Efficiency and Stability Enhancement in Perovskite Solar Cells by Inserting Lithium-Neutralized Graphene Oxide as Electron Transporting Layer

This work proposes a new perovskite solar cell structure by including lithium-neutralized graphene oxide (GO-Li) as the electron transporting layer (ETL) on top of the mesoporous TiO2 (m-TiO2) substrate. The modified work-function of GO after the intercalation of Li atoms (4.3 eV) exhibits a good energy matching with the TiO2 conduction band, leading to a significant enhancement of the electron injection from the perovskite to the m-TiO2. The resulting devices exhibit an improved short circuit current and fill factor and a reduced hysteresis. Furthermore, the GO-Li ETL partially passivates the oxygen vacancies/defects of m-TiO2 by resulting in an enhanced stability under prolonged 1 SUN irradiation

Fusion of Unobtrusive Sensing Solutions for Home-Based Activity Recognition and Classification Using Data Mining Models and Methods

From MDPI via Jisc Publications RouterHistory: accepted 2021-09-24, pub-electronic 2021-09-29Publication status: PublishedFunder: interreg VA; Grant(s): IVA5034This paper proposes the fusion of Unobtrusive Sensing Solutions (USSs) for human Activity Recognition and Classification (ARC) in home environments. It also considers the use of data mining models and methods for cluster-based analysis of datasets obtained from the USSs. The ability to recognise and classify activities performed in home environments can help monitor health parameters in vulnerable individuals. This study addresses five principal concerns in ARC: (i) users’ privacy, (ii) wearability, (iii) data acquisition in a home environment, (iv) actual recognition of activities, and (v) classification of activities from single to multiple users. Timestamp information from contact sensors mounted at strategic locations in a kitchen environment helped obtain the time, location, and activity of 10 participants during the experiments. A total of 11,980 thermal blobs gleaned from privacy-friendly USSs such as ceiling and lateral thermal sensors were fused using data mining models and methods. Experimental results demonstrated cluster-based activity recognition, classification, and fusion of the datasets with an average regression coefficient of 0.95 for tested features and clusters. In addition, a pooled Mean accuracy of 96.5% was obtained using classification-by-clustering and statistical methods for models such as Neural Network, Support Vector Machine, K-Nearest Neighbour, and Stochastic Gradient Descent on Evaluation Test

A modular framework for modelling and verification of activities in ambient intelligent systems

There is a growing need to introduce and develop formal techniques for computational models capable of faithfully modelling systems of high complexity and concurrent. Such systems the are ambient intelligent systems. This article proposes an efficient framework for the automated modelling and verification of the behavioural models capturing daily activities that occur in ambient intelligent systems based on the modularity and compositionality of Petri nets. This framework consists of different stages that incorporate Petri net techniques like composition, transformation, unfolding and slicing. All these techniques facilitate the modelling and verification of the system activities under consideration by allowing the modelling in different Petri net classes and the verification of the produced models either by using model checking directly or by applying Petri net slicing to alleviate the state explosion problem that may emerge in very complex behavioural models. Illustrative examples are provided to demonstrate the practicality and effectiveness of the proposed approach. Finally, to show the flexibility of the proposed framework in terms of verification, both an evaluation and comparison of the state space required for the property checking are conducted with respect to the typical model checking and slicing approach respectively