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With the development of the information age, big data analysis has become the basis for social management and the development of new ideas for intelligent governance. In this paper, the governance process of colleges and universities is presented and stored in the form of data by simulating the content of governance through the full domain data set, and the relationship between different subjects and object features is mapped through the generalized Cartesian product. From there, various data patterns are clustered to effectively manage various types of operations’ linkage. The multi-objective optimization algorithm is used to solve the Pareto optimal solution for different governance subjects, i.e., the mutual coordination between teachers and students. The objective optimization function is established to identify the Pareto frontiers of various governance programs and emergency plans. The results show that in the driving pathway of college big data governance, the correct rate of classification of policing grid governance events in 28 colleges and universities is 0.8279 on average, which effectively improves governance efficiency. Big data governance in colleges and universities enables college governance to continuously move from decentralized to centralized, from part to whole, and to achieve accurate and efficient governance modes.

The Beijing radioactive ion beam facility isotope separator on-line (BRISOL) is a radioactive ion beam facility based on a 100 MeV@200 μA proton cyclotron. The radioactive ion beams are produced by bombarding the thick target with proton beam provided by cyclotron. The mass resolving power of the BRISOL is better than 20 000. The first radioactive beams of 37K+ and 38K+ were generated at BRISOL with CaO targets in 2015, and the maximum current of the 38K+ beam is only 1×106 pps when the current of proton beam is 05 μA. The collinear laser spectroscopy experiments using 3743K+ beams will be carried out on BRISOL. The yields of 3743K+ would not be less than 1×106 pps in the experiment. In order to fulfill the requirements, it is necessary to increase the yield of radioactive ion beams. So a series of studies were carried out. The first step is the targets preparation. According to the requirements of the ISOL targets, CaO was selected as the targets material for the generation of radioactive ion beams. The CaO targets were prepared by cold pressing methods with Ca(OH)2, carboxymethyl cellulose (CMC) and deionized water. CMC was added as a binder and poreforming agent. Those materials were mixed in proportion and were cold pressed into desired shape. The thermal treatment was performed in a graphite furnace. After sintering, the targets were porous and then loaded into the targets container. The ion source with the ability of dual ionization mode, including surface ionization mode and FEBIAD ionization mode, was applied in the study. In order to improve the surface ionization efficiency for K, a 01 mm thick rhenium metal lining was installed on the inner wall of the cathode. Then an offline experiment was conducted and the spectra of CaO targets were measured. The experimental results show that Ca+ is ionized when the cathode current is high enough. Therefore, it is necessary to optimize the cathode currents for larger yield of K+. After that, an online experiment at BRISOL was carried out. The experiment results show that the 36.38,43,45.47K beams are produced successfully. The yield of 38K+ is increased to 1.12×1010 pps when the proton beam is 10 μA, the much larger yields are achieved through increasing proton intensities, optimizing both the structure of ion source and the targets working temperature. The development of CaO targets and the results of on-line experiment will be presented in this paper.

In the athermal and apochromatic design of optical systems, the distribution of lens' optical powers and the selection of optical glass and structural materials are crucial. In this paper, an athermal and apochromatic design method is proposed for optical systems with a long focal length, large relative aperture, and wide spectrum. Firstly, a complex optical system composed of multiple lenses is equivalent to a two-component, single-lens system consisting of a replacement and an equivalent lens group. The optical glass for the replacement lens group is selected based on weight and the principle of material replacement in the 3D glass diagram, thus achieving an athermal and apochromatic design. Secondly, an athermal and apochromatic optical system with a focal length of 130 mm, an F-number of 2.0, a spectral range of 480 nm~800 nm, a field of view angle of 22°, and an operating temperature of −40 °C~+60 °C is designed. The modulation transfer function (MTF) at each field of view is greater than 0.6 at 50 lp/mm in the −40 °C~+60 °C temperature range, and the secondary spectrum aberration is 0.0056 mm, which is within the focal depth range of the optical system. [ABSTRACT FROM AUTHOR]

Identifying the association and corresponding types of miRNAs and diseases is crucial for studying the molecular mechanisms of disease-related miRNAs. Compared to traditional biological experiments, computational models can not only save time and reduce costs, but also discover potential associations on a large scale. Although some computational models based on tensor decomposition have been proposed, these models usually require manual specification of numerous hyperparameters, leading to a decrease in computational efficiency and generalization ability. Additionally, these linear models struggle to analyze complex, higher-order nonlinear relationships. Based on this, we propose a novel framework, KBLTDARD, to identify potential multiple types of miRNA–disease associations. Firstly, KBLTDARD extracts information from biological networks and high-order association network, and then fuses them to obtain more precise similarities of miRNAs (diseases). Secondly, we combine logistic tensor decomposition and Bayesian methods to achieve automatic hyperparameter search by introducing sparse-induced priors of multiple latent variables, and incorporate auxiliary information to improve prediction capabilities. Finally, an efficient deterministic Bayesian inference algorithm is developed to ensure computational efficiency. Experimental results on two benchmark datasets show that KBLTDARD has better Top-1 precision, Top-1 recall, and Top-1 F1 for new type predictions, and higher AUPR, AUC, and F1 values for new triplet predictions, compared to other state-of-the-art methods. Furthermore, case studies demonstrate the efficiency of KBLTDARD in predicting multiple types of miRNA-disease associations. Author summary: Many studies have shown that miRNAs contribute to the onset and development of disease through a variety of potential mechanisms. Therefore, predicting multi-category associations between miRNAs and diseases is helpful for the diagnosis and treatment of complex diseases. Different from existing methods, our KBLTDARD combines the logistic tensor decomposition and Bayesian methods, realises the automatic search of hyperparameters by introducing sparse induction priors of multiple latent variables, and incorporates auxiliary information to improve the predictive ability of the Bayesian model. Unlike the simple overfitting of point estimation and the slow convergence of MCMC, we developed an efficient deterministic Bayesian inference algorithm to ensure solution efficiency. We performed 5-fold cross-validation to evaluate the performance of KBLTDARD, which can outperform other state-of-the-art methods. Moreover, the average AUC value of KBLTDARD for the prediction of 447 diseases reached 0.8165. For the prediction of miRNAs and association types of four common diseases, 17, 16, 16 and 17 of the top 20 triples were verified from the literature. [ABSTRACT FROM AUTHOR]

Background: Acute coronary syndrome (ACS) is a severe cardiovascular disease with globally rising incidence and mortality rates. Traditional risk assessment tools are widely used but are limited due to the complexity of the data. Methods: This study introduces a gated Transformer model utilizing machine learning to analyze electronic health records (EHRs) for an enhanced prediction of major adverse cardiovascular events (MACEs) in ACS patients. The model's efficacy was evaluated using metrics such as area under the curve (AUC), precision–recall (PR), and F1-scores. Additionally, a patient management platform was developed to facilitate personalized treatment strategies. Results: Incorporating a gating mechanism substantially improved the Transformer model's performance, especially in identifying true-positive cases. The TabTransformer+Gate model demonstrated an AUC of 0.836, a 14% increase in average precision (AP), and a 6.2% enhancement in accuracy, significantly outperforming other deep learning approaches. The patient management platform enabled healthcare professionals to effectively assess patient risks and tailor treatments, improving patient outcomes and quality of life. Conclusion: The integration of a gating mechanism within the Transformer model markedly increases the accuracy of MACE risk predictions in ACS patients, optimizes personalized treatment, and presents a novel approach for advancing clinical practice and research. [ABSTRACT FROM AUTHOR]

The available metal species are significant for the growth and development of plants. Thus, it is important to detect the content of available metal species in soil at real‐time. Available iron in soil used to be determined by atomic absorption spectrometry or inductive coupled plasma emission spectrometer (ICP) method, which is quite expensive and not easy to operate, especially for farmers. This paper reports a rapid electrochemical method only by applying an electrochemical workstation equipment. Compared to stripping voltammetry method, differential pulse voltammetry (DPV) in this work can detect the available iron species accurately just by directly using the soil extraction solution and does not need the acidic digestion procedure. The DPV strategy we adopted in this work has the advantages of convenience, rapidity and high precision, and can reach the measurement level of ICP method. [ABSTRACT FROM AUTHOR]