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Recycled waste materials obtained from industrial and agricultural processes are becoming promising thermal and acoustic insulating solutions in building applications; their use can play an important role in the environmental impact reduction. The aim of the present paper is the evaluation of the thermal performance of recycled waste panels consisting of cork scraps, rice husk, coffee chaff, and end-life granulated tires, glued in different weight ratios and pressed. Six panels obtained from the mixing of these waste materials were fabricated and analyzed. In particular, the scope is the selection of the best compromise solutions from the thermal and environmental points of view. To this aim, thermal resistances were measured in laboratory and a Life Cycle Assessment (LCA) analysis was carried out for each panel; a cross-comparative examination was performed in order to optimize their properties and find the best panels solutions to be assembled in the future. Life Cycle Analysis was carried out in terms of primary Embodied Energy and Greenhouse Gas Emissions, considering a ‘‘cradle-to-gate” approach. The obtained thermal conductivities varied in the 0.055 to 0.135 W/mK range, in the same order of magnitude of many traditional systems. The best thermal results were obtained for the panels made of granulated cork, rice husk, and coffee chaff in this order. The rubber granulate showed higher values of the thermal conductivity (about 0.15 W/mK); a very interesting combined solution was the panel composed of cork (60%), rice husk (20%), and coffee chaff (20%), with a thermal conductivity of 0.08 W/mK and a Global Warming Potential of only 2.6 kg CO2eq/m2. Considering the Embodied Energy (CED), the best solution is a panel composed of 56% of cork and 44% of coffee chaff (minimum CED and thermal conductivity).

This paper proposes a device of sensing that could be integrated into the instruments of any surgical robot. Despite advances in robot-assisted laparoscopic surgery, the tools currently supplied to surgical robots have limited functions, due to the absence of sensorization. With this motivation, we present a preliminary work based on the design, development, and early stages of experimentation with smart and multifunctional devices of sensing for surgical tools. The proposed device of sensing has a proximity sensor, colorimetric sensor, and BLE connection for different surgical instruments to connect to each other. The proximity feedback allows the surgeon to know the distance of the instrument from a particular tissue, to operate in conditions of greater safety. With the colorimetric feedback, on the other hand, we intend to proceed to the identification of specific tissue areas with characteristics that are not typical of the physiological tissue. The results show that the device is promising and can be further developed for multiple clinical needs in robotic procedures. This system can effectively increase the functionality of surgical instruments by overcoming the sensing limitations introduced by using robots in laparoscopic surgery.

Façade elements are a building component that satisfies multiple performance parameters. Among other things, “advanced façades” take advantage of hybrid solutions, such as assembling laminated materials. In addition to the enhanced mechanical properties that are typical of optimally composed hybrid structural components, these systems are energy-efficient, durable, and offer lighting comfort and optimal thermal performance, an example of which is the structural solution developed in collaboration with the University of Zagreb and the University of Ljubljana within the Croatian Science Foundation VETROLIGNUM project. The design concept involves the mechanical interaction of timber and glass load-bearing members without sealing or bonding the glass-to-timber surfaces. Following earlier research efforts devoted to the structural analysis and optimization of thus-assembled hybrid Cross-Laminated Timber (CLT)-glass façade elements, in this paper, special focus is given to a thermal and energy performance investigation under ordinary operational conditions. A simplified numerical model representative of a full-size building is first presented by taking advantage of continuous ambient records from a Live-Lab mock-up facility in Zagreb. Afterwards, a more detailed Finite Element (FE) numerical analysis is carried out at the component level to further explore the potential of CLT–glass façade elements. The collected numerical results show that CLT–glass composite panels can offer stable and promising thermal performance for façades similar to national and European standard requirements.

Street lighting characterizes many smart city initiatives around the world. In fact, significant savings can be achieved by not only replacing traditional luminaires with low-power LEDs, but also providing streetlights with smart light controllers and network connectivity, allowing the introduction of a sensible light intensity management and reduction of maintenance costs. Moreover, if designed with a far-looking view, smart lighting infrastructure could also support city-wide Internet of Things services, becoming key enablers of the smart city revolution, also in the 5G perspective. In this paper, we provide a thorough discussion on network architectures and communication technologies that could be adopted for smart public lighting applications, showing their benefits and downsides. Starting with significant activity on research, implementation and in-field testing, we also outline the steps required for the deployment of a smart public lighting infrastructure, each discussed in accordance with the network topology considered. Finally, we introduce some additional services that a smart public lighting infrastructure could support and discuss the benefits that would arise from integration with the upcoming 5G cellular network.

Slope instability phenomena are among the most hazardous natural events affecting our society, and therefore the physical–mechanical behavior of rock masses with reference to these is a relevant topic for engineering geological practice, as it conditions the realization and functionality of structures and infrastructures, as well as the quality of life and economy [...]

Wind turbine performance monitoring is a complex task because of the non-stationary operation conditions and because the power has a multivariate dependence on the ambient conditions and working parameters. This motivates the research about the use of SCADA data for constructing reliable models applicable in wind turbine performance monitoring. The present work is devoted to multivariate wind turbine power curves, which can be conceived of as multiple input, single output models. The output is the power of the target wind turbine, and the input variables are the wind speed and additional covariates, which in this work are the blade pitch and rotor speed. The objective of this study is to contribute to the formulation of multivariate wind turbine power curve models, which conjugate precision and simplicity and are therefore appropriate for industrial applications. The non-linearity of the relation between the input variables and the output was taken into account through the simplification of a polynomial LASSO regression: the advantages of this are that the input variables selection is performed automatically. The k-means algorithm was employed for automatic multi-dimensional data clustering, and a separate sub-model was formulated for each cluster, whose total number was selected by analyzing the silhouette score. The proposed method was tested on the SCADA data of an industrial Vestas V52 wind turbine. It resulted that the most appropriate number of clusters was three, which fairly resembles the main features of the wind turbine control. As expected, the importance of the different input variables varied with the cluster. The achieved model validation error metrics are the following: the mean absolute percentage error was in the order of 7.2%, and the average difference of mean percentage errors on random subsets of the target data set was of the order of 0.001%. This indicates that the proposed model, despite its simplicity, can be reliably employed for wind turbine power monitoring and for evaluating accumulated performance changes due to aging and/or optimization.

As a critical indictor in the Battery Management System (BMS), State of Charge (SOC) is closely related to the reliable and safe operation of lithium-ion (Li-ion) batteries. Model-based methods are an effective solution for accurate and robust SOC estimation, the performance of which heavily relies on the battery model. This paper mainly focuses on battery modeling methods, which have the potential to be used in a model-based SOC estimation structure. Battery modeling methods are classified into four categories on the basis of their theoretical foundations, and their expressions and features are detailed. Furthermore, the four battery modeling methods are compared in terms of their pros and cons. Future research directions are also presented. In addition, after optimizing the parameters of the battery models by a Genetic Algorithm (GA), four typical battery models including a combined model, two RC Equivalent Circuit Model (ECM), a Single Particle Model (SPM), and a Support Vector Machine (SVM) battery model are compared in terms of their accuracy and execution time.