Quantitative Method for Network Security Situation Based on Attack Prediction

Multistep attack prediction and security situation awareness are two big challenges for network administrators because future is generally unknown. In recent years, many investigations have been made. However, they are not sufficient. To improve the comprehensiveness of prediction, in this paper, we quantitatively convert attack threat into security situation. Actually, two algorithms are proposed, namely, attack prediction algorithm using dynamic Bayesian attack graph and security situation quantification algorithm based on attack prediction. The first algorithm aims to provide more abundant information of future attack behaviors by simulating incremental network penetration. Through timely evaluating the attack capacity of intruder and defense strategies of defender, the likely attack goal, path, and probability and time-cost are predicted dynamically along with the ongoing security events. Furthermore, in combination with the common vulnerability scoring system (CVSS) metric and network assets information, the second algorithm quantifies the concealed attack threat into the surfaced security risk from two levels: host and network. Examples show that our method is feasible and flexible for the attack-defense adversarial network environment, which benefits the administrator to infer the security situation in advance and prerepair the critical compromised hosts to maintain normal network communication

Preparation of N-, O-, and S-tri-doped biochar through one-pot pyrolysis of poplar and urea formaldehyde and its enhanced removal of tetracycline from wastewater

In this study, biochar was prepared via hybrid doping of N, O, and S by applying one-pot pyrolysis of poplar wood and S-containing urea formaldehyde at 900 °C. Different doping ratios were adopted, and the contents of O, N, and S were in the ranges of 2.78 – 5.56 %, 2.16 – 4.92 %, and 1.42 – 4.98 %, respectively. This hybrid doping significantly enhanced the efficiency of the removal of tetracycline (40 mg/L) from wastewater to 71.84 % in comparison with that attained by using normal poplar biochar (29.45 %). The adsorption kinetics and isotherms indicated that the adsorption process was favorable and was dominated by chemisorption instead of physisorption; the dominant adsorption process may be justified by the existence of abundant functional groups. The adsorption capacity was barely related to the surface area (R2 = 0.478), while it was closely related to the concentration of graphitic N (R2 = 0.985) because graphitic N enhanced the π–π interactions. The adsorption capacity was also highly related to the proportion of oxidized N and oxidized S owing to hydrogen bonding, which may have overlapped with the contribution of O-containing functional groups. This study presents a simple hybrid doping method for biochar modification and provides fundamental insights into the specific effects of O-, N- and S-containing functional groups on the performance of biochar for tetracycline removal

N evolution and physiochemical structure changes in chars during co-pyrolysis: Effects of abundance of glucose in fiberboard

© 2020 by the authors. The simple incineration of wood-based panels (WBPs) waste generates a significant amount of NOx, which has led to urgency in developing a new method for treating the N-containing biomass residues. This work aims to examine the N evolution and physiochemical structural changes during the co-pyrolysis of fiberboard and glucose, where the percentage of glucose in the feedstock was varied from 0% to 70%. It was found that N retention in chars was monotonically increased with increasing use of glucose, achieving ~60% N fixation when the glucose accounted for 70% in the mixture. Pyrrole-N (N-5) and Pyridine-N (N-6) were preferentially formed at high ratios of glucose to fiberboard. While the relevant importance of volatile–char interactions to N retention and transformation could be observed, the volatile–volatile reactions from the two feedstocks played a vital role in the increase in abundance of glucose. With the introduction of glucose, the porous structure and porosity in chars from the co-pyrolysis were dramatically altered, whereas the devolatilization of glucose tended to generate larger pores than the fiberboard. The insignificant changes in carbon structure of all chars revealed by Raman spectroscopy would practically allow us to apply the monosaccharides to the WBPs for regulating N evolution without concerns about its side effects for char carbon structures

RingMo-lite: A Remote Sensing Multi-task Lightweight Network with CNN-Transformer Hybrid Framework

In recent years, remote sensing (RS) vision foundation models such as RingMo have emerged and achieved excellent performance in various downstream tasks. However, the high demand for computing resources limits the application of these models on edge devices. It is necessary to design a more lightweight foundation model to support on-orbit RS image interpretation. Existing methods face challenges in achieving lightweight solutions while retaining generalization in RS image interpretation. This is due to the complex high and low-frequency spectral components in RS images, which make traditional single CNN or Vision Transformer methods unsuitable for the task. Therefore, this paper proposes RingMo-lite, an RS multi-task lightweight network with a CNN-Transformer hybrid framework, which effectively exploits the frequency-domain properties of RS to optimize the interpretation process. It is combined by the Transformer module as a low-pass filter to extract global features of RS images through a dual-branch structure, and the CNN module as a stacked high-pass filter to extract fine-grained details effectively. Furthermore, in the pretraining stage, the designed frequency-domain masked image modeling (FD-MIM) combines each image patch's high-frequency and low-frequency characteristics, effectively capturing the latent feature representation in RS data. As shown in Fig. 1, compared with RingMo, the proposed RingMo-lite reduces the parameters over 60% in various RS image interpretation tasks, the average accuracy drops by less than 2% in most of the scenes and achieves SOTA performance compared to models of the similar size. In addition, our work will be integrated into the MindSpore computing platform in the near future

Analysis of risk factors for deep vein thrombosis after spinal infection surgery and construction of a nomogram preoperative prediction model

ObjectiveTo investigate the differences in postoperative deep venous thrombosis (DVT) between patients with spinal infection and those with non-infected spinal disease; to construct a clinical prediction model using patients’ preoperative clinical information and routine laboratory indicators to predict the likelihood of DVT after surgery.MethodAccording to the inclusion criteria, 314 cases of spinal infection (SINF) and 314 cases of non-infected spinal disease (NSINF) were collected from January 1, 2016 to December 31, 2021 at Xiangya Hospital, Central South University, and the differences between the two groups in terms of postoperative DVT were analyzed by chi-square test. The spinal infection cases were divided into a thrombotic group (DVT) and a non-thrombotic group (NDVT) according to whether they developed DVT after surgery. Pre-operative clinical information and routine laboratory indicators of patients in the DVT and NDVT groups were used to compare the differences between groups for each variable, and variables with predictive significance were screened out by least absolute shrinkage and operator selection (LASSO) regression analysis, and a predictive model and nomogram of postoperative DVT was established using multi-factor logistic regression, with a Hosmer- Lemeshow goodness-of-fit test was used to plot the calibration curve of the model, and the predictive effect of the model was evaluated by the area under the ROC curve (AUC).ResultThe incidence of postoperative DVT in patients with spinal infection was 28%, significantly higher than 16% in the NSINF group, and statistically different from the NSINF group (P < 0.000). Five predictor variables for postoperative DVT in patients with spinal infection were screened by LASSO regression, and plotted as a nomogram. Calibration curves showed that the model was a good fit. The AUC of the predicted model was 0.8457 in the training cohort and 0.7917 in the validation cohort.ConclusionIn this study, a nomogram prediction model was developed for predicting postoperative DVT in patients with spinal infection. The nomogram included five preoperative predictor variables, which would effectively predict the likelihood of DVT after spinal infection and may have greater clinical value for the treatment and prevention of postoperative DVT

Survival benefit of local consolidative therapy for patients with single-organ metastatic pancreatic cancer: a propensity score-matched cross-sectional study based on 17 registries

BackgroundThe continuous exploration of oligometastatic disease has led to the remarkable achievements of local consolidative therapy (LCT) and favorable outcomes for this disease. Thus, this study investigated the potential benefits of LCT in patients with single-organ metastatic pancreatic ductal adenocarcinoma (PDAC).MethodsPatients with single-organ metastatic PDAC diagnosed between 2010 - 2019 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Propensity score matching (PSM) was performed to minimize selection bias. Factors affecting survival were assessed by Cox regression analysis and Kaplan-Meier estimates.ResultsA total of 12900 patients were identified from the database, including 635 patients who received chemotherapy combined with LCT with a 1:1 PSM with patients who received only chemotherapy. Patients with single-organ metastatic PDAC who received chemotherapy in combination with LCT demonstrated extended median overall survival (OS) by approximately 57%, more than those who underwent chemotherapy alone (11 vs. 7 months, p < 0.001). Furthermore, the multivariate Cox regression analysis revealed that patients that received LCT, younger age (< 65 years), smaller tumor size (< 50 mm), and lung metastasis (reference: liver) were favorable prognostic factors for patients with single-organ metastatic PDAC.ConclusionThe OS of patients with single-organ metastatic pancreatic cancer who received LCT may be prolonged compared to those who received only chemotherapy. Nevertheless, additional prospective randomized clinical trials are required to support these findings

Arteriovenous fistulas in the craniocervical junction region: With vs. without spinal arterial feeders

ObjectiveArteriovenous fistulas (AVFs) in the craniocervical junction (CCJ) region are a rare occurrence with special clinical manifestations. This study retrospectively reviewed patients with CCJ AVFs treated at our neurosurgical center, aiming to enhance the understanding of CCJ AVFs.MethodsA total of 113 patients with CCJ AVFs treated at our neurosurgical center between January 2013 and December 2020 were enrolled. They were grouped as patients with CCJ AVFs with spinal arterial feeders (n = 20) and patients with CCJ AVF without spinal arterial feeders (n = 93). Clinical presentation, angiographic characteristics, intraoperative findings, and treatment outcomes were analyzed.ResultsThe patients’ median age was 55 years (IQR 47.5–62 years). The proportion of males in the group without spinal arterial feeders was significantly higher (p = 0.001). Subarachnoid hemorrhage (SAH) was the most common clinical presentation, especially in the group with spinal arterial feeders (p < 0.001). There were significant differences in AVF type, fistula location, and direction of the venous drainage between the two groups (p < 0.001). Intervention embolization combined with microsurgery was more common in treating AVFs with spinal arterial feeders (p = 0.006). Spinal arterial feeders did not affect the outcome (p = 0.275).ConclusionsSAH was the most common presentation of CCJ AVFs in this study. Microsurgery and interventional embolization were optional treatment strategies. The angioarchitecture of CCJ AVFs was essential for selecting treatment strategies

CRISPR-Cas9-Based Functional Interrogation of Unconventional Translatome Reveals Human Cancer Dependency on Cryptic Non-Canonical Open Reading Frames

Emerging evidence suggests that cryptic translation beyond the annotated translatome produces proteins with developmental or physiological functions. However, functions of cryptic non-canonical open reading frames (ORFs) in cancer remain largely unknown. To fill this gap and systematically identify colorectal cancer (CRC) dependency on non-canonical ORFs, we apply an integrative multiomic strategy, combining ribosome profiling and a CRISPR-Cas9 knockout screen with large-scale analysis of molecular and clinical data. Many such ORFs are upregulated in CRC compared to normal tissues and are associated with clinically relevant molecular subtypes. We confirm the in vivo tumor-promoting function of the microprotein SMIMP, encoded by a primate-specific, long noncoding RNA, the expression of which is associated with poor prognosis in CRC, is low in normal tissues and is specifically elevated in CRC and several other cancer types. Mechanistically, SMIMP interacts with the ATPase-forming domains of SMC1A, the core subunit of the cohesin complex, and facilitates SMC1A binding to cis-regulatory elements to promote epigenetic repression of the tumor-suppressive cell cycle regulators encoded by CDKN1A and CDKN2B. Thus, our study reveals a cryptic microprotein as an important component of cohesin-mediated gene regulation and suggests that the \u27dark\u27 proteome, encoded by cryptic non-canonical ORFs, may contain potential therapeutic or diagnostic targets

Removal of phenol using sulphate radicals activated by natural zeolite-supported cobalt catalysts

Two Co oxide catalysts supported on natural zeolites from Indonesia (INZ) and Australia (ANZ) were prepared and used to activate peroxymonosulphate for degradation of aqueous phenol. The two catalysts were characterized by several techniques such as X-ray diffraction, scanning electron microscopy, energy dispersive X-ray spectroscopy (EDS) and N2 adsorption. It was found that Co/INZ and Co/ANZ are effective in activation of peroxymonosulphate to produce sulphate radicals for phenol degradation. Co/INZ and Co/ANZ could remove phenol up to 100 and 70 %, respectively, at the conditions of 25 ppm phenol (500 mL), 0.2 g catalyst, 1 g oxone and 25 °C. Several parameters such as amount of catalyst loading, phenol concentration, oxidant concentration and temperature were found to be the key factors influencing phenol degradation. A pseudo first order would fit to phenol degradation kinetics, and the activation energies on Co/INZ and Co/ANZ were obtained as 52.4 and 61.3 kJ/mol,respectively