Genuine nonlocality of generalized GHZ states in many-partite systems

A set of orthogonal multipartite quantum states is said to be distinguishability-based genuinely nonlocal (also genuinely nonlocal, for abbreviation) if the states are locally indistinguishable across any bipartition of the subsystems. In this work, we study the (distinguishability-based) genuine nonlocality of the generalized GHZ states, primarily for the case when a large number of partites are considered. For the N-qubit case, we show that genuinely nonlocal subsets of the GHZ basis with cardianlity {\Theta}(2^(N/2)) exist. We also generalize this result to the cases when d > 2 is an even number

A computational offloading optimization scheme based on deep reinforcement learning in perceptual network

Currently, the deep integration of the Internet of Things (IoT) and edge computing has improved the computing capability of the IoT perception layer. Existing offloading techniques for edge computing suffer from the single problem of solidifying offloading policies. Based on this, combined with the characteristics of deep reinforcement learning, this paper investigates a computation offloading optimization scheme for the perception layer. The algorithm can adaptively adjust the computational task offloading policy of IoT terminals according to the network changes in the perception layer. Experiments show that the algorithm effectively improves the operational efficiency of the IoT perceptual layer and reduces the average task delay compared with other offloading algorithms

Associations of fecal microbial profiles with breast cancer and non-malignant breast disease in the Ghana Breast Health Study

The gut microbiota may play a role in breast cancer etiology by regulating hormonal, metabolic and immunologic pathways. We investigated associations of fecal bacteria with breast cancer and nonmalignant breast disease in a case-control study conducted in Ghana, a country with rising breast cancer incidence and mortality. To do this, we sequenced the V4 region of the 16S rRNA gene to characterize bacteria in fecal samples collected at the time of breast biopsy (N = 379 breast cancer cases, N = 102 nonmalignant breast disease cases, N = 414 population-based controls). We estimated associations of alpha diversity (observed amplicon sequence variants [ASVs], Shannon index, and Faith's phylogenetic diversity), beta diversity (Bray-Curtis and unweighted/weighted UniFrac distance), and the presence and relative abundance of select taxa with breast cancer and nonmalignant breast disease using multivariable unconditional polytomous logistic regression. All alpha diversity metrics were strongly, inversely associated with odds of breast cancer and for those in the highest relative to lowest tertile of observed ASVs, the odds ratio (95% confidence interval) was 0.21 (0.13-0.36; Ptrend < .001). Alpha diversity associations were similar for nonmalignant breast disease and breast cancer grade/molecular subtype. All beta diversity distance matrices and multiple taxa with possible estrogen-conjugating and immune-related functions were strongly associated with breast cancer (all Ps < .001). There were no statistically significant differences between breast cancer and nonmalignant breast disease cases in any microbiota metric. In conclusion, fecal bacterial characteristics were strongly and similarly associated with breast cancer and nonmalignant breast disease. Our findings provide novel insight into potential microbially-mediated mechanisms of breast disease

Genetic algorithm-based congestion control optimisation for mobile data network

Mobile data network is featured by long delay and moving terminals, which affect the user service quality performance of transmission control protocol’s (TCP) congestion control algorithm Vegas. To solve this problem, this paper first proposed to optimise the congestion control algorithm using a genetic algorithm, build ns-3 network topology structure and adopt mobile data network trace for optimisation and simulation; and the Vegas optimisation problem as a multivariate dual-objective problem was solved with Non-dominated Sorting Genetic Algorithm II (NSGA-II). The ns-3 simulation results indicate that Vegas with optimised parameters have high throughput and short delay, which significantly promotes TCP Vegas’s QoS under a mobile scene

Construction and Validation of a Nomogram Predicting Depression Risk in Patients with Acute Coronary Syndrome Undergoing Coronary Stenting: A Prospective Cohort Study

Purpose: To construct and validate a nomogram for predicting depression after acute coronary stent implantation for risk assessment. Methods: This study included 150 patients with acute coronary syndrome (ACS) who underwent stent implantation. Univariate analysis was performed to identify the predictors of postoperative depression among the 24 factors. Subsequently, multivariate logistic regression was performed to incorporate the significant predictors into the prediction model. The model was developed using the “rms” software package in R software, and internal validation was performed using the bootstrap method. Results: Of the 150 patients, 82 developed depressive symptoms after coronary stent implantation, resulting in an incidence of depression of 54.7%. Univariate analysis showed that sleep duration ≥7 h, baseline GAD-7 score, baseline PHQ-9 score, and postoperative GAD-7 score were associated with the occurrence of depression after stenting in ACS patients (all p p = 0.027), GAD-7 score after operation (OR = 1.165, 95% CI: 1.275–2.097, p = 0.000), and baseline PHQ-9 score (OR = 3.221, 95%CI: 2.065–5.023, p = 0.000) were significant independent risk factors for ACS patients after stent implantation. Based on these results, a predictive nomogram was constructed. The model demonstrated good prediction ability, with an AUC of 0.857 (95% CI = 0.799–0.916). The correction curve showed a good correlation between the predicted results and the actual results (Brier score = 0.15). The decision curve analysis and prediction model curve had clinical practical value in the threshold probability range of 7 to 94%. Conclusions: This nomogram can help to predict the incidence of depression and has good clinical application value. This trial is registered with ChiCTR2300071408

A New Short-term Power Load Forecasting Model Based on Chaotic Time Series and SVM

This paper presents a model for power load forecasting using support vector machine and chaotic time series. The new model can make more accurate prediction. In the past few years, along with power system privatization and deregulation, accurate forecast of electricity load has received increasing attention. According to the chaotic and non-linear characters of power load data, the model of support vector machines (SVM) based on chaotic time series has been established. The time series matrix has also been established according to the theory of phase-space reconstruction. The Lyapunov exponents, one important component of chaotic time series, are used to determine time delay and embedding dimension, the decisive parameters for SVM. Then support vector machines algorithm is used to predict power load. In order to prove the rationality of chosen dimension, another two random dimensions are selected to compare with the calculated dimension. And to prove the effectiveness of the model, BP algorithm is used to compare with the results of SVM. Findings show that the model is effective and highly accurate in the forecasting of short-term power load. It means that the model combined with SVM and chaotic time series learning system have more advantage than other models

A New Short-term Power Load Forecasting Model Based on Chaotic Time Series and SVM

This paper presents a model for power load forecasting using support vector machine and chaotic time series. The new model can make more accurate prediction. In the past few years, along with power system privatization and deregulation, accurate forecast of electricity load has received increasing attention. According to the chaotic and non-linear characters of power load data, the model of support vector machines (SVM) based on chaotic time series has been established. The time series matrix has also been established according to the theory of phase-space reconstruction. The Lyapunov exponents, one important component of chaotic time series, are used to determine time delay and embedding dimension, the decisive parameters for SVM. Then support vector machines algorithm is used to predict power load. In order to prove the rationality of chosen dimension, another two random dimensions are selected to compare with the calculated dimension. And to prove the effectiveness of the model, BP algorithm is used to compare with the results of SVM. Findings show that the model is effective and highly accurate in the forecasting of short-term power load. It means that the model combined with SVM and chaotic time series learning system have more advantage than other models

Electroacupuncture Pretreatment Regulates Apoptosis of Myocardial Ischemia-Reperfusion Injury in Rats Through RhoA/p38MAPK Pathway Mediated by miR-133a-5p

The electroacupuncture (EA) pretreatment possesses a beneficial effect on myocardial ischemia/reperfusion (I/R) injury. However, the molecular mechanism of the EA effect is not fully understood. The study aimed to explore the protective effect of EA pretreatment on myocardial ischemia-reperfusion injury (MIRI) and apoptosis-related mechanisms in rats. Rats underwent in vivo myocardial ischemia-reperfusion, EA pretreatment, or intravenous injection of antagomirs. Cardiac function, infarct area, and myocardial cell apoptosis were measured. Meanwhile, the expressions of MKK3, MKK6, p38MAPK, Bax, Bcl-2, and Caspase-3 were also detected. We found that EA pretreatment significantly reduced infarct area and myocarpal cell apoptosis and enhanced cardiac function. EA pretreatment decreased the expression of Bax, Caspase-3, MKK3, MKK6, p38MAPK, Bax, and Caspase-3. In conclusion, The EA pretreatment down regulated the expression of MKK3, MKK6, and p38MAPK through the RhoA/p38MAPK pathway. EA pretreatment protect MIRI rats from apoptosis by down regulating the expression of MKK3, MKK6, and p38MAPK, thereby reducing the expression of Bax, Caspase-3 and up regulating the expression of Bcl-2, which mechanism is closely related to the RhoA/p38MAPK pathway mediated by miR-133a-5p

Evaluation of microbial communities of Chinese Feng-flavor Daqu with effects of environmental factors using traceability analysis

Abstract Analysis of the changes of microorganisms during Chinese Feng-flavor Daqu fermentation, and the specific contribution of different environmental factors to Daqu microorganisms. High throughput sequencing technology and SourceTracker software were used to analyze the microbial diversity of Feng-flavor Daqu before and after fermentation. 85 fungal and 105 bacterial were detected in the newly pressed Feng-flavor Daqu, while 33 fungal and 50 bacterial in the mature Daqu, and 202 fungal and 555 bacterial in the environmental samples. After fermentation, the microbial community structure of Daqu changed and decreased significantly. 94.7% of fungi come from raw materials and 1.8% from outdoor ground, 60.95% of bacteria come from indoor ground, 20.44% from raw materials, and 8.98% from tools. By comparing the changes of microorganisms in Daqu before and after fermentation, the microorganisms in mature Daqu may mainly come from not only the enhanced strains but also the environment.The source of main microorganisms in Feng-flavor Daqu and the influence of environmental factors on the quality of Daqu were clarified, which provided a basis for improving the quality of Feng-flavor Daqu