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Sleep disturbances, present in more than 90% of major depressive disorder (MDD) patients, are moderated by sex in adult MDD. In particular, slow-wave electroencephalographic activity (SWA; 0.5-4 Hz) accumulation is low and dissipation impaired. This SWA abnormality in depressed adult males does not change with age, suggesting that SWA abnormality appears at early ages. The present study evaluated sex differences in SWA in adolescents with MDD compared to healthy controls. We evaluated regularized sleep-wake schedules at home for 5-7 days, followed by two consecutive nights of sleep EEG recording. The study included 104 participants, 52 symptomatic and depressed subjects (MDD: 20 males and 32 females) and 52 healthy controls (HC: 20 males and 32 females), aged 13-18 years. SWA power and dissipation, and duration and latencies to each Non-Rapid Eye Movement (NREM) sleep period were calculated for each group. Results showed that SWA accumulation in the first NREM period was lower and its dissipation across the night more irregular in MDD males compared to HC males (P<0.009). By contrast, SWA was equivalent in MDD and HC females. In conclusion, as reported in adult MDD, the accumulation and dissipation of SWA was abnormal in depressed adolescents, but only in males. SWA abnormalities in adolescent MDD may relate to different depressive symptoms in females and males. These results underscore the need to develop sex-specific therapies to enhance and restore SWA in depressed adolescents.

Major public cloud providers, such as AWS, Azure or Google, offer seamless experiences for infrastructure as a service (IaaS), platform as a service (PaaS) and software as a service (SaaS). With the emergence of the public cloud's vast usage, administrators must be able to have a reliable method to provide the seamless experience that a public cloud offers on a smaller scale, such as a private cloud. When a smaller deployment or a private cloud is needed, OpenStack can meet the goals without increasing cost or sacrificing data control.

To demonstrate these enablement goals of resiliency and elasticity in IaaS and PaaS, the authors design a private distributed system cloud platform using OpenStack and its core services of Nova, Swift, Cinder, Neutron, Keystone, Horizon and Glance on a five-node deployment.

Through the demonstration of dynamically adding an IaaS node, pushing the deployment to its physical and logical limits, and eventually crashing the deployment, this paper shows how the PackStack utility facilitates the provisioning of an elastic and resilient OpenStack-based IaaS platform that can be used in production if the deployment is kept within designated boundaries.

The authors adopt the multinode-capable PackStack utility in favor of an all-in-one OpenStack build for a true demonstration of resiliency, elasticity and scalability in a small-scale IaaS. An all-in-one deployment is generally used for proof-of-concept deployments and is not easily scaled in production across multiple nodes. The authors demonstrate that combining PackStack with the multi-node design is suitable for smaller-scale production IaaS and PaaS deployments.

Cloud computing originated in central data centers that are connected to the backbone of the Internet. The network transport to and from a distant data center incurs long latencies that hinder modern low-latency applications. In order to flexibly support the computing demands of users, cloud computing is evolving toward a continuum of cloud computing resources that are distributed between the end users and a distant data center. The purpose of this review paper is to concisely summarize the state-of-the-art in the evolving cloud computing field and to outline research imperatives.

The authors identify two main dimensions (or axes) of development of cloud computing: the trend toward flexibility of scaling computing resources, which the authors denote as Flex-Cloud, and the trend toward ubiquitous cloud computing, which the authors denote as Ubi-Cloud. Along these two axes of Flex-Cloud and Ubi-Cloud, the authors review the existing research and development and identify pressing open problems.

The authors find that extensive research and development efforts have addressed some Ubi-Cloud and Flex-Cloud challenges resulting in exciting advances to date. However, a wide array of research challenges remains open, thus providing a fertile field for future research and development.

This review paper is the first to define the concept of the Ubi-Flex-Cloud as the two-dimensional research and design space for cloud computing research and development. The Ubi-Flex-Cloud concept can serve as a foundation and reference framework for planning and positioning future cloud computing research and development efforts.

Posttraumatic stress disorder (PTSD), an anxiety disorder with lifetime prevalence of 7.8%, is characterized by symptoms that develop following exposure to traumatic life events and that cause an immediate experience of intense fear, helplessness or horror. PTSD is marked by recurrent nightmares typified by the recall of intrusive experiences and by extended disturbance throughout sleep. Individuals with PTSD respond poorly to drug treatments for insomnia. The disadvantages of drug treatment for insomnia underline the importance of non-pharmacological alternatives. Thus, the present study had three aims: first, to compare the efficiency of two relaxation techniques (muscular relaxation and progressive music relaxation) in alleviating insomnia among individuals with PTSD using both objective and subjective measures of sleep quality; second, to examine whether these two techniques have different effects on psychological indicators of PTSD, such as depression and anxiety; and finally, to examine how initial PTSD symptom severity and baseline emotional measures are related to the efficiency of these two relaxation methods. Thirteen PTSD patients with no other major psychiatric or neurological disorders participated in the study. The study comprised one seven-day running-in, no-treatment period, followed by two seven-day experimental periods. The treatments constituted either music relaxation or muscle relaxation techniques at desired bedtime. These treatments were randomly assigned. During each of these three experimental periods, subjects' sleep was continuously monitored with a wrist actigraph (Ambulatory Monitoring, Inc.), and subjects were asked to fill out several questionnaires concerned with a wide spectrum of issues, such as sleep, depression, and anxiety. Analyses revealed a significant increase in objective and subjective sleep efficiency and a significant reduction in depression level following music relaxation. Moreover, following music relaxation, a highly significant negative correlation was found between improvement in objective sleep efficiency and reduction in depression scale. The study‘s findings provide evidence that music relaxation at bedtime can be used as treatment for insomnia among individuals with PTSD.

The purpose of this paper is to describe how emerging open standards are replacing traditional industrial networks. Current industrial Ethernet networks are not interoperable; thus, limiting the potential capabilities for the Industrial Internet of Things (IIoT). There is no forthcoming new generation fieldbus standard to integrate into the IIoT and Industry 4.0 revolution. The open platform communications unified architecture (OPC UA) time-sensitive networking (TSN) is a potential vendor-independent successor technology for the factory network. The OPC UA is a data exchange standard for industrial communication, and TSN is an Institute of Electrical and Electronics Engineers standard for Ethernet that supports real-time behaviour. The merging of these open standard solutions can facilitate cross-vendor interoperability for Industry 4.0 and IIoT products.

A brief review of the history of the fieldbus standards is presented, which highlights the shortcomings for current industrial systems in meeting converged traffic solutions. An experimental system for the OPC UA TSN is described to demonstrate an approach to developing a three-layer factory network system with an emphasis on the field layer.

From the multitude of existing industrial network schemes, there is a convergence pathway in solutions based on TSN Ethernet and OPC UA. At the field level, basic timing measurements in this paper show that the OPC UA TSN can meet the basic critical timing requirements for a fieldbus network.

This paper uniquely focuses on the specific fieldbus standards elements of industrial networks evolution and traces the developments from the early history to the current developing integration in IIoT context.

The purpose of this study was to determine the effectiveness of a Qigong program on older adults in the Thai community suffering from mild to moderate depression.

A randomized controlled trial study was conducted in a public health service (PHS) center setting. Sixty-six older adults aged 60–90 years with mild to moderate depressive symptoms measured by the Thai Geriatric Depression Scale (TGDS: scores 13–24) were recruited and randomly allocated into two groups. The subjective sleep quality was assessed using the Thai version of the Pittsburgh Sleep Quality Index (TPSQI). The Qigong program group was given 12 weeks of Qigong training including three sessions per week, while the control group followed normal PHS activities (singing and praying). The outcome measure was the change in the TGDS from baseline to 12 weeks.

Participants in the Qigong program group had a significantly improved TPSQI global score (p < 0.001), subjective sleep quality (p < 0.001), and sleep latency (p < 0.05) after 12 weeks of intervention, while those in the control group showed no significant difference in sleep quality. Compared with the control group, the Qigong program group reported significantly better sleep quality throughout the 12-week period. The prevalence of poor sleep quality in this population was 82 percent.

This study confirmed that the Qigong program could improve sleep quality in older adults with mild to moderate depressive symptoms as the Qigong program conferred more significant improvements than the usual program.

The recent innovations of Industry 4.0 have made it possible to easily collect data related to a production environment. In this context, information about industrial equipment – gathered by proper sensors – can be profitably used for supporting predictive maintenance (PdM) through the application of data-driven analytics based on artificial intelligence (AI) techniques. Although deep learning (DL) approaches have proven to be a quite effective solutions to the problem, one of the open research challenges remains – the design of PdM methods that are computationally efficient, and most importantly, applicable in real-world internet of things (IoT) scenarios, where they are required to be executable directly on the limited devices’ hardware.

In this paper, the authors propose a DL approach for PdM task, which is based on a particular and very efficient architecture. The major novelty behind the proposed framework is to leverage a multi-head attention (MHA) mechanism to obtain both high results in terms of remaining useful life (RUL) estimation and low memory model storage requirements, providing the basis for a possible implementation directly on the equipment hardware.

The achieved experimental results on the NASA dataset show how the authors’ approach outperforms in terms of effectiveness and efficiency the majority of the most diffused state-of-the-art techniques.

A comparison of the spatial and temporal complexity with a typical long-short term memory (LSTM) model and the state-of-the-art approaches was also done on the NASA dataset. Despite the authors’ approach achieving similar effectiveness results with respect to other approaches, it has a significantly smaller number of parameters, a smaller storage volume and lower training time.

The proposed approach aims to find a compromise between effectiveness and efficiency, which is crucial in the industrial domain in which it is important to maximize the link between performance attained and resources allocated. The overall accuracy performances are also on par with the finest methods described in the literature.

The proposed approach allows satisfying the requirements of modern embedded AI applications (reliability, low power consumption, etc.), finding a compromise between efficiency and effectiveness.

With the increasing acclaim of Wireless Sensor Networks and its diverse applications, research has been directed into optimising and prolonging the network lifetime. Energy efficiency has been a critical factor due to the energy resource impediment of batteries in sensor nodes. The proposed routing algorithm therefore aims at extending lifetime of sensors by enhancing load distribution in the network. The scheme is based on the chain-based routing technique of the PEGASIS (Power Energy GAthering in Sensor Information Systems) protocol and uses Ant Colony Optimisation to obtain the optimal chain. The contribution of the proposed work is the integration of the clustering method to PEGASIS with Ant Colony Optimisation to reduce redundancy of data, neighbour nodes distance and transmission delay associated with long links, and the employment an appropriate cluster head selection method. Simulation results indicates proposed method’s superiority in terms of residual energy along with considerable improvement regarding network lifetime, and significant reduction in delay when compared with existing PEGASIS protocol and optimised PEG-ACO chain respectively.

Minimizing the energy consumption in a wireless sensor node is important for lengthening the lifetime of a battery. Radio transmission is the most energy-consuming task in a wireless sensor node, and by compressing the sensor data in the online mode, it is possible to reduce the number of transmission periods. This study aims to demonstrate that temporal compression methods present an effective method for lengthening the lifetime of a battery-powered wireless sensor node.

In this study, the energy consumption of LoRa-based sensor node was evaluated and measured. The experiments were conducted with different LoRaWAN data rate parameters, with and without compression algorithms implemented to compress sensor data in the online mode. The effect of temporal compression algorithms on the overall energy consumption was measured.

Energy consumption was measured with different LoRaWAN spreading factors. The LoRaWAN transmission energy consumption significantly depends on the spreading factor used. The other significant factors affecting the LoRa-based sensor node energy consumption are the measurement interval and sleep mode current consumption. The results show that temporal compression algorithms are an effective method for reducing the energy consumption of a LoRa sensor node by reducing the number of LoRa transmission periods.

This paper presents with a practical case that it is possible to reduce the overall energy consumption of a wireless sensor node by compressing sensor data in online mode with simple temporal compression algorithms.

Blockchain technology is provoking significant disruptions, thereby affecting supply chain management. This study endeavoured to advance research regarding blockchain-based supply chain traceability by identifying the opportunities and limitations that accompany the adoption of public blockchains. Therefore, the purpose of the study is to contribute to contemporary supply chain research by an assessment of blockchain technology and its linkages to traceability.

This paper is conceptual. The authors summarised the relevant literature on the concepts of supply chain traceability, conceptualised key elements exclusive to the public blockchain and highlighted opportunities and limitations in implementing traceability using blockchains.

Incompatibilities were identified between general traceability and the public blockchain. However, when embracing the blockchain's privacy model, the blockchains can support information exchange in supply chains where vulnerability towards third parties, the confidentiality of information, or the privacy of participants are concerns. Furthermore, the public blockchain can support areas of supply chains where institutional interest is lacking.

This is one of the first papers in an international supply chain management journal to critically analyse the intersection of specific blockchain characteristics and supply chain traceability requirements. The authors thereby add to the discussion of designs for a disintermediated, peer-to-peer models and guide researchers and practitioners alike in exploring the application of disruptive change from blockchain technologies. By setting focus on the privacy model, the paper identifies the potential application and future research approaches to exploit the elementary strength of the blockchain.