Miracle-dz/UniFAR-FTUs huggingface dataset:
Construction of bilingual knowledge graph based on meteorological simulation

Geographical simulation is the core function of virtual geographic environments (VGEs). Providing knowledge support has become a key issue in current research. The literature involves massive knowledge, especially for meteorological simulation, an important component of the geographic environment. However, this knowledge is noisy, unstructured, and difficult to share and reuse. Based on weather research and forecasting models, this study constructs a cross‐language knowledge graph to manage difficult‐to‐handle knowledge and to make complex knowledge structured and more complete. First, we propose a cross‐language knowledge graph construction framework. Second, the bidirectional long short‐term memory‐conditional random fields and translation‐based multilingual knowledge graph embedding model are used to extract knowledge and align cross‐lingual entities. Finally, the bilingual knowledge graph is visually stored with a graph database. The framework and method can provide efficient queries and analyses for the meteorological field, while helping to provide a knowledge base for knowledge‐driven VGEs.
Polyoxomolybdate Layered Crystals Constructed from a Heterocyclic Surfactant: Syntheses, Pseudopolymorphism and Introduction of Metal Cations

Crystals with layered structures are crucial for the construction of functional materials exhibiting intercalation, ionic conductivity, or emission properties. Polyoxometalate crystals hybridized with surfactant cations have distinct layered packings due to the surfactants which can form lamellar structures. Introducing metal cations into such polyoxometalate-surfactant hybrid crystals is significant for the addition of specific functions. Here, polyoxomolybdate–surfactant hybrid crystals were synthesized as single crystals, and unambiguously characterized by X-ray structure analyses. Octamolybdate ([Mo8O26]4–, Mo8) and heterocyclic surfactant of 1-dodecylpyridinium (C12py) were employed. The hybrid crystals were composed of α-type and β-type Mo8 isomers. Two crystalline phases containing α-type Mo8 were obtained as pseudopolymorphs depending on the crystallization conditions. Crystallization with the presence of rubidium and cesium cations caused the formation of metal cation-introduced hybrid crystals comprising β-Mo8 (C12py-Rb-Mo8 and C12py-Cs-Mo8). The yield of the C12py-Rb-Mo8 hybrid crystal was almost constant within crystallization temperatures of 279–303 K, while that of C12py-Cs-Mo8 decreased over 288 K. This means that the C12py-Mo8 hybrid crystal can capture Rb+ and Cs+ from the solution phase into the solids as the C12py-Rb-Mo8 and C12py-Cs-Mo8 hybrid crystals. The C12py-Mo8 hybrid crystals could be applied to ion-capturing materials for heavy metal cation removal.
Payment for Order Flow and Asset Choice

The paper documents important differences in payment for order flow (PFOF), spreads, and price improvement across asset classes. In stocks we show that PFOF is small. While many retail trades are executed off-exchange, we find that they receive meaningful price improvement, particularly when spreads are at their minimum. In single-name equity options, we show that PFOF is large. While all option trades are executed on-exchange, option exchanges have rules that facilitate internalization. We exploit variation in the Designated Market Maker (DMM) assignments at option exchanges to show that retail traders receive less price improvement, and worse prices, from those DMMs who pay PFOF to brokers. Current debate concerning PFOF has focused on equity routing. We show that option routing is comparatively worse, and this gives rise to a second potential conflict of interest of brokers: encouraging customers to trade assets offering higher PFOF. As fintech has eliminated retail commissions, these cross-asset differences in PFOF have become far more consequential to broker incentives. First Draft: October 2021. For helpful comments, we are thankful to Zhi Da, Terrence Hendershott, Steve Heston, Craig Lewis, Pete Kyle, John Shim, Robert Van Ness, Michael Golding and Edward Monrad at Optiver, several anonymous industry participants, and seminar participants at Carnegie Mellon University, the University of Maryland, the Microstructure Exchange, and the University of Mississippi, Hofstra University, the SFS Cavalcade, the Econometric Society and for financial support we gratefully acknowledge the Block Center at Carnegie Mellon. University of Maryland, Robert H. Smith School of Business: ternst@umd.edu Carnegie Mellon University, Tepper School of Business: cspatt@andrew.cmu. edu
Biofilm in Endodontics: In Vitro Cultivation Possibilities, Sonic-, Ultrasonic- and Laser-Assisted Removal Techniques and Evaluation of the Cleaning Efficacy

Incomplete and inadequate removal of endodontic biofilm during root canal treatment often leads to the clinical failure. Over the past decade, biofilm eradication techniques, such as sonication of irrigant solutions, ultrasonic and laser devices have been investigated in laboratory settings. This review aimed to give an overview of endodontic biofilm cultivation methods described in papers which investigated sonic-, ultrasonic- and Er:Yag laser-assisted biofilm removal techniques. Furthermore, the effectiveness of these removal techniques was discussed, as well as methods used for the evaluation of the cleaning efficacy. In general, laser assisted agitation, as well as ultrasonic and sonic activation of the irrigants provide a more efficient biofilm removal compared to conventional irrigation conducted by syringe/needle. The choice of irrigant is an important factor for reducing the bacterial contamination inside the root canal, with water and saline being the least effective. Due to heterogeneity in methods among the reviewed studies, it is difficult to compare sonic-, ultrasonic- and Er:Yag laser-assisted techniques among each other and give recommendations for the most efficient method in biofilm removal. Future studies should standardize the methodology regarding biofilm cultivation and cleaning methods, root canals with complex morphology should be introduced in research, with the aim of simulating the clinical scenario more closely.
A REVIEW OF ATTACKS ON WIRELESS SENSOR NETWORKS

Abstract- The wireless sensor network is an emerging technology. It has become popular as it has variant applications in real world such as for medical monitoring, homeland security, industrial automation and verity of military applications. Sensor network provides endless opportu-nities but at same time pose various challenges as there are resource constraints like energy i.e. it has limited battery life, memory computa-tion etc. The sensor network can also be used to monitor phenomena which discourage human presence. The sensor network deploy in hos-tile environment which increase the probability of being attack by adversary. The wireless communication technology also acquire various types of security threats, it is very important to understand the security challenges and issues in order to design appropriate security mecha-nisms. This paper will discuss about sensor networks, security requirements, various challenges and issues and attacks. Keywords- wireless sensor networks, constraints, security challenges and issues, attacks Journal of Information Systems and Communication ISSN: 0976-8742 & E-ISSN: 0976-8750, Volume 3, Issue 1, 2012
Modification of the Principal Component Analysis Method Based on Feature Rotation by Class Centroids

Feature engineering is a branch of science that provides tools to support, for example, the preparation of feature spaces for a pattern recognition task. The present work focuses on the problem of feature extraction. The proposed model is based on the mechanisms of PCA principal component analysis. It fills a gap in the implementation of feature extraction by looking for spaces that best discriminate between classes. This was realized by rotating the features according to the centroids of the classes. In addition, a measure of their consistency was determined which allows precise estimation of the number of features for a particular component. Four experiments were conducted in this study. The first two were done on synthetic datasets, while the next two were conducted on ten real datasets. The synthetic data allowed to determine the characteristics depending on the percentage of informative features, the number of input features, the level of imbalance and the number of output components in the extraction task. The obtained results showed that the developed solution allows for a more precise extraction, thus increasing the quality of classification. Moreover, it was shown that the method based on class centroids allows to construct efficient ensembles of classifiers.
Single pressure transducer probe for 3D flow measurements

A new method of measuring 3D flow with a single pressure transducer mounted inside the head of a single probe is presented in this paper. The 3D flow field around the hemispherical or ellipsoidal probe head is used to derive the 3D flow vector in virtual 5-sensor mode. A pressure tap located in the vicinity of the probe head connects the instantaneous pressure of the measurement volume to the pressure transducer. By turning the probe to five different positions around the axis of the stem, a set of five pressures is formed. The relevant flow parameters such as total and static pressure, yaw and pitch angle as well as Mach number are derived from these five pressures using the proposed calibration model. A selection of six different probe head geometries with different pressure tap positions have been manufactured and calibrated in a free jet facility in order to find the ideal probe geometry. The results of the steady probe calibration showed that the probe captures 3D flow at a slightly higher error band compared to other probe techniques such as pneumatic multiple hole probes. A summary of the achieved model accuracy for each probe is given. Finally, the calibration model can be extended to any single sensor cylindrical probe, provided that the pressure coefficient shows a measurable variation with pitch angle.
Unifications of Continuous and Discrete Fractional Inequalities of the Hermite–Hadamard–Jensen–Mercer Type via Majorization

The main objective of the paper is to develop an innovative idea of bringing continuous and discrete inequalities into a unified form. The desired objective is thus obtained by embedding majorization theory with the existing notion of continuous inequalities. These notions are applied to the latest generalized form of the inequalities, popularly known as the Hermite–Hadamard–Jensen–Mercer inequalities. Moreover, the frequently-used Caputo fractional operators are employed, which are rightly considered critical, especially for applied problems. Both weighted and unweighted forms of the developed results are discussed. In addition to this, some bounds are also provided for the absolute difference between the left- and right-sides of the main results.
An Integrated Cooperative Control Strategy for EVs Accessed Community Uninterruptible Power System

The energy storage capacity of electric vehicles (EVs) enables them to have the potential to form an uninterruptible power system (UPS) when the power supply is interrupted in the community. To solve the problems related to the reasonable power distribution and accurate voltage tracking control for EVs accessed community UPS, an integrated cooperative control strategy based on the multi-agent system is proposed in this paper. First, an improved power distribution strategy based on the <inline-formula><tex-math notation="LaTeX">$P$</tex-math></inline-formula>-<inline-formula><tex-math notation="LaTeX">$f$</tex-math></inline-formula> droop control is developed to realize the state-of-charge balancing of EVs having different capacities and voltages. Then, a cooperative voltage tracking control strategy for EV energy storage units is developed to realize the accurate voltage tracking control, and a second-order sliding mode differentiator is designed to estimate the derivative of the virtual control law. Besides, the unknown part of the model is approximated by the fuzzy logic system, and the neighborhood synchronization error is upper-bounded by the prescribed performance control. The integrated cooperative control strategy designed is completely distributed. Results show the effectiveness of the proposed integrated cooperative control strategy.
In situ size and motility measurement of aquatic invertebrates with an underwater stereoscopic camera system using tilted lenses

In situ observation of traits of aquatic organisms, including size and motility, requires three‐dimensional measurements that are commonly done with a stereoscopic imaging system. However, to observe traits of small aquatic invertebrates, the imaging system requires relatively high magnification, which results in a small overlapping volume between the two cameras of a conventional stereoscopic system. The provision of a larger shared volume would therefore be of great advantage, especially, when the organism abundance is low. We implement a stereoscopic system that utilizes a tilted lens approach, known as the Scheimpflug principle, to increase the common imaging volume of two cameras. The system was calibrated and tested in the laboratory and then deployed in a saltmarsh to observe water boatmen Trichocorixa californica. Processing of the image data from the field deployments resulted in the simultaneous estimation of the traits of body length and swimming speed of the aquatic insects. Our stereo setup with tilted lenses increased the sampling volume by 3.1 times compared to a traditional stereo setup with the same optical parameters. The in situ data and subsequent processing reveal that the instrument can capture stereoscopic images that resolve both body length and swimming speed of the aquatic insects. Results indicate that the relationship between the body length and the swimming speed of the water boatmen is linear in the log–log space with an exponent of 0.81±0.12 . Furthermore, the insects experience Reynold's number in the range of 100 – 103 . Our results demonstrated that the system can be used to observe key traits of small aquatic organisms in an ecologically relevant context. This work expands the capability of underwater imaging systems to measure important traits of an individual aquatic invertebrate in its natural environment and aids in providing a trait‐based approach to zooplankton ecology.
The Associated Decision and Management Factors on Cattle Tick Level of Infestation in Two Tropical Areas of Ecuador

Decision-making on tick control practices is linked to the level of knowledge about livestock farming and to the social context in which individuals practice them. Tick infestation is one of the main problems in tropical livestock production. The objective of this study was to characterize tick-control related practices in two tropical livestock areas and their potential association with the level of tick infestation. A total of 139 farms were included in this survey. To determine this association, a multivariate logistic regression model was used. A stepwise model selection procedure was used and model validation was tested. Cattle husbandry as a main activity, the use of external paddocks, the use of amitraz, and the lack of mechanization on the farm were related with high tick infestation. On the other hand, owner involvement in the preparation of acaricide solution was identified as a protective factor against high tick infestation. At animal level, age (old), body condition status (thin), and lactation were also associated with high tick infestations, while Bos primigenius indicus cattle and their crosses reduced the probability of high tick infestations. The factors studied, such as herd size, education level of the owners, and veterinary guidance, varied from farm to farm. Nonetheless, these differences did not generate changes in the level of tick infestation. According to the area under the receiver operating characteristic curve (AUC-ROC), the model at farm level predicts a high level of infestation, with an accuracy of 72.00% and high sensitivity. In addition, at animal level, crossbreeding with indicus cattle and breeding selection for host resistance will be useful against high tick infestation. Likewise, the implementation of programs of capacitation and research on tick control for farmers, cowboys, and vets in these areas is necessary.
Free-Floating Bike-Sharing Demand Prediction with Deep Learning

 Abstract —As a solution to the last mile problem in big metropolitan cities, free-floating bike-sharing service is becoming a new choice for short travels all over the world. Unlike the docked bikes which requires the users to borrow and return at fixed stations, free-floating bikes can be used everywhere. However, this feasibility also brings a higher management cost. The bikes should be scheduled from the regions with less demand to those with higher demand, based on a precise demand prediction. In this paper, we use deep learning techniques including Multi-Layer Perceptron and ConvLSTM networks for this task. We find that in the case of the insufficient training data, e.g., one-month data of Mobike, Multi-Layer Perceptron performs better than both ConvLSTM and two simple historical methods.
Congregating in the Church of Google: Why Teachers Are Integrating Google Apps into Their Classrooms to Promote Student Collaboration

This qualitative research study investigates why teachers are integrating Google Apps (GAPS) into their classrooms to promote student collaboration. To do so, this study involves a review of the literature on this topic and semi-structured interviews with three teacher participants who have integrated GAPS into their classrooms to promote student collaboration. The findings of this study report that teachers are integrating GAPS into their classrooms for reasons related to its collaborative properties and what they allow students to do. The findings, however, also report that there are a number of potential obstacles that teachers can confront as they attempt to integrate GAPS into their classrooms. Nevertheless, the findings of this study present a compelling case for teachers to integrate GAPS into their classrooms to promote student collaboration.
A Low-Cost Beam Steering Antenna for Indoor Wireless Communication

Low-cost beam coverage is one of the main goals of indoor wireless communication. We propose a low-cost beam steering antenna by using a hemispherical active frequency selective surface and its embedded varactors. At the same time, in the three-dimensional spherical coordinate system, we propose a universal design method for this type of antenna based on the principle of phase compensation. The designed beam steering antenna can realize energy gathering and beam switching in the quasi-full airspace on the ground. Beam steering without phase shifter and reasonable allocation of energy bring the advantages of low cost. The proposed approach provides a feasible scheme for improving the robustness and energy saving of wireless communication.
Molecular Authentication of Twelve Meat Species Through a Promising Two-Tube Hexaplex Polymerase Chain Reaction Technique

Frequent meat frauds have aroused significant social attention. The aim of this study is to construct a two-tube hexaplex polymerase chain reaction (PCR) method offering accurate molecular authentication of twelve meat species in actual adulteration event. Deoxyribonucleic acid (DNA) sequencing demonstrates that designed primers can specifically amplify target species from genomic DNA mixture of six species in each tube reaction, which showed 100% accuracy of horse (148 bp), pigeon (218 bp), camel (283 bp), rabbit (370 bp), ostrich (536 bp), and beef (610 bp) as well as turkey (124 bp), dog (149 bp), chicken (196 bp), duck (277 bp), cat (380 bp), and goose (468 bp). A species-specific primer pair produced the target band in the presence of target genomic DNA but not non-target species. Through multiplex PCR assays with serial concentration of the DNA mixture of six species in each PCR reaction, the detection limit (LOD) of the two-tube hexaplex PCR assay reached up to 0.05–0.1 ng. Using genomic DNA isolated from both boiled and microwave-cooked meat as templates, PCR amplification generated expected PCR products. These findings demonstrate that the proposed method is specific, sensitive and reproducible, and is adequate for food inspection. Most importantly, this method was successfully applied to detect meat frauds in commercial meat products. Therefore, this method is of great importance with a good application foreground.
Data-Driven Vehicle Rebalancing With Predictive Prescriptions in the Ride-Hailing System

Rebalancing vacant vehicles is one of the most critical strategies in ride-hailing operations. An effective rebalancing strategy can significantly reduce empty miles traveled and reduce customer wait times by better matching supply and demand. While the supply (vehicles) is usually known to the system, future passenger demand is uncertain. There are two ways to handle uncertainty. First, the point-prediction-driven optimization framework involves predicting the future demand and then producing rebalancing decisions based on the predicted demand. Second, the data-driven optimization approaches directly prescribe rebalancing decisions from data. In this study, a predictive prescription framework is introduced to this problem, where the benefits of predictive and data-driven optimization models are combined. Based on a state-of-the-art vehicle rebalancing model, the matching-integrated vehicle rebalancing (MIVR) model, predictive prescriptions are introduced to handle demand uncertainty. Model performances are evaluated using real-world simulations with New York City (NYC) ride-hailing data under four demand scenarios. When demand can be accurately predicted, a point-prediction-driven optimization framework should be adapted. The proposed predictive prescription models achieve shorter customer wait times over the point-prediction-driven optimization models when future demand predictions are not so accurate, and achieve a competitive performance with respect to the cutting-edge robust optimization models.
Application of Green Gold Nanoparticles in Cancer Therapy and Diagnosis

Nanoparticles are currently used for cancer theranostics in the clinical field. Among nanoparticles, gold nanoparticles (AuNPs) attract much attention due to their usability and high performance in imaging techniques. The wide availability of biological precursors used in plant-based synthesized AuNPs allows for the development of large-scale production in a greener manner. Conventional cancer therapies, such as surgery and chemotherapy, have significant limitations and frequently fail to produce satisfying results. AuNPs have a prolonged circulation time, allow easy modification with ligands detected via cancer cell surface receptors, and increase uptake through receptor-mediated endocytosis. To exploit these unique features, studies have been carried out on the use of AuNPs as contrast agents for X-ray-based imaging techniques (i.e., computed tomography). As nanocarriers, AuNPs synthesized by nontoxic and biocompatible plants to deliver therapeutic biomolecules could be a significant stride forward in the effective treatment of various cancers. Fluorescent-plant-based markers, including AuNPs, fabricated using Medicago sativa, Olax Scandens, H. ambavilla, and H. lanceolatum, have been used in detecting cancers. Moreover, green synthesized AuNPs using various extracts have been applied for the treatment of different types of solid tumors. However, the cytotoxicity of AuNPs primarily depends on their size, surface reactivity, and surface area. In this review, the benefits of plant-based materials in cancer therapy are firstly explained. Then, considering the valuable position of AuNPs in medicine, the application of AuNPs in cancer therapy and detection is highlighted with an emphasis on limitations faced by the application of such NPs in drug delivery platforms.