Energy-aware lazy scheduling algorithm for energy-harvesting sensor nodes

The main problem in dealing with energy-harvesting (EH) sensor nodes is represented by the scarcity and non-stationarity of powering, due to the nature of the renewable energy sources. In this work, the authors address the problem of task scheduling in processors located in sensor nodes powered by EH sources. Some interesting solutions have appeared in the literature in the recent past, as the lazy scheduling algorithm (LSA), which represents a performing mix of scheduling effectiveness and ease of implementation. With the aim of achieving a more efficient and conservative management of energy resources, a new improved LSA solution is here proposed. Indeed, the automatic ability of foreseeing at run-time the task energy starving (i.e. the impossibility of finalizing a task due to the lack of power) is integrated within the original LSA approach. Moreover, some modifications have been applied in order to reduce the LSA computational complexity and thus maximizing the amount of energy available for task execution. The resulting technique, namely energy-aware LSA, has then been tested in comparison with the original one, and a relevant performance improvement has been registered both in terms of number of executable tasks and in terms of required computational burden.     

Flexible electronics: Prediction of substrate deformation during different steps of the lithography process

A numerical model was developed to simulate the micro-deformations of a polymeric substrate due to lithographic processing of different layers of a transistor-like structure. The results of the model were validated with the results from experiments. The model, a mechanical-thermal-hygroscopic model, takes into account the dimensional effects of temperature, moisture and stresses. It also includes the temperature dependent visco-elastic behaviour of the polymer substrate. The model can be used to predict overlay accuracies between different functional layers introduced by the lithographic process. It can also be used to understand the underlying processes such that it provides a tool to improve the overlay accuracy during actual processing.     

Mix design and performance analysis of asphalt concretes with electric arc furnace slag

The paper presents the results of a laboratory study, aimed at verifying the possibility to use two particular typologies of electric arc furnace (EAF) steel slags, in substitution of the natural aggregates, in the composition of wearing course asphalt concrete for flexible pavements. The experimental research has been articulated in a preliminary study of the chemical, leaching, physical, and mechanical properties of the EAF steel slag, and in the following mix design and performance characterisation of the bituminous conglomerates, through gyratory compaction tests, permanent deformations tests, Stiffness Modulus tests at various temperatures, fatigue tests and indirect tensile strength tests. All the mixtures with EAF slag have satisfied the requisites for acceptance in the road sector technical standards, thus resulting as suitable for use in the construction of road infrastructures, moreover presenting higher mechanical characteristics than those of the corresponding asphalts with full natural aggregate.     

Post-cracking behaviour of steel and macro-synthetic fibre-reinforced concretes

The present paper describes the results of an experimental investigation on the performances of concrete specimens reinforced with either steel or macro-synthetic fibres under three-point bending. Steel fibres are often used to improve the flexural toughness of concrete and are used in various structural applications while synthetic fibres are more often used to reduce crack opening due to shrinkage. Macro-synthetic fibres have been proposed more recently with the aim of creating an alternative to steel fibres in structural applications but their use is still limited.In the tests performed, specimens cast with the same concrete mix, but containing different dosages of either steel or macro-synthetic fibres, were used and compared. In general steel fibres were more efficient in increasing the toughness of concrete than macro-synthetic fibres, even though their results were significantly more scattered.Test results were used to calculate the parameters of stress-crack opening relations via inverse analysis using a cracked hinge model. This numerical model provided results, in terms of force-Crack Mouth Opening Displacement (CMOD) curves, in very good agreement with the experimental data.     

Thermoacoustic Emission from Carbon Nanotubes Imaged by Atomic Force Microscopy

The thermoacoustic effect of isolated single‐wall carbon nanotubes aligned between electrodes is experimentally observed for the first time by imaging the emitted acoustic wave using an atomic force microscopy‐based technique specifically developed for the task. The capability of such a technique for single‐point thermoacoustic measurements is first verified on carbon nanotubes layers with two electrodes for injecting alternate electric current. The technique is then demonstrated to allow the acquisition, simultaneously with the topography, of images reflecting the pressure of the acoustic wave at fixed distance from the sample. Such a capability is used to collect images reflecting the amplitude of acoustic waves generated by isolated nanotubes and nanotube bundles by the thermoacoustic effect.     

A Low-Cost and Low-Power Messaging System Based on the LoRa Wireless Technology

Mobile Networks and Applications - In this paper we describe a low-cost and low-power consumption messaging system based on LoRa technology. More that one billion people worldwide cannot access...     

Performance evaluation of an adaptive GSM receiver with frequency-selective fading and adjacent- and co-channel interference

This study represents the sequel and conclusion of a previous one already published in this Journal a few issues ago.¹ In that article, we tackled the problem of evaluating theoretically the performance of an adaptive MLSE receiver for MSK signals on a frequency-selective stationary radio propagation channel, and we validated the theoretical findings (in the form of upper and lower bounds to the BER of the receiver) by means of computer simulations. In the present paper, we report on the performance of the very same scheme of channel-estimator-plus-Viterbi-equalizer receiver for mobile communications in a more realistic operating environment. In particular, we completely encompass here the groupe spécial mobile (GSM) transmission format, based on the Gaussian minimum shift keying (GMSK) modulation scheme and on narrowband time-division multiple access (TDMA) transmission. Also, we remove the assumption of a time-invariant propagation channel, assuming the recommended standard GSM channels as benchmark transmission conditions. Finally, with the aid of extensive computer simulations, we analyse the sensitivity of the MLSE receiver to unwanted co-channel and adjacent-channel interference coming from other users of the mobile cellular GSM system.     

A new method for the analysis of integrated transmission lines

A flexible and efficient technique for the analysis of transmission lines is described. The transmission line is considered as the union of « elementary waveguides », where the medium is uniformly stratified. The modes are determined by matching the field at the interfaces using a procedure that leads to a linear eigenvalue problem involving a limited number of variables.     

Applied data science in patient-centric healthcare: Adaptive analytic systems for empowering physicians and patients

We define the emerging research field of applied data science as the knowledge discovery process in which analytic systems are designed and evaluated to improve the daily practices of domain experts. We investigate adaptive analytic systems as a novel research perspective of the three intertwining aspects within the knowledge discovery process in healthcare: domain and data understanding for physician- and patient-centric healthcare, data preprocessing and modelling using natural language processing and (big) data analytic techniques, and model evaluation and knowledge deployment through information infrastructures. We align these knowledge discovery aspects with the design science research steps of problem investigation, treatment design, and treatment validation, respectively. We note that the adaptive component in healthcare system prototypes may translate to data-driven personalisation aspects including personalised medicine. We explore how applied data science for patient-centric healthcare can thus empower physicians and patients to more effectively and efficiently improve healthcare. We propose meta-algorithmic modelling as a solution-oriented design science research framework in alignment with the knowledge discovery process to address the three key dilemmas in the emerging “post-algorithmic era” of data science: depth versus breadth, selection versus configuration, and accuracy versus transparency.