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This paper proposes an improved exponential curvature-compensated bandgap reference circuit to exploit the exponential relationship between the current gain β of the bipolar junction transistor (BJT) and the temperature as well as reduce the influence of resistance-temperature dependency. Considering the degraded circuit performance caused by the process deviation, the trimmable module of the temperature coefficient (TC) is introduced to improve the circuit stability. The circuit has the advantages of simple structure, high linear stability, high TC accuracy, and trimmable TC. It consumes an area of 0.09 ​mm2 when fabricated by using the 0.25-μm complementary metal-oxide-semiconductor (CMOS) process. The proposed circuit achieves the simulated power supply rejection (PSR) of about −78.7 ​dB@1 ​kHz, the measured TC of ∼4.7 ​ppm/°C over a wide temperature range from −55 ​°C to 125 ​°C with the 2.5-V single-supply voltage, and the tested line regulation of 0.10 ​mV/V. Such a high-performance bandgap reference circuit can be widely applied in high-precision and high-reliability electronic systems.

Background: Inhibition of the programmed death ligand 1, programmed death 1 pathway has been successfully used for treatment of multiple advanced adult cancers. However, its use in pediatric osteosarcoma is still in its infancy. In this study, we investigated programmed death ligand 1 and other checkpoint molecules' expression to determine the potential usefulness as targets for drug therapy. Methods: We incubated human wild-type osteosarcoma cells with incremental concentrations of doxorubicin to create a doxorubicin-resistant cell line. Matrigel in vitro invasion assays were used to compare invasiveness. Comparative programmed death ligand 1 expression was evaluated by Western blot assays. An immuno-oncology checkpoint protein panel was used to compare concentrations of 16 other checkpoint molecules. Chi-square tests and Wilcoxon rank-sum tests were used to determine significant differences. Results: A doxorubicin-resistant cell line was successfully created and was significantly more invasive than wild-type cells (0.47 vs 0.07, P < .001). On Western blot assay, doxorubicin-resistant but not wild-type cells expressed programmed death ligand 1. Doxorubicin-resistant cells had significantly higher levels of T-cell immunoglobulin-3 and cluster of differentiation 86 and higher cluster of differentiation 27, cluster of differentiation 40, lymphocyte-activation gene-3, cluster of differentiation 80, programmed death ligand 1, programmed death ligand 2, and inducible T-cell costimulatory expression than wild-type cells. Both lines expressed B- and T-lymphocyte attenuator, cluster of differentiation 28, herpesvirus entry mediator, and programmed death 1. Herpesvirus entry mediator, cluster of differentiation 40, and programmed death ligand 2 were also present in the culture media of both cell lines. Conclusion: Doxorubicin-resistant osteosarcoma seems to express higher programmed death ligand 1 than nonresistant wild-type cells. Benchmarking checkpoint molecules may provide the basis for future studies that elucidate pathways of drug resistance and tumor metastasis, biomarkers for cancer prognosis or recurrence, and future targets for directed drug therapy.

The power detection of the distribution network is essential for reliable and secure distribution. In this paper, a flexible dual-threshold SVDD fault warning algorithm with fault samples is proposed to deal with problems concerning complex network topology, accessible data, and missing fault data in the power grid. For the problems of complicated network topology and a wide variety of signal types, we propose to combine wavelet packet energy features with Spearman to extract electric signal features, and finally achieve accurate feature extraction of multiple signal types. In the case of the problems of untimely judgement and low accuracy of the original SVDD, a relaxed SVDD fault warning algorithm with fault samples is correspondingly proposed. We turn the original SVDD boundary into a double-layer boundary, and divides the hypersphere space into three regions to increase the sensitivity to the fault samples and lessen the risk of missed detection. Besides, an adaptive update strategy is developed, which reduces the computational effort of the model and is proven more applicable to the distribution. Finally, the method is applied to numerical examples and fault detection experiments, and the experimental results in turn verified its effectiveness and superiority.

Session-based recommendation (SBR) aims to recommend the next items based on anonymous behavior sequences over a short period of time. Compared with other recommendation paradigms, the information available in SBR is very limited. Therefore, capturing the item relations across sessions is crucial for SBR. Recently, many methods have been proposed to learn article transformation relationships over all sessions. Despite their success, these methods may enlarge the impact of noisy interactions and ignore the complex high-order relationship between non-adjacent items. In this study, we propose a self-supervised global context graph neural network (SGC-GNN) to model high-order transition relations between items over all sessions by using virtual context vectors, each of which connects to all items in a given session and enables to collect and propagation information beyond adjacent items. Moreover, to improve the robustness of the proposed model, we devise a contrastive self-supervised learning (SSL) module as an auxiliary task to jointly learn more robust representations of the items in sessions and train the model to fulfill the SBR task. Experimental results on three benchmark datasets demonstrate the superiority of our model over the state-of-the-art (SOTA) methods and validate the effectiveness of context vectors and the self-supervised module.

In order to compensate for the low convergence accuracy, slow rate of convergence, and easily falling into the trap of local optima for the original Harris hawks optimization (HHO) algorithm, an improved multi-strategy Harris hawks optimization (MSHHO) algorithm is proposed. First, the population is initialized by Sobol sequences to increase the diversity of the population. Second, the elite opposition-based learning strategy is incorporated to improve the versatility and quality of the solution sets. Furthermore, the energy updating strategy of the original algorithm is optimized to enhance the exploration and exploitation capability of the algorithm in a nonlinear update manner. Finally, the Gaussian walk learning strategy is introduced to avoid the algorithm being trapped in a stagnant state and slipping into a local optimum. We perform experiments on 33 benchmark functions and 2 engineering application problems to verify the performance of the proposed algorithm. The experimental results show that the improved algorithm has good performance in terms of optimization seeking accuracy, the speed of convergence, and stability, which effectively remedies the defects of the original algorithm.

Average particle size is a critical quality variable for optimal operation of the cobalt oxalate synthesis process. However, the measurement for this variable is often achieved from offline laboratory assaying procedure with low sampling rate and low reliability. Therefore, a data-driven soft sensor model based on adaptive Gaussian mixture regression (AGMR) is presented in this paper. Firstly, a GMR based soft sensor model is developed for predicting the average particle size. Secondly, the prediction uncertainty obtained from the GMR model is used to assess the performance of the current soft sensor model. Thirdly, a dual updating algorithm based on the model performance assessment is constructed to track the time-varying behavior of the synthesis process. In the updating method, bias updating and moving window model updating methods are performed in turns based on the results of model performance assessment. The dual updating mechanism can avoid blind updating. Finally, a numerical example and a real industrial cobalt oxalate synthesis process application are used to demonstrate the effectiveness of the proposed method.

Stress changes due to earthquake rupture can disturb geomagnetic fields significantly. In order to investigate the impact of the 2021 MW 7.3 Maduo earthquake on geomagnetic fields, a piezomagnetic model is constructed based on the coseismic slip to calculate the static coseismic piezomagnetic anomalies (PMs). The PMs are considerable in near-field. However, the PMs are negligible in regions tens of kilometers from the fault rupture. The PMs of our model are consistent with those of other strike-slip earthquakes, indicating that our piezomagnetic model is reasonable. The east component of observed coseismic geomagnetic changes and calculated PMs on a geomagnetic repeat station located about 6 km from fault trace are +4.8 ± 2.2 nanotesla and +4.3 nanotesla, respectively. It seems that the piezomagnetic model can explain the observed data. The PMs are up to 10 nanotesla in the near-field with the initial magnetization of 3 A/m and stress sensitivity of 2 × 10−3 MPa−1. Consequently, considerable coseismic geomagnetic changes that are above error could be observed along the fault, especially at locations with geometrical complexities.

For grinding processes, optimal-setting control (OSC) is becoming a hot topic. However, there is no configurable platform to assist researchers and engineers to design such a controller. This paper proposes a novel software platform named OSC to address this problem. The major superiority is that the platform not only provides a configurable environment by developing a powerful controller design tool and a Petri net model to schedule algorithm modules for parallel computation but also integrates several mainstream intelligent and data-driven algorithms (e.g., case based reasoning, fuzzy logic, and neural network) within a unified framework. The overall framework and key technologies are introduced in detail. Using a hardware-in-the-loop experiment system, the platform is verified and validated through a case of application where an intelligent optimal-setting controller is developed for a classical grinding process.

Production quality indices of complex industrial processes are usually hard to be measured in real time, which leads to unavailability of closed-loop operational optimization and control. Therefore, data-driven modeling techniques have been extensively employed to estimate production quality indices online. However, the conventional data-driven modeling methods often fail to achieve good performance because of interference from outliers. To solve the above-mentioned problem, this paper proposes an improved random vector functional link network (RVFLN) using a novel training method, which adopts a ridge regularized model with weighted factor for each training sample to evaluate the output weights. The robustness of the model has been achieved by employing a nonparametric kernel density estimation method to assign the weighted factors according to the training sample. To ensure the quality and computational load of the network in online applications, various online learning versions are presented according to the scope of data sampling. The improved RVFLN called robust regularized RVFLN has been validated using UCI, Statlib standard data sets, and an industrial grinding operation data. Results show that our proposed modeling technique perform favorably, and demonstrate its good potential for real world applications.

An accurate model is important for design and optimization control of centrifugal compressor. However, due to the varying operation conditions and the complexity of the flow dynamics inside it, establishing a satisfaction model of a new compressor is time and cost consuming. This paper proposes a rapid modeling method for centrifugal compressor based on model migration and support vector machines (SVMs). A general framework for centrifugal compressor model migration is given between two similar compressors, the base model of an existing old compressor is revised to fit for the new compressor by SVM using a small number of data. The effective of the proposed method is contrasted with pure SVM by a simulation case and the results show that, compared with the pure SVM, the migrated model can fit the new compressor faster with better accuracy, which is useful for reducing the cost and time in developing the model for new compressors.

Histone deacetylase (HDAC) inhibitors, especially vorinostat, are currently under investigation as potential adjuncts in the treatment of neuroblastoma. The effect of vorinostat co-treatment on the development of resistance to other chemotherapeutic agents is unknown. In the present study, we treated two human neuroblastoma cell lines [SK-N-SH and SK-N-Be(2)C] with progressively increasing doses of doxorubicin under two conditions: with and without vorinsotat co-therapy. The resultant doxorubicin-resistant (DoxR) and vorinostat-treated doxorubicin resistant (DoxR-v) cells were equally resistant to doxorubicin despite significantly lower P-glycoprotein expression in the DoxR-v cells. Whole genome analysis was performed using the Ilumina Human HT-12 v4 Expression Beadchip to identify genes with differential expression unique to the DoxR-v cells. We uncovered a number of genes whose differential expression in the DoxR-v cells might contribute to their resistant phenotype, including hypoxia inducible factor-2. Finally, we used Gene Ontology to categorize the biological functions of the differentially expressed genes unique to the DoxR-v cells and found that genes involved in cellular metabolism were especially affected.