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Data-intensive sciences are becoming increasingly important for modern sciences. The transport control plane (TC-Plane) of the networks supporting data-intensive sciences can be important to achieve efficient and controlled transport of data for data-intensive sciences. In this paper, we analyze FTS, which is the de facto TC-Plane of the largest data-intensive network, revealing both efficiency and resource control issues of the current design. We then present the design and initial evaluation of Transport Control Networking (TCN), a design that is based on FTS but introduces (1) network-application co-design/coordination, which uses ALTO to realize network-wide resource control, and (2) a general, efficient, flexible optimization framework for TC-Plane, which allows both zero-order and first-order (e.g., bottleneck structure) gradient-based algorithms. We also discuss future work to engage the broad networking and data-intensive sciences communities.

Objective To construct a theoretical model for cognitive domain information transmission (COIT) to promote the establishment of an attack and defense system for COIT, and further to validate it by simulating data in mathematical model. Methods A hub-susceptible-infected-recovery (HSIR) model was proposed theoretically to conceptualize COIT, and then the model was validated with Runge-Kutta nonlinear integral equation for mathematical simulation. Results Standard HSIR model showed when cognitive domain information was transmitted in a large population of 10 000 people, the peak transmission was observed at Day 46 and 7 480 members were covered, with a basic reproduction number of 1.3. Further, when some measures were taken to obstruct COIT at Days 8, 4 and 2, respectively, the number of peak infects people was decreased to 5 822, 4 892 and 4 126, with a peak transmission duration shortened by 92, 62 and 41 d, in order. Conclusion Basing on HSIR model, we bring forward a dynamic model of COPT, and primarily construct a defense system of COPI combat.

ObjectiveTo understand the relationship between depression and mental health literacy or the attitude towards mental illness among grassroots administrators， in order to provide a reference for further improving mental health services and building a psychosocial service system.MethodsIn October 2020， a total of 690 grassroots administrators from street offices in Licheng district of Ji'nan city were selected as the research subjects. Self-designed general situation questionnaire， mental health care knowledge questionnaire， mental illness related attitude questionnaire and Self-rating Depression Scale （SDS） were used to evaluate. Multi-factor linear stepwise regression analysis was used to explore the influencing factors of mental health literacy and attitude towards mental illness among the grassroots administrators.ResultsA total of 248 people （37.07%） were found to have depression. There were significant differences in the detection rate of depression among different ages （χ2=16.110）， education level （χ2=18.949）， marital status （χ2=8.611） and position （χ2=11.584） （P˂0.05 or 0.01）. In such personnel， the score of mental health care knowledge questionnaire was （15.20±2.77）， the awareness rate was 75.99%， and the score of mental illness related attitude questionnaire was 36（31~38）. The severity of depression was negatively correlated with the score of mental health care knowledge questionnaire （r=-0.379， P˂0.01）， and positively correlated with the scores of mental illness related attitude questionnaire （r=0.103， P=0.007）. Regression analysis showed that the education level （β=0.141， P˂0.01） and the severity of depression （β=-0.305， P˂0.01） were the influencing factors of mental health literacy. And the severity of depression （β=0.083， P=0.034） was the influencing factor of the attitude towards mental illness.ConclusionGrassroots administrators has a high detection rate of depression. And the more depressed they are， the lower the mental health literacy， but the attitude of mental illness is more positive.

Objective To explore the change trend and pattern of sleep quality in time dimension in high-altitude recruits during new training, and the longitudinal moderation of negative cognitive processing bias in the process of depression affecting sleep quality. Methods A self-designed demographic data questionnaire, Pittsburgh Sleep Quality Index (PQSI), Negative Cognitive Processing Bias Questionnaire and Self-Rating Depression Scale were used to survey 952 cluster-sampled military recruits during training period who served in high-altitude regions along the Qinghai-Tibet Supply Line. Group assessment was performed in 1, 2 and 3 months after new training. The score for depression at the first month and the score of negative cognitive processing bias at the second month were collected and used to predict the sleep quality at the end of the 3 months of new training. Results The military recruits had a mean score of PSQI of 4.45± 2.44, 4.01±2.15 and 4.45±2.54, respectively in the 3 time points, significantly higher than the national norms for Chinese (P < 0.01). Their sleep quality showed a trend of improving first and then decreasing with the extension of training time (P < 0.01). Nationality, age and educational level significantly affected the sleep quality (P < 0.05). The total PSQI score was positively correlated with educational level, depressed mood, negative cognitive processing bias and its dimensions among these individuals (r=0.131~0.330, P < 0.01). Regulatory effect analysis showed that negative memory bias could modulate the relationship between depression and sleep quality (t=2.498, P < 0.05), and explained 15.7% of the variance rate of sleep quality. Conclusion The recruits serving in high-altitude regions have a severe sleep quality. Nationality, age, educational level, depression and negative cognitive processing bias are important factors affecting the sleep quality in them during new training. Negative memory bias has a significant longitudinal moderate effect between depression and sleep quality and is a risk factor of depression affecting sleep quality, in long-term risk effectiveness.

The honeycomb core replacement scarf repair method was used to repair the one side face-sheet and honeycomb core damage of aluminum honeycomb sandwich composite plate. The drop hammer impact tests were carried out on the intact honeycomb sandwich panel and the repaired honeycomb sandwich panel, and the influence of honeycomb core replacement scarf repair on the impact resistance properties was compared and analyzed. X-ray digital imaging technology and macroscopic observation were used to investigate the damage forms and internal failure mechanisms of composite repair structure under the low velocity impact. The residual compression property after impact was characterized. The results show that with the increase of impact energy, the damage areas of both intact and repaired honeycomb sandwich panels are increased, the damage area of intact honeycomb sandwich panel is larger than that of repaired ones under the same impact energy. The impact load curve type of repaired honeycomb sandwich panel is changed completely, and the repaired honeycomb sandwich panel shows better impact resistance. Under the same impact energy, the CAI strength of the repaired honeycomb sandwich panel is higher than that of the intact ones. The failure mechanism includes the expansion of impact damage and compression damage. The impact resistance of the honeycomb splicing area is the best.

Ammonium inhibition is a key limiting factor for anaerobic digestion when using chicken manure as the main substrate, especially in a digestion system with biogas slurry reflux. Air stripping is usually used as a recycled biogas slurry treatment. In this study, we carried out the anaerobic co-digestion of chicken manure and corn straw. The fermentation performance was investigated with and without air stripping at different biogas slurry reflux ratios and with an increasing organic loading rate. The results show that air stripping enhanced biogas production, system stability, and volatile solid removal efficiency via the mitigation control of ammonium inhibition. The total ammonium nitrogen in the digesters with air stripping was 20.24–46.40% lower than in those without air stripping. The highest specific biogas production and volatile solid removal efficiency values were obtained in the digesters at an organic loading rate of 3.3 g volatile solid (VS)/(L·d) and a reflux ratio of 75% with air stripping, reaching 480.43 mL/gVSadd and 63.36%, respectively. Moreover, air stripping also improved the organic loading rate and reflux ratio. Stable operation was achieved at an organic loading rate of 5.3 gVS/(L·d) and a reflux ratio of 75%, with specific biogas production of 392.35 mL/gVSadd and a volatile solid removal efficiency of 50.33%. The fermentation performance deteriorated when the organic loading rate was increased to 8.0 gVS/(L·d) at a reflux ratio of 75%, even when air stripping was conducted, indicating that a slighter lower reflux ratio (50%) could be more feasible at a higher organic loading rate (8.0 gVS/(L·d). Additionally, the methanogen community structure varied according to the use of air stripping, with a shift in the methanogenic pathway from hydrogenotrophic to acetoclastic methanogens. Overall, our findings support the adoption of air stripping for ammonium mitigation in anaerobic digestion with biogas slurry reflux. [ABSTRACT FROM AUTHOR]

Background: Airborne pollen is a crucial risk factor in allergic rhinitis (AR). The severity of AR symptoms can vary based on pollen type and concentration. This study aimed to estimate the association between exposure to different pollen types and AR risk. Methods: We obtained data from patients admitted to the Beijing Tongren Hospital for AR, and data on pollen concentration, meteorological factors, and fine particulate matter (PM2.5) from 13 districts in Beijing from 2016 to 2019. We used a time‐stratified case‐crossover study design and calculated odds ratios (ORs) related to the risk of AR associated with a 10 grain/1000 mm2 increase in total pollen concentrations for specific pollen types. A stratified analysis was conducted to assess whether the associations were varied by age and sex. Results: The OR of AR associated with a 10 grain/1000 mm2 increase in the 7‐day average pollen concentration was 1.014 (95% CI: 1.014, 1.015), 1.076 (95% CI: 1.070, 1.082), 1.024 (95% CI: 1.023, 1.025), 1.042 (95% CI: 1.039, 1.045), 1.142 (95% CI: 1.137, 1.147), 1.092 (95% CI: 1.088, 1.097), 1.046 (95% CI: 1.035, 1.058), and 1.026 (95% CI: 1.024, 1.028) for total pollen, Ulmus, Cupressaceae, Populus, Fraxinus, Pinus, Betula, and Artemisia, respectively. Both tree pollen (Ulmus, Cupressaceae, Populus, Fraxinus, Betula, and Pinus) and weed pollen (Artemisia, Chenopodium, and Humulus) were correlated with an increased risk of AR. These associations remained consistent across distinct subgroups defined by both age and sex. Conclusion: Exposure to pollen from trees and weeds might be associated with an increased risk of AR. This research provides valuable scientific support for both clinical practitioners and patients with AR regarding the hazards of pollen exposure. [ABSTRACT FROM AUTHOR]

Objective To explore the characteristics of electroencephalogram (EEG) microstates of individuals with different levels of depressive symptoms in resting state so as to provide objective biomarkers in screening and identification of depressive symptoms in the population. Methods A total of 129 healthy volunteers aged 18 to 40 years were recruited from July to October 2020 in Chongqing. According to the results of Self-Rating Depression Scale (SDS), they were divided into following 3 groups: non-depressive group (n=107), mild depressive group (n=18) and moderate to severe depressive group (n=4). EEG signals of subjects in 8-min resting state were collected, and then were preprocessed and analyzed by MATLAB, EEGLAB and other tools to obtain 4 types (A, B, C and D) of microstate and their parameters, including global explanation variance (GEV), duration, occurrence frequency, coverage rate and conversion rate. SPSS24.0 was used for statistical analysis of each parameter. Results ① Among the subjects, 17.05% showed depressive symptoms. The average GEV of resting EEG microstates in healthy volunteers was 76.49%. ②The mean duration of microstate A in the mild depressive group was lower than that in the non-depressive and moderate to severe depressive group (P < 0.001). ③EEG microstate A had higher occurrence frequency than the other 3 types of microstates in the non-depressive and moderatet to severe depressive group (P < 0.001). ④The mild depressive group presented lower coverage rate of microstate A than the other 2 groups (P < 0.001). ⑤There were differences in conversion rates among different types of microstates (P < 0.05). Conclusion The duration of microstate A may be an early warning signal of the occurrence and development of depressive symptoms, which characterizes the preclinical state of depression. The declined conversion rate of microstates A and C may indicate the occurrence of depressive symptoms, and the alteration in the conversion rate of microstates C and D may be a key feature of the development and progression of depressive symptoms.