Lightweight Authentication Mechanism for Industrial IoT Environment Combining Elliptic Curve Cryptography and Trusted Token.

With the promotion of Industry 4.0, which emphasizes interconnected and intelligent devices, several factories have introduced numerous terminal Internet of Things (IoT) devices to collect relevant data or monitor the health status of equipment. The collected data are transmitted back to the backend server through network transmission by the terminal IoT devices. However, as devices communicate with each other over a network, the entire transmission environment faces significant security issues. When an attacker connects to a factory network, they can easily steal the transmitted data and tamper with them or send false data to the backend server, causing abnormal data in the entire environment. This study focuses on investigating how to ensure that data transmission in a factory environment originates from legitimate devices and that related confidential data are encrypted and packaged. This paper proposes an authentication mechanism between terminal IoT devices and backend servers based on elliptic curve cryptography and trusted tokens with packet encryption using the TLS protocol. Before communication between terminal IoT devices and backend servers can occur, the authentication mechanism proposed in this paper must first be implemented to confirm the identity of the devices and, thus, the problem of attackers imitating terminal IoT devices transmitting false data is resolved. The packets communicated between devices are also encrypted, preventing attackers from knowing their content even if they steal the packets. The authentication mechanism proposed in this paper ensures the source and correctness of the data. In terms of security analysis, the proposed mechanism in this paper effectively withstands replay attacks, eavesdropping attacks, man-in-the-middle attacks, and simulated attacks. Additionally, the mechanism supports mutual authentication and forward secrecy. In the experimental results, the proposed mechanism demonstrates approximately 73% improvement in efficiency through the lightweight characteristics of elliptic curve cryptography. Moreover, in the analysis of time complexity, the proposed mechanism exhibits significant effectiveness.

Retinal Vascular Image Segmentation Using Improved UNet Based on Residual Module.

In recent years, deep learning technology for clinical diagnosis has progressed considerably, and the value of medical imaging continues to increase. In the past, clinicians evaluated medical images according to their individual expertise. In contrast, the application of artificial intelligence technology for automatic analysis and diagnostic assistance to support clinicians in evaluating medical information more efficiently has become an important trend. In this study, we propose a machine learning architecture designed to segment images of retinal blood vessels based on an improved U-Net neural network model. The proposed model incorporates a residual module to extract features more effectively, and includes a full-scale skip connection to combine low level details with high-level features at different scales. The results of an experimental evaluation show that the model was able to segment images of retinal vessels accurately. The proposed method also outperformed several existing models on the benchmark datasets DRIVE and ROSE, including U-Net, ResUNet, U-Net3+, ResUNet++, and CaraNet.

Inhibitory Effects of High-Hydrostatic-Pressure Processing on Growth and Histamine Formation of Histamine-Forming Bacteria in Yellowfin Tuna Meat during Storage.

In the research, we evaluated the effects of high-pressure processing (HPP) on the growth and histamine formation of histamine-forming bacteria (HFB) in yellowfin tuna meat during storage. Tuna meat samples inoculated with the individual HFB species Morganella morganii and Photobacterium phosphoreum were subjected to HPP treatment at 250, 350, 450, and 550 MPa for 5 min, and the changes in bacterial count, total volatile basic nitrogen (TVBN) content, pH, and histamine content during storage at 4 and 15 °C were analyzed. The results indicate that the bacterial counts of the HFB species decreased significantly with increasing pressure, and HFB became undetectable in the samples treated at 450 and 550 MPa. At a storage temperature of 15 °C, the bacterial counts of both HFB species in the control group and samples subjected to HPP treatment at 250 and 350 MPa increased significantly with storage time. The bacterial counts of M. morganii in the samples stored at 4 °C decreased, whereas those of P. phosphoreum increased gradually owing to its psychrophilic nature. HPP treatment (>250 MPa) inhibited the increases in pH and TVBN content of the samples stored at 15 °C and delayed histamine formation in the samples during storage; these effects were more significant as the pressure during HPP treatment was increased.

TTAS: Trusted Token Authentication Service of Securing SCADA Network in Energy Management System for Industrial Internet of Things.

The vigorous development of the Industrial Internet of Things brings the advanced connection function of the new generation of industrial automation and control systems. The Supervisory Control and Data Acquisition (SCADA) network is converted into an open and highly interconnected network, where the equipment connections between industrial electronic devices are integrated with a SCADA system through a Modbus protocol. As SCADA and Modbus are easily used for control and monitoring, the interconnection and operational efficiency between systems are highly improved; however, such connectivity inevitably exposes the system to the open network environment. There are many network security threats and vulnerabilities in a SCADA network system. Especially in the era of the Industrial Internet of Things, any security vulnerability of an industrial system may cause serious property losses. Therefore, this paper proposes an encryption and verification mechanism based on the trusted token authentication service and Transport Layer Security (TLS) protocol to prevent attackers from physical attacks. Experimentally, this paper deployed and verified the system in an actual field of energy management system. According to the experimental results, the security defense architecture proposed in this paper can effectively improve security and is compatible with the actual field system.

Newly diagnosed panic disorder and the risk of erectile dysfunction: A population-based cohort study in Taiwan.

Previous studies indicated that panic disorder is correlated with erectile dysfunction (ED). The primary aim of this study was to explore the incidence rate of ED among panic disorder patients in an Asian country. The secondary aim was to compare the risk of ED in panic disorder patients that were treated with different kinds of antidepressants, and to explore the possible mechanism between these two disorders. We identified 1393 male patients with newly diagnosed panic disorder from the Taiwan's National Health Insurance Database. Four matched controls per case were selected for the study group by propensity score. After adjusting for age, obesity and comorbidities, the panic disorder patients had a higher hazard ratio of ED diagnosis than the controls, especially among the untreated panic disorder patients. This retrospective dynamic cohort study supports the link between ED and prior panic disorder in a large sample of panic disorder patients. This study points out the need of early antidepressant treatment for panic disorder to prevent further ED.