Miracle-dz/UniFAR-FTUs huggingface dataset:
Statistical Inference for Alpha-Series Process with the Generalized Rayleigh Distribution

In the modeling of successive arrival times with a monotone trend, the alpha-series process provides quite successful results. Both selecting the distribution of the first arrival time and making an optimal statistical inference play a crucial role in the modeling performance of the alpha-series process. In this study, when the distribution of the first arrival time is the generalized Rayleigh, the problem of statistical inference for the α, β, and λ parameters of the alpha-series process is considered. Further, in order to obtain optimal modeling performance from the mentioned alpha-series process, various estimators for the model parameters are obtained by employing different estimation methodologies such as maximum likelihood, modified maximum spacing, modified least-squares, modified moments, and modified L-moments. By a series of Monte Carlo simulations, the estimation efficiencies of the obtained estimators are evaluated through the different sample sizes. Finally, two real datasets are analyzed to illustrate the importance of modeling with the alpha-series process.
A Systematic Review of Transfusion-Transmissible Infections Among Blood Donors and Associated Safety Challenges in Pakistan

Abstract The blood transfusion (BT) system in Pakistan is fragmented, demand-driven and depends on weakly regulated transfusion practices. There is a considerable possibility that transfusion-transmissible infections (TTIs) are contributing to the current epidemic of hepatitis B virus (HBV) and hepatitis C virus (HCV) (affecting 7.4% of the general population) in the country. To study this issue, we conducted a systematic review to identify articles related to TTIs and transfusion safety in Pakistan from January 1, 2010 to January 31, 2020. A review of 33 articles met the final criteria for qualitative synthesis. Analysis of these studies showed a cumulative frequency of HBV 2.04%, HCV 2.44%, HIV 0.038%, syphilis 1.1% and malaria 0.11%. The frequency of coinfections among blood donors varied from 0.0099% to 0.35%. The highest number of coinfections were HCV and syphilis, followed by HCV and HBV infections. Syphilis and malaria were tested in only 38% and 46% of all the blood donations in one study. The rate of voluntary non-remunerated donations (VNRDs) was less than 13%, and male donors were 95% to 100% in these studies. There was a significant difference in the frequency of HBV and HCV in VNRDs (0.48%) as compared to replacement donors (RDs) (4.15%). In short, this review shows a high frequency of TTIs, especially HBV, HCV and syphilis in the blood donor population in Pakistan. There is a high dependency on RDs, minimal use of healthy voluntary blood donation practices, inadequate screening of high-risk donors, repeated collections of the blood from RDs, poor quality of screening methods and limited knowledge of donor health. Without standardized safe transfusion practices, there will be an ongoing increase in transmission of TTIs, especially HBV, HCV, syphilis, and HIV leading to a significant adverse public health impact.
Information–Theoretic Radar Waveform Design under the SINR Constraint

This study investigates the information–theoretic waveform design problem to improve radar performance in the presence of signal-dependent clutter environments. The goal was to study the waveform energy allocation strategies and provide guidance for radar waveform design through the trade-off relationship between the information theory criterion and the signal-to-interference-plus-noise ratio (SINR) criterion. To this end, a model of the constraint relationship among the mutual information (MI), the Kullback–Leibler divergence (KLD), and the SINR is established in the frequency domain. The effects of the SINR value range on maximizing the MI and KLD under the energy constraint are derived. Under the constraints of energy and the SINR, the optimal radar waveform method based on maximizing the MI is proposed for radar estimation, with another method based on maximizing the KLD proposed for radar detection. The maximum MI value range is bounded by SINR and the maximum KLD value range is between 0 and the Jenson–Shannon divergence (J-divergence) value. Simulation results show that under the SINR constraint, the MI-based optimal signal waveform can make full use of the transmitted energy to target information extraction and put the signal energy in the frequency bin where the target spectrum is larger than the clutter spectrum. The KLD-based optimal signal waveform can therefore make full use of the transmitted energy to detect the target and put the signal energy in the frequency bin with the maximum target spectrum.
Hyperloop transport technology assessment and system analysis

ABSTRACT The Hyperloop concept, pod speed competitions and current project developments have recently attracted much publicity. In this paper the transport technology of the vacuumed tube transport project Hyperloop is assessed through a system analysis of its principal aims, functional design, transport capacity and demand in comparison with existing commercial airlines, high-speed rail, and Maglev lines. First, the potential for high-speed long-distance travel demand for Hyperloop based on existing airline transport volumes between major airports in Germany on the one hand, and the proposed Hyperloop link from Los Angeles to San Francisco in California on the other, is assessed in general terms. Second, the technical feasibility of the proposed Hyperloop concept for vehicle design, capacity, operations, propulsion, guidance, energy supply, traffic control, safety, alignment, and construction is discussed in more detail. Third, possible environmental impacts and uncertain investment, operating and maintenance costs for implementation of a Hyperloop line are described. Finally, the risks for further Hyperloop project development and the need for more transparent research are emphasized.
GRADE approach to drawing conclusions from a network meta-analysis using a partially contextualised framework

This article describes GRADE (grading of recommendations assessment, development and evaluation) guidance on how to make conclusions from a network meta-analysis of interventions that includes individual randomised controlled trials for one outcome at a time. The guidance is based on a partially contextualised approach in which review authors must establish ranges of magnitudes of effect that represent a trivial to no effect, small but important effect, moderate effect, and large effect. The principles guiding this framework are that interventions should be grouped in categories, based on the magnitude of the effect; and that the judgments that place interventions in such categories should consider the estimates of effect, the certainty of the evidence, and the rankings. We describe and illustrate the four steps of this framework using an example.
Help Wanted: Employer Demand for Less-Skilled Temporary Foreign Worker Visas in an Era of Declining Unauthorized Immigration

Employer demand for less-skilled foreign workers admitted on temporary worker visas has increased considerably in recent years. Issuances of H-2A visas for agricultural workers and J-1 visas for exchange visitors have soared, and the cap for H-2B visas for nonagricultural workers is reached well before the end of the issuance period. This article examines the rise in employer demand for these programs, focusing on the roles of improved economic conditions, tougher immigration enforcement, and the drop in the number of less-skilled workers, including unauthorized immigrants. Economic conditions appear to be the most important determinant of employer demand. The upward trend in employer usage of the programs suggests that they can be a viable alternative to hiring unauthorized workers, and even more so if restructured appropriately.
The Policy Effect of Negative Interest Rate Policy on the Japanese Stock Market

We explored the policy effect of the Japanese negative interest rate policy (NIRP) on seven domestic stock market indices in this article. To represent the whole Japanese stock market, the Nikkei 225 and TOPIX were two commonly representative indices. Moreover, we employed four financial-industry indices and one non-financial index to detail the NIRP effect on the financial sector and the non-financial sector. After controlling the quantitative easing (QE) effect and the interaction effect between NIRP and QE, we investigated the NIRP effect over Japanese equity indices in the generalized autoregressive conditional heteroscedasticity (GARCH) model. The benefits of such modeling strategies could gauge the policy impact on market profitability and market stability simultaneously. For the entire market, the estimated independent NIRP effect illustrated a positive reaction on the conditional mean and a negative response on the conditional variance of the empirical GARCH model. The aggregated outcomes for NIRP that combined the independent NIRP effect and the NIRP effect of interacting with QE would be contingent on the magnitude of QE, and large enough QE reversed the market profitability and market stability. The estimation results of the non-financial equity index and financial-industry equity indices implied the conclusion of the market profitability and market stability, respectively.
Balanced Dominating Top-k Queries over Uncertain Data

Uncertainty of data is inherent in many important applications. Effectively extracting valuable information to enable better decisions is important but not a trivial task over uncertain data. We have witnessed a great deal of significant researches for this purpose, such as top-k queries, skyline queries and dominated top-k queries. As for uncertainty, the common challenge that those researches face is to answer the ranking methods in consideration of user's function score and probability. In this paper, we propose a novel ranking method to select reliable and worthy results. In our method the coordinated and balanced degree of score and probability is also an evaluation target. After constructing of balance degree, we design the balanced dominating top-k query semantic and effective algorithms to identify the top-k answers. Comprehensive experiments with both real and synthetic data sets demonstrate the effectiveness and efficiency of our proposed approach.
EE-Navi: Energy-Efficient Navigation Service for Electric Vehicle

With the increasing tension of global energy and the situation of worsening ecological environment, the development of electric vehicle, especially efficiency, has become the focus of automobile industries in various countries. However, there is still no energy-efficient navigation service available due to lack of data support. In this paper, we develop a navigation service, called EE-Navi, that finds the most energy-efficient route of electric vehicle. A service framework is designed to map energy consumption of electric vehicles on city streets using driving dataset of electric vehicles and map data so that users find the most energy-efficient route between any two endpoints. The most energy-efficient route does not always coincide with the shortest and fastest routes, which is closely related to road conditions. We collected electric vehicles driving dataset in a Chinese city, Xi'an. The dataset includes more than 30,000 electric vehicles with a data volume of 1TB. We adopted a novel method of power consumption prediction to derive the energy consumption regression model directly from the physical model, which is more professional. If the most energy-saving route recommended by the EE-Navi service is used, an average 7% savings in energy is achieved from the experiment.
Filtering STARR-Seq Peaks for Enhancers with Sequence Models

STARR-Seq is a high-throughput technique for directly identifying genomic regions with enhancer activity [1]. Genomic DNA is sheared, inserted into artificial plasmids designed so that DNA with enhancer activity trigger self-transcription, and transfected into culture cells. The resulting RNA is converted back into cDNA, sequenced, and aligned to a reference genome. "Peaks" are called by comparing observed read depth at each point to an expected read depth from control DNA using a statistical test. Examples of peak calling methods based on read depth include MACS2 [4], basicSTARRSeq, and STARRPeaker [3]. It is challenging to accurately distinguish between real peaks and artifacts in regions where mean read depth is low but the variance is high. Fortunately, enhancer activity is strongly correlated with sequence content. We propose using sequence-based machine learning models in a semi-supervised framework to filter peaks. 501-bp sequences centered on the ≈11k STARR peaks from [1] were extracted from the Drosophila melanogaster dm3 genome. Randomly-sampled 501-bp sequences were used as a negative set. Peaks were filtered using a Bonferroni-corrected significance value (α = 0.05) to create a "high-confidence" subset of ≈2.2k peaks. A Logistic Regression model with k-mer count features was trained on the high-confidence peak sequences and their negatives and used to classifying the remaining ≈8.8k peak sequences. The self-trained, sequenced-based model identified an additional ≈3.7k candidate enhancers ("medium confidence"). The remaining ≈5k STARR peaks were considered "low confidence" peaks. We plotted histograms of the read depth log-fold change for the three sets of peaks (high, medium, and low confidence) (see Figure 1). The distributions for the medium- and low-confidence peaks overlapped significantly. The sequence-based model identified enhancer candidates that would otherwise be filtered out using read depth alone. We called peaks for the 4 D. melanogaster FAIRE-Seq data sets from [2]. Sequencing data were cleaned with Trimmomatic, aligned to the dm3 genome with bwa backtrack, and filtered for mapping quality (q < 10) with samtools. MACS2 called ≈61k FAIRE peaks. The STARR peaks overlapped with the FAIRE peaks with precisions of 52.7% (high-confidence peaks), 40.6% (medium-confidence peaks), and 22.5% (low-confidence peaks).
OpenCollBench - Benchmarking of Collision Detection & Proximity Queries as a Web-Service

We present a server-based benchmark that enables a fair analysis of different collision detection & proximity query algorithms. A simple yet interactive web interface allows both expert and non-expert users to easily evaluate different collision detection algorithms’ performance in standardized or optionally user-definable scenarios and identify possible bottlenecks. In contrast to typically used simple charts or histograms to show the results, we additionally propose a heatmap visualization directly on the benchmarked objects that allows the identification of critical regions on a sub-object level. An anonymous login system, in combination with a server-side scheduling algorithm, guarantees security as well as the reproducibility and comparability of the results. This makes our benchmark useful for end-users who want to choose the optimal collision detection method or optimize their objects with respect to collision detection but also for researchers who want to compare their new algorithms with existing solutions.
First Report on Molecular Characterization of Taenia multiceps Isolates From Sheep and Goats in Faisalabad, Pakistan

Coenurus cerebralis is the larval stage of Taenia multiceps commonly found in the brain (cerebral form), intramuscular and subcutaneous tissues (non-cerebral form) of ungulates. Globally, few reports exist on the molecular characterization and genetic diversity of C. cerebralis with none available for Pakistan. The current study molecularly characterized 12 C. cerebralis isolates surgically recovered from sheep (n = 4) and goats (n = 8) from a total of 3,040 small ruminants using a portion of the cytochrome c oxidase subunit 1 (cox1) mitochondrial (mt) gene. NCBI BLAST search confirmed the identity of each isolate. A high haplotype and a low nucleotide diversity with three haplotypes from the 12 isolates were observed. The findings suggest the existence of unique haplotypes of C. cerebralis in Pakistan. The negative value of Tajima's D and the positive value of Fu's Fs were inconsistent with population expansion, however, the sample size was small. Bayesian phylogeny revealed that all Pakistani isolates alongside the Chinese sequences (obtained from GenBank) constituted a cluster while sequences from other regions constituted another cluster. This is the first molecular study to determine the genetic diversity of C. cerebralis in Pakistan and serves as a foundation for prospective studies on the prevalence and population structure of C. cerebralis in the country. Furthermore, in this study, we amplified only a partial segment of the cox1 gene from a limited sample size. This could have implications on the interpretation of the actual population structure in reality. Thus, we recommend future studies to consider a larger sample size in a massive epidemiological survey for further insights.
Non-structural Carbohydrate Metabolism in the Flesh of Stone Fruits of the Genus Prunus (Rosaceae) – A Review

Non-structural carbohydrates are abundant constituents of the ripe flesh of all stone fruits. The bulk of their content comprises sucrose, glucose, fructose and sorbitol. However, the abundance of each of these carbohydrates in the flesh differs between species, and also with its stage of development. In this article the import, subcellular compartmentation, contents, metabolism and functions of non-structural carbohydrates in the flesh of commercially cultivated stone fruits of the family Rosaceae are reviewed.
Efficiently mining rich subgraphs from vertex-attributed graphs

With the rapid collection of large network data such as biological networks and social networks, it has become very important to develop efficient techniques for network analysis. In many domains, additional attribute data can be associated with entities and relationships in the network, where the network data represents relationships among entities in the network and the attribute data represents various characteristics of the corresponding entities and relationships in the network. Simultaneous analysis of both network and attribute data results in detection of subnetworks that are contextually meaningful. We propose an efficient algorithm for enumerating all connected vertex sets in an undirected graph. Extending this enumeration approach, an algorithm for enumerating all maximal cohesive connected vertex sets in a vertex-attributed graph is proposed. To prune search branches that will not yield maximal patterns, we also present three pruning techniques for efficient enumeration of the maximal cohesive connected vertex sets. Our comparative runtime analyses show the efficiency and effectiveness of our proposed approaches. Gene set enrichment analysis shows that protein-protein interaction subnetworks with similar cancer dysregulation attributes are biologically significant. Availability: The implementation of the algorithm is available at http://www.cs. ndsu.nodak. edu/~ssalem/richsubgraphs.html
Functional Analysis of the Rice Type-B Response Regulator RR22

The phytohormone cytokinin plays a critical role in regulating growth and development throughout the life cycle of the plant. The primary transcriptional response to cytokinin is mediated by the action of the type-B response regulators (RRs), with much of our understanding for their functional roles being derived from studies in the dicot Arabidopsis. To examine the roles played by type-B RRs in a monocot, we employed gain-of-function and loss-of-function mutations to characterize RR22 function in rice. Ectopic overexpression of RR22 in rice results in an enhanced cytokinin response based on molecular and physiological assays. Phenotypes associated with enhanced activity of RR22 include effects on leaf and root growth, inflorescence architecture, and trichome formation. Analysis of four Tos17 insertion alleles of RR22 revealed effects on inflorescence architecture, trichomes, and development of the stigma brush involved in pollen capture. Both loss‐ and gain-of-function RR22 alleles affected the number of leaf silica-cell files, which provide mechanical stability and improve resistance to pathogens. Taken together, these results indicate that a delicate balance of cytokinin transcriptional activity is necessary for optimal growth and development in rice.
The Role of Properdin in Killing of Non-Pathogenic Leptospira biflexa

Properdin (P) is a positive regulatory protein that stabilizes the C3 convertase and C5 convertase of the complement alternative pathway (AP). Several studies have suggested that properdin can bind directly to the surface of certain pathogens regardless of the presence of C3bBb. Saprophytic Leptospira are susceptible to complement-mediated killing, but the interaction of properdin with Leptospira spp. has not been evaluated so far. In this work, we demonstrate that properdin present in normal human serum, purified properdin, as well as properdin oligomers P2, P3, and P4, interact with Leptospira. Properdin can bind directly to the bacterial surface even in the absence of C3b. In line with our previous findings, AP activation was shown to be important for killing non-pathogenic L. biflexa, and properdin plays a key role in this process since this microorganism survives in P-depleted human serum and the addition of purified properdin to P-depleted human serum decreases the number of viable leptospires. A panel of pathogenic L. interrogans recombinant proteins was used to identify putative properdin targets. Lsa30, an outer membrane protein from L. interrogans, binds to unfractionated properdin and to a lesser extent to P2-P4 properdin oligomers. In conclusion, properdin plays an important role in limiting bacterial proliferation of non-pathogenic Leptospira species. Once bound to the leptospiral surface, this positive complement regulatory protein of the AP contributes to the formation of the C3 convertase on the leptospire surface even in the absence of prior addition of C3b.