Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning-Based Economic Forecasting for the New Energy Vehicle Industry

Recently the issues of insufficient energy and serious air pollution around the world have been rising. Henceforth, there is a need to carry out a research of new energy. Soon, new energy vehicles will be the mainstream trend, which can not only reduce the burden of consumers due to rising fuel prices but also solve the air pollution problem caused by the exhaust emissions of fuel vehicles. With the rapid development of science and technology, deep learning continues to make breakthroughs, and, in the field of economy with huge information data, we have more powerful weapons available to predict and research important economic data with infinite value, which can not only provide reference information to policy makers but also help enterprises and even economic markets to develop more healthily and sustainably. Therefore, this article uses deep learning algorithms to forecast and analyze the new energy industry, starting from the financial information released by new energy vehicle companies in their annual reports, in order to make basic judgments and help policy makers and enterprises in the new energy vehicle industry.
Detecting change-points in a Compound Poisson Process

A Statistical change point problem was first studied in the mid-1950s in the context of quality control in industrial processes. A change point is defined as a point in the time order when the probability distribution of a sequence of observations differs before and after that point. The literature of statistical change point has evolved over time and now includes a significant amount of scholarly work on change point analysis with many important applications in other disciplines such as economics, geosciences, medicine, and genetics, to name a few. This work examines the problem of locating changes in the distribution of a Compound Poisson Process where the variables being summed are iid normal and the number of variable follows Poisson. The maximum likelihood ratio for the location of the change point will be explored as well as an information criterion developed, for the case of known variance, while a Bayesian approach is used to deal with the case including change in variance. These results can be applied in any field of study where an interest in locating changes not only in the parameter of a normally distributed data set but also in the rate of their occurrence. It has direct application to the
Radar imageries information extraction and its use in pre-hail estimation algorithm

सार – एक बहत बड़ ेडॉ लर मौसम रेडार ु (DWR) िचत्र संसािधत उपकरण िवकिसत िकया गया और PHDA म इसकी उपयोिगता का परीक्षण कर इसे प्रामािणक माना गया। ओला िनयंत्रण प्रिक्रया के िलए परावतर् कता (ART पिरकलन के िलए) समय और सम वय (िदशा और गित परकलन के िलए) वाले रेडार िचत्र के ROL से सचना प्रा त ू करने की आव यकता होती है िजससे PHDA को सचनाय िमलती ह। इस शोधपत्र का मख् य ू ु उ दे य इन सचनाओ ंको ू प्रा त कर उिचत आकँड़ा बेस म संग्रिहत करना है तािक PHDA वारा उ ह कशलतापवर् क संसािधत िकया जा सके। इन ु ू सचना ू ओ ंको ‘एक् सेल फाइल’ म संग्रिहत िकया जाता है तािक पिरणाम को प्र तुत करने के िलए PHDA वारा िकसी भी प्रकार की अपेिक्षत सचनाओ ंको पन ू ु : प्रा त कर त काल उनका िव लेषण िकया जा सके।
MicroRNA-Enriched Exosomes from Different Sources of Mesenchymal Stem Cells Can Differentially Modulate Functions of Immune Cells and Neurogenesis

Adult Mesenchymal stem cells-derived exosomes carry several biologically active molecules that play prominent roles in controlling disease manifestations. The content of these exosomes, their functions, and effect on the immune cells may differ depending on their tissue sources. Therefore, in this study, we purified the exosomes from three different sources and, using the RNA-Seq approach, highly abundant microRNAs were identified and compared between exosomes and parental cells. The effects of exosomes on different immune cells were studied in vitro by incubating exosomes with PBMC and neutrophils and assessing their functions. The expression levels of several miRNAs varied within the different MSCs and exosomes. Additionally, the expression profile of most of the miRNAs was not similar to that of their respective sources. Exosomes isolated from different sources had different abilities to induce the process of neurogenesis and angiogenesis. Moreover, these exosomes demonstrated their varying effect on PBMC proliferation, neutrophil survival, and NET formation, highlighting their versatility and broad interaction with immune cells. The knowledge gained from this study will improve our understanding of the miRNA landscape of exosomes from hMSCs and provide a resource for further improving our understanding of exosome cargo and their interaction with immune cells.
Analysis of the Manufacturing Variables of Binderless Panels Made of Leaves of Olive Tree (Olea europaea L.) Pruning Waste

While the construction industry consumes more raw materials than any other industrial sector, agriculture generates a large amount of waste that is not managed properly. The olive industry produces more than 7.5 million tons of waste that could be recovered. This paper presents a new method to valorize the leaves of olive tree pruning waste consisting of the manufacture of ecologic boards without adhesives by hot pressing. In order to analyze their influence, three manufacturing variables were varied to obtain the boards: leaf type (shredded and whole leaves), temperature (130, 140 and 150 °C) and time (4, and 12 min). Twenty-four boards were made and were then tested for their mechanical, physical and thermal properties according to the EN standards. The boards showed good results of thickness swelling (TS), water absorption (WA) and of thermal conductivity and can be used as an alternative for manufacturing thermal insulation boards. With a smaller particle size of shredded leaves, longer pressing times and higher pressing temperatures, the mechanical behavior of the boards could improve. The olive leaves are a low-cost renewable resource, and manufacturing products with a long, useful life can be beneficial to the environment.
The Neuroactive Steroid Pregnanolone Glutamate: Anticonvulsant Effect, Metabolites and Its Effect on Neurosteroid Levels in Developing Rat Brains

Pregnanolone glutamate (PA-G) is a neuroactive steroid that has been previously demonstrated to be a potent neuroprotective compound in several biological models in vivo. Our in vitro experiments identified PA-G as an inhibitor of N-methyl-D-aspartate receptors and a potentiator of γ-aminobutyric acid receptors (GABAARs). In this study, we addressed the hypothesis that combined GABAAR potentiation and NMDAR antagonism could afford a potent anticonvulsant effect. Our results demonstrated the strong age-related anticonvulsive effect of PA-G in a model of pentylenetetrazol-induced seizures. PA-G significantly decreased seizure severity in 12-day-old animals, but only after the highest dose in 25-day-old animals. Interestingly, the anticonvulsant effect of PA-G differed both qualitatively and quantitatively from that of zuranolone, an investigational neurosteroid acting as a potent positive allosteric modulator of GABAARs. Next, we identified 17-hydroxy-pregnanolone (17-OH-PA) as a major metabolite of PA-G in 12-day-old animals. Finally, the administration of PA-G demonstrated direct modulation of unexpected neurosteroid levels, namely pregnenolone and dehydroepiandrosterone sulfate. These results suggest that compound PA-G might be a pro-drug of 17-OH-PA, a neurosteroid with a promising neuroprotective effect with an unknown mechanism of action that may represent an attractive target for studying perinatal neural diseases.
Machine Algorithm for Heartbeat Monitoring and Arrhythmia Detection Based on ECG Systems

Cardiac arrhythmia is an illness in which a heartbeat is erratic, either too slow or too rapid. It happens as a result of faulty electrical impulses that coordinate the heartbeats. Sudden cardiac death can occur as a result of certain serious arrhythmia disorders. As a result, the primary goal of electrocardiogram (ECG) investigation is to reliably perceive arrhythmias as life-threatening to provide a suitable therapy and save lives. ECG signals are waveforms that denote the electrical movement of the human heart (P, QRS, and T). The duration, structure, and distances between various peaks of each waveform are utilized to identify heart problems. The signals' autoregressive (AR) analysis is then used to obtain a specific selection of signal features, the parameters of the AR signal model. Groups of retrieved AR characteristics for three various ECG kinds are cleanly separated in the training dataset, providing high connection classification and heart problem diagnosis to each ECG signal within the training dataset. A new technique based on two-event-related moving averages (TERMAs) and fractional Fourier transform (FFT) algorithms is suggested to better evaluate ECG signals. This study could help researchers examine the current state-of-the-art approaches employed in the detection of arrhythmia situations. The characteristic of our suggested machine learning approach is cross-database training and testing with improved characteristics.
Association of Oxidative Stress Markers with Vascular Stiffness Parameters in Patients with Diabetic Neuropathy

Introduction: One of the most common chronic complications of diabetes mellitus is diabetic neuropathy. The aim of the study was to elucidate the association between selected markers of oxidative stress and markers of vascular stiffness and to contribute to the understanding of the pathophysiological links between oxidative stress and micro- and macrovascular complications of diabetes. Methods: We enrolled patients with type 2 DM (n = 49), with moderate to severe diabetic polyneuropathy of lower extremities, and a control group without microvascular complications (n = 29). The neuropathy group received alpha-lipoic acid infusion therapy. Sampling was performed before and after treatment to determine the level of oxidative markers (advanced glycation end-products—AGEs, glycation products of AOPP proteins, MDA malondialdehyde and oxidized LDL), parameters of metabolic control and parameters of vascular wall stiffness were measured by sphygmomanometry. Results: After the administration of alpha-lipoic acid, we demonstrated a significant reduction in the level of three selected oxidation markers (AOPP: p < 0.001, AGE: p < 0.001, oxLDL: p < 0.05). In contrast, the level of MDA did not change significantly (p = 0.83). Throughout the group, oxLDL was significantly correlated with central BP (SBP and DBP in the aorta, p < 0.05 and <0.01) and with the augmentation index (AiX/75 bpm, p < 0.01). AOPP significantly correlated with systolic BP in the aorta (p < 0.05). We did not find significant associations in the remaining oxidation markers. Conclusion: In our study, we demonstrated a reduction in the level of oxidative markers after alpha-lipoic acid administration and also an association between markers of oxidative damage to lipids and proteins (oxLDL and AOPP) and some parameters of vascular stiffness.
Host plants and associated trophobionts of the weaver ant Oecophylla longinoda Latreille (Hymenoptera Formicidae) in Benin

The African weaver ant Oecophylla longinoda builds woven leaf nests inside tree canopies and is a major conservation biocontrol agent in sub‐Saharan Africa. Weaver ant colonies provide well‐protected and resource‐rich environments for many associated trophobionts, thereby boosting their establishment on host plants. There is very little published literature on O. longinoda, their hosts plants and their associated trophobionts in West Africa. These tri‐trophic interactions were studied over a period of four consecutive years (2010–2013) from south to north Benin. Our fieldwork revealed that all O. longinoda colonies were consistently associated with trophobionts. Oecophylla longinoda nests were recorded on 82 plant species belonging to 30 families, with 35 associated trophobiont species representing 11 families. Among cultivated plants, Mangifera indica was the most common species hosting O. longinoda, while Sarcocephalus latifolius the most frequent native one. Among trophobionts, Parasaissetia nigra, Udinia catori, Udinia farquharsoni (Coccidae) and Stictococcus sjostedti (Stictococcidae) were the most common hemipterans associated with O. longinoda. We identified a wide range of host plants that could be preserved (or planted) to promote the establishment of weaver ants to control different insect pests in fruit plantations in sub‐Saharan Africa. When planted around fruit plantations with their nests and tended hemipterans, these host plants could facilitate biocontrol of mango fruit flies (Tephritidae) and cashew bugs (Coreidae, Miridae), by O. longinoda in the fruit plantations.
An Unsupervised Approach of Truth Discovery from Multi-sourced Text Data

In the era of information explosion, multi-sourced data may exist conflicts or even errors. To address this issue, plenty of truth discovery methods have been proposed to get trustworthy information from conflicting data. However, most existing truth discovery methods are designed for structured data and cannot meet the strong need to extract trustworthy information from raw text data. For text data, there are no completely correct or wrong answers, most answers may be partially correct. It is quite different from the situation of traditional truth discovery. In addition, tradition methods estimate the reliability of source based on plenty of observations provided. Unfortunately, for the scene of text truth discovery, it is not easy to get enough observations for sources. Besides, traditional methods ignore the importance of structure information and semantic information of text data, which leads to suboptimal results. To solve these challenges, we propose a Graph Convolutional Network (GCN) based truth discovery model to discover trustworthy information from text data. Firstly, Smooth Inverse Frequency (SIF) is utilized to learn real-valued vector representations for text data. Then, we construct undirected graph with these vectors to capture the structure information of answers. After that, the GCN is used to store and update the reliability of these answers, which sums up all the feature vectors of all neighboring answers to improve the accuracy and efficiency of truth discovery. Different from traditional methods, we use vectors to store the reliability of answers which have higher representation capability compared with real numbers, and network is adopted to capture complex relationships between answers rather than simplified functions. The experiment results on real datasets show that though text data structures are complex, our model can still find reliable answers compared with retrieval-based and state-of-the-art approaches.
Chromatin Detection in Malaria Thick Blood Film Using Automated Image Processing

Malaria is a serious global health problem and rapid, accurate diagnosis is required to control the disease. An image processing algorithm to aid the diagnosis of malaria on thick blood films is developed. Morphological and automatic threshold selection techniques are applied on two color components from the HSI color model to identify chromatins of P. Falciparum and P. Vivax malaria species on the images. Chromatins are positively identified with good sensitivities for both species. After identifying the position of chromatins, the algorithm splits the image into small sub-images, each with a chromatin in the center. These small images can subsequently be used by technician to classify malaria species more conveniently.
Gastronomi Araştırmalarına Bütüncül Bir Bakış: Bibliyometrik Bir Analiz (A Holistic View of Gastronomy Research: A Bibliometric Analysis)

Anahtar Kelimeler Gastronomi Bibliyometrik analiz Turizm Öz Bu çalışmanın amacı gastronomi araştırmalarındaki gelişmeleri incelemektir. Bu amaç doğrultusunda WoS veri tabanında yer alan 1975 – 2021 yılları arasındaki 2130 çalışma bibliyometrik analiz yöntemi ile incelenmiştir. Araştırma sonuçlarına göre 2018 yılı en fazla araştırmanın yayımlandığı yıl olmuştur. Ayrıca yıllara göre artış oranı ise %11,3’tür. Lotka yasasına dayanarak gastronomi alanının henüz gelişmekte olan bir bilim dalı olduğu ortaya çıkmıştır. Ayrıca Bradford yasasına göre çekirdek kümede 13 derginin yer aldığı tespit edilmiştir. Gastronomi araştırmalarında kavramsal kümelenmenin kültür ve turizm tabanlı gastronomi çalışmaları ve yiyecek içecek üretimi tabanlı gastronomi çalışmaları olarak iki merkezde kümelendiği görülmektedir.
Astragaloside IV Alleviates Cerebral Ischemia-Reperfusion Injury through NLRP3 Inflammasome-Mediated Pyroptosis Inhibition via Activating Nrf2

As one of the fundamental components of Astragalus membranaceus, astragaloside IV (AST IV) exerts protective effects against cerebral ischemia-reperfusion injury (CIRI). Nevertheless, the underlying mechanisms have not yet been conclusively elucidated. To do so, here, we report on the regulatory effects of Nrf2 on NLRP3 inflammasome-mediated pyroptosis. CIRI was induced by middle cerebral artery occlusion-reperfusion (MCAO/R) in Sprague Dawley rats and modeled by oxygen and glucose deprivation/reoxygenation (OGD/R) in SH-SY5Y cells. Cerebral infarct volume and neurological deficit score served as indices to evaluate MCAO/R injury. In addition, the CCK-8 assay was used to assess cell viability, the LDH leakage rate was used as a quantitative index, and propidium iodide (PI) staining was used to visualize cells after OGD/R injury. The NLRP3/Caspase-1/GSDMD pathway, which produces the pores in the cell membrane that are central to the pyroptosis process, was assessed to investigate pyroptosis. Nrf2 activation was assessed by detecting Nrf2 protein levels and immunofluorescence analysis. We show that after MCAO/R of rats, the infarct volume and neurological deficit score of rats were strongly increased, and after OGD/R of cell cultures, cell viability was strongly decreased, and the LDH leakage rate and the proportion of PI-positive cells were strongly increased. In turn, MCAO/R and OGD/R enhanced the protein levels of NLRP3, Caspase-1, IL-1β, GSDMD, and GSDMD-N. Moreover, Nrf2 protein levels increased, and Nrf2 translocation was promoted after CIRI. Interestingly, AST IV (i) reduced the cerebral infarct volume and the neurological deficit score in vivo and (ii) increased the cell viability and reduced the LDH leakage rate and the proportion of PI-positive cells in vitro. AST IV reduced the protein levels of NLRP3, Caspase-1, IL-1β, GSDMD, and GSDMD-N, inhibiting NLRP3 inflammasome-mediated pyroptosis. AST IV also increased the protein levels of Nrf2 and promoted the transfer of Nrf2 to the nucleus, accelerating Nrf2 activation. Particularly revealing was that the Nrf2 inhibitor ML385 partly blocked the above effects of AST IV. Taken together, these results demonstrate that AST IV alleviates CIRI through inhibiting NLRP3 inflammasome-mediated pyroptosis via activating Nrf2.
Comprehensive Analysis of Pertinent Genes and Pathways in Atrial Fibrillation

Purpose Atrial fibrillation (AF) is the most frequent arrhythmia in clinical practice. The pathogenesis of AF is not yet clear. Therefore, exploring the molecular information of AF displays much importance for AF therapy. Methods The GSE2240 data were acquired from the Gene Expression Omnibus (GEO) database. The R limma software package was used to screen DEGs. Based on the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) databases, we conducted the functions and pathway enrichment analyses. Then, the STRING and Cytoscape software were employed to build Protein-Protein Interaction (PPI) network and screen for hub genes. Finally, we used the Cell Counting Kit-8 (CCK-8) experiment to explore the effect of hub gene knockdown on the proliferation of AF cells. Result 906 differentially expressed genes (DEGs), including 542 significantly upregulated genes and 364 significantly downregulated genes, were screened in AF. The genes of AF were mainly enriched in vascular endothelial growth factor-activated receptor activity, alanine, regulation of histone deacetylase activity, and HCM. The PPI network constructed of significantly upregulated DEGs contained 404 nodes and 514 edges. Five hub genes, ASPM, DTL, STAT3, ANLN, and CDCA5, were identified through the PPI network. The PPI network constructed by significantly downregulated genes contained 327 nodes and 301 edges. Four hub genes, CDC42, CREB1, AR, and SP1, were identified through this PPI network. The results of CCK-8 experiments proved that knocking down the expression of CDCA5 gene could inhibit the proliferation of H9C2 cells. Conclusion Bioinformatics analyses revealed the hub genes and key pathways of AF. These genes and pathways provide information for studying the pathogenesis, treatment, and prognosis of AF and have the potential to become biomarkers in AF treatment.
Biomechanical Evidence From Ultrasonography Supports Rigid Foot Orthoses in Children With Flatfoot

atic and flexible; however, it can be a source of pain, gait pattern change, or scoliosis [1]. The interest in the flatfoot in children has recently increased. Various management options, including medication for pain, orthoses, stretching, footwear selection, activity modification, and surgical management, have been used. Custom foot orthosis is commonly prescribed with the expectation of normal foot and leg function via arch realignment and stability increment; however, the effect is still controversial [2]. A recent study published by Annals of Rehabilitation Medicine demonstrated the clinical improvement in rigid foot orthoses (RFOs) in children with flatfoot [3]. To support the evidence of the effectiveness of RFO, they published new findings as follow-up studies on RFO and the changing muscle properties in this issue of the Annals of Rehabilitation Medicine [4]. The authors [4] investigated changes in the cross-sectional area (CSA) of the ankle invertors and evertors using ultrasonographic measurements and symptoms after 12 months of RFO application in children with symptomatic flatfoot. They explained the clinical effects of RFOs with changes in the CSA ratios of the ankle invertors and evertors and provided an interesting clinical reasoning, wherein PFO reduces the compensatory activities of the ankle invertors, thereby increasing the peroneus longus ratio and reducing pain. Many biomechanical changes have explained the mechanism of flatfoot, including moment and joint motion changes in the lower extremity [5,6]. An imbalance between ankle invertor and evertor has been proposed as another mechanism. Although a previous study failed to reveal the strength of invertors and evertors affects flatfoot, Yalcin et al. [7] revealed that the change in the CSA of the invertor and evertor affects the clinical symptoms for the first time. For assessment of lower limb biomechanical changes, most studies have used simple radiography and have used computed tomography or magnetic resonance imaging in some cases. Moreover, kinematic or kinetic assessment using a motion capture system, various sensors, or electromyography could provide more detailed information. However, these methods have shortcomings such as radiation-related risks or high cost and require elaborate processes and additional expertise. Therefore, ultrasonography could be a good alternative because it is a safe and painless procedure, especially for flatfoot assessment, which is common in pediatric patients [8]. Annals of Rehabilitation Medicine Editorial