Miracle-dz/UniFAR-FTUs huggingface dataset:
When long appears short: Effects of auditory distraction on event‐related potential correlates of time perception

Attentional models of time perception assume that the perceived duration of a stimulus depends on the extent to which attentional resources are allocated to its temporal information. Here, we studied the effects of auditory distraction on time perception, using a combined attentional‐distraction duration‐discrimination paradigm. Participants were confronted with a random sequence of long and short tone stimuli, most of which having a uniform (standard) pitch and only a few a different (deviant) pitch. As observed in previous studies, pitch‐deviant tones impaired the discrimination of tone duration and triggered a sequence of event‐related potentials (ERPs) reflecting a cycle of deviance detection, involuntary attentional distraction and reorientation (MMN, P3a, RON). Contrasting ERPs of short and long tone durations revealed that long tones elicited a more pronounced fronto‐central contingent negative variation (CNV) in the time interval after the expected offset of the short tone as well as a more prominent centro‐parietal late positive complex (LPC). Relative to standard‐pitch tones, deviant‐pitch tones especially impaired the correct discrimination of long tones, which was associated with a reduction of the CNV and LPC. These results are interpreted within the theoretical framework of resource‐based models of time perception, in which involuntary distraction due to a deviant event led to a withdrawal of attentional resources from the processing of time information.
Deep-Learning-Based Community Detection Approach on Multimedia Social Networks

Exploiting multimedia data to analyze social networks has recently become one the most challenging issues for Social Network Analysis (SNA), leading to defining Multimedia Social Networks (MSNs). In particular, these networks consider new ways of interaction and further relationships among users to support various SNA tasks: influence analysis, expert finding, community identification, item recommendation, and so on. In this paper, we present a hypergraph-based data model to represent all the different types of relationships among users within an MSN, often mediated by multimedia data. In particular, by considering only user-to-user paths that exploit particular hyperarcs and relevant to a given application, we were able to transform the initial hypergraph into a proper adjacency matrix, where each element represents the strength of the link between two users. This matrix was then computed in a novel way through a Convolutional Neural Network (CNN), suitably modified to handle high data sparsity, in order to generate communities among users. Several experiments on standard datasets showed the effectiveness of the proposed methodology compared to other approaches in the literature.
The Effects of Design Features on Users’ Trust in and Reliance on a Combat Identification System

In a previous study, we found that users’ trust in and reliance on an individual combat identification system is influenced by the system’s reliability as well as users’ awareness of the reliability. In this exploratory study we test the effects of design features of the same system on users’ target identification performance as well as their trust in and reliance on the system. In a simulated task environment, we varied the automation activation mode (i.e., automatic vs. manual) and the presentation of the “unknown” feedback (i.e., explicit vs. implicit). Participants responded fastest when the “unknown” feedback was provided automatically with explicit indication. In addition, participants trusted the explicit “unknown” feedback more than the implicit feedback. However, neither reliance behavior nor identification accuracy changed significantly across conditions. This study has implications for the design of combat identification systems to achieve appropriate trust. In addition, the results suggest that when studying trust in automation using a simulation, it is important to simulate the design features.
Navigating Student Challenges: From the Lens of First-Year Doctor of Physical Therapy Students

Supplemental Digital Content is Available in the Text. Introduction. Anxiety, depression, and burnout are being discussed across health professions. Despite rising concern, studies investigating stress in students enrolled in Doctor of Physical Therapy (DPT) programs remain limited. Only recently have studies exploring stress in DPT students surfaced with any consistency. In this study, our aim was to elucidate the self-identified challenges first-year DPT students faced, how they reacted, and what they did to manage them. Review of the Literature. Evidence suggests that DPT students, like other health professional students, report high levels of anxiety. Despite rising concern, studies investigating the impact of stress on DPT students remain limited. This concern also raises the question of the role of health professions educators in helping students develop the coping strategies needed to manage stress. Programs across the health professions have been proffered to address student stress; however, limited data exist to effectively guide educators. From the insights gained, we offer recommendations linked to the emic or student perspective that may help educators facilitate adaptive coping skills in their learners. Subjects. Participants included first-year DPT students from 3 private universities. Methods. A critical incident questionnaire was used to capture the student experience. Narratives were submitted electronically. Responses were deidentified, and researchers were blinded to participation. An inductive interpretivist approach was used to analyze the data. Strategies to ensure trustworthiness included prolonged engagement, triangulation of investigators, and peer review. Results. Eighty-two first-year DPT students responded; 70 complete responses were analyzed. Three major themes were identified: 1) first-year DPT students faced academic, personal, and mixed challenges; 2) challenges evoked a range of negatively charged emotions; and 3) students relied on adaptive and some potentially maladaptive personal characteristics, behaviors, and strategies to manage their challenges. Discussion and Conclusion. First-year DPT students face many of the same challenges as other health professional students. Most successfully navigated their challenges, however, not without some degree of emotion. As educators, we must prepare students to develop the coping strategies needed to manage not only current academic stressors but ultimately the stressors inherent in clinical practice. Toward that end, we offer recommendations, linked to the emic perspective obtained, that may help educators facilitate adaptive coping skills in their learners.
Performance Improvement of a Domestic Refrigerator by using PCM (Phase Change Material)

The paper investigates the performance improvement provided by a phase change material associated with the evaporator in a domesticrefrigerator. The heat release and storage rate of a refrigerator is depends upon the characteristics of refrigerant and its properties. Theusage of PCM as TS will help to improve the COP (Coefficient of performance) of new refrigeration cycle by introducing a new sub coolingroutine. The analysis of the experiment exemplifies the improvement of the system coefficient of performance considerably. Using water asPCM and for a certain thermal load it is found that the coefficient of performance of the conventional refrigerator increased by 55-60%. This improvement by sub cooling can be done for single evaporator refrigeration system. Because of prolonging of the compressor off timeby using the latent heat of energy of the PCM capsulated ice, used as the thermal energy storage material, has been investigatednumerically. We can have better food quality due to lower hysteresis cycles of on/off for a given period of operation.
3D LASER SCANNERS ON ARCHAEOLOGICAL EXCAVATIONS

To be able to fully reconstruct the part of the site destroyed by excavating, the surfaces of the excavated deposits have to be fully documented in 3D (“single surface planning”). During recent years we developed a GIS based procedure for the digital documentation of stratigraphic excavations. The outstanding importance of 3D single-surface recording for the stratigraphic record demands the use of high resolution documentation techniques. During the last three years, we have been testing the use 3D laser scanners combined with digital imagery as a recording tool on archaeological excavations using various instruments (Riegl LMS Z210i, Riegl LMS Z360). They showed a high reliability and efficiency for topographic single surface recording in every day archaeological work.
Gender-affirming multidisciplinary care for transgender and non-binary children and adolescents

ABSTRACT Modern transgender and non-binary (TGNB) pediatric health care originated in the 1990s. This patient population is adversely affected by minority stress, victimization, mental health disparities, and barriers to health care With improving social and cultural support for TGNB identities and favorable evidence for affirming social and medical interventions, the need for pediatric gender services clinics has grown. Gender-affirming care requires collaboration between social and medical entities, including school personnel, community services, medical providers, and mental health professionals, which is best served within a multidisciplinary treatment model of care. This article provides an overview of the components within multidisciplinary pediatric gender clinics.
Modeling and analysis of 96.5Sn-3.5Ag lead-free solder joints of wafer level chip scale package on buildup microvia printed circuit board

In this study, time-temperature-dependent nonlinear analyses of lead-free solder bumped wafer level chip scale package (WLCSP) on microvia buildup printed circuit board (PCB) assemblies subjected to thermal cycling conditions are presented. The lead-free solder considered is 96.5Sn-3.5Ag. The 62Sn-2Ag-36Pb solder is also considered to establish a baseline. These two solder alloys are assumed to obey the Garofalo-Arrhenius steady-state creep constitutive law. The shear stress and shear creep strain hysteresis loops, shear stress history, shear creep strain history, and creep strain density range at the corner solder joint are presented for a better understanding of the thermal-mechanical behavior of the lead-free solder bumped WLCSP on microvia buildup PCB assemblies.
Genetic dissection of maize plant architecture using a novel nested association mapping population

The leaf angle (LA), plant height (PH), and ear height (EH) are key plant architectural traits influencing maize (Zea mays L.) yield. However, their genetic determinants have not yet been well‐characterized. Here, we developed a maize advanced backcross‐nested association mapping population in Henan Agricultural University (HNAU‐NAM1) comprised of 1,625 BC1F4/BC2F4 lines. These were obtained by crossing a diverse set of 12 representative inbred lines with the common GEMS41 line, which were then genotyped using the MaizeSNP9.4K array. Genetic diversity and phenotypic distribution analyses showed considerable levels of genetic variation. We obtained 18–88 quantitative trait loci (QTLs) associated with LA, PH, and EH by using three complementary mapping methods, named as separate linkage mapping, joint linkage mapping, and genome‐wide association studies. Our analyses enabled the identification of ten QTL hot‐spot regions associated with the three traits, which were distributed on nine different chromosomes. We further selected 13 major QTLs that were simultaneously detected by three methods and deduced the candidate genes, of which eight were not reported before. The newly constructed HNAU‐NAM1 population in this study will further broaden our insights into understanding of genetic regulation of plant architecture, thus will help to improve maize yield and provide an invaluable resource for maize functional genomics and breeding research.
Ontological Model for Contextual Data Defining Time Series for Emotion Recognition and Analysis

One of the major challenges facing the field of Affective Computing is the reusability of datasets. Existing affective-related datasets are not consistent with each other, they store a variety of information in different forms, different formats, and the terms used to describe them are not unified. This paper proposes a new ontology, ROAD, as a solution to this problem, by formally describing the datasets and unifying the terms used. The developed ontology allows information about the origin and meaning of the data to be modeled, i.e., time series, representing both emotional states and features derived from biosignals. Furthermore, the ROAD ontology is extensible and not application-oriented, thus it can be used to store data from a wide range of Affective Computing experiments. The ontology was validated by modeling data obtained from one experiment on the AMIGOS dataset. The approach proposed in the paper can be used both by researchers who create new datasets or want to reuse existing ones, and for those who want to process data from experiments in a more automated way.
Trichloroacetic Acid/Acetone Precipitation Method to Optimize Canine Synovial Fluid for One and Two-Dimensional Gel Electrophoresis Studies

The challenge associated with the use of synovial fluid for osteoarthritic proteome studies is the optimization step, which involves the depletion of high abundant proteins from the samples. The objective of this study was to develop a cost efficient and effective method to remove albumin from canine synovial fluid for proteome studies. Pooled synovial fluid samples were obtained from clinically healthy dogs (n=5), with no radiographic features of osteoarthritis. The acetone precipitation method and 10% w/v of trichloroacetic acid/acetone were chosen to deplete the albumin from canine synovial fluid and the outcome from the different methods were compared using one dimensional and two-dimensional gel electrophoresis studies. The results showed that the 10% w/v TCA/acetone precipitation method removed highly abundant proteins from synovial fluid for gel electrophoresis studies compared to the acetone precipitation method. The 10% w/v TCA/acetone precipitation method provides an effective method to remove albumin from the synovial fluid using gel electrophoresis, especially two-dimensional gel electrophoresis. The accessibility and cost of TCA and acetone make this method of precipitation a simple and cost-effective technique in preparing a canine synovial fluid for two-dimensional gel electrophoresis analysis.
An adapted fan volume sampling scheme for 3-D algebraic reconstruction in linear tomosynthesis

We study the reconstruction process when the X-ray source translates along a finite straight line, the detector moving or not. This process, called linear tomosynthesis, induces a limited angle of view, which causes the vertical spatial resolution to be poor. To improve this resolution, we use iterative algebraic reconstruction methods, which are commonly used for tomographic reconstruction from a reduced number of projections. With noisy projections, such algorithms produce poor quality reconstructions. To prevent this, we use a first object prior knowledge, consisting of piecewise smoothness constraint. To reduce the computation time associated with both reconstruction and regularization processes, we introduce a second geometrical prior knowledge, based on the linear trajectory of the X-ray source. This linear source trajectory allows us to reconstruct a series of two-dimensional (2-D) planes in a fan organization of the volume. Using this adapted fan volume sampling scheme, we reduce the computation time by transforming the initial three-dimensional (3-D) problem into a series of 2-D problems. Obviously, the algorithm becomes directly parallelizable. Focusing on a particular region of interest becomes easier too. The regularization process can easily be implemented with this scheme. We test the algorithm using experimental projections. The quality of the reconstructed object is conserved, while the computation time is considerably reduced, even without any parallelization of the algorithm.
Rapid Alternative for Laboratory Determination of Resilient Modulus Input Values on Stabilized Materials for AASHTO Mechanistic-Empirical Design Guide

NCHRP is in the final stages of developing and implementing a new mechanistic-empirical pavement design guide (MEPDG) to be used by all state highway agencies for layer thickness design. Once adopted, it will become the national procedure required on all federally aided projects. A key material property to be used in the design will be the resilient modulus value, which either can be obtained from laboratory testing or can be backcalculated from deflection data collected with falling weight deflectometers. It is important that realistic design moduli values be used. The standard test method for resilient modulus of subgrade soils and untreated base and subbase materials is AASHTO T307-99. However, the setup for this procedure is both difficult and time-consuming, and the equipment required to run this test is expensive. With the low strain levels used, it is likely that accurate instrumentation will be problematic for most state departments of transportation. The free-free resonant column test is a simple, nondestructive laboratory test method for determining the seismic modulus of pavement materials; it takes approximately 5 min to setup and complete. The equipment costs less than $4,000. For the tests conducted, it appears that the seismic modulus device is a better and more repeatable test for estimating the resilient modulus of stabilized materials, and it shows great potential for acquiring reasonable resilient modulus input values for the new AASHTO MEPDG.
Double Optical Feedback Interferomety for biomedical applications

Optical Feedback Interferometry (OFI) and Double Optical Feedback interferometry (DOFI) are studied as non-invasive techniques for biomedical measurements. Due to its compactness and costeffectiveness, both sensors provide a very interesting approach for sensing different biomedical parameters. After a brief introduction to the two techniques, a modified signal processing algorithm developed within this MSc Thesis is presented. DOFI is then discussed as a technique for arterial pulse wave shape reconstruction, showing its limitations in large amplitude measurements, which, nevertheless, enable an OFI sensor to accurately measure it. Experimental results on arterial pulse wave measurements will be presented. DOFI is then proposed as a candidate technique for monitoring the displacement of cantilevers in atomic force microscopy which enhances the performance of present cantilever sensing solutions due to its robustness and size.
Variable frame rate analysis for automatic speech recognition

In this paper we investigate the use of variable frame rate (VFR) analysis in automatic speech recognition (ASR). First, we review VFR technique and analyze its behavior. It is experimentally shown that VFR improves ASR performance for signals with low signal-to-noise ratios since it generates improved acoustic models and substantially reduces insertion and substitution errors although it may increase deletion errors. It is also underlined that the match between the average frame rate and the number of hidden Markov model states is critical in implementing VFR. Secondly, we analyze an effective VFR method that uses a cumulative, weighted cepstral-distance criterion for frame selection and present a revision for it. Lastly, the revised VFR method is combined with spectral- and cepstral-domain enhancement methods including the minimum statistics noise estimation (MSNE) based spectral subtraction and the cepstral mean subtraction, variance normalization and ARMA filtering (MVA) process. Experiments on the Aurora 2 database justify that VFR is highly complementary to the enhancement methods. Enhancement of speech both facilitates the frame selection in VFR and provides de-noised speech for recognition.
A Communication-Efficient Federated Learning Scheme for IoT-Based Traffic Forecasting

Federated learning (FL) is widely adopted in traffic forecasting tasks involving large-scale IoT-enabled sensor data since its decentralization nature enables data providers’ privacy to be preserved. When employing state-of-the-art deep learning-based traffic predictors in FL systems, the existing FL frameworks confront overlarge communication overhead when transmitting these models’ parameter updates since the modeling depth and breadth renders them incorporating an enormous number of parameters. In this article, we propose a practical FL scheme, namely, Clustering-based hierarchical and Two-step-optimized FL (CTFed), to tackle this issue. The proposed scheme follows a divide et impera strategy that clusters the clients into multiple groups based on the similarity between their local models’ parameters. We integrate the particle swarm optimization algorithm and devises a two-step approach for local model optimization. This scheme enables only one but representative local model update from each cluster to be uploaded to the central server, thus reduces the communication overhead of the model updates transmission in FL. CTFed is orthogonal to the gradient compression- or sparsification-based approaches so that they can orchestrate to optimize the communication overhead. Extensive case studies on three real-world data sets and three state-of-the-art models demonstrate the outstanding training efficiency, accurate prediction performance, and robustness to unstable network environments of the proposed scheme.
Imprint Lithography for Large Scale Angular Encoders

Institute of Physical Electronics of Kaunas University of Technology, Savanorių 271, LT-5013 Kaunas, Lithuania Profactor GmbH, Im Stadtgut A2, A-4407 Steyr, Austria EV Group, E. Thallner GmbH, DI Erich Thallner Strasse 1, A-4782 St.Florian/Inn, Austria Brown&Sharpe-Precizika , Žirmūnų 139, LT-09120 Vilnius, Lithuania International Studies Center of Kaunas University of Technology, Mickeviciaus 37, LT-44244 Kaunas, Lithuania