Abstract
Time of use (TOU) rate has been regarded as an effective strategy to associate utility companies to avoid peak time financial risks and make the most profit out of the market, while most programs are not effective as expected to reduce peak time demand of residents. Exploring the impact factors of peak demand reduction (PDR) can help policy makers find reasons that weaken effects of programs and corresponding measures can be carried out to maximize the benefits. However, averaging quantitative indicators for program assessment and incomplete impactor analysis method in existing research show limitations of revealing the complex reasons behind it. In this paper, an association rule mining based quantitative analysis framework is built to explore the impact of household characteristics on PDR under TOU price making up for the deficiencies in current research. Firstly, a probability distribution based customer PDR characterizing model is proposed, in which difference-indifference model is adopted to quantify the effect of PDR and probability distribution fitting method is used to characterize the feature of PDR for households. Then a comprehensive association rule mining analysis using Apriori algorithm is presented to explore the impacts factors of PDR covering four categories of household characteristics including dwelling characteristics, socio-demographic, appliances and heating and attitudes towards energy. Finally, analysis results of a case study based on 2993 household records containing smart metering data and survey data illustrate that PDR level cannot be obtained simply based on the appliance's ownership and its usage habits. Socio-demographic information of households should be taken into consideration together; Internet connection and good house insulation contribute to the increase of PDR level. Moreover, the percentage of renewable generation for households also show a certain relationship with PDR. The proposed analysis framework and findings will associate retailer to improve the benefits of TOU programs and guide policy makers to design more efficient energy saving policies for residents.

Abstract
The Business Model is recognized as a valuable tool that helps companies leverage their competitive advantage while dealing with market uncertainty. It enables startups to organize in an efficient way, helping them to rapidly adapt to the different challenges they might face. By using a multiple case study this research aims at understanding how and why startups commercializing technology-based products that support healthcare providing activities changed their business model in the path to a sustainable growth. Based on the Business Model Canvas (BMC), it was possible to analyze different approaches used by those startups to face the challenges encountered. Three main challenges were identified that drove change in the business models: lack of knowledge about the customers' needs, lack of knowledge about the best fitting configuration of a standard product to the market, and lack of knowledge about configuration and interests of the different players. Moreover, even though the literature argues that business model evolution is driven by market and technology conditions, the business models of the startups included in this study changed exclusively due to market conditions.

Abstract
The growing importance of Radar solutions in automotive applications results in new standards to be met and more demanding performances to be assured. As such, Radar developers require testers capable of guaranteeing compliance with the specified criteria. This paper presents a preliminary approach to allow over-the-air (OTA) testing and validation of 76-77 GHz and 77-81 GHz Radar units in production lines, covering not only pass/fail conditions, but also determination of the Radar antenna array radiation diagrams (in azimuth and elevation) through a calibrated external power sensor. Preliminary measurements on an open environment test bench are shown for different Radar bandwidths (1 GHz and 4 GHz), illustrating the importance of a clean and shielded anechoic chamber environment as a baseline approach.

Abstract
Integrate-and-fire (IFN) model of a biological neuron is an amplitude-to-time conversion technique that encodes information in the time-spacing between action potentials (spikes). In principle, this encoding scheme can be used to modulate signals in an impulse radio ultra wide-band (IR-UWB) transmitter, making it suitable for low-power applications, such as in wireless sensor networks (WSN) and biomedical monitoring. This paper then proposes an architecture based on IFN encoding method applied to a UWB transceiver scenario, referred to herein as impulse-radio integrate-and-fire (IRIF) transceiver, followed by a system-level study to attest its effectiveness. The transmitter is composed of an integrate-and-fire modulator, a digital controller and memory block, followed by a UWB pulse generator and filter. At the receiver side, a low-noise amplifier, a squarer, a low-pass filter and a comparator form an energy-detection receiver. A processor reconstructs the original signal at the receiver, and the quality of the synthesized signal is then verified in terms of effective number of bits (ENOB). Finally, a link budget is performed. (C) 2019 Published by Elsevier GmbH.

Abstract
The increasing penetration of distributed energy resources, along with developments in house management systems, are supporting prosumers to play an active role in the electricity market. In particular, a peer-to-peer (P2P) electricity market is an attractive framework to allow direct transactions between prosumers. However, this may raise several challenges in the operation and management of the distribution grid. In this context, the main contribution of this paper is the design of P2P electricity markets taking into account grid characteristics while solving potential congestion and voltage problems. The paper proposes a coordination methodology between P2P markets and distribution system operator (DSO) allocating a grid tariff to the prosumers that cause grid limit violations. Such tariff is defined based on the euclidean distance among trading prosumers, which is implemented in the P2P market through product differentiation approach. The proposed methodology is compared to a benchmark model in a representative distribution grid with 138 nodes and 109 prosumers. An important conclusion is that the proposed methodology is capable of achieving a trade-off between prosumers P2P transactions and grid operation.

Abstract
Bipolar Disorder (BD) is chronic and severe psychiatric illness presenting with mood alterations, including manic, hypomanic and depressive episodes. Due to the high clinical heterogeneity and lack of biological validation, both BD treatment and diagnostic are still problematic. Patients and clinicians would benefit from better clinical and biological characterization, ultimately opening a new possibility to distinct forms of treatment. In this context, we studied genome wide association (GWA) data from the Wellcome Trust Case Control Consortium (WTCCC). After an exploratory analysis, we found a higher prevalence of homozygous compared with heterozygous in different single nucleotide polymorphisms (SNPs) in genes previously associated with BD risk. Results from our association rules analysis indicate that there is a group of patients presenting with different groups of genotypes, including pairs or triples, while others present only one. We performed the same analysis with a control group from the same cohort (WTCCC) and found that although healthy subjects may present the same SNPs combinations, the risky alleles occur in a lower frequency. Moreover, no subject in the control group presented the same pairs or triples of genotypes found in the BD group, and if a pair or triple is found, the support and confidence are lower than in the BD group (< 50 %). © Springer Nature Switzerland AG 2020.