Abstract
The vehicle routing problem with backhauls (VRPB) allows to integrate inbound and outbound routes, which is an efficient strategy to reduce routing costs and also to reduce the environmental and social impacts of transportation. In this paper, we analyze the VRPB literature with a sustainability perspective, which covers environmental and social objectives, collaborative networks and reverse logistics. First, to better understand and analyze the VRPB literature, all related works are characterized according to a common taxonomy provided for routing problems. This taxonomy is extended to differentiate between economic, environmental and social objectives. After identification of all VRPB papers that include sustainability issues, these are analyzed and discussed in more detail. The analysis reveals that research on VRPBs with sustainability concerns is recent and relatively scarce and the most popular aspects investigated are the minimization of fuel consumption andCO2 emissions. Future research lines driven by sustainability concerns are suggested for the VRPB as a promoter of green logistics.

Abstract
In this paper, we describe the design of an electrically large anechoic chamber for usage on millimetre-wave bands. Ansys Savant sotware was used to perform a simulation of the chamber, using physical optics coupled with uniform theory of diffraction (PO/UTD). Moreover, a method based on an open waveguide probe is described in this paper to obtain the electrical properties of the RF absorbers at millimetre-wave frequencies. Two different source antennas were simulated in this work and the corresponding quiet zones predicted. The largest quiet zone was 30 mm x 30 mm x 50mm, for a chamber size of 1.2 m x 0.6 m x 0.6 m.

Abstract
The European Union (EU) energy strategy towards decarbonization led EU countries to elaborate their corresponding National Energy and Climate Plans (NECP) for the period 2021 to 2030. This paper analyzes the Portuguese and Spanish NECPs concerning their power systems. CEVESA, a model for the long-term planning and operation of the Iberian electricity system, is used. The analysis is based on simulating the reference NECP scenario, as well as other alternative scenarios with different solar and wind generation shares, CO2 prices and fuel costs. Results provide insights on the MIBEL electricity market evolution under the current decarbonization national strategies. © 2020 IEEE.

Abstract
Recent developments in the field of smart grid have led to renewed interest in load monitoring strategies for achieving effective energy management schemes. There are vast amount of published studies describing the role of non-intrusive load monitoring (NILM) system based on various learning algorithms. It is widely known that the accuracy of load identification depends strongly on utilized methods and its features. Thus, the main aim of this study is to investigate the comparative accuracy of machine learning algorithms which have the same training data with different feature subsets. Afterwards, a low-cost data acquisition system for NILM using bagged tree ensemble algorithm is developed and demonstrated in detail. The proposed structure is tested on the ThingSpeak IoT platform to reveal the effectiveness of the evaluated concept.

Abstract
Deep learning, due to its unprecedented success in tasks such as image classification, has emerged as a new tool in image reconstruction with potential to change the field. In this paper, we demonstrate a crucial phenomenon: Deep learning typically yields unstable methods for image reconstruction. The instabilities usually occur in several forms: 1) Certain tiny, almost undetectable perturbations, both in the image and sampling domain, may result in severe artefacts in the reconstruction; 2) a small structural change, for example, a tumor, may not be captured in the reconstructed image; and 3) (a counterintuitive type of instability) more samples may yield poorer performance. Our stability test with algorithms and easy-to-use software detects the instability phenomena. The test is aimed at researchers, to test their networks for instabilities, and for government agencies, such as the Food and Drug Administration (FDA), to secure safe use of deep learning methods.

Abstract