Hybrid islanding detection method based on the rate of change of frequency and load impedance

Interconnection of distributed generation (DG) in distribution system will result in formation of islands in the event of loss of main supply. This scenario is harmful to the power system, hence quick detection is critical to halt the formation of islands. Among the common passive and active detection methods available, the hybrid detection method is identified as the most reliable method. This paper proposes a new hybrid method using the combination of passive and active technique which is the rate of change of frequency (ROCOF) and load impedance, respectively. The passive method works when the value of ROCOF exceeds the threshold value which is set at 0.3Hz/s. The active method works when it detects low value of ROCOF and immediately inject a pre-specified load into the system to increase the ROCOF value up to its threshold value. Simulation study on different case studies is carried out on distribution test system to evaluate the performance of the proposed method. Results show that this method is effective in detecting any events that could result in islanding.

Deepfake Detection Method Based on Cross-Domain Fusion

In recent years, despite its wide use in various fields, deepfake has been abused to generate hazardous contents such as fake movies, rumors, and fake news by manipulating or replacing facial information of the original sources and, thus, exerts huge security threats to the society. Facing the continuous evolution of deepfake, research on active detection and prevention technology becomes particularly important. In this paper, we propose a new deepfake detection method based on cross-domain fusion, which, on the basis of traditional spatial domain features, realizes the fusion of cross-domain image features by introducing edge geometric features of the frequency domain and, therefore, achieves considerable improvements on classification accuracy. Further evaluations of this method have been performed on publicly deepfake datasets, and the results show that our method is effective particularly on the Meso-4 DeepFake Database.

An active detection of compromised nodes based on en-route trap in wireless sensor network

With the development and wide use of wireless sensor network, security arises as an essential issue since sensors with restrict resources are deployed in wild areas in an unattended manner. Most of current en-route filtering schemes could filter false data effectively; however, the compromised nodes could take use of the filtering scheme to launch Fictitious False data Dropping attack, the detection of this attack is extremely difficult since the previous hop node is unable to distinguish whether the forwarding node dropt a false data report with incorrect Message Authentication Codes or a legitimate report. This is the first attempt to address the Fictitious False data Dropping attack; in this article, we propose an Active Detection of compromised nodes based on En-route Trap to trap compromised nodes in the scenario of a false data dropping. A trust model is used to evaluate trust level of neighbor nodes with respect to their authentication behaviors. A detecting algorithm of compromised node is used to detect compromised nodes. Simulation results showed that our scheme can address the Fictitious False data Dropping attack and detect 60% of compromised nodes with a low false positive rate; consequently, the packet accuracy of an Active Detection of compromised nodes based on En-route Trap increases rapidly and reaches to 86%.

Evaluating active versus passive sources of human brucellosis in Jining City, China

Human brucellosis (HB) remains a serious public health concern owing to its resurgence across the globe and specifically in China. The timely detection of this disease is the key to its prevention and control. We sought to describe the differences in the demographics of high-risk populations with detected cases of HB contracted from active versus passive sources. We collected data from a large sample population from January to December 2018, in Jining City, China. We recruited patients that were at high-risk for brucellosis from three hospitals and Centers of Disease Control and Prevention (CDCs). These patients were classified into two groups: the active detection group was composed of individuals receiving brucellosis counseling at the CDCs; the passive detection group came from hospitals and high-risk HB groups. We tested a total of 2,247 subjects and 13.3% (299) presented as positive for HB. The positive rates for active and passive detection groups were 20.5% (256/1,249) and 4.3% (43/998), respectively (p < 0.001). The detection rate of confirmed HB cases varied among all groups but was higher in the active detection group than in the passive detection group when controlled for age, sex, ethnicity, education, career, and contact history with sheep or cattle (p < 0.05). Males, farmers, those with four types of contact history with sheep or cattle, and those presenting fever, hyperhidrosis and muscle pain were independent factors associated with confirmed HB cases in multivariate analysis of the active detection group. Active detection is the most common method used to detect brucellosis cases and should be applied to detect HB cases early and avoid misdiagnosis. We need to improve our understanding of brucellosis for high-risk populations. Passive HB detection can be supplemented with active detection when the cognitive changes resulting from brucellosis are low. It is important that healthcare providers understand and emphasis the timely diagnosis of HB.

The role of cytomegalovirus and interleukin-8 in destabilization of atherosclerotic lesions in humans

Relevance. Currently, the role of persistent infections in the atherogenesis development mechanism is not fully understood. Therefore, its important to analyze the role of viral infection against the background of the pro-inflammatory cytokines expression in atherosclerotic plaque destabilization. The aim of the work was a comparative immunohistochemical study of cytomegalovirus (CMV) and IL-8 in different types of human atherosclerotic lesions during their destabilization. Materials and methods. The study was carried out on 130 autopsy samples of human aorta. CMV was detected by direct immunofluorescent antibody staining. IL-8 was detected by two-stage streptavidin-biotin antibody staining. Results. It has been shown that active detection of both CMV and IL-8 is characteristic of atherogenesis foci of the arterial intima with the most intense immune-inflammatory changes. The obtained results indicate the synergism of the cellular response to CMV and IL-8 in the vascular wall during the destabilization atherosclerotic lesions process. Conclusion. According to the results of the work, it can be concluded that both the presence of CMV in atherosclerotic lesions and the high production of IL-8 play a significant role in the formation of unstable atherosclerotic plaques in the vascular wall.