Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward Fair and Thrilling Autonomous Racing: Governance Rules and Performance Metrics for the Autonomous One

This letter is the second report from a series of IEEE TIV's Decentralized and Hybrid Workshops (DHWs) on Intelligent Vehicles for Education (IV4E). It outlines the prospect of The Autonomous One (TAO), a future autonomous racing competition modeled after Formula One to advance IV4E by pushing the boundaries of artificial intelligence. Existing autonomous races face challenges, including compromised fairness and low participant enthusiasm. These issues limit spectator engagement, thereby reducing the races’ educational value. To prevent similar flaws in organizing TAO, our focus lies in setting governance rules from an organizer's perspective. In these DHWs, we analyzed the rules of existing autonomous races and suggested rule-making guidelines for TAO. To improve fairness, we recommend a balanced scoring system with rigorous monitoring of participating teams’ performances. To discourage consistent victories by a single team, we suggest that leading teams share their source codes, thereby setting the championship-level performance as the baseline in the next season and accelerating the evolution of all participating teams’ abilities. For enhancing suspense and spectator interest, an on-site adaptive rewarding scheme should be deployed to create thrilling turnarounds. Our strategies aim to maintain fairness, increase spectator interest, inspire competitiveness, and ultimately contribute to the advancement of IV4E in organizing TAO.
Hypercomplex algebras for dictionary learning

This paper presents an application of hypercomplex algebras combined with dictionary learning for sparse representation of multichannel images. Two main representatives of hypercomplex algebras, Clifford algebras and algebras generated by the Cayley-Dickson procedure are considered. Related works reported quaternion methods (for color images) and octonion methods, which are applicable to images with up to 7 channels. We show that the current constructions cannot be generalized to dimensions above eight.
Combined QTL mapping, GWAS and transcriptomic analysis revealed a candidate gene associated with the timing of spring bud flush in tea plant (Camellia sinensis)

The timing of the spring bud flush (TBF) is a crucial agronomic trait for the tea plant, as it strongly influences the yield and economic value of harvested fresh tea leaves. The TBF of tea plant is generally defined as the date when > 30% of the growing tender shoots have reached the stage of one bud with one to three leaves, referred as to the stage of one and
Assessing Ionospheric Convection Estimates From Coherent Scatter From the Radio Aurora

Coherent radar backscatter spectra from auroral E region plasma irregularities can be measured with great precision and accuracy. The Doppler spectra obtained from those measurements can be used to estimate auroral convection patterns using an empirical model. These estimates are consistent with rocket, optical, and incoherent scatter radar data, but direct comparison between the different data sets is not straightforward. In order to establish the quality of the empirical model, alternative approaches are then needed. In this work, we use the electrostatic nature of the convection electric field to assess the physical consistency of the empirical model: The model will be satisfactory to the extent that the convection patterns derived from them are incompressible. The incompressibility criteria were assessed for the estimated convection patterns obtained from the substorm of 20 December 2015. Two different approaches were used for this assessment. First, an electrostatic potential was fitted to the convection pattern produced by the empirical model. A good fit implies that there exists an incompressible convection pattern consistent with the Doppler spectra measurements. Second, the uncertainties on the Doppler spectral estimates where propagated through the empirical model to calculate the expected uncertainties on the divergence of the convection field obtained directly from the empirical model. The convection field will be incompressible if its divergence lies within the uncertainty estimates. Both approaches indicate that the evaluated convection patterns are consistently incompressible.
Dispersion with connectivity in wireless mesh networks

We study a multi-objective access point dispersion problem, where the conflicting objectives of maximizing the distance and maximizing the connectivity between the agents are considered with explicit coverage (or Quality of Service) constraints. We model the problem first as a multi-objective model, and then, we consider the constrained single objective alternatives, which we propose to solve using three approaches: The first approach is an optimal tree search algorithm, where bounds are used to prune the search tree. The second approach is a beam search heuristic, which is also used to provide lower bound for the first approach. The third approach is a straightforward integer programming approach. We present an illustrative application of our solution approaches in a real wireless mesh network deployment problem.
Topological derivatives in piezoelectricity

Asymptotic formulae for the mechanical and electric fields in a piezoelectric body with a small void are derived and justified. Such results are new and useful for applications in the field of design of smart materials. In this way the topological derivatives of shape functionals are obtained for piezoelectricity. The asymptotic formulae are given in terms of the so-called polarization tensors (matrices) which are determined by the integral characteristics of voids. The distinguished feature of the piezoelectricity boundary value problems under considerations is the absence of positive definiteness of an differential operator which is non self-adjoint. Two specific Gibbs' functionals of the problem are defined by the energy and the electric enthalpy. The topological derivatives are defined in different manners for each of the governing functionals. Actually, the topological derivative of the enthalpy functional is local i.e., defined by the pointwise values of the governing fields, in contrary to the energy functional and some other suitable shape functionals which admit non-local topological derivatives, i.e., depending on the whole problem data. An example with the weak interaction between mechanical and electric fields provides the explicit asymptotic expansions and can be directly used in numerical procedures of optimal design for smart materials.
Using GitHub Cloud Service in Training Future IT Professionals: Local Study

: In today’s IT industry, it is important to develop the ability of IT students to collaboratively develop software, professional and personal skills. An effective method for developing such skills in future IT specialists is to organize different types of educational projects related to different programming technologies during the execution of mini projects, group and individual project assignments, term papers, academic training within the academic disciplines. The paper summarizes the results of a pedagogical study involving 29 expert students who study Computer Science and Software Engineering and used cloud service for GitHub collaborative IT development projects. The research findings testify, the most effective characteristics of this service, according to experts, identified the possibility of collaborative development of software (i1), the convenience of bug tracking (i3) and the convenience of the code editor (i7). It offers examples and results of using GitHub cloud service in the process of executing educational projects by future IT specialists.
Enhancement of laser-driven betatron x-rays by a density-depressed plasma structure

We report a significant enhancement of betatron radiation from a laser wakefield accelerator (LWFA) by inserting a density-depressed plasma structure. By using a technique of transverse laser machining, a longitudinally density-depressed plasma structure with tunable length and position has been fabricated to increase the betatron amplitude of the electron beam in the LWFA, leading to an enhanced photon number and critical energy of the betatron x-ray beam. By adjusting the length and position of the plasma density depression region, the photon number of the betatron x-ray is enhanced by a factor of 3, and the critical energy is enhanced by a factor of 1.4.
Multiscale Context Deep Hashing for Remote Sensing Image Retrieval

With the advancement of remote sensing satellites and sensor technology, the quantity and diversity of remote sensing imagery have exhibited a sustained trend of growth. Remote sensing image retrieval has gained significant attention in the realm of remote sensing. Hashing methods have been widely applied in remote sensing image retrieval due to their high computational efficiency, low storage cost, and effective performance. However, existing remote sensing image retrieval methods often struggle to accurately capture the intricate information of remote sensing images. They often lack high attention to key features. The neglect of multiscale and saliency information in remote sensing images can result in feature loss and difficulties in maintaining the balance of hash codes. In response to the issues, we introduce a multiscale context deep hashing network (MSCDH). First, we can obtain finer-grained multi-scale features and achieve a larger receptive field by incorporating the proposed multiscale residual blocks. Then, the proposed multicontext attention modules can increase the perceptual field and suppress the interference from irrelevant information by aggregating contextual information along channels and spatial dimensions. The experimental results on the UCMerced dataset and WHU-RS dataset demonstrate that the proposed method achieves state-of-the-art retrieval performance.
Advances in the simplification of Fault Trees automatically generated from AltaRica 3.0 models

Safety and risk analyses rely on models. These models have several important characteristics. They are event-oriented. The system under study changes of state when events, such as failure, hazard, repair and so on, occur. They are probabilistic. The exact moment of the occurrence of a failure is in essence unpredictable. They are discrete. States are represented by means of variables that take their values into finite, usually very small, domains. The most widely used modeling formalisms such as Fault Trees, Block Diagrams and Event Trees rely on Boolean algebra. There are cases however where binary states are not sufficient. For instance, it is sometimes necessary to represent the level of degradation of a component, the quality of a signal, and so on. This kind of models can be easily represented with AltaRica 3.0-a high level modeling language dedicated to safety analyses. AltaRica 3.0 is at the core of the OpenAltaRica project which aim is to develop a complete set of assessment tools for the language, including among others compilers to Fault Trees and Markov Chains, stochastic and stepwise simulators. In this article we study how the notion of prime implicants can be extended to finite domain calculus. We discuss the efficient implementation of finite domain calculus and show how these results can be applied to simplify Fault Trees, automatically generated from AltaRica 3.0 models. This simplification in its turn significantly improves the efficiency of the assessment of the automatically generated Fault Trees.
Development and Validation of RP-HPLC Method for Estimation of Epigallocatechin -3-gallate (EGCG) in Lipid based Nanoformulations

ABSTRACT:Epigallocatechin-3-gallate (EGCG) is a potent antioxidant and free radical scavenger present in green tea. It is extensively used as antioxidant, immunomodulator, anticancer, neuroprotective, cardioprotective, antiviral,antibacterial, photo protective and anti aging agent. The objective of the present work was to develop and validate an RP-HPLC method for determination of EGCG in lipid based nanoformulations. RP-HPLC analysis was performed by isocratic elution using a Phenomenex C18 column (4.6 × 250 mm, 5 µm) at 25 °C. A mixture of acetonitrile and 1% v/v acetic acid (25:75) of pH 2.8 was used as a mobile phase with a flow rate of 0.8 mL/min. The analysis was performed using a UV detector at 280 nm. The results showed that the peak area response was linear within the concentration range of 0.5-40 µg/mL (r =0.9998). The values of LOD (limit of detection) and LOQ (limit of quantification) were 0.125 µg/mL and 0.272 µg/mL respectively. The % RSD for peak area of intraday and interday precision was 0.08 and 0.2%, respectively. The results of accuracy for the different concentrations of EGCG (8, 10 and 12 μg/mL) were 100.29±0.19, 101.10±0.10 and 100.44±0.25 % respectively. The validated method was successfully applied for the estimation of EGCG content in lipid based nanoformulations.
Association between Dietary Patterns and the Risk of Hyperemesis Gravidarum

(1) Background: Although studies have suggested that dietary interventions may have potential benefits over conventional medical treatments, research on the association between dietary patterns and hyperemesis gravidarum (HG) in pregnant women is scarce. (2) Methods: To explore the relationship between dietary patterns and the risk of HG, a cross-sectional study was conducted in Xi’an, China from April 2021 to September 2022. Dietary intake was assessed by a semi-quantitative food-frequency questionnaire, and then factor analysis was used to derive dietary patterns. HG was defined as persistent and severe nausea and vomiting with weight loss ≥ 5%, pregnancy-unique quantification of emesis (PUQE) score ≥ 13, or hospitalization due to vomiting. Logistic regression models were used to estimate ORs and 95% CIs for HG according to dietary pattern scores. Stratified analyses and tests for interaction were performed by potential confounders. (3) Results: Of the 3122 pregnant women enrolled, 2515 individuals (mean age: 31.2 ± 3.4 years) were included in the final analysis. In total, 226 (8.9%) pregnant women were identified as having HG. Five dietary patterns were identified. After adjusting for covariates, the highest quartile of the “fish, shrimp and meat” and “egg, milk and water drinking” patterns was associated with a 37% and 58% lower risk of HG compared with the lowest quartile, respectively (p-trend < 0.05). Conversely, the highest quartile of the “beverage” pattern was associated with a 64% higher risk of HG compared with the lowest quartile (p-trend = 0.02). Furthermore, significant interactions were observed between the “egg, milk and water drinking” pattern and parity, employment status and nutritional supplement use (p-interaction < 0.05). (4) Conclusions: A diet rich in eggs, milk, seafood and unprocessed poultry and animal meat may be a protective factor against HG, while a diet high in beverages may be detrimental to HG. These associations may vary by parity, employment status and nutritional supplement use.
Automatic and Targetless LiDAR–Camera Extrinsic Calibration Using Edge Alignment

In this article, an automatic and targetless extrinsic calibration method is proposed for the light detection and ranging (LiDAR)–camera system, which can calibrate extrinsic parameters from coarse to fine in natural scenes. The calibration method contains a motion-based stage and a feature-based stage. In the motion-based stage, LiDAR and camera motions are inferred during the localization. Also, we proposed a motion selection algorithm to identify sensor motions with stable scales to simplify the cost function of the calibration process. Then, the initial extrinsic parameters are estimated by a joint optimization. In the subsequent feature-based stage, a novel recalibration algorithm using edge alignment is proposed to refine the translation vector of the extrinsic parameters. A novel framework is designed in this stage to extract LiDAR edge points, and a new cost function is proposed to refine the extrinsic parameters. Experiments on public datasets and real-world scenes show the practicability and validity of our calibration method.
The effect of fabrication methods (conventional, computer-aided design/computer-aided manufacturing milling, three-dimensional printing) and material type on the fracture strength of provisional restorations

ABSTRACT Background: Fracture is the most common reason for the failure of provisional restorations. This study aimed to assess the effects of the fabrication method (conventional, computer-aided design/computer-aided manufacturing [CAD/CAM] milling, three-dimensional [3D] printing) and material type on the fracture strength of provisional restorations. Materials and Methods: In this in vitro study, 60 provisional restorations were made through the conventional (Tempron and Master Dent), CAD/CAM milling (Ceramill and breCAM.HIPC) and 3D Printing (3D Max Temp) methods based on a scanned master model. The provisional restorations were designed by the CAD unit and fabricated with milling or 3D printing. Then, an index was made based on the CAD/CAM milling specimen and used for fabricating manual provisional restorations. To assess the fracture resistance, a standard force was applied by a universal testing machine until the fracture occurred. One-way ANOVA and Tukey's test were used to compare the groups (α = 0.05). Results: The mean fracture strength was significantly different among the five groups (P < 0.001), being significantly higher in the breCAM.HIPC group (P < 0.001), followed by the Tempron group (P < 0.05). However, the three other groups were not significantly different (P < 0.05). Conclusion: Despite the statistical superiority of some bis-acrylics over methacrylate resins, the results are material specific rather than category specific. Besides, the material type and properties might be more determined than the manufacturing method.
Novel homozygous leptin receptor mutation in an infant with monogenic obesity

Monogenic obesity can be caused by a mutation in one of the single genes involved in hunger and satiety. The most common mutations affect melanocortin 4 (MC4) followed by the leptin gene and its receptor. Leptin receptor (LEPR) gene mutation is an extremely rare endocrine disease characterized by early-onset obesity, hyperphagia in addition to pituitary hormone deficiency, and metabolic abnormalities. We report the case of a 12-month-old male infant born of a non-consanguineous marriage. He presented to us with rapid weight gain from 2 months of age along with hyperphagia. Biochemistry revealed a deranged lipid profile, elevated transaminases, and markedly raised serum leptin levels. On genetic analysis, a novel mutation was detected, which was a homozygous variation In exon 12 of the LEPR gene (chr1:g.65608901G>A) that resulted in the synonymous amino acid change of lysine at codon 584 proximal to donor splice site (p.Lys584). The in silico prediction of the variant was ‘damaging’ by MutationTaster2. The mutation was classified as a ‘variant of uncertain significance’ due to a lack of published literature and had to be correlated carefully with the clinical symptoms. It was recommended to do Sanger sequencing of the parents and other family members. However, due to financial constraints, the family could not afford the same. At the time of writing, funds were being arranged for procuring setmelanotide, which is a novel and effective therapy for monogenic obesity due to LepR mutation.
Investigating and Modeling Ageing Effects on Polymeric Insulator Electrical Properties

Polymeric insulators, in particular silicone rubber (SIR) are lightweight, have good hydrophobicity characteristics, and are easy to carry and install. They are commonly used for outdoor insulation in power lines. However, pollution, UV radiation, temperature, discharge, wetness, and stress can cause them to degrade over time, losing their electrical properties. Therefore, evaluating the ageing and degradation of polymeric insulators under different conditions becomes crucial. This paper investigates the ageing effects of the polymeric insulators with differences in pollution, applied voltage, hydrophobicity class, and geometrical structures of insulators. The investigation includes the experimental tests of the insulators’ electrical properties such as leakage current and flashover voltage, after assessing the initial characteristics of insulators based on their age and supply voltage. In addition, the aged polymeric insulator model based on an equivalent circuit model was developed to determine the leakage current and breakdown voltage of aged insulators. Moreover, an artificial neural network model is carried out to predict the critical leakage current and flashover voltage of the insulator under the ageing effect. The experiment results were used to validate the accuracy of the proposed models; with an aggregate error of less than 10%, the proposed models appeared to be satisfactory. These models can serve as a scholarly resource for designing, operating, and maintaining insulators, especially in polluted environments.
Artificial Neural Networks-Based Ignition Delay Time Prediction for Natural Gas Blends

ABSTRACT Energy production from natural gas has considerably increased worldwide. The composition of natural gas is diverse, and each blend has a different reactivity. Understanding the reactivities of these fuels enables the designer to develop fuel-source independent combustors. Also, future engines such as scramjet and homogeneous charge compression ignition (HCCI) will rely heavily on the reactivity of the fuel. Ignition delay time (IDT) is a direct measure of a fuel’s reactivity. In the current study, an artificial neural network (ANN) based model is developed to predict the IDT of different natural gas blends. The model has 13 inputs and three hidden layers and is trained using a back-propagation approach. The developed model is superior compared to a multiple linear regression approach and is validated with shock tube experiments. Furthermore, the model is used to predict the IDT of six different liquified natural gas blends (LNG), and the predicted results match the experimental data accurately. Additionally, the IDTs of four different commercial natural gas blends are predicted using the ANN model, showcasing the application of the tool in a real-world scenario.
Globalization and innovation: Evidence from Vietnamese small and medium sized enterprises

<p style="text-align:justify"><span style="font-size:10.5pt"><span style="font-family:等线"><span dir="ltr" lang="EN-US" style="font-family:"Cambria",serif"><span style="color:black">The purpose of this research is to investigate how globalization affects innovation in Vietnam’s small and medium sized enterprises (SMEs). Utilizing Probit model and the data from Vietnam’s SMEs Survey during the period 2005-2015, the research examines the micro and macro effects of globalization on the innovation of these businesses. The main results show that at macro level, globalization is negatively correlated with innovation. However, at micro level, increasing competition pressure and knowledge transfer due to globalization are positively correlated with innovation of Vietnam’s SMEs. </span></span></span></span></p>