Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Matching Optimization in Ridesharing System Based on Reinforcement Learning

Modern urban transportation, has concurrently posed environmental challenges such as traffic congestion and increased greenhouse gas emissions. In response to these issues, ridesharing systems have emerged as a viable solution. By fostering ridesharing among individuals with similar travel routes, ridesharing, effectively, optimizes vehicle utilization, offering a sustainable and practical alternative to address contemporary transportation challenges. In this work, we delve into intricacies of dynamic ridesharing systems. Focusing on the dynamic matching problem within ridesharing, we propose a solution leveraging reinforcement learning. Our contribution involves the distinct modeling of two scenarios: one-to-one and one-to-many ridesharing. In the one-to-one scenario, spatiotemporal constraints are considered with the objective of minimizing passengers’ waiting times. In the more complex one-to-many scenario, additional constraints are introduced focusing on both minimizing passengers’ waiting times and drivers’ detour times. The proposed modeling is time-focused assuming that time is a cutting parameter in the decision-making. The results obtained through our experiments demonstrate the system’s effectiveness, robustness and adaptability to diverse constraints.
Application of Short Video Description Technology in College English Teaching

In order to avoid students' negative learning mood, contemporary teachers are required to abandon the application of spoon-feeding teaching method in English classroom teaching, adopt micro-class teaching method, highlight the teaching characteristics of being close to the people, and create an efficient, short, and special teaching space to meet students' learning needs. In this study, short video description technology is applied to college English teaching, and a generation model of short video natural language description based on Attention mechanism is established. The video feature sequence may be out of sync with the generated word sequence, that is to say, the order of objects and behaviors appearing in the video may be different from their positions before and after the description sentence. In this article, a new generation model of short video natural language description based on attention mechanism is designed.
Rapid reduction of 4-nitrophenol into 4-aminophenol and industrial wastewater treatment in the presence of UiO-66/btb/MgO catalyst

The increase in population, the expansion of industries, and technological progress are the factors that have caused an increase in water consumption and wastewater production, environmental pollution, and forced investment for treatment. In this research work, the goal is to convert 4-NP (4-nitrophenol) into 4-AP (4-aminophenol) as a non-toxic and valuable product in industries, which is near the nanocatalyst during a reduction reaction. MgO nanoparticles (NPs) formed a complex on the surface of UiO-66-NH 2 supported by 1,3-bis((dimethyl thio carbamoyl)oxy)benzene (UiO-66/ btb/MgO). The effect of various physical and chemical parameters on the catalyst was investigated, and Fourier-transform infrared spectroscopy, thermal gravimetric, X-ray diffraction, transmission electron microscopy, and scanning electron microscopy analyses were used to confirm the morphology of the nanocatalyst. The results showed, the reduction of 4-NP and conversion to 4-AP in the model reaction, and industrial wastewater was carried out by the catalyst. The reaction in the presence of water solvent, NaBH 4 (4 mmol), time 3 min and catalyst (20 mg) had 96% yield. The UiO-66/ btb/MgO heterogeneous catalyst was readily recycled by filtration from the reaction solution, and the efficiency of the recycled catalyst was slightly reduced. This research can be an effective option for the final treatment of wastewater containing low to medium concentrations of 4-nitrophenol compounds.
Le complexe récifal kimméridgien - tithonien du Jura méridional interne (France), évolution multifactorielle, stratigraphique et tectonique

Le Complexe recifal du Jurassique superieur du Jura meridional interne presente une grande variete de lithologies. Les bioconstructions sont petites, variees et ne forment jamais de barrieres recifales. Les Calcaires en plaquettes se developpent dans une zone topographiquement basse heritee d'une phase tectonique ancienne. Une analyse structurale, basee sur le depliage en trois dimensions d'un niveau qui replace les affleurements dans leurs positions relatives originelles, nous apprend que la faille du Vuache provoque un decrochement de plus de 10 kilometres dans la region du Mont de Musieges et qu'un raccourcissement de 20 kilometres affecte la region etudiee. Des schemas paleogeographiques sont proposes pour le Kimmeridgien superieur et le Tithonien inferieur de la region genevoise. La plate-forme jurassienne prograde en direction de l'est au cours du Jurassique superieur. La topographie heritee et les fluctuations du niveau marin influencent la repartition des facies. Les zones topographiquement basses se comblent graduellement.
Technical report series on global modeling and data assimilation. Volume 1: Documentation of the Goddard Earth Observing System (GEOS) General Circulation Model, version 1

This technical report documents Version 1 of the Goddard Earth Observing System (GEOS) General Circulation Model (GCM). The GEOS-1 GCM is being used by NASA's Data Assimilation Office (DAO) to produce multiyear data sets for climate research. This report provides a documentation of the model components used in the GEOS-1 GCM, a complete description of model diagnostics available, and a User's Guide to facilitate GEOS-1 GCM experiments.
Sensor based Dance Coherent Action Generation Model using Deep Learning Framework

Dance Coherent Action Generation is a popular research task in recent years to generate movements and actions for computer-generated characters in a simulated environment. It is sometimes referred to as "Motion Synthesis". Motion synthesis algorithms are used to generate physically believable, visually compelling, and contextually appropriate movement using motion sensors. The Dance Coherent Action Generation Model (DCAM) is a generative framework for producing aesthetically pleasing movements using deep learning from small amounts of data. By learning an internal representation of motion dynamics, DCAM can synthesize long sequences of movements in which coherent patterns can be created through latent space interpolation. This framework provides a mechanism for varying the amplitude of the generated motion, allowing further realistic thinking and expression. The proposed model obtained 93.79% accuracy, 93.79% precision, 97.75% recall and 92.92% F1 score. DCAM exploits the balance between imitation and creativity by enabling the production of novel outputs from limited input data and can be trained in an unsupervised manner or fine-tuned with sparse supervision. Furthermore, the framework is easily extended to handle multiple layers of abstraction and can be further personalized to a particular type of movement, enabling the generation of highly individualized outputs.
An experimental comparison of multi-resolution segmentation, SLIC and K-means clustering for object-based classification of VHR imagery

ABSTRACT With smaller pixel coverages from recent sensors, considering single pixels in the classification process has become ineffective and incapable for delineation of the characteristics of targeted land use land cover (LULC) types. Object-based image analysis (OBIA) has been recently applied to enhance the identification performance of the classifiers considering not only the spectral but also the contextual and textural information. In this study, three segmentation approaches having a different theoretical basis, namely multi-resolution segmentation (MRS), Simple Linear Iterative Clustering (SLIC) superpixel algorithm and K-means clustering, were utilized to produce image objects, from which thematic maps were generated using random forest classifier. The most widely used segmentation evaluation metrics were applied by considering manually digitized reference polygons to evaluate the goodness of the constructed segments. Quality analyses were performed on four specific LULC types (residential, industrial and public buildings, and coniferous trees) for analysing the effectiveness of the approaches. Overall, the MRS algorithm produced the most accurate results in terms of both segmentation quality and classification accuracy. On the other hand, the difference in classification accuracy varied by about 4% for the segmentation algorithms. Results confirmed the importance of quality metrics for evaluating the goodness of generated segments because a direct link was observed between the quality of the segments and the classification accuracy achieved. However, the NSR metric is not favoured since it solely considers the number of segments, not the congruity of reference and corresponding image objects.
Exploring 3D Object Detection for Autonomous Factory Driving: Advanced Research on Handling Limited Annotations with Ground Truth Sampling Augmentation

: Autonomously driving vehicles in car factories and parking spaces can represent a competitive advantage in the logistics industry. However, the real-world application is challenging in many ways. First of all, there are no publicly available datasets for this specific task. Therefore, we equipped two industrial production sites with up to 11 LiDAR sensors to collect and annotate our own data for infrastructural 3D object detection. These form the basis for extensive experiments. Due to the still limited amount of labeled data, the commonly used ground truth sampling augmentation is the core of research in this work. Several variations of this augmentation method are explored, revealing that in our case, the most commonly used is not necessarily the best. We show that an easy-to-create polygon can noticeably improve the detection results in this application scenario. By using these augmentation methods, it is even possible to achieve moderate detection results when only empty frames without any objects and a database with only a few labeled objects are used.
PT-Net: Pyramid Transformer Network for Feature Matching Learning

In this article, we propose a novel pyramid transformer network (PT-Net) for feature matching problems. Recent studies have used the dense motion field to transform unordered correspondences into ordered motion vectors and have used convolutional neural networks (CNNs) to extract deep features. However, the limited receptive field of CNNs restricts the ability of the network to capture global information within the motion field. To tackle this limitation, we devise a pyramid transformer (PT) block to enhance the models ability to extract both local and global information from the motion field, which fuses multiscale motion field information by constructing a pyramid-structured motion field. Furthermore, to alleviate the high memory demands of spatial attention in the transformer, we introduce dilated sparse attention (DSA), a novel attention block that reduces the computational difficulty of multihead self-attention (MHSA) through regular interval sampling and deconvolution operations and focuses on the essential regions to establish long-range dependencies between the correct motion vectors. The proposed PT-Net is effective in inferring the probabilities of correspondences belonging to either inliers or outliers, while simultaneously estimating the essential matrix. Extensive experiments demonstrate that PT-Net network outperforms state-of-the-art methods for outlier removal tasks and camera pose estimation on different datasets, including YFCC100M and SUN3D. The code is available at https://github.com/gongzhepeng/PT-Net.
Effects of Physical Cues on Stem Cell-Derived Extracellular Vesicles toward Neuropathy Applications

The peripheral nervous system undergoes sufficient stress when affected by diabetic conditions, chemotherapeutic drugs, and personal injury. Consequently, peripheral neuropathy arises as the most common complication, leading to debilitating symptoms that significantly alter the quality and way of life. The resulting chronic pain requires a treatment approach that does not simply mask the accompanying symptoms but provides the necessary external environment and neurotrophic factors that will effectively facilitate nerve regeneration. Under normal conditions, the peripheral nervous system self-regenerates very slowly. The rate of progression is further hindered by the development of fibrosis and scar tissue formation, which does not allow sufficient neurite outgrowth to the target site. By incorporating scaffolding supplemented with secretome derived from human mesenchymal stem cells, it is hypothesized that neurotrophic factors and cellular signaling can facilitate the optimal microenvironment for nerve reinnervation. However, conventional methods of secretory vesicle production are low yield, thus requiring improved methods to enhance paracrine secretions. This report highlights the state-of-the-art methods of neuropathy treatment as well as methods to optimize the clinical application of stem cells and derived secretory vesicles for nerve regeneration.
Shifts of Sediment Microbial Community Structure along a Salinized and Degraded River Continuum

ABSTRACT Wang, Y. and Wang, Z. -L. , 2018. Shifts of sediment microbial community structure along a salinized and degraded river continuum. Succession of sediment microbial community along salinized and degraded river continuums where discharge declines and nutrient loading increases has received little attention. This issue was studied in a 120-km sediment continuum from upstream to estuary of the Jiyun River, northern China. Microbial community was investigated by phospholipid fatty acid (PLFA) analysis and 16S ribosomal ribonucleic acid (rRNA) gene sequencing. Microbial PLFA contents varied from 1.48 to 7.64 nmol g−1. Salinity decreased microbial, bacterial, and actinomycetic PLFA contents (p < 0.05), but had little effect on fungal PLFA contents. Dissolved organic carbon and dissolved nitrogen increased microbial, bacterial, and actinomycetic PLFAs (p < 0.05), but did not affect fungal PLFAs. Fungal PLFAs were mainly associated with the contents of sediment organic carbon (SOC) and total nitrogen (TN). Salinity decreased the ratio of Gram-positive/Gram-negative PLFAs but not the ratio of fungal/bacterial PLFAs. Bacterial communities were dominated by Proteobacteria, Firmicutes, and Bacteroidetes phyla. PLFA analysis and 16S rRNA sequencing returned similar results with respect to the change in bacterial community composition. Shannon–Wiener indices of bacterial operational taxonomic units were negatively correlated with SOC and TN. In conclusion, salinity and nutrients are collectively regulating the shifts in sediment microbial community structure along the river continuum.
Deep-Learning Uncovers certain CCM Isoforms as Transcription Factors.

BACKGROUND Cerebral Cavernous Malformations (CCMs) are brain vascular abnormalities associated with an increased risk of hemorrhagic strokes. Familial CCMs result from autosomal dominant inheritance involving three genes: KRIT1 (CCM1), MGC4607 (CCM2), and PDCD10 (CCM3). CCM1 and CCM3 form the CCM Signal Complex (CSC) by binding to CCM2. Both CCM1 and CCM2 exhibit cellular heterogeneity through multiple alternative spliced isoforms, where exons from the same gene combine in diverse ways, leading to varied mRNA transcripts. Additionally, both demonstrate nucleocytoplasmic shuttling between the nucleus and cytoplasm, suggesting their potential role in gene expression regulation as transcription factors (TFs). Due to the accumulated data indicating the cellular localization of CSC proteins in the nucleus and their interaction with progesterone receptors, which serve dual roles as both cellular signaling components and TFs, a question has arisen regarding whether CCMs could also function in both capacities like progesterone receptors. METHODS To investigate this potential, we employed our proprietary deep-learning (DL)-based algorithm, specifically utilizing a biased-Support Vector Machine (SVM) model, to explore the plausible cellular function of any of the CSC proteins, particularly focusing on CCM gene isoforms with nucleocytoplasmic shuttling, acting as TFs in gene expression regulation. RESULTS Through a comparative DL-based predictive analysis, we have effectively discerned a collective of 11 isoforms across all CCM proteins (CCM1-3). Additionally, we have substantiated the TF functionality of 8 isoforms derived from CCM1 and CCM2 proteins, marking the inaugural identification of CCM isoforms in the role of TFs. CONCLUSIONS This groundbreaking discovery directly challenges the prevailing paradigm, which predominantly emphasizes the involvement of CSC solely in endothelial cellular functions amid various potential cellular signal cascades during angiogenesis.
Reproduktionsmedizin und Endokrinologie

Background: The majority of women 40–49 years of age need an effective method of contraception because the decline in fertility with age is an insufficient protection against unwanted pregnancy. Although pregnancy is less likely after the age of 40 years, the clinical and social consequences of an unexpected pregnancy are potentially detrimental. No contraceptive method is contraindicated by advanced reproductive age alone; thus there is a need to discuss the effectiveness, risks and non-contraceptive benefits of all family planning methods for women in this age group. Methods: MEDLINE searches were done by topic (epidemiology, age and reproduction, sexual function, delayed childbearing and specific contraceptive methods). The topic summaries were presented to the Workshop Group and omissions or disagreements were resolved by discussion. Results: The decline in fecundity in the fifth decade is insufficient for contraceptive purposes. Thus a family planning method is needed. Sterilization is by far the most common method in several countries. Copper intrauterine devices and hormone intrauterine systems have similar effectiveness, with fewer than 1 % failures in the first year of typical use. Special considerations in this age group include the frequency of menstrual irregularity, sexual problems and the possibility of menopausal symptoms, all of which may respond to hormonal methods of contraception. Conclusions: Women should be advised to continue with a contraceptive method until they have reached the menopause with its natural state of sterility. J Reproduktionsmed Endokrinol 2010; 7 (Special Issue 1): 78–89.
INSL3 Variation in Dogs Following Suppression and Recovery of the HPG Axis

Simple Summary Insulin-like peptide 3 (INSL3) is a hormone produced by the steroidogenic Leydig cells of the testes and in most mammals acts as a robust blood biomarker for pubertal development and gonadal function. Using a new immunoassay to measure INSL3 in adult male dogs, we show that it is much lower than in other species. When a cohort of male beagle dogs is implanted with a GnRH agonist to induce contraceptive suppression of male fertility, the average circulating INSL3 is first reduced and then recovers slowly when the implant is removed. Importantly, however, and unlike in other species, in dogs there is considerable variation in INSL3 expression, with some dogs showing little or no initial reduction of INSL3, and similarly, there is often only a poor response during the recovery phase. We conclude that in dogs, unlike in most other mammals, the Leydig-cell production of INSL3 appears to be less tightly coupled to the functioning of the hypothalamic–pituitary–gonadal (HPG) hormone axis. Abstract Insulin-like peptide 3 (INSL3) is a constitutive product of mature, adult-type Leydig cells of the testes and consequently in most mammals is an ideal biomarker with which to monitor pubertal development. A new heterologous time-resolved fluorescence immunoassay was developed and validated to measure circulating INSL3 in the blood of adult male dogs. Compared to other species, INSL3 concentration is low with marked variation between individuals, which appears to be independent of breed, age, or weight. A model system was then used in which a cohort of beagle dogs was subject to a GnRH-agonist implant to suppress the HPG axis and spermatogenesis, followed by implant removal and recovery. Unlike testosterone, INSL3 levels were not fully suppressed in all animals by the GnRH agonist, nor was the recovery of Leydig cell function following implant removal uniform or complete, even after several weeks. In dogs, and dissimilar from other species (including humans), Leydig-cell INSL3 appears to be quite variable between individual dogs and only weakly connected to the physiology of the HPG axis after its suppression by a GnRH-agonist implant and recovery. Consequently, INSL3 may be less useful in this species for the assessment of testis function.
Experimental Investigation of Void Coalescence in XTral-728 Plate Containing Three-Void Cluster

Void clustering effect is investigated by performing uniaxial tensile test on commercial 95% aluminum alloy XTral 728 by strategically placing an additional hole. Incorporating additional void induces strong stress and strain localization and reduces incipient coalescence strain approximately 17%. Also, two different cluster orientations with respect to applied loading and hole spacing ratios have been considered. The experimental revealed that material ductility is significantly decreased with increasing hole spacing ratios and for certain cluster orientation. To evaluate existing void coalescence models, numerical simulations are also performed and found that existing models overestimate the incipient coalescence strains for considered three-void cluster.
Partition Resolvability of Nanosheet and Nanotube Derived from Octagonal Grid

Chemical graph theory, a branch of computational and applied mathematics, covers a very wide range of topics. As a result, the world of applied sciences heavily relies on graph theory. The concept of partition dimension has significant importance in the field of chemical graph theory. Although certain graphs have bounded partition dimensions, a graph’s partition dimension may be constant. In this study, we look at two alternative chemical structures made of an octagonal grid: nanosheets and nanotubes. We determined the partition dimension of an octagonal grid-generated nanosheet to be 3, and the partition dimension of a nanotube to be limited from 4.
When Unsuspected Crystallinity Ruins Biological Testing in Early Discovery: A Case Study

The impact of the crystalline or amorphous structure of a solid on the solubility and pharmacokinetic properties of a drug candidate is always considered by the pharmaceutical industry during the development of a new drug; however, it is not so frequently considered during the early drug discovery process by organic and medicinal chemists, particularly those working in academia. We want to share, as an example, the false negative obtained in the biological testing of a solid sample of a tyrosine kinase inhibitor due to its unexpected crystallinity and lower solubility with respect to a solid amorphous batch of the same compound and the experimentation carried out to establish the origin of such a discrepancy.