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BackgroundSchizophrenic patients have metabolic disorders， impaired language and information processing function. Inflammatory factors may play an important role in the occurrence and development of schizophrenia.ObjectiveTo explore the relationship of the inflammatory factor levels with metabolic levels， language fluency and information processing function in patients with schizophrenia， so as to provide references for clinical understanding of the neuropathological mechanisms of schizophrenia.MethodsA total of 96 patients with schizophrenia were included in the study group， who were hospitalized in the Fourth People's Hospital of Hefei from January 2021 to December 2022 as well as met the diagnostic criteria of Diagnostic and Statistical Manual of Mental Disorders， fifth edition （DSM-5） and Mini-International Neuropsychiatric Interview （MINI） 6.0 .Meanwhile， population who underwent physical examination at the same hospital were included in the control group （n=42）. A high-sensitivity multi factor electrochemiluminescence analyzer was used to detect the levels of inflammatory factors IL-4， IL-5， IL-7， IL-8， IL-10 and IL-13. A fully automated biochemical analyzer was used to detect the levels of metabolic indicators such as fasting blood glucose， triglycerides， high-density lipoprotein， apolipoprotein A， creatinine and urea nitrogen. Verbal fluency and information processing function of all participants were assessed by using Verbal Fluency Test （VFT） and Stroop Color Word Test （SCWT）.ResultsThere were statistically significant differences in the levels of IL-4， IL-5， IL-7， IL-8， IL-10， IL-13 and IL-15 between the study group and the control group （P

Purposes Aiming at the problem that Granger Causality (GC) analysis in EEG emotion recognition does not consider the interaction between nodes when constructing brain network, a method of constructing brain network by combining fuzzy cognitive map (FCM) and GC analysis is proposed. Methods First, on the basis of the correspondence between the structure of FCM and GC brain network, the GC brain network is modeled and improved by using the causal attributes among FCM nodes, and the FCM-GC brain network is constructed, by taking into account the cooperative interaction among nodes. Furthermore, in order to deeply integrate FCM with GC brain network, the spatial position information of EEG electrode channel is added to FCM training, and a new IFCM-GC brain network is constructed. On the basis of DEAP emotional EEG database, the features of IFCM-GC brain network are extracted, and the support vec tor machine is used as the recognition model. The average recognition rates in valence dimension and incentive dimension are 97.10% and 97.00%, respectively, which are more than 8% higher than the existing research on GC improvement. Findings The GC brain network constructed by this method takes into account the cooperative interaction among multiple nodes, and effectively improves the performance of emotion recognition system.

Objective To explore the influencing factors of intracranial infection in patients after ventricular drainage, and to build and verify the established risk prediction model. Methods A retrospective analysis was carried out on 757 patients who underwent external ventricular drainage (EVD) in our department from January 2010 to December 2018. According to the occurrence of postoperative intracranial infection, they were divided into intracranial infection group (n=79) and non-intracranial infection group (n=678). Their clinical data were collected and compared. Univariate analysis and multivariate logistic regression analysis were used to explore the influencing factors of intracranial infection after ventricular drainage. A risk prediction model was established and anomogram was drawn. Another 148 patients with EVD in our hospital from 2019 to 2020 were selected as the validation group, including 8 patients with intracranial infection after surgery and 140 patients without. The area under receiver operating characteristic (ROC) curve and Hosmer-Lemeshow test were employed to evaluate the prediction efficacy of the model. Results Hypoproteinemia, lumbar cisternae drainage, EVD time >7 d, pulmonary infection, tracheotomy, open craniocerebral trauma, surgical incision infection, and complicated with bacteremia/sepsis were influencing factors of intracranial infection after EVD. The validation results for the prediction model showed that the area under ROC curve for the risk of postoperative intracranial infection was 0.923 in the modeling group and 0.920 in the verification sample (P < 0.01). As to the Hosmer-Lemeshow test, the P value was 0.450 in the modeling patients and 0.877 in those from the verification group. Conclusion There are 8 high-risk factors for incidence of intracranial infection in patients after EVD. Our established model can effectively predict the occurrence of intracranial infection in these patients.

ObjectiveTo investigate the radioactive level of gross alpha and beta in drinking water in one district in Shanghai and provide a scientific basis for measuring the level of radiation in case of drinking water pollution due to potential nuclear accidents.MethodsA total of154 samples collected across the district were monitored by using the standard examination method for drinking water - radiological parameters GB/T 5750.13-2006.ResultsAll the samples of the drinking water conformed to the standard for drinking water quality GB 5749-2006. The differences between different seasons were significant. The difference of gross alpha and beta radioactivity of drinking water was significant between the high water period and the dry water period. The former was higher than the latter.ConclusionIt is very important to monitor and study radioactivity of drinking water regularly for the prevention and control of health damage caused by radioactive water pollution.

Industrial Internet security is crucial for strengthening the manufacturing and network sectors of China. Deep learning, owing to its strong expression ability, good adaptability, and high portability, can support the establishment of an intelligent and autonomous industrial Internet security system and method. Therefore, it is of great value to promote the integrated innovation of deep learning and industrial Internet security. In this study, we analyze the development demand for industrial Internet security from the perspective of macro industrial environment, security technology, and deep learning system, and summarize the application status of deep learning to industrial Internet security in terms of device, control, network, application, and data layers. The security challenges faced by deep learning application to industrial Internet primarily lie in model training and prediction. Furthermore, we identify key research directions including interpretability of deep neural networks, cost control of sample collection and calculation, imbalance of sample sets, reliability of model results, and tradeoff between availability and security. Finally, some suggestions are proposed: A dynamic defense system in depth should be established in terms of overall security strategy; an application-driven and frontier exploration integrated method should be adopted to achieve breakthroughs regarding key technologies; and resources input should be raised for such interdisciplinary fields to establish an industry–university–research institute joint research ecosystem.

A new discrete firefly algorithm was proposed to solve the shortest escape path problem in underground coal mine. Firstly, the firefly individual was initialized using transfer probability method. And then, a new efficient encoding and decoding method was proposed to redefine space distance, the maximum fluorescence intensity and fluorescence relative brightness of the firefly. So the firefly individual state can be expressed as an effective path from the starting point to the target point. In order to increase the diversity of solutions and to prevent the solutions falling into the local optimum, disturbed operation was carried out to the represented path of the firefly by a certain probability. After several iterations, the shortest path of the solution can be obtained. The experimental results show that the proposed algorithm can converge to the optimal solution with the smaller population size and less iterations than the other algorithms, and has strong convergence and flexibility, which can be used to solve any problem of the shortest path.

Under the new situation, the improvement of investment efficiency and efficiency will be the basic criteria for evaluating the quality of power supply enterprises’ investment activities in the distribution network. To this end, based on the characteristics of distribution network construction, combined with the internal and external development trends of power supply enterprises and development and promotion needs, this paper constructs a scientific comprehensive evaluation index system and evaluation method for the investment benefits of distribution networks, which is useful for scientifically grasping the investment needs and investment capabilities of power supply enterprises. It is of great significance to achieve a more precise and lean level of investment in the distribution network.

For low prediction accuracy of D-S evidence theory used in water inrush prediction of coal floor because of poor robust and fusion effect under the condition of large conflict probability of evidence source, an adaptive robust evidence theory was proposed based on robust evidence theory. A water inrush prediction model of coal floor was established by combining the adaptive robust evidence theory and multiple neural networks. The model was experimentally researched by use of real hydrogeololgy data of a coal mine working face. The experimental result shows that the model has high prediction accuracy and good stability.

In view of problem that positioning accuracy of existing underground positioning algorithm is difficult to meet actual demand, a three-dimensional positioning algorithm based on TDOA and AOA used in coal mine underground was proposed combining with special environmental conditions in coal mine underground. The positioning algorithm uses sensor base station to measure the time difference that radiation emitted by unknown node arrived to different base stations and relative angle between them, and realizes positioning through coordinate information of the unknown node and base station itself. The simulation results show that the algorithm based on TDOA and AOA can effectively eliminate the noise and random error of measurements, and improves positioning accuracy combining with positioning model.

For high misjudgment rates of existing extension identification method which was applied in coal mine water bursting source discrimination, an improved extensicon identification method was put forward. The ranges of classical domain, joint domain and the value of evaluated matter-element of the existing method were normalized, and then the selection of optimal value was optimized. Finally, a new location value calculation method was proposed. Experiment results indicate that application of the improved method to coal mine water bursting source discrimination improves the level of matter-element belongs accurately to a certain aquifer, thus improves accuracy rate of water source discrimination.

In view of problem that using traditional wavelet transform to analyze singularity of metal magnetic memory signal easily suffered from noise interference, an early fault detection model combining empirical mode decomposition with wavelet transform was proposed. Firstly, metal magnetic memory signal of steel cord conveyor belt is decomposed into intrinsic mode function components through empirical mode decomposition, then singularity characteristic of the signal is extracted by use of wavelet transform modulus maxima method. The experimental results show that the model can reflect local characteristic of the signal with stronger anti-interference ability, and determine abnormal stress concentration zone of steel cord conveyor belt effectively, which provides basis for early fault diagnosis.

In view of disadvantages of long time-consuming and low prediction accuracy existed in prediction model of water inrush from coal seam floor based on traditional CART algorithm, a decision tree model based on improved CART algorithm was introduced, and the decision tree model was used to establish prediction model of water inrush from coal seam floor. The experiment results show that running time of the prediction model of water inrush from coal seam floor based on improved CART algorithm is reduced from 1.0411 s to 0.6125 s, and prediction accuracy rate is improved from 88.78% to 95.54%.

There is considerably greater variation in metabolic rates between men than between women, in terms of basal, activity and total (daily) energy expenditure (EE). One possible explanation is that EE is associated with male sexual characteristics (which are known to vary more than other traits) such as musculature and athletic capacity. Such traits might be predicted to be most prominent during periods of adolescence and young adulthood, when sexual behaviour develops and peaks. We tested this hypothesis on a large dataset by comparing the amount of male variation and female variation in total EE, activity EE and basal EE, at different life stages, along with several morphological traits: height, fat free mass and fat mass. Total EE, and to some degree also activity EE, exhibit considerable greater male variation (GMV) in young adults, and then a decreasing GMV in progressively older individuals. Arguably, basal EE, and also morphometrics, do not exhibit this pattern. These findings suggest that single male sexual characteristics may not exhibit peak GMV in young adulthood, however total and perhaps also activity EE, associated with many morphological and physiological traits combined, do exhibit GMV most prominently during the reproductive life stages.

BackgroundCertain product characteristics, such as flavor, may increase adolescents' willingness to try vaped nicotine and cannabis (marijuana) products.MethodsA discrete choice experiment embedded within the 2021-2022 California Teens Nicotine and Tobacco Project Online Survey was administered to a non-probability sample of N = 2342 adolescents ages 12-17. Participants were sequentially presented four randomly-generated pairs of hypothetical vape products that varied in device type (disposable, refillable), content (nicotine, marijuana, "just vapor"), and flavor (seven options) and asked which of these (or neither) they would be more willing to try if a best friend offered. Conditional logistic regression quantified associations between product characteristics and participants' selections, including interactions by past 30-day use of e-cigarettes, marijuana, or both.ResultsCandy/dessert, fruit, and fruit-ice combination flavors were all associated with greater willingness to try a vape product (versus tobacco flavor) among participants not using e-cigarettes or marijuana, those using only e-cigarettes, and those co-using e-cigarettes and marijuana. Among participants only using marijuana, the most preferred flavors were no flavor, candy/dessert, and icy/frost/menthol. Among participants not using e-cigarettes or marijuana, model-predicted willingness to try a displayed vape product was greater when products were sweet or fruit flavored than tobacco or unflavored, regardless of whether displayed options contained nicotine (fruit/sweet: 21 %, tobacco/unflavored: 4 %), marijuana (fruit/sweet: 18 %, tobacco/unflavored: 6 %), or "just vapor" (fruit/sweet: 29 %, tobacco/unflavored: 16 %).ConclusionsIn this online non-probability sample, flavors in nicotine and cannabis vape products increased adolescents' willingness to try them. Comprehensive bans on flavored vapes would likely reduce adolescent use.

Obesity is caused by a prolonged positive energy balance1,2. Whether reduced energy expenditure stemming from reduced activity levels contributes is debated3,4. Here we show that in both sexes, total energy expenditure (TEE) adjusted for body composition and age declined since the late 1980s, while adjusted activity energy expenditure increased over time. We use the International Atomic Energy Agency Doubly Labelled Water database on energy expenditure of adults in the United States and Europe (n = 4,799) to explore patterns in total (TEE: n = 4,799), basal (BEE: n = 1,432) and physical activity energy expenditure (n = 1,432) over time. In males, adjusted BEE decreased significantly, but in females this did not reach significance. A larger dataset of basal metabolic rate (equivalent to BEE) measurements of 9,912 adults across 163 studies spanning 100 years replicates the decline in BEE in both sexes. We conclude that increasing obesity in the United States/Europe has probably not been fuelled by reduced physical activity leading to lowered TEE. We identify here a decline in adjusted BEE as a previously unrecognized factor.