Abstract
This article proposes a new method of identity recognition in sanitary facilities based on electrocardiography (ECG) signals. Our team previously proposed a novel approach of invisible ECG at the thighs using polymeric electrodes, leading to the creation of a proof-of-concept system integrated into a toilet seat. In this work, a biometrics pipeline was devised, which tested four different classifiers, varying the population from 2 to 17 subjects and simulating a residential environment. However, for this approach to be industrially viable, further optimization is required, particularly regarding electrode materials that are compatible with industrial processes. As such, we also explore the use of a conductive silicone material as electrodes, aiming at the industrial-scale production of a toilet seat capable of recording ECG data, without the need for body-worn devices. A desirable aspect when using such a system is matching the recorded data with the monitored user, ideally using a minimal sensor set, further reinforcing the relevance of user identification through ECG signals collected at the thighs. Our approach was evaluated against a reference device for a population of 17 healthy and pathological individuals, covering a wide age range (24-70 years). With the silicone composite, we were able to acquire signals in 100% of the sessions, with a mean heart rate deviation between a reference system and our experimental device of 2.82 +/- 1.99 beats per minute (BPM). In terms of ECG waveform morphology, the best cases showed a Pearson correlation coefficient of 0.91 +/- 0.06. For biometric detection, the best classifier was the Binary Convolutional Neural Network (BCNN), with an accuracy of 100% for a population of up to four individuals.

Abstract

Abstract
As humanity progresses and globalization advances, humanized environment and associated systems increase in complexity and size. In earth systems, oceans represent an essential element of equalization and normal functioning. Ocean-atmospheric interactions are nowadays believed to be at the heart of all earth vital signs and climatic behaviours, and therefore are essential to accurate monitoring and understanding of earth systems. The work presented is a preliminary result of the K2D- Knowledge and Data from the Deep to Space, project which addresses the challenge of creating underwater network nodes to provide power and communication to land through the submarine fiber optic cable repeaters. The N2ODE system will consist of a set of subsystems that will allow continuous monitoring and interaction with fixed and mobile underwater devices.

Abstract
Social media has become a crucial point for brands to establish a connection with their consumers and potential consumers, being many times responsible for developing the need and converting it into a purchase. Thus, it is worth highlighting the role of influencers in social media that affect fashion purchase. Given the growth of sustainable fashion, it is necessary to verify the relationship between influencers and social media and the intention to purchase sustainable fashion. A conceptual model that aims to understand the effect of influencers' characteristics in the intention to purchase sustainable fashion is presented. The results show that consumer knowledge and willingness to pay more are the only factors that positively affect the purchase intention of sustainable fashion. Furthermore, the authors highlight that consumer knowledge is the construct that has a distinctly greater impact on the intention to purchase sustainable fashion. © 2023, IGI Global. All rights reserved.

Abstract
Advances in technology are leading to more and more devices integrating sensors capable of acquiring data quickly and with high accuracy. Point clouds are no exception. Therefore, there is increased research interest in the large amount of available light detection and ranging (LiDAR) data by point cloud classification using artificial intelligence. Nevertheless, point cloud labeling is a time-consuming task. Hence the amount of labeled data is still scarce. Data synthesis is gaining attention as an alternative to increase the volume of classified data. At the same time, the amount of Building Information Models (BIMs) provided by manufacturers on website databases is increasing. In line with these recent trends, this paper presents a deep-learning framework for classifying point cloud objects based on synthetic data sets created from BIM objects. The method starts by transforming BIM objects into point clouds deriving a data set consisting of 21 object classes characterized with various perturbation patterns. Then, the data set is split into four subsets to carry out the evaluation of synthetic data on the implemented flexible two-dimensional (2D) deep neural framework. In the latter, binary or greyscale images can be generated from point clouds by both orthographic or perspective projection to feed the network. Moreover, the surface variation feature was computed in order to aggregate more geometric information to images and to evaluate how it influences the object classification. The overall accuracy is over 85% in all tests when orthographic images are used. Also, the use of greyscale images representing surface variation improves performance in almost all tests although the computation of this feature may not be robust in point clouds with complex geometry or perturbations. (C) 2022 American Society of Civil Engineers.

Abstract
Firms have available many forms of collaboration, including cooperatives or joint ventures, in this way leveraging their market power. Customers, however, are atomic agents with few mechanisms for collaborating, leading to an unbalanced buyer-supplier relationship and economic surpluses that shift to producers. Some group buying websites helped alleviate the problem by offering bulk discounts, but more advancements can be made with the emergence of technologies, such as the blockchain. In this article, we propose a customer-push e-marketplace built on top of Ethereum, where customers can aggregate their proposals, and suppliers try to outcompete each other in reverse auction bids to fulfil the order. Furthermore, smart contracts make it possible to automate many operational activities, such as payment escrows/release upon delivery confirmation, increasing the efficiency along the supply chain. The implementation of this network is expected to improve market efficiency by reducing transaction costs, time delays, and information asymmetry. Furthermore, concepts such as increased bargaining power and economies of scale, and their effects in buyer-supplier relationships, are also explored.