based on Splitting Performance and the number of Expected Segments, called SPES1 and SPES2, which determine the optimal cut point, as follows. First, several candidate cut points are selected based on their splitting performances being the closest to the optimal. Second, we compute the number of expected segments for each candidate cut point. Finally, we combine these two measures by introducing a weighting factor $\alpha $ to determine the optimal one from several candidate cut points. To validate the effectiveness of our methods, we perform them on 25 benchmark datasets. The experimental results demonstrate that the classification accuracies of the proposed algorithms are superior to the current state-of-the-art methods in tackling the multichotomous question, about 5% in some cases. In particular, according to the proposed methods, the number of candidate cut points converges to a certain extent.

Data offloading through vehicular ad hoc networks (VANETs) is one of the most promising methods for overcoming the overload problem in cellular networks. As data delivery by service providers consumes resources such as bandwidth, storage and power, the incentive scheme with the optimal pricing strategy must be identified. In the literature, most incentive schemes focus on offloading through fixed nodes, such as roadside units (RSUs). It remains very challenging to motivate a moving vehicle to help other users deliver their data due to the high mobility of vehicles. Game theory is a widely adopted method for analyzing pricing issues in wireless networks. Therefore, this paper proposes an optimal pricing strategy that uses the Stackelberg game to model the interaction between a service provider and a service requester. Then, the Stackelberg equilibrium is derived under the corresponding conditions. Next, an algorithm is proposed for selecting the service provider that offers the lowest price based on the results of the Stackelberg equilibrium. Finally, the simulation results demonstrate that the proposed algorithm can effectively reduce the downloading time of a task while maximizing the utilities of both the service provider and the requester.

The primary challenge encountered by unmanned technology during the unloading phase in open-pit mines is safety hazards, particularly concerning the stability and normative detection of engineering structures at the edges of unloading area. To tackle this issue, a point cloud model analysis algorithm, driven by parallel perception theory and named AC-VIT, is proposed for the real-time and stable detection of the stability and normativity of engineering structures at the edges of open-pit coal mine unloading areas. Initially, three-dimensional point cloud data are captured using unmanned dump trucks equipped with rearward LiDAR scanning. These data are then processed through grid averaging methods, statistical filtering, and mapping to discrete grid models. Preliminary terrain marking is conducted via height field gradient feature extraction, in conjunction with the improved AC-VIT neural network for normative recognition and classification. The AC-VIT model, leveraging parallel computation solely based on a self-attention mechanism and multi-level attention mechanisms, effectively captures long-distance dependencies. Furthermore, a parallel simulation environment for the unloading area is established based on the actual production environment of the Haerwusu open-pit coal mine in Inner Mongolia, within a simulated artificial scene environment, to gather a vast array of diverse artificial scene data. Utilizing this data, in conjunction with actual scene data, the algorithm undergoes a parallel execution to design and perform parallel perception computing experiments, facilitating the effective training of the detection algorithm and scientific evaluation. Experimental outcomes demonstrate that the AC-VIT algorithm, underpinned by parallel perception theory, attains an accuracy rate of 98%, surpassing the accuracy and efficiency of traditional neural network models. The successful deployment of the AC-VIT algorithm not only elevates the intelligence level in open-pit mine unloading operations, but also furnishes robust technical support for the safety detection of other analogous engineering structures. The algorithm introduced herein presents a more efficient, safe, and intelligent approach for the detection of engineering structures at unloading area edges, bearing significant relevance for achieving high-performance, high-reliability, and high-automation in open-pit mine operations.

ObjectivesResults from studies were inconsistent with regard to the effect of folic acid on the primary prevention of stroke. The aim of this study was to analyze the association between folic acid and the primary prevention of stroke using the data from observational studies and randomized controlled trials (RCTs).MethodsEligible publications published until June 2024 were searched in the database of PubMed, Web of Science and Embase. This study included all observational studies and RCTs of folic acid with first stroke as the reporting endpoints. Relative risks (RRs) and 95% confidence intervals (CIs) were pooled in the random-effects model to assess the effect of folic acid on the primary prevention of stroke.ResultsResults from 12 observational publications with 16 research, including 312,320 participants, were combined to explore the association between dietary folic acid intake and the primary prevention of stroke. The results showed that high dietary folic acid intake was associated with a 17% reduction in stroke incidence (RR:0.83; 95% CI: 0.73–0.94), and the effect of dietary folic acid was greater in areas without grain fortification (RR:0.80; 95% CI: 0.67–0.95). The pooled results from 12 RCTs, totaling 75,042 participants, indicated that folic acid supplementation was not associated with the stroke primary prevention (RR:0.92; 95% CI: 0.80–1.05), but folic acid supplementation was effective in areas without grain fortification (RR:0.78; 95% CI: 0.68–0.89).ConclusionOur meta-analysis demonstrated that dietary folic acid is effective in stroke primary prevention, and folic acid supplementation is effective in stroke primary prevention only in areas without grain fortification.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/#myprospero, identifier CRD42024516991.

The article presents the discussion on comparative study of Multiple Fitting Regression and Bayes and Probabilistic Support Vector Machine methods in classification of single-cell RNA data. Topics include supplementing and improving the mining algorithm of single-cell RNA data for understanding the heterogeneity of single-cell RNA; and application of preimplantation genetic diagnosis, preimplantation genetic screening (PGS), and tumor target cell determination and treatment.

Different network techniques are developing autonomously within their own region, besides, various new network techniques come out increasingly.However, the different network techniques cannot coordinate with each other to support the various business efficiently.Based on polymorphic networks and business environments, the researches about the coordinated scheduling of networks and businesses were illustrated from the perspective of the operator’s management and operation.The intent-based cooperative architecture of polymorphic network and service was proposed, which was driven by polymorphic business services.Furthermore, the way to orchestrate the network capabilities intelligently, and to promote the business innovation were explicated.

With the rapid development of intelligent technology, the smart heightened-containment biological laboratory (sHCBL) has moved from concept to reality. Experimental activities and laboratory construction, operation, and management will undoubtedly lead to disruptive changes. Conventional laboratories are increasingly being replaced by smart laboratories; however, the key technologies involved in this transition remain at an exploratory stage. It is necessary for HCBLs to absorb the advanced ideas of smart laboratories to guarantee the establishment of biosafety and biosecurity in a more automated way. This study examines in detail sHCBL module structures, the functions of each module, laboratory operation processes, and the advanced nature of smart laboratories. It may provide a theoretical foundation for the future transformation and smart construction of sHCBLs.

Coal–gangue sorting is a vital component of intelligent mine construction. As intelligent manufacturing continued to advance, data-driven coal–gangue recognition emerged as a prominent research topic. However, conventional data-driven methods for coal–gangue recognition heavily rely on expert-extracted features. The process of feature extraction is labor-intensive and significantly impacts the final outcome. Deep learning (DL) offers an effective approach to automatically extract features from raw data. Among the various DL techniques, convolutional neural networks (CNNs) have proven to be particularly effective. In this paper, we propose an intelligent method for recognizing coal–rock by fusing multiple preprocessing techniques applied to near-infrared spectra and employing dual attention. Initially, a signal-to-RGB image conversion method is applied to fuse three types of preprocessing data, namely first-order differential, second-order differential, and standard normal transform, into an RGB image representation. Subsequently, we propose a neural network model (CBAM-VIT) that integrates the convolutional block attention mechanism (CBAM) and Vision Transformer (VIT). When evaluated on the coal–rock dataset, this model achieves an accuracy of 98.5%, surpassing the performance of VIT (95.3%), VGG-16 (89%), and AlexNet (82%). The comparative results clearly demonstrate that the proposed coal–gangue recognition method yields significant improvements in classification outcomes.

Abstract Aiming at the problem of coal gangue identification in the current fully mechanized mining face and coal washing, this article proposed a convolution neural network (CNN) coal and rock identification method based on hyperspectral data. First, coal and rock spectrum data were collected by a near-infrared spectrometer, and then four methods were used to filter 120 sets of collected data: first-order differential (FD), second-order differential (SD), standard normal variable transformation (SNV), and multi-style smoothing. The coal and rock reflectance spectrum data were pre-processed to enhance the intensity of spectral reflectance and absorption characteristics, as well as effectively remove the spectral curve noise generated by instrument performance and environmental factors. A CNN model was constructed, and its advantages and disadvantages were judged based on the accuracy of the three parameter combinations (i.e., the learning rate, the number of feature extraction layers, and the dropout rate) to generate the best CNN classifier for the hyperspectral data for rock recognition. The experiments show that the recognition accuracy of the one-dimensional CNN model proposed in this paper reaches 94.6%. Verification of the advantages and effectiveness of the method were proposed in this article.

The long–term stability of proton conductors is one of the most important factors in evaluating materials. Guest molecules can act as “bridges” for proton conduction channels and reside in the channels of covalent organic frameworks, but they are prone to leakage. Therefore, it is important to develop proton conductors with intrinsic proton conductivity. In this paper, we synthesized an intrinsically sulfonated covalent organic framework, TpPa–SO3H, which has a more stable proton conducting performance than that of TpPa@H2SO4 by loading guest molecules. Meanwhile, the proton conductivity of TpPa–SO3H was further improved by coating a superabsorbent polymer through an in situ reaction to obtain PANa@TpPa–SO3H (PANa: sodium polyacrylate). As a result, the modified composite exhibits an ultrahigh proton conductivity of 2.33 × 10−1 S cm−1 at 80 °C under 95% relative humidity (RH). The stability of PANa@TpPa–SO3H makes it an efficient proton transport platform with excellent proton conductivity and long–term durability.

For the study of coal and gangue identification using near-infrared reflection spectroscopy, samples of anthracite coal and gangue with similar appearances were collected, and different dust concentrations (200 ug/m3, 500 ug/m3 and 800 ug/m3), detection distances (1.2 m, 1.5 m and 1.8 m) and mixing gangue rates (one-third coal, two-thirds coal, full coal) were collected in the laboratory by the reflection spectroscopy acquisition device and the gangue reflection spectral data. The spectral data were pre-processed using three methods, first-order differentiation, second-order differentiation and standard normal variable transformation, in order to enhance the absorption characteristics of the reflectance spectra and to eliminate the effects of changes in the experimental environment. The PCViT gangue identification model is established, and the disadvantages of the violent patch embedding of the ViT model are improved by using the stepwise convolution operation to extract features. Then, the interdependence of the features of the hyperspectral data is modeled by the self-attention module, and the learned features are optimized adaptively. The results of gangue recognition under nine working conditions show that the proposed recognition model can significantly improve the recognition accuracy, and this study can provide a reference value for gangue recognition using the near-infrared reflection spectra of gangue.

Background The repetitive antigen stimulation during chronic infection often leads to the accumulation of CD8+CD57+ T cells. These cells express high levels of interferon-γ, granzyme B and perforin with elevated cytolytic effect, and are considered as the most potent cells for combating chronical viral infection. The status of CD8+CD57+ T cells in non-small cell lung cancer (NSCLC) has not been well defined.Methods We used flow cytometry and undertook a systemic approach to examine the frequency, immunophenotyping and functional properties of CD8+CD57+ T cells in the peripheral blood, tumor tissue and the corresponding normal tissue, as well as lung draining lymph nodes, of patients with NSCLC.Results CD57+ T cells expressed high levels of programmed cell death-1 (PD-1) in all tested compartments and were predominantly CD8+ T cells. These cells in the peripheral blood displayed a terminally differentiated phenotype as defined by loss of CD27 and CD28 while expressing KLRG1. CD8+CD57+ T cells exhibited enhanced cytotoxic potencies and impaired proliferative capability. Unlike CD57+ T cells in the peripheral blood, a significant proportion of CD57+ T cells in the primary tumors expressed CD27 and CD28. CD8+CD57+ T cells in tumors lacked cytotoxic activity. The proliferative activity of these cells was also impaired. CD8+CD57+ T cells in the corresponding normal lung tissues shared similarities with their counterparts in peripheral blood rather than their counterparts in tumors. The vast majority of CD8+CD57+ T cells in lung draining lymph nodes were positive for CD27 and CD28. These cells were unable to produce perforin and granzyme B, but their proliferative activity was preserved. CD8+CD57+ T cells in tumors displayed an inferior response to PD-1 blockade compared with their CD8+CD57- counterparts. Interleukin (IL)-15 preferentially restored the effector function of these cells. Additionally, IL-15 was able to restore the impaired proliferative activity of CD8+CD57+ T cells in tumors and peripheral blood.Conclusions Our data indicate that the failure of the immune system to fight cancer progression could be a result of impaired CD8+ T-cell functional maturation into fully differentiated effector T cells within the tumor microenvironment. Boosting IL-15 activity might promote tumor-reactive CD8+ T-cell functional maturation while preserving their proliferative activity.

In order to meet the needs of intelligent perception of the driving environment, a point cloud registering method based on 3D NDT-ICP algorithm is proposed to improve the modeling accuracy of tunneling roadway environments. Firstly, Voxel Grid filtering method is used to preprocess the point cloud of tunneling roadways to maintain the overall structure of the point cloud and reduce the number of point clouds. After that, the 3D NDT algorithm is used to solve the coordinate transformation of the point cloud in the tunneling roadway and the cell resolution of the algorithm is optimized according to the environmental features of the tunneling roadway. Finally, a kd-tree is introduced into the ICP algorithm for point pair search, and the Gauss–Newton method is used to optimize the solution of nonlinear objective function of the algorithm to complete accurate registering of tunneling roadway point clouds. The experimental results show that the 3D NDT algorithm can meet the resolution requirement when the cell resolution is set to 0.5 m under the condition of processing the point cloud with the environmental features of tunneling roadways. At this time, the registering time is the shortest. Compared with the NDT algorithm, ICP algorithm and traditional 3D NDT-ICP algorithm, the registering speed of the 3D NDT-ICP algorithm proposed in this paper is obviously improved and the registering error is smaller.