Miracle-dz/UniFAR-FTUs huggingface dataset:
ESTIMATION OF VISCOSITY ARRHENIUS PRE-EXPONENTIAL FACTOR AND ACTIVATION ENERGY OF SOME ORGANIC LIQUIDS

Information concerning fluid’s physico-chemical behaviors is of utmost importance in the design, running and optimization of industrial processes, in this regard, the idea of fluid viscosity quickly comes to mind. Models have been proposed in literatures to describe the viscosity of liquids and fluids in general. The Arrhenius type equations have been proposed for pure solvents; correlating the pre-exponential factor A and activation energy Ea. In this paper, we aim at extending these Arrhenius parameters to simple organic liquids such as water, ethanol and Diethyl ether. Hence, statistical method and analysis are applied using viscosity data set from the literature of some organic liquids. The Arrhenius type equation simplifies the estimation of viscous properties and the calculations thereafter.
The Human Rights of Women and Social Transformation in the Arab Middle East

Although recently much attention has been paid to signs of reform and liberalization in the Arab world, there is also considerable evidence of trends in regressive social transformation. One such manifestation is the prevailing attitudes and social policies that continue to deny women their fundamental human rights and freedoms. Arab women continue to suffer major deficiencies in both oil-rich and poorer Arab countries.
Analyses of 3D numerical simulation of toppling deformation mechanism of Jiefanggou left slope in Jingping Step 1 hydropower station

The paper discussed the problem of the slope's deformation mechanism.It adopted 3D-σ limit elements numerical modeling and FLAC-3D numerical modeling.The purpose of 3D-σ modeling was the knowledge of slope evolvement mechanism.The purpose of FLAC-3D had two.The one of them was reciprocally validate results between the 3D-σ and FLAC-3D,and the other got deformation characters in cutting valley to understand the deformation cracking mechanism of left slope.The paper gained some conclusions by analyzing.The way that deformation mechanism of valley slope is analyzed from the point of terra stress is feasible. The unloading of slope accelerated the developing of bending and toppling deformation and drew a line of demarcation. The deformation mechanism of Jiefanggou left slope that is explained by author is rational. The unloading of slope also accelerated the developing of deep bending and toppling deformation and drew a line of demarcation too. The figure of breakage face of toppling deformation is double fold line.
A gentle introduction to dark matter

III. DM properties and basics on models and production mechanisms 10 A. Cosmologically inferred properties 10 1. DM abundance 10 2. DM velocity distribution 10 3. DM lifetime 10 4. DM mass 10 5. DM self-interactions: elastic collisions 11 6. DM self-interactions: inelastic collisions 11 7. DM interactions with SM. 11 B. Predicting DM abundance WIMPs 11 1. More on the annihilation cross-section 14 C. Intro to alternatives 14 1. FIMP 15 2. SIMPs and friends 15 3. Beyond 15
How Socio-Economic Conditions Affect Voting Turnouts in Pakistan? A District-Level Analysis

In democracies, we expect the people to voice their opinion through voting. Voting is important because this is a process through which the incumbents can be held accountable and the prospective candidates are encouraged to deliver to the people. Therefore, many studies highlight the determinants of political participation or voting turnout. However, political participation and its determinants is an understudied area in Pakistan’s case. Using the calculus-of-voting model as our theoretical reference point, we study the relationship between voting turnouts and literacy rates (male and female), households’ satisfaction with the service delivery, and households’ economic perception as compared to the previous year, for 2008 and 2013 general elections, at district level in Pakistan. Our OLS results indicate that citizens in districts with high literacy, in particular high female literacy, are more likely to cast a vote. Similarly, citizens in districts whose households have strong perception of betterment in their economic conditions are more likely to turn out to vote. However, we find a positive but insignificant relationship between households’ satisfaction with the provision of services/facilities, and voting turnouts at district level in Pakistan.
One-Clock Priced Timed Games with Negative Weights

Priced timed games are two-player zero-sum games played on priced timed automata (whose locations and transitions are labeled by weights modelling the cost of spending time in a state and executing an action, respectively). The goals of the players are to minimise and maximise the cost to reach a target location, respectively. We consider priced timed games with one clock and arbitrary integer weights and show that, for an important subclass of them (the so-called simple priced timed games), one can compute, in pseudo-polynomial time, the optimal values that the players can achieve, with their associated optimal strategies. As side results, we also show that one-clock priced timed games are determined and that we can use our result on simple priced timed games to solve the more general class of so-called negative-reset-acyclic priced timed games (with arbitrary integer weights and one clock). The decidability status of the full class of priced timed games with one-clock and arbitrary integer weights still remains open.
The social-ecological dynamic of small-scale fisheries in Coastal Area of Dumai City

Small-scale fishery activities that often occur in coastal areas Dumai City are part of the socio-ecological system, where the waters around the coastal areas provide services to several fishermen who live around and are interrelated with each other. This study aimed to identify the dynamic of social and ecological systems to the sustainability of small-scale fisheries in the waters of Dumai City. The are 3 research location in village, namely Pelintung, Tanjung Palas, and Bangsal Aceh. The research method is a survey using interview techniques with a questionnaire. The data analysis in this research is descriptive qualitative, fishing gear productivity, fishermen’s income, and the relationship dynamics of the catch. The social characteristics of fishermen can be shown from their education, where most of the fishermen in Dumai City have elementary school education. The wives of fishers in Dumai City are mostly still taking care of the household, and only a few are processing fish. Fishers in Dumai City also carry out the duration of fishing operations based on the type of fishing gear (gillnet, scoop net, and portable trap). For the last seven years, the productivity of fishing with gillnet fishing gear has increased, while for scoop net and portable trap fishing gear, it has tended to decrease. The RPUE value of gillnet fishers shows the highest profit, where the average income in one trip is IDR 1,086,050.00 compared to other fishers. The fishermen’s perception also shows that the catch of gillnet fishers during the last five years has increased.
Assessment of marine debris in seagrass beds of Pramuka Island, Kepulauan Seribu

Indonesia is referred as the second largest contributor of marine debris in the world after China, with an estimated 0.48-1.29 million metric tons per year. High anthropogenic activities from locals and tourists can produce large marine debris and impact the seagrass ecosystem. This research aimed to identify marine debris in the habitat of seagrass beds on Pramuka Island. Extensive surveys include observing seagrass community structures, the measurement of chemical and physical parameters of the water, and marine debris inventory at three stations in Pramuka Island. Observation results found six seagrass species, with coverage ranged from 1.67-47.32% while the density 28-112.76 stand/m2. Marine debris can be classified into six types: plastic, metal, rubber, glass, organic matter, and others. The weight of marine debris for each type ranges from 0.03–2.38 kg. In addition, microplastic found consisted of fiber, film, and fragment with a unit weight of 20–440 particles/kg. In conclusion, the seagrass conditions of each station are classified as damaged. The marine debris that has the most impact on the seagrass beds is plastic. Fiber type microplastic has the most abundance. this can interfere with respiration and the photosynthesis process of seagrass.
Research on Visual Odometer of Wheeled Robot with Motion Constraints

In this paper, aiming at the planar motion characteristics of wheeled robots, the Red Green Blue Depth Camera (RGBD camera) was used as an image acquisition device to reduce the motion of wheeled robots to two-dimensional plane processing, which simplified the calculation method of visual mileage of planar wheeled robots. In addition, according to the two-dimensional motion characteristics of the robot camera, the contour constraint condition of feature point matching between frames was proposed, and the linear constraint condition was proposed by linearizing the motion equation of the camera between frames. The research showed that the contour constraint condition and linear constraint condition could be used to screen the mismatched feature points, this condition could not only screen the mismatched point pairs of color images, but also screen the matching point pairs with correct color image matching but large depth error, which provided high quality matching point pairs for inter-frame motion estimation of visual odometer. Finally, the filtered matching point pairs were reduced in dimension and combined with the two-dimensional Iterative Closest Point (ICP) algorithm to estimate the trajectory of the robot camera. The experimentation results showed that compared with the original three-dimensional ICP algorithm, the combination of contour constraint condition, linear constraint condition and two-dimensional ICP algorithm could significantly improve the computational speed of visual odometry.
A Study of an Engine Anomaly Detection Model IForest-ADOA

In the environment of the Industrial Internet, automatic and effective anomaly detection methods are of great importance to ensure the safety and stability of engines. However, in practical engine applications, traditional machine learning anomaly detection methods often have limitations due to a large number of unmarked samples and high latitude data. In this paper, an engine anomaly detection model based on the ADOA model is proposed, combining the unsupervised learning iForest model with the semi-supervised learning ADOA model. Through the anomalous samples detected by iForest, the ADOA model is used to learn their features and patterns and perform clustering, to achieve the anomaly detection of the samples and improve its accuracy at the same time. Experiments based on a real engine dataset show that this model has good results in terms of anomaly recognition rate and anomaly misclassification rate for anomaly detection of high-dimensional data generated by real environments.
Semi-supervised LDA and Multi-distance Metric Learning for Person Re-identification

The problem of person re-identification has attracted a lot of attention in the field of machine vision. In practice, the non-overlapping sample images change drastically and the sample size is small, which makes the metric model overfitting phenomenon. In this paper, based on the k-NN and the sample normality property, we propose a resampling linear discriminant analysis (LDA) algorithm to suppress the local constraints caused by small samples, then train it to obtain the person re-identification metric learning model. A semi-supervised LDA algorithm with semi-supervised characteristics is developed by optimizing the inter-class scatter for weighting. A joint distance metric-based approach is also proposed to learn both the Mahalanobis distance and Euclidean distance. The improved algorithm is tested on the VIPeR and CUHK01 datasets, and the results indicate that, despite the change in the total number of training samples, the algorithm in this paper shows high recognition accuracy.
Natural Scene Chinese Recognition Based on Deep Learning

Text recognition is one of the research focuses in the field of computer vision. In recent years, many researchers have proposed recognition methods based on codec frameworks, such as CRNN, but most of these methods are based on English, and relatively few are based on Chinese. Chinese language, because of its large number of character types and contains a large number of similar characters, only using CNN to extract features will lose a large amount of information. Meanwhile, the method of giving combined attention is an emerging direction. In this paper, we study the recognition of the Chinese language and introduce attention to BiLSTM to obtain more contextual information. Compared with the original algorithm, the performance of this algorithm is improved on the synthetic dataset.
Research on Safety Helmet Detection Algorithm of Power Workers Based on Improved YOLOv5

The traditional helmet detection algorithm in power industry has low precision and poor robustness. In response to this problem, the helmet detection algorithm based on improved YOLOv5 (You only look once) is put forward in this paper. Firstly, the YOLOv5 network structure is improved. By increasing the size of the feature map, one scale is added to the original three scales, and the added 160*160 feature map can be used for the detection of small targets; Secondly, the K-means is used for re-clustering the helmet data set to get more suitable priori anchor boxes. The experimental results illustrate that the average accuracy of the improved YOLOv5 algorithm is increased by 2.9% and reaching 95% compared with the initial model, and the accuracy of helmet recognition is increased by 2.4% and reaching 94.6%. This algorithm reduces the rates of missing detection and misdetection of small target detection in original network, and has strong practicability and advanced nature. It can satisfy the requirements of real-time detection and has a certain role in promoting the safety of power industry.
Discovery of SiO Masers in the “Water Fountain” Source IRAS 16552−3050

In this paper, we report new detections of SiO v = 1 and v = 2 J = 1 → 0 masers in the “water fountain” source IRAS 16552−3050, which was observed with the Nobeyama 45 m telescope during 2021 March–April. Water fountains are evolved stars whose H2O maser spectra trace high-velocity outflows of >100 km s−1. This is the second known case of SiO masers in a water fountain, after their prototypical source, W 43A. These SiO masers should shed light on the evolutionary status of this category of evolved stars, which are likely to be at the end of the asymptotic giant branch phase, when the star exhibits the most copious stellar mass loss, followed by development of the complicated morphologies of planetary nebulae. The origin of a large (up to 25 km s−1) velocity offset of the SiO masers with respect to the systemic velocity derived from the spectrum of CO J = 2 → 1 line is discussed here.
Extracting Parallel Sentences from Low-Resource Language Pairs with Minimal Supervision

At present, machine translation in the market depends on parallel sentence corpus, and the number of parallel sentences will affect the performance of machine translation, especially in low resource corpus. In recent years, the use of non parallel corpora to learn cross language word representation as low resources and less supervision to obtain bilingual sentence pairs provides a new idea. In this paper, we propose a new method. First, we create cross domain mappings in a small number of single languages. Then a classifier is constructed to extract bilingual parallel sentence pairs. Finally, we prove the effectiveness of our method in Uygur Chinese low resource language by using machine translation, and achieve good results.
OpenGL Based 3D Visualization of Underground Mining Systems

An OpenGL based 3D visual modeling system of underground mine excavations was introduced. The key techniques for developing the 3D visual modeling system with OpenGL were presented, including entity drawing, geometric transformation, projective transformation, illumination and rendering. Examples of OpenGL application were given and a prototype system for visualizing the underground mine excavation system of a Chinese iron mine was characteriaed. Some problems for further study were proposed.
Transformer Model for Remaining Useful Life Prediction of Aeroengine

Accurate aeroengine remaining useful life (RUL) prediction plays a vital role in ensuring safe operation and reducing maintenance losses. In order to improve the accuracy of aeroengine RUL prediction, an aeroengine RUL prediction method based on the Transformer model is proposed, which gives greater weight to the characteristics of important time steps through self attention mechanism, and solves the memory degradation problem caused by too long sequence in engine RUL prediction, and excavates the complex mapping relationship between input features and aeroengine RUL. Experiments on the C-MAPSS data set show that the Transformer model can better predict the aeroengine RUL based on the aeroengine degradation data. Compared with the long short term memory network model, the root mean square error of the two sub data sets is reduced by 6.57% and 5.63% respectively.
Development of analog signal transmission in LAr for DUNE

The Deep Underground Neutrino Experiment (DUNE) is currently investigating a new prototype design for its second far detector module. The new concept proposes a vertical drift liquid argon time projection chamber, with a cathode at mid-height in the detector and anodes made of printed circuit boards, located at the top and bottom of the detector. In this context, the design of the photo-detection system needs to be revisited, opening a window of opportunity for further optimization and new developments. It is envisaged to distribute the photo-sensors (x-ARAPUCA) on the cathode surface. Such a system is required to operate within high-voltage surfaces, with both power supply and signal delivered using non-conductive materials. This article describes the on-going work to collect and read-out the signal of the photo-sensors, which are re-shaped into large tiles containing 160 SiPMs each. A new ganging scheme for the SiPMs is introduced. In particular, this article will focus on the proposed option to read out the sensors using an analog optical transmitter, that should ensure the transmission of the signals — with a wide dynamic range — to the outside of the cryostat to be digitized.
Document-level Core Events Extraction based on QA

Document-level core events extraction model based on QA is provided in this paper. Question with prior information is constructed input into the BTBiLSTM model. Firstly, extract events from documents. Secondly finds event arguments from the text to be extracted. Lastly fuse events to select core events. The experimental results on ACE2005 Chinese corpus show that the event argument role question with prior information has a good effect on event argument extraction. Compared with existing method, our method in this paper realizes cross sentences events arguments extraction, it also applicable to documents without headline.
End-to-end Saliency Face Detection and Recognition

Face recognition is a long-lasting hot topic in compute vision. The face recognition system mainly includes face detection, alignment and feature extraction. In the forward task, the extracted features are used to measure the similarity between faces, and outputs whether those are same person or not or which person it is in the registered set. Typically, the three stages of recognition system training independently of each other have the following shortcomings: 1) redundant calculation of feature maps; 2) unable to end-to-end optimization; 3) detecting an extracting so much useless face. A lightweight model for saliency face detection and recognition that can be optimized end-to-end is proposed. While maintaining accuracy, it meets the real-time and memory limitation requirements in embedded devices or terminals.
Directed Association-based User Identification Algorithm

This paper studies the social network with directed association semantics, and proposes an unsupervised user identification algorithm (DAUM-P). The algorithm construct a user associations graph based on multiple types of directed user behaviors, and define directed associations; then, walk based on directed associations to obtain a walking sequence, and then combine the network embedding model to learn user characteristics vector representation; finally, the identification result is given based on the representation vector distance. Finally, the experiments show that the user identification effect is better than that of the baseline method.
Improving Word Alignment by Adding Gromov-Wasserstein into Attention Neural Network

Statistical machine translation systems usually break the translation task into two or more subtasks and an important one is finding word alignments over a parallel sentence bilingual corpus. We address the problem of introducing word alignment for language pairs by developing a novel neural network model that can applied to other generative alignment models. We use Multi-layer attention model and multi-layer model with multi-head-attention mechanism on each layer provides superior translation quality. It can be trained on bilingual data without relying on word alignment. In this paper, we cast the correspondence problem directly as an optimal distance problem. We use the Gromov-Wasserstein distance to calculated how similarities between word pairs are related across languages. The resulting alignments dramatically outperform the GIZA++ and FastAlign approach, these alignments are comparable on public data sets.
Polyphyletic Loss: Litchi Flower Detection with Occlusion

Controlling shoot and promoting flower of litchi is one of the key technologies for the high yield of fruit trees, but the occlusion caused by dense growth has a great impact on the detection of flowers. To solve the problem, this paper designs a polyphyletic loss function to detect the occlusive litchi flowers. A new aggregation loss term is proposed in the loss function to force the proposal box to approach and compactly locate the corresponding target. At the same time, the segmentation loss of the bounding box specially designed for the dense crop scene is added to keep the prediction box away from the surrounding objects and improve the robustness of detecting a large number of flowers. We conducted experiments on the self-built litchi flower data set and the multi species fruit flower data set at the same time. Compared with other methods, the proposed method has higher robustness and detection accuracy, which provides an important idea and method for flower number statistics and fruit yield prediction in dense scenes.
Runet: Convolutional Networks for Crack Detection

In daily life, crack is a common phenomenon. Such as road cracks, stone cracks. These cracks are usually not easy to be quickly and intuitively identified, because these cracks are characterized by poor continuity and low contrast. Among the existing crack detection methods based on deep learning, the model is too large to be directly used in life. We design an end-to-end neural network, its name is Runet, which greatly reduces the parameters of the model by depthwise separable convolution, increases the receptive field of the network by hole convolution. At the same time, we introduce the trainingtime and inferencetime architecture to increase the running speed and precision.