Abstract
This study presents subject specific lower limb joint angular kinematic and dynamic analysis at time and frequency domain as well as joint mechanical work, power and dynamic stiffness assessment during normal gait, stiff knee gait and slow running for indicative range operation of personalized active gait aid device. Gait aid devices present increasing interest for the generalization of gait rehabilitation, as an answer to the growth demand of population with gait rehabilitation need, as well as the insufficient health care personnel. Nevertheless, the large costs and standardized equipment leave out many patients without gait rehabilitation, with the need for low cost, personalized gait rehabilitation equipment, based on subject-specific analysis. In vivo and noninvasive case study was assessed of a healthy male subject 70 kg mass and 1.86 m height on human gait lab. Reflective adhesive marks were applied at skin surface of lower limb selected anatomical points and images captured with eight 100 Hz camera Qualisys along with ground reaction forces and force moments acquired at 2000 Hz with two AMTI force plates during foot contact with the ground on normal gait (NG) at comfortable auto-selected velocity, stiff knee gait (SKG) with lower knee flexion and slow running (SR) at minimum run velocity on stiff knee condition. Inverse kinematics and dynamics were performed using AnyGait with TLEM model and lower limb joint angular signal analyzed. Indicative range operation from lower limb joint mechanical assessment were obtained at complementary domain for subject specific gait aid device selection and parametrization.

Abstract
This study describes a computationally efficient model for the optimal sizing and siting of Electrical Energy Storage Devices (EESDs) in Smart Grids (SG), accounting for the presence of time-varying electricity tariffs due to Demand Response Program (DRP) participation. The joint planning and operation problem for optimal siting and sizing of the EESD is proposed in a two-stage optimization problem. In this regard, the long-term decision variables deal were the size and location of the EESDs and have been considered at the master level while the operating point of the generation units and EESDs is determined by the slave stage of the model utilizing a standard mixed-integer linear programming model. To examine the effectiveness of the model in the slave sub- problem, the operation model is solved for different working days of different seasons. Binary Particle Swarm Optimization (BPSO) and Binary Genetic Algorithm (BGA) have been used at the master level to propose different scenarios for investment in the planning stage. The slave problem optimizes the model in terms of the short-term horizon (day-ahead). Additionally, the slave problem determines the optimal schedule for an SG considering the presence of EESD (with sizes and locations provided by the upper level). The electricity price fluctuates throughout the day, according to a Time-of-Use (ToU) DRP pricing scheme. Moreover, the impacts of DRPs have been addressed in the slave stage. The proposed model is examined on a modified IEEE 24-Bus test system

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.