Miracle-dz/UniFAR-FTUs huggingface dataset:
Convection Initiation During the Meiyu Environment in the Yangtze‐Huai River Basin of China

Convection is the main contributor to heavy rainfall over China's Yangtze‐Huai River Basin (YHRB) during Meiyu season; however, the mechanisms of convection initiation (CI) associated with the Meiyu front are still not well understood. In this study, a large set of 86,099 CI events, identified from composite reflectivity data in YHRB over six Meiyu seasons, were used to investigate the characteristics of the spatiotemporal distribution of CI in YHRB. The result showed that the overwhelming majority of CI events (∼90%) occurred in the region of existing stratiform clouds. Meanwhile, CI tended to concentrate in mountainous areas and exhibited two triggering modes. By relating the CI events with an objective analysis of ERA5 reanalysis data, it was also revealed that the characteristics of CI occurrence varied with patterns of Meiyu circulation and their interactions with local topography, and the warm air advection pattern dominated the Meiyu CI. We further illustrated that CI on the plains occurred with a morning peak corresponding to environments of high 0–3 km shear (SHR3) and low most unstable convective available potential energy (MUCAPE), while the CI near or over mountains had an afternoon peak corresponding to low SHR3 and high MUCAPE environments.
Handwriting pattern matching and retrieval with binary features

This dissertation explores the role of binary features in pattern matching and retrieval with applications to handwriting processing. Specifically, this dissertation focuses on examining metric/non metric properties of similarity measures for binary feature vectors, accelerating pattern searching with nonmetric binary vector similarity measures, and developing effective binary features from handwritten word images for handwriting identification and retrieval tasks. Metric or non-metric verification of dissimilarity measures is important as efficient nearest neighbor (nn) search algorithms using metrics are usually inapplicable in searching a space based on non-metrics. We formulate a set of dissimilarity measures for binary vectors, and comprehensively examine their metric or non-metric properties. Especially, we discover and mathematically prove a special property, the Tri-Edge Inequality (TEI), associated with several measures. As the TEI property disables conventional fast search algorithms exploiting the triangle inequality, a cluster-tree based algorithm is developed to accelerate k-nn classification without any presuppositions about the metric form and properties of a dissimilarity measure. A mechanism of early decision making and minimal side-operations for choosing searching paths largely contribute to the efficiency of the algorithm. The algorithm is evaluated through extensive experiments. We develop a set of effective binary features from handwritten word images for tasks of handwriting identification, word image retrieval and signature authentication. Handwriting identification utilizes sixty-two alphanumeric characters and four characteristic words, from which binary features are extracted. Discriminability of each of sixty-two characters and four characteristic words is examined through two models of handwriting identification, writer identification and verification. Individuality of handwritten characters is quantitatively established. The ranking order of sixty-two alphanumeric characters in writer discriminability, for the first time, provides a scientific guidance for selecting most-discriminative characters in examining forensic documents. Binary features from sixty-two characters and four words lead to very high identification and verification rates. Word-level binary features are also applied to word image retrieval and signature authentication with very promising results. (Abstract shortened by UMI.)
Assessment of 6-Month Lead Prediction Skill of the GloSea5 Hindcast Experiment

This study explores the 6-month lead prediction skill of several climate indices that influence on East Asian climate in the GloSea5 hindcast experiment. Such indices include Nino3.4, Indian Ocean Diploe (IOD), Arctic Oscillation (AO), various summer and winter Asian monsoon indices. The model’s prediction skill of these indices is evaluated by computing the anomaly correlation coefficient (ACC) and mean squared skill score (MSSS) for ensemble mean values over the period of 1996~2009. In general, climate indices that have low seasonal variability are predicted well. For example, in terms of ACC, Nino3.4 index is predicted well at least 6 months in advance. The IOD index is also well predicted in late summer and autumn. This contrasts with the prediction skill of AO index which shows essentially no skill beyond a few months except in February and August. Both summer and winter Asian monsoon indices are also poorly predicted. An exception is the Western North Pacific Mon-soon (WNPM) index that exhibits a prediction skill up to 4-to 6-month lead time. However, when MSSS is considered, most climate indices, except Nino3.4 index, show a negligible prediction skill, indicating that conditional bias is significant in the model. These results are only weakly sensitive to the number of ensemble members.
Effect of different sources of organic fertilizers on crop growth and yield of cabbage

Cabbage is (Brassica oleracea var. capitata) a popular leafy vegetable in Nepal, but depleted soil fertility and inefficient production techniques can limit productivity. To address this, we conducted a study in the Jumla district of Karnali Province, Nepal during the 2022 main cropping season. We evaluated the impact of different organic fertilizers on the growth and yield of cabbage using a Randomized Complete Block Design with seven treatments and three replications with the hybrid cultivar 'Green Coronet'. The treatments included Farm Yard Manure, Poultry Manure, FYM + Vermicompost, Vermicompost, Black Wonder, FYM + Poultry Manure, and Sheep Manure. The parameters, including plant height, stem diameter, leaf length and width, head weight, polar and equatorial diameter, head circumference, root and shoot weight, and weight of the head (g) of cabbage were taken for measurement. Our findings showed that organic fertilizers significantly influenced the growth and yield of cabbage, with Vermicompost showing the most potential as a nutrient source for achieving higher growth and yield. The Vermicompost treatment resulted in the largest size of cabbage head (1026 g), while the Farm Yard Manure and Poultry Manure treatments had limited impact. Our results suggest that the use of organic fertilizers such as Vermicompost can lead to higher cabbage production, improved soil fertility, and reduced reliance on chemical fertilizers and pesticides. This has significant implications for promoting sustainable agriculture practices, improving food security, and providing economic benefits for farmers and communities in Nepal and beyond.
Parasitism and bees: A review of basic concepts from an ecological perspective

Parasitism and bees: a concept revision from an ecological perspective. From the beginning all, parasites, pathogens and viruses are organisms that have a negative connotation in the field of medicine. However, the use of methodologies and terms of medicine in the field of ecology and, above all, veterinary medicine, led to this understanding of "pathogenic" organisms reaching such studies. When we look into the apicultural research bibliography, we find that these concepts sometimes are poorly distinguished and only a few early studies have been focused on bringing light on this conflictive conceptualization. Today we find a large number of scientific articles that misuse these terms and often consider them as synonyms. Throughout this review we will seek to differentiate each of these terms and generate consensus that avoids the confusion of such important ecological concepts.
Research on recommendation algorithm based on neural network fusing user behavior sequence

The traditional neural network collaborative filtering algorithm is developed from the matrix decomposition algorithm, and compared with the matrix decomposition, the neural network collaborative filtering algorithm uses a multi-layer perceptron to replace the original dot product operation. Although this method can fully cross user vectors and item vectors, it cannot learn the timing problems on the user side. To solve this, a collaborative filtering model based on deep neural network fusion user sequence is proposed. The model still models the interaction information between the user and the object, with the difference being that recurrent neural networks are introduced when modeling the user. The probability of the user's interest in the item is calculated from the obtained user characteristics and item characteristics. Experiments on the MovieLens and MIND datasets show that the proposed model is higher than the matrix decomposition algorithm and neural network co-filtering algorithm on the AUC and F1 Score indicators, which verifies the accuracy of the model's recommendation effect.
Research on the opinion evolution process under the effect of time delay

To address the problem of time delays in the information dissemination process, the classical Hegselmann-Krause model is extended by introducing concepts such as time delays. The role of network models, trust thresholds and time delays in the opinion evolution process is explored. The results show that scale-free networks are more likely to reach opinion consensus than random networks under the same trust threshold, the larger the trust threshold in the same network model the easier it is for opinions to reach consensus, and conversely the smaller the trust threshold the more fragmented the opinions tend to be. secondly, time delay speeds up the initial stage of opinion evolution. Although this feature is counterintuitive, the value of this feature is verified in example simulations. The simulation results considering time delays are closer to the evolutionary process of the real case.
Dynamic soil and high rise structure interaction analysis by coupling method

The coupling equation of particular solution boundary element (BE) and finite element (FE)is derived. The freedom of coupling equation is reduced so that not only the freedom of finite element domain but also that of coupling boundary of both FE domain and BE domain are included. The equation does not increase the width of the matrix of original finite element equation,and it is easy to be programmed. Numerical analysis of shear wall and soil system is also given.
Human gait recognition algorithm based on MobileNetV1 with attention mechanism

For embedded modern equipment, the current gait recognition algorithm model is difficult to deploy on it due to a large amount of gait frame image data, slow network processing speed, complex structure and low computational efficiency. In this paper, a lightweight convolutional network model integrating the attention mechanism is proposed. The algorithm first performs morphological processing on the image, extracts the gait contour image, and calculates the gait energy image; integrates the attention mechanism with MobileNetV1. The feature information of the image is effectively extracted, and the parameters of the network are reduced. A number of body method validation experiments are conducted in the CAISIA-B gait database of the Chinese Academy of Sciences, and the experimental results are significantly improved with other deep learning models.
Is it Positive or Negative? On Determining

Abstract— In most experiments using event-related brain poten-tials (ERPs), there is a straightforward way to define—on theoret-ical grounds—which of the conditions tested is the experimentalcondition and which is the control condition. If, however, theoret-ical assumptions do not give sufficient and unambiguous informa-tiontodecidethisquestion,thentheinterpretationofanERPeffectbecomes difficult, especially if one takes into account that certaineffects can be both a positivity or a negativity on the basis of themorphology of the pattern as well as with respect to peak latency(regard for example, N400 and P345). Exemplified with an ERPexperiment on language processing, we present such a critical caseand offer apossible solution on the basis ofnonlinear data analysis.We show that a generalized polarity histogram, the word statisticsof symbolic dynamics, is in principle able to distinguish negativegoing ERP components from positive ones when an appropriateencoding strategy, the half wave encoding is employed. We pro-pose statistical criteria which allow to determine ERP componentson purely methodological grounds.
Optimal Allocation and Assessment of Distributed Generation Units with Multi-Objective Planning in Unbalanced Distribution Networks

Abstract—Real-world distribution networks are generally unbalanced due to unbalanced loads and asymmetric line parameters. Proper allocation of distributed generation (DG) units in unbalanced radial distribution networks to extract their maximum potential benefits is still quite difficult. Converter-coupled DG units have individual phase real and reactive power regulating ability and provide voltage unbalance compensation. This article proposes a new technique for the suitable allocation and capacity of converter-based multiple DG units considering the unbalanced operation of distribution networks. The objective function contains power loss minimization, multiphase voltage stability augmentation, and reduction of voltage unbalances among different phases in the system. A complex multi-objective optimization problem has been synthesized with the help of a Fast and Flexible Radial Power Flow (FFRPF) and solved by employing a Weighted aggregation (WA) based particle swarm optimization (PSO) approach. The performance of the proposed technique is validated on 19-bus, 25-bus, and 34-bus unbalanced radial distribution networks that consider various power factor modes of the converter-coupled DG units. The analysis of the simulation results indicates a significant enhancement in the power delivery efficiency in all the unbalanced test systems
Determinants of Bank Profitability in the Context of Financial Liberalization: Evidence from Morocco

This article aims, in the context of financial liberalization, to examine the impact of several internal and external factors in relation to the profitability of banks listed on the Moroccan stock exchange. The study uses the panel data method on a sample of the six banks with the largest market shares over the 2010–2019 period to investigate the influence of ownership structure, bank governance, capital structure, prudential regulation, and also a macroeconomic variation on bank profitability. Accordingly, the empirical results indicate a negative relationship between concentration, foreign ownership, and bank profitability. Further, high capital structure ratios, especially solvency, liquidity, and capitalization ratios appear to have an undesirable effect on the performance of Moroccan banks. In contrast, the impact of domestic institutional ownership is positive, implying that profitability is more favored by domestic ownership than by foreign ownership. Our findings also highlight the importance of independent directors and board size. Therefore, liberalizing ownership and opening up the board to the expertise of outside directors is essential for the banking sector’s development. In terms of policy implications, central banks and regulators need to ensure that banks rigorously adhere to specified leverage and fixed capital adequacy ratios while ensuring optimal liberalization for bank performance.
Frontal Fibrosing Alopecia Associated With Oral Erosive Lichen Planus: Two Locations, One Disease

Accepted: July 27, 2023; Published: April 2023 Copyright: ©2023 Toso et al. This is an open-access article distributed under the terms of the Creative Commons AttributionNonCommercial License (BY-NC-4.0), https://creativecommons.org/licenses/by-nc/4.0/, which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original authors and source are credited.
Effect of Multi-gram Term Expansion and Reduction in Text Classification

High dimensional feature is the main significant characteristic and problem in text classification. More features generally increase the effectiveness of text classification; however, it is difficult and time consuming for traditional classifier learners to process a huge number of term features. In this paper, we investigate the effect of increasing multi-gram term features in feature reducing methods for text classification. Sentence-based 2-gram terms are applied to expand the number of features and the log-scaled discriminant coefficient is used to reduce the number of features as a small fix number of dimensions. For demonstrating the property of terms expanding in feature reduction, two data sets are used to test and compare the methods in our experiments of text classification. The experimental results show that the single-label classification can be improved by multi-gram expansion. However, the results of multi-label classification are dependent on the distribution of instances in classes.
Bottom hole pressure estimation and 
 ℒ1 adaptive control in managed pressure drilling system

In the past decade, managed pressure drilling, a technology aiming at precise well pressure control, has been gaining increasing popularity and been a key enabler for some of the most challenging well drilling cases such as the offshore deep water well drilling. This paper attempts to solve two of the main challenges involved in the managed pressure drilling systems: first, the bottom‐hole states measurements are updated at a low rate, which can be practically viewed as unmeasured and thus need to be estimated in real time for both monitoring and control purposes and second, the drilling process is subject to uncertainties including unknown system parameters (e.g., frictions and densities), unmodeled actuator dynamics, and noise, which require a robust adaptive controller for control of the bottom‐hole pressure. Towards this objective, an integrated estimator and ℒ1 adaptive control scheme is proposed. The estimator provides estimation of the bottom‐hole pressure and flow rate, based on the available measurements from the topside. The ℒ1 adaptive controller drives the bottom‐hole pressure to the desired value following a reference model, which also handles the time delays in the input signal. The design is based on a recently developed nonlinear drilling model. The results demonstrate that the ℒ1 adaptive controller has guaranteed performance bounds for both the input and the output signals of the system while using the estimation of the regulated outputs. Simulation results covering different operational conditions verify the theoretical findings. Copyright © 2016 John Wiley & Sons, Ltd.
Shortest Path Analysis and Its Application in Network Systems

According to the characteristics of digital map,a key technique of shortest path analysis was presented,and a shortest path analysis system was realized by a practical and efficient DIKB algorithm based on the digital map.In the paper some techniques such as the vector map representation,construction of the topological structure of network and efficient implementation of shortest path algorithm etc.are described in some detail.
Research of structured light measurement auto-focus system

In order to help the structured light measurement system for further calculation and measurement,an auto-focus system is designed based on the control of computer.This system can adjust the brightness of the image and complete the auto-focus process in different scenes automatically.Using "Sobel-Tenengrad function" as criterion function and "climbing method"as focus search strategy,the system precisely focuses in 10 seconds.It can also be used to calculate the function values for four criterion functions in different environments.After analyzing the results,a conclusion is drawn about the unimodality and robustness of these four criterion functions.Sobel-Tenengrad function,squared gradient function and Laplace function perform well in several experiments.However,variance function does not show the properties which criterion function should own.
Numerical solution of semidefinite constrained least squares problems

In this thesis, we are concerned with computing the least squares solution of the linear matrix equation AX = B subject to the constraint that the matrix X is positive semidefinite. For symmetric X, this is the previously studied semidefinite least squares (SDLS ) problem; for nonsymmetric X, we introduce the nonsymmetric semidefinite least squares (NS-SDLS ) problem. An application of this second problem is the estimation of the compliance matrix at some location on a deformable object. This is an important step in the process of making interactive computer models of deformable objects, a technique which is used in the area of medical simulation. We also introduce a third semidefinite constrained least squares problem called the linear matrix inequality least squares (LMI-LS ) problem, which is a generalization of the first two problems. Sufficient conditions for the existence and uniquenss of solutions for each of these three problems is provided. These solutions are characterized as solutions of nonlinear systems of equations known as the Karush-Kuhn-Tucker (KKT ) equations. It is shown that the KKT equations for each of these problems can be stated as a semidefinite linear complementarity problem (SDLCP). Interior-point methods are proposed for the numerical solution of each of these three problems. Computational experiments are conducted which indicate that predictorcorrector interior-point methods solve these semidefinite constrained least squares problems efficiently. A noticable improvement is made over the current computational methods used for solving the SDLS problem.
Comparison of ResNet-50 and vision transformer models for trash classification

Paper, glass, fruit, scrap metal equipment, and other materials have become domestic garbage as people's living standards have dramatically improved. Plastic trash, including bags, and disposable lunch boxes, has become a global environmental threat. Carrying out waste separation and encouraging waste reduction at the source is a meaningful way to realize the quantification, harmlessness, and resourcefulness of domestic waste. Pictures contain lots of information data and many methods for successfully extracting and examining that information data. Consequently, one of the primary research projects in the field of pictures is the picture classification problem. Traditional image classification techniques can no longer process and extract information from vast amounts of image data quickly. Throughout the experiments, the accuracy of ResNet-50 is lower than ViT, most likely due to its less extensive set of parameters. And the more the parameters of the model, the higher the precision of training. Additionally, the experiment examined the performance of the ResNet-50 model with and without pretraining, and the results showed that the performance of the model without pretraining was noticeably inferior to the model with pretraining. It is better to have a model which has numerous parameters.