Miracle-dz/UniFAR-FTUs huggingface dataset:
General Torque Enhancement Approach for a Nine-Phase Surface PMSM With Built-In Fault Tolerance

This article investigates maximum possible torque improvement in a two-pole surface permanent magnet synchronous machine with a reduced magnet span, which causes production of highly nonsinusoidal back electromotive force (EMF). It contains a high third and a fifth harmonics, which can be used for the torque enhancement, using stator current harmonic injection. Optimal magnet span is studied first, and it is shown that with such a value, the machine would be able to develop an insignificantly lower maximum torque than with the full magnet span. Next, field-oriented control algorithm, which considers all nonfundamental EMF components lower than the machine phase number, is devised. Using maximum torque per ampere principles, optimal ratios between fundamental and all other injected components are calculated and then used in the drive control. The output torque can be in this way increased up to 45% with respect to the one obtainable with fundamental current only. Alternatively, for the same load torque, stator current rms value can be reduced by 45%. Last but not least, a method for position sensor fault mitigation is introduced. It is based on the alternative use of a back-EMF harmonic for rotor position estimation, instead of the torque enhancement. Experimental verification is provided throughout for all the relevant aspects.
Constrained actin dynamics emerges from variable compositions of actin regulatory protein complexes

Assemblies of actin and its regulators underlie the dynamic morphology of all eukaryotic cells. To begin to understand how diverse regulatory proteins work together to generate actin-rich structures we tracked the assembly of actin regulators and their relative proportions in a cell-free system that generates filopodia-like structures (FLS). We found that heterogeneous mixtures of regulators could give rise to morphologically similar structures and that the FLS actin bundles exhibited simple dynamic behaviour of growth and shrinkage. To explain these observations, we combined experiment with theory, and found that stochastic fluctuations between redundant actin regulatory subcomplexes can account for the actin dynamics. Comparing the localizations of a variety of endogenous actin regulators in Drosophila embryos and distributions of filopodia lengths yielded similar conclusions of heterogenous actin regulatory complexes and filopodia lengths governed by a stochastic growth process. Our results explain how weakly-associating assemblies of regulatory proteins can produce robust functional outcomes.
OBTAINING THE WILD BERGAMOT ESSENTIAL OIL WITH HIGH CONTENT OF THYMOQUINONE

Background: The Wild bergamot ( Monarda fistulosa L.) is mentioned among plants with high content of thymol and carvacrol in the composition of essential oil. At the same time, it also produces significant amounts of thymoquinone – a substance with antimycotic, anticancerous and antituberculous activity. The objective of this study was to evaluate the factors which affect the thymoquinone formation during the processing of plant material, and to create a technological procedure for obtaining the Wild bergamot essential oil with maximum content of this active compound. Material and methods: Aerial parts of the Wild bergamot have been harvested in the budding-flowering phase from the collection of the Scientific Centre for Medicinal Plants Cultivation of Nicolae Testemitanu State Uiversity of Medicine and Pharmacy. Essential oil isolation has been performed by hydrodistillation. Analysis of plant material and the obtained samples of essential oil have been performed by high performance liquid chromatography method with diode-array UV detection. results: The content of thymoquinone increases considerably during the process of fermentation of humidified plant material in the air stream prior to the essential oil distillation. Additionally, the content of thymoquinone in the essential oil can be increased by performing the distillation with a mixture of steam and air. Conclusions: A technological procedure was created, which allowed obtaining the Wild bergamot essential oil, that contains 20-32% of thymoquinone and 23-32% of thymol and carvacrol, with a yield of 12.5-14.5 g from 1 kg of dried plant material.
Modeling of RRAM With Embedded Tunneling Barrier and Its Application in Logic in Memory

This paper proposes a modeling technique for the evaluation of RRAM with embedded tunneling barrier that serves as an embedded selector, enabling high density integration while reducing the leakage current in a memory array. The further exploration of various biasing and pulsing schemes is provided so as to optimize programming efficiency for logic-in-memory application.
Tracking and association of body weight and body fat in elementary school children

Background: Excess body fat is a major health risk to various chronic diseases. Given the ease of measurement and low cost of body weight and height, body mass index (BMI) has been widely used to estimate body fatness. The present study evaluates the accuracy of BMI as an indicator of body fatness, measured via bioelectric impedance (BIA) during the elementary school years in Austrian children. Methods: Out of six schools in Innsbruck, Austria, anthropometric assessments and BIA measurements were performed in 199 children starting elementary school in fall 2013. Annual follow-up measurements were performed by the same measurement team until 4th grade. Results: Cross-sectional analyses revealed a significant moderate correlation between BMI and percent body fat (%BF) (r=0.61) and an AUC of 0.88 indicated high accuracy of BMI as an indicator for body fatness. There was also high tracking of BMI and %BF over the 4-year observation period (r ≥ 0.84). Follow-up measurements further showed that the association between BMI and %BF increased with age during the elementary school years (rgrade 4 = 0.78; AUCgrade 4 = 0.96). Conclusions: Given the health problems associated with excess body fat an accessible and accurate assessment of body composition is warranted. The BMI provides a low-cost and easily administered surrogate measure of body fatness in elementary school children for initial screening. Children considered at risk, however, may require more elaborate assessments that directly measure fat mass and fat-free mass.
Comparative Effect of Sodium Chloride and Sodium Sulfate on the Biomass and Quantitative Changes in Starch, Protein and Total Soluble Sugar in Pea Seeds.(Pisum Sativum L.)

Premise of the Research: The nature of soil is very important factor in the growth and development of a crop. Crop plants suffer a decline in growth and yield, when exposed to saline condition. Pea considers one of the main leguminous crops, due to its ability to produce significant quantities of protein, carbohydrates and nutrient rich seeds. Plants were subjected to four salt treatments, 4, 8, 12 and 16 mmhos/cm of Sodium Chloride and Sodium Sulfate and the Biomass and biochemical responses were measured. Methodology: All growth attributes such as stem, root and leaf fresh and dry weight decrease with the salinities doses increased. 4 mmhos/cm of both, salt treatment were no significant effects on the Biomass and Quantitative changes in Starch, Protein and Soluble sugar in seeds of pea, but an evident from the data. Pivotal Results: It was noted that the, starch contents were much reduced in16 mmhos/cm, salinity level of Sodium Chloride and Sodium Sulfate as compared to control. The protein content and sugar content value were increased in higher concentration of Sodium Chloride and Sodium sulfate, when compared to control in Pea, CV. Azad P-1. Proline content increased with salt stress up to 8mmhos/cm in CV.Azad P-1. Conclusions: It was observed that the high dose of sodium sulfate is declined Biomass and Quantitative changes in starch, than that of sodium chloride solution in pea seeds.
The practicalities of scaling Bayesian neural networks to real-world applications

In this work, I will focus on ways in which we can build machine learning models that appropriately account for uncertainty, whether with computationally cheap estimates or with more expensive and reliable ones. In particular, I will explore how we can model distributions with Bayesian neural networks and how we can manipulate them depending on the task. The two main techniques for performing inference in Bayesian neural networks are variational inference and Markov chain Monte Carlo. I will look into the advantages and disadvantages of both methods and apply them to real-world problems. The emphasis is on how to achieve calibrated uncertainty estimates without compromising scalability. One contribution of this work is to offer a new method for implementing Bayesian neural networks within the framework of Bayesian decision theory, where Bayesian decision theory is important in all decision-making applications. A further contribution is developing sampling techniques that provide more reliable uncertainties, especially over data that lie outside the training distribution. Finally I also introduce a method for using Bayesian neural networks in an astrophysical application where it is vital that uncertainties are calibrated appropriately for the task.
On the Distribution Modeling of Heavy-Tailed Disk Failure Lifetime in Big Data Centers

It has become commonplace to observe frequent multiple disk failures in big data centers in which thousands of drives operate simultaneously. Disks are typically protected by replication or erasure coding to guarantee a predetermined reliability. However, in order to optimize data protection, real life disk failure trends need to be modeled appropriately. The classical approach to modeling is to estimate the probability density function of failures using nonparametric estimation techniques such as kernel density estimation (KDE). However, these techniques are suboptimal in the absence of the true underlying density function. Moreover, insufficient data may lead to overfitting. In this article, we propose to use a set of transformations to the collected failure data for almost perfect regression in the transform domain. Then, by inverse transformation, we analytically estimated the failure density through the efficient computation of moment generating functions, and hence, the density functions. Moreover, we developed a visualization platform to extract useful statistical information such as model-based mean time to failure. Our results indicate that for other heavy-tailed data, the complex Gaussian hypergeometric distribution and classical KDE approach can perform best if the overfitting problem can be avoided and the complexity burden is overtaken. On the other hand, we show that the failure distribution exhibits less complex Argus-like distribution after performing the Box–Cox transformation up to appropriate scaling and shifting operations.
A Multimodality Fusion Deep Neural Network and Safety Test Strategy for Intelligent Vehicles

Multimodality fusion based on deep neural networks (DNN) is a significant method for intelligent vehicles. The special characteristics of DNN lead to the issue of AI safety and safety test. In this paper, we firstly propose a multimodality fusion framework called Integrated Multimodality Fusion Deep Neural Network (IMF-DNN), which can flexibly accomplish both object detection and end-to-end driving policy for prediction of steering angle and speed. Then, we propose a DNN safety test strategy, which systematically analyzes DNN's robustness and generalization ability in large amounts of diverse driving environment conditions. The test in this paper is based on our IMF-DNN model and the strategy can be widely used for other DNNs. Finally, the experiment analysis is performed on KITTI for object detection and the dateset DBNet for end-to-end tasks. The results show the superior accuracy of the proposed IMF-DNN model and the test strategy's potential ability to improve the robustness and generalization of autonomous vehicle deep learning model. Code is available at https://github.com/ennisnie/IMF-DNN
Bidding for the Better Jobs: An Experiment on Gender Differences in Competitiveness without a Real-Effort Task

We model the competitive striving for high-level positions in firms by letting experimental participants compete in bidding for prizes of different sizes in a hierarchy. Our set-up includes both a flat hierarchy and a steep hierarchy. We mainly focus on whether men and women behave differently with respect to bidding for higher and lower positions, but also consider other possible sources of heterogeneity in behavior. On average, women bid higher than men, but not significantly so, except for the top position of the flat hierarchy. For lower positions, bids are generally close to optimal bidding whereas they are relatively lower for higher positions. Women do win the top positions significantly more often, but there are no significant gender differences in earnings, the difference between prizes and bids. Our results suggest that the strong gender differences in attitudes towards competition that were found in numerous previous studies based on competition in tournaments with real-effort tasks may be specific to that environment. An implication of our results thus is that a particular phenomenon should be studied using more than one experimental design.
Effects of inundation duration on southeastern Louisiana oyster reefs

Abstract Understanding the effects of predicted rising sea levels, combined with changes in precipitation and freshwater inflow on key estuarine ecosystem engineers such as the eastern oyster would provide critical information to inform restoration design and predictive models. Using oyster ladders with shell bags placed at three heights to capture a range of inundation levels, oyster growth of naturally recruited spat was monitored over the course of 6 months. Oyster numbers and shell heights were consistently highest in bottom and mid bags experiencing greater than 50% inundation (mid: 63 ± 7%; bottom: 95 ± 3%). Identifying thresholds for optimal oyster growth and survival to enhance restoration engineering would require finer scale evaluation of inundation levels.
A Comparative Study on Attribute Recognition and Word of Mouth Intention of SNS Advertising - Focused on Facebook, Instagram, KaKaoStory and Twitter

SNS media is gaining its media share with the benefits of digital technology, such as the convenience of physical access and the entertainment and interactivity of contents, and are becoming a part of users' lives. As media contents consumers move from traditional media to SNS, marketing communication activities are rapidly adapting to leading SNS platforms such as Facebook. This study compares how users perceive four advertisement attributes in each SNS, focusing on Facebook, Instagram, Kakao Story, and Twitter, where the media content creation and consumption systems are relatively similar to each other. The impact on eWOM intention was identified. In addition, we discussed effective SNS operation.
Model Predictive Control of DC–DC SEPIC Converters With Autotuning Weighting Factor

In this article, a model predictive control (MPC) method for dc–dc single-ended primary-inductor converters with autotuning weighting factor capability is presented. The conventional MPC requires retuning of the weighting factor when the operating condition of the converter is changed. The effect of this change is observed as inability of the controller in maintaining the switching frequency constant. The weighting factor avoids excessive switching frequency in the dc–dc converters. Based on the relation between the inductor current ripple and switching frequency, an autotuning weighting factor based MPC is proposed. The effect of the weighting factor on the switching frequency is investigated. The proposed MPC eliminates the need for retuning the weighting factor when the operating point of the converter is changed. The proposed control strategy is verified experimentally under input voltage, load, and parameter variations. The results obtained from conventional and proposed MPC methods are compared. It is shown that the proposed MPC method controls the average switching frequency when the operating mode of the converter is changed. Furthermore, parameter mismatch results are presented for both conventional and proposed MPC methods.
Development of Decision Support System for Quality Management of University Education

— The subject of the study is the process of studying at University. The purpose of the research is the development of an information system, which could record all the events related to the process of obtaining education for a particular student at all its stages. The article describes the main information systems out both Russian and foreign. The analysis of the considered systems allows concluding that none of the proposed products allows for an in-depth analysis and forecasting of a possible option for the development of the educational process. A number of works on the issues of building a competency model of a graduate were studied, including mathematical models and approaches to evaluate learning outcomes. Based on the research of developments in this area, we can conclude that the available information systems make it possible to solve individual problems associated with the support of the educational process, there is also no methodology for the design and synthesis of such ISs, taking into account their integration into existing ISs at universities. The paper proposes the use of system analysis methods in order to: obtaining the structure of an IS for analysis and management of the competence-building process; the study of the design object, methods and means of its interaction with the environment; identification of functional subsystems implementing individual tasks, their structural elements that implement individual information processing and analysis algorithms; identification of functional relationships in the form of interacting input and output information flows of system elements; determining the order and logic of their interaction in the implementation of individual tasks of the information system. The article proposes to use the developed information system for analysis and management to identify common system properties and the main laws of information processing in assessing the competence of graduates taking into account professionally significant personal qualities.
Atlas of Subcellular RNA Localization Revealed by APEX-Seq

We introduce APEX-seq, a method for RNA sequencing based on spatial proximity to the peroxidase enzyme APEX2. APEX-seq in nine distinct subcellular locales produced a nanometer-resolution spatial map of the human transcriptome, revealing extensive and exquisite patterns of localization for diverse RNA classes and transcript isoforms. We uncover a radial organization of the nuclear transcriptome, which is gated at the inner surface of the nuclear pore for cytoplasmic export of processed transcripts. We identify two distinct pathways of messenger RNA localization to mitochondria, each associated with specific sets of transcripts for building complementary macromolecular machines within the organelle. APEX-seq should be widely applicable to many systems, enabling comprehensive investigations of the spatial transcriptome.
A Design Technique for Optimizing Resonant Coils and the Energy Transfer of Inductive Links

Power transfer efficiency (PTE) is a key performance parameter in development work on resonant inductive power transfer (IPT) systems. Geometrically optimizing the transmitter (Tx) and receiver (Rx) coil pair is a way of improving the IPT system’s efficiency. In this article, a new figure-of-merit (FoM) is proposed to find an optimum coil geometry which maximizes the PTE. The employed FoM parameter, called the “strong coupling factor” (<inline-formula> <tex-math notation="LaTeX">${P_{\text {scf}}}$ </tex-math></inline-formula>), is defined such that its value indicates how strongly the Tx and Rx coils are linked together. Considering the IPT application and its physical size constraints, a proper selection method for identifying the numerical value of <inline-formula> <tex-math notation="LaTeX">${P_{\text {scf}}}$ </tex-math></inline-formula> is essential for optimal coil geometry design. This article presents an iterative algorithm to assist in the selection of the most favorable <inline-formula> <tex-math notation="LaTeX">${P_{\text {scf}}}$ </tex-math></inline-formula> value which provides maximized PTE for the designed optimum coil geometry. Design examples of two nominal IPT systems at frequencies of 415 and 0.1 MHz are used to investigate the design algorithm. Theoretical calculations show the optimum geometry designed for the IPT system operating at 415 MHz, with coupling coefficient (<inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>) of 0.2, can achieve maximum PTE of 85.70%. Measurements presented from a practical Tx/Rx coil pair in the IPT link operating at 0.1 MHz, with <inline-formula> <tex-math notation="LaTeX">$K=0.05$ </tex-math></inline-formula>, show a PTE of 83.10% against a calculated PTE of 84.11% validating the design process.
Effect of groundwater quality on adsorptive iron removal

Adsorptive iron removal from groundwater involves the adsorption of iron(II) onto the surface of the filter media and the subsequent oxidation of adsorbed iron(II) in the presence of oxygen to regenerate the adsorption site for the continuation of the process. Different ions and compounds present in groundwater might influence iron(II) adsorption onto filter media. Laboratory scale batch adsorption experiments were conducted to investigate the effect of major inorganic ions present in groundwater, i.e. Mn 2+ , Ca 2+ , NH 4 + , SO 4 2− , Cl − , alkalinity and background ionic strength on the iron(II) adsorption onto new silica sand and iron oxide coated sand. Alkalinity, background ionic strength, NH 4 + and Cl − had no significant effect on iron(II) adsorption onto both the media tested. An increase in concentration of Mn 2+ and Ca 2+ decreased the iron(II) adsorption onto new sand whereas an increase in iron(II) adsorption was observed when SO 4 2− concentration was increased. The effect of Mn 2+ , Ca 2+ and SO 4 2− on iron(II) adsorption onto iron oxide coated sand, however, was minimal. This indicates that once the iron oxide coating is developed, iron(II) adsorption is not significantly hindered due to the presence of other inorganic ions in concentrations common in groundwater.
Accumulation of Mitochondrial DNA Microsatellite Instability in Malaysian Patients with Primary Central Nervous System Tumors.

AIM To determine the mitochondrial microsatellite instability (mtMSI) status in a series of Malaysian patients with brain tumors. Furthermore, we analyzed whether the mtMSI status is associated with the clinicopathological features of the patients. MATERIAL AND METHODS Forty fresh frozen tumor tissues along with blood samples of brain tumor patients were analyzed for mtMSI by PCR amplification of genomic DNAs, and the amplicons were directly sequenced in both directions using Sanger sequencing. RESULTS Microsatellite analysis revealed that 20% (8 out of 40) of the tumors were mtMSI positive with a total of 8 mtMSI changes. All mtMSI markers were detected in D310 and D16184 of the D-loop region. Additionally, no significant association was observed between mtMSI status and clinicopathological features. CONCLUSION The variations, specifically the mtMSI, suggest that the mitochondrial DNA (mtDNA) can be targeted for genomic alteration in brain tumors. Therefore, the specific role of mtDNA alteration in brain tumor development and prognosis requires further investigation.