Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of Bubble Bouncing on Mass Transfer and Chemical Reaction

The hydrodynamics of bubbly flows is dominated by bubble induced turbulence and bubble-bubble interactions. Both phenomena influence the gas-liquid mass transfer as well as the mixing of reactants. If the time scales of mass transfer and mixing are in the same order as the time scales of a parallel-consecutive reaction, the yield and selectivity will be affected by the local hydrodynamics. In this study, an experimental setup is presented that enables the investigation of mass transfer during well-defined and adjustable bubble collisions. The influence of CO2 bubble collisions on mass transfer is measured and modeled with a modified Sherwood number correlation. Further visualization of the concentration field in the vicinity of O2 bubbles by means of laser induced fluorescence shows the dependency of mass transfer from a chemical reaction and enables the development of a first model approach.
Application of Carbon Quantum dot Fluorescent Materials in Metal Ions Detection

The research situation of the application of carbon quantum dot fluorescent materials to the detection of metal ions, including the current research status and preface of recent years, and development trends are summarized. The application of carbon quantum dot fluorescent materials to the detection of metal ions is highlighted, including iron ions, copper ions, mercury ions, lead ions, and silver ions. Finally, the problems and development prospects of carbon quantum dot fluorescent materials for detecting metal ions are discussed.
The role of mTOR signaling pathway in regulating autophagy in liver injury of TX mice with Wilson’s disease

Wilson disease (WD), known as hepatolenticular degeneration (HLD), is a treatable autosomal recessive disorder of copper metabolism. Because copper deposits in the liver first, the liver is not only the original defective organ but also the most affected organ. The liver injury is also one of the main causes of death throughout the course of the disease. Therefore, the treatment of liver injury is the main task of WD treatment, which is of great significance to improve the prognosis of patients. Autophagy is a process that promotes cell survival through degradation, recycling, and absorption in order to maintain the normal physiological function of cells, while excessive autophagy can aggravate cell death. In view of the abnormal damage of liver cells in patients with WD, which may be related to the change of autophagy level, in this study, we established an animal model of WD through toxic milk (TX) mice, observed the change of autophagy level in the liver, and observed the change of liver damage in mice after treatment with autophagy inhibitors. It was found that the mTOR signaling pathway was activated and autophagy was inhibited in Wilson mouse liver. After treatment with rapamycin, the autophagy level of mice liver was upregulated, and the copper content of mice liver was reduced, and the damage was alleviated. TX mouse hepatocytes were isolated, after using siRNA to interfere with mTOR expression, the copper accumulation was significantly reduced, which was the same with RAPA treatment. The results showed that in TX mice, the damage caused by copper accumulation in the liver may be related to the decrease of autophagy level caused by the activation of the mTOR signaling pathway. Our findings suggested that RAPA or the use of siRNA targeting mTOR may have potential applications in the treatment of Wilson’s disease.
A Text classification algorithm based on topic model and convolutional neural network

Based on the neural topic model ProdLDA and convolutional neural network, this paper proposes a text classification algorithm based on topic model and convolutional neural network. Firstly, the text information is modeled on the word vector model, then the convolutional neural network is used to extract the granularity features of high-dimensional text, and the neural topic model ProdLDA is used to extract the potential topic features. Then, the connection layer is established to connect the text features, and finally the classification layer is processed. At the same time, a new topic feature introduction method is used in the process of extracting topic features. Experimental results show that this algorithm can effectively improve the performance of text classification.
Target motion detection algorithm based on dynamic threshold

Realizing moving target detection through visual algorithms is a major branch of computer technology. Moving target detection has a wide range of applications in many fields such as video surveillance, unmanned driving, and behavior research. The current algorithm has been able to achieve moving target detection well in a static background, but the detection of multiple moving targets in a dynamic background is still a challenging research area. The main pain points are complex background, light changes, noise interference and Occlusion and real-time detection requirements, etc. Aiming at the detection of moving targets in complex scenes, a moving target detection algorithm based on dynamic threshold is proposed. The algorithm first uses the gray difference gradient of three adjacent frames of the video sequence to calculate the segmentation threshold of foreground moving targets to realize the dynamic threshold. Then use dynamic thresholds to achieve multi-target motion detection in complex backgrounds or scenes with changing lighting. Because the gradient change of adjacent frames of the scene is considered in the process of moving target detection and segmentation, and the gradient change is converted into a dynamic threshold to participate in moving target detection and segmentation, the algorithm can adapt to scenes with changing lighting. Experiments show that this algorithm is feasible for moving target detection in complex scenes, and has a lower error rate than that of traditional algorithms, and is more suitable for to complex or changing lighting scenes.
A Multi-Task Matrix Factorized Graph Neural Network for Co-Prediction of Zone-Based and OD-Based Ride-Hailing Demand

Ride-hailing service has witnessed a dramatic growth over the past decade but meanwhile raised various challenging issues, one of which is how to provide a timely and accurate short-term prediction of supply and demand. While the predictions for zone-based demand have been extensively studied, much less efforts have been paid to the predictions for origin-destination (OD) based demand (namely, demand originating from one zone to another). However, OD-based demand prediction is even more important and worth further explorations, since it provides more elaborate trip information in the near future as reference for fine-grained operations, such as the routing and matching of shared ride-hailing services that pick up and drop off two or more passengers in each ride. Simultaneous prediction of both zone-based and OD-based demand can be an interesting and practical problem for the ride-hailing platforms. To address the issue, we propose a multi-task matrix factorized graph neural network (MT-MF-GCN), which consists of two major components: (1) a GCN (graph convolutional network) basic module that captures the spatial correlations among zones via mixture-model graph convolutional (MGC) network, and (2) a matrix factorization module for multi-task predictions of zone-based and OD-based demand. By evaluations on the real-world on-demand data in Manhattan and Haikou, we show that the proposed model outperforms the state-of-the-art baseline methods in both zone- and OD-based predictions.
Ameliorated Methodology To Meta Model UML Sequence Diagram In The Table Format

------------------------------------------------------------------- ABSTRACT --------------------------------------------------------------- The unified modeling language (UML) consists of 13 diagrams. The sequence diagram is one of the UML diagrams which captures the dynamic behavior of a system. It represents how the objects communicate to the other objects using a message(s) such as simple, synchronous or asynchronous. These messages are executed in time order from top to bottom and left to right. In this work, an automated tool is proposed which meta-models by taking the UML sequence diagram as an input and then translate the diagram to XMI format using the available whitestar tool and stored in the file. We abstract the various components such as objects, messages, activation, loops (for, while), conditional statements alt (if, if-else), etc. The objects, messages, activation, loops, conditional statements alt are abstracted from the file and are represented in first-order logic, and are stored in form of a table. Finally, using the abstracted information an equivalent sequence diagram is generated.
An interpretive structural modeling approach to enablers of green supply chain management on construction projects

The objective of this study is to understand and evaluate the interactions of the Green Supply Chain Management enablers from a construction project's perspective in Imo State, Nigeria. This paper discusses the mix of practical intuition and determination through an interpretive structural modeling (ISM)-driven methodology. Eight (8) enablers were identified from a literature review, expert consultation, and real-world examples. While Matrice d’Impacts croisesmultipication applique a classement analysis (MICMAC) was used to identify dependence and driving power, it was used as a way to understand the relationship between the enablers. The study found that strong, yet fragile, forces drive GSCM adoption, with enhanced awareness of GSCM, increased market appeal for green construction projects, and government support through incentives and tax rebates.
EXPERIMENTAL VALIDATION OF THE CHARLESBY AND HORIKX MODELS APPLIED TO DE-VULCANIZATION OF SULFUR AND PEROXIDE VULCANIZATES OF NR AND EPDM

The theoretical model developed by Charlesby to quantify the balance between cross-links creation of polymers and chain scission during radiation cross-linking and further modifications by Horikx to describe network breakdown from aging were merged to characterize the balance of both types of scission on the development of the sol content during de-vulcanization of rubber networks. There are, however, disturbing factors in these theoretical considerations vis-a-vis practical reality. Sulfur- and peroxide-cured NR and EPDM vulcanizates were de-vulcanized under conditions of selective cross-link and random main-chain scissions. Cross-link scission was obtained using thiol-amine reagents for selective cleavage of sulfur cross-links. Random main-chain scission was achieved by heating peroxide vulcanizates of NR with diphenyldisulfide, a method commonly employed for NR reclaiming. An important factor in the analyses of these experiments is the cross-linking index. Its value must be calculated using the sol fraction of the cross-linked network before de-vulcanization to obtain reliable results. The values for the cross-linking index calculated with sol-gel data before de-vulcanization appear to fit the experimentally determined modes of network scission during de-vulcanization very well. This study confirms that the treatment of de-vulcanization data with the merged Charlesby and Horikx models can be used satisfactorily to characterize the de-vulcanization of NR and EPDM vulcanizates.
Clinical and electrophysiological efficacy of leaf extract of gingko biloba L (Ginkgoaceae) in subjects with diabetic sensorimotor polyneuropathy

Purpose: To investigate the health benefits of Gingko biloba L. leave extract EGb 761 in patients with diabetic sensorimotor polyneuropathy (DSP). Method: In a randomized, double-blind, placebo trial, patients ( n = 156) received Gingko biloba (120 mg/day) or placebo for 6 months. Efficacy of Gingko biloba extract EGb 761 was assessed on the reduction in the scores of “Short-Form McGill Pain Questionnaire” (SF-MPQ) and improvements in latency, amplitude and conduction velocity of four sensory (ulnar, median, peroneal and sural) and four motor (ulnar, median, peroneal and tibial) nerves. Results: Gingko biloba significantly improved the mean scores of sensory, affective, visual analogue score (VAS) and present pain intensity (PPI) of SF-MPQ on 3 and 6 month post-treatment. At the start of experiment in the Gingko biloba-treated group, scores for sensory, affective, VAS and PPI domains were 12.90 ± 7.21, 4.40 ± 1.79, 4.38 ± 1.69 and 3.24 ± 0.78 respectively. After 6-months treatment with Gingko biloba , scores for sensory, affective, VAS and PPI domains were reduced (p < 0.01) to 7.24 ± 4.91, 3.11 ± 1.81, 3.37 ± 1.35 and 2.45 ± 0.65 respectively. Contrarily, in the placebo-administered group, sensory domain was (p < 0.01) deteriorated after 6 month (4.70 ± 1.87) compared to the baseline (4.04 ± 1.67). Gingko biloba extract EGb 761 failed to influence the electrophysiological measures when compared with the placebo group. The common adverse effects observed with Gingko biloba were somnolence and dryness of mouth compared with the placebo-treated patients Conclusion: Gingko biloba extract EGb 761 seems to be an effective and safe drug for reducing symptoms of DSP without any apparent effect on the electrophysiological attributes of sensorimotor nerves. Keywords: Diabetic polyneuropathy, Diabetes mellitus, Electrophysiological parameters, Gingko biloba , Sensorimotor nerves, Short-Form McGill Pain Questionnaire
Effect of Drying Methods on Phenolic Compounds and Antioxidant Activity of Urtica dioica L. Leaves

Stinging nettle (Urtica dioica) is a plant well known in traditional medicine for its many beneficial properties, but the lack of standardization regarding the product to offer to consumers limits its diffusion. To this end, drying appears to be a useful technique to offer a low-cost product that can be stored for long time, but the different drying procedures may give rise to end-products of very different quality as nutraceutical and antioxidant compounds. Nettle leaves have been dehydrated employing freeze-drying (FD), oven-drying (OD) or heat pump drying (HPD) and compared with fresh leaves following water extraction to emulate the use by final consumers. Results indicate that the best dehydration technique is HPD, which apparently gives rise to more than a doubling of total phenols and antioxidant activity in the extract compared to the water extract obtained from fresh leaves but a reduction in the level of ascorbic acid of about 39%. In addition, the content of some phenolic compounds is 10 to over a hundred times higher in the extract after HPD than that obtained from fresh samples. This confirms that the dehydration technique should be tuned in relation to the compounds of greatest interest or value.
Management of non-vital anterior teeth with open apex: A report of two cases

) was described as an alternative to traditional apexification treatment [10] which incorporates the application of the material in the apical third of the canal to create an apical barrier. MTA is a bio-material with excellent biocompability and superior sealing abilities even in the presence of moisture. This report describes two cases of apexification in teeth with open apex managed with apical barrier technique. Treatment of teeth with open apex requires careful handling and diligence in clinical practice. Prognosis of endodontic treatment in such cases is directly related to the quality of apical obturation. Previously, a calcified barrier using calcium hydroxide treatment was recommended as an approach for these cases however due to various drawback of this method, current management approach suggests apexification through an apical plug at the apical segment.This report describes two cases of apexification of upper left incisor using mineral trioxide aggregate plug. After two year follow up, both teeth are clinically and radiographically asypmtomatic and the healing of the apical area is visible.
Laboratory Glassware Identification: Supervised Machine Learning Example for Science Students

This paper provides a supervised machine learning example to identify laboratory glassware. This project was implemented in an Introduction to Scientific Computing course for first-year students at our institution. The goal of the exercise was to present a typical machine learning task in the context of a chemistry laboratory to engage students with computing and its applications to scientific projects. This is an end-to-end data science experience with students creating the dataset, training a neural network, and analyzing the performance of the trained network. The students collected pictures of various glassware in a chemistry laboratory. Four pre-trained neural networks, Inception-V1, Inception-V3, ResNet-50, and ResNet-101 were trained to distinguish between the objects in the pictures. The Wolfram Language was used to carry out the training of neural networks and testing the performance of the classifier. The students received hands-on training in the Wolfram Language and an elementary introduction to image classification tasks in the machine learning domain. Students enjoyed the introduction to machine learning applications and the hands-on experience of building and testing an image classifier to identify laboratory equipment.
SYSTEM RELIABILITY OF REINFORCED CONCRETE RIBBED ARCH BRIDGE

According to the mechanical characteristics of reinforced concrete ribbed arch bridges, the rib failure mode is proposed as the main failure mode of such bridges, and a mathematical model for reliability analysis of a reinforced concrete ribbed arch bridge system is established. The failure of such a system is studied by the method of critical strength with support constraints. By comparing various methods, the formal method of system reliability solution is determined to be the reliability calculation method of reinforced concrete arch bridge systems, and the calculation equation of the saliency coeﬃcient of reinforced concrete ribbed arch bridge components is given. The process of reliability analysis of a reinforced concrete ribbed arch bridge system is summarized and its eﬀectiveness veriﬁed by an example. The research results can provide references for the reliability analysis of reinforced concrete ribbed arch bridge systems.
Influence of a spectral cumulus parametrization on simulating global tropical cyclone activity in an AGCM

The influence of a new spectral cumulus parametrization (the spectral scheme) on simulating global tropical cyclone (TC) activity in an Atmospheric General Circulation Model (AGCM) is investigated. Experiments are performed using AFES (AGCM for the Earth Simulator) and two different convection schemes – the original convection scheme of the AGCM (the Emanuel scheme) and the spectral scheme. The result shows that the both schemes can capture qualitative features of global TC activity in terms of climatology, variability, and intensity. However, the spectral scheme simulates TC genesis and track density better than the original convection scheme by improving the cold bias in the upper troposphere. The spectral scheme also provides better results for TC response to interannual variability (i.e., El Niño Southern Oscillation, ENSO) by suppressing excessive convection response to ENSO. The spectral scheme is capable of simulating the variability of TC genesis related to the Madden–Julian Oscillation (MJO) much better than the original convection scheme. Consequently, the spectral scheme can outperform the original convection scheme in simulating global TC activity from various perspectives, and thus the scheme is expected to be useful for TC prediction and future projections.
An Unbalanced Security Protocol for IoT devices

Authenticated key exchange (AKE) protocol is a crucial process in Internet of Things. This paper aims to customize the Elliptic Curve Diffie-Hellman (ECDH)-based AKE according to the computational capability of devices. We propose a method to unbalance the computations in the ECDH key exchange scheme by configuring the more powerful device undertake one scalar multiplication on behalf of the computationally limited one. The analysis results show that the computationally unbalanced protocols are more friendly to computationally limited devices than the original one.
Oil-modified alkyd type resin based on secondary esters of castor oil

This study investigates the preparation of an oil-modified alkyd resin, starting from secondary esters of castor oil. Secondary esters of castor oil with mixed fatty acids of sunflower oil were subjected to thermal splitting. Partial glycerides resulting from the reaction between the thermal splitting mixture and glycerol were reacted with phthalic anhydride to obtain the proposed oil-modified alkyd resin. Due to the occurrence of diene conjugation on the ricinoleic acid chain during thermal splitting, this process can be viewed as an alternate way of preparing an alkyd resin partially modified with dehydrated castor oil
Application of Biomimetic Nanomaterials in Biological Detection and the Intelligent Recognition Method of Nanoparticle Images

New nanomaterials (metal nanoclusters, graphene, etc.) are favored by researchers due to their unique properties and are widely used in biomedical detection. The excellent fluorescence characteristics of gold nanoclusters are utilized to develop a fast and highly sensitive bionic nanomaterial with non-label and dual functions, which can detect silver ions and mercury ions and study the particularity of TEM nanoparticle images. The particle segmentation of TEM nanoparticle images is studied to compare the traditional watershed algorithm and watershed transformation algorithm. The experiment results show that silver ions can enhance the fluorescence of gold nanoclusters to form gold-silver nanoclusters with strong yellow fluorescence, and mercury ions can quickly weaken the fluorescence of gold-silver nanoclusters. Based on the biomimetic nanomaterials, a dual-function fluorescent probe is designed to detect silver ions and mercury ions in lake with detection accuracy of 8 nM and 33 nM respectively; the sensing excitation of the fluorescent probe is further analyzed. Because the metal-enhanced fluorescence (MEF) effect enables the silver element and Au nanoparticles to form fluorescence-enhancing effect, the high metalphilic interaction between mercury ions and silver ions quenches the fluorescence effect of gold nanocluster; the rapid watershed transformation/region fusion method can achieve better particle image segmentation combined with the image segmentation algorithms of different TEM nanoparticles, which can be better applied to the characterization analysis of the preparation of gold nanomaterials.
Secrecy Analysis of SSK Modulation: Adaptive Antenna Mapping and Performance Results

In this paper, we investigate the physical layer security based on space shift keying (SSK) in which bit-to-spatial mapping of antenna indices dynamically adapts according to the partial channel state information (CSI) of the legitimate channel at the transmitter (Tx). Further, the Tx also adapts transmit power allocation to each transmit antenna according to partial CSI. Since an eavesdropper (Eve) is not (usually) aware of the legitimate CSI, it cannot successfully decode the antenna index; thus, transmission over the legitimate channel is secured from the wiretapping of Eve. An important virtue of the proposed scheme is that the Tx does not require full CSI of the legitimate and eavesdropper channels to secure the confidential information. The proposed work focuses on the secrecy rate (SR) and bit error rate (BER) of the considered multiple-input single-output system over Rayleigh fading channels. Approximate expressions of Bob’s data rate for 2-ary and 4-ary SSK modulations are derived in closed-form. Furthermore, an upper-bound of Bob’s data rate for $N_{a}$ -ary SSK modulation is also derived. In order to obtain the secrecy rate for the proposed scheme, we also evaluate Eve’s data rate. Analytical results of SR and BER demonstrate the robustness of the proposed scheme. All numerical results are thoroughly verified by using extensive Monte Carlo simulations.