Network Traffic Statistics Method for Resource-Constrained Industrial Project Group Scheduling under Big Data

With the advent of the Internet era, the demand for network in various fields is growing, and network applications are increasingly rich, which brings new challenges to network traffic statistics. How to carry out network traffic statistics efficiently and accurately has become the focus of research. Although the current research results are many, they are not very ideal. Based on the era background of big data and machine learning algorithm, this paper uses the ant colony algorithm to solve the typical resource-constrained project scheduling problem and finds the optimal solution of network traffic resource allocation problem. Firstly, the objective function and mathematical model of the resource-constrained project scheduling problem are established, and the ant colony algorithm is used for optimization. Then, the project scheduling problem in PSPLIB is introduced, which contains 10 tasks and 1 renewable resource. The mathematical model and ant colony algorithm are used to solve the resource-constrained project scheduling problem. Finally, the data quantity and frequency of a PCU with a busy hour IP of 112.58.14.66 are analyzed and counted. The experimental results show that the algorithm can get the unique optimal solution after the 94th generation, which shows that the parameters set in the solution method are appropriate and the optimal solution can be obtained. The schedule of each task in the optimal scheduling scheme is very compact and reasonable. The peak time of network traffic is usually between 9 : 00 and 19 : 00-21 : 00. We can reasonably schedule the network resources according to these time periods. Therefore, the network traffic statistics method based on the solution of resource constrained industrial project group scheduling problem under big data can effectively carry out network traffic statistics and trend analysis

Experimental and numerical studies on spray characteristics of an internal oscillating nozzle

© 2019 by Begell House, Inc. An open atomization test bench based on high-speed Schlieren technology and a Malvern particle size analyzer was developed to investigate the effects of different injection pressures (0.12 MPa to 0.24 MPa) on spray characteristics of an internal oscillating nozzle, including the spatial distribution of flow rate, oscillation frequency, spray cone angle, spatial distribution of spray particle size, and velocity. A numerical investigation that simultaneously considered the internal flow field and the external spray within the same computational field was performed to reveal the oscillation mechanism. The experimental results indicated that the spray of the internal oscillating nozzle shows a fan shape distribution with small flow in the middle and large distribution on both sides. The flow rate gradually increases with the rising of injection pressure and reaches its maximum at 0.24 MPa when the distance from the nozzle is constant. The oscillating frequency keeps an upward tendency with a maximum growth rate when the injection pressure ascends from 0.15 MPa to 0.18 MPa. The spray cone angle does not change significantly with the increase of the injection pressure, fluctuating at approximately 41.8 degrees. Moreover, a critical injection pressure is obtained, below which the droplet size increases with the rise of the injection pressure and above which the droplet size declines moderately. The numerical investigation revealed that the oscillation phenomenon was generated due to the periodic establishing and vanishing of the pressure gradient within the feedback channels, and the Coanda effect occurred in the main flow passage

A Novel Polyvinylidene Fluoride Tree-Like Nanofiber Membrane for Microfiltration

A novel polyvinylidene fluoride (PVDF) tree-like nanofiber membrane (PVDF-TLNM) was fabricated by adding tetrabutylammonium chloride (TBAC) into a PVDF spinning solution via one-step electrospinning. The structure of the prepared membranes was characterized by field emission scanning electron microscopy (FE-SEM), Fourier transform infrared spectroscopy (FT-IR) and pore size analysis, and the hydrophilic property and microfiltration performance were also evaluated. The results showed that the tree-like nanofiber was composed of trunk fibers and branch fibers with diameters of 100–500 nm and 5–100 nm, respectively. The pore size of PVDF-TLNM (0.36 μm) was smaller than that of a common nanofiber membrane (3.52 μm), and the hydrophilic properties of the membranes were improved significantly. The PVDF-TLNM with a thickness of 30 ± 2 μm showed a satisfactory retention ratio of 99.9% against 0.3 μm polystyrene (PS) particles and a high pure water flux of 2.88 × 104 L·m−2·h−1 under the pressure of 25 psi. This study highlights the potential benefits of this novel PVDF tree-like nanofiber membrane in the membrane field, which can achieve high flux rates at low pressure

Targeting the Pulmonary Microbiota to Fight against Respiratory Diseases

The mucosal immune system of the respiratory tract possesses an effective “defense barrier” against the invading pathogenic microorganisms; therefore, the lungs of healthy organisms are considered to be sterile for a long time according to the strong pathogens-eliminating ability. The emergence of next-generation sequencing technology has accelerated the studies about the microbial communities and immune regulating functions of lung microbiota during the past two decades. The acquisition and maturation of respiratory microbiota during childhood are mainly determined by the birth mode, diet structure, environmental exposure and antibiotic usage. However, the formation and development of lung microbiota in early life might affect the occurrence of respiratory diseases throughout the whole life cycle. The interplay and crosstalk between the gut and lung can be realized by the direct exchange of microbial species through the lymph circulation, moreover, the bioactive metabolites produced by the gut microbiota and lung microbiota can be changed via blood circulation. Complicated interactions among the lung microbiota, the respiratory viruses, and the host immune system can regulate the immune homeostasis and affect the inflammatory response in the lung. Probiotics, prebiotics, functional foods and fecal microbiota transplantation can all be used to maintain the microbial homeostasis of intestinal microbiota and lung microbiota. Therefore, various kinds of interventions on manipulating the symbiotic microbiota might be explored as novel effective strategies to prevent and control respiratory diseases

Validity of a multiphase CT-based radiomics model in predicting the Leibovich risk groups for localized clear cell renal cell carcinoma: an exploratory study

Abstract Objective To develop and validate a multiphase CT-based radiomics model for preoperative risk stratification of patients with localized clear cell renal cell carcinoma (ccRCC). Methods A total of 425 patients with localized ccRCC were enrolled and divided into training, validation, and external testing cohorts. Radiomics features were extracted from three-phase CT images (unenhanced, arterial, and venous), and radiomics signatures were constructed by the least absolute shrinkage and selection operator (LASSO) regression algorithm. The radiomics score (Rad-score) for each patient was calculated. The radiomics model was established and visualized as a nomogram by incorporating significant clinical factors and Rad-score. The predictive performance of the radiomics model was evaluated by the receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA). Results The AUC of the triphasic radiomics signature reached 0.862 (95% CI: 0.809–0.914), 0.853 (95% CI: 0.785–0.921), and 0.837 (95% CI: 0.714–0.959) in three cohorts, respectively, which were higher than arterial, venous, and unenhanced radiomics signatures. Multivariate logistic regression analysis showed that Rad-score (OR: 4.066, 95% CI: 3.495–8.790) and renal vein invasion (OR: 12.914, 95% CI: 1.118–149.112) were independent predictors and used to develop the radiomics model. The radiomics model showed good calibration and discrimination and yielded an AUC of 0.872 (95% CI: 0.821–0.923), 0.865 (95% CI: 0.800–0.930), and 0.848 (95% CI: 0.728–0.967) in three cohorts, respectively. DCA showed the clinical usefulness of the radiomics model in predicting the Leibovich risk groups. Conclusions The radiomics model can be used as a non-invasive and useful tool to predict the Leibovich risk groups for localized ccRCC patients. Critical relevance statement The triphasic CT-based radiomics model achieved favorable performance in preoperatively predicting the Leibovich risk groups in patients with localized ccRCC. Therefore, it can be used as a non-invasive and effective tool for preoperative risk stratification of patients with localized ccRCC. Key points • The triphasic CT-based radiomics signature achieves better performance than the single-phase radiomics signature. • Radiomics holds prospects in preoperatively predicting the Leibovich risk groups for ccRCC. • This study provides a non-invasive method to stratify patients with localized ccRCC. Graphical Abstrac

Development and validation of a CT-based deep learning radiomics nomogram to predict muscle invasion in bladder cancer

Objective: This study aimed to develop a nomogram combining CT-based handcrafted radiomics and deep learning (DL) features to preoperatively predict muscle invasion in bladder cancer (BCa) with multi-center validation. Methods: In this retrospective study, 323 patients underwent radical cystectomy with pathologically confirmed BCa were enrolled and randomly divided into the training cohort (n = 226) and internal validation cohort (n = 97). And fifty-two patients from another independent medical center were enrolled as an independent external validation cohort. Handcrafted radiomics and DL features were constructed from preoperative nephrographic phase CT images. Least absolute shrinkage and selection operator (LASSO) regression was used to identify the most discriminative features in train cohort. Multivariate logistic regression was used to develop the predictive model and a deep learning radiomics nomogram (DLRN) was constructed. The predictive performance of models was evaluated by area under the curves (AUC) in the three cohorts. The calibration and clinical usefulness of DLRN were estimated by calibration curve and decision curve analysis. Results: The nomogram that incorporated radiomics signature and DL signature demonstrated satisfactory predictive performance for differentiating non-muscle invasive bladder cancer (NMIBC) from muscle invasive bladder cancer (MIBC), with an AUC of 0.884 (95 % CI: 0.813–0.953) in internal validation cohort and 0.862 (95 % CI: 0.756–0.968) in external validation cohort, respectively. Decision curve analysis confirmed the clinical usefulness of the nomogram. Conclusions: A CT-based deep learning radiomics nomogram exhibited a promising performance for preoperative prediction of muscle invasion in bladder cancer, and may be helpful in the clinical decision-making process

Genotype Change in Circulating JEV Strains in Fujian Province, China

Japanese encephalitis (JE), found in pigs, is a serious mosquito-borne zoonotic infectious disease caused by the Japanese encephalitis virus (JEV). JEV is maintained in an enzootic cycle between mosquitoes and amplifying vertebrate hosts, mainly pigs and wading birds. It is transmitted to humans through the bite of an infected mosquito, allowing the pathogen to spread and cause disease epidemics. However, there is little research on JEV genotype variation in mosquitoes and pigs in Fujian province. Previous studies have shown that the main epidemic strain of JEV in Fujian Province is genotype III. In this study, a survey of mosquito species diversity in pig farms and molecular evolutionary analyses of JEV were conducted in Fujian, China, in the summer of 2019. A total of 19,177 mosquitoes were collected at four sites by UV trap. Four genera were identified, of which the Culex tritaeniorhynchus was the most common mosquito species, accounting for 76.4% of the total (14,651/19,177). Anopheles sinensi (19.25%, 3691/19,177) was the second largest species. High mosquito infection rateswere an important factor in the outbreak. The captured mosquito samples were milled and screened with JEV-specific primers. Five viruses were isolated, FJ1901, FJ1902, FJ1903, FJ1904, and FJ1905. Genetic affinity was determined by analyzing the envelope (E) gene variants. The results showed that they are JEV gene type I and most closely related to the strains SH-53 and SD0810. In this study, it was found through genetic evolution analysis that the main epidemic strain of JE in pig farms changed from gene type III to gene type I. Compared with the SH-53 and SD0810 strains, we found no change in key sites related to antigenic activity and neurovirulence of JEV in Fujian JEV and pig mosquito strains, respectively. The results of the study provide basic data for analyzing the genotypic shift of JEV in Fujian Province and support the prevention and control of JEV

Comparative Analyses of the Gut Microbiota in Growing Ragdoll Cats and Felinae Cats

Today, domestic cats are important human companion animals for their appearance and favorable personalities. During the history of their domestication, the morphological and genetic portraits of domestic cats changed significantly from their wild ancestors, and the gut microbial communities of different breeds of cats also apparently differ. In the current study, the gut microbiota of Ragdoll cats and Felinae cats were analyzed and compared. Our data indicated that the diversity and richness of the gut microbiota in the Felinae cats were much higher than in the Ragdoll cats. The taxonomic analyses revealed that the most predominant phyla of the feline gut microbiota were Firmicutes, Bacteroidota, Fusobacteriota, Proteobacteria, Actinobacteriota, Campilobacterota, and others, while the most predominant genera were Anaerococcus, Fusobacterium, Bacteroides, Escherichia-Shigella, Finegoldia, Porphyromonas, Collinsella, Lactobacillus, Ruminococcus_gnavus_group, Prevotella, and others. Different microbial communities between the Ragdoll group and the Felinae group were observed, and the compared results demonstrated that the relative abundances of beneficial microbes (such as Lactobacillus, Enterococcus, Streptococcus, Blautia, Roseburia, and so on) in the Ragdoll group were much higher than in the Felinae group. The co-occurrence network revealed that the number of nodes and links in the Felinae group was significantly higher than the Ragdoll group, which meant that the network of the Felinae group was larger and more complex than that of the Ragdoll group. PICRUSt function analyses indicated that the differences in microbial genes might influence the energy metabolism and immune functions of the host. In all, our data demonstrated that the richness and diversity of beneficial microbes in the Ragdoll group were much higher than the Felinae group. Therefore, it is possible to isolate and identify more candidate probiotics in the gut microbiota of growing Ragdoll cats