Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated Student Classroom Behaviors’ Perception and Identification Using Motion Sensors

With the rapid development of artificial intelligence technology, the exploration and application in the field of intelligent education has become a research hotspot of increasing concern. In the actual classroom scenarios, students’ classroom behavior is an important factor that directly affects their learning performance. Specifically, students with poor self-management abilities, particularly specific developmental disorders, may face educational and academic difficulties owing to physical or psychological factors. Therefore, the intelligent perception and identification of school-aged children’s classroom behaviors are extremely valuable and significant. The traditional method for identifying students’ classroom behavior relies on statistical surveys conducted by teachers, which incurs problems such as being time-consuming, labor-intensive, privacy-violating, and an inaccurate manual intervention. To address the above-mentioned issues, we constructed a motion sensor-based intelligent system to realize the perception and identification of classroom behavior in the current study. For the acquired sensor signal, we proposed a Voting-Based Dynamic Time Warping algorithm (VB-DTW) in which a voting mechanism is used to compare the similarities between adjacent clips and extract valid action segments. Subsequent experiments have verified that effective signal segments can help improve the accuracy of behavior identification. Furthermore, upon combining with the classroom motion data acquisition system, through the powerful feature extraction ability of the deep learning algorithms, the effectiveness and feasibility are verified from the perspectives of the dimensional signal characteristics and time series separately so as to realize the accurate, non-invasive and intelligent children’s behavior detection. To verify the feasibility of the proposed method, a self-constructed dataset (SCB-13) was collected. Thirteen participants were invited to perform 14 common class behaviors, wearing motion sensors whose data were recorded by a program. In SCB-13, the proposed method achieved 100% identification accuracy. Based on the proposed algorithms, it is possible to provide immediate feedback on students’ classroom performance and help them improve their learning performance while providing an essential reference basis and data support for constructing an intelligent digital education platform.
Differential Serotonergic Modulation of Synaptic Inputs to the Olfactory Cortex

Serotonin (5-hydroxytriptamine, 5-HT) is an important monoaminergic neuromodulator involved in a variety of physiological and pathological functions. It has been implicated in the regulation of sensory functions at various stages of multiple modalities, but its mechanisms and functions in the olfactory system have remained elusive. Combining electrophysiology, optogenetics and pharmacology, here we show that afferent (feed-forward) pathway-evoked synaptic responses are boosted, whereas feedback responses are suppressed by presynaptic 5-HT1B receptors in the anterior piriform cortex (aPC) in vitro. Blocking 5-HT1B receptors also reduces the suppressive effects of serotonergic photostimulation of baseline firing in vivo. We suggest that by regulating the relative weights of synaptic inputs to aPC, 5-HT finely tunes sensory inputs in the olfactory cortex.
ArtiSynth designing a modular 3D articulatory speech synthesizer

ArtiSynth is a modular, component‐based system for performing dynamic 3D simulations of the human vocal tract and face. It provides a test bed for research in areas such as speech synthesis, linguistics, medicine, and dentistry. ArtiSynths framework enables researchers to construct, refine, and exchange models of all parts of the vocal tract and surrounding structures. ArtiSynth introduces a probe concept to unify input and output data flow, which allows control of and access to models with time varying data series. ArtiSynth supports interconnected heterogeneous models, such as rigid body, mass‐spring, and parametric, using a point‐set connection method, called markers, for constraint satisfaction. Using ArtiSynth, we created a muscle‐driven rigid body jaw model, a parametric principle component tongue model from MRI images, a parametric lip model, and mass‐spring face tissue model. We combined them in various ways. Data from medical imaging (MRI, CT, and ultrasound) and other technologies such as optica.
Reduction in Pollutants Emissions From Domestic Boilers: Computational Fluid Dynamics Study

This study is concerned with building a computational fluid dynamics (CFD) model to simulate the combustion process occurring in the combustion chamber of some domestic boilers. The burner used in this boiler is a conventional cylindrical premix burner with small inlet holes on its surface. A two-dimensional CFD model is built to simulate the combustion chamber domain, and the partially premixed combustion model with a post-processor for NO x calculations is used to simulate the combustion process inside the combustion chamber. A complete description of the formation characteristics of NO x produced from the boiler is discussed in detail. A comparison between the CFD numerical results and the experimental measurements at different boiler loads is performed in order to validate the numerical model and investigate the accuracy of the CFD model. The validated CFD model is used to investigate the effect of different boundaries temperatures and the mixture inlet velocity on the flue gas average temperature, residence time, and hence the CO and NO x concentrations produced from the combustion chamber. The concept of changing the mixture inlet velocity is found to be an effective method to improve the design of the burner in order to reduce the pollutant emissions produced from the boiler with no effect on the boiler efficiency.
Landslide Hazard Assessment Method Considering the Deformation Factor: A Case Study of Zhouqu, Gansu Province, Northwest China

Landslides are geological disasters that can cause great damage to natural and social environments. Landslide hazard assessments are crucial for disaster prevention and mitigation. Conventional regional landslide hazard assessment results are static and do not take into account the dynamic changes in landslides; thus, areas with landslides that have been treated and stabilized are often still identified as high-risk areas. Therefore, a new hazard assessment method is proposed in this paper that combines the deformation rate results obtained by interferometric synthetic aperture radar (InSAR) with the results of conventional hazard assessments to obtain the hazard assessment level while considering the deformation factor of the study area, with Zhouqu, Gansu Province, selected as the case study. First, to obtain the latest landslide inventory map of Zhouqu, the hazard assessment results of the study area were obtained based on a neural network and statistical analysis, and an innovative combination of the deformation rate results of the steepest slope direction from the ascending and descending data were obtained by InSAR technology. Finally, the hazard assessment level considering the deformation factor of Zhouqu was obtained. The method proposed in this paper allows for a near-term hazard assessment of the study area, which in turn enables dynamic regional landslide hazard assessments and improves the efficiency of authorities when conducting high-risk-area identification and management.
The novel compact microwave SIW filter based on L-ridged rectangular waveguide

The problem of electrodynamic analysis and synthesis of band-pass microwave filter based on L-ridged rectangular waveguide is solved. The possibilities of multilevel substrate integrated waveguide (SIW) filter structure implementation for ridged rectangular waveguides are discussed. The compact SIW filter with 4% pass-band and a stop-band attenuation level −60 dB is described.
Integrate satellite observing images for the Gulf of Mexico

A number of satellite image sources providing meteorological and environmental information are available over the Gulf of Mexico (GOM). Those satellite images are taken periodically in order to observe weather changes. However, users usually face problems when they want to search, retrieve and combine images from multiple data sources. Without an adequate system and skilled personnel for managing data, the magnitude of the effort needed to deal with such large and complex data sets can be a substantial barrier to the GOM research community to track hurricanes and protect ecosystem. As a result, a mediator-based data integration system is proposed and developed to retrieve and integrate the images of satellite observational systems for the GOM. In addition, a visual segmentation-based data record extraction method is adapted and implemented to extract images from the retrieved Web pages.
Perspectives of the Saudi medical students toward the impact of lockdowns on their physical activity level and lifestyle during the COVID-19 pandemic

Background Physical activity (PA) level is affected by various factors in university students. Due to the pandemic of COVID-19, the Saudi Ministry of Education announced the closure of schools and universities as a preventive measure. This cross-sectional study aimed to evaluate the impact of the COVID-19 pandemic lockdown on PA levels and other lifestyle aspects among the Saudi medical students and to explore their perspectives toward it. Methods Three hundred ninety-six medical students have completed the survey, which consisted of three sections: (1) participant’s demographic data; (2) five statements about the PA and the lockdown; and (3) the International PA Questionnaire—Short Form (IPAQ-SF). Results About 63.9% of the participants were female, and 60.4% were of normal weight. Approximately 80% of participants were classified as inactive. Regarding the perspective of the students, 52.8% reported that they were not exercising regularly in the gym before the lockdown, and 46.9% reported that the lockdown and transition to online learning affected their compliance with exercise. During the lockdown and shift to online learning, the majority of participants experienced decreased PA levels, 42.4% experienced weight gain, and 53.6% reported a negative impact on their psychological status. Conclusion Generally, the results of this study showed that the COVID-19 had a negative influence on the PA level, as well as other lifestyle aspects (e.g., gaining weight) and psychological status of medical students. This study highlights aspects where universities and institutions delivering medical education can use resources to improve students’ well-being during pandemics.
Dielectric Properties of Polymer Nanocomposite Interphases Using Electrostatic Force Microscopy and Machine Learning

Knowing the dielectric properties of the interfacial region in polymer nanocomposites is critical to predicting and controlling dielectric properties. They are, however, difficult to characterize due to their nanoscale dimensions. Electrostatic force microscopy (EFM) provides a pathway to local dielectric property measurements, but extracting local dielectric permittivity in complex interphase geometries from EFM measurements remains a challenge. This paper demonstrates a combined EFM and machine learning (ML) approach to measuring interfacial permittivity in 50 nm silica particles in a PMMA matrix. We show that ML models trained to finite-element simulations of the electric field profile between the EFM tip and nanocomposite surface can accurately determine the interface permittivity of functionalized nanoparticles. It was found that for the particles with a polyaniline brush layer, the interfacial region was detectable (extrinsic interface). For bare silica particles, the intrinsic interface was detectable only in terms of having a slightly higher or lower permittivity. This approach fully accounts for the complex interplay of filler, matrix, and interface permittivity on the force gradients measured in EFM that are missed by previous semianalytic approaches, providing a pathway to quantify and design nanoscale interface dielectric properties in nanodielectric materials.
Prediction of structure-borne sound transmission in large welded ship structures using statistical energy analysis

Abstract An efficient method is presented for the prediction of structure-borne sound transmission in large welded ship structures. SEA (Statistical Energy Analysis) is used, and the equations used for the SEA parameters are also presented. Traditionally, the SEA method requires a great deal of work when steel structures are modelled. It is almost impossible to prepare models manually for large structures such as ships. In the method developed, the preprocessing programs used in the context of the finite element method (FEM) are applied to reduce the modelling work. To date, one-dimensional beam, two-dimensional triangular and quadrilateral plate, and three-dimensional volume elements have been implemented. The assemblage of the loss factor matrix is made in a manner analogous to the stiffness matrix in FEM. In the computer implementation of the SEA program, standard FEM techniques are used to reduce calculation time, including the skyline matrix technique andLDLT-matrix decomposition of the loss factor matrix. The effectiveness of the present method is illustrated by the computer run of the model of the passenger cruise vessel, which contained over 5000 elements and 17 000 coupling branches. It took only 17 min in the Iris Indigo R4000 workstation. Application calculations for an echo sweeping vessel, a timber-container carrier, and a passenger cruise vessel are discussed, and comparison is made with full scale measurements.
Biogenic Selenium Nanoparticles in Biomedical Sciences: Properties, Current Trends, Novel Opportunities and Emerging Challenges in Theranostic Nanomedicine

Selenium is an important dietary supplement and an essential trace element incorporated into selenoproteins with growth-modulating properties and cytotoxic mechanisms of action. However, different compounds of selenium usually possess a narrow nutritional or therapeutic window with a low degree of absorption and delicate safety margins, depending on the dose and the chemical form in which they are provided to the organism. Hence, selenium nanoparticles (SeNPs) are emerging as a novel therapeutic and diagnostic platform with decreased toxicity and the capacity to enhance the biological properties of Se-based compounds. Consistent with the exciting possibilities offered by nanotechnology in the diagnosis, treatment, and prevention of diseases, SeNPs are useful tools in current biomedical research with exceptional benefits as potential therapeutics, with enhanced bioavailability, improved targeting, and effectiveness against oxidative stress and inflammation-mediated disorders. In view of the need for developing eco-friendly, inexpensive, simple, and high-throughput biomedical agents that can also ally with theranostic purposes and exhibit negligible side effects, biogenic SeNPs are receiving special attention. The present manuscript aims to be a reference in its kind by providing the readership with a thorough and comprehensive review that emphasizes the current, yet expanding, possibilities offered by biogenic SeNPs in the biomedical field and the promise they hold among selenium-derived products to, eventually, elicit future developments. First, the present review recalls the physiological importance of selenium as an oligo-element and introduces the unique biological, physicochemical, optoelectronic, and catalytic properties of Se nanomaterials. Then, it addresses the significance of nanosizing on pharmacological activity (pharmacokinetics and pharmacodynamics) and cellular interactions of SeNPs. Importantly, it discusses in detail the role of biosynthesized SeNPs as innovative theranostic agents for personalized nanomedicine-based therapies. Finally, this review explores the role of biogenic SeNPs in the ongoing context of the SARS-CoV-2 pandemic and presents key prospects in translational nanomedicine.
Assessing Chaos in Time Series: Statistical Aspects and Perspectives

Chaos theory offers to time series analysis new perspectives as well as concepts and ideas that have a through contribution to statistics. On the other hand, statistical methodology has shown to play a crucial role for the comprehension of nonlinear and chaotic phenomena. One peculiar feature of chaotic systems is sensitivity to initial conditions, which is responsible of the unpredictability we experience in such phenomena. One of the most popular quantity that measures this property is the maximum Lyapunov characteristic exponent (MLCE). In this paper we discuss from a statistical perspective the problems arising in estimating both the MLCE and its generalizations in time series, issues that have recently deserved attention in the community of time series analysts. We also present a method based on resampling in order to assign confidence interval to the estimates of the MLCE. It is shown that in addition to answering the question of the presence of chaos, these methods give relevant contributions to the characterization of many other aspects of nonlinear time series.
Commercial sexual trafficking of males: A pilot training with law enforcement

ABSTRACT Law enforcement agents are often the first professionals to come into contact with victims of commercial sexual trafficking (CST). While they likely have encountered female victims of CST, they may need increased understanding of the identification of male victims. This study evaluated the effectiveness of a training program aimed to increase awareness of male victims of CST among law enforcement professionals. Participants’ (N = 132) knowledge level was examined prior to the training and immediately thereafter and results showed that pretest knowledge scores were low. The majority of participants (77%) did not have prior training in CST in males and there was a significant gain in knowledge from pre to posttest. None of the participants’ demographic variables were significant predictors of pretest or posttest scores. Implications for training law enforcement in male CST are provided.
Theory of sonogram characterization of optical pulses

We derive a formula for retrieving the amplitude and phase of an optical pulse from its sonogram. When the transfer function of the frequency filter is known, the pulse amplitude and phase are completely retrieved from the sonogram without iterative calculations by using the derived formula. Based on this formula, we find that the width of a sampling pulse, which is used for cross-correlation measurement of the sonogram, must be much shorter than the width of the pulse under test, and that it is not necessarily possible to reconstruct the pulse when we use the pulse under test itself as a sampling pulse. Finally, we discuss possible schemes for sonogram characterization of optical pulses.
Signature scheme based on composite discrete logarithm

In this paper, we proposed a digital signature scheme based on composite discrete logarithm. We show that the proposed scheme is secure against existential forgery under adaptive chosen-message attack relative to the hardness of discrete logarithm. Furthermore, we show that the proposed scheme is not only computationally efficient but is also more efficient than the other schemes. In signature generation, the proposed signature scheme is 13.5 times faster than RSA [R.L. Rivest et al., 1978], 3 times faster than DSA [A, Odlyzko, 1995], at least 1.5 times faster than Pointcheval scheme [G. Poupard et al., 1998] and Poupard-Stern scheme [T. Okamoto et al., 1998] respectively. In signature verification, the proposed signature scheme is 6 times faster than Poupard-Stern scheme and has the same verification time as that of RSA and Pointcheval scheme.
Evaluation of the Efficacy of Antioxidant Extract from Lemon By-Products on Preservation of Quality Attributes of Minimally Processed Radish (Raphanus sativus L.)

The aim of this work was to enhance the use of a food-grade antioxidant extract obtained from lemon processing byproducts (peel, pulp and seeds) to extend the shelf life of minimally processed radishes. The extract (LPE) was previously characterized in terms of total phenolic (6.75 ± 0.34 mg GAE g−1 d.w.) and flavonoid content (2.04 ± 0.09 mg CE g−1 d.w.) and antioxidant activity, and eriocitrin and hesperidin were identified as the most prevalent phenolic compounds by a UHPLC system. The effects of different dipping aqueous solutions (UCR, DRa, DRb) and alginate-based edible coating formulations (CRc, CRd) with and without the antioxidant extract were studied on the quality parameters of minimally processed radishes, characterized regarding their microbiological and physicochemical characteristics for up to 14 days at 3 °C. The coating formulated with LPE delayed the radish respiration process, as well as resulting in less color variation (ΔE < 3) and reduced mesophilic aerobic count values (4.49 ± 1.43 log CFU g−1), proving the effectiveness of LPE as a value-added ingredient in developing post-harvest strategies to prolong the shelf life of minimally processed vegetables. Indeed, coated samples without the extract showed a clear development of rotting, which led to the end of their shelf life on their 7th day of storage.
The Discriminative Discrete Basis Problem: Definitions, Algorithms, Benchmarking, and Application to Brain's Functional Dynamics

In this paper, considering two Boolean matrices, we tackle the problem of finding a set of Boolean basis vectors that capture the latent components most discriminative of one matrix from another. We refer to this problem as the discriminative discrete basis problem (DDBP). Depending on the symmetry/asymmetry of the problem about the two input matrices, different classes of the DDBP are defined. We present an ensemble of new algorithms, called the discriminative-associative (DASSO) algorithms, to find solutions for these classes of the DDBP. Furthermore, we design a comprehensive, simulation-based benchmarking framework for evaluating the performance of such algorithmic solutions to the DDBP over a wide range of variables, including the effects of disturbances, noise, the geometric features of the latent components within each matrix, and the relationships among these components across the contrasted matrices. As an example application, we demonstrate the utility of the DASSO algorithms in studying the dynamics of the brain function by identifying the cortical activities that discriminate motor from non-motor tasks using recordings obtained via electroencephalography (EEG). The results highlight the location of such discriminating activities in the cortex and the time of their occurrence, which can lead to the design of efficient and accurate brain computer interfaces (BCIs). To the best of our knowledge, this is the first time the DDBP is recognized, defined, and addressed through the development of algorithmic solutions. The DASSO algorithms can be utilized in a variety of data mining and feature selection applications, in which finding discriminating structures across two datasets is of interest.