Miracle-dz/UniFAR-FTUs huggingface dataset:
Formas químicas, disponibilidade de manganês e produtividade de soja em solos sob semeadura direta

This field study had the following objectives: to determine the most appropriate micronutrient extractor to evaluate manganese bioavailability in soils under no-tillage; to evaluate the interaction of soil manganese with humic acids in soils under no-tillage with and without manganese deficiency through electronic paramagnetic resonance (EPR); and to evaluate manganese fractions (exchangeable, organic, oxides and residual) after manganese addition in soils under no-tillage with and without previous manganese deficiency for soybean. Four sites (I to IV) were selected to study the response curve to manganese (0 to 48 kg ha-1) fertilization in soybean. Sites I to III were located in Tibagi, state of Parana (PR), Brazil and site IV was in Castro (PR). Manganese rates affected the manganese concentrations in the soil, but not the manganese concentrations in leaves and the amount of manganese taken up by plants. Exchangeable, organic and oxide forms of manganese increased with the increasing rates applied at site I, with the largest increase being observed for the organic fraction (changed from 5.4 mg kg-1 manganese in the control to 35.1 mg kg-1 of manganese in the highest manganese dose). Manganese was not detected by EPR in the solid samples of humic acid - pH 3.0. Manganese quantification in the same samples after nitric-perchloric digestion, along clear signals of manganese detected by EPR in samples of humic acid - pH 2.4, indicated that manganese in the samples was strongly linked to the functional (probably carboxylic) groups of humic acid. The fact that there was no effect of manganese on soybean yield might be due to the complexation of the nutrient by soil organic matter. In general, DTPA-TEA was the most reliable manganese extractor to evaluate manganese availability to soybean under no-tillage.
Human-Interpretable Rules for Anomaly Detection in Time-Series

Human-interpretable rules for anomaly detection refer to abnormal data presented in a format that cannot be intelligible to analysts. Learning these rules is a challenging issue, while only a few works address the problem of different types of anomalies in time-series. This paper presents an extended decision tree based on patterns to generate a minimized set of human-comprehensible rules for anomaly detection in univariate time-series. This method uses Bayesian optimization to avoid manual tuning of hyper-parameters. We define a quality measure to evaluate both the accuracy and the intelligibility of the produced rules. The performance of the proposed method is experimentally assessed on real-world and benchmark datasets. The results show that our approach generates rules that outperforms state-of-the-art anomaly detection techniques.
Visualizing and Exploring Big Datasets based on Semantic Community Detection

The extended use of the RDF model has made available many datasets from heterogeneous sources that are of interest to a wide audience. Their exploration, however, is a highly demanding task requiring extensive training and knowledge of the SPARQL language. In this demo, we present an innovative system, which supports the exploration of large RDF datasets without requiring any knowledge about the RDF or SPARQL. The system is based on a novel algorithm that detects semantically similar communities capitalizing on hyperbolic network embedding and a weighted similarity metric. The detected communities are visualized in a user-friendly way and presented to the user through a two level abstraction interface with a toolbox of exploration functionalities.
TEMP and copper cocatalyzed oxygenation of ketones with molecular oxygen: chemoselective synthesis of α-ketoesters

A novel and practical method for the synthesis of α-ketoesters through TEMP and copper cocatalyzed chemoselective oxidative coupling of commercially available methyl ketones with alcohols is developed. Mild conditions and broad substrate scope make this chemistry an efficient tool for the late-stage modification of bioactive compounds. Detailed mechanistic studies demonstrate a novel mechanism involving an organocatalytic cycle and SET process. The oxygenation process with molecular oxygen enables this transformation.
ALKOXY-DERIVED FERROELECTRIC PZT FILMS: THE EFFECT OF LEAD ACETATE DEHYDRATION TECHNIQUES AND LEAD CONTENT IN THE ELECTROCHEMICALLY PREPARED SOLUTIONS ON THE PROPERTIES OF THE FILMS

Abstract Different ways of dehydration of lead acetate trihydrate for its application in sol-gel techniques are discussed. Reaction of Pb(CH3COO)2·3H2O with acetic acid anhydride proved to be the most efficient way of its dehydration. Characteristics of the solutions of lead acetate in methoxyethanol dehydrated by different methods are presented. Solutions for preparation of PZT films are prepared using electrochemical dissolution of titanium and zirconium in methoxyethanol. Physico-chemical properties of these solutions alongside with structural and electrical characteristics of the PZT films obtained from these solutions are presented. The effect of lead content in solutions on the properties of the films obtained is discussed.
Theory and Realization of Secondary Task Assignment for Multi-UAV Pickup Based on Green Scheduling

e development of artificial intelligence technology has brought changes to various industries. Under the concept of green sustainable development, how to use the progress of science and technology to implement low-carbon strategies is a problem that every enterprise should consider. Aiming at the problem of picking up goods in logistics industry, this paper proposed a secondary task assignment theory for multiple unmanned aerial vehicle (Multi-UAV) based on green scheduling. e theory greatly improves the utilization rate of unmanned aerial vehicle (UAV) and reduces the energy consumption.We analyzed the advantages and disadvantages of local optimal algorithm and global optimal algorithm in time and energy consumption. rough repeated experiments in different ranges, we have well verified the high efficiency and general applicability of this theory, which can provide theoretical and practical implications for logistics enterprises using UAV to achieve low-carbon sustainable development in the future.
Spatial Network Structures of Urban Agglomeration Based on the Improved Gravity Model: A Case Study in China's Two Urban Agglomerations

Research on urban agglomerations from the perspective of network spatial structure is important to promote their sustainable development. Based on online and traditional data, this paper first improves three aspects of the traditional spatial gravity model—city quality, the gravitation coefficient, and city distance—considering urban center functional intensity and population mobility tendencies. +e resulting improved directional gravity model is applied to analyze the structure of the city network for two urban agglomerations in China, the Beijing-Tianjin-Hebei urban agglomeration (BTHUA) and the Yangtze River Delta urban agglomeration (YRDUA). +e results of the study are as follows: (1) the existing urban connections have obvious hierarchies and imbalances, with the YRDUA urban hierarchical connections being of larger scale. (2) Cities are closely connected, but city networks are unbalanced, though the YRDUA has more balanced urban development. (3) Each node city has a clear radiation range limit, and spatial distance remains an important constraint on urban connections. +e backbone network of the BTHUA has a triangular shape and trends toward a “sparse north and dense south,” while the YRDUA is characterized by multiple axes and an overall distribution that trends toward a “dense north and sparse south. ” (4) Cities with poor comprehensive strength are more likely to be captured, forming an attract and be attracted relationship. (5) +e BTHUA and the YRDUA each form three communities.
Structure Detection in Verbose CSV Files

Numerous data are stored in semi-structured files with ad-hoc layout. Such data are valuable digital assets for various data-driven applications. This work introduces the notion of verbose CSV files . Verbose CSV files include content serving different purposes in various positions. They are designed for human visual inspection or statistical report collection. An important preliminary task for extracting information from such files is structure detection , in particular classifying lines or cells by their purpose. As manual efforts are infeasible and error-prone for large files or large sets of files, automatic approaches are desirable. This work addresses both the line and the cell classification problems on verbose CSV files. Strudel is a supervised learning approach based on a random forest classifier, combined with a set of novel features that fall into three categories: content features, contextual features, and computational features. We annotated five real-world datasets from various domains, on which we tested our approach. Our in-depth experiments show the advantages of Strudel over baseline and state-of-the-art approaches in both line and cell classification tasks.
Indexed Log File: Towards Main Memory Database Instant Recovery

Main Memory Database Systems (MMDBSs) may significantly increment IOPS (Input/Output Operations per Second) rates by avoiding access to secondary memory. This occurs because they maintain the database in Random Access Memory (RAM). Similar to traditional Disk-Based Database Systems (DBSs), MMDBSs are expected to trigger recovery activities after system failures to restore the database to its last consistent state before the failure. Nonetheless, MMDBSs executes the recovery process in an offline way, thus the database becomes available for new transactions only after the full recovery process has been performed. Systems can keep database replicas for high availability. However, replication is not immune to some failure sources that can cause multiple and shared malfunctions. Therefore, software techniques are required to prevent failures and repair crashed systems as soon as possible. This work proposes a novel MMDBS instant recovery process which makes MMDBS able to schedule new transactions simultaneously with the recovery activities. In order to validate this new approach, simulations with a prototype implemented on Redis have been conducted over Memtier benchmark. The achieved results evidence the suitability of the proposed recovery mechanism.
SOJA: A Memory-efficent Small–large Outer Join for MPI

The join is a fundamental and widely used operation in data analytics but equally, it is also one of the most expensive ones. Considerable work has been carried out to improve and evaluate join approaches based on popular distributed processing systems such as Spark and Hadoop, however, it has not been widely studied on MPI. In this paper, we first implement, analyse and compare existing algorithms for the common small-large outer join operation and develop a novel approach, the swap-based outer join algorithm (SOJA). SOJA is designed to minimise the expensive communication between the distributed nodes while also reducing the cost of the local join operations. We demonstrate the benefits of SOJA experimentally, showing that it achieves at worst an execution time similar to its competitors. More importantly, SOJA requires substantially less memory (typically half the memory compared to the best competitor) and that memory usage scales very well.
SUSTAIN OPHTHALMIC DEL IVERY OF LEVOFLOXACIN FROM A pH - TRIGGERED IN SITU GELLING SYSTEM

Most eye diseases are treate d with topical application of eye drops. The poor bioavailability and therapeutic response exhibited by these conventional ey e drops due to rapid precorneal elimination of the drug may be overcome by the use of in situ gelling systems that are instilled as drops into the eye and undergo a sol - to - gel transition in the cul - de - sac. Hence, the purpose of the present work was to develop Sustain Ophthalmic delivery of Levofloxacin from a pH triggered in situ gelling system. Polyacrylic acid (Carbopol 934) was u sed as the gelling agent in combination with hydroxypropyl methylcellulose (Methocel K4M) and HPMC K100LV which acted as a viscosity enhancing agent. Compatibility studies of the drug excipients were carried out using FTIR. The pre pared formulations were c haracterized for clarity, pH, drug content, Gelling capacity, Rheological studies, in vitro drug release study , sterility study and stability study.
Reinforcement Learning-Based Collision Avoidance Guidance Algorithm for Fixed-Wing UAVs

A deep reinforcement learning-based computational guidance method is presented, which is used to identify and resolve the problem of collision avoidance for a variable number of fixed-wing UAVs in limited airspace. The cooperative guidance process is first analyzed for multiple aircraft by formulating flight scenarios using multiagent Markov game theory and solving it by machine learning algorithm. Furthermore, a self-learning framework is established by using the actor-critic model, which is proposed to train collision avoidance decision-making neural networks. To achieve higher scalability, the neural network is customized to incorporate long short-term memory networks, and a coordination strategy is given. Additionally, a simulator suitable for multiagent high-density route scene is designed for validation, in which all UAVs run the proposed algorithm onboard. Simulated experiment results from several case studies show that the real-time guidance algorithm can reduce the collision probability of multiple UAVs in flight effectively even with a large number of aircraft.
Tin Silsesquioxanes as Models for the “Open” Site in Tin‐Containing Zeolite Beta

The use of biomass as a resource to produce value‐added products has garnered significant interest as a means of reducing reliance on fossil fuels. This task is complicated by the complex, highly functionalized nature of abundant biomass derivatives, such as glucose. Tin‐containing zeolite Beta (Sn‐Beta) can selectively isomerize glucose to fructose through a 1,2‐intramolecular hydride shift (1,2‐HS) or selectively produce mannose through a 1,2‐intramolecular carbon shift (1,2‐CS) by titration of the silanol groups with sodium (Na‐Sn‐Beta). To understand the structure–activity relationships between the conditions of the active sites in the zeolite, two molecular models (tin silsesquioxanes) of the tin sites in the zeolite were synthesized. Tin silsesquioxanes that contain an octahedral tin site with and without an adjacent silanol group selectively form fructose through a 1,2‐HS and mannose through a 1,2‐CS, respectively, and provide further evidence for the nature of the active sites in Sn‐Beta.
Analysis and Classification of Mobile Apps Using Topic Modeling: A Case Study on Google Play Arabic Apps

Mobile app stores provide an extremely rich source of information on app descriptions, characteristics, and usage, and analyzing these data provides insights and a deeper understanding of the nature of apps. However, manual analysis of this vast amount of information on mobile apps is not a simple and straightforward task; it is costly in terms of human effort and time. Computational methods such as topic modeling can provide an efficient and satisfactory approach to mobile app information analysis. Topic modeling is a type of statistical modeling technique for discovering abstract topics that occur in a set of documents. This study explores the relationship between features of Arabic apps and investigates how well the current predefined Google Play app categories represent the type and genre of Arabic mobile apps. Based on the textual app description analysis, we aim to design and develop a sustainable classification system using the Latent Dirichlet Allocation (LDA) method of topic modeling in order to cover the Arabic apps classification in Google Play app store. Our study supports the hypothesis that the textual app descriptions are effective in suggesting new categories for Arabic mobile apps in Google Play app store. Also, the results indicated that the current classification on Google Play app store is not suitable for our case study “Arabic apps,” as well as it is not sustainable, as it can not cover the new app types including Arabic apps. This study offers an important contribution to Arabic app analysis and design, to improve app search and exploration in several domains such as business, marketing, and technical development. Furthermore, it provides insights for the future of Arabic app research and provides guidance for the development of an Arabic app dashboard that will support users on how to select an app based on their specific needs.
The Analysis of Opinion Evolution and Control Based on the Prisoner's Dilemma Game in Social Networks

In a social network, a user is greatly influenced by their neighbors’ opinions, and the user’s opinion updating can be regarded as the prisoner’s dilemma game. In view of such considerations, this paper proposes an opinion evolution and control model based on the prisoner’s dilemma game and gives the corresponding opinion evolution and control algorithm. Under different initial positive opinion proportions, different opinion control levels, and the same control threshold value and under different initial positive opinion proportions, different opinion control levels, and different opinion control threshold values in a scale-free network, the experiments illustrate the opinion evolution trends and control strategies according to the measures of changing the opinion control levels and opinion control threshold values for network regulators. The experiments show that the lower the initial positive opinion proportion is and the smaller (resp., larger) the control opinion threshold value chosen by the network regulators is, the lower (resp., higher) the opinion control level is; the larger the initial positive opinion proportion is and the larger the control opinion threshold value chosen by the network regulators is, the lower the opinion control level is.
Big Sequence Management: Scaling up and Out

Data series are a prevalent data type that has attracted lots of interest in recent years. Specifically, there has been an explosive interest towards the analysis of large volumes of data series in many different domains. This is both in businesses (e.g., in mobile applications) and in sciences (e.g., in biology). In this tutorial, we focus on applications that produce massive collections of data series, and we provide the necessary background on data series storage, retrieval and analytics. We look at systems historically used to handle and mine data in the form of data series, as well as at the state of the art data series management systems that were recently proposed. Moreover, we discuss the need for fast similarity search for supporting data mining applications, and describe efficient similarity search techniques, indexes and query processing algorithms. Finally, we look at the gap of modern data series management systems in regards to support for efficient complex analytics, and we argue in favor of the integration of summarizations and indexes in modern data series management systems. We conclude with the challenges and open research problems in this domain.
Early Detection of Seasonal Outbreaks from Twitter Data Using Machine Learning Approaches

Seasonal outbreaks have several different periods that occur primarily during winter in temperate regions, while influenza may occur throughout the year in tropical regions, triggering outbreaks more irregularly. Similarly, dengue occurs in the star of the rainy season in early May and reaches its peak in late June. Dengue and flu brought an impact on various countries in the years 2017–2019 and streaming Twitter data reveals the status of dengue and flu outbreaks in the most affected regions. This research work presents that Social Media Analysis (SMA) can be used as a detector of the epidemic outbreak and to understand the sentiment of social media users regarding various diseases. Providing awareness about seasonal outbreaks through SMA is an effective approach for researchers and healthcare responders to detect the early outbreaks. The proposed model aims to find the sentiment about the disease in tweets, and the seasonal outbreaks-related tweets are classified into two classes as disease positive and disease negative. This work proposes a machine-learning-based approach to detect dengue and flu outbreaks in social media platform Twitter, using four machine learning algorithms: Random Forest (RF), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Decision Tree (DT), with the help of Term Frequency and Inverse Document Frequency (TF-IDF). For experimental analysis, two datasets (dengue and flu) are analyzed individually. The experimental results show that the RF classifier has outperformed the comparison models in terms of improved accuracy, precision, recall, F1-measure, and Receiver Operating Characteristic (ROC) curve. The proposed work offers favorable performance with total precision, accuracy, recall, and F1-measure ranging from 84% to 88% for conventional machine learning techniques.
Decay Estimates for a Type of Fuzzy Viscoelastic Integro-Differential Model

We consider a type of fuzzy viscoelastic integro-differential model in this paper. With the aid of some appropriate hypotheses, a unified method and the multiplier technique are implemented to get priori estimates precisely without constructing any auxiliary function. By establishing the estimation of energy function, we derive the stability result of the global solution, and we calculate the estimations of energy attenuation in exponential and polynomial forms, respectively.
Content-Enhanced Network Embedding for Academic Collaborator Recommendation

It is meaningful for a researcher to find some proper collaborators in complex academic tasks. Academic collaborator recommendation models are always based on the network embedding of academic collaborator networks. Most of them focus on the network structure, text information, and the combination of them. The latent semantic relationships exist according to the text information of nodes in the academic collaborator network. However, these relationships are often ignored, which implies the similarity of the researchers. How to capture the latent semantic relationships among researchers in the academic collaborator network is a challenge. In this paper, we propose a content-enhanced network embedding model for academic collaborator recommendation, namely, CNEacR. We build a content-enhanced academic collaborator network based on the weighted text representation of each researcher. The content-enhanced academic collaborator network contains intrinsic collaboration relationships and latent semantic relationships. Firstly, the weighted text representation of each researcher is obtained according to its text information. Secondly, a content-enhanced academic collaborator network is built via the similarity of the weighted text representation of researchers and intrinsic collaboration relationships. Thirdly, each researcher is represented as a latent vector using network representation learning. Finally, top- k similar researchers are recommended for each target researcher. Experiment results on the real-world datasets show that CNEacR achieves better performance than academic collaborator recommendation baselines.