Miracle-dz/UniFAR-FTUs huggingface dataset:
DEVELOPMENT AND VALIDATION OF SPECTROPHOTOMETRIC METHODS FOR SIMULTANEOUS DETERMINATION OF LEVOCETIRIZINE DIHYDROCHLORIDE AND MONTELUKAST SODIUM IN TABLET DOSAGE FORM

Four simple and accurate spectrophotometric methods have been described for the simultaneous determination of levocetirizine dihydrochloride and montelukast sodium in tablet dosage form. The first method is a bivariate calibration algorithm involving the use of two selected wavelengths 220 nm and 230 nm for the determination of studied drugs. The second method is a dual wavelength which is based on determination of levocetirizine dihydrochloride at the absorbance difference between 208 nm and 214.4 nm in which montelukast sodium absorbance difference was zero for any concentration, also montelukast sodium can be determined at absorbance difference between 355 and 390 nm in which levocetirizine dihydrochloride has absorbance zero for any concentration also. The third method is a derivative spectrophotometry, in which the two drugs were quantified using second derivative responses at 244 nm for levocetirizine dihydrochloride and at 293.2 or 335.6 nm for montelukast sodium. The fourth method is ratio difference which depends on measuring the peak amplitudes for levocetirizine dihydrochloride at 216 and 232 nm using 4 μg mL -1 montelukast sodium as a divisor, while montelukast sodium can be measured at 296.4 and 344.2 nm using 4 μg mL -1 of levocetirizine dihydrochloride as divisor. Beer’s law was obeyed in the concentration range 4–28 μg mL -1 for both drugs in all methods. The developed methods were used to determine the studied drugs in bulk powder, laboratory prepared mixtures and pharmaceutical dosage form with good accuracy and precision. All methods were validated W
A Globally Arbitrated Memory Tree for Mixed-Time-Criticality Systems

Embedded systems are increasingly based on multi-core platforms to accommodate a growing number of applications, some of which have real-time requirements. Resources, such as off-chip DRAM, are typically shared between the applications using memory interconnects with different arbitration polices to cater to diverse bandwidth and latency requirements. However, traditional centralized interconnects are not scalable as the number of clients increase. Similarly, current distributed interconnects either cannot satisfy the diverse requirements or have decoupled arbitration stages, resulting in larger area, power and worst-case latency. The four main contributions of this article are: 1) a Globally Arbitrated Memory Tree (GAMT) with a distributed architecture that scales well with the number of cores, 2) an RTL-level implementation that can be configured with five arbitration policies (three distinct and two as special cases), 3) the concept of mixed arbitration policies that allows the policy to be selected individually per core, and 4) a worst-case analysis for a mixed arbitration policy that combines TDM and FBSP arbitration.We compare the performance of GAMT with centralized implementations and show that it can run up to four times faster and have over 51 and 37 percent reduction in area and power consumption, respectively, for a given bandwidth.
High-Performance and Energy-Effcient Memory Scheduler Design for Heterogeneous Systems

When multiple processor cores (CPUs) and a GPU integrated together on the same chip share the off-chip DRAM, requests from the GPU can heavily interfere with requests from the CPUs, leading to low system performance and starvation of cores. Unfortunately, state-of-the-art memory scheduling algorithms are ineffective at solving this problem due to the very large amount of GPU memory traffic, unless a very large and costly request buffer is employed to provide these algorithms with enough visibility across the global request stream. Previously-proposed memory controller (MC) designs use a single monolithic structure to perform three main tasks. First, the MC attempts to schedule together requests to the same DRAM row to increase row buffer hit rates. Second, the MC arbitrates among the requesters (CPUs and GPU) to optimize for overall system throughput, average response time, fairness and quality of service. Third, the MC manages the low-level DRAM command scheduling to complete requests while ensuring compliance with all DRAM timing and power constraints. This paper proposes a fundamentally new approach, called the Staged Memory Scheduler (SMS), which decouples the three primary MC tasks into three significantly simpler structures that together improve system performance and fairness. Our evaluation shows that SMS provides 41.2% performance improvement and fairness improvement compared to the best previous state-of-the-art technique, while enabling a design that is significantly less complex and more power-efficient to implement.
Autologous platelets as a source of proteins for healing and tissue regeneration

Summary Platelets are known for their role in haemostasis where they help prevent blood loss at sites of vascular injury. To do this, they adhere, aggregate and form a procoagulant surface leading to thrombin generation and fibrin formation. Platelets also release substances that promote tissue repair and influence the reactivity of vascular and other blood cells in angiogenesis and inflammation. They contain storage pools of growth factors including PDGF, TGF-β and VEGF as well as cytokines including proteins such as PF4 and CD40L. Chemokines and newly synthesised active metabolites are also released. The fact that platelets secrete growth factors and active metabolites means that their applied use can have a positive influence in clinical situations requiring rapid healing and tissue regeneration. Their administration in fibrin clot or fibrin glue provides an adhesive support that can confine secretion to a chosen site. Additionally, the presentation of growth factors attached to platelets and/or fibrin may result in enhanced activity over recombinant proteins. Dental implant surgery with guided bone regeneration is one situation where an autologous platelet-rich clot clearly accelerates ossification after tooth extraction and/or around titanium implants. The end result is both marked reductions in the time required for implant stabilisation and an improved success rate. Orthopaedic surgery, muscle and/or tendon repair, reversal of skin ulcers, hole repair in eye surgery and cosmetic surgery are other situations where autologous platelets accelerate healing. Our aim is to review these advances and discuss the ways in which platelets may provide such unexpected beneficial therapeutic effects.
Building Models for Biopathway Dynamics Using Intrinsic Dimensionality Analysis

An important task for many if not all the scientific domains is efficient knowledge integration, testing and codification. It is often solved with model construction in a controllable computational environment. In spite of that, the throughput of in-silico simulation-based observations become similarly intractable for thorough analysis. This is especially the case in molecular biology, which served as a subject for this study. In this project, we aimed to test some approaches developed to deal with the curse of dimensionality. Among these we found dimension reduction techniques especially appealing. They can be used to identify irrelevant variability and help to understand critical processes underlying high-dimensional datasets. Additionally, we subjected our data sets to nonlinear time series analysis, as those are well established methods for results comparison. To investigate the usefulness of dimension reduction methods, we decided to base our study on a concrete sample set. The example was taken from the domain of systems biology concerning dynamic evolution of sub-cellular signaling. Particularly, the dataset relates to the yeast pheromone pathway and is studied in-silico with a stochastic model. The model reconstructs signal propagation stimulated by a mating pheromone. In the paper, we elaborate on the reason of multidimensional analysis problem in the context of molecular signaling, and next, we introduce the model of choice, simulation details and obtained time series dynamics. A description of used methods followed by a discussion of results and their biological interpretation finalize the paper.
1 Forecasting oil prices

The paper examines the importance of combining high frequency information, along with the market fundamentals, in order to gain incremental forecasting accuracy for oil prices. Inspired by French et al. (1986) and Bollerslev et al. (1988), who maintain that future asset returns are also influenced by past volatility, we use daily volatilities and returns from financial and commodity markets to generate real out-ofsample forecasts for the monthly oil futures prices. Our results convincingly show that although the oil market fundamentals are useful for long term forecasting horizons, the combination of the latter with asset realized volatilities, as these are constructed using ultra-high frequency data, significantly improve oil price forecasts in short-run horizons. These findings are both statistically and economically significant, as suggested by several robustness tests.
Comparing legal attitudes of Anglo- and Latino-Americans: Confirming the factor structure of the Legal Attitudes Questionnaire

ABSTRACT The Legal Attitudes Questionnaire (LAQ) is a commonly used measure of juror bias; however, the factor structure of the English-language version of this measure has yet to be confirmed. This research compared the model fit of three competing models of the English-language LAQ within 504 Anglo-American and 290 Latino-American students. Results indicated that the Couch and Sundre (2001) three-factor structure provided the best fit to the LAQ. Latent mean differences indicated that Latino-Americans endorsed more authoritarian attitudes and more negative views of the police than Anglo-Americans. Interpretation of these findings and implications for future use are discussed.
Examining Intraracial Dynamics and Engagement Between Native-Born and Foreign-Born Black Collegians in STEM

For decades, higher education research has recognized the importance of cross-cultural interaction among students. However, this body of scholarship has largely examined this phenomenon across races, with few researchers examining within race interactions. Accordingly, the purpose of this study is to examine how native-born and foreign-born Black students interact in science, technology, engineering, and mathematics (STEM) classrooms. Findings reveal that there were both similarities and differences in how each group perceived each other, and that ultimately, cross-cultural engagement was beneficial for their academic development.
RIFT: Multi-modal Image Matching Based on Radiation-invariant Feature Transform

Traditional feature matching methods such as scale-invariant feature transform (SIFT) usually use image intensity or gradient information to detect and describe feature points; however, both intensity and gradient are sensitive to nonlinear radiation distortions (NRD). To solve the problem, this paper proposes a novel feature matching algorithm that is robust to large NRD. The proposed method is called radiation-invariant feature transform (RIFT). There are three main contributions in RIFT: first, RIFT uses phase congruency (PC) instead of image intensity for feature point detection. RIFT considers both the number and repeatability of feature points, and detects both corner points and edge points on the PC map. Second, RIFT originally proposes a maximum index map (MIM) for feature description. MIM is constructed from the log-Gabor convolution sequence and is much more robust to NRD than traditional gradient map. Thus, RIFT not only largely improves the stability of feature detection, but also overcomes the limitation of gradient information for feature description. Third, RIFT analyzes the inherent influence of rotations on the values of MIM, and realizes rotation invariance. We use six different types of multi-model image datasets to evaluate RIFT, including optical-optical, infrared-optical, synthetic aperture radar (SAR)-optical, depth-optical, map-optical, and day-night datasets. Experimental results show that RIFT is much more superior to SIFT and SAR-SIFT. To the best of our knowledge, RIFT is the first feature matching algorithm that can achieve good performance on all the above-mentioned types of multi-model images. The source code of RIFT and multi-modal remote sensing image datasets are made public .
Blind and passive digital video tamper detection based on multimodal fusion

In this paper, we propose novel algorithmic models based on feature transformation in cross-modal subspace and their multimodal fusion for different types of residue features extracted from several intraframe and inter-frame pixel sub-blocks in video sequences for detecting digital video tampering or forgery. An evaluation of proposed residue features - the noise residue features and the quantization features, their transformation in cross-modal subspace, and their multimodal fusion, for emulated copy-move tamper scenario shows a significant improvement in tamper detection accuracy as compared to single mode features without transformation in cross-modal subspace.
It Is Easy to Be Wise After the Event: Communicating Finite-State Machines Capture First-Order Logic with "Happened Before"

Message sequence charts (MSCs) naturally arise as executions of communicating finite-state machines (CFMs), in which finite-state processes exchange messages through unbounded FIFO channels. We study the first-order logic of MSCs, featuring Lamport's happened-before relation. We introduce a star-free version of propositional dynamic logic (PDL) with loop and converse. Our main results state that (i) every first-order sentence can be transformed into an equivalent star-free PDL sentence (and conversely), and (ii) every star-free PDL sentence can be translated into an equivalent CFM. This answers an open question and settles the exact relation between CFMs and fragments of monadic second-order logic. As a byproduct, we show that first-order logic over MSCs has the three-variable property.
HELIOSEISMOLOGY OF A REALISTIC MAGNETOCONVECTIVE SUNSPOT SIMULATION

We compare helioseismic travel-time shifts measured from a realistic magnetoconvective sunspot simulation using both helioseismic holography and time–distance helioseismology, and measured from real sunspots observed with the Helioseismic and Magnetic Imager instrument on board the Solar Dynamics Observatory and the Michelson Doppler Imager instrument on board the Solar and Heliospheric Observatory. We find remarkable similarities in the travel-time shifts measured between the methodologies applied and between the simulated and real sunspots. Forward modeling of the travel-time shifts using either Born or ray approximation kernels and the sound-speed perturbations present in the simulation indicates major disagreements with the measured travel-time shifts. These findings do not substantially change with the application of a correction for the reduction of wave amplitudes in the simulated and real sunspots. Overall, our findings demonstrate the need for new methods for inferring the subsurface structure of sunspots through helioseismic inversions.
Overview of automatic seed selection methods for biomedical images segmentation

In biomedical image processing, image segmentation is a relevant research area due to its wide spread usage and application. Seeded region growing is very attractive for semantic image segmentation by involving the high-level knowledge of image components in the seed point selection procedure. However, the seeded region growing algorithm suffers from the problems of automatic seed point generation. A seed point is the starting point for region growing and its selection is very important for the success of segmentation process. This paper presents an extensive survey on works carried out in the area of automatic seed point selection for biomedical images segmentation by seeded region growing algorithm. The main objective of this study is to provide an overview of the most recent trends for seed point selection in biomedical image segmentation.
TNT-NLG , System 1 : Using a Statistical NLG to Massively Augment Crowd-Sourced Data for Neural Generation

Ever since the successful application of sequence to sequence learning for neural machine translation systems (Sutskever et al., 2014), interest has surged in its applicability towards language generation in other problem domains. In the area of natural language generation (NLG), there has been a great deal of interest in end-to-end (E2E) neural models that learn and generate natural language sentence realizations in one step. In this paper, we present TNT-NLG System 1, our first system submission to the E2E NLG Challenge, where we generate natural language (NL) realizations from meaning representations (MRs) in the restaurant domain by massively expanding the training dataset. We develop two models for this system, based on Dusek et al.’s (2016a) open source baseline model and context-aware neural language generator. Starting with the MR and NL pairs from the E2E generation challenge dataset, we explode the size of the training set using PERSONAGE (Mairesse and Walker, 2010), a statistical generator able to produce varied realizations from MRs, and use our expanded data as contextual input into our models. We present evaluation results using automated and human evaluation metrics, and describe directions for future work.
Obesity prevalence in Mexico: impact on health and economic burden

Abstract Objective Along with other countries having high and low-to-middle income, Mexico has experienced a substantial change in obesity rates. This rapid growth in obesity prevalence has led to high rates of obesity-related diseases and associated health-care costs. Design Micro-simulation is used to project future BMI trends. Additionally thirteen BMI-related diseases and health-care costs are estimated. The results are simulated for three hypothetical scenarios: no BMI reduction and BMI reductions of 1 % and 5 % across the population. Setting Mexican Health and Nutrition Surveys 1999 and 2000, and Mexican National Health and Nutrition Survey 2006. Subjects Mexican adults. Results In 2010, 32 % of men and 26 % of women were normal weight. By 2050, the proportion of normal weight will decrease to 12 % and 9 % for males and females respectively, and more people will be obese than overweight. It is projected that by 2050 there will be 12 million cumulative incidence cases of diabetes and 8 million cumulative incidence cases of heart disease alone. For the thirteen diseases considered, costs of $US 806 million are estimated for 2010, projected to increase to $US 1·2 billion and $US 1·7 billion in 2030 and 2050 respectively. A 1 % reduction in BMI prevalence could save $US 43 million in health-care costs in 2030 and $US 85 million in 2050. Conclusions Obesity rates are leading to a large health and economic burden. The projected numbers are high and Mexico should implement strong action to tackle obesity. Results presented here will be very helpful in planning and implementing policy interventions.
Clarifying the Relationship Between Public Service Motivation and In-Role and Extra-Role Behaviors: The Relative Contributions of Person-Job and Person-Organization Fit

A core proposition of public service motivation (PSM) theory is that PSM is positively related to individual performance. Some studies, however, suggest that this relationship is mediated by person-job or person-organization fit. This study aims to further clarify the relationship between PSM and performance by, first, studying the mediation role of both person-job and person-organization fits and, second, by investigating this mediation for both in-role and extra-role behavior. Whereas in-role behavior is aimed at the individual task, extra-role is aimed at helping colleagues. This difference may matter for the role of PSM and fit. To this end, we conducted structural equation modeling with bootstrapping on self-reported survey data from public employees (n = 1,031). The analysis showed that person-job, but not person-organization fit, fully mediated the relationship between PSM and in-role behavior. The relationship with extra-role behavior was not mediated. The PSM-performance relationship may thus be more complex than previously envisioned, as both type of performance and person-job fit matter.
The Effects of Item and Respondent Characteristics on Midpoint Response Option Endorsement: A Mixed-Methods Study.

As the demand for accountability and transparency in higher education has increased, so too has the call for direct assessment of student learning outcomes. Accompanying this increase of knowledge-based, cognitive assessments administered in a higher education context is an increased emphasis on assessing various noncognitive aspects of student growth and development over the course of their college career. Noncognitive outcomes are most often evaluated via self-report instruments associated with Likert-type response scales, posing unique challenges for researchers and assessment practitioners hoping to draw valid conclusions based upon this data. One long-debated characteristic of such assessments is the midpoint response option. More specifically, prior research suggests that respondents may be more or less likely to endorse the midpoint response option under different measurement and respondent dispositional conditions thus introducing construct-irrelevant variance within respondent scores. The current study expanded upon previous work to examine the effects of various item and respondent characteristics on endorsement and conceptualization of the midpoint response option in a noncognitive assessment context. A mixed-methods approach was employed in order to fully address research questions associated with two studies – one quantitative and one qualitative in nature. Study 1 employed hierarchical generalized linear modeling to simultaneously examine the effects of respondent characteristics and experimentally manipulated item characteristics on the probability of midpoint response option endorsement. Respondent characteristics included self-reported effort expended on the assessments administered and respondent levels of verbal aptitude (SAT verbal scores). Respondents were
Model-Driven Development of Performance Sensitive Cloud Native Streaming Applications

The number of applications that process data in a stream basis has increased significantly over recent years due to the proliferation of sensors. Additionally, in cyber-physical systems, physical and software components are deeply intertwined, adding the ability to act on the environment. In many cases, cloud resources are used for the processing, exploiting their flexibility, but these sensor streaming applications often need to support operational and control actions that have real-time and lowlatency requirements that go beyond the cost effective and flexible solutions supported by cloud platforms. The development of these applications cannot be delegated to the magical properties of frameworks and services that promise simple solutions, hiding the inherent underlying complexity of cloud resources. It raises the difficulty of developing complex streaming processing in the cloud and highlights the need for a suitable developing methodology. Moreover, during the developing lifecycle, a number of facets have to be considered such as the design of functional parallel solutions, the impact of a target cloud platform that exhibits different degrees of performance variability, or the need for more complex performance requirement support. This talk will present our experiences in developing Petri Net models for performance sensitive cloud applications thus leveraging the use of formal models in complex scenarios.