Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Iron Mining Activity on the Endophytic Fungal Community of Aspilia grazielae

Aspilia grazielae (J. U. Santos) is an endemic plant species in Morro do Urucum in the Pantanal wetland (Brazil). A. grazielae is used for the restoration of areas impacted by iron mining activities. This study evaluates the diversity (composition, value and abundance) of endophytic fungal communities, considering parts of the plant and soil condition. The leaves and roots of A. grazielae were collected from native vegetation areas (NVA) and recovery areas (RCA) in Morro do Urucum. Illumina sequencing technology was used to investigate variation in endophytic fungal biodiversity. The operational taxonomic units detected in NVA ranged from 183 to 263 (leaf) and 115 to 285 (root), while RCA samples ranged from 200 to 282 (leaf) and 156 to 348 (root). Ascomycota phylum was the most common species among all plant samples. The most significant classes identified were Lecanoromycetes and Dothideomycetes that differed significantly (p ≤ 0.05) according to their plant hosts and soil stress. The relative abundance of Pestalotiopsis (Sordariomycetes class) and Stereocaulon (Lecanoromycetes class) genera was influenced by the iron mining activities according to the leaf samples analysed. However, the abundance and wealth of endophytic fungal communities in A. grazielae from RCA were evidence that could explain their high resilience to environmental disturbances and the source-sink dynamics of fungal propagules.
Empagliflozin Ameliorates Bleomycin-Induced Pulmonary Fibrosis in Rats by Modulating Sesn2/AMPK/Nrf2 Signaling and Targeting Ferroptosis and Autophagy

Pulmonary fibrosis (PF) is a life-threatening disorder that severely disrupts normal lung architecture and function, resulting in severe respiratory failure and death. It has no definite treatment. Empagliflozin (EMPA), a sodium-glucose cotransporter 2 (SGLT2) inhibitor, has protective potential in PF. However, the mechanisms underlying these effects require further elucidation. Therefore, this study aimed to evaluate the ameliorative effect of EMPA against bleomycin (BLM)-induced PF and the potential mechanisms. Twenty-four male Wister rats were randomly divided into four groups: control, BLM treated, EMPA treated, and EMPA+BLM treated. EMPA significantly improved the histopathological injuries illustrated by both hematoxylin and eosin and Masson’s trichrome-stained lung tissue sections, as confirmed by electron microscopic examination. It significantly reduced the lung index, hydroxyproline content, and transforming growth factor β1 levels in the BLM rat model. It had an anti-inflammatory effect, as evidenced by a decrease in the inflammatory cytokines’ tumor necrosis factor alpha and high mobility group box 1, inflammatory cell infiltration into the bronchoalveolar lavage fluid, and the CD68 immunoreaction. Furthermore, EMPA mitigated oxidative stress, DNA fragmentation, ferroptosis, and endoplasmic reticulum stress, as evidenced by the up-regulation of nuclear factor erythroid 2-related factor expression, heme oxygenase-1 activity, glutathione peroxidase 4 levels, and a decrease in C/EBP homologous protein levels. This protective potential could be explained on the basis of autophagy induction via up-regulating lung sestrin2 expression and the LC3 II immunoreaction observed in this study. Our findings indicated that EMPA protected against BLM-induced PF-associated cellular stress by enhancing autophagy and modulating sestrin2/adenosine monophosphate-activated protein kinase/nuclear factor erythroid 2-related factor 2/heme oxygenase 1 signaling.
Comparative Study of Isoflavone Synthesis Genes in Two Wild Soybean Varieties Using Transcriptomic Analysis

Soybean is an important food crop that contains high amounts of isoflavones. However, due to the expression of multiple genes, different soybean seeds have different isoflavone compositions. The underlying mechanisms for this complexity remain unknown. In this study, we identified potential differentially expressed genes (DEGs) in two wild soybean cultivars, ZYD7068 (high isoflavone) and ZYD7194 (low isoflavone), at different seed developmental stages using RNA-seq technology and compared their differences in isoflavone content. A total of 1067 and 6479 differentially metabolized genes were identified at R6 and R8 stages, respectively. Subsequent analysis of the KEGG pathway revealed that three of these differential metabolized genes were involved in the Isoflavonoid biosynthesis and Flavone and flavonol biosynthesis at the R6 stage. A total of 80 TF genes encoding differential expression of MYB, bZIP, and WRKY were identified in A1 vs. B1 and A3 vs. B3. Eight differentially expressed genes were identified in duplicates at both stages, and three genes showed the same expression trend at both stages. To confirm the results of RNA-seq, qRT-PCR was performed to analyze the expression of the six identified differentially expressed genes (DEGs). The results of qRT-PCR were consistent with the results of RNA-seq. We found that four genes (Glyma.13G173300, Glyma.13G173600, Glyma.14G103100, and Glyma.17G158900) may be involved in the positive regulation of isoflavone synthesis, while two genes (Glyma.04G036700 and Glyma.19G030500) may be involved in the negative regulation of isoflavone synthesis. These findings suggest that the observed difference in isoflavone levels between the two cultivars may be attributable to the differential expression of these six genes at later stages of seed development.
Terminal sliding mode-based cooperative tracking control for non-linear dynamic systems

This paper investigates cooperative tracking problems for multiple non-linear dynamic systems. The desired trajectory is only available to a portion of the agents. Treating the desired trajectory as a virtual agent, a terminal sliding mode (TSM)-based distributed control algorithm is designed only using the neighbours’ information. A decentralized observer is provided firstly to estimate the unknown external disturbances. Then, the Gaussian functions are introduced to approximate the second-order derivative of the inaccessible states. On this basis, we further develop a TSM-based cooperative tracking control algorithm for the non-linear dynamic systems such that the tracking errors of each agent converge to an adjustable neighbourhood of the origin in finite time. Finally, a simulation example is presented to illustrate the feasibility and effectiveness of the proposed approaches.
Evaluating the Impact of County-to-District Transformation on Urban Residential Land Supply: A Multi-Period Difference-in-Differences Model Analysis

Utilizing panel data from 264 prefecture-level cities in mainland China between 2009 and 2017, this study employs a multi-period difference-in-differences model and propensity score matching to assess the effects of county-to-district transformation (CDT) on the scale, proportion, and price of the urban residential land supply. The findings reveal the following details: (1) CDT led to a short-term increase in the overall proportion and price of this land, whereas its influence on the scale of the supply exhibited a time lag; (2) the policy’s impact on residential land supply varied across different types of cities, with a more pronounced effect on the scale, proportion, and price in large cities; and (3) the current implementation of CDT primarily modified the urban land’s supply–demand relationship through the expansion of built-up space, conversion of spatial function, and agglomeration of population and the labor force, consequently affecting the supply of the aforementioned land. Finally, this paper puts forward relevant policy suggestions on how to adjust land supply and effectively regulate the land market during the process of promoting the withdrawal of counties and districts in the future.
Towards a fully packaged high-performance RF sensor featuring slotted photonic crystal waveguides

A low loss and high sensitivity X-band RF sensor based on electro-optic (EO) polymer filled silicon slot photonic crystal waveguides (PCW) and bowtie antenna is proposed. By taking advantage of the slow light enhancementt in the PCW(>20X), large EO coefficient of the EO polymer(r33>200pm/V), as well as significant electric field enhancement of bowtie antenna on silicon dioxide substrate(>10000X), we can realize a large in-device EO coefficient over 1000pm/V so as to realize a high performance RF wave sensor. In addition, on-chip Mach-Zender interferometer (MZI) layout working under push-pull configuration is adopted to further increase the sensitivity of the sensor. Furthermore, inverse taper couplers and slotted photonic crystal waveguides are carefully designed and discussed in this paper to reduce the insertion loss of the device so as to increase the device signal-to-noise ratio. The minimum detectable electromagnetic power density is pushed down to 2.05 mW/m2, corresponding to a minimum sensing electric field of 0.61 V/m. This photonic RF sensor has several important advantages over conventional electronics RF sensors based on electrical scheme including high data throughput, compact in size, and great immunity to electromagnetic interference (EMI).
Streaming the Video Presentation Slides based on Frame Distance Technique

The lockdown of the society that caused by COVID 19 couldn’t affect the education sector as there is an alternative option that the students can take their education in online classes. Online classes start been widely used over the Internet as all the education sectors start using the e-learning system. E-learning system use the online tools to stream the contents of the lecture, such contents will increase the traffic load on the networks. The load of the networks will affect the smoothness of the online classes, as there is a possibility that the video frames could be lost or corrupted. Frame distance technique is proposed to reduce the traffic on the networks, by dropping the redundancy slides that are related to the same display time segment. Three different frame distance technique are considered in this study (low, medium and high) to show the amount of bandwidth that are required to stream the video to a group of students. From the outcomes it can be found from that, adaption will be necessary to reduce the video traffic load on the networks that will have a positive effect on the online classes.
Intelligent monitoring small target detection algorithm based on improved faster R-CNN

Current applications of the small target detection algorithm include enterprise logo detection, small face detection, pedestrian detection, traffic sign detection, automatic driving, remote sensing image detection, and criminal investigation. Practical applications are primarily still in the research phase. Consequently, this paper proposes an optimized small target detection algorithm on the basis of Faster R-CNN. Utilizing ResNet-18 with fewer convolutional layers in place of ResNet-50 as the backbone network of the original Faster R-CNN algorithm, the feature extraction network is strengthened, reducing the number of parameters of the algorithm. Moreover, by analyzing the characteristics of small targets, we can eliminate missed detection and false detection of small targets, Faster R-CNN algorithm uses feature fusion to amplify the contrast between small target features and background features in the feature pyramid network; Enhance prediction box deviations caused by pooling regions of interest by utilizing ROI Align. Lastly, the improved algorithm is experimentally verified on the MSCOCO data set. The faster R-CNN small target detection algorithm proposed in this thesis improves the optimized algorithm’s average accuracy from 89.14% to 93.39%, which is 4.25% higher than the original Faster R-CNN algorithm. Intelligent monitoring significantly enhances the detection and recognition of smaller and medium-sized targets.
Novel integrated matching algorithm using a deep learning algorithm for Wi-Fi fingerprint-positioning technique in the indoors-IoT era

The Internet-of-Things (IoT) has been used with greater frequency to track peoples’ daily activities, particularly those conducted indoors. Wi-Fi technology has been also been used as an alternative to global navigation satellite system (GNSS) technologies to track indoor activities. The received signal strength indicator (RSSI) is widely used to assist in the positioning of Wi-Fi signals. However, the RSSI-based technique suffers from multipath, non-line-of-sight (NLOS) problems and the fluctuation of RSSI measurements via Wi-Fi chipsets. One of the most well-known RSSI-based approaches is to apply the fingerprinting method to do the positioning. However, the fingerprinting-based form has an additional problem due to the lack of RSSI data samples, specifically in harsh area with a huge number of classes or reference points (RPs) and an unstable matching process algorithm. To mitigate the problems of the RSSI-based fingerprinting approach, this research proposes a novel matching process algorithm called Norm_MSATE_LSTM. We first performed the augmentation process to increase the RSSI data records via the Mean Stander deviation Augmentation TEchnique (MSATE). The RSSI records were normalized (norm), and the long short-term memory (LSTM) technique was applied to estimate the correct positions. Finally, the proposed matching algorithm was compared with the stand-alone matching algorithms, including the weighted k-nearest neighbors (WkNN) and LSTM. The results obtained from the experiments and the simulated experiments using OMNeT++ show that the proposed matching algorithm may improve positioning accuracy by 33.1% and 57.5% when only augmentation and augmentation with normalization are applied, respectively.
Stylosanthes guianensis CIAT 184 – review of a tropical forage legume

A comprehensive review, based on about 180 references, synthesizing research and development about Stylosanthes guianensis accession CIAT 184 is presented. This genotype has been widely tested across the tropics and was developed into commercial cultivars in several countries. Agronomic evaluations in a range of disciplines and other research in numerous countries of tropical America, Africa, Southeast Asia and China, and utilization of the legume by farmers are reported. CIAT 184 is particularly successful in southern China, where it gave origin to 5 cultivars. Its outstanding feature is an apparently durable tolerance of the fungal disease, anthracnose (Colletotrichum gloeosporioides), throughout the tropics, with exception of the savanna ecozone in tropical America. Further assets include adaptation to acid, infertile soils, drought tolerance and high production of nutritious dry matter that can be used as traditional forage for ruminants and as leaf meal or pellets to feed monogastrics. CIAT 184 has also found application for soil conservation and improvement (such as erosion control, mulch and green manure), alone or in association with crops and trees. Published work dealing with basic research, mainly conducted in China, is presented. The review concludes with a brief discussion on the current adoption of CIAT 184 and with suggestions regarding future perspectives and research needs.
Classification of Cocoa Pod Maturity Using Similarity Tools on an Image Database: Comparison of Feature Extractors and Color Spaces

Côte d’Ivoire, the world’s largest cocoa producer, faces the challenge of quality production. Immature or overripe pods cannot produce quality cocoa beans, resulting in losses and an unprofitable harvest. To help farmer cooperatives determine the maturity of cocoa pods in time, our study evaluates the use of automation tools based on similarity measures. Although standard techniques, such as visual inspection and weighing, are commonly used to identify the maturity of cocoa pods, the use of automation tools based on similarity measures can improve the efficiency and accuracy of this process. We set up a database of cocoa pod images and used two feature extractors: one based on convolutional neural networks (CNN), in particular, MobileNet, and the other based on texture analysis using a gray-level co-occurrence matrix (GLCM). We evaluated the impact of different color spaces and feature extraction methods on our database. We used mathematical similarity measurement tools, such as the Euclidean distance, correlation distance, and chi-square distance, to classify cocoa pod images. Our experiments showed that the chi-square distance measurement offered the best accuracy, with a score of 99.61%, when we used GLCM as a feature extractor and the Lab color space. Using automation tools based on similarity measures can improve the efficiency and accuracy of cocoa pod maturity determination. The results of our experiments prove that the chi-square distance is the most appropriate measure of similarity for this task.
An improved method for thyroid nodule ultrasound image segmentation based on U2‐Net

To achieve accurate segmentation of thyroid nodule ultrasound images, obtain information on the physiological parameters of the lesion area and guide the clinical formulation of individualized treatment plan, an improved network based on the U2‐Net model is proposed in this paper. Thyroid images of 264 patients and 215 healthy volunteers at the First Hospital of Shanxi Medical University from February 2016 to June 2022 are studied, and the digital database thyroid image (DDTI) data set is proposed for data expansion. The experimental results show that the Dice coefficient on the test set was 80.58%, and the mean intersection over union was 81.21%. The improved U2‐Net model has the best segmentation accuracy compared with similar models, realizes the automatic segmentation of thyroid nodules, provides help for manual segmentation, and has good application prospects and clinical value.
Automatic 10 m Forest Cover Mapping in 2020 at China’s Han River Basin by Fusing ESA Sentinel-1/Sentinel-2 Land Cover and Sentinel-2 near Real-Time Forest Cover Possibility

Given the increasingly fragmented forest landscapes, it is necessary to map forest cover with fine spatial resolution in a large area. The European Space Agency (ESA) released the 10 m global land cover map in 2020 based on Sentinel-1 and Sentinel-2 images, and Dynamic World provides near real-time possibilities of many land cover classes based on Sentinel-2 images, but they are not designed particularly for forest cover. In this research, we aimed to develop a method to automatically estimate an accurate 10 m forest cover map in 2020 by fusing the ESA forest cover map and Dynamic World near real-time forest cover possibilities. The proposed method includes three main steps: (1) generating stable forest samples, (2) determining the threshold T and (3) producing the fused forest cover map. China’s Han River Basin, dominated by complex subtropical forests, was used as the study site to validate the performance of the proposed method. The results show that the proposed method could produce a forest cover map with the best overall accuracy of 98.02% ± 1.20% and more accurate spatial details compared to using only one of the two data sources. The proposed method is thus superior in mapping forest cover in complex forest landscapes.
Δ8-THC Induces Up-Regulation of Glutamatergic Pathway Genes in Differentiated SH-SY5Y: A Transcriptomic Study

Cannabinoids, natural or synthetic, have antidepressant, anxiolytic, anticonvulsant, and anti-psychotic properties. Cannabidiol (CBD) and delta-9-tetrahydrocannabinol (Δ9-THC) are the most studied cannabinoids, but recently, attention has turned towards minor cannabinoids. Delta-8-tetrahydrocannabinol (Δ8-THC), an isomer of Δ9-THC, is a compound for which, to date, there is no evidence of its role in the modulation of synaptic pathways. The aim of our work was to evaluate the effects of Δ8-THC on differentiated SH-SY5Y human neuroblastoma cells. Using next generation sequencing (NGS), we investigated whether Δ8-THC could modify the transcriptomic profile of genes involved in synapse functions. Our results showed that Δ8-THC upregulates the expression of genes involved in the glutamatergic pathway and inhibits gene expression at cholinergic synapses. Conversely, Δ8-THC did not modify the transcriptomic profile of genes involved in the GABAergic and dopaminergic pathways.
An analytical solution of the Laplace equation with Robin conditions by applying Legendre transform

ABSTRACT We derived the analytical solution of the Laplace equation with Robin conditions on a sphere with azimuthal symmetry by applying Legendre transform, which was expressed in terms of the Appell hypergeometric function. Δu = 0 in a unit sphere ∂u(r, ζ)/∂r|r=1 + h u(1, ζ) = f(ζ) on a unit sphere, ζ = cos (θ), θ is the azimuthal angle and h ∈ . The function f(ζ) is a prescribed function and is assumed to be a square-integrable function. Moreover the analytical expression of the integral is given in terms of the Appell function F1. In many experimental approaches, the Robin coefficient « h » is the main unknown parameter for example in transport phenomena where the Robin coefficient is the dimensionless Biot number. The usefulness of this formula is illustrated by some examples of inverse problems.
“I am (oppressed), therefore I see”: Multiple stigmatized identities predict belief in generalized prejudice and intraminority coalition

ABSTRACT Integrating lay theory of generalized prejudice (LTGP) and intersectionality frameworks, the present research demonstrated that, across four samples (N = 7,121), people with a greater number of stigmatized identities (based on race/ethnicity, gender, and sexual orientation) endorsed LTGP more strongly, perceived greater similarities across marginalized groups, and ultimately indicated stronger support for intraminority coalitions (Studies 1–3) and specifically stronger policy support for low-SES people (Study 3). Notably, multiply stigmatized people (especially those with three stigmatized identities) endorsed LTGP and intraminority coalitions more strongly than did singly stigmatized and non-stigmatized people, who did not significantly differ from each other on these outcomes. Together, these findings highlight the importance of intersectionality in understanding intraminority relations and contribute to coalition-building efforts across oppressed groups.
Capital Structure, Corporate Governance and Cost Efficiency in Selected Listed Financial Firms in Nigeria

The study analyzed the cost efficiency of a subset of Nigerian listed financial firms and looked at the impact of corporate governance on the subset of listed financial enterprises. Additionally, it evaluated how corporate governance affected the chosen listed financial organizations in Nigeria taking into consideration capital structure and cost efficiency. They were with the intention of supplying details on the interactions between capital structure, corporate governance, and cost efficiency in a number of Nigerian financial organizations between 2005 and 2020 which is the post-consolidation period and the time the country was affected by the infamous virus that shook the whole world. The objective of this study is to examine the capital structure, and corporate governance on cost efficiency of selected listed financial firms in Nigeria. The study used secondary data from 20 quoted, carefully chosen financial firms in Nigeria and used a descriptive survey design. These data were analyzed using Stochastic Frontier Analysis (SFA) and the findings indicated that the deposit money banks in Nigeria had an average cost of efficiency of 54.6%. The capital structure was significantly impacted by corporate governance factors such as board size (t= 2.285, p0.05) and board expertise (t=-2.311, p0.05). Finally, the outcome demonstrated that elements of corporate governance such as board size (t=-2.807, p 0.05), board independence, and board composition, which acted as intermediary variables between corporate governance and cost-effectiveness, were both statistically significant at the 5% level. According to the study's findings, there was a significant association between cost-effectiveness, corporate governance, and capital structure.