Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of large delay on pinning impulsive synchronization control: A strict comparison principle

Over the past decades, various successful results have shown the discoveries and progress of impulsive synchronization. Yet, little attention has been devoted to the effects of the impulsive elasticity coefficient on network synchronization for both impulsive control problems and impulsive disturbance problems. In this work, we study impulsive pinning synchronization for coupled neural networks from a large delay perspective. Some flexible synchronization criteria are derived based on the average impulsive interval method. It has shown that synchronization of coupled neural networks with arbitrarily finite delay can be achieved via controlling a small fraction of nodes. Specifically, by applying a strict comparison principle for impulsive delayed neural networks, the effects of the impulsive elasticity coefficient on synchronization are revealed to be dissimilar from the case of delay‐free neural networks. One can verify that increasing the elasticity coefficient may bring a desynchronizing impact on the original synchronized networks. More ulteriorly, the generated criteria will still hold if the delay is sufficiently large, which implies that the delay is independent of the size of the impulsive interval. Our work is an essential step toward investigating the role of the elasticity coefficient on synchronization. Furthermore, the synchronization region of different impulsive weights, impulsive intervals, and impulsive control proportions are described thoroughly.
Effects of cannabis legalization on the use of cannabis and other substances

Purpose of review As more jurisdictions legalize cannabis for non-medical use, the evidence on how legalization policies affect cannabis use and the use of other substances remains inconclusive and contradictory. This review aims to summarize recent research findings on the impact of recreational cannabis legalization (RCL) on cannabis and other substance use among different population groups, such as youth and adults. Recent findings Recent literature reports mixed findings regarding changes in the prevalence of cannabis use after the adoption of RCL. Most studies found no significant association between RCL and changes in cannabis use among youth in European countries, Uruguay, the US, and Canada. However, some studies have reported increases in cannabis use among youth and adults in the US and Canada, although these increases seem to predate RCL. Additionally, there has been a marked increase in unintentional pediatric ingestion of cannabis edibles postlegalization, and an association between RCL and increased alcohol, vaping, and e-cigarette use among adolescents and young adults. Summary Overall, the effects of cannabis legalization on cannabis use appear to be mixed. Further monitoring and evaluation research is needed to provide longer-term evidence and a more comprehensive understanding of the effects of RCL.
Imaging of the internal structure of an asteroid analogue from quasi-monostatic microwave measurement data. II. The time domain approach

Context. The internal structures of small solar system bodies (SSSBs) are still poorly understood. In this paper, we find an experimental tomographic reconstruction of coarse high-contrast details inside a complex-structured target object using multipoint full-wave radar data. Aims. Our aim is to advance the development of inversion techniques to be used in potential planetary scientific radar investigations targeting SSSBs, which have complex shapes and whose internal structure is largely unknown. Finding out the structure is an important scientific objective of Solar System research in order to understand its history and evolution. Methods. This is the second part (Paper II) of a joint study considering the methods to analyse and invert quasi-monostatic microwave measurement data of an asteroid analogue. We focused on incorporating advanced, full-wave, forward simulation in time domain with experimental data obtained from multiple measurement points. In particular, this study investigates multiple scattering and multipath effect suppression (MES) to reduce artefacts in the reconstructions. MES is necessary since the high-contrast and complex-shaped target and, especially, its back wall in high curvature regions cause intense reflections that deteriorate the reconstruction quality if not treated correctly. We considered the following two approaches to obtain MES: (i) geometrical optics-based pathlength thresholding and (ii) a peak detection method to investigate whether a data-driven approach could be used. At the inversion stage, we investigated marginalisation of random effects due to modelling by splitting a larger point set into several sparse sets of measurements. Results. Based on the results, MES is crucial to localise a void inside the complex analogue target. A reconstruction can be found when the maximum signal propagation time approximately matches that of the first back-wall echo for each measurement point. The marginalisation approach allows us to find a reconstruction that is comparable in quality to the case of full data, while reducing the computation effort per subsystem, which is advantageous when inverting a large data set.
Three level automatic segmentation of optic disc using LAB color space contours and morphological operation

Fundus disorders like Glaucoma are a serious health risk that affects people all over the world, lowering their quality of life. Optic disc (OD) segmentation is a crucial step for automatic detection of glaucoma with fundus images. This paper presents an efficient and accurate OD segmentation methodology from eye fundus images. In the proposed methodology, fundus images are transformed from RGB (Red, Green, and Blue) color space to LAB (Luminance, A‐axis, B‐axis) color space. A new image is generated using the luminance, A‐axis component, and image information content. As a pre‐segmentation stage, the unsupervised approach extracts the region of interest containing OD. The adaptive threshold method, followed by morphological operations, extracts the contours of the OD region and removes the spur. Segmentation of OD is a challenging task due to similar intensity levels with neighborhood, vascular occlusion, and retinal atrophy. The proposed work overcomes the challenges by segmentation of the OD at three levels, which enhances the functionality at different illumination conditions. The features are extracted at various levels to determine whether the segmented image contains the OD or not. The efficiency of the proposed methodology demonstrates by applying it to a variety of fundus images from datasets, namely ISBI IDRiD, DRISHTI‐GS, and SYSU. Key performance metrics like Intersection over Union and Dice Coefficient are used to evaluate the performance of the proposed methodology. The proposed methodology achieves Intersection over Union of 0.996, 0.997, and 0.994 on ISBI IDRiD, DRISHTI‐GS, and SYSU datasets, respectively. Similarly, the proposed methodology obtains 0.96, 0.97, and 0.98 Dice Coefficient on the three datasets.
Robustness and Tuning of Incremental Backstepping Approach

Incremental Backstepping (IBS) approach is robust to the uncertainties in the plant dynamics term f(x). However, its robustness to the uncertainties in the control effectiveness term g(x) remains unknown especially in the presence of actuator dynamics. Furthermore, it requires the assumption of the availability of fast actuator dynamics. In this paper, the robustness is analyzed with and without the actuator dynamics. It is found that uncertainties with γ > 1 are advantageous for the stability of the complete system. Then, the tuning of the IBS is introduced. Two methods which can increase the robustness of the IBS are proposed: γ tuning and actuator compensator. The design of an actuator compensator requires the parameter of the actuator whereas the γ tuning methods does not. Finally, the robustness to model uncertainties is verified by simulation examples, which show the effectiveness of the proposed approaches.
A Model of the 2-35 micron Point Source Infrared Sky

The Point Source Infrared Sky Model of Wainscoat et al. [ApJS, 83, 111 (1992)] is extended to make predictions for any filter lying wholly within the range 2.0 to 35.0 μm. The development of a library of complete 2-35 μm low-resolution spectra (with 0. 1 μm step size) that represent the 87 categories of Galactic object and four types of extragalactic source implicit in the Model supports this extension. This library is based upon the «spectral template» technique whereby existing spectral fragments for individual sources (from ground-based, airborne, and satellite-borne instruments) are combined into complete spectra. Templates provide a natural way to represent the complete spectral energy distributions of celestial sources for which only infrared photometry and/or partial spectroscopy are available
Extraction and Characterization of Type I Collagen from Parrotfish (Scarus sordidus Forsskål, 1775) Scale solubilized with the Aid of Acetic Acid and Pepsin

Waste from marine fish processing is an important source of valuable products. Fish collagen is considered a alternative biomaterial due to its excellent properties, and it is widely used for industrial purposes. Thus, this present study aimed to characterize acid and pepsin-soluble collagens from the waste of parrotfish (Scarus sordidus Forsskål, 1775) scales. The yields (p > 0.05) of acid-soluble collagen (ASC-PFS) and pepsin-soluble collagen (PSC-PFS) were 1.17 g/100 g and 1.00 g/100 g, respectively. Both collagen samples were categorized as type I owing to the presence of two alpha chain subunits (α1 and α1) after being confirmed by a sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE). Under the fourier transform infrared (FTIR) test, the triple helical structure of type I collagens from the ASC-PFS and PSC-PFS was maintained. Moreover, the study of UV visible spectra and X-ray diffraction (XRD) showed the similarity of collagens derived from different fish species, and the thermostability (Tmax) evaluation of all extracted collagens was in the range of 36.22–37.78°C, and their values were comparable to previous research on the fish scale collagens. The effect of various pH and sodium chloride (NaCl) treatments on solubility exhibited that the ASC-PFS and PSC-PFS were highly soluble in an acidic condition (pH < 5.0) and low concentration of sodium chloride (<30 g/L). Taken together, collagens extracted from parrotfish scale waste can be an alternative source for industries.
Do Double‐Moment Microphysics Schemes Make Reliable Predictions on the Raindrop Number Concentration?: A Squall‐Line Case Study

Utilizing raindrop size distribution observations in the evaluation of a cloud microphysics scheme helps identifying systematic deficiencies of the scheme and improving it. In this study, we evaluate the performance of seven different double‐moment bulk microphysics schemes in predicting raindrop number concentration Nr in squall‐line simulations using ground‐based disdrometer observations. While all seven schemes show good performances in the prediction of rainwater mass, they show significant biases from the observation and severe discrepancies from each other in the prediction of Nr. The Thompson, Thompson aerosol‐aware, and Weather Research and Forecasting double moment 6‐class (WDM6) schemes, where the main source of Nr is melting of snow and graupel, overestimate Nr by four times, which may be caused by overestimation of Nr production via melting. The Morrison and predicted particle properties (P3) schemes, where the autoconversion of cloud droplets into raindrops is an Nr source that is as important as melting, exhibit negative biases of Nr, which may be attributed to insufficient collisional breakup of raindrops. The P3_Nc scheme, the P3 scheme that prognoses cloud droplet number concentration, shows two prominent modes in Nr distribution, one of which consists of overestimated Nr produced by overly active autoconversion. The ice‐spheroids habit model with aspect‐ratio evolution scheme overestimates Nr but not as much as the two Thompson schemes and WDM6 scheme, showing the most realistic prediction of mean raindrop diameter. The high uncertainty in Nr prediction needs to be reduced by carefully selecting parameterizations for important microphysical processes, in order to maximize the advantage of using double‐moment microphysics schemes.
Implementation of Quizizz Educational Game Media in Increasing Students' Learning Motivation During the Covid-19 Pandemic in Islamic Religious Education Learning

This study aims to explain and describe how the implementation of learning using the Quizizz educational game media in increasing students' learning motivation during the Covid-19 pandemic in learning Islamic religious education. In this study the authors used a qualitative approach, the data sources were obtained from interviews, observations and documentation to teachers and students of SMAN 1 Campaka class X MIPA 1 and 2. The theoretical analysis used was Leony about learning media with Quizizz and Sardiman AM theory for analysis. motivation to learn.Based on data analysis, it can be concluded that: (1)The implementation of the Quizizz educational game media is well implemented, creating effective and fun learning and responding to the challenges of teachers in the very rapid development of the times and the emergence of the Covid-19 pandemic which requires learning to be carried out remotely, the impact of using Quizizz is to increase learning motivation, learning that attracts students' enthusiasm and interest in learning, a busy but still conducive learning environment (2) Supporting factors for the application of Quizizz media are the availability of facilities in the form of projectors, loudspeakers, electrical connecting cables, wifi in every class, while the inhibiting factor is the internet network which is less strong and stable (3) the results of the learning evaluation show a very good score, the highest score is 100% and the lowest score is 53%.
First record of Tanaorhinus viridiluteata Walker, 1861 (Lepidoptera: Geometridae: Geometrinae) from Mizoram, India

Very little work has been done to document the moth fauna of the Mizoram state in northeast India. An emerald moth collected from three localities in Aizawl District of Mizoram was identified as Tanaorhinus viridiluteata Walker, 1861 based on morphological and molecular studies. This species has been described briefly with colour photographs of male and female genitalia. Partial mitochondrial COI gene was amplified from these specimens for molecular analysis. This study represents a first record of the genus Tanaorhinus and species T. viridiluteata from Mizoram State.
Characterization of Intrinsic Radiation Sensitivity in a Diverse Panel of Normal, Cancerous and CRISPR-Modified Cell Lines

Intrinsic radiosensitivity is a major determinant of radiation response. Despite the extensive amount of radiobiological data available, variability among different studies makes it very difficult to produce high-quality radiosensitivity biomarkers or predictive models. Here, we characterize a panel of 27 human cell lines, including those derived from lung cancer, prostate cancer, and normal tissues. In addition, we used CRISPR-Cas9 to generate a panel of lines with known DNA repair defects. These cells were characterised by measuring a range of biological features, including the induction and repair of DNA double-strand breaks (DSBs), cell cycle distribution, ploidy, and clonogenic survival following X-ray irradiation. These results offer a robust dataset without inter-experimental variabilities for model development. In addition, we used these results to explore correlations between potential determinants of radiosensitivity. There was a wide variation in the intrinsic radiosensitivity of cell lines, with cell line Mean Inactivation Doses (MID) ranging from 1.3 to 3.4 Gy for cell lines, and as low as 0.65 Gy in Lig4−/− cells. Similar substantial variability was seen in the other parameters, including baseline DNA damage, plating efficiency, and ploidy. In the CRISPR-modified cell lines, residual DSBs were good predictors of cell survival (R2 = 0.78, p = 0.009), as were induced levels of DSBs (R2 = 0.61, p = 0.01). However, amongst the normal and cancerous cells, none of the measured parameters correlated strongly with MID (R2 < 0.45), and the only metrics with statistically significant associations are plating efficiency (R2 = 0.31, p = 0.01) and percentage of cell in S phase (R2 = 0.37, p = 0.005). While these data provide a valuable dataset for the modelling of radiobiological responses, the differences in the predictive power of residual DSBs between CRISPR-modified and other subgroups suggest that genetic alterations in other pathways, such as proliferation and metabolism, may have a greater impact on cellular radiation response. These pathways are often neglected in response modelling and should be considered in the future.
AI-Predicted mTOR Inhibitor Reduces Cancer Cell Proliferation and Extends the Lifespan of C. elegans

The mechanistic target of rapamycin (mTOR) kinase is one of the top drug targets for promoting health and lifespan extension. Besides rapamycin, only a few other mTOR inhibitors have been developed and shown to be capable of slowing aging. We used machine learning to predict novel small molecules targeting mTOR. We selected one small molecule, TKA001, based on in silico predictions of a high on-target probability, low toxicity, favorable physicochemical properties, and preferable ADMET profile. We modeled TKA001 binding in silico by molecular docking and molecular dynamics. TKA001 potently inhibits both TOR complex 1 and 2 signaling in vitro. Furthermore, TKA001 inhibits human cancer cell proliferation in vitro and extends the lifespan of Caenorhabditis elegans, suggesting that TKA001 is able to slow aging in vivo.
Indoor Point Cloud Segmentation Using a Modified Region Growing Algorithm and Accurate Normal Estimation

With the development of 3D sensors, 3D point cloud data can now be obtained conveniently. Therefore, it is crucial to process point cloud data automatically. Region growing is a commonly used algorithm to segment point clouds, which greatly depends on the accuracy of points’ normals and requires tuning two thresholds; i.e., the increment of curvature ( $\sigma _{th}$ ) and normal angles ( $\theta _{th}$ ). In this paper, we improve the region growing algorithm in two ways: Accurate normal estimation and strengthening the region growing criteria. For the first aspect, principal component analysis (PCA) is utilized to estimate the initial normals of the point cloud. Then, the points are divided into regular points (RP) and sharp feature points (SFP), according to their initial normals. A robust estimator-based PCA is then applied to refine the SFP normals. For the latter aspect, non-connected and non-coplanar points are detected and ignored when region grows. Finally, the segmentation performance of the proposed method is evaluated using internal and external indices. The results indicate that the proposed method can accurately estimate the point normals within an acceptable time, and obtain a better result than the classic PCA-based region growing algorithm and advanced DetMM-based methods.
Moments of Standardized Fernandez-Steel Skewed Distributions: Applications to the Estimation of GARCH-Type Models

We provide general expressions for obtaining raw, absolute and conditional moments for a standardized version of the class of skewed distributions proposed by Fernandez and Steel (1998). We show that these expressions are readily programmable in addition of greatly reducing the computational cost. We discuss several applications that are relevant for the purpose of estimating asymmetric conditional volatility models under skewed distributions.
Height Measurement for Meter Wave MIMO Radar based on Matrix Pencil Under Complex Terrain

Height measurement with meter wave (MW) MIMO radar in the actual complex terrain environment is a difficult problem because of terrain fluctuation, ground inclination, and multiple reflection points. Therefore, a practical signal model is studied, and a height measurement method of matrix pencil based on this signal model is proposed. Firstly, a more practical signal model of MW MIMO radar with the complex terrain is derived, and then the modified generalized MUSIC and maximum likelihood estimation algorithms are proposed. However, the modified algorithms need to know a priori information of reflected terrain angle. Therefore, the height measurement methods based on matrix pencil are studied which includes forward matrix pencil method, forward and backward matrix pencil method and unitary matrix pencil method. These method can quickly estimate the elevation and height without a priori information. Finally, the advantages/disadvantages of proposed algorithms under this complex terrain are compared by computer simulations. The results of simulations show that the effectiveness of our proposed algorithms.
On the Askey-Wilson and Rogers Polynomials

The q-shifted factorial (a)n or (a; q)n is and an empty product is interpreted as 1. Recently, Askey and Wilson [6] introduced the polynomials 1.1 where 1.2 and 1.3 We shall refer to these polynomials as the Askey-Wilson polynomials or the orthogonal 4ϕ3 polynomials. They generalize the 6 — j symbols and are the most general classical orthogonal polynomials, [2].
Assessing the Effects of Extreme Events on Machine Learning Models for Electricity Price Forecasting

: Forecasting electricity prices in the face of extreme events, including natural disasters or abrupt shifts in demand, is a difﬁcult challenge given the volatility and unpredictability of the energy market. Traditional methods of price forecasting may not be able to accurately predict prices under such conditions. In these situations, machine learning algorithms can be used to forecast electricity prices more precisely. By training a machine learning model on historical data, including data from extreme events, it is possible to make more accurate predictions about future prices. This can assist in ensuring the stability and dependability of the electricity market by assisting electricity producers and customers in making educated decisions regarding their energy usage and generation. Accurate price forecasting can also lessen the likelihood of ﬁnancial losses for both producers and consumers during extreme events. In this paper, we propose to study the effects of machine learning algorithms in electricity price forecasting, as well as develop a forecasting model that excels in accurately predicting said variable under the volatile conditions of extreme events.
Adaptive Regulator Design for Collocated Parabolic Distributed Parameter Systems

In this paper an adaptive regulator is designed for collocated parabolic distributed parameter systems with bounded deterministic disturbances in the case of the input and output operators being unbounded. The adaptive regulator is constructed by the concept of high-gain output feedback and the estimation mechanism of the unknown parameters for the disturbance. In the control system, the convergence of the system state to zero will be guaranteed.