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With the development of edge computing and deep learning, intelligent human behavior recognition has spawned extensive applications in smart worlds. However, current edge computing technology faces performance bottlenecks due to limited computing resources at the edge, which prevent deploying advanced deep neural networks. In addition, there is a risk of privacy leakage during interactions between the edge and the server. To tackle these problems, we propose an effective, privacy-preserving edge–cloud collaborative interaction scheme based on WiFi, named P-CA, for human behavior sensing. In our scheme, a convolutional autoencoder neural network is split into two parts. The shallow layers are deployed on the edge side for inference and privacy-preserving processing, while the deep layers are deployed on the server side to leverage its computing resources. Experimental results based on datasets collected from real testbeds demonstrate the effectiveness and considerable performance of the P-CA. The recognition accuracy can maintain 88%, although it could achieve about 94.8% without the mixing operation. In addition, the proposed P-CA achieves better recognition accuracy than two state-of-the-art methods, i.e., FedLoc and PPDFL, by 2.7% and 2.1%, respectively, while maintaining privacy.

Due to mobile Internet technology's rapid popularization, the Industrial Internet of Things (IIoT) can be seen everywhere in our daily lives. While IIoT brings us much convenience, a series of security and scalability issues related to permission operations rise to the surface during device communications. Hence, at present, a reliable and dynamic access control management system for IIoT is in urgent need. Up till now, numerous access control architectures have been proposed for IIoT. However, owing to centralized models and heterogeneous devices, security and scalability requirements still cannot be met. In this paper, we offer a smart contract token-based solution for decentralized access control in IIoT systems. Specifically, there are three smart contracts in our system, including the Token Issue Contract (TIC), User Register Contract (URC), and Manage Contract (MC). These three contracts collaboratively supervise and manage various events in IIoT environments. We also utilize the lightweight and post-quantum encryption algorithm-Nth-degree Truncated Polynomial Ring Units (NTRU) to preserve user privacy during the registration process. Subsequently, to evaluate our proposed architecture's performance, we build a prototype platform that connects to the local blockchain. Finally, experiment results show that our scheme has achieved secure and dynamic access control for the IIoT system compared with related research.

IntroductionIn the middle of December 2022, the Chinese government adjusted the lockdown policy on coronavirus disease 2019 (COVID-19), a large number of infected patients flooded into the emergency department. The emergency medical staff encountered significant working and mental stress while fighting the COVID-19 pandemic. We aimed to investigate the workload change, and the prevalence and associated factors for depression symptoms among emergency medical staff after the policy adjustment.MethodsWe conducted a cross-sectional online survey of emergency medical staff who fought against COVID-19 in Shandong Province during January 16 to 31, 2023. The respondents’ sociodemographic and work information were collected, and they were asked to complete the 9-item Patient Health Questionnaire (PHQ-9) then. Univariate and multivariate logistic regression analyses were applied to identify the potential associated factors for major depression.ResultsNine hundred and sixteen emergency medical personnel from 108 hospitals responded to this survey. The respondents’ weekly working hours (53.65 ± 17.36 vs 49.68 ± 14.84) and monthly night shifts (7.25 ± 3.85 vs 6.80 ± 3.77) increased after the open policy. About 54.3% of the respondents scored more than 10 points on the PHQ-9 standardized test, which is associated with depressive symptoms. In univariate analysis, being doctors, living with family members aged ≤16 or ≥ 65 years old, COVID-19 infection and increased weekly working hours after the open policy were significantly associated with a PHQ-9 score ≥ 10 points. In the multivariate analysis, only increased weekly working hours showed significant association with scoring ≥10 points.ConclusionEmergency medical staff’ workload had increased after the open policy announcement, which was strongly associated with a higher PHQ-9 scores, indicating a very high risk for major depression. Emergency medical staff working as doctors or with an intermediate title from grade-A tertiary hospitals had higher PHQ-9 scores, while COVID-19 infection and weekly working hours of 60 or more after the open policy were associated with higher PHQ-9 scores for those from grade-B tertiary hospitals. Hospital administrators should reinforce the importance of targeted emergency medical staff support during future outbreaks.

BackgroundImmune checkpoint inhibitor-related myocarditis is the deadliest complication of immunotherapy. However, the underlying pathophysiological mechanisms of its occurrence and development remain unclear. Due to the long-term lack of effective early diagnosis and treatment options, it is of great significance to understand the pathophysiological mechanism of immune checkpoint inhibitor-related myocarditis.MethodsTissue samples from three patients with immune checkpoint inhibitor-related myocarditis and three control tissue samples were collected for protein analysis. Differentially expressed proteins were screened out using quantitative proteomics technology based on TMT markers. Protein–protein interaction (PPI) and Gene Ontology (GO) functional enrichment analyses of cross-factors were subsequently performed. Combined with the PD-L1 subcellular organelle- level protein interaction network, we searched for hub proteins involved in immune checkpoint inhibitor-related myocarditis and explored potential drug sensitivity and disease correlation.ResultsA total of 306 differentially expressed proteins were identified in immune checkpoint inhibitor-related myocarditis. Enrichment analysis showed that the differentially expressed proteins were closely related to mitochondrial metabolism. By analyzing mitochondria-related proteins and PD-L1-related proteins, we found four hub proteins, mammalian target of rapamycin (mTOR), Glycogen synthase kinase 3β (GSK3β), Protein tyrosine phosphatase non-receptor type 11 (PTPN11), and Mitofusin 2 (MFN2), indicating that they are closely related to immune checkpoint inhibitor-related myocarditis. Finally, we explored potential drugs for the treatment of immune checkpoint inhibitor-related myocarditis.ConclusionMitochondrial metabolism is involved in the process of immune checkpoint inhibitor-related myocarditis, and we identified four hub proteins, which may become new biomarkers for the early diagnosis and treatment of immune checkpoint inhibitor-related myocarditis.

BackgroundThe subxiphoid approach has been widely used recently. However, there is little data focusing on neurological outcomes in patients with thymomatous myasthenia gravis (MG) who underwent subxiphoid thoracoscopic thymectomy. The purpose of this study was to compare the neurological outcomes of patients with thymomatous MG who underwent extended thymectomy with a subxiphoid or transthoracic approach 1 year postoperatively.MethodsThe records of patients with Masaoka stage I and II thymomas who underwent extended thymectomy from January 2019 to December 2020 with tumor size less than 5 cm and thymomatous MG were retrospectively reviewed and evaluated. Neurological outcomes were measured by a quantitative myasthenia gravis score (QMGS), with a 2.3-point reduction in QMGS associated with improvement in clinical MG status. The clinical efficacy and variables affecting the outcomes were assessed using the Kaplan–Meier method and Cox proportional hazard regression analysis.ResultsA total of 89 patients were included in the analysis, of which 44 had a subxiphoid approach and 45 had a trans-sternal approach. Mean QMGS decreased from 12 at initial diagnosis to 8.7 preoperatively and 5.6 at 12 months postoperatively in the subxiphoid group and from 12.1 to 8.9 to 6.0 in the transthoracic group. Thirteen patients (28.9%) who underwent the trans-sternal approach and 10 (22.7%) who underwent the subxiphoid approach did not have an improved clinical status compared with their preoperative status. The median time to clinical improvement was 3 months (95% CI, 2.15–3.85) for the subxiphoid approach and 6 months (95% CI, 5.54–6.46) for the trans-sternal approach. Univariate results showed that the subxiphoid approach was associated with a faster improvement in clinical status (HR = 1.701, 95% CI, 1.044–2.773, P

Blockchain has become a well-known, secured, decentralized datastore in many domains, including medical, industrial, and especially the financial field. However, to meet the requirements of different fields, platforms that are built on blockchain technology must provide functions and characteristics with a wide variety of options. Although they may share similar technology at the fundamental level, the differences among them make data or transaction exchange challenging. Cross-chain transactions have become a commonly utilized function, while at the same time, some have pointed out its security loopholes. It is evident that a secure transaction scheme is desperately needed. However, what about those nodes that do not behave? It is clear that not only a secure transaction scheme is necessary, but also a system that can gradually eliminate malicious players is of dire need. At the same time, integrating different blockchain systems can be difficult due to their independent architectures, and cross-chain transactions can be at risk if malicious attackers try to control the nodes in the cross-chain system. In this paper, we propose a dynamic reputation management scheme based on the past transaction behaviors of nodes. These behaviors serve as the basis for evaluating a node’s reputation to support the decision on malicious behavior and enable the system to intercept it in a timely manner. Furthermore, to establish a reputation index with high precision and flexibility, we integrate Particle Swarm Optimization (PSO) into our proposed scheme. This allows our system to meet the needs of a wide variety of blockchain platforms. Overall, the article highlights the importance of securing cross-chain transactions and proposes a method to prevent misbehavior by evaluating and managing node reputation.

The Internet of Things (IoT) is an emerging paradigm, seamlessly integrating a great quantity of smart objects that are connected to the Internet [...]

Internet of Things (IoT) technology has been widely adopted by the vast majority of businesses and is influencing every aspect of the world. However, the nature of the internet, communication means, embedded OS, and backend recourses make IoT objects vulnerable to cyber-attacks. For example, the Mirai botnet attack used the IoT to perform the DDoS attack that took down many popular websites. In addition, most standard and conventional security solutions designed for enterprise systems are not applicable to IoT devices. As a result, we are facing a big IoT security and protection challenge, and there is an urgent need to analyze current IoT-specific cyber-attacks and to design suitable and efficient security mechanisms.

Abstract Background Role of biomarkers for promotion of tumor proliferation (BPTPs) and for promotion of apoptosis (BPAs) in thymic malignant tumors is still unclear. The purpose of this study was to evaluate the relationship between BPTPs and/or BPAs and malignancy of thymic malignant tumors. Methods Studies on thymic malignant tumors and biomarkers were searched in PubMed, ISI Web of Knowledge, and Embase databases, and all statistical analyses were conducted using Review Manager. Results Twelve articles related to biomarkers and thymic malignant tumors were selected and analyzed. A relationship between BPAs and Masaoka stage was demonstrated for four markers, namely Bax, p73, Casp-9 and Bcl-2, included 138 stage I/II patients and 74 stage III/IV patients, and BPAs were significantly correlated with high Masaoka staging (P = 0.03). We further found a relationship between BPAs and degree of malignancy for four markers, namely Bax, p73, Casp-9 and Bcl-2, included 176 thymoma patients and 36 thymic carcinoma patients, and BPAs were significantly correlated with thymic carcinoma (P = 0.010). In addition, a relationship between BPTP and Masaoka staging was demonstrated for seven markers, namely Podoplanin, Glut-1, Muc-1, Egfr, Igf1r, c-Jun, and n-Ras, included 373 patients with stage I/II and 212 patients with stage III/IV, and BPTPs were significantly correlated with high Masaoka staging (P

Recently, in order to guarantee security against quantum adversaries, several identification (ID) schemes based on computational problems which are supposed to be hard even for quantum computers have been proposed. However, their security are only proven against non-quantum adversaries. In this paper, we proposed a novel four-pass code-based identification scheme. By using quantum random oracle model, we provide a security proof for our scheme against quantum adversaries which aim to impersonate the prover under concurrent active attacks, based on the hardness assumption of syndrome decoding (SD) problem. Our security proof is interesting in its own right, since it only requires a non-programmable quantum random oracle, in contrast to existing security proofs of digital signatures generated from ID scheme via Fiat-Shamir transform which require programmable quantum random oracles.

Micro-video sharing is prevalent nowadays due to the rapid development of personal smart devices, fast growing of network bandwidth and the easy-to-use cloud computing. However, the micro-video sharing system is more vulnerable compared to the conventional video service model because of the vulnerability of the cloud and the hardware computing constraints of personal devices. Ensuring the security and privacy of the micro-videos has become critical for micro-video sharing systems. Recently, a number of encryption schemes have been proposed to protect the security of the micro-videos stored in the cloud. However, these approaches cannot support micro-video sharing functionality. Moreover, the encryption-based method cannot provide the fine-grained access control on encrypted micro-videos. In this paper, we present a notion of fine-grained identity-based broadcast proxy re-encryption to address the issue of fine-grained encrypted micro-video sharing. Further, we present a concrete scheme in the proposed notion and proved its semantic security. Performance and evaluation reveal that our scheme is practical and efficient.

Abstract Background Fibronectin (FN) is a high-molecular-weight glycoprotein component of the extracellular matrix involved in cell adhesion, migration, metastasis, proliferation and differentiation, as well as embryogenesis, wound healing, and blood coagulation. Considerable recent research has established that tumor expression of FN is closely associated with tumor formation and development as well as disease prognosis. However, the mechanisms underlying this relationship have remained unclear. The aim of this study was to investigate FN protein expression in esophageal squamous cell carcinoma (ESCC) and determine its potential prognostic relevance, while also elucidating the source and function of FN. Methods We conducted immunohistochemical analyses of protein expression in primary tumors of ESCC patients and analyzed their association with standard prognostic parameters and clinical outcomes. Expression of FN in two ESCC cell lines (Eca-109 and TE-1) was also examined by RT-PCR, immunofluorescence, and ELISA. ESCC cells were cultured in a microenvironment containing a high FN content, and changes in their morphology and migration ability were assessed by microscopy, wound-healing assays, and Transwell assays. Results FN expression in ESCC specimens was mainly detected in the tumor stroma, with very little FN detected in tumor cells. Stromal FN content in ESCC specimens was associated with lymphatic metastasis (P = 0.032) and prognosis. In this latter context, patients with high tumor stromal expression of FN showed worse overall survival (P = 0.002) and progression-free survival (P

Cyber-Physical Systems (CPS) are integrations of computation, networking, and physical processes. Its process control is often referred to as embedded systems. Generally, CPS and Internet of Things have the same basic architecture, whereas the former shows a higher combination and coordination between physical and computational elements, i.e., wireless sensor networks can be a vital part of CPS applications. With the rapid development, CPS has been applied to healthcare industry, where a wide range of medical sensors are used within a healthcare organization. However, these sensors may generate a large number of false alarms in practice, which could significantly reduce the system effectiveness. Targeting on this issue, in this work, we attempt to design a Medical Fuzzy Alarm Filter (named MFAFilter) for healthcare environments by means of fuzzy logic, especially fuzzy if-then rules, which could handle the vague and imprecise among data. In the evaluation, we conducted two major experiments to explore the performance of our approach in a simulated and a real network environment, respectively. Experimental results demonstrate that the use of fuzzy if-then rules could achieve a better accuracy as compared to the traditional supervised algorithms, and that our designed filter is effective in the practical environment.

Internet of Things (IoT) has been widely used in our daily life, which enables various objects to be interconnected for data exchange, including physical devices, vehicles, and other items embedded with network connectivity. Wireless sensor network (WSN) is a vital application of IoT, providing many kinds of information among sensors, whereas such network is vulnerable to a wide range of attacks, especially insider attacks, due to its natural environment and inherent unreliable transmission. To safeguard its security, intrusion detection systems (IDSs) are widely adopted in a WSN to defend against insider attacks through implementing proper trust-based mechanisms. However, in the era of big data, sensors may generate excessive information and data, which could degrade the effectiveness of trust computation. In this paper, we focus on this challenge and propose a way of combining Bayesian-based trust management with traffic sampling for wireless intrusion detection under a hierarchical structure. In the evaluation, we investigate the performance of our approach in both a simulated and a real network environment. Experimental results demonstrate that packet-based trust management would become ineffective in a heavy traffic environment, and that our approach can help lighten the burden of IDSs in handling traffic, while maintaining the detection of insider attacks.

Recently, the popularity of the Internet of Things (IoT) has led to a rapid development and significant advancement of ubiquitous applications seamlessly integrated within our daily life. Owing to the accompanying growth of the importance of privacy, a great deal of attention has focused on the issues of secure management and robust access control of IoT devices. In this paper, we propose the design of a blockchain connected gateway which adaptively and securely maintains user privacy preferences for IoT devices in the blockchain network. Individual privacy leakage can be prevented because the gateway effectively protects users' sensitive data from being accessed without their consent. A robust digital signature mechanism is proposed for the purposes of authentication and secure management of privacy preferences. Furthermore, we adopt the blockchain network as the underlying architecture of data processing and maintenance to resolve privacy disputes.

The deployment of IoT devices with significant data collection capabilities around the world raises concerns about user privacy. People are worried about ubiquitous IoT devices collecting and sharing their data with unknown parties without their awareness or consent. Currently, several governmental agencies have stated that IoT service providers should obtain user consent before collecting and using their personal data. However, to the best of our knowledge, there is no standard means for users to reach agreements on privacy practices for IoT applications. Among different types of IoT applications, this paper focuses on the scenario in which people use their personal smartphones to access nearby IoT devices via Bluetooth Low Energy (BLE). To address the privacy issue in the scenario, this paper proposes a privacy preferences expression framework for BLE-based applications named PrivacyBat. The framework defines specifications for users to achieve agreements on privacy practices with nearby BLE devices. In addition, this framework provides guidelines for a device to process user requests according to the agreement. To demonstrate how the framework operates, this paper further provides a proof of concept implementation. As the proposed framework can improve the privacy policy agreement process in IoT applications, this paper can hopefully contribute to increasing user trust in IoT applications.

Recently, the popularity and universality of smart-devices has led to rapid advancement in the development of applications for mobile commerce around the world. Novel mobile payment schemes, such as Apple pay, Android pay, and Samsung pay are becoming an increasingly popular ways to conduct online transactions, no matter what type of smart devices are used. Due to the attendant growth in the importance of security, significant attention has been devoted to the challenge of designing and implementing a robust mobile payment scheme for securing online transactions. In this paper, we demonstrate a robust mobile payment scheme based on sturdy certificateless signatures with bilinear pairing. We elegantly refine the proposed mobile payment scheme to make it suitable for computation-constrained mobile devices. The practicability of the proposed mobile payment scheme is then certified via a rigorous security analysis and thorough performance evaluation using the Raspberry PI as the implementation platform for our proposed scheme. Furthermore, we implement a transaction repository with the aid of smart contract technology. The simulation results, based on Ethereum, demonstrate the feasibility of employing the smart contract technology to secure mobile payments.

In the context of climate change, natural gas is becoming increasingly important for low-carbon development in China. The gap between the demand and supply of natural gas, domestically, and China’s high dependence on foreign sources, highlights the importance of ensuring a secure system for supplying natural gas in the country. This study applied ecological network analysis, a powerful systems-oriented method, to simulate interactions between different nodes of the natural gas supply system and to evaluate the system’s security level. Two network models were constructed at the regional and national layers, respectively, by dividing external natural gas suppliers into multiple regions and countries. These models were used to evaluate the overall security level and related characteristics of China’s natural gas supply system from 2000 to 2012. The results showed stable improvement in the system’s security during this period. With the exceptions of some specific indicators (e.g., the mutualism index (MI)), analyses of network information and structure yielded the mostly similar results for the two models. In conclusion, a regional layer (RL) network model is considered more economical than a national layer (NL) model for evaluating the overall security of China’s natural gas supply system, especially when available data are limited.

With the development of information technology, films, music, and other publications are inclined to be distributed in digitalized form. However, the low cost of data replication and dissemination leads to digital rights problems and brings huge economic losses. Up to now, existing digital rights management (DRM) schemes have been powerless to deter attempts of infringing digital rights and recover losses of copyright holders. This paper presents a YODA-based digital watermark management system (Y-DWMS), adopting non-repudiation of smart contract and blockchain, to implement a DRM mechanism to infinitely amplify the cost of infringement and recover losses copyright holders suffered once the infringement is reported. We adopt game analysis to prove that in Y-DWMS, the decision of non-infringement always dominates rational users, so as to fundamentally eradicate the infringement of digital rights, which current mainstream DRM schemes cannot reach.

Device-free localization (DFL) locates targets without being equipped with the attached devices, which is of great significance for intrusion detection or monitoring in the era of the Internet-of-Things (IoT). Aiming at solving the problems of low accuracy and low robustness in DFL approaches, in this paper, we first treat the RSS signal as an RSS-image matrix and conduct a process of eliminating the background to dig out the variation component with distinguished features. Then, we make use of these feature-rich images by formulating DFL as an image classification problem. Furthermore, a deep convolutional neural network (CNN) is designed to extract features automatically for classification. The localization performance of the proposed background elimination-based CNN (BE-CNN) scheme is validated with a real-world dataset of outdoor DFL. In addition, we also validate the robust performance of the proposal by conducting numerical experiments with different levels of noise. Experimental results demonstrate that the proposed scheme has an obvious advantage in terms of improving localization accuracy and robustness for DFL. Particularly, the BE-CNN can maintain the highest localization accuracy of 100%, even in noisy conditions when the SNR is over −5 dB. The BE-based methods can outperform all the corresponding raw data-based methods in terms of the localization accuracy. In addition, the proposed method can outperform the comparison methods, deep neural network with autoencoder, K-nearest-neighbor (KNN), support vector machines (SVM), etc., in terms of the localization accuracy and robustness.

Device-free localization (DFL) locates target in a wireless sensors network (WSN) without equipping with wireless devices or tags, which is an emerging technology in the fields of intrusion detection and monitoring. In order to achieve an accurate result of DFL, the conventional works adopt l 1 norm as a regularizer to take the full potential of sparsity for locating targets. Contrasting to the previous works, we exploit the l 2 , 1 norm as the regularizer and devise an efficient optimization method with a proximal operator-based scheme, which leads the proposed improved-sparse-coding algorithm with proximal operator (ISCPO). Compared with the state-of-the-art methods that adopt l 1 norm as the regularizer, the proposed algorithm can improve the joint sparsity of sparse solution. Experimental results on our real testbeds of indoor DFL show that, in scenarios of living room and corridor, the proposed approach can achieve high localization accuracies of about 100% and 90%, respectively. In addition, the proposed ISCPO algorithm outperforms the compared state-of-the-art methods and has a more robust performance in challenged environments for target localization.

The Internet-of-Things (IoT) is an emerging paradigm seamlessly integrating a great number of smart objects ubiquitously connected to the Internet. With the rise in interest in the IoT, industry and academia have introduced a variety of authentication technologies to deal with security challenges. Authentication in IoT involves not only shifting intelligent access control down to the end smart objects, but also user identification and verification. In this paper, we build an authentication system based on brainwave reactions to a chain of events. Brainwaves, as external signals of a functioning brain, provide a glimpse into how we think and react. However, seen another way, we could reasonably expect that a given action or event could be linked back to its corresponding brainwave reaction. Recently, commercial products in the form of wearable brainwave headsets have appeared on the market, opening up the possibility of exploiting brainwaves for various purposes and making this more feasible. In the proposed system, we use a commercially available brainwave headset to collect brainwave data from participants for use in the proposed authentication system. After the brainwave data collection process, we apply a machine learning-based approach to extract features from brainwaves to serve as authentication tokens in the system and support the authentication system itself.

Rapid advances in wireless communications and pervasive computing technologies have resulted in increasing interest and popularity of Internet-of-Things (IoT) architecture, ubiquitously providing intelligence and convenience to our daily life. In IoT-based network environments, smart objects are embedded everywhere as ubiquitous things connected in a pervasive manner. Ensuring security for interactions between these smart things is significantly more important, and a topic of ongoing interest. In this paper, we present a certificateless signature scheme for smart objects in IoT-based pervasive computing environments. We evaluate the utility of the proposed scheme in IoT-oriented testbeds, i.e., Arduino Uno and Raspberry PI 2. Experiment results present the practicability of the proposed scheme. Moreover, we revisit the scheme of Wang et al. (2015) and revealed that a malicious super type I adversary can easily forge a legitimate signature to cheat any receiver as he/she wishes in the scheme. The superiority of the proposed certificateless signature scheme over relevant studies is demonstrated in terms of the summarized security and performance comparisons.

BackgroundNestin is associated with neoplastic transformation, but the mechanisms by which nestin contributes to invasion and malignancy of lung cancer remain unknown. Considering that proliferation is necessary for malignant behavior, we investigated the mechanism of nestin action in association with the proliferative properties of non-small cell lung cancer (NSCLC).MethodsNestin expression was examined in NSCLC specimens and cell lines. Associations with clinicopathological features, including prognosis and proliferative markers, were evaluated. Effects of nestin knockdown on proliferation and the signaling pathways involved were further investigated.ResultsNestin was expressed in most cancer specimens and all the tumor cell lines analyzed. High nestin expression in malignant tissue was associated with high Ki-67 or PCNA levels and poor patient outcomes. Conversely, knockdown of nestin expression led to significant inhibition of tumor cell proliferation, decreased colony forming ability, and cell cycle G1 arrest. Furthermore, nestin knockdown resulted in inhibition of Akt and GSK3β activation.ConclusionsOur data demonstrate that nestin expression in NSCLC cells is associated with poor prognosis of patients and tumor cell proliferation pathway. Downregulation of nestin efficiently inhibited lung cancer cell proliferation, which might be through affecting cell cycle arrest and Akt-GSK3β-Rb signaling pathway.