Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Modeling of the Support System for Vehicle-Mounted Spherical Magnetic Suspension Flywheel Battery

In this article, the dynamic model of the support system for vehicle-mounted spherical magnetic suspension flywheel battery (SMSFB) is established and analyzed. Firstly, a seven degree-of-freedom (DOF) automotive model is established. Based on this, the effects on the SMSFB support system caused by the vehicle start-up acceleration, braking deceleration as well as the uneven road caused vibrations are analyzed. Then, the detailed expression of the radial suspension force generated by the suspension control current is derived. Finally, the dynamic model of the SMSFB support system is built and analyzed by the co-simulation of ADAMS and MATLAB. The simulation results show that the SMSFB support system provides a fast rotor suspension performance and is capable of maintaining the flywheel rotor stability under complex road conditions and changing vehicle driving conditions.
Sequence Labeling using Conditional Random Fields

The aim of this paper is to get some experience with sequence labeling, specifically, assigning tags or labels to each member in the sequences of utterances in conversations from a corpus. Since nowadays predicting single class label or tag is not adequate. Predicting large number of variables that depends on each other is required. In sequence labeling it is often beneficial to optimize the tags assigned to the sequence as a whole rather than treating each tag decision separately. A machine learning technique termed as Conditional Random Fields, which is designed for sequence labeling will be used in order to take advantage of the surrounding context. Conditional random fields (CRFs), is a scheme for building probabilistic models to divide and tag sequence data. With a given a labeled set of data, baseline set of features will be created and the accuracy of the CRF suite model created using those features will be measured.
IRDC-Net: Lightweight Semantic Segmentation Network Based on Monocular Camera for Mobile Robot Navigation

Computer vision plays a significant role in mobile robot navigation due to the wealth of information extracted from digital images. Mobile robots localize and move to the intended destination based on the captured images. Due to the complexity of the environment, obstacle avoidance still requires a complex sensor system with a high computational efficiency requirement. This study offers a real-time solution to the problem of extracting corridor scenes from a single image using a lightweight semantic segmentation model integrating with the quantization technique to reduce the numerous training parameters and computational costs. The proposed model consists of an FCN as the decoder and MobilenetV2 as the decoder (with multi-scale fusion). This combination allows us to significantly minimize computation time while achieving high precision. Moreover, in this study, we also propose to use the Balance Cross-Entropy loss function to handle diverse datasets, especially those with class imbalances and to integrate a number of techniques, for example, the Adam optimizer and Gaussian filters, to enhance segmentation performance. The results demonstrate that our model can outperform baselines across different datasets. Moreover, when being applied to practical experiments with a real mobile robot, the proposed model’s performance is still consistent, supporting the optimal path planning, allowing the mobile robot to efficiently and effectively avoid the obstacles.
Small perturbations of nonlocal biharmonic problems with variable exponent and competing nonlinearities

— The present paper deals with the analysis of combined e¤ects of an absorption term and a small perturbation of the reaction term in a pðxÞ-biharmonic Kirchho¤ problem with Navier boundary condition. The main result in this work establishes the existence of a continuous spectrum consisting in an interval. The proofs combine variational methods with energy estimates.
Simpler Standard Errors for Multi-Stage Regression-Based Estimators: Illustrations in Health Economics

With a view towards lessening the analytic and computational burden faced by practitioners seeking to correct the standard errors of two-stage estimators, we offer a heretofore unexploited simplification of the conventional formulation for the most commonly encountered cases in empirical application – two-stage estimators that, in either stage, involving maximum likelihood estimation or the nonlinear least squares method. Also with the applied researcher in mind, we cast the discussion in the context of nonlinear regression models involving endogeneity – a sampling problem whose solution often requires two-stage estimation. We detail our simplified standard error formulations for three very useful estimators in applied contexts involving endogeneity in a nonlinear setting (endogenous regressors, endogenous sample selection, and causal effects). The analytics and Stata/Mata code for implementing our simplified formulae are demonstrated with illustrative real-world examples and simulated data.
Ineliminable idealizations, phase transitions, and irreversibility

The dissertation examines two putative explanations from statistical mechanics with the aim of understanding the nature and role of idealizations in those accounts, namely, the Yang-Lee account of phase transitions and the Boltzmannian account of irreversible behavior. Like most explanations in physics, these accounts involve idealizations to some extent. Many idealized explanations hold out the hope that the idealizations can be removed or eliminated with further work. However, the idealizations that occur in the accounts of phase transitions and irreversibility are ineliminable. The only way (in principle) to obtain a description – let alone an explanation – of these phenomena is to invoke various idealizing assumptions. Ineliminably idealized explanations are not well-understood from a philosophical point of view. Indeed, most philosophers of science would probably hold that no idealizations are ineliminable. The dissertation argues that this view is mistaken, showing where and why extant accounts of idealization miss this fact by distinguishing the widely-accepted understanding of idealizations as falsehoods from a novel understanding of idealizations as abstractions. As abstractions, idealizations are devices for ignoring certain details about the real world. The dissertation argues that ineliminable idealizations cannot be falsehoods, and that they should be understood as abstractions.
Positive solutions to a Dirichlet problem with non-Lipschitz nonlinearities

Let \(Omega\) be a bounded smooth domain in \(R^N\). We study the existence of positive solutions to the Dirichlet problem $$\displaylines{ -\Delta u=(1-u)u^{s-1}-\lambda u^{r-1}, \quad\text{in } \Omega,\cr u=0, \quad \text{on } \partial\Omega,}$$ where \(1<;r<\leq 2\), and \(\lambda>0\). In particular, we answer some questions posed in the recent paper [3] where this problem was considered. For more information see https://ejde.math.txstate.edu/Volumes/2021/30/abstr.html
A wideband dual-mode complementary dipole antenna

ABSTRACT A wideband dual-mode complementary dipole antenna is proposed. The antenna is composed of a wideband slotline radiator and an electric dipole. It is excited by a coaxial cable at the center. A pair of slot stubs is used to perturb the second odd-order mode within the slotline radiator, and a dual-resonant wideband radiation characteristic is resulted in. An antenna prototype is then designed, fabricated and measured. The measured results show the antenna has a fractional impedance bandwidth of 40.2% with two resonances. Unidirectional radiation pattern and an average gain of about 7 dBi are satisfactorily achieved within the operating bandwidth as well. The antenna has a simple structure and exhibits a dual-resonant, wideband radiation characteristic.
Research on Finger Pressure Tactile Sensor with Square Hole Structure Based on Fiber Bragg Grating

Aiming at the problems of lateral force interference and non-uniform strain of robot fingers in the process of pressure tactile sensing, a flexible tactile sensor with a square hole structure based on fiber Bragg grating (FBG) is proposed in this paper. Firstly, the optimal embedding depth of the FBG in the sensor matrix model was determined by finite element simulation. Secondly, according to the size of the finger knuckle and the simulation analysis based on the pressure tactile sensor element for the robot finger, the square hole structure was designed, and the overall dimensions of the sensing element and size of the square hole were determined. Thirdly, the FBG was embedded in the polydimethylsiloxane (PDMS) elastic matrix to make a sensor model, and the tactile sensor was fabricated. Finally, the FBG pressure tactile sensing system platform was built by using optical fiber sensing technology, and the experiment of the FBG tactile sensor was completed through the sensing system platform. Experimental results show that the tactile sensor designed in this paper has good repeatability and creep resistance. The sensitivity is 8.85 pm/N, and the resolution is 0.2 N. The loading sensitivity based on the robot finger is 27.3 pm/N, the goodness of fit is 0.996, and the average value of interference in the sensing process is 7.63%, which is lower than the solid structure sensor. These results verify that the sensor can effectively reduce the lateral force interference and solve the problem of non-uniform strain and has high fit with fingers, which has a certain application value for the research of robot pressure tactile intelligent perception.
Fault-tolerant control of flexible air-breathing hypersonic vehicles in linear ODE-beam systems

ABSTRACT This paper addresses the fault-tolerant control issue for a class of flexible air-breathing hypersonic vehicles. Firstly, a longitudinal dynamic model with process faults is established, which contains an ordinary differential equation (ODE) for rigid body, an Euler–Bernoulli beam equation for flexible modes, and a new boundary connection between them; Secondly, a novel fault-tolerant control scheme is proposed to accommodate process faults and suppress vibrations, which relies on the direct Lyapunov method and the bilinear matrix inequalities (BMIs) technique; Thirdly, in order to compute the gain matrices of the fault-tolerant control law, a two-step algorithm is provided to solve the BMI feasibility problem in terms of linear matrix inequality optimisation technique. Finally, the simulation results are provided to illustrate the effectiveness of the theoretical results.
IoT-Enabled Multimodal Biometric Recognition System in Secure Environment

A multimodal biometric recognition system on Internet of Things (IoT) with Blockchain environments has been proposed in this article. This system distributes a decentralized biometric authentication process mechanism and improves security in the IoT environment. The implementation of this system consists of five components: 1) image preprocessing; 2) feature representation; 3) cancelable biometrics; 4) classification; and 5) encryption–decryption of multimodal biometrics templates. A region of interest is segmented from each biometric trait during image preprocessing. Then, a discriminant feature extraction technique has been employed for feature computation. A cancellable biometric system (CBS) is introduced to secure and preserve the original biometric features from external hazards and misuse. The extracted cancelable features undergo classification to perform the subjects’ authentication. Then, a method of encryption–decryption of templates is performed to handle the various online authentication attacks and improve IoT-enabled authentication. Finally, the recognition scores due to iris, periocular, palmprint, and face biometrics are fused to increase the performance of the proposed IoT-enabled multimodal biometric system. The proposed system obtains identification performance 99.92%, 100% for CASIA-V4-distance (CASIA-DIST), UBIRIS-v2 iris, 100% for periocular (CASIA-DIST, UBIRIS-v2), 100% for Bosphorus palmprint, and 100% for FERET face databases using 30-D cancelable features that show the superiority of the proposed system as compared with state-of-the-art methods.
BUDGET ANALYSIS OF TURBULENT KINETIC ENERGY IN CORNER SEPARATION : RANS VS LES

Abstract. The aim of the study is to compare the LES of corner separation, considered as a reference, in the LMFA-NACA65 linear compressor cascade to RANS simulations using Wilcox two-equation models (k − ω). The LES was carried-out by Gao et al. [1] using the Shearimproved Smagorinsky model (SISM), and has been validated by comparison with the experimental data obtained by Ma et al. [2]. Two steady RANS simulations are carried out using k− ω models: Wilcox 1988 model and Wilcox 1988 model with quadratic constitutive relation. First, the analysis will consider the total pressure losses on a two-dimensional section downstream of the cascade. The Reynolds stresses and the turbulent kinetic energy budget are analysed within the separation region, on a two-dimensional section at the trailing edge of the blade. The RANS simulations mostly overestimate the total pressure losses and the size of the corner separation, compared to the LES and the experiment. The RANS simulations also overestimate the level of the Reynolds stresses and the terms of the turbulent kinetic energy budget, compared to the LES. The quadratic constitutive relation yields some modifications, but not significant enough to have results comparable to LES.
Phenol–Furfural Resin/Graphite/Ag-Based Electrically Conductive Adhesive Composites from Waste Bagasse with Enhanced Thermo-Electric Properties

This study describes the preparation and evaluation of phenol–furfural resin (PFR) from bagasse and its nanocomposites for electrically conductive adhesive (ECA) application. PFR was prepared with furfural extracted from bagasse using a modified acid digestion method. Three different formulations of PFR nanocomposites with conductive nanoparticles, i.e., PFR-silver, PFR-graphite, and PFR-silver + graphite, were prepared using 20, 40, and 60 w/w% of fillers via the impregnation method. The resultant products were characterized using FT-IR, SEM, EDS, and XRD spectroscopy. Electrical conductivity was measured using a four-probe technique, while band gap was calculated via Tauc plots. The results exhibited a significant rise in electrical conductivity of insulating virgin PFR from 2.6 × 10−4 Scm−1 to 8.2 × 10−1 Scm−1 with a 40 and 20 w/w% blend of Ag and graphite in PFR. This synergism was exhibited because graphite and Ag NPs supply excellent junctions for building networks. Both tend to coalesce due to van der Waals forces and high surface energies. Therefore, conductive pathway numbers can be increased, and the contact area can be effectively enlarged. This ternary composite exhibited the lowest bandgap energy value, i.e., 3.1 eV. Thermogravimetric temperature values T0 and Tdeg were increased up to 120 °C and 484 °C, respectively, showing a significant increase in thermal stability. Therefore, the resultant nanocomposite material has good potential to be employed as an ECA in the electronic industry.
A Study on the Use of Suggestion Strategies among Yemeni Learners of English

The current study attempts to investigate the use of suggestion strategies among Yemeni learners of English at private universities. The participants of the study were 41 undergraduate students from English Department in the three private universities. The data were collected using a Discourse Completion Test (DCT). They were analyzed according to the models proposed by Martinez Flor. (2005), whose analytical framework classified suggestion strategies into three types: direct, conventionalized forms, and indirect. The results of the current study revealed that Yemeni learners of English at private universities almost tend to use the same types of suggestion strategies in the six situations with slight differences in their percentages. The results showed that there are three types of suggesting strategies used by the students namely; direct suggestion56.5%, conventionalized form 24.4%, and indirect suggestion 19.1%. Moreover, the results indicated that there are no significant statistical differences in the use of suggestion strategies according to the participants of the three universities. However, the results of the study showed that there is a slight difference in favor of Al-Nasser University in situation one and for the National University in situation four.
Computation of Least Angle Regression coefficient profiles and LASSO estimates.

Variable selection plays a signiﬁcant role in statistics. There are many variable selection methods. Forward stagewise regression takes a diﬀerent approach among those. In this thesis Least Angle Regression ( LAR ) is discussed in detail. This approach has similar principles as forward stagewise regression but does not suﬀer from its computational diﬃculties. By using a small artiﬁcial data set and the well-known Longley data set, the LAR algorithm is illustrated in detail and the coeﬃcient proﬁles are obtained. Furthermore a penalized approach to variable reduction called the LASSO is discussed, and it is shown how to compute its coeﬃcient proﬁles eﬃciently using the LAR algorithm with a small modiﬁcation. Finally, a method called K -fold cross validation used to select the constraint parameter for the LASSO is presented and illustrated with the Longley data.
Contourlet Transform with Sparse Representation-Based Integrated Approach for Image Pansharpening

ABSTRACT Pansharpening is an algorithmic approach to achieve high spatial resolution multispectral image. This paper proposes a new pansharpening method that is an integration of contourlet transform with sparse representation (CT-SR). CT is used to perform multiscale decomposition of input images to separate high-frequency and low-frequency subbands. High-frequency subbands are fused based on local energy calculation whereas SR using training dictionary is used to extract salient features to fuse low-frequency subbands. The synthesized multispectral image is obtained by combining fused low- and high-frequency subbands. It is observed that spatial resolution of synthesized multispectral image is improved, but at the same time, the original spectral resolution is also preserved. The detailed comparison between the resultant images of CT-SR and commonly used pansharpening techniques including wavelet transform with sparse representation are carried out at visual assessment level as well as through calculation of various performance indices. It is found that CT-SR performs is improved compared to other algorithms considered in this work.
Performance Analysis of a VM-PM Repair Strategy in MEC-Enabled Wireless Systems With Bursty Traffic

With the rapid development of wireless communication technology, Internet of Things (IoT) is more widely used in diverse application domains, hence bringing massive timely and bursty data flows. Mobile edge computing (MEC) enables computation and energy constrained IoT devices to offload tasks on powerful servers deployed at the edge of networks. Despite considerable advancements and merits, infrastructures in MEC systems are still lack of robustness. In order to better guarantee the response performance and effectively improve the dependability of the MEC-enabled wireless system, we propose a VM-PM repair strategy with PM preventive maintenance. By introducing Markovian arrival process (MAP) to capture bursty traffic, we model the local computing and edge computing as a MAP/M/1 queue and a MAP/M/$\bm N$ queue, respectively. Then, by using matrix-geometric solution, we obtain several key performance indexes, including task response latency, throughput, energy consumption level, and computation resource availability. Next, we carry out system experiments to explore the change trends of the system performance. Finally, we jointly optimize the key performance indexes by formulating a multi-objective mixed-integer optimization (MIO) problem. In addition, we develop a sine cosine based task scheduling and server maintenance (SC-TSSM) algorithm to obtain the optimal offload rate and preventive maintenance threshold.
Machine Learning Model Application and Comparison in Actuated Traffic Signal Forecasting

Traffic signal forecasting plays a significant role in intelligent traffic systems since it can predict upcoming traffic signal without using traditional radio-based direct communication with infrastructures, which causes high risk in the communication security. Previously, mathematical and statistical approach has been adopted to predict fixed time traffic signals, but it is no longer suitable for modern traffic-actuated control systems, where signals are dependent on the dynamic requests from traffic flows. And as a large amount of data is available, machine learning methods attract more and more attention. This paper views signal forecasting as a time-series problem. Firstly, a large amount of real data is collected by detectors implemented at an intersection in Hanover via IoT communication among infrastructures. Then, Baseline Model, Dense Model, Linear Model, Convolutional Neural Network, and Long Short-Term Memory (LSTM) machine learning models are trained by one-day data and the results are compared. At last, LSTM is selected for a further training with one-month data producing a test accuracy over 95%, and the median of deviation is only 2 s. Moreover, LSTM is further evaluated as a binary classifier, generating a classification accuracy over 92% and AUC close to 1.
Effect of Vitamin E on Transcriptomic Alterations in Alzheimer’s Disease

Research into ageing is focused on understanding why some people can maintain cognitive ability and others lose autonomy, affecting their quality of life. Studies have revealed that age-related neurodegenerative disorders like Alzheimer’s disease (AD) are now major causes of death among the elderly, surpassing malignancy. This review examines the effects of vitamin E on transcriptomic changes in ageing and neurodegenerative diseases, using AD as an example, and how different transcriptome profiling techniques can shape the results. Despite mixed results from transcriptomic studies on AD patients’ brains, we think advanced technologies could offer a more detailed and accurate tool for such analysis. Research has also demonstrated the role of antioxidant modifiers in preventing AD. This review will explore the key findings regarding AD and its modulation by vitamin E, emphasizing the shift in its epidemiology during the ageing process.
A Generalized Forced Oscillation Method for Tuning Proportional-Resonant Controllers

This brief proposes a tuning methodology for proportional-resonant (PR) controllers by using the design philosophy of the Ziegler–Nichols forced oscillation method. Unlike such related methods that are usual for proportional–integral–differential design, and those that have been recently proposed for PR controllers, the method in this brief is not restricted to plants whose Nyquist diagram crosses the negative real axis. It involves a feedback experiment with an adjustable phase relay, which allows the identification of the most appropriate point of the frequency response for each class of plants. The proposed method, which also includes tuning formulas that provide appropriate stability margins for each class of plants, is analyzed in detail for three examples, showing its wide applicability.