Miracle-dz/UniFAR-FTUs huggingface dataset:
Large signal modeling of quasi-resonant converters using regulated unified model

This paper analyzes the large signal model of quasi-resonant converters (QRCs) using a unified model. The internal resistance of each component in the basic quasi-resonant switch model (QBSW) is taken into consideration. The parameters can be predicted under varying supply voltage and load current. In other words, the variation of switching frequency, which is a controller parameter, can be determined while output voltage is kept regulating. It is useful for designing an adaptive feedback controller. The power dissipation is analyzed so that the efficiency of the QRSW can be estimated. Moreover, the conduction loss of each component can be found. The critical component in power dissipation is known and the maximum efficiency can be predicted.
Waveguide Sources of Photon Pairs

This thesis describes various methods for producing photon pairs from waveguides. It covers relevant topics such as waveguide coupling and phase matching, along with the relevant measurement techniques used to infer photon pair production. A new proposal to solve the phase matching problem is described along with two conceptual methods for generating entangled photon pairs. Photon pairs are also experimentally demonstrated from a third novel structure called a Bragg Reflection Waveguide (BRW). The new proposal to solve the phase matching problem is called Directional QuasiPhase Matching (DQPM). It is a technique that exploits the directional dependence of the non-linear susceptiblity (χ(2)) tensor. It is aimed at those materials that do not allow birefringent phase-matching or periodic poling. In particular, it focuses on waveguides in which the interplay between the propagation direction, electric field polarizations and the nonlinearity can change the strength and sign of the nonlinear interaction periodically to achieve quasi-phasematching. One of the new conceptual methods for generating entangled photon pairs involves a new technique that sandwiches two waveguides from two differently oriented but similar crystals together. The idea stems from the design of a Michelson interferometer which interferes the paths over which two unique photon pair processes can occur, thereby creating entanglement in any pair of photons created in the interferometer. By forcing or sandwiching the two waveguides together, the physical space that exists in the standard Micheleson type interferometer is made non-existent, and the interferometer is effectively squashed. The result is that the two unique photon pair processes actually occupy the same physical path. This benefits the stability of the interferometer in addition to miniaturizing it. The technical challenges involved in sandwiching the two waveguides are briefly discussed. The main result of this thesis is the observation of photon pairs from the BRW. By analyzing the time correlation between two single photon detection events, spontaneous parametric down conversion (SPDC) of a picosecond pulsed ti:sapph laser is demonstrated. The process is mediated by a ridge BRW. The results show evidence for type-0, type-I and type-II phase matching of pump light at 783nm, 786nm and 789nm to down converted light that is strongly degenerate at 1566nm, 1572nm, and 1578nm respectively. The inferred efficiency of the BRW was 9.8·10−9 photon pairs per pump photon. This contrasts with the predicted type-0 efficiency of 2.65·10−11. This data is presented for the first time in such waveguides, and represents significant advances towards the integration of sources of quantum information into the existing telecommunications infrastructure.
Displacement and dissociation of oligonucleotides during DNA hairpin closure under strain

The simple hybridization kinetic of an oligonucleotide to its template is a fundamental step in many biological processes such as replication arrest, CRISPR recognition, DNA sequencing, DNA origami, etc. Although approaches exist that address special cases of this problem, there are no simple general prediction schemes. In this work, we have measured experimentally, with no fluorescent labelling, the displacement of an oligonucleotide from its substrate in two situations: one corresponding to oligonucleotide binding/unbinding on ssDNA and one where the oligonucleotide is displaced by the refolding of a dsDNA fork. In this second situation, the fork is expelling the oligonucleotide thus reducing significantly its residence time. To account for our data in these two situations, we have constructed a mathematical model, based on the known nearest neighbor dinucleotide free energies, and provided a good estimate of the residence times of different oligonucleotides (DNA, RNA, LNA) with various length in different experimental conditions (force, temperature, buffer conditions, presence of mismatches, etc.). This study provides a foundation for the dynamics of oligonucleotide displacement, a process of importance in a number of biological and bioengineering contexts.
Statistical correction of input gradients for black box models trained with categorical input features

Gradients of a deep neural network’s predictions with respect to the inputs are used in a variety of downstream analyses, notably in post hoc explanations with feature attribution methods. For data with input features that live on a lower-dimensional manifold, we observe that the learned function can exhibit arbitrary behaviors off the manifold, where no data exists to anchor the function during training. This leads to a random component in the gradients which manifests as noise. We introduce a simple correction for this off-manifold gradient noise for the case of categorical input features, where input values are subject to a probabilistic simplex constraint, and demonstrate its effectiveness on regulatory genomics data. We find that our correction consistently leads to a significant improvement in gradient-based attribution scores.
STUDI ETNOFARMAKOLOGI OBAT TRADISIONAL SEBAGAI ANTI DIARE DI KECAMATAN BATURADEN KABUPATEN BANYUMAS

ABSTRAK Telah dilakukan penelitian yang bertujuan untuk mengetahui pemanfaatan obat tradisional sebagai antidiare di Kecamatan Baturaden.Metode yang digunakan metode kualitaif, dan pemilihan responden dilakukan purposive sampling. Dari hasil penelitian ditemukan 10 tanaman untuk pengobatan diare jambu biji, kara, ketumbel, kunyit, lengkuas, manggis, nangka, pala, patikan kebo, pepaya. Simplisia yang digunakan adalah daun, umbi, kulit buah, buah, biji, cara penggunaan diremas-remas, ditumbuk, diseduh. Kata kunci : obat tradisional, diare, baturaden ABSTRACT A research has been conducted to know the usage of traditional antidhiarea in Baturaden Sub distric. This research uses qualitative metodhe and the responden was chosen by purposive sampling method. The research result shows that 10 plants, mangosteen, jackfruit, nutmeg, patikan kebo, and papaya. The simplisia or raw material used were leaf, root, fruir, and seed. Those plant are prepared as traditional medicine by squeezing, pounding, or pouring with hot water. Key words: traditional, antidiarhea, Baturaden.
Characterization of metabolic changes associated with floral transition in Arabidopsis: RAFFINOSE SYNTHASE 5 contributes to determine the timing of floral transition

Integration of environmental and endogenous cues triggers floral induction at the optimal time during the plant life cycle. Flowering is a tightly regulated process, which involves an intricated genetic network, as expected for a process crucial for plant fitness and survival. Individual metabolites are known to contribute to the determination of flowering time, including carbohydrates and hormones. However, a global analysis of metabolic changes associated with flowering was still lacking. We performed a metabolomic study to characterize global metabolic changes associated with photoperiodic floral induction. By using an inducible system, with the CONSTANS (CO) promoter driving the expression of CO fused to the rat glucocorticoid receptor (CO::GR), we induce flowering and identify metabolites that increase or decrease in leaves and apices during floral induction. Combining metabolomic with transcriptomic data, we identify that raffinose metabolism was altered in apices that are induced to flower. Loss-of-function mutants affecting RAFFINOSE SYNTHASE 5 (RS5), a key enzyme of the raffinose metabolism, show an early flowering phenotype. Also, RS5 expression changes during floral transition, suggesting a role for raffinose catabolism on the release of simple sugars at the apex. We propose that variation on the differential accumulation of raffinose and mono- and disaccharides during floral transition contributes to the induction of floral transition, by influencing expression of THEHALOSE-6-PHOSPHATE SYNTHASE 1 (TPS1) and SQUAMOSA PROMOTER BINDING PROTEIN-LIKE 3 (SPL3), which affect expression of the florigen FLOWERING LOCUS T (FT).
Compressive stress drives adhesion-dependent unjamming transitions in breast cancer cell migration

Cellular unjamming is the collective fluidization of cell motion and has been linked to many biological processes, including development, wound repair, and tumor growth. In tumor growth, the uncontrolled proliferation of cancer cells in a confined space generates mechanical compressive stress. However, because multiple cellular and molecular mechanisms may be operating simultaneously, the role of compressive stress in unjamming transitions during cancer progression remains unknown. Here we investigate which mechanism dominates in a dense, mechanically stressed monolayer. We find that long-term mechanical compression triggers cell arrest in benign epithelial cells and enhances cancer cell migration in transitions correlated with cell shape, leading us to examine the contributions of cell-cell adhesion and substrate traction in shape-dependent unjamming transitions. We show that cadherin-mediated cell-cell adhesion regulates differential cellular responses to compressive stress and predominantly controls unjamming transitions in dense monolayers. Importantly, compressive stress does not induce the epithelial—mesenchymal transition in unjammed cells. Using traction force microscopy, traction forces are attenuated in compressed cells in the bulk of the monolayer regardless of cell type and motility. Intercellular adhesion thus is the dominant regulator of compression-induced unjamming transitions and may impact collective cell motion in tumor development and breast cancer progression.
DC-Link Voltage Constraint Strategy for DC Power Supply Film-Capacitor Drive System Based on Improved Model Predictive Control

DC-link voltage oscillation is a common problem that needs special attention in dc power supply film-capacitor drive systems. For this problem, an improved model predictive control method is presented in this article, which achieves system stability control by adding voltage stability constraint of dc-link in the cost function. First, the discrete predictive equations of motor current and LC filter voltage for permanent magnet synchronous motor are derived in this article. The system stability is proved by analyzing the system damping with voltage constraint in the meantime. The weight factor of the cost function based on exponential function is designed to accelerate the suppression of dc-link voltage oscillation in the dynamic process. Second, a full-order observer is designed for observing the LC filter inductor current, which is designed to reduce hardware costs. Third, an improved control set method is proposed to improve the steady-state performance of each control item in the cost function. Finally, a hardware platform based on DSP is built to verify the validity of the stability constraint scheme proposed in this article.
Boosting polygenic risk scores

Polygenic risk scores (PRS) evaluate the individual genetic liability to a certain trait and are expected to play an increasingly important role in the field of clinical risk stratification. Most often, PRS are estimated based on summary statistics of univariate effects derived from genome-wide association studies. To improve the predictive performance of PRS, it is desirable to fit multivariable models directly on the genetic data. Due to the large and high-dimensional data, a direct application of existing methods is often not feasible and new efficient algorithms are required to overcome the computational burden regarding efficiency and memory demands. We develop an adapted component-wise L2-boosting algorithm to fit genotype data from large cohort studies to continuous outcomes using linear base-learners for the genetic variants. Similar to the snpnet approach implementing lasso regression, the proposed snpboost approach iteratively works on smaller batches of variants. By restricting the set of possible base-learners in each boosting step to variants most correlated with the residuals from previous iterations, the computational efficiency can be substantially increased without losing prediction accuracy. Furthermore, for large-scale data based on various traits from the UK Biobank we show that our method yields competitive prediction accuracy and computational efficiency compared to the snpnet approach. Due to the modular structure of boosting, our framework can be further extended to construct PRS for different outcome data and effect types.
RKHS based State Estimator for Radar Sensor in Indoor Application

For the estimation of targets' states (location, velocity, and acceleration) from nonlinear radar measurements, usually, the improved version of well known Kalman filter: extended Kalman filter (EKF) and unscented Kalman filter (UKF) are used. However, EKF and UKF approximates the nonlinear measurement function either by Jacobian or using sigma points. Consequently, because of the approximation of the measurement function, the EKF and UKF cannot achieve high estimation accuracy. The potential solution is to replace the approximation of nonlinear measurement function with its estimate, obtained in high dimensional reproducing kernel Hilbert space (RKHS). An ample amount of research has been done in this direction, and the combined filter is termed RKHS based Kalman filter. However, there is a shortage of literature dealing with estimating the dynamic state of the target in an indoor environment using RKHS based Kalman filter. Therefore, in this paper, we propose the use of RKHS based Kalman filter for indoor application. Specifically, we validate the suitability of the RKHS based Kalman filtering approach using simulations performed over three different target motion models.
River Water Quality Classification using a Hybrid Machine Learning Technique

Water is essential for the life and nourishment of all living things. River water quality has deteriorated in recent years as a result of toxic wastes and contaminants. This rapid augmentation in water pollution is a major source of concern since it degrades water quality and renders it unsuitable for any usage. In India, Yamuna is considered one of the most important rivers in the northern region which plays a major role in development. In this work, we have focused on the Yamuna River water quality classification according to its usability as excellent, poor and unsuitable for drinking. To achieve this objective, we have presented a novel hybrid machine learning technique by taking the advantage of an ensemble learning mechanism. The proposed approach considers relief-based feature selection which helps to reduce the dimensionality, forward feature ranking which helps to consider the stage-wise features and a decision tree classifier. The performance of proposed water quality classification technique is compared with other classification techniques including Naïve Bayes, Support Vector Machine (SVM), bagged tree and boosted tree classifiers. The proposed approach achieves an overall accuracy of 99.65% which proves the robustness of this scheme in terms of predicting the water quality.
Combining measurements and modeling/simulations analysis to assess carbon nanotube memory cell characteristics

A simulation package for CNT memory cells is developed, based on computational modeling of both the mesoscopic structure of carbon nanotube films and the electrical conductivity of inter-CNT contacts. The developed package enables the modeling of various electrical measurements and identification of a range of operation conditions delivering desirable device characteristics. This approach opens the path for optimization of the CNT fabric to meet performance requirements.
DEVELOPING GASTRO RETENTIVE TABLETS: PROSPECTIVE PLANNING

Objective of presented work is to project prospective action plan to be following while formulation development of gastro retentive tablets (GRTB). Research in the field of developing oral dosage forms directed to improve their performance through combating its major drawback of short gastric residence time (GRT) and unpredictable gastric emptying time (GET). This drawback is solving by retarding their GET and improving GRT. Several systems were developed but extensively exploited systems are floating system, mucoadhesive system, magnetic system, expandable system, and so on. These systems presented mostly as tablets, for convenience and improved patient compliance. Development of product is aiming to have a reproducible and high quality attributes (process) producing an optimised and validated product. Besides it has to ensure acceptable and reproducible product quality and performance throughout shelf life and life cycle. Design and development of product is a difficult task that requires proper planning with extensive knowledge. Scarce publications are there for complying described shortcomings. This manuscript will help in combating tabled issues and will aid researchers while planning for designing gastro retentive tablet with wished performance.
Live-action mass-casualty training and virtual world training

Live-action training simulations with expert facilitators are considered by many to be the gold-standard in training environments. However, these training environments are expensive, provide many logistical challenges, and may not address the individual’s learning needs. Fortunately, advances in distance-based learning technologies have provided the foundation for inexpensive and effective learning environments that can simultaneously train and educate students on a much broader scale than live-action training environments. Specifically, intelligent tutoring systems (ITSs) have been proven to be very effective in improving learning outcomes. The Virtual Civilian Aeromedical Evacuation Sustainment Training (VCAEST) interface takes advantage of both of these technologies by enhancing a virtual world with a web-based ITS, AutoTutor LITE (Learning in Interactive Training Environments). AutoTutor LITE acts as a facilitator in the virtual world by providing just-in-time feedback, presenting essential domain knowledge, and by utilizing tutoring dialogues that automatically assess user input. This paper will discuss the results of an experimental evaluation of the VCAEST environment compared to an expert-led live-action training simulation.
Argumentation and risk communication about genetic testing

As genetic testing for the presence of potentially health-affecting mutations becomes available for more genetic conditions, many people will soon be faced with the decision of whether or not to have a genetic test. Making an informed decision requires an understanding and evaluation of the arguments for and against having the test. As a case in point, this paper considers argumentation involving the decision of whether to have a BRCA gene test, one of the first commercially available genetic tests. First, argumentation in a five-page patient brochure from a for-profit company that provides BRCA gene testing is analyzed. Next, for comparison, argumentation on BRCA testing in materials for healthcare consumers written by a not-for-profit health plan and a government organization is analyzed. In view of the challenges identified by considering these examples, this chapter discusses how argumentation-oriented computer systems may be able to help the healthcare consumer to make informed decisions about genetic testing.
The Resiliency Advantage Master Change Thrive Under Pressure And Bounce Back From Setbacks

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Reliable analysis of settling errors in SC integrators: application to ΣΔ modulators

A study is presented into the transient response of SC integrators considering amplifier finite bandwidth, slew-rate, and parasitic capacitors during, unlike previous models, both the integration and sampling phases. The model is validated by experimental results on a second-order /spl Sigma//spl Delta/ modulator and provides more reliable estimations of the defective settling in high-speed designs than previously reported models.
The Review of Factors that Influence the Supply Chain Performance

This article aims to highlight the factors that influence the supply chain (SC) performance. SC performance is an aggregate of the performance of every company operating in the chain. The relationship between the companies in the SC plays a key role in the SC overall performance. There are different factors that may influence directly a company’s performance but indirectly they affect the entire chain. Some of these factors are: longevity of the relationship, supply base reduction, supplier involvement, information sharing, cross-functional teams, trust and commitment. In addition, this paper identifies several other factors that influence the SC performance, such as: environmental uncertainty, top-management support, customer focus, information technology, strategic purchasing, logistic integration and supply network structure. By knowing them, strategies can be build to overcome problems, the opportunities offered can be exploit and lastly these factors can be used to develop SC models with focus on SC performance. DOI: 10.5901/ajis.2016. v5n2p181
DCGrid

We introduce Dynamic Constrained Grid (DCGrid), a hierarchical and adaptive grid structure for fluid simulation combined with a scheme for effectively managing the grid adaptations. DCGrid is designed to be implemented on the GPU and used in high-performance simulations. Specifically, it allows us to efficiently vary and adjust the grid resolution across the spatial domain and to rapidly evaluate local stencils and individual cells in a GPU implementation. A special feature of DCGrid is that the control of the grid adaption is modeled as an optimization under a constraint on the maximum available memory, which addresses the memory limitations in GPU-based simulation. To further advance the use of DCGrid in high-performance simulations, we complement DCGrid with an efficient scheme for approximating collisions between fluids and static solids on cells with different resolutions. We demonstrate the effectiveness of DCGrid for smoke flows and complex cloud simulations in which terrain-atmosphere interaction requires working with cells of varying resolution and rapidly changing conditions. Finally, we compare the performance of DCGrid to that of alternative adaptive grid structures.