Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating the Efficiency of Four-year Public Master's Universities in Arkansas Using Data Envelopment Analysis

For the fourth time in the past 20 years, the state of Arkansas has implemented a performance-funding program. Although directly referenced in the title of Act 1203 of 2011, the implemented performance-funding program does not include a measure of how efficiently institutions are converting their inputs into outputs. The purpose of this study was two-fold: (1) to examine, through the conceptual framework of agency theory, why the state of Arkansas adopted performance funding policy after experiencing three adopt-implement-abandon cycles in the past 20 years, and (2) to estimate the efficiency of four-year public Master’s universities in Arkansas in achieving one of the mandatory measures in the performance-funding program: number of bachelor’s degrees awarded. The institutions included in this study were Arkansas State University, Arkansas Tech University, and University of Central Arkansas in the larger group, and Henderson State University, Southern Arkansas University, and University of Arkansas at Monticello in the medium/smaller group. The analytical technique utilized in this study was based on Simar and Wilson’s (2007) double bootstrap truncated regression approach. This study found that the six Arkansas institutions were inefficient in the production of bachelor’s degrees during the 2011-2012 academic year, given the inputs included in the model and in relationship to the national sample of Master’s Colleges and Universities. Through the second-stage regression analysis, this study also found that a state’s per capita real gross domestic product (GDP) was a positive and statistically significant determinant of estimated efficiency for both groups of institutions (larger and medium/smaller). For the medium/smaller group, higher education’s share of total state expenditures was a negative and statistically significant determinant of estimated efficiency, while the share of operating revenues based on net tuition was a positive and statistically significant determinant of estimated efficiency for the larger group institutions. The existence of a performance-funding program was found to not be a significant determinant of institutional efficiency through the second-stage regression analysis. Using Eisenhardt’s (1989) taxonomy of behaviorversus outcome-based contracts, this study also found that agency theory served as a viable conceptualization of why the state of Arkansas chose to adopt performance-funding policy for the fourth time since its initial adoption in 1995.
SALIVARY ALPHA-AMYLASE GENE SUGGESTS A METABOLIC SHIFT TOWARDS LIPID-BASED ENERGY PRODUCTION

Low serum salivary amylase levels have been associated with a range of metabolic abnormalities, including obesity and insulin resistance. We recently suggested that low copy-number at the AMY1 gene, associated with lower enzyme levels, also increases susceptibility to obesity. To advance our understanding of the effect of AMY1 copy-number variation on metabolism, we compared the metabolomic signatures of high and low copy-number carriers. We analysed, using mass spectrometry and NMR, the sera of healthy normal-weight women carrying either low (LA:≤4 copies; n =50) or high (HA:≥8 copies; n =50) AMY1 copies. Best fitting multivariate models (empirical P <1x10 -3 ) of MS and NMR data were concordant in showing differences in lipid metabolism between the two groups. In particular, LA carriers showed lower levels of long- and medium-chain fatty acids, and higher levels of dicarboxylic fatty acids and 2-hydroxybutyrate (known marker of glucose malabsorption). Taken together, these observations suggest increased metabolic reliance on fatty acids in LA carriers through β- and ω-oxidation and reduced cellular glucose uptake with consequent diversion of acetyl-CoA into ketogenesis. Our observations are in line with previously-reported delayed glucose uptake in LA carriers after starch consumption. Further functional studies are needed to extrapolate from our findings to implications for biochemical pathways.
Ra-SBRT is Potential Immune Adjuvant for Innate Immune Cell Populations in Advance Stage NSCLC Patients

: Bystander toxicity and tissue fibrosis are the major complications with conventional radiation therapy for cancer patients. In this context, we here propose RapidArc - Stereotactic Body Radiation Therapy (Ra-SBRT) as a noninvasive and immune adjuvant approach for the successful eradication of advance stage NSCLC. Ra-SBRT is highly focused and capable of destroying tumors with high grade metastatic lesions and spared normal tissues. Follow up of stage 4 th NSCLC patient revealed that Ra-SBRT is potentially immunogenic which was evident by increased number of iNOS+ Tumor Associated macrophages (M1-TAM), Siglac-8+ eosinophils, basophils and subsequent prolongation of disease free survival of 4 th stage NSCLC patients by 3 years. This study demonstrated M1 retuning potential of Ra-SBRT which is a pre-requisite of effective management of inoperable and highly metastatic tumors of lung with least or no bystander impact.
Improving Hadoop Performance through scheduling and prefetching

Abstract. In this paper, we continue investigating how to improve Hadoop performance, we will investigate further strategies, tackling the data locality with respect to data access patterns, cluster memory and effective scheduling. In a Hadoop cluster, users are generally accessing data according to their business needs, which make some data more accessed than others, reason why we consider data patterns as a crucial element in our approach. In fact, the performance can be affected by the latency of data access, which is best when blocks are already in memory while requested for processing, knowing that caching too much can lead to memory overhead or even severe delays when wrong data are always taking memory space. Actually, clusters are nowadays offering more capabilities in term of nodes memory, which is considered in most cases as highly underutilized according to several studies. In our approach we considered the cluster memory to improve efficiency by using the underutilized memory space in a way to offer better data locality for future tasks, through a new prefetching/scheduling algo-rithm. The idea behind our approach is to differentiate blocks by popularity based on the previous access patterns, then proceed by memory allocation or memory eviction in optimal and efficient way across all the cluster.
Analysis of Structure and Composition of Macro-Invertebrate Assemblages in Natural Lukaya River Stream

Biological assessment of aquatic ecosystems is widely employed as an alternative or complement to chemical and toxicity testing due to numerous advantages of using biota to determine ecosystem condition. These advantages, especially to developing countries, include the relatively low cost and technical requirements. This study was carried out to establish relationship between water quality and macrozoobenthos assemblages along the Lukaya stream and to analyze fauna structure assemblages. Seven sites were selected along a 50-km stretch of the stream, which drained land under agricultural, residential and industrial uses. Water physico-chemical data was explored using multivariate analysis of Canonical Component Analysis to detect environmental trends. Water physico-chemical characteristics (temperature, pH, turbidity, conductivity, calcium, magnesium, nitrate, nitrite, sulphate and phosphate) and biodiversity indices (species richness, diversity, redundancy, evenness, abundance) did not show significant difference ( P = 1) between sites along the stream but some correlation between variables and biodiversity indices were detected by spearman’s correlation analysis. Sixty-two taxonomic groups identified are dominated by odonata, diptera, shelfish and molluscs but three taxa: Lymnaeidae , Chironomyidae and Atyidae presented the highest abundance in all assemblages. The model of DIMO made it possible to split the various sites in two groups in function of H’ , Hmax and J’ . Moreover, rank-frequency diagrams of Frontier characterized stage 1 and middle between stages 1 and 2 structured curves.
Modeling the Distribution of Infectious Diseases Using Parallel Calculations

Global warming and related climate changes are the subjects of intensive research as they affect the social, economic, and medical aspects of human life. Large-scale climate change: melting glaciers and the spread of new infections are likely to have a negative impact on the world economy. The spread of infectious diseases is indeed relevant, but the amount of data needed to model the spread is large, and therefore the processing time of this data is large. Therefore, the use of parallel algorithms will speed up the solution to this problem, and, accordingly, faster obtaining the results of modeling the spread of infectious diseases. A parallel algorithm for modeling the spread of infectious diseases based on OpenMP technology has been developed and has been investigated; the benefits of using such a feature as multi-core personal computers have been analyzed in the paper. Parallel modeling uses a graph of an imaginary city. The results of speed and acceleration of the parallel algorithm, which indicates the optimization of the computational process have been analyzed. You can increase the acceleration rate, as well as direct the efficiency to one by varying the number of processor cores.
Subcategorization acquisition

Manual development of large subcategorised lexicons has proved difficult because predicates change behaviour between sublanguages, domains and over time. Yet access to a comprehensive subcategorization lexicon is vital for successful parsing capable of recovering predicate-argument relations, and probabilistic parsers would greatly benefit from accurate information concerning the relative likelihood of different subcategorisation frames (scfs) of a given predicate. Acquisition of subcategorization lexicons from textual corpora has recently become increasingly popular. Although this work has met with some success, resulting lexicons indicate a need for greater accuracy. One significant source of error lies in the statistical filtering used for hypothesis selection, i.e. for removing noise from automatically acquired scfs. This thesis builds on earlier work in verbal subcategorization acquisition, taking as a starting point the problem with statistical filtering. Our investigation shows that statistical filters tend to work poorly because not only is the underlying distribution zipfian, but there is also very little correlation between conditional distribution of scfs specific to a verb and unconditional distribution regardless of the verb. More accurate back-off estimates are needed for scf acquisition than those provided by unconditional distribution. We explore whether more accurate estimates could be obtained by basing them on linguistic verb classes. Experiments are reported which show that in terms of scf distributions, individual verbs correlate more closely with syntactically similar verbs and even more closely with semantically similar verbs, than with all verbs in general. On the basis of this result, we suggest classifying verbs according to their semantic classes and obtaining back-off estimates specific to these classes. We propose a method for obtaining such semantically based back-off estimates, and a novel approach to hypothesis selection which makes use of these estimates. This approach involves automatically identifying the semantic class of a predicate, using subcategorization acquisition machinery to hypothesise conditional scf distribution for the predicate, smoothing the conditional distribution with the back-off estimates of the respective semantic verb class, and employing a simple method for filtering, which uses a threshold on the estimates from smoothing. Adopting Briscoe and Carroll’s (1997) system as a framework, we demonstrate that this semantically-driven approach to hypothesis selection can significantly improve the accuracy of large-scale subcategorization acquisition.
l4C Fixation by Leaves and Leaf Cell Protoplasts of the Submerged Aquatic Angiosperm Potamogeton lucens: Carbon Dioxide or Bicarbonate?1

Protoplasts were isolated from leaves of the aquatic angiosperm Potamogeton lucens L. The leaves utilize bicarbonate as a carbon source for photosynthesis, and show polarity; that is, acidification of the periplasmic space of the lower, and alkalini- zation of the space near the upper leaf side. At present there are two models under consideration for this photosynthetic bicarbonate utilization process: conversion of bicarbonate into free carbon dioxide as a result of acidification and, second, a bicarbonate-proton symport across the plasma membrane. Carbon fixation of protoplasts was studied at different pH values and compared with that in leaf strips. Using the isotopic disequilibrium technique, it was established that carbon dioxide and not bicarbonate was the form in which DIC actually crossed the plasma membrane. It is concluded that there is probably no true bicarbonate transport system at the plasma membrane of these cells and that bicarbonate utilization in this species apparently rests on the conversion of bicarbonate into carbon dioxide. Experiments with acetazolamide, an inhibitor of periplasmic carbonic anhydrase, and direct measurements of carbonic anhydrase activity in intact leaves indicate that in this species the role of this enzyme for periplasmic conversion of bicarbonate into carbon dioxide is insignificant.
Post-Train Adaptive MobileNet for Fast Anti-Spoofing (keynote)

Many applications require high accuracy of neural networks as well as low latency and user data privacy guaranty. Face anti-spoofing is one of such tasks. However, a single model might not give the best results for different device performance categories, while training multiple models is time consuming. In this work we present Post-Train Adaptive (PTA) block. Such a block is simple in structure and offers a drop-in replacement for the MobileNetV2 Inverted Residual block. The PTA block has multiple branches with different computation costs. The branch to execute can be selected on-demand and at runtime; thus, offering different inference times and configuration capability for multiple device tiers. Crucially, the model is trained once and can be easily reconfigured after training, even directly on a mobile device. In addition, the proposed approach shows substantially better overall performance in comparison to the original MobileNetV2 as tested on CelebA-Spoof dataset. Different PTA block configurations are sampled at training time, which also decreases overall wall-clock time needed to train the model. While we present computational results for the anti-spoofing problem, the MobileNetV2 with PTA blocks is applicable to any problem solvable with convolutional neural networks, which makes the results presented practically significant.
Research of the Neural Network Module for Detecting Anomalies in Network Traffic

The aim of this article was to study, develop and apply neural network methods to analyze traffic of local area network for detection of the anomalies in network traffic. The theoretical significance of the presented material is coverage of the problem of research and development of methods of computational intelligence, namely artificial neural networks to detect intrusions in networks. In particular, a method of detecting anomalies was created on the base of a Kohonen self-organizing map, the introduction of which into the structure of the existing attack detection system allows to increase the accuracy of detecting anomalies in network traffic by 35-40% compared to other existing methods. The practical significance of the results is the possibility of applying the developed method to detect intrusions and anomalies in modern corporate networks and the possibility of building a real-time detection system based on it, as well as solving of other tasks of data mining such as classification or clustering of suspect events in network traffic.
Prototyping a Gamified System to Persuade School-Age Children in Developing Countries: Using Kahoot in Online Environments

Socio-economically disadvantaged school-age children in developing countries, e.g., primary school students, often feel isolated and lack the opportunity to use modern technology for their educational growth. This may negatively impact their schooling through low grades and, in the worst-case, even dropping out of their classes. Gamification is the use of game design elements in non-gaming contexts. It is envisaged that using a gamified learning system can significantly impact different learners. Our aim is to conduct a case study with a Kahoot-based gamified learning system. The design of the system followed a de-sign thinking iterative process. The test result informs us that those participants using a Kahoot based online platform for solving primary level math quizzes and puzzles preferred the reward-based tasks in the gamified system. Our future effort is to utilize Cognitive Social Learning Theory (CSLT) that supports design instruction and the gamification of learning to deploy and implement our Kahoot-based gamified application, followed by an experimental design which will be carried out with students to measure behavioural change and learning.
Improved DC Model and Transfer Functions for the Negative Output Elementary Super Lift Luo Converter

– Negative output elementary super lift Luo converter (NOESLLC), which has the significant advantages including high-voltage transfer gain, high efficiency, high power density, and reduced output voltage/inductor current ripples when compared to the traditional DC-DC converters, is an attractive DC-DC converter for the field of negative DC voltage applications. In this study, in consideration of the voltage across the energy transferring capacitor changing abruptly at the beginning of each switching cycle, the improved averaged model of the NOESLLC operating in continuous conduction mode (CCM) is established. The improved DC model and transfer functions of the system are derived and analyzed. The current mode control is applied for this NOESLLC. The results from the theoretical calculations, the PSIM simulations and the circuit experiments show that the improved DC model and transfer functions here are more effective than the existed ones of the NOESLLC to describe its real dynamical behaviors.
Simulation for the Separation of VDR Voice Signal Base on the Local-Wave Method

The Voyage Data Recorder (VDR) is becoming one of the most important equipment of the modern ship, especially for the combat vessel. In the one hand, Many types of data of the combat vessel can be recorded in time and be saved for a long time, which could be benefit for replaying some status or scenes of the vessel on some occasion. On the other hand, For the reason of the complex sources and instability of the data in VDR, It is difficult to separate the VDR voice signal completely, then it is inconvenient for the data retrieval. In this paper, a new method named Local-wave method has been used to resolve the problem in theory, which usually focus on the local feature of the signal. According to the simulation, this method separated the VDR voice signal more effective, and it will be useful to the data retrieve of VDR voice signal.
Mitoquinone mesylate targets SARS-CoV-2 infection in preclinical models

To date, there is no effective oral antiviral against SARS-CoV-2 that is also anti-inflammatory. Herein, we show that the mitochondrial antioxidant mitoquinone/mitoquinol mesylate (Mito-MES), a dietary supplement, has potent antiviral activity against SARS-CoV-2 and its variants of concern in vitro and in vivo. Mito-MES had nanomolar in vitro antiviral potency against the Beta and Delta SARS-CoV-2 variants as well as the murine hepatitis virus (MHV-A59). Mito-MES given in SARS-CoV-2 infected K18-hACE2 mice through oral gavage reduced viral titer by nearly 4 log units relative to the vehicle group. We found in vitro that the antiviral effect of Mito-MES is attributable to its hydrophobic dTPP+ moiety and its combined effects scavenging reactive oxygen species (ROS), activating Nrf2 and increasing the host defense proteins TOM70 and MX1. Mito-MES was efficacious reducing increase in cleaved caspase-3 and inflammation induced by SARS-CoV2 infection both in lung epithelial cells and a transgenic mouse model of COVID-19. Mito-MES reduced production of IL-6 by SARS-CoV-2 infected epithelial cells through its antioxidant properties (Nrf2 agonist, coenzyme Q10 moiety) and the dTPP moiety. Given established safety of Mito-MES in humans, our results suggest that Mito-MES may represent a rapidly applicable therapeutic strategy that can be added in the therapeutic arsenal against COVID-19. Its potential long-term use by humans as diet supplement could help control the SARS-CoV-2 pandemic, especially in the setting of rapidly emerging SARS-CoV-2 variants that may compromise vaccine efficacy. One-Sentence Summary Mitoquinone/mitoquinol mesylate has potent antiviral and anti-inflammatory activity in preclinical models of SARS-CoV-2 infection.
Dynamic interplay of protrusive microtubule and contractile actomyosin forces drives tissue extension

In order to shape a tissue, cell-based mechanical forces have to be integrated into global force patterns. Over the last decades, the importance of actomyosin contractile arrays, which are the key constituents of various morphogenetic processes, has been established for many tissues. Intriguingly, recent studies demonstrate that the microtubule cytoskeleton mediates folding and elongation of the epithelial sheet during Drosophila morphogenesis, placing microtubule mechanics en par with actin-based processes. While these studies establish the importance of both cytoskeletal systems during cell and tissue rearrangements, a mechanistic explanation of their functional hierarchy is currently missing. Here, we dissect the individual roles of these two key generators of mechanical forces during epithelium elongation. We demonstrate that microtubules dictate cell shape changes and actomyosin refines them. Combining experimental and numerical approaches, we find that altering the microtubule and actomyosin functions results in predictable changes in tissue shape. We further show that planar polarized microtubule patterning is independent of cell geometry and actomyosin-based mechanics. These results support a hierarchical mechanism, whereby microtubule-based forces in some epithelial systems prime actomyosin-generated forces.
Weighted Adaptive Multiple Decision Functions for False Discovery Rate Control

Efforts to develop more efficient multiple hypothesis testing procedures for false discovery rate (FDR) control have focused on incorporating an estimate of the proportion of true null hypotheses (such procedures are called adaptive) or exploiting heterogeneity across tests via some optimal weighting scheme. This paper combines these approaches and provides an assessment of the resulting methodology. Specifically, weighted adaptive multiple decision functions (WAMDFs) that control the FDR when test statistics from true null hypotheses are independent and independent of test statistics from false null hypotheses are provided, and optimal weights for a random effects model are derived. Assessment reveals that, under a weak dependence structure, WAMDFs dominate procedures which are either adaptive or weighted but not both. In particular, they allow for more rejected null hypotheses and have false discovery proportion that is asymptotically less than or equal to the nominal level, even when the employed weights are only positively correlated with optimal weights. The method is demonstrated on a real data set.