Miracle-dz/UniFAR-FTUs huggingface dataset:
Predictable Robots for Autistic Children—Variance in Robot Behaviour, Idiosyncrasies in Autistic Children’s Characteristics, and Child–Robot Engagement

Predictability is important to autistic individuals, and robots have been suggested to meet this need as they can be programmed to be predictable, as well as elicit social interaction. The effectiveness of robot-assisted interventions designed for social skill learning presumably depends on the interplay between robot predictability, engagement in learning, and the individual differences between different autistic children. To better understand this interplay, we report on a study where 24 autistic children participated in a robot-assisted intervention. We manipulated the variance in the robot’s behaviour as a way to vary predictability, and measured the children’s behavioural engagement, visual attention, as well as their individual factors. We found that the children will continue engaging in the activity behaviourally, but may start to pay less visual attention over time to activity-relevant locations when the robot is less predictable. Instead, they increasingly start to look away from the activity. Ultimately, this could negatively influence learning, in particular for tasks with a visual component. Furthermore, severity of autistic features and expressive language ability had a significant impact on behavioural engagement. We consider our results as preliminary evidence that robot predictability is an important factor for keeping children in a state where learning can occur.
3D nanorhombus nickel nitride as stable and cost-effective counter electrodes for dye-sensitized solar cells and supercapacitor applications

Transition metal nitride based materials have attracted significant interest owing to their excellent properties and multiple applications in the field of electrochemical energy conversion and storage devices. Herein we synthesize 3D nanorhombus nickel nitride (Ni3N) thin films by adopting a reactive radio frequency magnetron sputtering process. The as-deposited 3D nano rhombus Ni3N thin films were utilized as cost-effective electrodes in the fabrication of supercapacitors (SCs) and dye-sensitized solar cells (DSSCs). The structure, phase formation, surface morphology and elemental composition of the as-deposited Ni3N thin films were characterized by X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), energy-dispersive X-ray spectroscopy (EDS) and atomic force microscopy (AFM). The electrochemical supercapacitive performance of the Ni3N thin films was examined by cyclic voltammetry (CV) and galvanostatic charge–discharge (GCD) techniques, in 3 M KOH supporting electrolyte. The areal capacitance of the Ni3N thin film electrode obtained from CV analysis was 319.5 mF cm−2 at a lower scan rate of 10 mV s−1. Meanwhile, the Ni3N thin film showed an excellent cyclic stability and retained 93.7% efficiency of its initial capacitance after 2000 cycles at 100 mV s−1. Interestingly, the DSSCs fabricated with a Ni3N CE showed a notable power energy conversion efficiency of 2.88% and remarkable stability. The prominent performance of the Ni3N thin film was ascribed mainly due to good conductivity, high electrochemically active sites with excellent 3D nano rhombus structures and high electrocatalytic activity. Overall, these results demonstrate that the Ni3N electrode is capable of being considered for efficient SCs and DSSCs. This investigation also offers an essential directive for the advancement of energy storage and conversion devices.
Physical exercise in pregnancy: benefits, risks and prescription

Abstract Objectives The aim of this article is to provide a comprehensive literature review, gathering the strongest evidence about the risks and benefits and the prescription of physical exercise during pregnancy. Content Regular physical exercise during pregnancy is associated with numerous benefits. In general women are not adequately advised on this matter. Along with their concerns regarding the potential associated risks, it contributes to the abandonment or refusal to start exercising during pregnancy. A systematic review was conducted in MEDLINE including articles considered to have the highest level of scientific evidence. Fifty-seven articles, including 32 meta-analysis, 9 systematic reviews and 16 randomized controlled trials were included in the final literature review. Summary Exercise can help preventing relevant pregnancy related disorders, such as gestational diabetes, excessive gestational weight gain, hypertensive disorders, urinary incontinence, fetal macrosomia, lumbopelvic pain, anxiety and prenatal depression. Exercise is not related with an increased risk of maternal or perinatal adverse outcomes. Compliance with current guidelines is sufficient to achieve the main benefits, and exercise type and intensity should be based on woman’s previous fitness level. Outlook Exercise in pregnancy is safe for both mother and fetus, contributing to prevent pregnancy related disorders. Exercise type and intensity should be adapted to woman’s previous fitness level, medical history and characteristics of the ongoing pregnancy.
Data Augmentation of Kinematic Time-Series From Rehabilitation Exercises Using GANs

Machine learning, especially deep learning, offers great potential for medical applications. However, deep learning algorithms need a vast amount of training data. Especially in the medical domain, it is challenging to collect larger datasets. Access to patients can be limited, and data recording is mainly bound to laboratory settings requiring expertise from medical professionals. When involving a healthy control group, datasets are often unbalanced, with most data belonging to the control group. This paper proposes a data augmentation method to generate pose data of repetitive rehabilitation exercises trained on a specific population, e.g., a specific neurological disease. Our method is based on a generative adversarial network (GAN) that uses convolutional and long short-term memory (LSTM) layers. We evaluated our method using a dataset that contains rehabilitation exercises from stroke and Parkinson’s disease patients and a healthy control group. We demonstrated that a classifier trained using our augmentation method could distinguish between healthy, stroke, and Parkinson’s disease patients with an accuracy of 81%. In contrast, the same classifier achieved only 75% when using a standard resampling technique.
Light-induced spin hyperpolarisation in condensed phase

ABSTRACT An overview of spin hyperpolarisation methods is given; such methods can be used for strong signal enhancement in nuclear magnetic resonance (NMR). We focus on techniques, which are operative in condensed phase and exploit light-induced processes to generate non-thermal spin hyperpolarisation. Such techniques are optical nuclear polarisation (ONP), Chemically induced dynamic nuclear polarisation (CIDNP) and optical pumping (OP). Since in all methods polarisation is transferred from primarily polarised spins to target spins, we also review existing methods for polarisation transfer. In the section dedicated to ONP, we provide its theoretical background as well as practical experimental aspects and describe applications of ONP in molecular crystals and in NV– centres in diamonds. In the CIDNP section, we discuss in detail the known mechanisms of CIDNP in liquids and in solids and outline CIDNP applications to detect elusive radicals, to determine their magnetic parameters and to enhance NMR signals. Finally, we provide a short description of OP in semiconductors, light-induced para-hydrogen induced polarisation and chemically induced dynamic electron polarisation. Advantages and challenges of light-induced spin hyperpolarisation are discussed and perspectives in this field are addressed.
Research Tools for Smart Electric Vehicle Charging: An introduction to the adaptive charging network research portal

Millions of Electric Vehicles (EVs) will enter service in the next decade, generating gigawatthours of additional energy demand. Charging these EVs cleanly, affordably, and without excessive stress on the grid will require advances in charging system design, hardware, monitoring, and control. Collectively, we refer to these advances as smart charging.
Literature Review on Co-Located Collaboration Modeling Using Multimodal Learning Analytics—Can We Go the Whole Nine Yards?

Collaboration is one of the important 21st-century skills. It can take place in remote or co-located settings. Co-located collaboration (CC) is a very complex process that involves subtle human interactions that can be described with indicators like eye gaze, speaking time, pitch, and social skills from different modalities. With the advent of sensors, multimodal learning analytics has gained momentum to detect CC quality. Indicators (or low-level events) can be used to detect CC quality with the help of measurable markers (i.e., indexes composed of one or more indicators) which give the high-level collaboration process definition. However, this understanding is incomplete without considering the scenarios (such as problem solving or meetings) of CC. The scenario of CC affects the set of indicators considered: For instance, in collaborative programming, grabbing the mouse from the partner is an indicator of collaboration; whereas in collaborative meetings, eye gaze, and audio level are indicators of collaboration. This can be a result of the differing goals and fundamental parameters (such as group behavior, interaction, or composition) in each scenario. In this article, we present our work on profiles of indicators on the basis of a scenario-driven prioritization, the parameters in different CC scenarios are mapped onto the indicators and the available indexes. This defines the conceptual model to support the design of a CC quality detection and prediction system.
Bipolar Spiral Midfield Wireless Power Transfer for Cardiac Implants Application

This letter proposes a midfield wireless power transfer system using a bipolar spiral-shape transmitter (BPSP-Tx) for cardiac implants application. Optimal current distribution is built on BPSP-Tx surface following spiral structure to generate a focused field inside human tissue and adapt the implantable receiver (Rx) misalignments. Source performance has been verified by implantable hooked-shape Rx (HKSD-Rx) that separated at a distance of 45 mm from Tx. Measurements are conducted in minced pork for validation. Experimental results show that the transmission coefficient can achieve −20.48 dB at a distance of 45 mm and −22.15 at 60 mm. Furthermore, the Rx misalignment is verified by output power level, which shows that the proposed system can maintain high performance on a rotation range of Rx from <inline-formula><tex-math notation="LaTeX">$-90^\circ$</tex-math></inline-formula> to <inline-formula><tex-math notation="LaTeX">$90^\circ$</tex-math></inline-formula>. Finally, the human safety regulations are verified to present a low specific absorption rate, which demonstrates the potential for cardiac implants.
Ultra-Wideband Millimeter-Wave Planar Array Antenna With an Upside-Down Structure of Printed Ridge Gap Waveguide for Stable Performance and High Antenna Efficiency

In this letter, a wideband millimeter-wave (mmWave) planar array antenna with a printed ridge gap waveguide (PRGW) feeding network is presented. A planar bowtie antenna fed by the PRGW is employed as the array radiation element which combines the bowtie with an upside-down electromagnetic bandgap part by using lamination. Thus, with this new upside-down structure, the proposed array antenna achieves a stable, wideband, and high-antenna-efficiency performance. A prototype of ${8} \times {\rm{8\ }}$array is fabricated and measured. The measured results show that the proposed array has an impedance matching bandwidth (|S11| < −10 dB) of 50.3% from 18.6 to 31.1 GHz. The maximum measured gain is 22.1 dBi at 29 GHz, and the total antenna efficiency is above 60% with a bandwidth of 45% from 18.8 to 29.8 GHz.
A Study on Design of Adaptive Assistant Devices for the Sit-To-Stand

A person's ability from sitting to standing is crucial for independent living. Especially for people with reduced mobility, Sit-to-Stand (STS) transitions are exceptionally difficult. In this paper, we propose novel solutions for home care of the disabled, build a model based on adaptive principle with monocular vision and design an assisting device. Some simulations were made to verify the correctness of the model. The simulation results show that the model can be in line with the evolving process of a velocity trajectory of STS at any Chair of Height (COH). This solution has the features of convenient, high precision and short time to reconstruct model.
Privacy Leakage: Gender Identification Based on Built In Smartphone Accelerator Usage

Personal information is a kind of sensitive data that users prefer keeping in private. However, with the development of Mobile Internet, various novel applications generate a large amount of data, which could be used to correlate the users and further infer their private information. This paper argues that smartphone application usage would incur potential privacy issue, and proposes a scheme to verify a case study that gender information leaks while the built-in smartphone accelerator is used. In particular, an Android OS application was firstly developed to collect the data generated by smartphone accelerator. Then, by extracting multiple features from the collected acceleration data, constructing an adjacent matrix to correlate users and features, and leveraging the Support Vector Machine (SVM) algorithm, users are classified into either males or females. Finally, while evaluating the scheme with real world data, experimental results show that, just by leveraging a four-second accelerator data segment of each user, the accuracy and F1 value can both achieve 84\%, respectively. This paper reveals the potential privacy leakage in smartphone usage, and provides valuable input for smartphone users and security service firms seeking to improve their products.
A Compact Eighth-Mode Circular SIW Cavity-Based MIMO Antenna

A verycompact and simple two-port multiple-input–multiple-output (MIMO) antenna is presented in this letter. The antenna element is designed based on the 1/8th mode modified circular substrate integrated waveguide cavity. The proposed MIMO antenna offers a bandwidth of 3.43% and resonates at 3.5 GHz. The isolation network consists of a single narrow linear half-wavelength slot and it offers a minimum isolation of 30 dB over the operating band. With a smaller physical area of 40 × 40 mm2, the proposed compact antenna offers a significant gain of 4.03 dBi. The envelope correlation coefficient values over the operational bandwidth are well within the accepted threshold limit. The proposed antenna is well suited for 5G wireless communication system applications. The design concept is further extended to a compact four-port MIMO antenna.
Blind Calibration of Air Quality Wireless Sensor Networks Using Deep Neural Networks

Temporal drift of low-cost sensors is crucial for the applicability of wireless sensor networks (WSN) to measure highly local phenomenon such as air quality. The emergence of wireless sensor networks in locations without available reference data makes calibrating such networks without the aid of true values a key area of research. While deep learning (DL) has proved successful on numerous other tasks, it is under-researched in the context of blind WSN calibration, particularly in scenarios with networks that mix static and mobile sensors. In this paper we investigate the use of DL architectures for such scenarios, including the effects of weather in both drifting and sensor measurement. New models are proposed and compared against a baseline, based on a previous proposed model and extended to include mobile sensors and weather data. Also, a procedure for generating simulated air quality data is presented, including the emission, dispersion and measurement of the two most common particulate matter pollutants: PM2.5 and PM10. Results show that our models reduce the calibration error with an order of magnitude compared to the baseline, showing that DL is a suitable method for WSN calibration and that these networks can be remotely calibrated with minimal cost for the deployer.
High Availability Acoustic Positioning for Targets Moving in Poor Coverage Areas

Currently, most acoustic positioning strategies are based on broadband coding and the pulse compression technique to obtain better accuracy and robustness against in-band noise. However, the accuracy and robustness of these systems are often seriously damaged in areas of low coverage and/or affected by multipath. This work proposes a high availability positioning algorithm (HAPA) that optimally processes the received signal to improve positioning results throughout the whole location area. This algorithm validates the Time-of-Arrival (ToA) from each beacon, corrects their Doppler shift, and compensates for multipath when it exists. If the number of validated beacons is enough to obtain a position, the algorithm tries to position with their optimal combination; if not, it tries to position by incorporating additional ToA candidates for the nonvalidated beacons. The HAPA performance has been evaluated through a set of experimental tests to compare its results with those obtained by the robust positioning algorithm (RPA) developed by the authors in previous work, and by a classical positioning algorithm (CPA) based on the emission of chirps. In particular, when using a mobile robot to follow trajectories that cross areas with low coverage and affected by multipath, the HAPA maintains availability of 97.89%, while the RPA drops to 83.25% and the CPA to 50.19%.
An Angular Displacement Sensor-Based Active Feedback Open Complementary Split-Ring Resonator

This letter presents an active feedback microwave sensor with a wide dynamic range and high sensitivity for detecting angular displacement. The sensor comprises two types of movable ring resonators (the rotor), split-ring resonator (SRR) and complement SRR (CSRR), and a modified convex-shaped open complementary split-ring resonator (the stator). The stator is designed with an active circuitry to enhance the quality factor over 4.7 K, and the feedback loop surrounds the sensing zone to enhance the mutual coupling for tracking a finite rotation of rotors in a wide dynamic range. The stator uses the separation between two reflection coefficients or the decibel magnitude variation at a fixed frequency, which is generated from its resonance frequency shift and whose values vary as a function of the rotation angle to be measured. After rotor loading, the average decibel sensitivity in terms of variation in reflection was calculated as 0.32 and 0.13 dB/degree for the SRR and CSRR, respectively, within a dynamic range of 0°–90°.
Research on Two-Level Energy Optimized Dispatching Strategy of Microgrid Cluster Based on IPSO Algorithm

This paper considers the economy and stability of the microgrid cluster system, and proposes a two-level energy optimization dispatching strategy for the microgrid cluster, which aims to coordinate the economic benefits and operational risks of the microgrid while reducing the interactive power and fluctuations between the microgrid cluster and the distribution network, and reduce line loss. The first level takes the microgrid as the research object, takes the highest economic benefit and the lowest operational risk as the optimization goals, uses the improved PSO algorithm to solve the problem, and builds the unit risk-economic benefit ratio screening solution set, and then formulates the internal dispatching candidate strategy of the microgrid. The second level is based on the premise that the interactive power between the microgrid cluster and the distribution network is minimized, and the optimal internal dispatching strategy of each microgrid is determined. Then the line loss of the microgrid cluster system is considered, and the microgrid cluster energy complementary scheme is formulated. Finally, a practical example verifies the feasibility and effectiveness of the two-level dispatching strategy and the improved algorithm.
Multiple Integrated Antennas for Wearable Fifth-Generation Communication and Internet of Things Applications

This paper proposes integrating multiple antennas into wearable devices for application in fifth-generation (5G) communications and the Internet of Things (IoT). The frequency bands covered were wireless local area network 2.4-GHz/5-GHz channels (2410–2484 MHz), the global positioning system (1575 MHz), narrowband IoT band 8 (880–960 MHz), Global System for Mobile Communications 850/900, and the 5G frequency range 1 n77/78/79 band (3300–5000 MHz). The antennas were configured as a <inline-formula> <tex-math notation="LaTeX">$2\times2$ </tex-math></inline-formula> multiple-input multiple-output system (MIMO). The antenna resonance contribution integrates the slot mode, monopole mode of the three-dimensional frame, and loop coupling monopole mode. The configuration of the independent matching circuits facilitated the effective control of the desired mode and the coverage of the desired bands. All integrated antennas were placed in a watch device (<inline-formula> <tex-math notation="LaTeX">$37\times 43\times8$ </tex-math></inline-formula> mm<sup>3</sup>) with a metal body and a metal strap. An integrated miniaturized antenna was designed. The diversity gain and multiplexing efficiency between the MIMO antennas were 9.97dB and above −2dB, respectively. Favorable diversity performance was confirmed. The channel capacity of the <inline-formula> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula> 2 MIMO array was determined to be 8.8 bps/Hz. Moreover, its performance was evaluated through the addition of a hand tissue module. The impact of metal straps on antenna radiation performance was discussed. The specific absorption rate (SAR) was examined, and the best results obtained under an SAR of 1 g and 10 g were 0.25 and 0.58 W/kg, respectively. The findings demonstrate the suitability of the antenna for practical applications in 5G communication systems and the IoT.
Data Privacy in a Time of Covid-19: How concerned are you?

—We introduce a study examining people’s privacy concerns in a time of Covid-19 and we reﬂect on people’s willingness to share their personal data in the interest of controlling the spread of the virus and save lives. T he signiﬁcant growth in the number of users with mobile phones as well as the adoption of key enabling technologies like cloud computing has led to the creation of an entire tracking ecosystem that could enable the use of pervasive surveillance methods. However, this development also brings serious privacy concerns, as current governance and regulation frameworks are lagging behind these technological advancements. This is visible in the current pandemic, where concerns around privacy and civil liberties have led several countries to respond differently in their approach of controlling the spread of Covid-19 and preserve human life.