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Objectives: Physical diseases are well-established risk factor for suicide, particularly among older adults. However, little is known about the underlying mechanism of the association. This study aimed to describe the prevalence of physical diseases and their influences on the elderly in rural China and to examine the underlying mechanisms of the relationship between physical diseases and suicide. Methods: This matched case–control psychological autopsy study was conducted from June 2014 to September 2015. Consecutive suicide cases (242) among people aged 60 years or above were identified in three Chinese provinces. The suicide cases were 1:1 matched with living comparisons based on age, gender and residential area. Two informants for each participant were interviewed to collect data on their demographic characteristics, the severity index of physical diseases, depressive symptoms, feelings of hopelessness, mental disorders and social support. Results: A significant difference was found between suicide cases and living comparisons regarding the prevalence of physical diseases (83.5% vs 66.5%, p < 0.001) and their severity (11.3 ± 6.2 vs 6.7 ± 5.3, p < 0.001). Independent risks of suicide included the following: not currently married (OR = 2.81, 95% CI = [1.04, 7.62]), mental disorders (OR = 7.18, 95% CI = [1.83, 28.13]), depressive symptoms (OR = 1.15, 95% CI = [1.05, 1.26]) and feelings of hopelessness (OR = 1.51, 95% CI = [1.20, 1.90]). The structural equation model indicated that the relationship between the severity index of physical diseases and suicide was mediated by depressive symptoms, feelings of hopelessness and mental disorders. Conclusion: The severity and number of physical diseases were found to be correlated with suicide among the elderly in rural China, after controlling for demographic characteristics. Physical diseases elevate one’s suicide risk by increasing depressive symptoms, feelings of hopelessness and mental disorders. Efforts for suicide prevention should be integrated with strategies to treat physical diseases along with psychological interventions.

SUMMARYKey management becomes more difficult in multiprivileged group communications due to the dynamic membership and the complex relations between users and resources. Because centralized key management schemes have the drawbacks of the single point of failure, and performance bottleneck and distributed key management schemes are not scalable and lack of central control, decentralized key management schemes are proposed as a tradeoff between them. In this paper, we propose a decentralized group key management scheme using multilinear forms for dynamic multiprivileged groups. Once users join/leave the group and change their privileges, the related session keys should be updated. The rekeying in the joining operation is relatively simple because the keys are deduced from the previous keys based on a one-way function. When rekeying for one leaving/switching operation, a uniform rekeying material is negotiated between the related service groups (SGs) by using multilinear forms. Compared with other schemes in which several rounds of negotiations are executed for rekeying in each joining/leaving/switching operation, only one round of negotiation is required in each leaving/switching operation of our decentralized group key management scheme. At last, the affected session keys can be deduced by the related SGs. Our proposed scheme also supports the dynamic formation and decomposition of SGs, which provides good scalability. Security analysis is provided to show that the proposed scheme is secure. The performance analysis and the simulation results show that the proposed scheme reduces the communication cost greatly.

Traditional Chinese medicine (TCM) is found on a long‐term medical practice in China. Rare human brains can fully grasp the deep TCM knowledge derived from a tremendous amount of experience. In this big data era, a big electronic brain might be competent via deep learning techniques. For this prospect, the electronic brain needs to process various heterogeneous data, such as images, texts, audio signals, and other sensory data. It used to be a challenge to analyze the heterogeneous data by the computer‐aided system until the advances of the powerful deep learning tools. We propose a multimodal deep learning framework to mimic a TCM practitioner to diagnose a patient on the basis of multimodal perceptions of see, listen, smell, ask, and touch. The framework learns common representations from various high‐dimensional sensory data, and fuse the information for final classification. We propose to use conceptual alignment deep neural networks to embed prior knowledge and obtain interpretable latent representations. We implement a multimodal deep architecture to process tongue image and description text data for TCM diagnosis. Experiments illustrate that the multimodal deep architecture can extract effective features from heterogeneous data, produce interpretable representations, and finally achieve a higher accuracy than either corresponding unimodal architectures.

As the world is now fighting against rampant virus COVID-19, the development of vaccines on a large scale and making it reach millions of people to be immunised has become quintessential. So far 40.9% of the world got vaccinated. Still, there are more to get vaccinated. Those who got vaccinated have the chance of getting the vaccine certificate as proof to move, work, etc., based on their daily requirements. But others create their own forged vaccine certificate using advanced software and digital tools which will create complex problems where we cannot distinguish between real and fake vaccine certificates. Also, it will create immense pressure on the government and as well as healthcare workers as they have been trying to save people from day 1, but parallelly people who have fake vaccine certificates roam around even if they are COVID/Non-COVID patients. So, to avoid this huge problem, this paper focuses on detecting fake vaccine certificates using a bot powered by Artificial Intelligence and neurologically powered by Deep Learning in which the following are the stages: a) Data Collection, b) Preprocessing to remove noise from the data, and convert to grayscale and normalised, c) Error level analysis, d) Texture-based feature extraction for extracting logo, symbol and for the signature we extract Crest-Trough parameter, and e) Classification using Den-seNet201 and thereby giving the results as fake/real certificate. The evaluation of the model is taken over performance measures like accuracy, specificity, sensitivity, detection rate, recall, f1-score, and computation time over state-of-art models such as SVM, RNN, VGG16, Alexnet, and CNN in which the proposed model (D201-LBP) outperforms with an accuracy of 0.94.

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Smart Assistants have rapidly emerged in smartphones, vehicles, and many smart home devices. Establishing comfortable personal spaces in smart cities requires that these smart assistants are transparent in design and implementation—a fundamental trait required for their validation and accountability. In this article, we take the case of Google Assistant (GA), a state-of-the-art smart assistant, and perform its diagnostic analysis from the transparency and accountability perspectives. We compare our discoveries from the analysis of GA with those of four leading smart assistants. We use two online user studies (N = 100 and N = 210) conducted with students from four universities in three countries (China, Italy, and Pakistan) to learn whether risk communication in GA is transparent to its potential users and how it affects them. Our research discovered that GA has unusual permission requirements and sensitive Application Programming Interface (API) usage, and its privacy requirements are not transparent to smartphone users. The findings suggest that this lack of transparency makes the risk assessment and accountability of GA difficult posing risks to establishing private and secure personal spaces in a smart city. Following the separation of concerns principle, we suggest that autonomous bodies should develop standards for the design and development of smart city products and services.

Edge computing is becoming increasingly commonplace, as consumer devices become more computationally capable and network connectivity improves (e.g., due to 5G). With the rapid development of edge computing and Internet of Things (IoT), the use of edge-cloud collaborative computing to provide service-oriented network application (i.e., task) in edge-cloud IoT has become an important research topic. In this paper, we present an edge-cloud collaborative computing framework and our resource deployment algorithm with task prediction (RDAP). Based on our paradigm, tasks in the cloud service center are predicted using the two-dimensional time series, and task classification aggregation and delay threshold determination are combined to optimize task resource deployment of edge servers. A task scheduling algorithm with Pareto improvement (TSAP) is also proposed. At the edge servers, the Pareto progressive comparison is conducted in two stages to obtain the tangent point or any intersection point of the two objective curves of user’s quality of service and effect of system service to optimize task scheduling. The experimental results show that for varying user task scales and different Zipf distribution α parameters, combining RDAP and TSAP (RDAP-TSAP) can improve the average user task hit rate. In addition, the average task completion time of users, the overall system service effect, and the total task delay rate of RDAP-TSAP are better than TSAP and the benchmark algorithms for task scheduling.