Enhanced Air Stability of Perovskite Quantum Dots by Manganese Passivation

Organic-inorganic perovskite quantum dots (PeQDs) have attracted attention due to their excellent optical properties, e.g., high photoluminescence quantum yields (PLQYs; >70%), a narrow full width at half maximum (FWHM; 25 nm or less), and color tunability adjusted by the halide components in an entire tunability (from 450 nm to 730 nm). On the other hand, PeQD stability against air, humidity, and thermal conditions has still not been enough, which disturbs their application. To overcome these issues, with just a focus on the air stability, Mn2+ ion passivated perovskite quantum dots (Mn/MAPbBr3 QDs) were prepared. Mn2+ could be expected to contract the passivating layer against the air condition because the Mn2+ ion was changed to the oxidized Mn on PeQDs under the air conditions. In this research, Mn/MAPbBr3 QDs were successfully prepared by ligand-assisted reprecipitation (LARP) methods. Surprisingly, Mn/MAPbBr3 QD films showed more than double PLQY stability over 4 months compared with pure MAPbBr3 ones against the air, which suggested that oxidized Mn worked as a passivating layer. Improving the PeQD stability is significantly critical for their application.

Assistive Technologies for Supporting the Wellbeing of Older Adults

As people age, they are more likely to develop multiple chronic diseases and experience a decline in some of their physical and cognitive functions, leading to the decrease in their ability to live independently. Innovative technology-based interventions tailored to older adults’ functional levels and focused on healthy lifestyles are considered imperative. This work proposed a framework of active and healthy ageing through the integration of a broad spectrum of digital solutions into an open Pan-European technological platform in the context of the SHAPES project, an EU-funded innovation action. In conclusion, the SHAPES project can potentially engage older adults in a holistic technological ecosystem and, therefore, facilitate the maintenance of a high-quality standard of life.

3D Scanning/Printing: A Technological Stride in Sculpture

The creation of innovative tools, objects and artifacts that introduce abstract ideas in the real world is a necessary step for the evolution process and characterize the creative capacity of civilization. Sculpture is based on the available technology for its creation process and is strongly related to the level of technological sophistication of each era. This paper analyzes the evolution of basic sculpture techniques (carving, lost-wax casting and 3D scanning/printing), and their importance as a culture footprint. It also presents and evaluates the added creative capacities of each technological step and the different methods of 3D scanning/printing concerning sculpture. It is also an attempt to define the term “material poetics”, which is connected to sculpture artifacts. We conclude that 3D scanning/printing is an important sign of civilization, although artifacts lose a part of material poetics with additive manufacturing. Subsequently, there are various causes of the destruction of sculptures, leaving a hole in the history of art. Finally, this paper showcases the importance of 3D scanning/printing in salvaging cultural heritage, as it has radically altered the way we “backup” objects.

A Simulated Environment for Robot Vision Experiments

Training on simulation data has proven invaluable in applying machine learning in robotics. However, when looking at robot vision in particular, simulated images cannot be directly used no matter how realistic the image rendering is, as many physical parameters (temperature, humidity, wear-and-tear in time) vary and affect texture and lighting in ways that cannot be encoded in the simulation. In this article we propose a different approach for extracting value from simulated environments: although neither of the trained models can be used nor are any evaluation scores expected to be the same on simulated and physical data, the conclusions drawn from simulated experiments might be valid. If this is the case, then simulated environments can be used in early-stage experimentation with different network architectures and features. This will expedite the early development phase before moving to (harder to conduct) physical experiments in order to evaluate the most promising approaches. In order to test this idea we created two simulated environments for the Unity engine, acquired simulated visual datasets, and used them to reproduce experiments originally carried out in a physical environment. The comparison of the conclusions drawn in the physical and the simulated experiments is promising regarding the validity of our approach.

A Simplified Tantalum Oxide Memristor Model, Parameters Estimation and Application in Memory Crossbars

In this paper, an improved and simplified modification of a tantalum oxide memristor model is presented. The proposed model is applied and analyzed in hybrid and passive memory crossbars in LTSPICE environment and is based on the standard Ta2O5 memristor model proposed by Hewlett–Packard. The discussed modified model has several main enhancements—inclusion of a simplified window function, improvement of its effectiveness by the use of a simple expression for the i–v relationship, and replacement of the classical Heaviside step function with a differentiable and flat step-like function. The optimal values of coefficients of the tantalum oxide memristor model are derived by comparison of experimental current–voltage relationships and by using a procedure for parameter estimation. A simplified LTSPICE library model, correspondent to the analyzed tantalum oxide memristor, is created in accordance with the considered mathematical model. The improved and altered Ta2O5 memristor model is tested and simulated in hybrid and passive memory crossbars for a state near to a hard-switching operation. After a comparison of several of the best existing memristor models, the main pros of the proposed memristor model are highlighted—its improved implementation, better operating rate, and good switching properties.

Traffic Flow Prediction for Smart Traffic Lights Using Machine Learning Algorithms

Nowadays, many cities have problems with traffic congestion at certain peak hours, which produces more pollution, noise and stress for citizens. Neural networks (NN) and machine-learning (ML) approaches are increasingly used to solve real-world problems, overcoming analytical and statistical methods, due to their ability to deal with dynamic behavior over time and with a large number of parameters in massive data. In this paper, machine-learning (ML) and deep-learning (DL) algorithms are proposed for predicting traffic flow at an intersection, thus laying the groundwork for adaptive traffic control, either by remote control of traffic lights or by applying an algorithm that adjusts the timing according to the predicted flow. Therefore, this work only focuses on traffic flow prediction. Two public datasets are used to train, validate and test the proposed ML and DL models. The first one contains the number of vehicles sampled every five minutes at six intersections for 56 days using different sensors. For this research, four of the six intersections are used to train the ML and DL models. The Multilayer Perceptron Neural Network (MLP-NN) obtained better results (R-Squared and EV score of 0.93) and took less training time, followed closely by Gradient Boosting then Recurrent Neural Networks (RNNs), with good metrics results but the longer training time, and finally Random Forest, Linear Regression and Stochastic Gradient. All ML and DL algorithms scored good performance metrics, indicating that they are feasible for implementation on smart traffic light controllers.

Does One Size Fit All? A Case Study to Discuss Findings of an Augmented Hands-Free Robot Teleoperation Concept for People with and without Motor Disabilities

Hands-free robot teleoperation and augmented reality have the potential to create an inclusive environment for people with motor disabilities. It may allow them to teleoperate robotic arms to manipulate objects. However, the experiences evoked by the same teleoperation concept and augmented reality can vary significantly for people with motor disabilities compared to those without disabilities. In this paper, we report the experiences of Miss L., a person with multiple sclerosis, when teleoperating a robotic arm in a hands-free multimodal manner using a virtual menu and visual hints presented through the Microsoft HoloLens 2. We discuss our findings and compare her experiences to those of people without disabilities using the same teleoperation concept. Additionally, we present three learning points from comparing these experiences: a re-evaluation of the metrics used to measure performance, being aware of the bias, and considering variability in abilities, which evokes different experiences. We consider these learning points can be extrapolated to carrying human–robot interaction evaluations with mixed groups of participants with and without disabilities.

Phosphating Modification with Metal Ions of Carbon Steel Surface to Improve the Influence of Anticorrosion Properties

The purpose of this research is to investigate the influence of the phosphatizing process with Ni2+, Ce3+, and Ti2+ ions on the properties of the coating to obtain better corrosion protection of the metal. Steel corrosion occurs through physicochemical interaction between the metal and its surrounding environment. This leads to a change in the metal’s physical, mechanical, and optical properties that can cause damage to the functionality of the metal, which in turn may result in accidents or other malfunctions. Carbon steel grade has limited resistance to corrosion, depending on the carbon content and alloying element, the microstructure, and the surrounding environment of the material. This paper present tests that have been carried out on some of the physicochemical properties of protective epoxy and polyurethane coating on carbon steel grade. Coatings represent one of the methods available to protect metal surfaces from corrosion. Coating properties such as thickness, hardness, and adhesion were investigated. The same properties were tested by exposing the sample plates to corrosive conditions of the humid chamber and seawater. Their anticorrosion properties were explored by electrochemical impedance spectroscopy (EIS) techniques under immersion in 3.5 wt.% NaCl solutions as a corrosive medium. Part of the samples prior to application of the coatingwere modified with a phosphate solution containing metal ions: Ni2+, Ce3+, and Ti2+ to further investigate the effects of phosphatization on the properties of the coating. After exposure of the plates to the salt and moist chamber conditions, no traces of corrosion products, cracking or peeling of the coating were found on the surfaces. The adhesion properties were tested by the pull-off adhesion test. It was found that metal/polymer adhesion was satisfied according to EN ISO 4624:2016 and had the same value for all samples. However, a detailed EIS analysis showed a higher resistance of phosphate samples with Ce3+ ions than samples that were phosphated with Ni2+ and Ti2+ ions and those that did not have a sparingly soluble phosphate salt layer.

Artwork Style Recognition Using Vision Transformers and MLP Mixer

Through the extensive study of transformers, attention mechanisms have emerged as potentially more powerful than sequential recurrent processing and convolution. In this realm, Vision Transformers have gained much research interest, since their architecture changes the dominant paradigm in Computer Vision. An interesting and difficult task in this field is the classification of artwork styles, since the artistic style of a painting is a descriptor that captures rich information about the painting. In this paper, two different Deep Learning architectures—Vision Transformer and MLP Mixer (Multi-layer Perceptron Mixer)—are trained from scratch in the task of artwork style recognition, achieving over 39% prediction accuracy for 21 style classes on the WikiArt paintings dataset. In addition, a comparative study between the most common optimizers was conducted obtaining useful information for future studies.

Encoding Two-Qubit Logical States and Quantum Operations Using the Energy States of a Physical System

In this paper, we introduce a novel coding scheme, which allows single quantum systems to encode multi-qubit registers. This allows for more efficient use of resources and the economy in designing quantum systems. The scheme is based on the notion of encoding logical quantum states using the charge degree of freedom of the discrete energy spectrum that is formed by introducing impurities in a semiconductor material. We propose a mechanism of performing single qubit operations and controlled two-qubit operations, providing a mechanism for achieving these operations using appropriate pulses generated by Rabi oscillations. The above architecture is simulated using the Armonk single qubit quantum computer of IBM to encode two logical quantum states into the energy states of Armonk’s qubit and using custom pulses to perform one and two-qubit quantum operations.