Suicide rate among rural elderly is the highest among all age groups in China, yet little is known about the suicide risks in this rapidly growing vulnerable population.
This matched case-control ...psychological autopsy study was conducted during June 2014 to September 2015. Consecutive samples of suicides aged 60 or above were identified in three provinces (Shandong, Hunan, and Guangxi) in China. Living comparisons were 1:1 matched with the suicides in age (±3 years old), gender, and living location. Risk factors included demographic characteristics, being left-behind, mental disorder, depressive symptoms, stressful life events, and social support.
A total of 242 suicides and 242 comparisons were enrolled: 135 (55.8%) were male, mean (s.d.) age was 74 (8) years. The most frequently used suicide means were pesticides (125, 51.7%) and hanging (95, 39.3%). Independent risks of suicide included unstable marital status odds ratio (OR) 4.19, 95% confidence interval (CI) 1.61-10.92, unemployed (compared with employed, OR 4.43, 95% CI 1.09-17.95), depressive symptoms (OR 1.34, 95% CI 1.21-1.48), and mental disorder (OR 6.28, 95% CI 1.75-22.54). Structural equation model indicated that the association between being left-behind and suicide was mediated by mental disorder, depressive symptoms, stressful life events, and social support.
Unstable marital status, unemployed, depressive symptoms, and mental disorder are independent risk factors for suicide in rural elderly. Being left-behind can elevate the suicide risk through increasing life stresses, depressive symptoms, mental disorder, and decreasing social support. Elderly suicide may be prevented by restricting pesticides, training rural physicians, treating mental disorders, mitigating life stress, and enhancing social connection.
In order to improve the security and efficiency of image encryption systems comprehensively, a novel chaotic S-box based image encryption scheme is proposed. Firstly, a new compound chaotic system, ...Sine-Tent map, is proposed to widen the chaotic range and improve the chaotic performance of 1D discrete chaotic maps. As a result, the new compound chaotic system is more suitable for cryptosystem. Secondly, an efficient and simple method for generating S-boxes is proposed, which can greatly improve the efficiency of S-box production. Thirdly, a novel double S-box based image encryption algorithm is proposed. By introducing equivalent key sequences {
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} related with image ciphertext, the proposed cryptosystem can resist the four classical types of attacks, which is an advantage over other S-box based encryption schemes. Furthermore, it enhanced the resistance of the system to differential analysis attack by two rounds of forward and backward confusion-diffusion operation with double S-boxes. The simulation results and security analysis verify the effectiveness of the proposed scheme. The new scheme has obvious efficiency advantages, which means that it has better application potential in real-time image encryption.
IoT devices generate data over time, which is going to be shared with other parties to provide high-level services. Smart City is one of its applications which aims to manage cities automatically. ...Because of the large number of devices, three critical challenges come up: heterogeneity, privacy-preserving of generated data, and providing high-level services. The existing solutions cannot even solve two of the mentioned challenges simultaneously. In this paper, we propose a three-module framework, named “Ontology-Based Privacy-Preserving” (OBPP) to address these issues. The first module includes an ontology, a data storage model, to address the heterogeneity issue while keeping the privacy information of IoT devices. The second one contains semantic reasoning rules to find abnormal patterns while addressing the quality of provided services. The third module provides a privacy rules manager to address the privacy-preserving challenges of IoT devices achieved by dynamically changing privacy behaviors of the devices. Extensive simulations on a synthetic smart city dataset demonstrate the superior performance of our approach compared to the existing solutions while providing affordability and robustness against information leakages. Thus, it can be widely applied to smart cities.
•An ontology is developed for the smart city to enable effective and seamless interactions/interoperations on heterogeneous devices/services provided by different vendors in IoT-based smart cities.•The nature of the system is changed with the aim of more accurate privacy preserving.•A three-layer ontology-based privacy service framework is designed for supporting privacy preservation in the process of interactions/interoperations.•Challenges and directions for future work on privacy-preserving in the smart city have been discussed.
Emerging network computing technologies extend the functionalities of industrial IoT (IIoT) terminals. However, this promising service-provisioning scheme encounters problems in untrusted and ...distributed IIoT scenarios because malicious service providers or clients may deny service provisions or usage for their own interests. Traditional nonrepudiation solutions fade in IIoT environments due to requirements of trusted third parties or unacceptable overheads. Fortunately, the blockchain revolution facilitates innovative solutions. In this paper, we propose a blockchain-based fair nonrepudiation service provisioning scheme for IIoT scenarios in which the blockchain is used as a service publisher and an evidence recorder. Each service is separately delivered via on-chain and off-chain channels with mandatory evidence submissions for nonrepudiation purpose. Moreover, a homomorphic-hash-based service verification method is designed that can function with mere on-chain evidence. And an impartial smart contract is implemented to resolve disputes. The security analysis demonstrates the dependability, and the evaluations reveal the effectiveness and efficiency.
Summary
With the popularity of Sensor‐Cloud, its security issues get more attention from industry and academia. Especially, Sensor‐Cloud underlying network is very vulnerable to internal attacks due ...to its limitations in computing, storage, and analysis. Most existing trust evaluation mechanisms are proposed to detect internal attack issues from the behavior level. However, there are some special internal attacks in the data level such as hidden data attacks, which are normal in the behavior level but generate malicious data to lead user to make wrong decisions. To detect this type of attacks, we design a fog‐based detection system (FDS), which is based on the trust evaluation mechanism in the behavior level. In this paper, three types of scenes (the redundant data, the parameter curve characteristic, and the data validation) are defined, and three detection schemes are given. Some experiments are conducted, which manifest that FDS has certain advantages in detecting hidden data attacks.
Due to the worldwide prevalence of multidrug-resistant pathogens and high incidence of diseases such as cancer, there is an urgent need for the discovery and development of new drugs. Nearly half of ...the FDA-approved drugs are derived from natural products that are produced by living organisms, mainly bacteria, fungi, and plants. Commercial development is often limited by the low yield of the desired compounds expressed by the native producers. In addition, recent advances in whole genome sequencing and bioinformatics have revealed an abundance of cryptic biosynthetic gene clusters within microbial genomes. Genetic manipulation of clusters in the native host is commonly used to awaken poorly expressed or silent gene clusters, however, the lack of feasible genetic manipulation systems in many strains often hinders our ability to engineer the native producers. The transfer of gene clusters into heterologous hosts for expression of partial or entire biosynthetic pathways is an approach that can be used to overcome this limitation. Heterologous expression also facilitates the chimeric fusion of different biosynthetic pathways, leading to the generation of “unnatural” natural products. The genus Streptomyces is especially known to be a prolific source of drugs/antibiotics, its members are often used as heterologous expression hosts. In this review, we summarize recent applications of Streptomyces species, S. coelicolor, S. lividans, S. albus, S. venezuelae and S. avermitilis, as heterologous expression systems.
As an alternative to current wired-based networks, wireless sensor networks (WSNs) are becoming an increasingly compelling platform for engineering structural health monitoring (SHM) due to ...relatively low-cost, easy installation, and so forth. However, there is still an unaddressed challenge: the application-specific dependability in terms of sensor fault detection and tolerance. The dependability is also affected by a reduction on the quality of monitoring when mitigating WSN constrains (e.g., limited energy, narrow bandwidth). We address these by designing a dependable distributed WSN framework for SHM (called DependSHM) and then examining its ability to cope with sensor faults and constraints. We find evidence that faulty sensors can corrupt results of a health event (e.g., damage) in a structural system without being detected. More specifically, we bring attention to an undiscovered yet interesting fact, i.e., the real measured signals introduced by one or more faulty sensors may cause an undamaged location to be identified as damaged (false positive) or a damaged location as undamaged (false negative) diagnosis. This can be caused by faults in sensor bonding, precision degradation, amplification gain, bias, drift, noise, and so forth. In DependSHM, we present a distributed automated algorithm to detect such types of faults, and we offer an online signal reconstruction algorithm to recover from the wrong diagnosis. Through comprehensive simulations and a WSN prototype system implementation, we evaluate the effectiveness of DependSHM.
Tracking mobile targets in wireless sensor networks (WSNs) has many important applications. As it is often the case in prior work that the quality of tracking (QoT) heavily depends on high accuracy ...in localization or distance estimation, which is never perfect in practice. These bring a cumulative effect on tracking, e.g., target missing. Recovering from the effect and also frequent interactions between nodes and a central server result in a high energy consumption. We design a tracking scheme, named t-Tracking, aiming to achieve two major objectives: high QoT and high energy efficiency of the WSN. We propose a set of fully distributed tracking algorithms, which answer queries like whether a target remains in a "specific area" (called a "face" in localized geographic routing, defined in terms of radio connectivity and local interactions of nodes). When a target moves across a face, the nodes of the face that are close to its estimated movements compute the sequence of the target's movements and predict when the target moves to another face. The nodes answer queries from a mobile sink called the "tracker", which follows the target along with the sequence. t-Tracking has advantages over prior work as it reduces the dependency on requiring high accuracy in localization and the frequency of interactions. It also timely solves the target missing problem caused by node failures, obstacles, etc., making the tracking robust in a highly dynamic environment. We validate its effectiveness considering the objectives in extensive simulations and in a proof-of-concept system implementation.
Social influence analysis has become one of the most important technologies in modern information and service industries. It will definitely become an essential mechanism to perform complex analysis ...in social networking big data. It is attracting an increasing amount of research ranging from popular topics extraction to social influence analysis, including analysis and processing of big data, social influence evaluation, influential users identification, and information diffusion modeling. We provide a comprehensive investigation of social influence analysis, and discuss the characteristics of social influence and the architecture of social influence analysis based on social networking big data. The relationship between big data and social influence analysis is also discussed. In addition, research challenges relevant to real-world issues based on social networking big data in social influence analysis are discussed, focusing on research issues such as scalability, data collection, dynamic evolution, causal relationships, network heterogeneity, evaluation metrics, and effective mechanisms. Our goal is to provide a broad research guideline of existing and ongoing efforts via social influence analysis in large-scale social networks, and to help researchers better understand the existing work, and design new algorithms and methods for social influence analysis.
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