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Thermal energy storage (TES) is a key technology to enhance the efficiency of energy systems as well as to increase the share of renewable energies. In this context, the present paper reports a literature review of the recent advancement in the field of adsorption TES systems. After an initial introduction concerning different heat storage technologies, the working principle of the adsorption TES is explained and compared to other technologies. Subsequently, promising features and critical issues at a material, component and system level are deeply analyzed and the ongoing activities to make this technology ready for marketing are introduced.

Sleep behavior is a key factor in maintaining good physiological and psychological health. A well-known approach to monitor sleep is polysomnography. However, it is costly and intrusive, which may disturb sleep. Consequently, polysomnography is not suitable for sleep behavior analysis. Other approaches are based on actigraphy and sleep diary. Although being a good source of information for sleep quality assessment, sleep diaries can be affected by cognitive bias related to subject’s sleep perception, while actigraphy overestimates sleep periods and night-time disturbance compared to sleep diaries. Machine learning techniques can improve the objectivity and reliability of the observations. However, since signal morphology vary widely between people, conventional machine learning is complex to set up. In this regard, we present an adaptive, reliable, and innovative computational approach to provide per-night assessment of sleep behavior to the end-user. We exploit heartbeat rate and wrist acceleration data, gathered via smartwatch, in order to identify subject’s sleep behavioral pattern. More specifically, heartbeat rate and wrist motion samples are processed via computational stigmergy, a bio-inspired scalar and temporal aggregation of samples. Stigmergy associates each sample to a digital pheromone deposit (mark) defined in a mono-dimensional space and characterized by evaporation over time. As a consequence, samples close in terms of time and intensity are aggregated into functional structures called trails. The stigmergic trails allow to compute the similarity between time series on different temporal scales, to support classification or clustering processes. The overall computing schema includes a parametric optimization for adapting the structural parameters to individual sleep dynamics. The outcome is a similarity between sleep nights of the same subject, to generate clusters of nights with different quality levels. Experimental results are shown for three real-world subjects. The resulting similarity is also compared with the dynamic time warping, a popular similarity measure for time series

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Sleep plays a vital role in good health and well-being throughout our life. Getting enough quality sleep at the right times can help protect mental and physical health, quality of life, and safety. Emerging wearable devices allow people to measure and keep track of sleep duration, patterns, and quality. Often, these approaches are intrusive and change the user's daily sleep habits. In this paper, we present an unobtrusive approach for the detection of sleep stages and positions. The proposed system is able to overcome the weakness of classic actigraphy-based systems, since it is easy to deploy and it is based on inexpensive technology. With respect to the actigraphy-based systems, the proposed system is able to detect the bed posture, that is crucial to support pressure ulcer prevention (i.e. bedsores). Results from our algorithm look promising and show that we can accurately infer sleep duration, sleep positions, and routines with a completely unobtrusive approach

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A smart Information and Communication Technology (ICT) enables a synchronized interplay of different key factors, aligning infrastructures, consumers, and governmental policy-making needs. In the harbor's logistics context, smart ICT has been driving a multiyear wave of growth. Although there is a standalone value in the technological innovation of a task, the impact of a new smart technology is unknown without quantitative analysis methods on the end-to-end process. In this paper, we first present a review of the smart ICT for marine container terminals, and then we propose to evaluate the impact of such smart ICT via Business Process Model and Notation (BPMN) modeling and simulation. The proposed approach is discussed in a real-world modeling and simulation analysis, made on a pilot terminal of the Port of Leghorn (Italy).