Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing Perceived Discomfort and Proxemic Behavior towards Robots: A Comparative Study between Real and Augmented Reality Presentations

This paper assesses the usefulness of immersive technology to evaluate perceived discomfort and proxemic behavior towards a robot depending on its size. Therefore, we compared a real and an augmented reality (AR) robot presentation. In a within-subject design, a service humanoid approached participants (N = 32) in four trials in a counterbalanced order. One trial presented a real robot, another showed a same-sized AR version, and two trials showed down-scaled AR versions of the robot. The perceived discomfort and the comfort distance were measured. For the presentation mode comparison, the distance estimation error was measured additionally. The results show that the comfort distance was greater for the AR robot than for the real one. The comfort distance was also greater for the largest robot compared to the smaller sizes, while there was no difference when comparing the smaller ones. There was no difference in perceived discomfort between the presentation modes or the robot sizes. The distance estimation error was greater in AR. The study indicates that results obtained with the AR and real robot are comparable relative to each other. Therefore, utilizing AR could effectively evaluate various versions of robots in terms of the discomfort they induce—a critical prerequisite prior to the manufacturing process. Finally, AR might provide better validity for the evaluation of subjective measures.
Effect of circadian rhythm on NAD and other metabolites in human brain

Nicotinamide Adenine Dinucleotide (NAD) plays a central role in the master circadian clock of the brain (the suprachiasmatic nuclei, SCN) as demonstrated in many model organisms. NAD acts as an enzyme co-factor and substrate and its modulation was found to be tightly regulated to the periodicity of the cycles. However, in human brain, the effect of the circadian rhythm (CR) on the metabolism of the SCN and other brain regions is poorly understood. We conducted a magnetic resonance spectroscopy (MRS) study at a high magnetic field, measuring the occipital brain NAD levels and other metabolites in two different morning and afternoon diurnal states in 25 healthy participants. Salivary cortisol levels were determined to confirm that the experiment was done in two chronologically different physiological conditions, and a behavioral test of risk-taking propensity was administered. Overall, we found that the CR did not significantly affect NAD levels in the occipital brain region. The other brain metabolites measured, including lactate, were not significantly affected by the CR either, except for taurine. The CR did impact risk-taking behavior and salivary cortisol level, confirming that the participants were in two circadian different behavioral and physiological states in the morning and in the afternoon. Measurement of the CR effect on NAD and taurine levels in other brain regions might provide stronger effects.
A Scalo Gram-Based CNN Ensemble Method With Density-Aware SMOTE Oversampling for Improving Bearing Fault Diagnosis

Machine learning (ML) based bearing fault detection is an emerging application of Artificial Intelligence (AI) that has proven its utility in effectively classifying various faults for timely measures. There are myriad studies dedicated to the effective classification of bearing faults under different conditions and experimental settings. In this study, we proposed a weighted voting ensemble (WVE) of three low-computation custom-designed convolutional neural networks (CNNs) to classify bearing faults at 48 KHz. Some of the recent studies have exploited 1-d time-series signals and time-frequency based 2-d transformations for bearing fault classification. However, 1-d signals lack contextual information and higher-dimensional interpretations whereas time-frequency based transformations provide a more appropriate, visually perceivable and explainable representation of the time and frequency changes. Therefore in this study, a scalogram based representation of the signals is leveraged for classification using the CNN. Furthermore, the class imbalance is a significant challenge that affects the modelling behavior and possibly create biases. This study provides a novel density and distance hybrid over-sampling approach namely Density-Aware SMOTE(DA-SMOTE) built upon the SMOTE methodology for a more refined representation of synthetic samples within the minority class distribution. The experimentation procedures were carried out before and after the oversampling and it was observed that the balanced dataset acquired much better accuracy then the imbalanced dataset. This is evident by the fact that the highest validation accuracy for the proposed ensemble method (WVCNN) reached at 0-HP and 1-HP reached 99.28% and 99.13% while for the over-sampled dataset the accuracy soared to 99.71% and 99.87% for 0 and 1-HP respectively. The performance was evaluated for other metrics apart from the accuracy to assess the model’s performance in terms of chance occurrences and the class wise performance.
A CPW Probe to Rectangular Waveguide Transition for On-Wafer Micromachined Waveguide Characterization

A new transition from coplanar waveguide probe to micromachined rectangular waveguide for on-wafer device characterization is presented in this article. The transition is fabricated in the same double H-plane split silicon micromachined waveguide technology as the devices under test (DUT), requiring no additional postprocessing or assembly steps. We outline the design and fabrication process of the transition for the frequency band of 220–230 GHz. A coplanar waveguide structure acts as the probing interface, with an E-field probe protruding in the waveguide cavity exciting the fundamental waveguide mode. Guard structures around the E-field probe increase the aspect ratio during deep reactive ion etching and secure its geometry. A full equivalent circuit model is provided by analyzing its working principle. RF characterization of fabricated devices is performed for both single-ended and back-to-back configurations. Measured $S$-parameters of the single-ended transition are obtained by applying a two-tiered calibration and are analyzed using the equivalent circuit model. The insertion loss of the single-ended transition lies between 0.3 and 1.5 dB over the whole band, with the return loss in excess of 8 dB. In addition to previously reported characterization of a range of DUT the viability of the transition for on-wafer device calibration is demonstrated by characterizing a straight waveguide line, achieving an insertion loss per unit length of 0.02–0.08 dB/mm in the frequency band of 220–230 GHz.
Multilingual Language Diversity Protects Native Language Production under Different Control Demands

The use of multiple languages has been found to influence individuals’ cognitive abilities. Although some studies have also investigated the effect of multilingualism on non-native language proficiency, fewer studies have focused on how multilingual experience affects native language production. This study investigated the effect of multilingualism on native language production, specifically examining control demands through a semantic Go/No-Go picture naming task. The multilingual experience was quantified using language entropy, which measures the uncertainty and diversity of language use. Control demands were achieved by manipulating the proportion of Go (i.e., naming) trials in different conditions. Results showed that as control demands increased, multilingual individuals exhibited poorer behavioral performance and greater brain activation throughout the brain. Moreover, more diverse language use was associated with higher accuracy in naming and more interconnected brain networks with greater involvement of domain-general neural resources and less domain-specific neural resources. Notably, the varied and balanced use of multiple languages enabled multilingual individuals to respond more efficiently to increased task demands during native language production.
Optimization of Offshore Wind Power Generation in Response to the 2022 Extreme Drought in Portugal

Portugal, in line with the European Union, is aiming for carbon neutrality by 2050 (Net Zero), which implies a transition to sustainable energy sources. Climate change is all too evident, as extreme weather periods are occurring in a cyclical manner with greater brevity to such an extent that the grid operator must deal with production scenarios where it can no longer rely on hydroelectric production given the recurring drought situation. This situation increases dependence on thermal production using natural gas and imports. This has significant economic implications. Portugal has exploited its onshore wind potential, reaching an installed capacity of 5.671 MW by 2022. However, the expansion of onshore wind energy is limited to reinforcing the existing infrastructure. To overcome these challenges, it is necessary to expand the exploitation of the offshore wind potential that is already underway. This article proposes the location of offshore wind production platforms along the Portuguese coast. This allows for an analysis of offshore production and its optimization according to the minimum cost per MWh in the face of extreme scenarios, i.e., in periods of extreme drought where the hydroelectric production capacity is practically non-existent. The model is fed by using market price indications and the amount of energy needed for the following day. Using forecast data, the model adapts offshore wind production for the following day according to the minimization of the average market price. This study presents an optimization model adapted to combat the unpredictability of extreme weather conditions. This strategic framework significantly increases the resilience and reliability of offshore wind energy production, marking a significant advance in the management of renewable energy under the pressure of climate variability. The results of the simulations allow us to conclude that despite the high cost of offshore technology (in deep waters), in extreme climate scenarios, it enables cost reduction and a clear decrease in imports.
Evaluation of the Breastfeeding Dynamics of Neonates with Ankyloglossia via a Novel Ultrasonographic Technique

To effectively address breastfeeding issues for neonates and mothers, one must understand the physiology of breastfeeding and the anatomical components involved in sucking, swallowing, and respiration. This study compared the tongue position and movement of neonates with tongue ties versus healthy controls during sucking. A new objective ultrasonography diagnostic approach was also introduced for the orofacial region. This retrospective study evaluated B-mode and M-mode ultrasonography images from 30 neonates clinically diagnosed with tongue tie, and a control group of 30 neonates. B-mode ultrasound images were used to examine several characteristics to locate the nipple in the oral cavity during breastfeeding. Anatomic M-mode ultrasound images were used to assess tongue movement during sucking. The nipple moved farther from the intersection of the hard and soft palates during the sucking cycle in the ankyloglossia group than in the control group (p < 0.05). Compared to the control group, neonates with ankyloglossia have a lower capacity to lift the anterior tongue toward the palate when sucking (p < 0.05). There was no significant difference in tongue movement metrics between the two groups (p > 0.05). Our findings were consistent with earlier research. The novel measurement method will offer a new perspective on breastfeeding.
Estimation of Three-Dimensional Ground Reaction Forces During Walking and Turning Using Insole Pressure Sensors Based on Gait Pattern Recognition

Three-dimensional ground reaction forces (3D GRFs) in various gait patterns (e.g., walking and turning) provide essential information for clinical assessment. Previous scholars proposed to estimate GRFs from insoles with few pressure sensors using machine learning techniques. However, estimating GRFs during turning using a model learned from walking proved difficult because relationships between plantar pressure and GRFs vary according to the gait pattern. In this study, GRFs were estimated based on gait pattern recognition. GRF estimation models were learned from various gait patterns in advance. These models represented relationships between the insole measurements and GRFs using a Gaussian process regression (GPR). The GRFs were estimated from the insole measurements using a maximum likelihood (ML) model recognized as the current gait pattern. The ML model had the largest time frames, in which its standard deviation (SD) of the probabilistically estimated vertical GRF was the smallest among all the models (i.e., its training data were closest to the inputs), in the initial and terminal stance phases (subphases). In addition, the time frames of the contralateral leg were considered to enhance the recognition accuracy based on interlimb coordination. Experiment results showed that the proposed system, which used a common model learned from all the gait patterns for the vertical GRF and the ML model in the subphases of both legs for the horizontal GRFs, estimated 3D GRFs during walking and turning with different curvatures and directions with higher accuracy. The system can be widely applied for clinical walking tests and monitoring in daily life.
Dynamic Impedance and Chaotic Behavior Modeling Featured Novel Robust Arc Fault Identification Approach

Accurate and reliable electric arc fault identification ensures the safety of personnel and equipment through in-field operations and maintenance work. However, due to the application scenarios and nonlinear arc dynamics, the distortion degree of the detectable signals is influenced by network/circuit structure, system parameter, operation manner, etc., the salience of the fault signatures may change significantly, which poses challenges to develop the “generic scheme” in arc faults identification. To address such issues, this paper proposed a robustness-improved arc fault identification approach that incorporates the modeling of dynamic impedance and chaotic behavior in faulting systems. For describing the “unique” nonlinear characteristics of arc faults among different operation scenarios, the HT-based dynamic impedance (HTDI) representation method is developed to extract the “signatures” of arc faults, and the mechanism of how system parameters impact the presentation form of arc fault features are also revealed. Meanwhile, for quantitively extracting the signatures, the chaotic behavior of arcing fault system has been analyzed in phase space in detail, with a series of chaos indicators. Additionally, HTDI and chaos indicators implemented LSTM classifier has been designed to achieve accurate and rapid arcing fault identification with time-series inputs. With the series of actual arc fault cases and simulation cases under different configurations, the effectiveness and robustness of the proposed method have been thoroughly validated.
The impact of robot co-location on student learning experiences when reasoning about geometry

The application of social robots in education is an emerging field that has the potential to transform the way we teach and learn. In this work, we compare the effects of a physically co-located social robot and a virtual social robot on learning experiences, as students reason about geometry conjectures. Our thematic analysis of interactions and interviews show that the co-located robot was treated more socially and improved learning experiences compared to a virtual robot. Specifically, it increased the perceived effectiveness of the interaction to lower anxiety, provide companionship, and support reasoning and comprehension. These results support the use of co-located, physically present robots to complement learning activities that benefit from social interaction, including reasoning about complex problems and use of gestures. This research contributes to the growing body of literature on the use of social robots in education and highlights the potential for further research on this subject.
Failure Explanation in Privacy-Sensitive Contexts: An Integrated Systems Approach

In this paper, we explore how robots can properly explain failures during navigation tasks with privacy concerns. We present an integrated robotics approach to generate visual failure explanations, by combining a language-capable cognitive architecture (for recognizing intent behind commands), an object- and location-based context recognition system (for identifying the locations of people and classifying the context in which those people are situated) and an infeasibility proof-based motion planner (for explaining planning failures on the basis of contextually mediated privacy concerns). The behavior of this integrated system is validated using a series of experiments in a simulated medical environment.
Measuring the Global Ocean Surface Circulation with Satellite and In Situ Observations

(1) Earth & Space Research, 2101 Fourth Ave, Suite 1310, Seattle WA, 98212, USA, Email: kdohan@esr.org; lager@esr.org (2) SAT-OCEAN, 53, Boulevard de la Reine, 78000 Versailles, France: Email: fabrice.bonjean@sat-ocean.com (3) Scripps Institution of Oceanography, UCSD (University of California, San Diego), 9500 Gilman Drive, MC 0213, La Jolla CA, 92093, USA, Email: lcenturioni@ucsd.edu; pniiler@ucsd.edu (4) NOAA (National Oceanic and Atmospheric Administration) Pacific Marine Environmental Laboratory, Ocean Climate Research Division, 7600 Sand Point Way NE, Seattle, WA, 98115, USA: Email: Meghan.F. Cronin@noaa.gov (5) Department of Marine Sciences, Pusan National University, Busan, 609-735, Korea, Email: lee@east.ocean. pusan.ac. kr (6) Physical Oceanography Division, NOAA/AOML (National Oceanic and Atmospheric Administration/Atlantic Oceanographic and Meteorological Laboratory), 4301 Rickenbacker Causeway, Miami, FL, 33149, USA, Email: Rick.Lumpkin@noaa.gov (7) International Pacific Research Center, SOEST (School of Ocean and Earth Science and Technology), University of Hawaii, 1680 East West Road, POST Bldg. #401, Honolulu, Hawaii, 96822, USA, Email: maximenk@hawaii.edu (8) IORGC ((Institute of Observational Research for Global Change), Japan Agency for Marine-Earth Science and Technology, 2-15 NatsushimaCho, Yokosuka, Kanagawa 237-0061, Japan, Email: huchida@jamstec.go. jp
Ophiostomatatoid fungi (Ascomycota) associated with Ips acuminatus (Coleoptera) in eastern Poland

Ips acuminatus (Coleoptera, Scolytinae) is a pine-infesting bark beetle that occurs throughout Europe and Asia. Recently, the insect has killed numerous Scots pines (Pinus sylvestris) in eastern Poland. Several species of ophiostomatoid fungi are associated with I. acuminatus in Europe, but no research has been done on the fungi associated with this bark beetle in Central Europe specifically. The aim of this study was to identify the ophiostomatoid fungal associates of I. acuminatus in eastern Poland, where tree mortality caused by this beetle species has recently increased. Field surveys in Puławy and Mircze Forest Districts yielded a total of 2 269 fungal isolates from 237 beetles and 204 beetle galleries. Isolates were grouped based on morphology and representatives of each group were identified based on DNA sequences of the ITS, LSU, β-tubulin, calmodulin and elongation factor 1-α gene regions. A total of seven previously described species of ophiostomatoid fungi were identified. The dominant species were Graphilbum acuminatum and Sporothrix pseudoabietina. This study revealed that the community of ophiostomatoid fungi associated with I. acuminatus in Poland is different from those reported in other regions of Europe. In addition, molecular data suggest that S. pseudoabietina is a synonym of S. villosa in the Sporothrix gossypina & S. stenoceras species complexes.
Simulating Microscale Urban Airflow and Pollutant Distributions Based on Computational Fluid Dynamics Model: A Review

Urban surfaces exert profound influences on local wind patterns, turbulence dynamics, and the dispersion of air pollutants, underscoring the critical need for a thorough understanding of these processes in the realms of urban planning, design, construction, and air quality management. The advent of advanced computational capabilities has propelled the computational fluid dynamics model (CFD) into becoming a mature and widely adopted tool to investigate microscale meteorological phenomena in urban settings. This review provides a comprehensive overview of the current state of CFD-based microscale meteorological simulations, offering insights into their applications, influential factors, and challenges. Significant variables such as the aspect ratio of street canyons, building geometries, ambient wind directions, atmospheric boundary layer stabilities, and street tree configurations play crucial roles in influencing microscale physical processes and the dispersion of air pollutants. The integration of CFD with mesoscale meteorological models and cutting-edge machine learning techniques empowers high-resolution, precise simulations of urban meteorology, establishing a robust scientific basis for sustainable urban development, the mitigation of air pollution, and emergency response planning for hazardous substances. Nonetheless, the broader application of CFD in this domain introduces challenges in grid optimization, enhancing integration with mesoscale models, addressing data limitations, and simulating diverse weather conditions.
Recurrent neural network implementation of digital integrated circuits to mitigate challenges in design verification

Design verification is the dominant stage that consumes the most resources in the digital integrated circuit (IC) design process. Design verification is important because human designers imperfectly convert high-level specifications to low-level circuit implementations using standard cell logic, which is nonlinear and complex to predict and characterize. The widening process variations in shrinking process technologies while digital designs grow in scale and complexity to the extent of being impossible to fully or intuitively identify all temporal interactions of a specific design. Deep neural networks (DNN) are being progressively integrated into the sophisticated software tool chain and design process flow of digital IC as artificial intelligence can learn relationships and correlations in complex, high-dimensional, and multi-factorial problems. In this work, we propose to apply DNN to implement digital IC to minimize the complexity of digital design verification. We posit that DNN can learn to implement circuit functions directly from high-level specifications without requiring detailed specifications from the designer. Trained neural networks can be implemented on neuromorphic hardware to achieve greater power and compute efficiencies than the conventional standard cell implementation. We demonstrate that over 150 randomly generated finite state machines (FSM) can be learned effectively with Recurrent Neural Network (RNN) comprising Gated Recurrent Units (GRU) with different complexity as indicated by the number of states and inputs to the FSM. Our proposed methodology of learning RNN GRU implementations of FSM demonstrates a way forward to reduce the cost and effort of design verification, ultimately leading towards faster digital IC design cycles.