Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Analysis of UWB MIMO Antenna for Smart Fabric Communications

This paper presents a flexible multiple-input, multiple-output (MIMO) antenna with ultrawideband (UWB) performance for smart clothing applications. The MIMO antenna is comprised of four octagonal-shaped radiators with several slots loaded into them, and it offers a frequency range of 2.9–12 GHz. The unit cell has a size of 0. 26λ0 × 0. 164λ0 × 0. 014λ0 and the MIMO antenna has a size of 0. 48λ0 × 0. 48λ0 × 0. 014λ0, where λ0 corresponds to the lowest operating frequency. The radiation and diversity performances of the antenna are evaluated, and the obtained metrics are envelope correlation coefficient (ECC) <0.045, diversity gain (DG) >9.9 dB, total active reflection coefficient (TARC) <−14 dB, and channel capacity loss (CCL) <0.13 bits/s/Hz. The bending analysis of the MIMO antenna is performed. The specific absorption rate (SAR) of the MIMO antenna is also investigated, and the obtained values are 0.229 W/Kg (4 GHz), 0.253 W/Kg (7 GHz), and 0.463 W/Kg (10 GHz).
A Preliminary Study on the Correlation Factor Analysis of Language Cognitive Assessment System Based on Scale Construction

This paper explores the 12 dimensions of language cognition assessment system using factor analysis. Principal component analysis and correlation analysis of factors within the system were conducted on the language cognition assessment system. Factor analysis and principal component analysis: KMO: 0.934, weak bias correlation, suitable for factor analysis, and the contribution of principal component variance was 79.837%, respectively. The correlations between the 12 factors ranged from 0.611-0.903. The correlation values between the remaining 11 factor assignments and the total score of the system were higher than the correlations between the subscales. The system is constructed with good structural validity, but the correlations between the factors are strong. It is suggested that the indicators be combined or the form of the questions be modified to provide guidance for the modification of the language cognition assessment system.
Lyapunov analysis of sliding motions: Application to bounded control

The results concern the fundamental problem of Lyapunov analysis of sliding motions. It consist first to estimate the useful part of the sliding surface (the so-called “sliding domain”) and second to estimate the useful part of the state domain that is the domain of all initial conditions for which the corresponding solutions converge to the sliding domain. The application of such results concern the design of a realistic bounded control. Several examples are exposed in order to illustrate the obtained results.
Safety Tunnel-Based Model Predictive Path-Planning Controller With Potential Functions for Emergency Navigation

The potential functions (PFs) have generally shown good performances in real-time path planning with computation efficiency conforming to the requirements of lower control systems in autonomous driving. However, several inherent limitations exist in using the PFs, including a local minimum in specific scenarios and no passage between closely spaced obstacles. Recent studies have focused on conventional scenarios where PFs are assumed to work normally, without malfunctioning, occurring during perilous situations. Therefore, we propose a specific safety tunnel (ST)-based model predictive controller (MPC) combined with PFs (PF-STMPC) to handle path-planning in extreme-emergency traffic scenarios (e.g., emergency braking and lane-changing obstacles). To further guarantee driving safety, we improve PFs with the responsibility-sensitive safety (RSS) model that accurately calculates the minimum safe longitudinal and lateral distances. Furthermore, a sigmoid-based ST is designed for emergency navigation if the PFs fail to plan a safe path due to the aforementioned inherent limitations, enabling the controller with planning functionality if necessary. The ST is embedded in the MPC-based tracking controller as a safe constraint sensitive to surrounding environments (e.g., road structure and obstacles). The proposed PF-STMPC was co-simulated using MATLAB/Simulink and CarSim Simulator under the constant speed condition. Compared with the state-of-the-art method, the proposed method demonstrated better performance in finding a safe path and eliminating severe yawing of the ego-vehicle (82.8% less in sideslip yawing amplitude and 57.7% shorter in the oscillation period of yaw angle) when facing traffic emergencies.
Metagenomic Insights into the Adaptive Responses of the Microbiome of a Spent Engine Oil-Perturbed Agricultural Soil to Iron Stress

Abstract In hydrocarbon-impacted matrices, accessibility to iron, an essential micronutrient poses a significant challenge to microorganisms due to its complexation with hydrocarbon pollutants. This necessitates the elicitation of diverse adaptive responses to counteract the iron stress and achieve cellular iron homeostasis. In this study, putative genes responsible for iron acquisition, iron regulation, and iron storage in agricultural soil (1S) and agricultural soil amended with spent engine oil (AB1) were evaluated through functional annotation of their open reading frames (ORFs). Functional annotation was done using KEGG KofamKOALA and BacMet. Annotation revealed 110 genes in AB1 of which 96 (87.3%) were annotated for iron acquisition. The iron acquisition genes were responsible for Fe2+/Fe3+ transport and efflux, heme transport, vitamin B12/cobalamin transport, siderophore transport and biosynthesis. In IS metagenome, 28 genes were detected, of which 20 (71.4%) were annotated for iron acquisition. More genes involved in iron regulation and oxidative stress protection were detected in AB1 metagenome while significant differences observed in the iron metabolism genes recovered from 1S and AB1 metagenomes were statistically validated (p < 0.05). The study revealed that spent engine oil contamination of the agricultural soil imposed significant iron stress on the AB1 microbiome necessitating the expression of several genes encoding high affinity iron uptake systems (siderophores, ABC iron transporters), global iron regulators, and iron-induced oxidative stress protection proteins.
COVID-Specific Long-term Sequelae in Comparison to Common Viral Respiratory Infections: An Analysis of 17 487 Infected Adult Patients

Abstract Background A better understanding of long-term health effects after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has become one of the health care priorities in the current pandemic. We analyzed a large and diverse patient cohort to study health effects related to SARS-CoV-2 infection occurring >1 month postinfection. Methods We analyzed 17 487 patients who received diagnoses for SARS-CoV-2 infection in a total of 122 health care facilities in the United States before April 14, 2022. Patients were propensity score–matched with patients diagnosed with the common cold, influenza, or viral pneumonia from March 1, 2020, to April 1, 2021. For each outcome, SARS-CoV-2 was compared with a generic viral respiratory infection (VRI) by predicting diagnoses in the period between 30 and 365 days postinfection. Both coronavirus disease 2019 (COVID-19) and VRI patients were propensity score–matched with patients with no record of COVID-19 or VRI, and the same methodology was applied. Diagnoses where COVID-19 infection was a significant positive predictor in both COVID-19 vs VRI and COVID-19 vs control comparisons were considered COVID-19-specific effects. Results Compared with common VRIs, SARS-CoV-2 was associated with diagnoses of palpitations, hair loss, fatigue, chest pain, dyspnea, joint pain, and obesity in the postinfectious period. Conclusions We identify that some diagnoses commonly described as “long COVID” do not appear significantly more frequent post–COVID-19 infection compared with other common VRIs. We also identify sequelae that are specifically associated with a prior SARS-CoV-2 infection.
Personality, Anxiety, and Stress in Patients with Small Intestine Bacterial Overgrowth Syndrome. The Polish Preliminary Study

Objective: Small intestinal bacterial overgrowth (SIBO) syndrome is associated with depression and anxiety. This study aimed to examine for the first time the correlation between personality traits, situational anxiety, and stress in Polish patients with SIBO. Methodology: This study included 26 patients with SIBO aged 20–35 years and 24 non-SIBO patients aged 20–35 years. The following instruments were used: NEO-FFI Personality Inventory, KPS Sense of Stress Questionnaire, and the anxiety-state subscale from the State-Trait Anxiety Inventory (STAI). Results: Compared to the non-SIBO subgroup, SIBO patients expressed specific patterns of personality traits: higher neuroticism, lower extroversion, and a higher state of anxiety and stress. Unlike the non-SIBO subgroup, stress (total emotional tension, external, and intrapsychic) correlated negatively only with extroversion. Conclusions: Personality is the primary regulator of experience and behavior. The specificity captured in the research is a premise for an in-depth study considering various psychological variables to determine cause-effect relationships.
A general comparison principle for Hamilton Jacobi Bellman equations on stratified domains 1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

This manuscript aims to study finite horizon, first order Hamilton Jacobi Bellman (HJB) equations on stratified domains. This problem is related to optimal control problems with discontinuous dynamics. We use nonsmooth analysis techniques to derive a strong comparison principle as in the classical theory and deduce that the value function is the unique viscosity solution. Furthermore, we prove some stability results of the Hamilton Jacobi Bellman equation. Finally, we establish a general convergence result for monotone numerical schemes in the stratified case.
The Quality of Argumentation in an Euclidean Geometry Context: A Case Study

The research reported in this study examined the quality of argumentation of South African Grade 11 learners through the lens of Toulmin’s argument pattern (TAP). Very little research has quantified the argumentation of learners in mathematics across the school grades. The focus was on measuring the mathematical knowledge and quality of arguments formulated by learners as they engage in a reasoning task set in a Euclidean geometry investigative context. Mathematics education reform efforts have highlighted the importance of argumentation in the acquisition of mathematical knowledge. To describe these participants’ quality of arguments, a sample of 135 Grade 11 learners was drawn from a target population of high schools located in one large South African province. Using an analytical framework modified from Osborne et al. (2004), the findings suggested that although learners’ knowledge of properties of parallel lines was encouragingly satisfactory, the level of their argumentation quality was low. The implication of this finding is that mathematics initial teacher education programs need to design investigations that feature the TAP (core) in their courses. It is recommended that future studies may need to design intervention strategies to address high school learners’ lack of argumentation skills.
Novel contrast enhancement scheme for infrared image using detail-preserving stretching

A novel contrast enhancement scheme for infrared (IR) image using detail-preserving stretching (DPS) is presented in this paper. Unlike CCD images, the histogram of the IR images is not distributed to the entire dynamic range of the input image. Therefore, in order to enhance the contrast, the proposed method adopts contrast stretching since it has a low computational complexity and preserves the global contrast of the original. However, the stretching often produces unpleasant images that do not contain clear details by limiting the output dynamic range. To solve this problem, we propose a new contrast stretching method based on gradient-domain processing (GDP) that can enhance the local image contrast while preserving the global contrast as well as the image details. Experimental results demonstrate that the proposed method outperforms the conventional stretching-based and other image enhancement methods with respect to the visual quality and computational complexity.
Development of Microalgae Biodiesel: Current Status and Perspectives

Microalgae are regarded as a promising source of biodiesel. In contrast with conventional crops currently used to produce commercial biodiesel, microalgae can be cultivated on non-arable land, besides having a higher growth rate and productivity. However, microalgal biodiesel is not yet regarded as economically competitive, compared to fossil fuels and crop-based biodiesel; therefore, it is not commercially produced. This review provides an overall perspective on technologies with the potential to increase efficiency and reduce the general costs of biodiesel production from microalgae. Opportunities and challenges for large-scale production are discussed. We present the current scenario of Brazilian research in the field and show a successful case in the research and development of microalgal biodiesel in open ponds by Petrobras. This publicly held Brazilian corporation has been investing in research in this sector for over a decade.
Unsupervised and efficient learning in sparsely activated convolutional spiking neural networks enabled by voltage-dependent synaptic plasticity

Spiking neural networks (SNNs) are gaining attention due to their energy-efficient computing ability, making them relevant for implementation on low-power neuromorphic hardware. Their biological plausibility has permitted them to benefit from unsupervised learning with bio-inspired plasticity rules, such as spike timing-dependent plasticity (STDP). However, standard STDP has some limitations that make it challenging to implement on hardware. In this paper, we propose a convolutional SNN (CSNN) integrating single-spike integrate-and-fire (SSIF) neurons and trained for the first time with voltage-dependent synaptic plasticity (VDSP), a novel unsupervised and local plasticity rule developed for the implementation of STDP on memristive-based neuromorphic hardware. We evaluated the CSNN on the TIDIGITS dataset, where, helped by our sound preprocessing pipeline, we obtained a performance better than the state of the art, with a mean accuracy of 99.43%. Moreover, the use of SSIF neurons, coupled with time-to-first-spike (TTFS) encoding, results in a sparsely activated model, as we recorded a mean of 5036 spikes per input over the 172 580 neurons of the network. This makes the proposed CSNN promising for the development of models that are extremely efficient in energy. We also demonstrate the efficiency of VDSP on the MNIST dataset, where we obtained results comparable to the state of the art, with an accuracy of 98.56%. Our adaptation of VDSP for SSIF neurons introduces a depression factor that has been very effective at reducing the number of training samples needed, and hence, training time, by a factor of two and more, with similar performance.
Is the Alt-Right Popular in Canada? Image Sharing, Popular Culture, and Social Media

Background: In popular coverage and social media analysis, the alt-right has been described as a popular phenomenon. Following Stuart Hall’s understanding of popular culture, we question the status of the alt-right in Canada as both a political and methodological problem that requires critical attention to social media metrics and critical experimentation in developing new digital methods. Analysis: Our study developed a novel method to analyse image circulation across major social media platforms. We find that image sharing is marginal, yet the spread of images distinguishes political communities between Twitter hashtags, subreddits, and Facebook pages. We found a distinct alt-right community in our sample, active but isolated from other popular sites. Conclusion and Implications: While the findings suggest the limited significance of image sharing to conceptualize popularity in cross-platform analysis, our novel method offers a compelling alternative to corporate social media analytics and raises new questions about how popular politics, especially the popularity of the alt-right, may be studied in the future.
Effect of CuO Nanoparticles on Performance and Emissions Behaviors of CI Engine Fueled with Biodiesel-Diesel Fuel Blends

- Recently the world has a very important need for replacement fossil fuel with renewable sources of energy. Greenhouse effect is considered one of bad effects of fossil fuels, which causes increase of global temperature. Biodiesel is one of fossil fuel alternatives, which can be produced from many organic sources. Scientists are searching for adding nano-materials to fueling system, which can modify fuel characteristic in compression champers. In this study diesel fuel will be replaced with blends of diesel and biodiesel produced from waste cooking oil (WCO) is created using a catalytic transesterification reaction (CTR). With the addition of a low concentration of alcohol over the period of an hour at a reaction temperature of 65 °C, (CTR) converts (WCO) to methyl esters. Blends consisting of (40 % diesel, 60 % biodiesel and CuO nano-martial with different concentration) will be prepared for fueling direct injection engine four-stroke. The engine will be run at 1400 rpm with natural aspiration under various loads. Using blends of (pure diesel, B40 [consist of 60 % biodiesel and 40 % diesel], 50b40 [consist of 60 % biodiesel, 40 % diesel and 50 mg CuO], 100B40 [consist of 60 % biodiesel, 40 % diesel and 100 mg CuO], 150 [consist of 60 % biodiesel, 40 % diesel and 150 mg CuO] and pure diesel). On engine performance and emissions, the impact of using copper oxide has been studied. The results of the experiment demonstrate that diesel engines can run on various mixtures of fuel, biodiesel, and CuO nano-material under the same operating conditions. The obtained data indicates that a 10% increase in brake thermal efficiency was noted, decrease in exhaust temperature with 11.6 % and decrease in brake specific fuel consumption with 6.66 %.
Machine Learning Method for Fatigue Strength Prediction of Nickel-Based Superalloy with Various Influencing Factors

The accurate prediction of fatigue performance is of great engineering significance for the safe and reliable service of components. However, due to the complexity of influencing factors on fatigue behavior and the incomplete understanding of the fatigue failure mechanism, it is difficult to correlate well the influence of various factors on fatigue performance. Machine learning could be used to deal with the association or influence of complex factors due to its good nonlinear approximation and multi-variable learning ability. In this paper, the gradient boosting regression tree model, the long short-term memory model and the polynomial regression model with ridge regularization in machine learning are used to predict the fatigue strength of a nickel-based superalloy GH4169 under different temperatures, stress ratios and fatigue life in the literature. By dividing different training and testing sets, the influence of the composition of data in the training set on the predictive ability of the machine learning method is investigated. The results indicate that the machine learning method shows great potential in the fatigue strength prediction through learning and training limited data, which could provide a new means for the prediction of fatigue performance incorporating complex influencing factors. However, the predicted results are closely related to the data in the training set. More abundant data in the training set is necessary to achieve a better predictive capability of the machine learning model. For example, it is hard to give good predictions for the anomalous data if the anomalous data are absent in the training set.
Available potential energy density for Boussinesq fluid flow

Abstract An exact expression ${\mathscr{E}}_{a} $ for available potential energy density in Boussinesq fluid flows (Roullet & Klein, J. Fluid Mech., vol. 624, 2009, pp. 45–55; Holliday & McIntyre, J. Fluid Mech., vol. 107, 1981, pp. 221–225) is shown explicitly to integrate to the available potential energy ${E}_{a} $ of Winters et al. (J. Fluid Mech., vol. 289, 1995, pp. 115–128). ${\mathscr{E}}_{a} $ is a positive definite function of position and time consisting of two terms. The first, which is simply the indefinitely signed integrand in the Winters et al. definition of ${E}_{a} $ , quantifies the expenditure or release of potential energy in the relocation of individual fluid parcels to their equilibrium height. When integrated over all parcels, this term yields the total available potential energy ${E}_{a} $ . The second term describes the energetic consequences of the compensatory displacements necessary under the Boussinesq approximation to conserve vertical volume flux with each parcel relocation. On a pointwise basis, this term adds to the first in such a way that a positive definite contribution to ${E}_{a} $ is guaranteed. Globally, however, the second term vanishes when integrated over all fluid parcels and therefore contributes nothing to ${E}_{a} $ . In effect, it filters the components of the first term that cancel upon integration, isolating the positive definite residuals. ${\mathscr{E}}_{a} $ can be used to construct spatial maps of local contributions to ${E}_{a} $ for direct numerical simulations of density stratified flows. Because ${\mathscr{E}}_{a} $ integrates to ${E}_{a} $ , these maps are explicitly connected to known, exact, temporal evolution equations for kinetic, available and background potential energies.
Trapping of an electron in coupled quantum dots in graphene

Due to Klein's tunneling the electronic states of a quantum dot in graphene have finite widths and an electron in quantum dot has a finite trapping time. This property introduces a special type of interdot coupling in a system of many quantum dots in graphene. The interdot coupling is realized not as a direct tunneling between quantum dots but as coupling through the continuum states of graphene. As a result the interdot coupling modifies both the positions and the widths of the energy levels of the quantum dot system. We study the system of quantum dots in graphene theoretically by analyzing the complex energy spectra of the quantum dot system. We show that in a double-dot system some energy levels become strongly localized with an infinite trapping time. Such strongly localized states are achieved only at one value of the interdot separation. We also study a periodic array of quantum dots in graphene within a tight-binding mode for a quantum dot system. The values of the hopping integrals in the tight-binding model are found from the expression for the energy spectra of the double quantum dot system. In the array of quantum dots the states with infinitely large trapping time are realized at all values of interdot separation smaller than some critical value. Such states have nonzero wave vectors.