Miracle-dz/UniFAR-FTUs huggingface dataset:
New directions and challenging computations in fluid dynamics modeling with stabilized and multiscale methods

In this paper, we provide a brief overview of the development of stabilized and multiscale methods in fluid dynamics. We mainly focus on recent developments and new directions in the variational multiscale (VMS) methods. We also discuss applications of the VMS techniques to fluid dynamics problems involving computational challenges associated with high-Reynolds-number flows, wall-bounded turbulent flows, flows with moving domains including subdomains in relative motion, and free-surface flows.
Deep Neural Networks based Detection and Analysis of Fake Tweets

Social networking is a wedge for interchanging thoughts, individual perspectives and views but without adversely affecting the sentimental, religious, or maybe private thoughts of the group. Furthermore, the spread of fake news flashes continues to be a pattern on social networking. This paper discusses a hybrid method used towards the control over this kind of ill intentions by developing a method that analysis and also detects posts, toxic comments, or fake news inside any multimedia or text format. This particular paper will help to identify the precision of fake news utilizing Deep Neural Networks. News articles extracted from Twitter is represented as embedding vectors. The hybridized Convolutional Neural Network and LSTM (Long-Short Term Memory) framework is built to learn the contextual dependencies between the words present in news articles. We compare many methods for detecting fake news. The Natural Language Interference (NLI) designs may also be qualified. The information compilation, interpretation, and then tests procedure are clarified completely along with existing different study analyses in the identity of linguistic variants to come down with truthful and false information. Next, we check as well as train a pair of learning breakthroughs to produce exact fake information detectors.
Co-Designing Digital Technologies for Improving Clinical Care in People with Parkinson’s Disease: What Did We Learn?

The healthcare model is shifting towards integrated care approaches. This new model requires patients to be more closely involved. The iCARE-PD project aims to address this need by developing a technology-enabled, home-based, and community-centered integrated care paradigm. A central part of this project is the codesign process of the model of care, exemplified by the active participation of patients in the design and iterative evaluation of three sensor-based technological solutions. We proposed a codesign methodology used for testing the usability and acceptability of these digital technologies and present initial results for one of them, MooVeo. Our results show the usefulness of this approach in testing the usability and acceptability as well as the opportunity to incorporate patients’ feedback into the development. This initiative will hopefully help other groups incorporate a similar codesign approach and develop tools that are well adapted to patients’ and care teams’ needs.
ESRTMDet: An End-to-End Super-Resolution Enhanced Real-Time Rotated Object Detector for Degraded Aerial Images

The degradation of image resolution reduces the detection performance in aerial imagery because it generates a large number of small objects, and accurately detecting these small objects remains a challenge. Existing methods mostly use a superresolution (SR) model to first obtain the SR image of the low-resolution degraded image (<inline-formula><tex-math notation="LaTeX">$I^{\text{LR}}$</tex-math></inline-formula>) and then use this image as the input of the object detection (OD) network to solve this problem. However, this architecture that involves executing a complex SR network before the detector is time-consuming and makes it hard to achieve real-time model inference. To address this challenge, we propose a simple and effective rotated small OD method, named end-to-end superresolution enhanced real-time rotated object detector (ESRTMDet). First, we design a lightweight embedded feature map superresolution module (ESRM) embedded in the detection model to enhance and amplify the backbone output features, making the detection heads detect small objects more easily. Furthermore, we train a parallel SR network branch (PSRB) simultaneously that uses the backbone feature to restore a high-resolution image. Through our proposed feature alignment loss and feature affinity layer, our PSRB effectively guides the feature map enhancement of ESRM. Finally, through end-to-end joint optimization of the detector and PSRB, the detection performance on <inline-formula><tex-math notation="LaTeX">$I^{\text{LR}}$</tex-math></inline-formula> is significantly improved. Extensive experiments over DOTA and UCAS-AOD demonstrate that our method can achieve state-of-the-art results. In addition, we discard our PSRB and use <inline-formula><tex-math notation="LaTeX">$I^{\text{LR}}$</tex-math></inline-formula> as the input during inference, reducing the inference time-consuming of our model. Therefore, our ESRTMDet-X not only achieves 77.11% mean of average precision on the degraded DOTA dataset, but also achieves an amazing inference speed of 337 FPS, thus obtaining the best speed–accuracy tradeoff.
States and temporal interpretation in Capeverdean

One known puzzle in Creole systems is that temporal interpretation seems to be constrained by stativity (Bickerton 1974). For decades, the relevant division has been, roughly: bare stative verbs mean present, bare nonstatives mean past. In Capeverdean, a Portuguese-based Creole, we do indeed have: N sabe risposta “I know the answer”, N kume pexe “I ate fish”. The above generalization, however, is inaccurate: most Capeverdean statives pattern with nonstatives in this respect. Crucially, also sabe “know” may pattern with nonstatives, challenging further this traditional view. In this paper I argue that the distinct temporal readings above can only be explained via the internal structure of events. A Become subevent (Dowty 1979) accounts for N sabe risposta – “I got to know the answer”, with its consequent state (Moens & Steedman 1988) being “[now] I know. ” In contrast, there is no consequent state as “I eat fish” for “I ate fish” (cf. “I’ve eaten.”).
Dispersive pressure, boundary jerk and configurational changes in debris flows

The granular rock material within a debris flow experiences jerk (change in acceleration) as it runs over a rough basal bed or collides with sidewalls. This creates a pressure – the so-called dispersive pressure – which acts to change the configuration of the granular mass and therefore the frictional relationship of the debris flow with the basal boundary. Normal pressures are no longer hydrostatic and pressure fluctuations are created in the fluid phase. In this paper we formulate relationships between internal shear work, free mechanical energy, dispersive pressure and configurational changes within a debris flow. We associate the potential energy of the debris flow configuration with dilatant kinematic motions and show why it is necessary to integrate the shear work over time to calculate boundary jerks which cannot be represented by closed-form, analytical pressure functions. The effect of the dispersive pressure is mediated by the presence of the viscous muddy fluid which consists of two types: a) the free fluid and b) the bonded fluid attached to the solid granular phase.
A Novel Two-Dimensional TiClO as a High-Performance Anode Material for Mg-Ion Batteries: A First-Principles Study

Searching for efficient electrode materials with excellent electrochemical performance is of great significance to the development of magnesium-ion batteries (MIBs). Two-dimensional Ti-based materials are appealing for use in MIBs due to their high cycling capability. On the basis of density functional theory (DFT) calculations, we comprehensively investigate a novel two-dimensional Ti-based material, namely, TiClO monolayer, as a promising anode for MIBs. Monolayer TiClO can be exfoliated from its experimentally known bulk crystal with a moderate cleavage energy of 1.13 J/m2. It exhibits intrinsically metallic properties with good energetical, dynamical, mechanical, and thermal stabilities. Remarkably, TiClO monolayer possesses an ultra-high storage capacity (1079 mA h g−1), a low energy barrier (0.41–0.68 eV), and a suitable average open-circuit voltage (0.96 V). The lattice expansion for the TiClO monolayer is slight (<4.3%) during the Mg-ion intercalation. Moreover, bilayer and trilayer TiClO can considerably enhance the Mg binding strength and maintain the quasi-one-dimensional diffusion feature compared with monolayer TiClO. All these properties indicate that TiClO monolayers can be utilized as high-performance anodes for MIBs.
Reflection and Transmission coefficients for SH wave in Plane Wave Domain

The calculation of reflection and transmission coefficients at any interface is an important problem of elastodynamic theory. Historically, reflec tion and transmission coefficients have been obtained in several domains according to their importance. It is known that for isotropic case reflection and transmission coefficients dep end on the acoustic impedance contrast and angle of incidence. The angle of incidence can be computed by using the cross product of two known unit normals. In this approach, derived reflection and transmission coefficients will be in plane wave domain. By derivin g reflection and transmission coefficients in plane wave domain for 3D media, determinatio n of dip and azimuth of interface are avoided, and, thereby, we avoid ray tracing ray tracing and exposure to caustics especially in anisotropic media. The problem that I solve, in this regard, is the problem of the special case of dipping interface and how to rotate the plane wave coordinate system from that determined by the computational grid, and the system determined by a dipping interface. Classical reflection and transmission coefficie nts in plane wave coordinates are worked out for reflectors aligned with the computational gri d. For non-aligned reflectors, those with dip and azimuth, computation of effective reflect ion and transmission coefficients is not straight forward, for this the coordinate syst em must be rotated. To do this, a normal for each individual plane wave based on local velocity and vector cross product of this normal with the normal to reflector are computed. T his cross product yields a ray parameter that presently is used to compute corresponding reflection and transmission coefficients for a given plane wave. The importance of this ap proach is the automatic adaptation of the reflection and transmission coefficients expre ssion to a special case of dipping interface. These coefficients can then be used to scale the am plitude component of plane wave extrapolation across a reflector as is done in seismic fo rward modeling. Another importance of reflection and transmission coefficients in plan e wave domain, is their use in Rayleigh Sommerfeld Modeling(RSM) of seismic data. In line traces and cross line traces are required in order to model the plane wave inputs. Presently, the problem associated with data acquisition is studied here by changing the number of cross line traces.
Hematological Changes in Gas Station Workers

(1) Background: Benzene, toluene, and xylene isomers (BTX) are present in gasoline. Exposure to benzene may lead to the appearance of a series of signs, symptoms, and complications, which are characterized by benzene poisoning, which is an occupational disease. This study evaluated the presence of signs and symptoms related to occupational exposure and whether occupational exposure to BTX is associated with the development of hematological changes. (2) Material and Methods: This cross-sectional epidemiological study included 542 participants, in which 324 were gas station workers (GSWs) and 218 were office workers (OWs) with no occupational exposure to benzene. To characterize the type of exposure (exposed and not exposed), trans,trans-Muconic acid (tt-MA), Hippuric acid (HA), and Methylhippuric acid (MHA) were used as exposure biomarkers. The tt-MA analysis revealed that the GSWs had 0.29 mg/g of urinary creatinine and the OWs had 0.13 mg/g of urinary creatinine. For HA, the GSWs presented 0.49 g/g of creatinine while the OWs presented 0.07. MHA analysis revealed that the GSWs had 1.57 g/g creatinine and the OWs had 0.01 g/g creatinine. Occupation habits and clinical symptoms were collected by questionnaire and blood samples were analyzed for hematological parameters. The persistence of hematological changes was evaluated with three serial blood collections every 15 days followed by laboratory hematological analysis. A descriptive analysis by the Chi-square test method was performed to evaluate the association between occupational exposure to fuels and the occurrence of changes in hematological parameters. (3) Results: In the GSWs, the most described signs and symptoms were somnolence (45.1%), headache (38.3%), dizziness (27.5%), tingling (25.4%), and involuntary movement (25%). Twenty GSWs that presented hematological alterations performed serial collections fifteen days apart. In addition, these workers presented total leukocyte counts above the upper limit and lymphocyte counts close to the lower limit. Leukocytosis and lymphopenia are hematological alterations present in chronic benzene poisoning. (4) Conclusions: The results found an initial change in different hematological parameters routinely used in clinics to evaluate health conditions. These findings reveal the importance of valuing clinical changes, even in the absence of disease, during the health monitoring of gas station workers and other groups that share the same space.
Improvement of Heat Dissipation Structure of Low Speed Permanent-Magnet Motor

A 160kW and 60r/min low speed and high torque permanent-magnet synchronous motor is studied. According to fluid mechanics and heat transfer theories, a motor temperature field calculation model is established. The motor fluid field and temperature field are coupling solved by the finite volume method, and the steady-state temperature distribution of the motor is obtained. In addition, the cooling wind thorn is added on the end face of rotor core in order to solve the high temperature rise problem of permanent magnet. The influences of the geometric parameters of the cooling wind thorn on the fluid field and temperature field are analyzed by simulation, and the optimum size of the wind thorn is obtained for the best thermal performance. As the highest temperature rise is located at the end of the stator winding, the winding temperature rise is reduced by sealing the thermal conductive adhesive between the motor shell and the end of the winding. The influences of different sealing solutions and thermal-conductivity adhesives on motor heat transfer are also simulated.
Comparison of the Efficacy of Diluted Polyethylene Glycol and Low-Density (0.1% w/v) Barium Sulfate Suspension for CT Enterography

Purpose To compare small bowel distension and side effects between a diluted polyethylene glycol (PEG) solution and a low-density (0.1% w/v) barium sulfate suspension (LDBSS) for CT enterography (CTE) preparation. Materials and Methods Total 173 consecutive patients who underwent CTE were enrolled in this study. The LDBSS (1 L) was used in 50 patients, and the diluted iso-osmotic PEG solution (1 L) was used in 123 patients. Two blinded radiologists independently scored jejunal and ileal distensions on a 5-point scale. To compare side effects between the two groups, the patients reported whether they had immediate complications after the administration of the oral contrast media. Results For ileal and jejunal distension, the diluted PEG solution showed no difference from the LDBSS for either reader (ileum: reader 1, median, 4; 4, interquartile range, 3–4; 3–4, p = 0.997; reader 2, median, 4; 4, interquartile range, 3.3–4.0; 3–4, p = 0.064; jejunum: reader 1, median, 2; 2, interquartile range, 2–3; 2–3, p = 0.560; reader 2, median, 3; 2, interquartile range, 2–3; 2–3, p = 0.192). None of the patients complained of immediate complications following administration of either of the oral contrast media. Conclusion The diluted PEG solution showed comparable bowel distension compared to LDBSS and no immediate side effects; thus, it can be a useful alternative.
Size Dependent Vibration Behavior of an AFM with Sidewall and Top-Surface Probes Based on the Strain Gradient Elasticity Theory

In this paper, the size-dependent vibration behavior of an atomic force microscope with assembled cantilever probe (ACP) is analyzed utilizing the modified strain gradient elasticity theory. The proposed ACP comprises a horizontal cantilever, a vertical extension and two tips located at the free ends of the cantilever and extension. Because the vertical extension is located between the clamped and free ends of the microcantilever, the cantilever is modeled as two beams. The results of the current model are compared to those evaluated by both modified couple stress and classical beam theories. The results indicate that the resonant frequency and sensitivity of the proposed ACP is strongly size-dependent especially when the contact stiffness is very low or it is very high. The results also declare that utilizing the strain gradient theory is essential in the analysis of the vibration behavior of the proposed ACP.
Comprehensive Survey of Sensor Data Verification in Internet of Things

The Internet of Things (IoT) has been a critical and emerging technology platform enabling the development and deployment of smart devices to solve real-world challenges and issues. In addition, IoT applications have gathered various and numerous data from various sources, such as sensor data from IoT devices, system data, status information of IoT devices, and others. However, the quality of sensor data should be thoroughly verified to exploit the benefits of sensor data fully. Due to the importance of sensor data quality, we comprehensively provide a survey on the verification of IoT sensor data in this paper. Thus, we conduct a survey of sensor data verification in IoT regarding six aspects. We focus on: 1) anomaly classification; 2) sensor data verification frameworks; 3) sensor data verification methods (including anomaly detection and anomaly correction); 4) evaluation methods to assess sensor data verification methods; 5) technology and tools to build sensor data verification systems; and 6) challenges and future research directions. In addition, we identify advantages and disadvantages of the methods in anomaly detection and correction. Hence, this survey provides a comprehensive and better understanding of sensor data verification in IoT, building background knowledge in related topics.
Complex-Domain Pitch Estimation Algorithm for Narrowband Speech Signals

We propose a complex-domain pitch estimation algorithm for narrowband speech signals, which utilizes a complex spectrum containing both amplitude and phase spectrum information. Traditional frequency-domain pitch estimation algorithms assume that a speech signal has a harmonic structure; they estimate a pitch by calculating the distance between the adjacent peaks of the amplitude spectrum corresponding to harmonics. However, only a few peaks can be detected from narrowband speech signals because of their limited bandwidth, resulting in pitch estimation errors. In this article, phase differences between harmonics are utilized as an additional cue for pitch estimation. The phase difference between harmonics refers to the two-step phase difference between successive analysis frames and between the lowest-order harmonic and other harmonics, which is theoretically derived using the pitch. When the phase spectrum for the higher-order harmonic is shifted by the theoretical value, it agrees with the phase spectrum for the lowest-order harmonic. Therefore, for each pitch candidate, the proposed method calculates the shifted phase spectra and combines them with the amplitude spectrum to generate complex spectra. When a pitch candidate is correct, the cumulative sum of the complex spectra is added in the same direction, which emphasizes harmonics even for narrowband speech signals. Results of the objective evaluation show that the proposed method accurately estimates the pitch from narrowband speech signals.
JudgeNet: Inverse Attention and Multiscale Fusion Networks With Boundary Determination in Polyp Segmentation

Colonoscopy is one of the most effective means of detecting intestinal polyps and colon cancer. With the development of machine vision, doctors often use automation to help diagnose intestinal polyps when using colonoscopy. The high rate of missed polyps during traditional intestinal polyp detection can lead to an increased recurrence rate and cancer of polyps. Therefore, we propose a multi-scale fusion network with boundary judgment function. The network can accurately segment smaller polyps and improve the blurred boundaries of segmented images. The network achieves the purpose of deepening the boundary information by using the attention mechanism by judging the global information generated by the encoder at different levels and the prediction mask map obtained by multi-scale fusion. Experimental results show that the network can accurately identify the boundaries of diminutive polyps and improve the overall accuracy of polyp segmentation. Compared with the latest network models, we achieved first place in several metrics in the mainstream datasets(e.t., Kvasir, CVC-ClinicDB, CVC-300, and ETIS LaribPolypDB). Codes available: https://github.com/Wdy1997/J–Net.git
Implementation of Multi-modal Speech Emotion Recognition Using Text Data and Audio Signals

Speech emotion detection is a difficult task, and models that employ audio characteristics in developing high-performing classifiers have received much attention in recent times. The effectiveness of emotional features extracted from speech has a significant impact on Speech Emotion Recognition's (SER) performance. On the other hand, most emotional features are sensitive to non-emotional factors such as the speaker, speaking styles, and environment. A deep dual recurrent encoder model is developed in this research. It sequentially uses text data and audio signals to comprehend speech data better and categorise it using various labels such as happy, sad, neutral, and angry. While emotional interaction comprises sound and speech, this model uses dual Recurrent Neural Networks (RNNs) to encode information from audio and text sequences and then integrate it to predict the emotion class. This architecture analyses audio signals from the signal to the language level, allowing it to use the data's information than models that only focus on audio characteristics. The model is further presented via a web application that recognises emotion from recorded spoken utterances. Cascading Style Sheets (CSS) and Hypertext Markup Language (HTML) are used to create the front-end components. On the other hand, the backend components are built with Django, SQLite as database, and Docker containers. TensorFlow, an open-source software framework for machine learning and deep neural networks were also used to create the model. Categorising utterances into one of four emotion categories, the model used on the USC-IEMOCAP dataset had an accuracy of 68.26%. This model has increasingly broadened its applications because it is an essential component of optimal human-computer interactions, such as dialogue systems.