Modeling and Joint Optimization of Security, Latency, and Computational Cost in Blockchain-based Healthcare Systems

In the era of the Internet of Things (IoT), blockchain is a promising technology for improving the efficiency of healthcare systems, as it enables secure storage, management, and sharing of real-time health data collected by the IoT devices. As the implementations of blockchain-based healthcare systems usually involve multiple conflicting metrics, it is essential to balance them according to the requirements of specific scenarios. In this paper, we formulate a joint optimization model with three metrics, namely latency, security, and computational cost, that are particularly important for IoT-enabled healthcare. However, it is computationally intractable to identify the exact optimal solution of this problem for practical sized systems. Thus, we propose an algorithm called the Adaptive Discrete Particle Swarm Algorithm (ADPSA) to obtain near-optimal solutions in a low-complexity manner. With its roots in the classical Particle Swarm Optimization (PSO) algorithm, our proposed ADPSA can effectively manage the numerous binary and integer variables in the formulation. We demonstrate by extensive numerical experiments that the ADPSA consistently outperforms existing benchmark approaches, including the original PSO, exhaustive search and Simulated Annealing, in a wide range of scenarios

INFLUENCES ANALYSIS OF CONFIGURATIONS ON THE PERFORMANCE OF PARALLEL TYPE SIX-AXIS ACCELEROMETERS

The development of parallel type six-axis accelerometers was hindered for their complicated forward kinematics and dynamics algorithms which make it difficult to decouple the six acceleration components timely, accurately and stably. This paper applies four parallel configurations with 6-DOF and a closed-form solution of the forward kinematics to six-axis accelerometers as the elastic bodies, where the piezoelectric ceramics act as the sensitive elements and play the role of prismatic pairs. An efficient decoupling algorithm was derived to calculate the six acceleration components completely by the use of Kane’s dynamics method in configuration space. Considering the differences in sensing properties of the four six-axis accelerometers, a quantitative comparison was conducted to reveal the configurations’ direct influences on some static characteristics, including accuracy, efficiency, sensitivity, isotropy, and working frequency range, which makes a theoretical foundation for the subsequent design of a reconfigurable prototype

INFLUENCES ANALYSIS OF CONFIGURATIONS ON THE PERFORMANCE OF PARALLEL TYPE SIX-AXIS ACCELEROMETERS

The development of parallel type six-axis accelerometers was hindered for their complicated forward kinematics and dynamics algorithms which make it difficult to decouple the six acceleration components timely, accurately and stably. This paper applies four parallel configurations with 6-DOF and a closed-form solution of the forward kinematics to six-axis accelerometers as the elastic bodies, where the piezoelectric ceramics act as the sensitive elements and play the role of prismatic pairs. An efficient decoupling algorithm was derived to calculate the six acceleration components completely by the use of Kane’s dynamics method in configuration space. Considering the differences in sensing properties of the four six-axis accelerometers, a quantitative comparison was conducted to reveal the configurations’ direct influences on some static characteristics, including accuracy, efficiency, sensitivity, isotropy, and working frequency range, which makes a theoretical foundation for the subsequent design of a reconfigurable prototype

Serum autoantibodies against human oxidized low-density lipoproteins are inversely associated with severity of coronary stenotic lesions calculated by Gensini score

Background: The relationship between autoantibodies against human oxidized low-density lipoprotein (anti-oxLDL) and the progression of atherosclerotic diseases is unclear. This study aimed to investigate the association between serum anti-oxLDL titers and the severity and extent of coronary stenotic lesions. Methods: We measured the titers of IgG anti-oxLDL by enzyme-linked immunosorbent assay (ELISA) in 154 consecutive patients undergoing coronary angiography for suspected coronary heart disease (CHD). The severity and extent of coronary stenotic lesions were evaluated on coronary angiography findings by Gensini score. Results: The anti-oxLDL titers were significantly lower in 117 patients with CHD than those in 37 controls (p < 0.01). The serum anti-oxLDL titers were significantly correlated to serum levels of globulin (r = 0.405), conjugated bilirubin (r = 0.280), high-density lipoprotein (HDL) cholesterol (r = 0.238), homeostatic model assessment for insulin resistance (HOMA-IR) (r = &#8211;0.267), high sensitivity C-reactive protein (hs-CRP) (r = &#8211;0.230), triglyceride (r = &#8211;0.207), advanced glycation end products (AGEs) (r = &#8211;0.200), and malondialdehyde (r = &#8211;0.165). However, only HDL cholesterol and AGEs remained independent predictors of the anti-oxLDL titers after adjusting for confounders. Multivariate regression analysis showed that the anti-oxLDL titers, as well as serum levels of hs-CRP, fasting glucose, and albumin, were significantly associated with Gensini scores. Conclusions: Titers of anti-oxLDL are inversely associated with complicated proatherogenic metabolic risk factors, and the severity of coronary stenotic lesions calculated by Gensini scores, supporting a protective role for anti-oxLDL against the progression of atherosclerosis. (Cardiol J 2011; 18, 4: 364&#8211;370

Security and Privacy Problems in Voice Assistant Applications: A Survey

Voice assistant applications have become omniscient nowadays. Two models that provide the two most important functions for real-life applications (i.e., Google Home, Amazon Alexa, Siri, etc.) are Automatic Speech Recognition (ASR) models and Speaker Identification (SI) models. According to recent studies, security and privacy threats have also emerged with the rapid development of the Internet of Things (IoT). The security issues researched include attack techniques toward machine learning models and other hardware components widely used in voice assistant applications. The privacy issues include technical-wise information stealing and policy-wise privacy breaches. The voice assistant application takes a steadily growing market share every year, but their privacy and security issues never stopped causing huge economic losses and endangering users' personal sensitive information. Thus, it is important to have a comprehensive survey to outline the categorization of the current research regarding the security and privacy problems of voice assistant applications. This paper concludes and assesses five kinds of security attacks and three types of privacy threats in the papers published in the top-tier conferences of cyber security and voice domain.Comment: 5 figure

Deep Graph Embedding for IoT Botnet Traffic Detection

Botnet attacks have mainly targeted computers in the past, which is a fundamental cybersecurity problem. Due to the booming of Internet of things (IoT) devices, an increasing number of botnet attacks are now targeting IoT devices. Researchers have proposed several mechanisms to avoid botnet attacks, such as identification by communication patterns or network topology and defence by DNS blacklisting. A popular direction for botnet detection currently relies on the specific topological characteristics of botnets and uses machine learning models. However, it relies on network experts’ domain knowledge for feature engineering. Recently, neural networks have shown the capability of representation learning. This paper proposes a new approach to extracting graph features via graph neural networks. To capture the particular topology of the botnet, we transform the network traffic into graphs and train a graph neural network to extract features. In our evaluations, we use graph embedding features to train six machine learning models and compare them with the performance of traditional graph features in identifying botnet nodes. The experimental results show that botnet traffic detection is still challenging even with neural networks. We should consider the impact of data, features, and algorithms for an accurate and robust solution

Security and privacy problems in voice assistant applications: A survey

Voice assistant applications have become omniscient nowadays. Two models that provide the two most important functions for real-life applications (i.e., Google Home, Amazon Alexa, Siri, etc.) are Automatic Speech Recognition (ASR) models and Speaker Identification (SI) models. According to recent studies, security and privacy threats have also emerged with the rapid development of the Internet of Things (IoT). The security issues researched include attack techniques toward machine learning models and other hardware components widely used in voice assistant applications. The privacy issues include technical-wise information stealing and policy-wise privacy breaches. The voice assistant application takes a steadily growing market share every year, but their privacy and security issues never stopped causing huge economic losses and endangering users' personal sensitive information. Thus, it is important to have a comprehensive survey to outline the categorization of the current research regarding the security and privacy problems of voice assistant applications. This paper concludes and assesses five kinds of security attacks and three types of privacy threats in the papers published in the top-tier conferences of cyber security and voice domain

HSPA12A Unstabilizes CD147 to Inhibit Lactate Export and Migration in Human Renal Cell Carcinoma

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions. Background: Metastasis accounts for 90% of cancer-associated mortality in patients with renal cell carcinoma (RCC). However, the clinical management of RCC metastasis is challenging. Lactate export is known to play an important role in cancer cell migration. This study investigated the role of heat shock protein A12A (HSPA12A) in RCC migration. Methods: HSPA12A expression was examined in 82 pairs of matched RCC tumors and corresponding normal kidney tissues from patients by immunoblotting and immunofluorescence analyses. The proliferation of RCC cells was analyzed using MTT and EdU incorporation assays. The migration of RCC cells was evaluated by wound healing and Transwell migration assays. Extracellular acidification was examined using Seahorse technology. Protein stability was determined following treatment with protein synthesis inhibitor cycloheximide and proteasome inhibitor MG132. Mass spectrometry, immunoprecipitation, and immunoblotting were employed to examine protein-protein interactions. Results: RCC tumors from patients showed downregulation of HSPA12A, which was associated with advanced tumor node metastasis stage. Intriguingly, overexpression of HSPA12A in RCC cells inhibited migration, whereas HSPA12A knockdown had the opposite effect. Lactate export, glycolysis rate, and CD147 protein abundance were also inhibited by HSPA12A overexpression but promoted by HSPA12A knockdown. An interaction of HSPA12A with HRD1 ubiquitin E3 ligase was detected in RCC cells. Further studies demonstrated that CD147 ubiquitination and proteasomal degradation were promoted by HSPA12A overexpression whereas inhibited by HSPA12A knockdown. Notably, the HSPA12A overexpression-induced inhibition of lactate export and migration were abolished by CD147 overexpression. Conclusion: Human RCC shows downregulation of HSPA12A. Overexpression of HSPA12A in RCC cells unstabilizes CD147 through increasing its ubiquitin-proteasome degradation, thereby inhibits lactate export and glycolysis, and ultimately suppresses RCC cell migration. Our results demonstrate that overexpression of HSPA12A might represent a viable strategy for managing RCC metastasis