Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of some pre-sowing treatments on seed germination and seedling growth of Melia azedarach l. and Tamarindus indica l. trees

The present study was conducted during two successive seasons of 2020 and 2021 to determine the effect of some pre-germination treatments on germination and seedling growth of Tamarindus indica and Melia azedarach . In this study, seeds of the two species were subjected to seven treatments i.e. soaking seeds in GA3 at 1000, 2000 and 3000 ppm for 24 h. ; immersing seeds in concentrated sulphuric acid for 10 and 20 min.; seed scarification by steel file and untreated (control) seeds. Seeds were sown in poly ethylene pots (25 x 30 cm) under normal environmental condition for germination. The highest germination percentage was recorded in seeds of the two species treated by mechanical scarification, followed by immersion in concentrated sulphuric acid (98%) for 20 and 10 minutes. Meanwhile, the lowest values of germination percentage were obtained with the control, followed by soaking seeds in GA3 at 1000 ppm compared to other treatments. Mechanical scarification, followed by concentrated H 2 SO 4 for 20 and 10 min. led to highly significant reduction in time to attain 50% germination as well as the highest values of vigor index for the tested species in the 1st and 2nd seasons compared to other treatments. Also, the results showed that the pre-germination treatments have a significant effect on the growth performance (shoot and root length, number of leaves, fresh and dry weight of shoots and roots as well as chlorophyll content) of M. azedarach and T. indica . The highest values of the growth parameters were obtained from applying scarification and concentrated sulphuric acid for 20 and 10 min., respectively. The study recommended mechanical scarification and sulphuric acid for 20 minutes for the best seed germination and seedling growth of these tree species.
How Classroom Environment Influences Academic Enjoyment in Mathematics Among Chinese Middle School Students: Moderated Mediation Effect of Academic Self-Concept and Academic Achievement

Purpose Substantial literature has documented the influence of classroom environment on academic enjoyment. However, little is known about the mediating and moderating mechanisms underlying this relationship. Based on the control-value theory and the individual-context interaction model, a moderated mediation model was constructed in this study to further examine whether academic self-concept mediated the relation between classroom environment and enjoyment in mathematics and whether this mediating effect was moderated by academic achievement. Methods We recruited 750 Chinese middle school students and they completed the classroom environment, academic self-concept, and academic enjoyment questionnaires. Results After controlling for gender and grade, the results of structural equation modeling showed that academic self-concept partially mediated the association between classroom environment and enjoyment in mathematics. The mediating path from classroom environment to academic self-concept was moderated by academic achievement. Classroom environment positively predicted academic self-concept for the higher achieving students. However, the effect of classroom environment on academic self-concept was not significant for the lower achieving students. Conclusion These findings highlight that classroom environment has a more salient impact on academic self-concept and enjoyment for higher achieving students than for lower achieving students. The study results provide guidelines for educators regarding effective interventions for fostering positive academic emotions.
Research on the identification of DCT vehicle driver’s starting intention based on LSTM neural network and multi-sensor data fusion

Currently, the objective evaluation of the DCT vehicle drivability requires the accurate identification of the driver’s intention and vehicle state as well as the selection of the targeted evaluation indicators. The existing identification methods usually cannot divide the driver’s intentions in detail and make full use of the characteristics of time-series signals. Simultaneously, external kinematic sensors are more commonly used than the sensors of vehicle powertrain, which impacts the recognition effect. This paper proposes a new method for identifying the DCT vehicle driver’s starting intentions based on an LSTM neural network and multi-sensor data fusion. The DCT vehicle driver’s starting intentions are subdivided and defined based on human–vehicle interaction analysis and K-means clustering. The input of the model consists of 11-dimensional variables that include motion parameters of the vehicle collected by the external sensors and the powertrain parameters collected by onboard sensors. The method proposed in this paper first establishes a recognition window, which is utilized to extract the starting process samples from the DCT vehicle driving data. Second, the 11 variables of each sample are used as one set of multi-dimensional time-series signals, which are preprocessed through wavelet denoising. Finally, the LSTM network is used to identify the samples. The identification results indicate that the highest recognition accuracy of the proposed algorithm is 94.27%, which is approximately 5% higher than conventional methods, such as fully connected neural networks and support vector machines. Furthermore, the model with 11 input variables outperforms the model with fewer input variables. The effectiveness and superiority of the identification model have been demonstrated.
On localization of Dirac fermions by disorder

This thesis is devoted to the effects of disorder on two-dimensional systems of Dirac fermions. Disorder localizes the usual electron system governed by the Schroedinger equation. The influence of disorder on Dirac fermions is qualitevely different. We concentrate on a random mass term in the Dirac equation. We have discovered that Dirac fermions in graphene are localized by a random mass, without any transition into metallic state. The situation is entirely different for Dirac fermions in a p-wave superconductor. There electrostatic disorder appears in the Dirac equation as a random mass, which localizes the excitation, but only if the disorder is relatively weak. For large mass fluctuations a transition into metallic state appears. This qualitatively different response to disorder in graphene and in p-wave superconductors is explained by the appearance of Majorana bound states, which allow for resonant tunneling and metallic state. Electrostatic disorder in a d-wave superconductor represented as random vector potential in the Dirac equation. We look at the transmission of Dirac fermions for electrostatic potential with long- and short-range fluctuations. We study the interplay of electrical and mechanical properties of suspended graphene by calculating the correction to the conductivity due to its deformation by a gate electrode.
UV properties of Galileons: Spectral Densities

We propose a picture for the UV properties of Galileon field theories. We conjecture that Galileons, and all theories incorporating the Vainshtein mechanism, fall into Jaffe's class of `non-localizable' field theories characterized by an exponential growth in their Kallen-Lehmann spectral densities. Similar properties have been argued to arise for Little String Theories and M-theory. For such theories, the notion of micro-causality and the time ordering used to define the S-matrix and correlation functions must be modified, and we give a Lorentz invariant prescription for how this can be achieved. In common with General Relativity (GR), the scattering amplitudes for Galileons are no longer expected to satisfy polynomial boundedness away from the forward scattering or fixed physical momentum transfer limits. This is a reflection of the fact that these theories are fundamentally gravitational and not local field theories. We attribute this to the existence of a locality bound for Galileons, analogous to the Giddings-Lippert locality bound for GR. We utilize the recently developed Galileon duality to define a UV finite, Lorentz invariant, quantization of a specific Galileon theory for which the energy of all states are positive definite. We perform an explicit computation of the Wightman functions for this theory, and demonstrate the exponential growth associated with the locality bound. In analogy with GR, the bound is correlated with the absence of Galileon Duality (i.e. Diffeomorphism) invariant local observables. We argue that these theories can be quantized in a manner which preserves Lorentz invariance and macro-causality and that the latter ensures that the superluminalities found in the low energy effective theory are absent in the full theory.
Methyl-Sensitive Amplification Polymorphism (MSAP) Analysis Provides Insights into the DNA Methylation Underlying Heterosis in Kenaf (Hibiscus Cannabinus L.) Drought Tolerance

ABSTRACT Kenaf (Hibiscus cannabinus L.) is an industrial fiber crop with significant heterosis and tolerance to abiotic stresses. However, the mechanism underlying heterosis in kenaf under drought conditions is completely unknown. In this study, F1 hybrids were remarkably tolerant to drought stress. Through methylation-sensitive amplification polymorphism (MSAP) analysis, we highlighted that total DNA methylation level under drought decreased by 11.2% in F1 and increased by 29.7% and 13.1% in parents CP079 and CP071, respectively. Unlike parents, the hypomethylation rate was 38% higher than hypermethylation in F1, the overall change in methylation pattern was higher in F1 (1.6%) than parents (0.4%). Furthermore, 35 key DNA fragments showing different methylation patterns were analyzed and found that the cytosine methylation status of DnaJ, ERF5, ZIP2, and PATL3 revealed changes in DNA methylation that may affect their respective expression level. Subsequently, virus-induced gene silencing (VIGS) mediated knockdown of the DnaJ, significantly increased the sensitivity of kenaf seedlings to drought. Overall, this study provides an epigenetic mechanism that could be used to improve the breeding strategy in kenaf.
Multi-Stage Hybrid Text-to-Image Generation Models.

Generative Adversarial Networks (GANs) have proven their outstanding potential in creating realistic images that can't differentiate between them and the real images, but text-to-image (conditional generation) still faces some challenges. In this paper, we propose a new model called (AttnDM GAN) stands for Attentional Dynamic Memory Generative Adversarial Memory, which seeks to generate realistic output semantically harmonious with an input text description. AttnDM GAN is a three-stage hybrid model of the Attentional Generative Adversarial Network (AttnGAN) and the Dynamic Memory Generative Adversarial Network (DM-GAN), the 1 st stage is called the Initial Image Generation, in which low resolution 64x64 images are generated conditioned on the encoded input textual description. The 2 nd stage is the Attention Image Generation stage that generates higher-resolution images 128x128, and the last stage is Dynamic Memory Based Image Refinement that refines the images to 256x256 resolution images. We conduct an experiment on our model the AttnDM GAN using the Caltech-UCSD Birds 200 dataset and evaluate it using the Frechet Inception Distance (FID) with a value of 19.78. We also proposed another model called Dynamic Memory Attention Generative Adversarial Networks (DMAttn-GAN) which considered a variation of the AttnDM GAN model, where the second and the third stages are switched together, its FID value is 17.04.
Polycyclic Aromatic Hydrocarbons in Home-Grown
Allium Fistulosum from an Industrial City
of China: Concentrations, Source
and Cancer Risks

Polycyclic aromatic hydrocarbons (PAHs) in vegetables may pose a cancer risk via consumption of contaminated vegetables. Thus, this study aims to analyze the concentrations of PAHs in home-grown allium fistulosum from an industrial city of China, and assess the exposure risks. The results indicate that adsorption of PAHs through roots and local anthropogenic pollution were major factors affecting accumulation of PAHs in edible parts of allium fistulosum. The sources of PAHs were identified as vehicle emissions, agricultural waste burning and pollution discharges of petroleum factories. Daily dietary exposures of consumers to PAHs in allium fistulosum ranged from 2.76×10 -4 to 7.02×10 -5 ng/(d·kg), children had the highest daily exposure dose, while the teenagers had the lowest. For individuals of different gender and age groups (children, teenagers, adults and old), the incremental lifetime cancer risks (ILCRs) ranged from 1.4×10 -6 to 3.5×10 -5 , which were all higher than safe risk level of 10 -6 . The findings of this study suggest that growing allium fistulosum in the urban areas of an industrial city may pose low cancer risks to consumers.
UHF RFID Tag Design Using Theory of Characteristics Modes for Platform-Tolerant and Harsh Metallic Environments

This paper presents a wideband, long-range RFID tag antenna design with platform tolerant features using characteristic mode analysis (CMA). The proposed design consists of a two-layer structure, that are separated by an air gap. The upper layer consists of T-match and dual loop-based meandered antenna optimized for achieving long read range characteristics in free space/low permittivity dielectrics. The second layer consists of multi-resonant strips printed on a grounded substrate carefully designed using CMA for producing multi-resonant modes in the required UHF RFID band. The multi-resonant strips help in achieving wide bandwidth and isolation between different environment surfaces. This tag antenna provides a wide bandwidth ranging from 900 – 940 MHz that covers both the US UHF RFID band (902 – 928 MHz) and the upper European UHF RFID band (915 – 921 MHz). Moreover, this design achieved a read range of 14.7 m in free space, 14 m on a metal plate, and 13 m on wood, and glass. Furthermore, the proposed antenna was also tested in harsh metal environments such as car number plates and cargo metallic containers. Therefore, the proposed tag can be used for tagging metallic containers, wood containers, and other harsh platforms for cargo management, Asset tracking, supply chain visibility, and Internet of Things (IoT) Applications.
Relationships between parenting style and sibling conflicts: A meta-analysis

Previous studies have shown that sibling conflicts are detrimental to physical and psychological development, as well as long-term human development. Although many studies have discovered relations between parenting style and sibling conflicts, these findings were contentious and did not provide a universal solution. Therefore, the meta-analysis was used as the method to determine the nature and magnitude of the relationships. There were a total of 14,356 participants in the 16 included studies, from which 55 effect sizes were extracted. According to the overall meta-analysis results, authoritative parenting (r = –0.201) did have the potential to reduce sibling conflicts. Neglectful (r = 0.389), inconsistent (r = 0.364), indulgent (r = 0.293), and authoritarian (r = 0.235) parenting styles were related to sibling conflicts in a positive way. Moreover, the study investigated the effectiveness of several moderator variables, such as children’s gender, age, region, outcome measure, and publication year. The research indicates that authoritative parenting is a protective factor of sibling conflicts. Moderator analyses found that gender, age, region, outcome measure, and publication year played moderating roles in the relationships. Finally, research limitations in the current study and expectations for future research, as well as theoretical contributions and implications for parenting practice, were presented. Systematic review registration [https://inplasy.com/inplasy-2022-8-0020/], identifier [INPLASY202280020].
Repositioning Budapest’s tourism milieu for a post-Covid-19 period: Visual content analysis

In order to propose a repositioning toolkit, this research addresses the essence of the daytime tourism milieu of the Hungarian capital Budapest’s nightly party zone and formulates the following two research questions: (1) What are the available elements of the daytime tourism milieu of Budapest’s party zone? and (2) How can this milieu enhance tourist experience for leveraging a sense of place in a future post-Covid-19 era? The data for this research were collected with the help of 85 undergraduates, who were given the task of taking 3 photos, as if they were tourists, aiming to capture the best reflection of the daytime tourism milieu of Budapest’s party zone. A database of 255 photos was analysed through visual content analysis. Additionally, each image was assigned a location of the photo, five hashtags and a short description. The descriptions of the photos were analysed by Python programming and calculations. The findings show the most important research outcomes concerning Budapest’s party zone, focusing on the daytime values of the district. The research identified the “creative milieu”’, “Jewish heritage milieu” and “gastronomic milieu” as the most important daytime profiles of the party zone. Based on the findings, the authors propose a repositioning toolkit and a strategy which, on the one hand, will develop a stronger sense of place in the case of the tourism milieu of Budapest’s party zone and, on the other hand, will position the party zone not only as a place of nightlife but also as a venue of daytime tourism.
RESEARCH NOTE ON THE HARRISON-THOMPSON GENERALIZATION OF BOHM'S SHEATH CONDITION

Harrison and Thompson (1959) generalized Bohm's condition for the formation of a sheath in an electrical discharge. One point of their generalization which restricts the claim that their analysis is applicable to an arbitrary incident ion velocity distrubution is discussed. The restriction lies in the divergence or their expansion of the integral for the ion density. A corrected analysis is suggested, and the physical interpretation of the results is described briefly. (D.L.C.)
efficient weather recognition algorithm on highway roads for vehicle guidance

Adverse weather has long been recognized as one of the major causes of motor vehicle crashes due to its negative impact on visibility and road surface. Automatic recognition of weather condition has important application value in traffic condition warning, automobile auxiliary driving, intelligent transportation system, and other aspects. Providing drivers with real- time weather information is therefore extremely important to ensure safe driving in adverse weather. Most of the Department of Transportations (DoTs) in the U.S. have installed roadside webcams mostly for operational awareness. This study leveraged these easily accessible data sources to develop affordable automatic road weather condition for vehicle guidance. The developed detection model is focused on four weather conditions; rainy, snowy, sunny and foggy. The main goal of the proposed work is to use two neural network models such as K-Nearest Neighbour and pre- trained Convolutional Neural Network (CNN) models to achieve the classified tasks. The ResNet50 issued to train and test on the “Weather Dataset” and desirable recognition results are obtained. AlexNet, GoogleNet, ResNet50 and SqueezeNet has been compared . The Result has been achieved with ResNet50. ResNet50 is proposed as higher accuracy as compared to AlexNet, GoogleNet ,ResNet and SqueezeNet.
Rheumatoid arthritis – symptoms, diagnosis, treatment

Rheumatoid arthritis is a systemic autoimmune disease of connective tissue of unknown etiology. Specific for it is the involvement of symmetrically small and medium-sized joints, leading to degradation of their structure, stiffening and muscle contractures. It is also characterized by extra-articular and generalized symptoms, such as fatigue, low-grade fever and sweating. It can occur with or without elevated RF and / or ACPA antibodies, causes disability and contributes to premature death. Drugs for this disease can be categorized into disease-modifying drugs as well as glucocorticosteroids and non-steroidal anti-inflammatory drugs. The first-line drug is methotrexate. In case of ineffectiveness of the treatment, biological treatment can be started.
Effect of an enzymatic-probiotic preparation on the microfauna of the rumen of the Russian Black Pied and Ayrshire cows

The main purpose of the research was to study the effect of “Rumit” enzyme-probiotic additive, obtained on the basis of reindeer rumen microorganisms (Rangifer tarandus), on the population and generic diversity of rumen protists of lactating Russian Black Pied and Ayrshire cows. In the conditions of one farm with the same feeding rations, control and experimental groups of two breeds of 15 animals each were formed. For 90 days, the experimental groups received 50 g per day of a probiotic preparation in addition to the main diet. At the beginning and at the end of the experiment, the contents of the rumen were selected for counting and determining the generic composition of the ciliate fauna. At the beginning of the experiment, the same density and uniform population of protists were determined in all groups. The use of the probiotic contributed to the growth of the rumen microfauna. In the experimental group of the Russian Black Pied cattle, the density of the ciliate fauna was 2 times higher than in the control group (p>0.95). In cows of the Ayrshire breed of the experimental group, in the cicatricial content, the population of ciliates exceeded this indicator of the control by 237.2 thousand ind. /ml. Also, the use of probiotics led to the emergence of new genera of protists and their active reproduction. It was concluded that the drug “Rumit” had a positive effect on the density and generic composition of the ciliate fauna of the rumen of cows, which indicates the optimal parameters of its functioning and the state of health of animals in general.
Atrial Fibrillation in Chronic Kidney Disease: An Overview

Chronic kidney disease (CKD) is a condition that can be caused due to any etiology leading to structural damage to the kidney, which can be measured by a decrease in estimated glomerular filtration rate (eGFR) and the presence of damage biomarkers for more than three months. This article has discussed the causal relationship between atrial fibrillation (AF) and CKD, a few of them being inflammation, renin-angiotensin-aldosterone system (RAAS) activation, anemia, and uremia associated with CKD. This review mentioned the clinical impact of the presence of AF in CKD patients. The presence of AF in CKD patients aggravates the renal dysfunction, which in turn adds to the generation of AF. This article explores the various pharmacological and interventional treatment modalities, including antiarrhythmics, anticoagulants, and cardiac ablation, and their complications, leading to restricted usage in CKD patients.
A Study on Chinese Students’ At-home Online Learning Practice at a UK University

: This study explores Chinese university students’ online learning practice during the at-home online learning period and the factors driving their learning practice. Based on the data collected from semi-structured interviews with eight postgraduate students at a UK university, I found that Chinese students prefer self-directed learning and collaborative learning through social media. Lack of metacognitive skills and deep roots in Chinese collective culture drive them to learn in these two ways. I advocate a) that students should learn and employ metacognitive learning strategies and b) that teachers should provide students with more flexible and appropriate learning materials from various platforms.