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Cloud service providers typically provide different types of virtual machines (VMs) to cloud users with various requirements. Thanks to its effectiveness and fairness, auction has been widely applied in this heterogeneous resource allocation. Recently, several strategy-proof combinatorial cloud auction mechanisms have been proposed. However, they fail to protect the bid privacy of users from being inferred from the auction results. In this paper, we design a differentially private combinatorial cloud auction mechanism (DPCA) to address this privacy issue. Technically, we employ the exponential mechanism to compute a clearing unit price vector with a probability proportional to the corresponding revenue. We further improve the mechanism to reduce the running time while maintaining high revenues, by computing a single clearing unit price, or a subgroup of clearing unit prices at a time, resulting in the improved mechanisms DPCA-S and its generalized version DPCA-M, respectively. We theoretically prove that our mechanisms can guarantee differential privacy, approximate truthfulness and high revenue. Extensive experimental results demonstrate that DPCA can generate near-optimal revenues at the price of relatively high time complexity, while the improved mechanisms achieve a tunable trade-off between auction revenue and running time., 12 pages, 6 figures

Cross-cloud data migration is one of the prevailing challenges faced by mobile users, which is an essential process when users change their mobile phones to a different provider. However, due to the insufficient local storage and computational capabilities of the smart phones, it is often very difficult for users to backup all data from the original cloud servers to their mobile phones in order to further upload the downloaded data to the new cloud provider. To solve this problem, we propose an efficient data migration model between cloud providers and construct a mutual authentication and key agreement scheme based on elliptic curve certificate-free cryptography for peer-to-peer cloud. The proposed scheme helps to develop trust between different cloud providers and lays a foundation for the realization of cross-cloud data migration. Mathematical verification and security correctness of our scheme is evaluated against notable existing schemes of data migration, which demonstrate that our proposed scheme exhibits a better performance than other state-of-the-art scheme in terms of the achieved reduction in both the computational and communication cost.

The large amount of driving data can help intelligent vehicles make decisions to drive safely, improve vehicular services and enhance driving experience. In traditional vehicular networks, data sharing needs to be done with roadside units (RSUs). However, RSUs cannot be entirely trusted and the data stored in the RSUs may be tampered with. In addition, the deployment of RSUs along roads consumes a large amount of social resources. Further, data sharing between vehicles lacks a trusted environment, and vehicles may be unwilling to share data with others because of data security and privacy concerns. Moreover, in the event of unauthorized data sharing, the source of the leaked data is difficult to trace. In this study, we exploit consortium blockchain technology to achieve traceable and anonymous vehicle-to-vehicle (V2V) data sharing, effectively preventing second-hand sharing of data. The combination of 5G and blockchain makes it possible to share data without using RSUs. We design an enhanced delegated proof-of-stake consensus algorithm to make it more suitable for applications in the distributed Internet of Vehicles (IoV). A comprehensive analysis shows that the proposed scheme is secure and efficient.

With the rapid development of the industrial Internet of Things (IIoT) and cloud computing, an increasing number of companies outsource their data to cloud servers to save costs. To protect data privacy, sensitive industrial data must be encrypted before being outsourced to cloud servers. A multi-user searchable encryption (MUSE) scheme was introduced to ensure high efficiency of encrypted data retrieval. In an IIoT system with numerous users, the existing MUSE schemes suffer from certain key exposure problems owing to the limited key protection of smart devices and frequent queries by users. In this study, we propose a parallel key-insulated MUSE scheme for IIoT. This scheme utilizes broadcast encryption technology to implement MUSE. In addition, our scheme introduces a key-insulated primitive to improve the tolerance to key exposure. The security of our scheme is proved in the random oracle model. The experimental results show that our scheme achieves high computational efficiency.

Vehicular Ad-Hoc Networks (VANETs) play an ever-increasing important role in improving traffic management and enhancing driving safety. However, vehicular communication using a wireless channel faces security and privacy challenges. The Conditional Privacy-Preserving Authentication (CPPA) scheme is suitable for solving the above challenges, but the existing identity-based CPPA schemes suffer from inborn key escrow issues. Motivated by this, we propose a lightweight CPPA scheme based on elliptic curve cryptography to solve the above issues, in which the pseudonym and public/private key pair of the vehicle is generated by itself, so that the proposed scheme avoids the key escrow issue. Furthermore, to achieve efficient vehicular communication, a CPPA scheme is proposed using a fog computing model that supports mobility, low latency, and location awareness. The pseudonym of the vehicle is generated by two hash chains in the proposed scheme, so that the storage overhead can be reduced efficiently under the condition that backward security is guaranteed. Security analysis shows that the scheme is secure under the random oracle and satisfies the security requirements of VANETs. Performance evaluation demonstrates that the proposed scheme outperforms related schemes in terms of computational and communication overhead.

Purpose Portal hypertension due to cirrhosis is common among patients with hepatocellular carcinoma (HCC). This study aimed to compare the outcomes of partial hepatectomy in patients with HCC and clinically significant portal hypertension (CSPH) with or without concurrent splenectomy and esophagogastric devascularization (CSED). Patients and methods From a multicenter database, patients with HCC and CSPH who underwent curative-intent hepatectomy were identified. Postoperative morbidity and mortality, and long-term overall survival (OS) were compared in patients with and without CSED before and after propensity score matching (PSM). Results Of the 358 enrolled patients, 86 patients underwent CSED. Before PSM, the postoperative 30-day morbidity and mortality rates were comparable between the CSED and non-CSED group (both P > 0.05). Using PSM, 81 pairs of patients were created. In the PSM cohort, the 5-year OS rate of the CSED group were significantly better than the non-CSED group (52.9%vs.36.5%, P=0.046). The former group had a significantly lower rate of variceal bleeding on follow-up (7.4%vs.21.7%, P=0.014). On multivariate analysis, CSED was associated with significantly better OS (HR: 0.39, P Conclusion Hepatectomy and CSED can safely be performed in selected patients with HCC and CSPH, which could improve postoperative prognosis by preventing variceal bleeding, and prolonging long-term survival.

Conditional privacy preservation and message authentication serve as the primary research issues in terms of security in vehicular networks. With the arrival of 5G era, the downloading speed of network services and the message transmission speed have significantly improved. Consequently, the content exchanged by users in vehicular networks is not limited to traffic information, and vehicles moving at high speeds can share a wide variety of contents. However, sharing content reliably and efficiently remains challenging owing to the fast-moving character of vehicles. To solve this problem, we propose a reliable and efficient content sharing scheme in 5G-enabled vehicular networks. The vehicles with content downloading requests quickly filter the adjacent vehicles to choose capable and suitable proxy vehicles and request them for content services. Thus, the purpose of obtaining a good hit ratio, saving network traffic, reducing time delay, and easing congestion during peak hours can be achieved. The security analysis indicates that the proposed scheme meets the security requirements of vehicular networks. Our cryptographic operations are based on the elliptic curve, and finally, the proposed scheme also displays favorable performance compared to other related schemes.

The cloud computing paradigm provides numerous tempting advantages, enabling users to store and share their data conveniently. However, users are naturally resistant to directly outsourcing their data to the cloud since the data often contain sensitive information. Although several fine-grained access control schemes for cloud-data sharing have been proposed, most of them focus on the access control of the encrypted data (e.g., restricting the decryption capabilities of the receivers). Distinct from the existing work, this article aims to address this challenging problem by developing a more practical bidirectional fine-grained access control scheme that can restrict the capabilities of both senders and receivers. To this end, we systematically investigate the access control for cloud data sharing. Inspired by the access control encryption (ACE), we propose a novel data sharing framework that combines the cloud side and the edge side. The edge server is located in the middle of all the communications, checking and preventing illegal communications according to the predefined access policy. Next, we develop an efficient access control algorithm by exploiting the attribute-based encryption and proxy re-encryption for the proposed framework. The experimental results show that our scheme exhibits superior performance in the encryption and decryption compared to the prior work.

Element doping has become an important means of designing and developing efficient and stable Oxygen Evolution Reaction (OER). In this work, a new type of CoAl LDH doped with Ce and MoS2 was designed and prepared. The catalyst first prepared Ce-doped CoAl LDH (Ce–CoAl LDH) by co-precipitation and one-step hydrothermal method. Then, MoS2 was introduced between Ce–CoAl LDH layers by hydrothermal method. The double doping of Ce and MoS2 could significantly improve the electrocatalytic activity of CoAl LDH. At the current density of 10 mA/cm2, the potential of 5% Ce–CoAl LDH@MoS2 is 1.508 V, which is much lower than the 1.733 V of CoAl LDH. And the ECSA of 5% Ce-CoAL LDH@MoS2 (23.4 mF/cm2) is nearly 8 times that of CoAl LDH (7.9 mF/cm2). Its excellent OER performance benefits from the increase of active sites and the enhancement of conductivity. This work provides a new research idea for doping design of effective OER electrocatalysts.

By equipping various sensors and analyzing sensed data, vehicles can perform automatic driving; these vehicles are known as connected and autonomous vehicles (CAVs). In CAVs, tampered data will result in incorrect driving decisions. Hence, secure data transmission should be ensured to enable correct lifecritical decisions. Untrusted resource-constrained modules allow attackers to obtain private data from CAVs, such as the key. Benefitting from trusted computing, the proposed scheme can verify the trusted status of internal modules and achieve secure data transmission by adopting the remote attestation and hash message authentication code. The scheme is proven to be secure in the random oracle model under the computational Diffie- Hellman problem. Furthermore, we perform experiments and evaluate the performance using Intel Software Guard eXtensions, which provides part of the trusted computing function. The experimental results show that the scheme could be efficient and suitable for CAVs.

As internet of things and other technologies are widely used in industrial manufacturing, automation and intelligence have witnessed rapid developments, resulting in the proposal of the industrial internet of things (IIoT). However, the IIoT still faces various network security threats; hence, data integrity, confidentiality, and anonymity need to be ensured. The use of cloud and edge servers as semitrusted third parties often results in the leaking of privacy sensitive user data. Meanwhile, existing security schemes treat the cloud and edge as fully trusted entities, which is not always valid. Considering edge servers as semitrusted entities, we propose a novel message authentication scheme that leverages group signature technology and proxy reencryption technology to ensure data integrity, confidentiality, and anonymity. Through theoretical analysis and performance comparison, we prove the security of our scheme. In addition, we implement our scheme on a real publish/subscribe system, and the experimental results show the feasibility of our scheme.

When a laser beam induces surface tension gradient at the free surface of a liquid, a weak surface depression is expected and has been observed. Here we report giant depression and rupture in “optothermocapillary fluids” under the illumination of laser and sunlight. Computational fluid dynamics models were developed to understand the surface deformation and provided desirable physical parameters of the fluid for maximum deformation. New optothermocapillary fluids were created by mixing transparent lamp oil with different candle dyes. They can be cut open by sunlight and be patterned to different shapes and sizes using an ordinary laser show projector or a common laser pointer. Laser driving and elevation of optothermocapillary fluids, in addition to the manipulation of different droplets on their surface, were demonstrated as an efficient controlling method and platform for optofluidic operations. The fundamental understanding of light-induced giant depression and creation of new optothermocapillary fluids encourage the fundamental research and applications of optofluidics.

The existing engineering empirical life analysis models are not capable of considering the constitutive behavior of materials under contact loads; as a consequence, these methods may not be accurate to predict fatigue lives of rolling bearings. In addition, the contact stress of bearing in operation is cyclically pulsating, it also means that the bearing undergo non-symmetrical fatigue loadings. Since the mean stress has great effects on fatigue life, in this work, a novel fatigue life prediction model based on the modified SWT mean stress correction is proposed as a basis of which to estimate the fatigue life of rolling bearings, in which, takes sensitivity of materials and mean stress into account. A compensation factor is introduced to overcome the inaccurate predictions resulted from the Smith, Watson, and Topper (SWT) model that considers the mean stress effect and sensitivity while assuming the sensitivity coefficient of all materials to be 0.5. Moreover, the validation of the model is finalized by several practical experimental data and the comparison to the conventional SWT model. The results show the better performance of the proposed model, especially in the accuracy than the existing SWT model. This research will shed light on a new direction for predicting the fatigue life of rolling bearings.

Data sharing is one important service provided by cloud storage. In order to share data conveniently and securely, Shen et al. proposed a cloud storage auditing scheme for data sharing, which uses the sanitizable signature to hide sensitive information. However, it may cause unauthorized access to the data, since anyone can access the data stored on the cloud server. This article proposes a privacy-preserving cloud storage auditing (PP-CSA) scheme for data sharing, where only authorized users can access the data. Furthermore, PP-CSA adopts the Diffie–Hellman protocol to avoid the secure channel between the data owner and the sanitizer. Finally, the security analysis and the experimental results prove that the security and efficiency of PP-CSA can be accepted.

A connected and autonomous vehicle (CAV) is often fitted with a large number of onboard sensors and applications to support autonomous driving functions. Based on the current research, little work on applications’ access to in-vehicle data has been done. Furthermore, most existing autonomous driving operating systems lack authentication and encryption units. As such, applications can excessively obtain confidential information, such as vehicle location and owner preferences and even upload it to the cloud, threatening the security of the vehicle and the privacy of the owner. In this study, we propose a fine-grained access control scheme to restrict applications’ access to data in CAVs (FGAC-inCAVs). First, we present a system model composed of the following elements: a trusted third party (TTP), which is a fully trusted authority; perception components like sensors, which can capture the road information (pictures, videos, etc.); and multiple applications. Then, a fast attribute-based encryption (ABE) is presented, and security analysis also shows it is secure against selective and chosen-plaintext attacks. Furthermore, we propose a key update scheme based on the Chinese remainder theorem (CRT). Finally, the theoretical analysis and simulation experiments demonstrate its feasibility and efficiency.

Background Repeat hepatectomy is a feasible treatment modality for intrahepatic recurrence after hepatectomy of hepatocellular carcinoma, yet the survival benefit remains ill-defined. The objective of the current study was to define long-term, oncologic outcomes after repeat hepatectomy among patients with early and late recurrence. Methods Patients undergoing curative-intent repeat hepatectomy for recurrent hepatocellular carcinoma were identified using a multi-intuitional database. Early and late recurrence was defined by setting 1 year after initial hepatectomy as the cutoff value. Patient clinical characteristics, overall survival, and disease-free survival were compared among patients with early and late recurrence before and after propensity score matching. Results Among all the patients, 81 had early recurrence and 129 had late recurrence from which 74 matched pairs were included in the propensity score matching analytic cohort. Before propensity score matching, 5-year overall survival and disease-free survival after resection of an early recurrence were 41.7% and 17.9%, respectively, which were worse compared with patients who had resection of a late recurrence (57.0% and 39.4%, both P Conclusion Repeat hepatectomy for early recurrence was associated with worse overall survival and disease-free survival compared with late recurrence. These data may help inform patients and selection of patients being considered for repeat hepatectomy of recurrent hepatocellular carcinoma.

Background China is one of the countries sharing the major burden of tuberculosis (TB) in the world. Health care workers (HCWs) are subject to a high risk of occupational latent tuberculosis infection (LTBI)-an asymptomatic state of TB disease. However, the heterogenic composition of healthcare professionals in terms of nature of their work leads to the inconsistency in predicting the prevalence of LTBI amongst them. Furthermore, the global statistics do not account for the analysis conducted within the Chinese population. Our study reflects a systemic and epidemiological meta-analysis to investigate the risk of contracting LTBI by the HCWs of China. Methods A systematic review of the literature was performed to identify studies reporting LTBI prevalence or incidence among HCWs and a control groups in China. Risk of infection, as well as subgroup analysis was calculated by pooled effect estimates. Review Manager 5.0 was used to perform the meta-analyses. Results Twenty studies containing 9,654 HCWs met the inclusion criteria. The average prevalence of LTBI among HCWs was 51.5%, ranging from 27.9-88.8%. HCWs had a higher risk of prevalence of LTBI than the control groups [odds ratio (OR), 1.78, 95% confidence interval (CI), 1.46-2.16]. In the subgroup analysis, the prevalence of LTBI in HCWs with respect to the control groups was observed to be highest in Eastern China (OR, 2.05; 95% CI, 1.35-3.11). Furthermore, the pooled OR for LTBI was 1.90 and 1.65 separately from the results of the tuberculin skin test (TST) and the interferon-gamma release assay. Lastly, upon comparing the HCWs with the control groups from the community and the nosocomial source, it was observed that the pooled OR favored for the prevalence of LTBI, which was primarily community-sourced (3.12 and 1.54). HCWs had an increased risk of prevalence of LTBI than the control groups, both in general hospitals and TB specific hospitals (pooled OR 2.4 and 1.57). Conclusions Risk of LTBI infection among HCWs is relatively high in China, especially in the eastern region, predisposed by the cumulative exposure to Mycobacterium tuberculosis from the community and the general hospitals. Overall, our data reflects an alarming risk posed to our HCWs, and calls for immediate reforms at the policy levels, so as to implement effective screening and treatment of affected HCWs in China.

Cloud auction provides a cost-effective approach to cloud resource allocation. Most existing cloud auction mechanisms simply assume that the auctioneer is trustable, and overlook the fairness of auctions. However, in fact, such a trustable auctioneer may not exist, and the fairness is non-trivial to guarantee. In this work, for the first time, we propose a decentralized auctioning and trading framework for cloud virtual machines based on blockchain. We realize both auction fairness and trade fairness among participants (e.g., cloud provider and cloud users), which guarantees each participant will not suffer any loss as long as it follows the protocol. Furthermore, we implement our system through the local analog network and Ethereum official test network, carry out experimental simulation, and demonstrate the feasibility of our system.

Existing security and identity-based vehicular communication protocols used in Vehicular Ad-hoc Networks (VANETs) to achieve conditional privacy-preserving mostly rely on an ideal hardware device called tamper-proof device (TPD) equipped in vehicles. Achieving fast authentication during the message verification process is usually challenging in such strategies and further they suffer performance constraints from resulting overheads. To address such challenges, this paper proposes a novel Chinese remainder theorem (CRT)-based conditional privacy-preserving authentication scheme for securing vehicular authentication. The proposed protocol only requires realistic TPDs, and eliminates the need for pre-loading the master key onto the vehicle's TPDs. Chinese remainder theorem can dynamically assist the trusted authorities (TAs) whilst generating and broadcasting new group keys to the vehicles in the network. The proposed scheme solves the leakage problem during side channel attacks, and ensures higher level of security for the entire system. In addition, the proposed scheme avoids using the bilinear pairing operation and map-to-point hash operation during the authentication process, which helps achieving faster verification even under increasing number of signature. Moreover, the security analysis shows that our proposed scheme is secure under the random oracle model and the performance analysis shows that our proposed scheme is efficient in reducing computation and communication overheads.

In recent years, the continuous development of smart healthcare has brought substantial convenience to our lives, especially for emerging edge-enabled smart healthcare systems that transmit and store a large amount of medical data. However, if private health data of users are leaked or tampered with, it will cause enormous damage to users’ rights, even threatening the safety of their lives. Traditionally, attribute-based encryption (ABE) is used to encrypt data and implement fine-grained access control for such sensitive information. However, the computing overhead of traditional ABE is relatively large, and in an edge-enabled environment, the outsourcing part of encryption and decryption to the edge node can reduce the computing cost of resource-constrained devices for edge-enabled smart healthcare. However, current schemes are not efficient for resource-constrained devices and edge nodes. Therefore, an efficient ABE scheme is proposed that outsources part of the encryption and decryption to the edge nodes as well as supports attribute updates, enabling flexible right control. A formal security proof is provided, verifying that our scheme is secure under the Decisional Bilinear Diffie–Hellman assumption. The performance of our scheme is evaluated at different security levels, and the experimental results demonstrate that our scheme is more efficient for resource-constrained devices than the traditional ABE.

Sensing data plays a pivotal role in connected and autonomous vehicles (CAVs), enabling CAV to perceive surroundings. For example, malicious applications might tamper this life-critical data, resulting in erroneous driving decisions and threatening the safety of passengers. Access control, one of the promising solutions to protect data from unauthorized access, is urgently needed for vehicle sensing data. However, due to the intrinsic complexity of vehicle sensing data, including historical and real time, and access patterns of different data sources, there is currently no suitable access control framework that can systematically solve this problem; current frameworks only focus on one aspect. In this article, we propose a novel and flexible access control framework, AC4AV , which aims to support various access control models, and provide APIs for dynamically adjusting access control models and developing customized access control models, thus supporting access control research on CAV for the community. In addition, we propose a data abstraction method to clearly identify data, applications, and access operations in CAV, and therefore is easily able to configure the permits of each data and application in access control policies. We have implemented a prototype to demonstrate our architecture on NATS for real-time data and NGINX for historical data, and three access control models as built-in models. We measured the performance of our AC4AV while applying these access control models to real-time and historical data. The experimental results show that the framework has little impact on real-time data access within a tolerable range.

BACKGROUND: Though robot-assisted minimally invasive esophagectomy (RAMIE) is demonstrated to offer a better visualization and provide a fine dissection of the mediastinal structures to facilitate the complex thoracoscopic operation, the superiorities of RAMIE over MIE have not been well verified. The aim of this study was to explore the actual superiorities through comparing short-term results of RAMIE with that of MIE. METHODS: PubMed, EMBASE and web of science databases were systematically searched up to September 1, 2020 for case-controlled studies that compared RAMIE with TLMIE. RESULTS: Fourteen studies were identified, with a total of 2,887 patients diagnosed with esophageal cancer, including 1,435 patients subjected to RAMIE group and 1,452 patients subjected to MIE group. The operative time in RAMIE was still significantly longer than that in MIE group (OR =0.785; 95% CI, 0.618–0.952; P

Background In the entire population, an aberrant right subclavian artery (ARSA) is closely associated with chromosomal abnormalities. ARSA with additional ultrasonic findings would increase risk of chromosomal abnormalities. The risk of fetal chromosomal abnormalities increased exponentially with the maternal age. These risks in the advanced maternal age (AMA) group are uncertain. This study aimed to determine the incidence of ARSA in Chinese AMA and non-AMA women and the frequency of aneuploidy among AMA and non-AMA women with ARSA. Methods This retrospective study included 13,690 singleton pregnancies, were divided into AMA and non-AMA groups. Integrated obstetric ultrasonic screening, biochemical screening, noninvasive prenatal screening, and fetal karyotype analysis were analyzed. Results The overall incidence of ARSA was 0.69%, with no difference between age groups. The incidence of chromosomal abnormalities in the AMA group (37 / 2860) was much higher than that of the non-AMA group. The risk of chromosomal abnormalities significantly increased with both ARSA detected and additional ultrasound findings. With combined ARSA and AMA, the likelihood of the incidence of chromosomal abnormalities increased. Chimerism (45X / 46XX) was found with isolated ARSA in AMA pregnancies. Conclusion There is a high prevalence of chromosomal abnormalities in fetuses of AMA women. ARSA increases the risk of chromosomal abnormalities in both age groups, especially combined with ARSA. When ARSA occurs in AMA women, it confers a high likelihood of chromosomal abnormalities.

Portal vein tumor thrombus (PVTT) and microvascular invasion (MVI) are types of intrahepatic vascular metastasis of hepatocellular carcinoma (HCC) and are highly correlated with poor prognosis. However, the underlying biomarkers of PVTT and MVI are unclear. We identified a PVTT/MVI-associated gene S100P by cDNA microarray analysis, and assess the potential value of serum S100P measurement in the differential diagnosis of HCC and prediction of MVI status with large retrospective and perspective cohort studies. The mRNA and protein of S100P was increased in HCCs with PVTT or MVI. High S100P immunostaining in tumors was correlated with inferior tumor-free survival. Serum S100P values discriminated patients with HCCs from those with benign liver tumors, and it showed predictive potential of MVI status in both retrospective and perspective cohorts. S100P may regulate HCC tumorigenicity and invasive ability; S100P also was associated with up-regulation of CD44, which may mediate HCC cell adhesion to form PVTT/MVI. Serum S100P may be a novel differential diagnostic marker for HCC and a potential predictor of MVI status pre-surgery for HCC patients. S100P overexpression in HCC is highly correlated with the formation of PVTT and MVI, which may make S100P as a potential therapeutic target for HCC metastasis.

Owing to the development of wireless communication technology and the increasing number of automobiles, vehicular ad hoc networks (VANETs) have become essential tools to secure traffic safety and enhance driving convenience. It is necessary to design a conditional privacy-preserving authentication (CPPA) scheme for VANETs because of their vulnerability and security requirements. Traditional CPPA schemes have two deficiencies. One is that the communication or storage overhead is not sufficiently low, but the traffic emergency message requires an ultra-low transmission delay. The other is that traditional CPPA schemes do not consider updating the system secret key (SSK), which is stored in an unhackable Tamper Proof Device (TPD), whereas side-channel attack methods and the wide usage of the SSK increase the probability of breaking the SSK. To solve the first issue, we propose a CPPA signature scheme based on elliptic curve cryptography, which can achieve message recovery and be reduced to elliptic curve discrete logarithm assumption, so that traffic emergency messages are secured with ultra-low communication overhead. To solve the second issue, we design an SSK updating algorithm, which is constructed on Shamir’s secret sharing algorithm and secure pseudo random function, so that the TPDs of unrevoked vehicles can update SSK securely. Formal security proof and analysis show that our proposed scheme satisfies the security and privacy requirements of VANETs. Performance analysis demonstrates that our proposed scheme requires less storage size and has a lower transmission delay compared with related schemes.

The rapid development of the Internet of Things (IoT) accumulates a large number of communication records, which are utilized for anomaly detection in IoT communication. However, only a small part of these records can be labeled, which increases the difficulty in anomaly detection. This article proposes a semisupervised hierarchical stacking temporal convolutional network (HS-TCN), which is the first semisupervised model for anomaly detection in IoT communication, and it can train unlabeled data based on a small number of labeled data. Furthermore, HS-TCN fully considers the features of streaming data in IoT communication and can weed out uncertain records. Finally, the experimental results demonstrate that HS-TCN promotes the performance of anomaly detection and attains better efficiency.

Scalable Video Coding (SVC) is a promising coding technique that enables flexible video transmission to support heterogeneous devices. However, the current best-effort delivery model characterized by the Internet cannot fully exploit the scalability feature of SVC because the network nodes are transparent for multimedia streaming applications. Software-defined networking (SDN) has emerged as an innovative network paradigm that decouples the control and forwarding planes while routing. This architecture with the OpenFlow protocol enables network operators to obtain per-flow QoS control in a more scalable, flexible, and finely granular manner compared to the conventional network architecture. Inspired by these facts, this paper designs a comprehensive QoS-aware multicast scheme for scalable video streaming over SDN networks. First, we present the designed multimedia streaming multicast system architecture and formulate the QoS-aware multicast routing problem as a non-linear programming model. We then propose a fundamental tree construction algorithm that decomposes the video streaming requests into subrequests and serves them in a bottom-up manner. Furthermore, we design a layer switching strategy based on the video distortion model to mitigate network-induced video quality distortion. Finally, the experimental results are presented to validate the performance of our methods in terms of scalability, network utility, video playout quality, and playback interruption ratio.

With the rising popularity of the Internet and communication technology, vehicles can analyze and judge the real-time data collected by various cloud service providers (CSPs) in a vehicular network. However, in a vehicular network environment, real-time data are transmitted via wireless channels, which can lead to security and privacy issues. To avoid illegal access by adversaries, vehicle authentication and key agreement mechanism has been considered as one of the promising security measures in vehicular network environments. Besides, most of the solutions focus on authentication between one vehicle and one CSP. In such strategies, the implementation of efficient authentication for multiple vehicles and CSPs simultaneously is usually challenging. Further, they are also subjected to performance limitations due to the overhead incurred. To solve these issues, we propose a many-to-many authentication and key agreement scheme for secure authentication between multiple vehicles and CSPs. The proposed scheme can prevent unauthorized access and provide SK-security even if temporary information is leaked. To improve the service, the CSP only needs to broadcast an anonymous message periodically instead of having to generate a unique anonymous message for each of vehicles. Similarly, when a vehicle wants to request the services of $m$ CSPs, it only needs to send one request message instead of $m$ . Therefore, the proposed scheme not only implements many-to-many communication but also significantly reduces the computation and communication overhead. Moreover, a thorough security analysis shows that the proposed scheme provides better security compared to other related schemes.

Existing conditional privacy-preserving communication protocols based on a tamper-proof device (TPD) in vehicular ad hoc networks (VANETs) face the challenges of master key compromise and vehicle revocation. To address these challenges, we propose a new identity-based anonymous authentication scheme. In this scheme, the master key of the system will not be directly installed in the TPD; hence, it is impossible for an adversary to obtain the system master key from the TPD. To generate the private key of the vehicle, an additional secret is required, and this secret is provided by the roadside unit (RSU). Therefore, revoking a malicious vehicle from the VANETs is efficient, since the RSU only needs to stop generating the up-to-date secret for the vehicle. In addition, the signature verification algorithm of the scheme involves no bilinear pairing operations, making the verification process more efficient. The security of the scheme is proved to be equivalent to the elliptic curve discrete logarithm assumption in the random oracle. The experimental results show that the authentication of the scheme is more efficient, and the communication cost has been diminished.

Although T cells are considered as the central component in immune-mediated diseases, supportive evidence has demonstrated that B cells also contribute to the progression of these diseases. B cells are divided into various subsets according to their secreted cytokines. Different B cell subsets play diverse roles in immune-mediated dermatoses. Regulatory B cells (Bregs) are defined functionally by their ability to secrete IL-10, which has been revealed to contribute to immunological tolerance. Drugs that deplete B cells, such as rituximab, are now used for the treatment of several immune-mediated dermatoses. In this review, we present and discuss the current knowledge on the roles of B cells in several immune-mediate dermatoses including psoriasis, pemphigus, bullous pemphigoid, and dermatomyositis, atopic dermatitis.

Background. Diagnoses of Skin diseases are frequently delayed in China due to lack of dermatologists. A deep learning-based diagnosis supporting system can facilitate pre-screening patients to prioritize dermatologists’ efforts. We aimed to evaluate the classification sensitivity and specificity of deep learning models to classify skin tumors and psoriasis for Chinese population with a modest number of dermoscopic images. Methods. We developed a convolutional neural network (CNN) based on two datasets from a consecutive series of patients who underwent the dermoscopy in the clinic of the Department of Dermatology, Peking Union Medical College Hospital, between 2016 and 2018, prospectively. In order to evaluate the feasibility of the algorithm, we used two datasets. Dataset I consisted of 7192 dermoscopic images for a multi-class model to differentiate three most common skin tumors and other diseases. Dataset II consisted of 3115 dermoscopic images for a two-class model to classify psoriasis from other inflammatory diseases. We compared the performance of CNN with 164 dermatologists in a reader study with 130 dermoscopic images. The experts’ consensus was used as the reference standard except for the cases of basal cell carcinoma (BCC), which were all confirmed by histopathology. Results. The accuracies of multi-class and two-class models were 81.49% ± 0.88% and 77.02% ± 1.81%, respectively. In the reader study, for the multi-class tasks, the diagnosis sensitivity and specificity of 164 dermatologists were 0.770 and 0.962 for BCC, 0.807 and 0.897 for melanocytic nevus, 0.624 and 0.976 for seborrheic keratosis, 0.939 and 0.875 for the “others” group, respectively; the diagnosis sensitivity and specificity of multi-class CNN were 0.800 and 1.000 for BCC, 0.800 and 0.840 for melanocytic nevus, 0.850 and 0.940 for seborrheic keratosis, 0.750 and 0.940 for the “others” group, respectively. For the two-class tasks, the sensitivity and specificity of dermatologists and CNN for classifying psoriasis were 0.872 and 0.838, 1.000 and 0.605, respectively. Both the dermatologists and CNN achieved at least moderate consistency with the reference standard, and there was no significant difference in Kappa coefficients between them (P > 0.05). Conclusions. The performance of CNN developed with relatively modest number of dermoscopic images of skin tumors and psoriasis for Chinese population is comparable with 164 dermatologists. These two models could be used for screening in patients suspected with skin tumors and psoriasis respectively in primary care hospital.

This article proposes a novel deep learning based fusion prognostic method for remaining useful life (RUL) prediction of engineering systems. The proposed framework strategically combines the advantages of bidirectional long short-term memory (BLSTM) networks and particle filter (PF) method and meanwhile mitigates their limitations. In the proposed framework, BLSTM networks are applied for further extracting, selecting, and fusing discriminative features to form predicted measurements of the identified degradation indicator. Simultaneously, PF is utilized to estimate system state and identify unknown parameters of the degradation model for RUL prediction. Hence, the proposed fusion prognostic framework has two innovative features: first, the preprocessed features from raw multisensor data can be intelligently extracted, selected, and fused by the BLSTM networks without specific domain knowledge of feature engineering; second, the predicted measurements with uncertainties obtained from the BLSTM networks will be properly represented by the PF in a transparent manner. Moreover, the developed approach is experimentally validated with machining tool wear tests on a computer numerical control (CNC) milling machine. In addition, the popular techniques employed in this field are also investigated to compare with the proposed method.

Allergic contact dermatitis is a common clinical allergic disease. The patch test is the gold standard for finding and clarifying contact allergens. With the deepening understanding of the epidemiology, pathogenesis, and clinical manifestations of contact dermatitis, as well as the increased requirements for the standardized application of patch tests, the relevant consensus needs to be continuously updated. This consensus is updated on the basis of the version issued in 2015. In this revised version, it refines the selection of clinical indications, improves the judgment and interpretation of results, adds delayed detection and patient education, and so on, which aims to further standardize clinical applications and improve the value of patch test.

Background and Aims: This study was designed to analyze the effects of age and clinicopathological characteristics on prognosis of Chinese patients with hepatocellular carcinoma (HCC). Methods: The clinical data of 2032 HCC patients who were first diagnosed with HCC and underwent curative hepatectomy in our hospital between January 2006 and January 2011 were retrospectively analyzed. Results: Younger HCC patients (age

Vehicular ad hoc networks (VANETs) are wireless self-organizing networks, whose communication is conducted through open wireless channels, thus making it vulnerable to various attacks. It is therefore necessary to employ encryption technology to ensure secure communication. In view of the foregoing, key agreement techniques are introduced to VANET communications. The group key agreement (GKA) protocol allows a group of participants to establish a public session key for a secure communication channel over an insecure network. The traditional GKA protocol is inefficient, however, and consumes considerable amounts of resources. With the aim of resolving problems related to information security and privacy preservation in VANETs, this paper proposes a full session key agreement scheme based on chaos mapping. In this paper, the first phase describes the key agreement between the fog server and group administrator; the second phase describes the group key agreement for vehicle nodes in the group where the vehicle can only communicate with vehicles having the same group key without the aid of a trusted key center or centralized key distributor. The proposed scheme is based on Chebyshev chaotic mapping algorithm, in which a chaotic map is utilized to provide a one-way hash; Chebyshev polynomial is also employed to establish a public multi-party key; it avoids the modular multiplication index or scalar multiplication on the elliptic curve. The proposed scheme does not only satisfy the security requirements in VANETs but also performs detailed analysis.

The internal clearance is a decisive parameter for life prediction and reliability analysis of rolling bearings. In this paper, a comprehensive reliability analysis model of rolling bearings is proposed by considering the internal clearance of rolling bearings. The model of bearings’ working clearance was established after investigating the effect mechanisms of effective interference, temperature, and centrifugal force on the clearance. The validation of the results is carried out by the reliability analysis of a real rolling bearing through the modeling of working clearance probability distribution and the research of life factor. The results indicate that the proposed methodology provides an additional reference way for the reliability analysis of rolling bearings.

Metabolic syndrome (MetS) is a clustering of at least three of the following four medical conditions: obesity, hypertension, dyslipidemia, and hyperglycemia. We aimed to discover the relationships between these diseases and osteoarthritis (OA) of the knee.We searched four databases (EMBASE, PubMed, Cochrane Library, and MEDLINE), as well as articles on websites and conference materials. Study effect estimates and their 95% confidence intervals (CIs) were extracted and calculated. Sensitivity analyses were undertaken to determine inter-study heterogeneity. Finally, we tested for publication bias to determine whether the outcome of the meta-analysis was robust.A total of 1609 articles were identified, 40 of which were included. In radiological studies, the relationships with OA were increased for people with the following diseases: metabolic syndrome (OR 1.418, 95% CI 1.162 to 1.730), hypertension (OR 1.701, 95% CI 1.411 to 2.052), and hyperglycemia (OR 1.225, 95% CI 1.054 to 1.424). In symptomatic studies, the outcomes were similar in metabolic syndrome (OR 1.174, 95% CI 1.034 to 1.332) and hypertension (OR 1.324, 95% CI 1.186 to 1.478) studies, while there were no associations in hyperglycemia (OR 0.975, 95% CI 0.860 to 1.106) studies. There was no correlation between dyslipidemia and OA, whether in radiological studies (OR 1.216, 95% CI 0.968 to 1.529) or symptomatic studies (OR 1.050, 95% CI 0.961 to 1.146).In both studies, metabolic syndrome and hypertension were positively associated with knee OA, and dyslipidemia showed no correlations. Hyperglycemia was associated with OA in radiological studies, while results were reversed in symptomatic studies. Key Points • The hypothesis that metabolic syndrome and its components increase the risk for knee osteoarthritis is attractive; thus, this meta-analysis may help us find out the answer. • There were lots of large-scale studies here, and the total participants were considerable; and this meta-analysis was relatively robust because of reasonable heterogeneity and publication bias. • Targeted education and effective management of risk factors may be helpful for reducing the prevalence of knee osteoarthritis.

Based on the functional characteristics of inter-vehicle communication networks under the current framework, a privacy-preserving authentication framework that combines fifth-generation communication technology (5G) and edge computing technology is proposed. The proposed framework is different from that used in the previous 802.11p-based inter-vehicle communication network architecture, and we use device-to-device technology to achieve communication between vehicles. One difference between a 5G-enabled model and a traditional model for vehicle ad hoc networks is that secure communication between vehicles in a 5G-enabled model is challenging. The authentication protocol proposed in this study can be divided into two parts. The first part involves authenticating and selecting an edge computing vehicle, wherein a fuzzy logic mathematical method is used in the selection process. The second part is the mutual authentication between edge computing and ordinary vehicles. In this process, the exchange of security information between vehicles in a group is possible. Simultaneously, the identity privacy and traceability of the vehicle are ensured. Moreover, the proposed signature scheme is proved to be secure under a random oracle model. Performance evaluation results demonstrate that our proposed scheme has relatively lower computational and communication overhead than existing schemes.

To investigate the effect of perioperative antiviral therapy on the prognosis of hepatitis B virus (HBV) DNA-negative patients with HBV-related hepatocellular carcinoma (HCC).The clinical data of 140 patients who were positive for hepatitis B surface antigen (HBsAg) but negative for HBV DNA before partial hepatectomy were retrospectively analyzed. Propensity score matching (PSM) was used to eliminate the influence of confounding factors on prognosis. Postoperative liver function, HBV reactivation rate, recurrence-free survival (RFS) and overall survival (OS) were compared between antiviral and non-antiviral therapy groups.Compared with the non-antiviral therapy group, the antiviral therapy group had a lower rate of HBV reactivation and better postoperative liver function (P 0.05). The 1-year, 2-year and 3-year survival rates of the antiviral therapy group were better than those of the non-antiviral therapy group before or after PSM (P 0.05). Prognostic analysis excluding 11 patients with HBV reactivation showed that perioperative antiviral therapy could significantly improve OS (P = 0.004), but had no significant effect on RFS (P = 0.056). Multivariate analyzes showed that antiviral therapy was associated with better OS.Perioperative antiviral therapy can significantly reduce the risk of HBV reactivation and improve postoperative liver function, RFS and OS.

With the advancements in social media and rising demand for real traffic information, the data shared in vehicular ad hoc networks (VANETs) indicate that the size and amount of requested data will continue increasing. Vehicles in the same area often have similar data downloading requests. If we ignore the common requests, the resource allocation efficiency of the VANET system will be quite low. Motivated by this fact, we propose an efficient and privacy-preserving data downloading scheme for VANETs, based on the edge computing concept. In the proposed scheme, a roadside unit (RSU) can find the popular data by analyzing the encrypted requests sent from nearby vehicles without having to sacrifice the privacy of their download requests. Further, the RSU caches the popular data in nearby qualified vehicles called edge computing vehicles (ECVs). If a vehicle wishes to download the popular data, it can download it directly from the nearby ECVs. This method increases the downloading efficiency of the system. The security analysis results show that the proposed scheme can resist multiple security attacks. The performance analysis results demonstrate that our scheme has reasonable computation and communication overhead. Finally, the OMNeT++ simulation results indicate that our scheme has good network performance.

Biologic drugs have been increasingly used in the treatment of psoriasis and are especially favorable for severe, recalcitrant, and special-type cases. Therefore, appropriate, effective, and safe use of biologic drugs has drawn attention from dermatologists. For this purpose, Chinese experts majoring in psoriasis analyzed domestic and international research data, summarized current clinical experiences, investigated features of Chinese patients with psoriasis, and finally formulated the present consensus, which provides detailed guidances to clinicians in terms of the principles and methods of the application of biologics, the efficacy and safety profile, patient screening and monitoring, main adverse events and corresponding solutions, and precautions for special patient populations.

Objectives Carbapenem-resistant Enterobacteriaceae (CRE) are a major public-health threat. The most important mechanism of carbapenem resistance in CRE is carbapenemase production. Early identification of carbapenemase-producing Enterobacteriaceae (CPE) leads to improved clinical outcomes. This systematic review aimed to assess the accuracy and applicability of the modified Hodge test (MHT), the carbapenemase Nordmann–Poirel (Carba NP) test, the modified carbapenem inactivation method (mCIM) and matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF/MS) for CPE detection. Methods The meta-analysis included pooled sensitivity, specificity, diagnostic odds ratio, and summary receiver operating characteristic (SROC) curve and area under the curve (AUC). Results A total of 67 studies were included in the analysis. Pooled effect sizes (95% confidence interval) of the MHT, Carba NP, mCIM and MALDI-TOF/MS, respectively, were as follows: sensitivity, 92% (87–95%), 97% (94–98%), 99% (99–100%) and 99% (96–100%); specificity, 93% (86–97%), 100% (99–100%), 99% (96–100%) and 99% (96–100%); diagnostic odds ratio, 98.156 (48.175–199.995), 1277.710 (751.391–2172.692), 3597.352 (1287.575–10000) and 1781.360 (651.827–4868.228); and AUC, 0.97, 1, 1 and 1. Conclusion Carba NP, mCIM and MALDI-TOF/MS all demonstrated high accuracy in CPE detection, whereas the MHT is not recommended owing to some clear drawbacks. We recommend the selection of carbapenemase detection tests in the order of mCIM, Carba NP and MALDI-TOF/MS according to their simplicity, cost, and equipment and skills involved.

Fan blade is one of the key parts used in aircraft engine and its failure is mainly caused by fatigue fracture. This paper aims to predict fatigue life of fan blade during its service operation. First, the effective load and stress of fan blade are obtained by using finite element analysis and simulation. Second, the fatigue notch factor of fan blade is determined by using the nominal stress method. Then, the material properties of fan blade are used to correct and obtain the S − N $S - N$ curve of fan blade. Finally, according to the actual load spectrum of three working loading cycles in 900h, the Miner’s damage accumulation rule is employed to predict the fatigue life of fan blade.

In recent years, research on the security of Industry 4.0 and the Internet of Things (IoT) has attracted a close attention from industry, government, and the scientific community. Smart vehicular networks, as a type of industrial IoT, inevitably exchange large amounts of security and privacy-sensitive data, which make them attractive targets for attackers. For protecting network security and privacy, we have proposed an extensible and effective anonymous batch authentication scheme. In contrast to traditional pseudonym authentication schemes, the same system private key need not to be preloaded in our scheme, effectively avoiding a system failure when destroying a vehicle. Besides, the certificate revocation list (CRL) size is merely related to the number of vehicles that have been revoked, regardless of the number of pseudonym certificates for revoked vehicles. Moreover, this scheme maintains the effectiveness of the traditional scheme, effectively reduces the scale of the CRL, and employs an identity revocation scheme that supports rapid distribution. The scheme supports conditional privacy protection, namely, only the trusted authority (TA) can uniquely trace and revoke vehicles. For illegal vehicles, the TA releases the two hashed seeds to facilitate traceability by all entities in its domain. Furthermore, security analysis indicates that our solution is secure under the random oracle model and fulfills a series of security requirements of vehicular networks. Compared to existing authentication schemes, performance evaluations show that the scheme offers relatively good performance in terms of time consumption.

Momentum wheels are the key components of the inertial actuators in the satellites, and the momentum wheel bearings are weak links of momentum wheels as they operate under harsh conditions. The reliability estimation for momentum wheel bearings are helpful to guarantee the mission successes for both momentum wheels and satellites. Hence, this paper put emphasis into reliability estimation of a momentum wheel bearing considering multiple coupling operating conditions and frictional heat by using the finite element analysis. The stress-strength interference model is employed to calculate the reliability of the momentum wheel bearing. A comparative analysis for reliability estimation with and without frictional heat of the momentum wheel bearing is conducted. The results show that the frictional heat cannot be ignored in the reliability analysis of momentum wheel bearings

PURPOSE We investigated the effectiveness, safety and compliance of Zheng's Supine Rehabilitation Exercise (ZSRE) as a rehabilitation programme among elderly patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). PATIENTS AND METHODS About 82 elderly patients with AECOPD were divided into a rehabilitation group and control group on their admission day, and both groups received routine medical treatment. Patients in the rehabilitation group started ZSRE on the second day of admission and continued until 8 weeks after discharge. RESULTS At the 9th week after discharge, the COPD Assessment Test (CAT), 6-minute walking distance (6MWD) and Modified Medical Research Council Dyspnea Scale (mMRC) in the rehabilitation group were all significantly better than those in the control group (P

Cloud storage auditing helps users to check the integrity of their data stored on the cloud. However, if the client's auditing secret key is exposed to the malicious cloud, the client's data may be deleted by the malicious cloud without being detected. In this paper, we propose a public auditing protocol with intrusion-resilience to relieve damage caused by the key-exposure problem. The proposed protocol divides the lifetime of files stored on the cloud into several time periods, and each time period is further divided into several refreshing periods. The auditing secret key is updated in each time period, and secret values used to update the auditing secret key change in every refreshing period. These two update operations are completed by the client and the third party auditor (TPA). This protocol is secure against the adversary as long as the client and TPA are not compromised in the same refreshing period. Security proof under random oracle model proves the protocol is secure, and the experimental results indicate that the performance of the protocol is acceptable.

Objective To develop a nomogram to estimate the risk of SPLD (International Study Group of Liver Surgery definition grade B or C) and long-term survival in patients with HCC before hepatectomy. Background SPLD is the leading cause of post-hepatectomy mortality. The decision to refer an HCC patient for hepatectomy is mainly based on the survival benefit and SPLD risk. Prediction of SPLD risk before hepatectomy is of great significance. Methods A total of 2071 consecutive patients undergoing hepatectomy for HCC were recruited and randomly divided into the development cohort (n = 1036) and internal validation cohort (n = 1035). Five hundred ninety patients from another center were enrolled as the external validation cohort. A nomogram was developed based on independent preoperative predictors of SPLD determined in multivariable logistic regression analysis. Results The SPLD incidences in the development, internal, and external validation cohorts were 10.1%, 9.5%, and 8.6%, respectively. Multivariable analysis identified total bilirubin, albumin, gamma-glutamyl transpeptidase, prothrombin time, clinically significant portal hypertension, and major resection as independent predictors for SPLD. Incorporating these variables, the nomogram showed good concordance statistics of 0.883, 0.851, and 0.856, respectively in predicting SPLD in the 3 cohorts. Its predictive performance in SPLD, 90-day mortality, and overall survival (OS) outperformed Child-Pugh, model for end-stage liver disease, albumin-bilirubin, and European Association for the Study of the Liver recommended algorithm. With a nomogram score of 137, patients were stratified into low and high risk of SPLD. High-risk patients also had decreased OS. Conclusions The nomogram showed good performance in predicting both SPLD and OS. It could help surgeons select suitable HCC patients for hepatectomy.