Catalyst Composition and Content Effects on the Synthesis of Single-Walled Carbon Nanotubes by Arc Discharge

Single-walled carbon nanotubes (SWCNTs) were prepared by a modified arc discharging furnace using Fe-Ni-Mg powders as catalyst at 600∘C. The effects of catalyst composition and content on the production rate and purity of SWCNTs are investigated in this paper. When the Fe-Ni-Mg catalyst composition is 2: 1: 2 wt% and the catalyst content is 5 wt%, the experimental results indicate that the production of SWCNTs is 12 grams per hour, and the purity and diameter of SWCNTs are 70% and 1.22 ∼1.38 nm, respectively. The results indicate that the cooperative function of catalyst composition and content plays an important role in the production of SWCNTs. The aim of this work is to control the production process of SWCNTs efficiently

Development and validation of nomogram models to predict radiotherapy or chemotherapy benefit in stage III/IV gastric adenocarcinoma with surgery

ObjectivesThe advanced gastric adenocarcinoma (GAC) patients (stage III/IV) with surgery may have inconsistent prognoses due to different demographic and clinicopathological factors. In this retrospective study, we developed clinical prediction models for estimating the overall survival (OS) and cancer-specific survival (CSS) in advanced GAC patients with surgeryMethodsA retrospective analysis was conducted using the Surveillance, Epidemiology, and End Results (SEER) database. The total population from 2004 to 2015 was divided into four levels according to age, of which 179 were younger than 45 years old, 695 were 45-59 years old, 1064 were 60-74 years old, and 708 were older than 75 years old. There were 1,712 men and 934 women. Univariate and multivariate Cox regression analyses were performed to identify prognostic factors for OS and CSS. Nomograms were constructed to predict the 1-, 3-, and 5-year OS and CSS. The models’ calibration and discrimination efficiency were validated. Discrimination and accuracy were evaluated using the consistency index, area under the receiver operating characteristic curve, and calibration plots; and clinical usefulness was assessed using decision curve analysis. Cross-validation was also conducted to evaluate the accuracy and stability of the models. Prognostic factors identified by Cox regression were analyzed using Kaplan-Meier survival analysis.ResultsA total of 2,646 patients were included in our OS study. Age, primary site, differentiation grade, AJCC 6th_TNM stage, chemotherapy, radiotherapy, and number of regional nodes examined were identified as prognostic factors for OS in advanced GAC patients with surgery (P < 0.05). A total of 2,369 patients were included in our CSS study. Age, primary site, differentiation grade, AJCC 6th_TNM stage, chemotherapy, radiotherapy, and number of regional nodes examined were identified as risk factors for CSS in these patients (P < 0.05). These factors were used to construct the nomogram to predict the 1-, 3-, and 5-year OS and CSS of advanced GAC patients with surgery. The consistency index and area under the receiver operating characteristic curve demonstrated that the models effectively differentiated between events and nonevents. The calibration plots for 1-, 3-, and 5-year OS and CSS probability showed good consistence between the predicted and the actual events. The decision curve analysis indicated that the nomogram had higher clinical predictive value and more significant net gain than AJCC 6th_TNM stage in predicting OS and CSS of advanced GAC patients with surgery. Cross-validation also revealed good accuracy and stability of the models.ConclusionThe developed predictive models provided available prognostic estimates for advanced GAC patients with surgery. Our findings suggested that both OS and CSS can benefit from chemotherapy or radiotherapy in these patients

MOCA: A Network Intrusion Monitoring and Classification System

Optimizing the monitoring of network traffic features to detect abnormal traffic is critical. We propose a two-stage monitoring and classification (MOCA) system requiring fewer features to detect and classify malicious network attacks. The first stage monitors abnormal traffic, and the anomalous traffic is forwarded for processing in the second stage. A small subset of features trains both classifiers. We demonstrate MOCA’s effectiveness in identifying attacks in the CICIDS2017 dataset with an accuracy of 99.84% and in the CICDDOS2019 dataset with an accuracy of 93%, which significantly outperforms previous methods. We also found that MOCA can use a pre-trained classifier with one feature to distinguish DDoS and Botnet attacks from normal traffic in four different datasets. Our measurements show that MOCA can distinguish DDoS attacks from normal traffic in the CICDDOS2019 dataset with an accuracy of 96% and DDoS attacks in non-IoT and IoT traffic with an accuracy of 99.94%. The results emphasize the importance of using connection features to discriminate new DDoS and Bot attacks from benign traffic, especially with insufficient training samples

Towards an Efficient Verification Approach on Network Configuration

Abstract-This paper presents our new design and implementation of a configuration verification system called ConfVS. With the increasing complexity of network configuration, verifying network behavior has become a highly time-consuming and errorprone process. Much research effort has been made to tackle this challenge. In this paper, we propose a formalization scheme based on binary decision diagram to model the entire network behavior specified by diverse configuration requirements (e.g., security policies, routing policies, and address translation rules), and design a set of algorithms to efficiently verify the compliance of network behavior to the requirements. Our experiments show that ConfVS can validate thousands of network devices configured by millions rules with ten times improved efficiency when compared to several well-known existing solutions

Local Path Searching Based Map Matching Algorithm for Floating Car Data

AbstractThe information acquisition of road traffic flow is requisite for urban traffic control and management. Floating car data (FCD) is emerging technique for traffic flow collection of urban large-scale road network, and it can provide effective means to model and analyze road traffic conditions. Map-matching is one of the key techniques for FCD. The typical navigation map-matching algorithms are not suitable for handling FCD with large sample interval. Through analyzing FCD characteristics, we first propose FCD map-matching algorithm based on local path searching. The information of the previous matched GPS point is utilized to reduce the search space significantly. Square confidence area is constructed to decrease the number of candidate paths. This algorithm can not only achieve FCD location with high accuracy, but also determine vehicle moving trajectory. The experimental results show our method is robust for the different sample intervals of FCD