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Purpose: To analyze the expression level of β-lactamase gene (LACTB) in gastric cancer cells and the main types of transcripts expressed, and then explore the effect and mechanism of LACTB transcripts on the biological function of gastric cancer cells. Methods: The expression level of LACTB and the main biological processes involved were analyzed by bioinformatics analysis and RT-qPCR. The expression level of LACTB transcript 1 was overexpressed or knocked down in AGS and HGC-27 cells, and the effect and mechanism of LACTB on gastric cancer cell migration and invasion were verified by in vitro cell experiments and western blotting experiments. Results: 1.The UALCAN analysis website showed that LACTB mRNA was highly expressed in gastric cancer tissue, and the expression level of LACTB transcript 1 in peripheral blood of gastric cancer patients was consistent with the bioinformatics analysis. LinkedOmics and DAVID enrichment analysis found that LACTB co-expressed genes were mainly involved in biological processes such as immune response, inflammatory response, and proteolysis through lysosomes and immune synapses. 2.Knocking down the expression level of LACTB transcript 1 can inhibit the migration and invasion ability of gastric cancer cells, and at the same time can activate autophagy and inhibit EMT. However, overexpression of LACTB transcript 1 resulted in the opposite. Conclusions: LACTB transcript 1 is an inhibitor that regulates autophagy to inhibit the migration and invasion of gastric cancer cells, and is highly expressed in peripheral blood, which has the potential as a biomarker of gastric cancer.

In recent years, more and more Flexible AC Transmission System (FACTS) devices have been used in power systems, making subsynchronous oscillation (SSO) more and more serious. Thyristor Controlled Phase Shifting Transformer (TCPST), as an advanced FACTS device, can alleviate SSO effectively. In this paper, PSCAD is used to build the transient model of the second standard test system with TCPST, and the complex torque coefficient method based on simulation analysis— test signal method is used to study how TCPST will influence the system's SSO under different conditions. The result of simulation indicates that TCPST can alleviate and even suppress SSO effectively.

Acting as a virtual sensor network for household appliance energy use monitoring, non-intrusive load monitoring is emerging as the technical basis for refined electricity analysis as well as home energy management. Aiming for robust and reliable monitoring, the ensemble approach has been expected in load disaggregation, but the obstacles of design difficulty and computational inefficiency still exist. To address this, an ensemble design integrated with multi-heterogeneity is proposed for non-intrusive energy use disaggregation in this paper. Firstly, the idea of utilizing a heterogeneous design is presented, and the corresponding ensemble framework for load disaggregation is established. Then, a sparse coding model is allocated for individual classifiers, and the combined classifier is diversified by introducing different distance and similarity measures without consideration of sparsity, forming mutually heterogeneous classifiers. Lastly, a multiple-evaluations-based decision process is fine-tuned following the interactions of multi-heterogeneous committees, and finally deployed as the decision maker. Through verifications on both a low-voltage network simulator and a field measurement dataset, the proposed approach is demonstrated to be effective in enhancing load disaggregation performance robustly. By appropriately introducing the heterogeneous design into the ensemble approach, load monitoring improvements are observed with reduced computational burden, which stimulates research enthusiasm in investigating valid ensemble strategies for practical non-intrusive load monitoring implementations.

As a pivotal technological foundation for smart home implementation, non-intrusive load monitoring is emerging as a widely recognized and popular technology to replace the sensors or sockets networks for the detailed household appliance monitoring. In this paper, a probability model framed ensemble method is proposed for the target of robust appliance monitoring. Firstly, the non-intrusive load disaggregation-oriented ensemble architecture is presented. Then, dictionary learning model is utilized to formulate the individual classifier, while the sparse coding-based approach is capable of providing multiple solutions under greedy mechanism. Furthermore, a fully probabilistic model is established for combined classifier, where the candidate solutions are all labelled with probability scores and evaluated via two-stage decision-making. The proposed method is tested on both low-voltage network simulator platform and field measurement datasets, and the results show that the proposed ensemble method always guarantees an enhancement on the performance of non-intrusive load disaggregation. Besides, the proposed approach shows high flexibility and scalability in classification model selection. Therefore, by initializing the architecture and approach of ensemble method-based NILM, this work plays a pioneer role in using ensemble method to improve the robustness and reliability of non-intrusive appliance monitoring.

Soil organic carbon (SOC) and soil inorganic carbon (SIC) are important C pools in the Loess Plateau of Northwest China, however, variations of SOC and SIC stocks under different cultivation practices and nitrogen (N) fertilization rates are not clear in this area. A long-term field experiment started in June 2003 was conducted to investigate the SOC and SIC stocks in a calcareous soil of the Chinese Loess Plateau under four cultivation practices, i.e., fallow (FA), conventional cultivation (CC), straw mulch (SM), and plastic film-mulched ridge and straw-mulched furrow (RF), in combination with three N fertilization rates, i.e., 0 (N0), 120 (N120), and 240 (N240) kg N/hm2. Results indicate that the crop straw addition treatments (SM and RF) increased the contents of soil microbial biomass C (SMBC) and SOC, and the SOC stock increased by 10.1%–13.3% at the upper 20 cm soil depth in comparison to the 8-year fallow (FA) treatment. Meanwhile, SIC stock significantly increased by 19% at the entire tested soil depth range (0–100 cm) under all crop cultivation practices in comparison to that of soil exposed to the long-term fallow treatment, particularly at the upper 60 cm soil depth. Furthermore, moderate N fertilizer application (120 kg N/hm2) increased SOC stock at the upper 40 cm soil depth, whereas SIC stock decreased as the N fertilization rate increased. We conclude that the combined application of crop organic residues and moderate N fertilization rate could facilitate the sequestrations of SOC and SIC in the calcareous soil.

In this study, we built a submerged macrophyte model (GBAEDM) that could simulate several macrophyte species simultaneously. The model was calibrated and validated using pilot-scale experimental data, with satisfactory results. The coefficient of determination (R2) and root-mean-square error (RMSE) values were 0.86 and 0.03 for TP, 0.71 and 0.38 for TN, 0.93 and 6.78 for Chl a, and 0.99 and 27.61 for submerged macrophyte biomass. The GBAEDM was then used to choose appropriate species and suitable initial plant biomass submerged macrophyte recovery in Gonghu Bay. The appropriate species at 1 m depth were Vallisneria spinulosa (1150 g/m2), Myriophyllum spicatum (525 g/m2), and V. spinulosa–M. spicatum mixed (600 g/m2; 3:1) communities. At 2 m depth, the appropriate species were M. spicatum (1450 g/m2) and V. spinulosa–M. spicatum mixed (1950 g/m2; 12:1) communities. Only M. spicatum (1608 g/m2) was recommended at 3 m depth. Finally, the GBAEDM was used to determine the critical water transparency thresholds for submerged macrophytes in Gonghu Bay. A transparency value of 0.62 m at 2 m (the WT/H value of 0.31) was determined as the critical underwater light threshold for the recovery of submerged macrophytes in Gonghu Bay.

Currently various types of aircraft booming and maturing, however, their long-time navigational capability should be improved urgently. This paper aims at studying laser power beaming, which includes the technology of high-efficient photoelectric conversion and APT(acquiring, pointing and tracking) technology, to provide power for flying UAV(unmanned aerial vehicles) and improve their flight endurance. The experiment of testing different types of solar cells under various conditions has been done to choose the solar cell which has the highest photoelectric conversion rate and find its most sensitive wavelength. In addition, the charge management module has been chose on the base of the characteristics of lithium batteries. Besides, a laser APT system was designed and set up, at the same time FSM (Fast Scan Mirror) control program and digital image processing program were used to control the system. The success of the indoor experiment of scan-tracking and charging for the moving UAV model via laser proves that this system is workable. And in this experiment, the photoelectric conversion rate of the whole system is up to 17.55%.