Miracle-dz/UniFAR-FTUs huggingface dataset:
Device–Edge Collaborative Differentiated Data Caching Strategy Toward AIoT

Caching AI of Things (AIoT) data at the edge can reduce the load on cloud centers while providing real-time services for AIoT users. Existing static caching strategies based on popularity prediction fail to meet users’ demands for time-varying entity data, while dynamic caching strategies focus only on evaluating the time-varying state characteristics of entity data, but ignore the differences in popularity among entities, resulting in poor service experience. To this end, a device–edge collaborative differentiated data caching strategy considering static entity popularity as well as dynamic system state is proposed. First, a hot entity recognition method centered on user interests is designed to achieve individual preference estimation by mining users’ long short-term interests, and then achieve group interest prediction based on social computing. Based on this, a dynamic caching optimization method is designed, which considers the timeliness of entity data and communication cost of the system to design the objective function of optimal cache decision and then solve it based on reinforcement learning. Simulation results demonstrate that the proposed caching strategy achieves better performance than other benchmark strategies in terms of cache hit rate and search cost.
ADVANCES IN MULTIDIMENSIONAL SIZE THEORY

Size Theory was proposed in the early 90's as a geometrical/topological approach to the problem of Shape Comparison, a very lively research topic in the fields of Computer Vision and Pattern Recognition. The basic idea is to discriminate shapes by comparing shape properties that are provided by continuous functions valued in R, called measuring functions and defined on topological spaces associated to the objects to be studied. In this way, shapes can be compared by using a descriptor named size function, whose role is to capture the features described by measuring functions and represent them in a quantitative way. However, a common scenario in applications is to deal with multidimensional information. This observation has led to considering vector-valued measuring functions, and consequently the multidimensional extension of size functions, namely the k-dimensional size functions. In this work we survey some recent results about size functions in this multidimensional setting, with particular reference to the localization of their discontinuities.
It Takes Two to Tango, Part II: Synthesis of A-Ring Functionalised Quinones Containing Two Redox-Active Centres with Antitumour Activities

In 2021, our research group published the prominent anticancer activity achieved through the successful combination of two redox centres (ortho-quinone/para-quinone or quinone/selenium-containing triazole) through a copper-catalyzed azide-alkyne cycloaddition (CuAAC) reaction. The combination of two naphthoquinoidal substrates towards a synergetic product was indicated, but not fully explored. Herein, we report the synthesis of 15 new quinone-based derivatives prepared from click chemistry reactions and their subsequent evaluation against nine cancer cell lines and the murine fibroblast line L929. Our strategy was based on the modification of the A-ring of para-naphthoquinones and subsequent conjugation with different ortho-quinoidal moieties. As anticipated, our study identified several compounds with IC50 values below 0.5 µM in tumour cell lines. Some of the compounds described here also exhibited an excellent selectivity index and low cytotoxicity on L929, the control cell line. The antitumour evaluation of the compounds separately and in their conjugated form proved that the activity is strongly enhanced in the derivatives containing two redox centres. Thus, our study confirms the efficiency of using A-ring functionalized para-quinones coupled with ortho-quinones to obtain a diverse range of two redox centre compounds with potential applications against cancer cell lines. Here as well, it literally takes two for an efficient tango!
Texture Inpainting for Photogrammetric Models

We devise a technique designed to remove the texturing artefacts that are typical of 3D models representing real‐world objects, acquired by photogrammetric techniques. Our technique leverages the recent advancements in inpainting of natural colour images, adapting them to the specific context. A neural network, modified and trained for our purposes, replaces the texture areas containing the defects, substituting them with new plausible patches of texels, reconstructed from the surrounding surface texture. We train and apply the network model on locally reparametrized texture patches, so to provide an input that simplifies the learning process, because it avoids any texture seams, unused texture areas, background, depth jumps and so on. We automatically extract appropriate training data from real‐world datasets. We show two applications of the resulting method: one, as a fully automatic tool, addressing all problems that can be detected by analysing the UV‐map of the input model; and another, as an interactive semi‐automatic tool, presented to the user as a 3D ‘fixing’ brush that has the effect of removing artefacts from any zone the users paints on. We demonstrate our method on a variety of real‐world inputs and provide a reference usable implementation.
SurvMetrics: An R package for Predictive Evaluation Metrics in Survival Analysis

Recently, survival models have found vast applications in biostatistics, bioinformatics, reliability engineering, finance and related fields. But there are few R packages focusing on evaluating the predictive power of survival models. This lack of handy software on evaluating survival predictions hinders further applications of survival analysis for practitioners. In this research, we want to fill this gap by providing an "all-in-one" R package which implements most predictive evaluation metrics in survival analysis. In the proposed SurvMetrics R package, we implement concordance index for both untied and tied survival data; we give a new calculation process of Brier score and integrated Brier score; we also extend the applicability of integrated absolute error and integrated square error for real data. For models that can output survival time predictions, a simplified metric called mean absolute error is also implemented. In addition, we test the effectiveness of all these metrics on simulated and real survival data sets. The newly developed SurvMetrics R package is available on CRAN at https://CRAN.R-project.org/package=SurvMetrics and GitHub at https://github.com/skyee1/SurvMetrics .
DSRC Versus LTE-V2X: Empirical Performance Analysis of Direct Vehicular Communication Technologies

Vehicle-to-Vehicle (V2V) communication systems have an eminence potential to improve road safety and optimize traffic flow by broadcasting Basic Safety Messages (BSMs). Dedicated Short-Range Communication (DSRC) and LTE Vehicle-to-Everything (V2X) are two candidate technologies to enable V2V communication. DSRC relies on the IEEE 802.11p standard for its PHY and MAC layer while LTE-V2X is based on 3GPP’s Release 14 and operates in a distributed manner in the absence of cellular infrastructure. There has been considerable debate over the relative advantages and disadvantages of DSRC and LTE-V2X, aiming to answer the fundamental question of which technology is most effective in real-world scenarios for various road safety and traffic efficiency applications. In this paper, we present a comprehensive survey of these two technologies (i.e., DSRC and LTE-V2X) and related works. More specifically, we study the PHY and MAC layer of both technologies in the survey study and compare the PHY layer performance using a variety of field tests. First, we provide a summary of each technology and highlight the limitations of each in supporting V2X applications. Then, we examine their performance based on different metrics.
A Novel Distributed Machine Learning Model to Detect Attacks on Edge Computing Network

To meet the growing number and variety of IoT devices in 5G and 6G network environments, the development of edge computing technology is a powerful strategy for ofﬂoading processes in data servers by processing at the network and nearby the user. Besides its benefits, several challenges related to decentralized operations for improving performance or security tasks have been identified. A new research direction for distributed operating solutions has emerged from these issues, leading to applying Distributed Machine Learning (DML) techniques for edge computing. It takes advantage of the capacity of edge devices to handle increased data volumes, reduce connection bottlenecks, and enhance data privacy. The designs of DML architectures have to use optimized algorithms (e.g., high accuracy and rapid convergence) and effectively use hardware resources to overcome large-scale problems. However, the trade-off between accuracy and data set volume is always the biggest challenge for practical scenarios. Hence, this paper proposes a novel attack detection model based on the DML technique to detect attacks at network edge devices. A modified voting algorithm is applied to core logic operation between sever and workers in a partition learning fashion. The results of numerical simulations on the UNSW-NB15 dataset have proved that our proposed model is suitable for edge computing and gives better attack detection results than other state of the art solutions.
Estimating Particulate Matter Concentration over Arid Region Using Satellite Remote Sensing: A Case Study in Makkah, Saudi Arabia

The air quality indicator approximated by satellite measurements is known as an atmospheric particulate loading, which is evaluated in terms of the columnar optical thickness of aerosol scattering. The effect brought by particulate pollution has gained interest among researchers to study aerosol and particulate matter. In this study we presents the potentiality of retrieving concentrations of particulate matter with diameters less than ten micrometer (PM10) in the atmosphere using the Landsat 7 ETM+ slc-off satellite images over Makkah, Mina and Arafah. A multispectral algorithm is developed by assuming that surface condition of study area are lambertian and homogeneous. In situ PM10 measurements were collected using DustTrak aerosol monitor 8520 and their locations were determined by a handheld global positioning system (GPS). The multispectral algorithm model shows that PM10 high during Hajj season compared to other season. The retrieval dataset gives the accuracy > 0.8 of R coefficient value over Makkah, Mina and Arafah. These results provide confidence that the multispectral algorithm PM10 models can make accurate predictions of the concentrations of PM10.
Rotated Feed-Combined Reconfigurable Transmit RIS With Disparate Deployment of 1-bit Hybrid Units for B5G/6G

This communication presents an initial disparate deployment of 1-bit hybrid units using p-i-n diodes in transmit reconfigurable intelligent surface (TRIS) combined with a rotated feed source to address the gain limitation of the existing 1-bit TRIS. Two types of hybrid units, with a radiation phase difference of 90° by the delay line, are proposed in this study. The TRIS aperture is divided into two sections based on two different radiation centers by rotated feed, and the hybrid units in each Section are initially configured to implement optimized quasi 2-bit beam collimation in the 0° and 40° for enhanced quantization loss. It is found that the array envelope is higher even in radiation directions other than the optimized directions, despite the initial disparate deployment of each section. Beamforming for –theta direction in the H-plane is only considered for the proof of concept with the smallest size of TRIS aperture. A <inline-formula> <tex-math notation="LaTeX">$16\times16$ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$7.6\times 7.6\,\,\lambda_{0}$ </tex-math></inline-formula> at 28 GHz) 1-bit hybrid TRIS offers gain enhancement of 0–5.6 dB in −70 ° to −0 ° directions of the array envelope compared with the conventional 1-bit TRIS. The proposed 1-bit hybrid TRIS yields an improved aperture efficiency of 24.4%.
Convergence of the generalized back-propagation algorithm with constant learning rates

A new generalized back-propagation algorithm which can effectively speed up the convergence rate and reduce the chance of being trapped in local minima was previously proposed by the authors (1996). In this paper, we will analyze the convergence of the generalized back-propagation algorithm. The weight sequences in generalized backpropagation algorithm can be approximated by a certain ordinary differential equation (ODE). When the learning rate tends to zero, the interpolated weight sequences of generalized back-propagation converge weakly to the solution of associated ODE.
A Novel Ambroxol-Derived Tetrahydroquinazoline with a Potency against SARS-CoV-2 Proteins

We report synthesis of a novel 1,2,3,4-tetrahydroquinazoline derivative, named 2-(6,8-dibromo-3-(4-hydroxycyclohexyl)-1,2,3,4-tetrahydroquinazolin-2-yl)phenol (1), which was obtained from the hydrochloride of 4-((2-amino-3,5-dibromobenzyl)amino)cyclohexan-1-ol (ambroxol hydrochloride) and salicylaldehyde in EtOH. The resulting compound was produced in the form of colorless crystals of the composition 1∙0.5EtOH. The formation of the single product was confirmed by the IR and 1H spectroscopy, single-crystal and powder X-ray diffraction, and elemental analysis. The molecule of 1 contains a chiral tertiary carbon of the 1,2,3,4-tetrahydropyrimidine fragment and the crystal structure of 1∙0.5EtOH is a racemate. Optical properties of 1∙0.5EtOH were revealed by UV-vis spectroscopy in MeOH and it was established that the compound absorbs exclusively in the UV region up to about 350 nm. 1∙0.5EtOH in MeOH exhibits dual emission and the emission spectra contains bands at about 340 and 446 nm upon excitation at 300 and 360 nm, respectively. The DFT calculations were performed to verify the structure as well as electronic and optical properties of 1. ADMET properties of the R-isomer of 1 were evaluated using the SwissADME, BOILED-Egg, and ProTox-II tools. As evidenced from the blue dot position in the BOILED-Egg plot, both human blood–brain barrier penetration and gastrointestinal absorption properties are positive with the positive PGP effect on the molecule. Molecular docking was applied to examine the influence of the structures of both R-isomer and S-isomer of 1 on a series of the SARS-CoV-2 proteins. According to the docking analysis results, both isomers of 1 were found to be active against all the applied SARS-CoV-2 proteins with the best binding affinities with Papain-like protease (PLpro) and nonstructural protein 3 (Nsp3_range 207–379-AMP). Ligand efficiency scores for both isomers of 1 inside the binding sites of the applied proteins were also revealed and compared with the initial ligands. Molecular dynamics simulations were also applied to evaluate the stability of complexes of both isomers with Papain-like protease (PLpro) and nonstructural protein 3 (Nsp3_range 207–379-AMP). The complex of the S-isomer with Papain-like protease (PLpro) was found to be highly unstable, while the other complexes are stable.
MVHGN: Multi-View Adaptive Hierarchical Spatial Graph Convolution Network Based Trajectory Prediction for Heterogeneous Traffic-Agents

The future trajectory prediction of heterogeneous traffic-agents for autonomous vehicles in mixed traffic scene is of great significance for safe and reliable driving. Thus, we propose the Multi-View Adaptive Hierarchical Spatial Graph Convolution Network (MVHGN) to predict the future trajectories of heterogeneous traffic-agents. Firstly, multiple logical correlations are obtained based on the time series data of traffic-agents and a multi-view logical network is constructed. The multi-view logical feature extraction is realized based on the graph convolution module. Then, combining the multi-view logical features and the adaptive spatial topology network, the logical-physical features at the micro level are mined through the graph convolution module; based on the logical-physical features at the micro level and the regional clustering network at the macro level, the global logical-physical features are obtained. Finally, the model predicts the future trajectories of traffic-agents based on the encoder-decoder structure of the GRU. For the Apolloscape trajectory data set, the performance of our proposed method MVHGN is better than that of the comparison models.
Active Noise Hybrid Time-Varying Control for Motorcycle Helmets

Recent noise at work regulations in the EU (2003) have been established to prevent noise induced hearing loss (NIHL). This imposes better performance results to traditional feedback active noise control (ANC) in motorcycle helmets, which suffer from well known limitations. Here two new ideas are applied to this problem. First, an hybrid (feedforward/feedback) linear time invariant (LTI) controller is designed for a motorcycle helmet ANC, which improves the resulting attenuation. This is achieved by adding an extra pair of microphones which measure the external noise that is then used as the feedforward input signal. In addition and to increase even more the resulting performance, the air velocity is measured in real-time and used as the parameter which schedules a linear parameter varying (LPV) feedback (FB) controller. This is combined with the previous feedforward (FF) controller, resulting in a time-varying hybrid controller. Both hybrid, LTI and LPV controllers are designed using linear matrix inequality (LMI)-based optimization. Two experiments have been carried out to measure the relation between external noise spectra and velocity: a wind tunnel test and a freeway ride experience. The resulting controllers are tested in a simulation which uses actual data obtained from the freeway experiment. The resulting attenuations in this motivating study seem promising for future controller tests to be performed in real-time, with the adequate hardware.
Design and Analysis of High-Performance and Small-Size Piezoresistive Pressure Sensors

In this article, a rapid thermal process is proposed to manufacture piezoresistance to improve the performance of piezoresistive devices, and an absolute piezoresistive pressure sensor is prepared by anodic bonding between silicon film and glass with the cavity to reduce the size of the device. The size of the sensor is only $1.0\times 1.0\times0.513$ mm. At the same time, the arrangement of piezoresistance is optimized by the finite element method. In addition, the deformation of the diaphragm is measured with a profiler, which verified the feasibility of Ti as the absorption of oxygen generated in the anodic bonding process. In the pressure range of 0–200 kPa, the output voltage and nonlinearity of the pressure sensor prepared by the rapid thermal annealing (RTP) process are 91.6 mV and 0.14%FSO, respectively, and the temperature coefficient of resistance (TCR) and the temperature coefficient of sensitivity (TCS) are 571 and −1676 ppm/°C respectively. Compared with the pressure sensor prepared by the common piezoresistive process, the TCR and TCS of the designed sensor are reduced by 73% and 24%, respectively, which has the advantages of high output voltage, low TCR and TCS, and small-size.
Duration as a Cue in the Recognition of Synthetic Vowels

Sets of synthetic vowel sounds which differed in first‐formant frequency, second‐formant frequency, and duration were presented to a group of listeners for identification. It was found that listeners' judgments depended on all of these factors. Duration was a relatively more important cue for vowels located in the center of the F1‐F2 space where a vowel might more readily be confused with one of its neighbors. The perceived duration of a vowel was found to be biased by the rhythm of the sounds that preceded it. In the case of sounds lying near perceptual boundaries, this was sometimes sufficient to change the identity of the vowel.
Bioinspired construction of Mg-Li alloys surfaces with stable superhydrophobicity and improved corrosion resistance

A facile method was utilized for the construction of the bioinspired superhydrophobic Mg–Li alloy surfaces with peonylike micronanoscale hierarchical structures. The resultant materials were characterized by scanning electron microscope, x-ray photoelectron spectroscopy, and water contact angle measurements. The obtained Mg–Li alloy surfaces presented the dramatically improved corrosion resistance and long-term stable superhydrophobic properties with a static water contact angle of about 160° and a small sliding angle of less than 5°, which may extend the practical application of Mg–Li alloys in industrial and high-technology fields.
Highly effective piezoelectric materials for various fields of application

This paper describes highly effective piezoelectric ceramic materials (developed at Rostov State University) which have a combination of parameters required in various fields of application including: materials stable to electrical and mechanical influences; materials possessing high dielectric permittivity; high sensitivity materials; materials possessing high stability of resonance frequency; materials possessing low dielectric permittivity; and high-temperature materials. A number of these materials are used in various industrial devices and instruments. A method for producing novel piezoceramic materials is considered.
Size, shape and dynamics of large-scale turbulent flow structures in a gravel-bed river

In this paper, we present a detailed investigation of the size, scale and dynamics of macro-turbulent flow structures in gravel-bed rivers. We used an array of seven electromagnetic current meters with high resolution in both space and time to measure the streamwise velocity fluctuations in a gravel-bed river. The array was deployed successively in various configurations in order to quantify the vertical, lateral and longitudinal extent of the flow structures and to estimate their advecting velocities. To depict the spatial and temporal properties of the flow structures, we used space–time velocity matrices, space–time correlation analysis and coherent-structure detection schemes. The results show that the large-scale turbulent flow structures in a gravel-bed river occupy the entire depth of the flow and that they are elongated and narrow. The length of the structures is 3 to 5 times the flow depth while the width is between 0.5 and 1 times flow depth. In spite of the high roughness of the bed, these values are similar to those reported in the literature for laboratory experiments on large-scale turbulent flow structures. The dynamics of the large-scale turbulent flow structures investigated using flow visualization highlight the interactions between the outer flow region and the near-bed region. Our evidence suggests that large-scale flow incursions trigger ejections in the near-bed region that can develop into megabursts that can reach the water surface.