Miracle-dz/UniFAR-FTUs huggingface dataset:
Microwave Production of Manganese from Manganese (IV) Oxide using Postconsumer Polypropylene as Reductant

This work investigates the production of manganese metal from MnO 2 by microwave irradiation using postconsumer polypropylene (PP) as reductant. Reagent grade MnO 2 was first calcined to Mn3O4 followed by reduction with pulverised PP in a domestic microwave oven (Pioneer, Model PM-25 L, 1000 W, 2.45 GHz) in a recorded temperature range 900-1200 °C. Calcined and reduced products were characterised by XRD, XRF and SEM/EDX. The results showed that microwave irradiation is effective at calcining MnO 2 to Mn 3 O 4 , evidenced by the complete disappearance of peaks of MnO 2 and appearance of peaks of Mn 3 O 4 after 40 minutes. SEM/EDX analysis revealed that calcined Mn 3 O 4 was first transformed to MnO and later to manganese metal. The range of temperature achieved in the microwave oven was below the equilibrium temperature for MnO reduction by solid carbon and accordingly solid carbon produced from PP cannot be wholly responsible for the production of manganese metal. It is therefore concluded that the production of manganese metal observed in this investigation was effected with CH 4 (generated from the thermal decomposition of PP) and solid amorphous carbon as the predominant reductants, with solid amorphous carbon providing the heat energy required for the reduction. The reduction of manganese oxides using postconsumer plastics as reductants is therefore a potential route for diverting plastics from landfill sites as well as decrease the amount of expensive metallurgical coke currently used in the ferromanganese process. Keywords: Microwave irradiation, Polypropylene, Calcination, Reduction, Manganese
High mortality of COVID-19 associated mucormycosis in France: a nationwide retrospective study

We studied COVID-19 associated mucormycosis based on 17 cases reported nationwide and assessed the differences with India. They differed by frequencies of diabetes mellitus (47% in France versus 95% in India), hematological malignancies (35% versus 1%), anatomical sites (53% lung versus >80% rhino-orbito-cerebral) and prognosis (>80% mortality versus <50%).
Path guidance based on landmark user preference fuzzy entropy weight model

Compared with electronic map or static point of interest (POI), the path guidance integrating landmarks with salience features can reduce the cognitive burden better. However, due to the influence of various factors on the significance of landmarks, specific mathematical formulas need to be constructed, and there is no unified formula construction standard. To solve this problem, considering the consistency of the idea between multiple factor comprehensive of landmark and multiple constraint optimal scheme decision of fuzzy decision, this paper constructs a fuzzy entropy weight model of landmark based on user preferences, which the actual values of landmark significance are converted to vague set values, and the weights of landmark factors are determined by fuzzy entropy of Vague soft sets. Subsequently, the landmark preference model and path guidance model are obtained and applied in the case of path guidance. Compared with the mathematical formula, the constructed landmark user preference model by fuzzy entropy of Vague soft sets reduces the construction complexity, and is an effective method to realize landmark preference and apply in path guidance.
The Electromagnetic Signal Track Correlation Algorithm based on Attribute Clustering and Space-time Constraints

In view of the problem that start-end correlation of ship electromagnetic signal, the electromagnetic signal track correlation algorithm, based on attribute clustering and space-time constraints, is proposed. The algorithm consists of K-means dynamic clustering stage, space-time constraints of the track association stage. By extracting the relevant properties of electromagnetic signals, through the clustering stage, obtaining an indefinite number of clusters, and through the space-time correlation stage, obtaining a number of tracks per cluster. The main contributions of this paper are: 1. the K-means dynamic clustering method is proposed; 2. the track correlation method of space-time constraints is proposed. In this paper, the results of the comparison experiment show that the accuracy of this algorithm is better than that of the comparison algorithm, and the practical feasibility is verified.
Guidance Cleaning Network for Sketch-Based 3D Shape Retrieval

The sketch-based query for 3D shapes has drawn growing attention from the academic community due to its flexibility and accessibility. However, most recent works focus on reducing the cross-modality discrepancy between 2D sketches and 3D shapes, which neglect the influence caused by noise information in some low-quality free-hand sketches. To address this issue, we proposed a novel network to decrease the impact in two ways: 1) an attention weighting module to detect the noisy samples by a self-attention mechanism; 2) a data cleaning module to clear up low-quality sketches according to a ranking regularization. Experiments on two widely-used datasets demonstrate the effectiveness of our method.
Image registration of SAR and Optical based on salient image sub-patches

As a fundamental and critical task in multi-source image fusion, the registration of optical image and synthetic aperture radar (SAR) image can identify corresponding identical or similar structures from two heterogeneous images. Although Pseudo Siamese network has achieved notable success in matching heterogeneous images, the network prone to mismatch when there are blurred, duplicate or similar scenes appeared in the search image. To improve the performance, in this paper, the OPT-to-SAR image pair is cut into sub-patch pairs through a pre-defined sliding window. Then a two-stage image filtering mechanism is proposed to maintain candidate sub-patches with ideal texture information. After sending all the qualified sub-patch pairs into the Pseudo Siamese network, the final matching result will be passed through a RANSAC module. In this way, the interference from invalid image areas can be reduced and the model robustness can be ensured by using the statistic information of the whole image pair. A series of experiments conducted under various data scenarios proved the effectiveness of the method.
Positioning accuracy estimation for on-board data processing at the road surface defects monitoring

In this work, an approach has been developed to analyze data on the movement of a vehicle obtained as a result of measurements carried out using a satellite navigation system. The issues of increasing the accuracy of such measurements are of great importance in quality monitoring of road maintenance for geo-referencing of detected roadway defects. When using the proposed approach, the speed of a vehicle is considered as a stationary random process, the correlation function of which belongs to one of the specified classes. Based on the monitoring results, the value of the correlation function parameter is selected, which, in a certain sense, best matches the measurement results. The problem is also solved in a certain sense of the optimal choice of the duration of the averaging interval of the velocity values obtained as a result of monitoring, while taking into account how much, for the selected duration of the averaging interval, the sample obtained correlation function of the velocity corresponds to the correlation function from the hypothetical class for the selected parameter value.
Multi-scale Salient Features for Person Re-identification

Person re-identification is a technology that uses computer vision to match and retrieve the same target person across cameras, which has a wide range of applications in the fields of intelligent video surveillance, intelligent security, and unmanned driving. Aiming at the problem of low accuracy caused by complex background information, occlusion and other factors in person re-identification, the extracted features are not discriminative and robust, resulting in low accuracy, a person re-identification method based on multi-scale saliency features is proposed. Firstly, the saliency detection is used to extract the distinctive feature areas with discriminative power in person, so that the features of distinguishing person inages are more prominent. Secondly, these saliency features are merged with global features and the fused features are divided into different scales, and the global features are divided into three different scales to enhance the expression of features. Finally, according to the difference of global and local features, three kinds of loss functions are combined for learning. In the inference stage, the global feature and the local feature are fused into a new feature vector for metric learning. The proposed method has been used in the person re-identification public datasets Market1501 and DukeMTMC-reID to do a lot of effective verification experiments. The experimental results show that the features extracted by the proposed method have strong distinguishability and robustness.
Relative trajectories of two charged sedimenting particles in a Stokes flow

We study the dynamics of two charged point particles settling in a Stokes flow.We find what ranges of initial relative positions and what ranges of system parameters lead to formation of stable doublets.The system is parameterized by the ratio of radii, ratio of masses and the ratio of electrostatic to gravitational force.We focus on opposite charges.We find a new class of stationary states with the line of the particle centers inclined with respect to gravity and demonstrate that they are always locallyasymptotically stable. Stability properties of stationary states with the vertical line of the particle centers are also discussed.We find examples of systems with multiple stable stationary states.We show that the basin of attraction for each stable stationary state has infinite measure, so that particles can capture one another even when they are very distant, and even if their charge is very small. This behavior is qualitatively different from the uncharged case where there only exists a bounded set of periodic relative trajectories.We determine the range of ratios of Stokes velocities and ratio masses which give rise to non-overlapping stable stationary states (given the appropriate ratio of electrostatic to gravitational force). For non-overlapping stable inclined or vertical stationary states the larger particle is always above the smaller particle. The non-overlapping stable inclined stationary states existonly if the larger particle has greater Stokes velocity, but there are non-overlapping stable vertical stationary states where the larger particle has higher or lower Stokes velocity.
A fuzzy adaptive extended Kalman filter exploiting the Student’s t distribution for mobile robot tracking

To solve the problem of non-Gaussian distribution of measurement noise during the actual process of trajectory tracking when the mobile robot is performing tasks, a novel fuzzy adaptive extended Kalman filter exploiting the Student’s t distribution for a robot path tracking is proposed. The distributions of process and measurement noise are modeled using the Student’s t distribution. With the adaptive fuzzy controller, the adaptive factors are designed to adjust the covariance matrices of the process and measurement noises simultaneously, which optimize the posterior state and tracking accuracy. The simulation results show that the proposed algorithm has better accuracy and is more robust than existing state-of-the-art algorithms.
Effect of working parameter on droplet deposition in pumpkin top dressing using multi-rotor UAV

With the wide utilization of UAV in agricultural experiment and field management, plant protection UAV became a highly usage equipment for famers. Topdressing for pumpkin is important, when pumpkin fruit are expanding. It is difficult for farmers and other spraying machines to work on the farm, because all the fields is covered with leaves and stalks. A field test were launched to explore UAV working parameters on pumpkin topdressing, and distribution of droplets on face or back leave were measured to reflect the spraying quality and quantity of machine. During filed experiment, parameters of each flight was set by auto-flight system, and flight speed was stable. Through field experiment and data analysis, it was found that when the flying height was 2.5 m and the flying speed was 3 m/s, the total amount of droplet deposition reached the maximum, and the droplet attachment level on the back leaves was the highest. This parameters could be a guidance when using UAV for topdressing in pumpkin or other kinds of crops.
Fine-Grained Image Classification on Agricultural Pest Larvae

Pest management is an essential part of the growth of crops. Accurately identifying the types of pests in the early stage is conducive to formulating targeted prevention and control measures to reduce pests’ impact on grain production. In order to identify the pests in the larval stage as early as possible, in this paper, we compare the conventional classification model and the fine-grained classification model and construct a fine-grained image classification model that can be used to classify the larvae of crop pests, which improves the ability to identify pests in the larval stage. Experiments show that our optimized fine-grained classification model surpasses the general convolution image classification model on the fine-grained agricultural pest dataset AgrFIP20.
Analysis of Codon Usage Bias in the chloroplast genome of Helianthus annuus J-01

In order to determine the usage of the codon against Helianthus annuus chloroplast genome, this study used Codon W 1.4. 2 and online software CUSP to perform neutral mapping, ENC-plot and PR2-plot analysis on the coding sequence codes of 51 genes in the H. annuus J-01 chloroplast genome. The results showed the GC content of H. annuus J-01 chloroplast genome codons was GC1 (47.14%) > GC2 (39.44%) > GC3 (28.42%), indicating that the preference for synonymous codons in the chloroplast genome ends with A/U. The average ENC value is greater than 45, of which 37 are greater than 45. It shows that the H. annuus J-01 chloroplast genome has a weak bias. Through neutral plot analysis, ENC-plot analysis and PR2-plot analysis, it is found that the preference of H. annuus J-01 chloroplast genome codons is affected by selection as the main factor. The 12 optimal codons in the H. annuus J-01 chloroplast genome were finally determined: CUU, AUA, UAU, UAG, CAU, AAA, CCA, ACU, GCU, GAU, AGU, GGA.
Research on crop disease recognition based on uniting multi-layer features

Traditional image processing has many problems in crop disease recognition, such as complicated manual design and low efficiency. This article studies the application of deep learning algorithms in crop disease recognition. This article analyzes the characteristics of feature maps extracted from different convolutional groups of ResNet, proposes multi-layer features united ResNet(MLFU-ResNet) for crop disease classification, and introduces label smoothing to modify the loss function. The experimental results show that the network structure proposed in this article has achieved better classification performance in crop disease data sets. Compared with ResNet, the classification accuracy of the network structure proposed in this article has increased by 0.8%.
A CYBER SECURITY ONTOLOGY FOR SMART CITY

: Smart city is an intelligent and interconnected city that aims to enhance the quality of living of its people by using information and communication technology (ICT). However, the intrinsic inter-connectivity makes it highly vulnerable to security attacks. Therefore, it is essential to build a robust architecture that not only offers smart city services but also satisfies its security issues. This paper presents an integrated layered architecture for smart city security named ICADS. Moreover, two semantic models of ICADS are proposed as ontologies, namely OntoICADS and Secure-OntoICADS, to deal with the dynamicity and security of smart city. Secure-OntoICADS provides formal description to four major elements of security i.e. vulnerability, attack, security requirement, and security mechanism. Additionally, to validate their adaptability, ontologies have been mapped to different smart city applications.
Prediction of Litecoin Prices using ARIMA and LSTM

Since its inception in 2009, cryptocurrency has attracted attention from investors and the academia as well. The significant growth in market capitalization, types and the volatility of cryptocurrencies has spurred considerable interest and studies by researchers. However, most studies are on the hugely popular bitcoin. The latest study shows that Litecoin though occupying the fourth place at 2.74% market cap as on 1st August 2019, has attracted institutional investors. This is a breakthrough, wherein for the first time, cryptocurrency was seen in equal terms with fiat currency. This significant development is attributed to Litecoin’s unique features of low cost of mining, real-time speed of transactions, backed by strong blockchain based cost-effective architecture which are capable of handling higher volumes than bitcoin. This research is aimed at the price prediction of Litecoin using machine models. Two models Auto-Regressive Integrated Moving Average (ARIMA) and Long short-term Memory (LSTM) are used for data analysis to understand the best possible model for price prediction. Data collected over a five-and-a-half-year period from 2014 to 2019 is analysed and both the models are evaluated with MAPE, ME, MAE, and RMSE performance parameters. The best results for Litecoin price prediction are achieved when LSTM model is used with a MAPE of 5.759%.
A Short and Efficient Approach for the Total Synthesis of (S)‐Zearalenone and (R)‐De‐O‐methyllasiodiplodin by Using Stille and RCM Protocols

A concise, flexible, and linear approach has been devised for the total synthesis of the resorcinylic acid lactones (S)-zearalenone (2) and (R)-de-O-methyllasiodiplodin (4) by using a Stille cross-coupling strategy. The other key steps of the synthesis include a ring-closing metathesis (RCM), a chemoselective reduction of an α,β-unsaturated ketone, and a transesterification reaction.
Fluoride Removal from Water using Bio-Char, a Green Waste, Low-Cost Adsorbent: Equilibrium Uptake and Sorption Dynamics Modeling

Drinking water containing fluoride >1 mg/L is unsafe for human consumption. Higher intake of fluoride can cause potential health hazards. Low-cost pine wood and pine bark chars, obtained as a byproduct from fast pyrolysis in an auger reactor at 400 and 450 °C, were characterized and used as received for water defluoridation. Sorption studies were performed at different temperatures, pH values, and solid to liquid ratios in the batch mode. Maximum fluoride adsorption occurred at pH 2.0. A kinetic study yielded an optimum equilibrium time of 48 h with an adsorbent dose of 10 g/L. Sorption isotherm studies were conducted over a concentration range of 1–100 mg/L. Fluoride adsorption decreased with an increase in temperature. The char performances were evaluated using the Freundlich, Langmuir, Redlich–Peterson, Toth, Temkin, Sips, and Radke adsorption models. Based on average percent error, the best isotherm fits follow the orders for pine wood and pine bark: PWLangmuir ≈ PWRedlich-Peterson > PWToth > PWSips >.
D-OPTIMAL OPTIMIZATION OF MICROWAVE- ASSISTED SYNTHESIS OF MORINGA OLEIFERA POD ACTIVATED CARBON AND APPLICATION TO METHYLENE BLUE ADSORPTION

Activated carbon was optimally produced from Moringa oleifera pod (AMOP), with the aid of microwave, based on the D-Optimal Design under the Response Surface Methodology of the Design Expert software (6.0.8). Process factors selected were time (20–60 mins), temperature (200–450 °C), and impregnation ratio (IMR) (1:1-1:5), while the responses selected were yield, Methylene Blue Number (MBN) and Iodine Value Number (IVN). Adsorption, kinetic and diffusion mechanism of Methylene Blue (MB) adsorption on the AMOP were investigated. Suitability of types 1-6 of pseudo-second-order (PSO) equation was analysed. Surface properties of the AMOP were characterized using Fourier Transform Infrared Spectroscopy (FTIR) and physicochemical analysis. The optimum conditions (20 mins, IMR of 1:1, and 450 °C) obtained for the activation/carbonization gave 22.11% yield, 78.53 mg/g MBN and 96 mg/g IVN. The FTIR spectra showed changes in the functional groups on the AMOP after the activation/carbonization process. Maximum adsorption capacity of AMOP for MB adsorption was 98.46 mg/g. The process fit well to Type3 of PSO (R2 >0.9). The process’s adsorption rate was controlled by diffusion mechanism. This study showed that Moringa oleifera pod is a viable precursor for the production of effective activated carbon.
Ovarian Cancer-Associated Ascites Have High Proportions of Cytokine-Responsive CD56bright NK Cells

Ovarian cancer is the most lethal gynecological malignancy, with serous histotype as the most prevalent epithelial ovarian cancer (EOC). Peritoneal ascites is a frequent comorbidity in advanced EOC. EOC-associated ascites provide a reliable sampling source for studying lymphocytes directly from tumor environment. Herein, we carried out flow cytometry-based analysis to readdress issues on NK and T lymphocyte subsets in women with advanced EOC, additionally evaluating phenotypic modulation of their intracellular pathways involved in interleukin (IL)-2 and IL-15 signaling. Results depicted ascites as an inflammatory and immunosuppressive environment, presenting significantly (p < 0.0001) higher amounts of IL-6 and IL-10 than in the patients’ blood, as well as significantly (p < 0.05) increased expression of checkpoint inhibitory receptors (programmed death protein-1, PD-1) and ectonucleotidase (CD39) on T lymphocytes. However, NK lymphocytes from EOC-associated ascites showed higher (p < 0.05) pS6 phosphorylation compared with NK from blood. Additionally, in vitro treatment of lymphocytes with IL-2 or IL-15 elicited significantly (p < 0.001) phosphorylation of the STAT5 protein in NK, CD3 and CD8 lymphocytes, both from blood and ascites. EOC-associated ascites had a significantly (p < 0.0001) higher proportion of NK CD56bright lymphocytes than blood, which, in addition, were more responsive (p < 0.05) to stimulation by IL-2 than CD56dim NK. EOC-associated ascites allow studies on lymphocyte phenotype modulation in the tumor environment, where inflammatory profile contrasts with the presence of immunosuppressive elements and development of cellular self-regulating mechanisms.
What to do if syndroms are corrupted also?

We consider a discrete version of Compressed Sensing Problem when one has to ﬁnd an error vector based on its syndrom for a given parity-check matrix but not all positions of the syndrom are correct. We establish analogs of some well-known bounds in coding theory and discuss how it can be applied to the original compressed sensing problem setting.