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【Background and Objective】 As renewable energy is expanding, complementary energy storage solutions such as pumped-storage power stations have been developed. By the end of 2020, the total installed capacity of pumped-storage power stations in China had surpassed 30 million kW. Differing from conventional hydropower stations, pumped-storage power stations operate under distinct thermal and dynamic conditions, which can affect the environment as the temperature of water discharged from these reservoirs is closely linked to river ecosystems. Low-temperature discharges not only delay or prevent fish spawning, but also adversely affect crop growth, leading to crop yield reduction. Despite these concerns, research on the change in outflow water temperature of hybrid pumped-storage power stations is limited. This paper is to bridge this knowledge gap. 【Method】 Taking the Jinshuitan Reservoir as an example, we simulate outflow water temperature under different inlet-outlet elevations and pumping rates using used the MIKE3 water temperature model. This allowed us to assess the impact of hybrid pumped-storage hydropower stations on outflow water temperature. 【Result】 Compared to the outflow water temperature without pumping and storage, pumping-storage reduced the outflow water temperature from May to September and increased it during the other month; the maximum decrease and increase in temperature were less than 0.7 ℃ and 0.8 ℃, respectively. Specifically, at a pumping rate of 110 m3/s, raising the outlet elevation from 130 m to 150 m led to the reduction in the outflow water temperature to decrease from 0.4 ℃ to 0.1 ℃ between May and October, and the rise in temperature to decrease from 0.5 ℃ to 0.02 ℃ during the remaining months, compared to conditions without pumping-storage. When the outlet elevation was maintained at 139 m, increasing the pumping rate from 50 m3/s to 70 m3/s only affected decrease in the outflow water temperature between May and September, with the decrease increasing from 0.1 ℃ to 0.2 ℃. Further increases in the pumping rate, while keeping the outlet elevation at 139 m, had no significant impact on the outflow water temperature. 【Conclusion】 The elevation of the inlet and outlet significantly affects the outflow water temperature; a lower elevation results in a greater temperature difference. While the pumping rate does influence the temperature difference, the effect is less pronounced, though the temperature difference does increase with pumping rates. To minimize the adverse impacts of pumped-storage operation on downstream ecosystems, it is advisable to position the inlet and outlet at the highest feasible elevations.

With the development of multi-source AC/DC distribution networks, DC fault detection technology has become the key to DC protection. Aiming at the characteristics of large fault current with a rapid rise and difficulty to extract fault features when distribution line faults occur in the DC part, a fault detection technology of AC/DC distribution network based on deep belief network (DBN) is proposed combining feature extraction in the time domain and frequency domain. By analyzing the characteristics of the fault equivalent circuit, Fourier transform and phase-mode transform are used to extract the frequency domain and time domain characteristics of the fault current and voltage signals as the input of DBN, and the Softmax classifier is used to output the fault pole selection and fault area identification results. An AC/DC distribution network model is built on PSCAD to test the algorithm. The simulation result shows that the proposed detection method still has high accuracy and the strong ability to tolerate noise under the influence of line-distributed capacitance and control strategy. The further algorithm comparison experiment shows that the necessary combination of fault feature extraction and effective deep learning model training can complete the complex fault detection.

IntroductionImmunity is involved in a variety of bone metabolic processes, especially osteoporosis. The aim of this study is to explore new bone immune-related markers by bioinformatics method and evaluate their ability to predict osteoporosis.MethodsThe mRNA expression profiles were obtained from GSE7158 in Gene expression Omnibus (GEO), and immune-related genes were obtained from ImmPort database (https://www.immport.org/shared/). immune genes related to bone mineral density(BMD) were screened out for differential analysis. protein-protein interaction (PPIs) networks were used to analyze the interrelationships between different immune-related genes (DIRGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of DIRGs function were performed. A least absolute shrinkage and selection operation (LASSO) regression model and multiple Support Vector Machine-Recursive Feature Elimination (mSVM-RFE) model were constructed to identify the candidate genes for osteoporosis prediction The receiver operator characteristic (ROC) curves were used to validate the performances of predictive models and candidate genes in GEO database (GSE7158,GSE13850).Through the RT - qPCR verify the key genes differentially expressed in peripheral blood mononuclear cells Finally, we constructed a nomogram model for predicting osteoporosis based on five immune-related genes. CIBERSORT algorithm was used to calculate the relative proportion of 22 immune cells.ResultsA total of 1158 DEGs and 66 DIRGs were identified between high-BMD and low-BMD women. These DIRGs were mainly enriched in cytokine−mediated signaling pathway, positive regulation of response to external stimulus and the cellular components of genes are mostly localized to external side of plasma membrane. And the KEGG enrichment analysis were mainly involved in Cytokine−cytokine receptor interaction, PI3K−Akt signaling pathway, Neuroactive ligand−receptor interaction,Natural killer cell mediated cytotoxicity. Then five key genes (CCR5, IAPP, IFNA4, IGHV3-73 and PTGER1) were identified and used as features to construct a predictive prognostic model for osteoporosis using the GSE7158 dataset.ConclusionImmunity plays an important role in the development of osteoporosis.CCR5, IAPP, IFNA4, IGHV3-73 and PTGER1were play an important role in the occurrences and diagnosis of OP.

Sensor-based human activity recognition has been widely used in various fields; however, there are still challenges involving recognition of daily complex human activities using sensors. In order to solve the problem of timeliness and homogeneity of recognition functions in human activity recognition models, we propose a human activity recognition model called 'BSTCA-HAR' based on a long short-term memory (LSTM) network. The approach proposed in this paper combines an attention mechanism and a temporal convolutional network (TCN). The learning and prediction units in the model can efficiently learn important action data while capturing long time-dependent information as well as features at different time scales. Our series of experiments on three public datasets (WISDM, UCI-HAR, and ISLD) with different data features confirm the feasibility of the proposed method. This method excels in dynamically capturing action features while maintaining a low number of parameters and achieving a remarkable average accuracy of 93 % , proving that the model has good recognition performance. [ABSTRACT FROM AUTHOR]

Objective To detect the expression of high-mobility group protein A2 (HMGA2) in gastric cancer tissues, investigate its relationship with clinicopathological features and prognosis, and explore its role and underlying mechanism in regulating the invasion and proliferation of gastric cancer cells. Methods TCGA database, GTEx database and bioinformatics analysis tools (GEPIA, KM plotter) were used to analyze the expression of HMGA2 gene in gastric cancer and the survival time of patients. The expression of HMGA2 protein in gastric cancer and adjacent tissues was detected by immunohistochemistry, and the correlation between HMGA2 expression and the clinicopathological parameters and its effect on prognosis of gastric cancer patients were analyzed. After HMGA2 was knocked down in gastric cancer cell line MGC-803, and overexpressed in gastric cancer cell line SGC-7901, plate cloning and transwell assay were used to detect the proliferation and invasion ability, respectively. The invasion ability was detected again in the HMGA2 overexpressed SGC-7901 cells after knockdown of Snail1. Western blotting was employed to measure the expression changes of HMGA2 and epithelial to mesenchymal transition (EMT) markers in the cells. Results The bioinformatics analysis showed that the mRNA expression of HMGA2 was significantly increased in gastric cancer tissues compared with normal gastric tissues (P < 0.05). Immunohistochemistry also indicated that the protein level of HMGA2 was obviously higher in gastric cancer tissues than para-cancer tissues, and it was associated with TNM staging, lymph node metastasis and survival rate. The gastric cancer patients with high HMGA2 expression had statistically shorter total survival and relapse-free survival (P < 0.05). Knockdown of HMGA2 resulted in decreased proliferation and invasion ability in MGC-803 cells decreased (P < 0.05), while its overexpression led to enhanced proliferation and invasion ability in SGC-7901 cells (P < 0.05). However, knocking down Snail1 would cause the invasion of HMGA2-overexpressed SGC-7901 cells reduced (P < 0.05), and the expression level of E-cadherin up-regulated and those of N-cadherin and Vimentin down-regulated. Conclusion High HMGA2 expression is associated with poor prognosis of gastric cancer patients, and it may promote invasion and metastasis of gastric cancer cells by regulating Snail1.

Promoting green innovation is an effective way to solve the increasingly serious environmental pollution problems in emerging economies. Information technology is constantly changing the operation mode of enterprises; however, whether information technology investment helps promote enterprises’ green innovation is still an important issue to be studied. According to resource-based and knowledge integration theory, this study constructs data from Chinese A-share listed heavy polluting enterprises during 2010–2018, adopting the panel data Tobit model to investigate the nexus between information technology investment and green innovation. Our empirical results demonstrate that the amount of information technology investment is positively correlated with the emerging levels of green patents in Chinese heavy polluting enterprises, and this positive correlation only significantly exists in state-owned enterprises (SOEs) and enterprises with a strong sense of environmental responsibility and strict environmental regulation. The findings of this study help understand in depth how information technology investment affects enterprises’ green innovation and its boundaries, which also have important policy implications for government departments and enterprises to make better use of information technology to deal with the challenge of environmental pollution.