Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization of Coat Protein Gene of Cucumber Mosaic Virus Isolates in Iran

Background: Cucumber mosaic virus (CMV) from the Bromoviridae family, is one of the most widespread plant viruses in the world. Objectives: In the present study tomato fields in Guilan, Isfahan, Khorasan Razavi, Khuzestan and Tehran provinces were surveyed to determine the presence of CMV subgroups during 2011-2012. Materials and Methods: Out of 305 symptomatic leaf samples tested by Enzyme-linked immuno sorbent assay (ELISA), 147 samples (48.2 %) were found to be infected by CMV with the highest percentage in Khorasan Razavi (67.4%) followed by Khuzestan (50.6%), Tehran (48%), Isfahan (38.2%) and Guilan (34.3%). The coat protein (CP) gene in the 19 sequenced CMV isolates composed of 657 nucleotides (nt) in a size that encodes 218 amino acids. Phylogenetic analysis based on the nt CP gene showed that the ToKz1, ToKz2, ToKz3 and ToKz4 from Khuzestan fell into subgroup IB and the rest of the Iranian isolates including those sequenced in this study fell into subgroup IA. Results: Subsequent analyses showed that the Iranian CMV isolates belonging to subgroup IA of CMV were most related phylogenetically to each other and they were distinct from the subgroup IB and subgroup II isolates. Bioassay on Nicotiana glutinosa and Solanum lycopersicum showed that the symptoms caused by subgroup IB isolates from Khuzestan were milder than those caused by CMV isolates from subgroup IA under this study. Conclusions: In Iran only subgroups IA and II have been reported, however for the first time this study shows the occurrence and phylogenetic relationships of CMV subgroup IB isolated from tomato fields in West Asia, Iran.
Relative Contributions of Sampling Error in Initial Population Size and Vital Rates to Outcomes of Population Viability Analysis

Abstract: We evaluated the relative contributions of sampling error (randomly chosen standard errors applied as 0–30% of parameter estimates) in initial population size and vital rates (survival and reproduction) to the outcome of a simulated population viability analysis for grizzly bears ( Ursus arctos). Error in initial population size accounted for the largest source of variation (model II analysis of variance, F 25,5= 10.8, p =0.00001) in simulation outcomes, explaining 60.5% of the variance. In contrast, error in vital rates contributed little to simulation outcomes (F 25,5= 0.61, p =0.70), accounting for only 2.4% of model variation. Reduced global variation in vital rates, as a result of independent random sampling of annual deviates for each parameter, likely contributed to the results. Errors in estimates of initial population size, if ignored in PVA, have the potential to leave managers with estimates of population persistence that are of little value for making management decisions.
Accelerating Itemset Sampling using Satisfiability Constraints on FPGA

Finding recurrent patterns within a data stream is important for fields as diverse as cybersecurity or e-commerce. This requires to use pattern mining techniques. However, pattern mining suffers from two issues. The first one, known as "pattern explosion", comes from the large combinatorial space explored and is the result of too many patterns outputed to be analyzed. Recent techniques called output space sampling solve this problem by outputing only a sampled set of all the results, with a target size provided by the user. The second issue is that most algorithms are designed to operate on static datasets or low throughput streams. In this paper, we propose a contribution to tackle both issues, by designing an FPGA accelerator for pattern mining with output space sampling. We show that our accelerator can outperform a state-of-the-art implementation on a server class CPU using a modest FPGA product.
Development of Molecularly Imprinted Polymers - from Environmental Sensors to Biotechnology Applications

Many researchers have concentrated on the development of new methods for rapid determination of endocrine disrupting chemicals (EDCs) that are widely detected in environmental waters. Exposure to EDCs is associated with an earlier onset of puberty, decreased fecundity or fertility, altered sexual behavior, and increased incidence of abnormalities or cancers of the reproductive tract in humans [1]. The findings of a new study suggest that some endocrine disruptors may play a role in the obesity epidemic [2]. Direct determination of EDCs in water remains a challenging problem due to their low concentrations (ng/L to μg/L), which is further complicated by the presence of numerous other compounds (pharmaceuticals, personal care products, detergents and natural organic matter). Such matrix effects are formidable even when sophisticated instrumental techniques are used [3,4].
Recommending Remedial Readings Using Student's Knowledge state

Assessment plays a vital role in learning, as it provides both instructors and students with feedback on the overall effectiveness of their teaching or learning. However, when a student fails to correctly answer certain questions in an assessment (such as a quiz), the student needs speciﬁc recommendations that are tailored to their learning needs and to the knowledge deﬁciency exposed by the assessment outcomes. In this paper, we explore the methods for automatically identifying the recommended textbook materials that are most relevant and suitable to the student. In particular, we conducted experiments on how to incorporate students’ current knowledge state on domain concepts associated with the activity to recommend personalized remedial sections to each student. The results show that incorporating student knowledge states can signiﬁcantly improve the quality of recommendations as compared to traditional content-based recommendations.
Pans

Closed desert depressions, called pans, have an extensive distribution in the world's dry lands, particularly on certain types of susceptible surface, which include shales, fine-grained sandstones and unconsolidated materials. They also occur in certain characteristic geomorphological situations, including interdunal areas, old river channels, and deflated lake beds. Pans often, but not invariably, have lunette dunes on their lee sides. Aridity is an important predisposing factor for pan development, and permits deflation, salt weathering, and disruption of drainage systems, all of which contribute to pan formation. Large numbers of hypotheses for pan development have been proposed, including animal activities and solution, but all these can be incorporated in a general model of pan development.
F97-C5 Page -1- National Textile Center Annual Report: November 1999 F97-C05 1 DEVELOPMENT AND EXPERIMENTAL EVALUATION OF NONLINEAR PHENOMENA IN HIGH-SPEED YARN TRANSPORT SYSTEMS F97-C5 Principal Investigators

During the last year of this project, our interdisciplinary, international group of researchers developed new models for several complex yarn manufacturing processes and new, modelverifying experiments. At North Carolina State University Subhash Batra, Tushar Ghosh, Bill Oxenham, and their students refined models for the unwinding process and conducted ring-spinning experiments. Barrie Fraser at the University of Sydney in Australia developed twist models for ring-spinning and two-plying operations. Under the guidance of Bhuvenesh Goswami (Textiles) and Chris Rahn (Mechanical Engineering), graduate students at Clemson University refined the Unwinding Analyzer for experimental measurement of high-speed video and tension data.
The migration of humpback whales along the northeast coast of Newfoundland

Humpback whales migrate along the northeast coast of Newfoundland in summer. Individuals were identified which moved from St. Vincent's on the south coast to Bonavista Bay on the northeast coast, and from there to southern Labrador. The mean migration rate northward along the northeast coast in summer was estimated to be 1.2° latitude per month, although animals moved through a study area at the end of the Bay de Verde Peninsula at about twice this rate. The animals arriving at Bay de Verde in early July, when humpback densities were higher, were on average larger than those arriving either earlier or later. Over periods of more than 1 day there was no evidence for consistent companionships (other than mothers with their 1st-year calves), and only one animal appeared to have a preferred range within the Bay de Verde study area, although some whales preferentially returned to certain stretches of the coastline in different years.
Optimal distribution system restoration using PHEVs

Power outages cost billions of dollars every year and jeopardise the lives of hospital patients. Traditionally, power distribution system takes a long time to recover after a major blackout, due to its top-down operation strategy. New technologies in modern distribution systems bring opportunities and challenges to distribution system restoration. As fast response energy resources, plug-in hybrid electric vehicles (PHEVs) can accelerate the load pickup by compensating the imbalance between available generation and distribution system load. This study provides a bottom-up restoration strategy to use PHEVs for reliable load pickup and faster restoration process. The optimisation problem of finding load pickup sequence to maximise restored energy is formulated as a mixed integer linear programming (MILP) problem. Moreover, the coordination between transmission and distribution restoration is developed to efficiently restore the entire system back to normal operating conditions. Simulation results on one 100-feeder test system demonstrate the efficiency of MILP-based restoration strategy and the benefit from PHEVs to restore more energy in given restoration time. The proposed restoration strategy has great potential to facilitate system operators to achieve efficient system restoration plans. It also provides incentives to deploy a large amount of PHEVs to improve system resiliency.
TourSense: A Framework for Tourist Identification and Analytics Using Transport Data

We advocate for and present TourSense, a framework for tourist identification and preference analytics using city-scale transport data (bus, subway, etc.). Our work is motivated by the observed limitations of utilizing traditional data sources (e.g., social media data and survey data) that commonly suffer from the limited coverage of tourist population and unpredictable information delay. TourSense demonstrates how the transport data can overcome these limitations and provide better insights for different stakeholders, typically including tour agencies, transport operators, and tourists themselves. Specifically, we first propose a graph-based iterative propagation learning algorithm to recognize tourists from public commuters. Taking advantage of the trace data from the identified tourists, we then design a tourist preference analytics model to learn and predict their next tour, where an interactive user interface is implemented to ease the information access and gain the insights from the analytics results. Experiments with real-world datasets (from over 5.1 million commuters and their 462 million trips) show the promise and effectiveness of the proposed framework: the Macro and Micro F1 scores of the tourist identification system achieve 0.8549 and 0.7154, respectively, whereas the tourist preference analytics system improves the baselines by at least 23.53 and 11.44 percent in terms of precision and recall.
On the phylogeny and evolutionary history of pterosaurs

Abstract Previous cladistic studies of pterosaur relationships suffer from restricted numbers of taxa and characters, incomplete data sets and absence of information on characters, tree structure and the robustness of tress. Parsimony analysis of a new character data set (60 characters, 20 terminal taxa, 97.75% complete) yielded six trees. In the strict consensus tree Preondactylus is the most basal taxon followed, stepwise, by the Dimorphodontidae and the Anurognathidae. Beyond this basal group, more derived pterosaurs (Campylognathoididae (Rhamphorhynchidae + Pterodactyloidea)) share a suite of characters principally associated with elongation of the rostrum. The Pterodactyloidea consists of four major clades. The Ornithocheiroidea is the most basal taxon consisting, stepwise, of Istiodactylus, the Ornithocheiridae, Nyctosaurus and the Pteranodontidae. The remaining taxa, Ctenochasmatoidea, Dsungaripteroidea and Azhdarchoidea, are weakly united in a clade of non-ornithocheiroid pterodactyloids, but their inter-relationships are difficult to resolve. Cycnorhamphus is the basal-most ctenochasmatoid, while the remaining taxa (Pterodactylus, Lonchodectidae, Ctenochasmatidae) form an unresolved trichotomy. The Dsungaripteroidea (Germanodactylus+Dsungaripteridae) is strongly supported by features of the skull and dentition. The Azhdarchoidea (Tapejara [Tupuxuara + Azhdarchidae]) is united by cranial characters such as elevation of the antorbital region, and relative shortening of the wing finger. The pattern of pterosaur evolution suggested by the results of this analysis is broadly similar to traditional ideas, but has greater resolution, more complexity and reveals several previously unrecognized ‘events’.
Understanding and Improving the Performance of Constructive Interference Using Destructive Interference in WSNs

The constructive interference (CI) phenomenon has been exploited by a number of protocols for providing energy-efficient, low-latency, and reliable data collection and dissemination services in wireless sensor networks. These protocols consider CI to provide highly reliable packet delivery. This has attracted attention to understand the working of CI; however, the existing works present inconsistent views. Furthermore, these works do not study in the real-world settings where the physical conditions of deployment and unreliable wireless channels also impact the performance of CI. Therefore, we study the phenomenon of CI, considering a receiver’s viewpoint and analyze the parameters that affect CI. We validate our arguments with results from extensive and rigorous experimentation in real-world settings. This paper presents comprehensive insights into the CI phenomenon. With the understanding, we develop the destructive interference-based power adaptation (DIPA), an energy-efficient and distributed algorithm, that adapts transmission power to improve the performance of CI. Since CI-based protocols cannot have an explicit acknowledgment packet, we make use of destructive interference on a designated byte to provide a feedback. We leverage this feedback to adapt transmission powers. We compared CI with and without DIPA in two real-life testbeds. On one testbed, we achieve around 25% lower packet losses while using only half of its transmission power for 64-B packets. On the other testbed, we achieve 25% lower packet losses while consuming only 47% of its transmission power for 128-B packets. Existing CI-based protocols can easily incorporate DIPA into them to achieve lower packet losses and higher energy efficiencies.
Regional-scale patterns and predictors of species richness and abundance across twelve major tropical inter-reef taxa

Species richness and abundance are biodiversity metrics widely used to describe and estimate changes in biodiversity. Studies of marine species richness and abundance typically focus on one, or just a few, taxa. Consequently, it is currently not possible to understand the performance of predictors of species richness and abundance across marine taxa. Using a taxonomically comprehensive dataset of twelve major taxa of flora and fauna from eight phyla sampled from the inter-reef seabed region of the Great Barrier Reef, Australia, we used boosted regression trees to test the performance of fourteen environmental and spatial predictors of species richness and abundance. Sediment composition predicted richness best for all taxa: gravel contributed up to 39% relative influence for one group and all taxa had low richness in muddy habitats. Sea surface temperature, seabed current shear stress, depth and latitude were also influential predictors for species richness for eight groups. Sediment was frequently an influential predictor for abundance also, while distance to domain (reef/coast) and longitude were relatively influential for six taxa. Within-site richness was correlated between nearly all pairs of taxa, as was within-site abundance, however ρ values were low. Overall, model performance was high, explaining up to 62% deviance of species richness, and 38% of abundance. Typically, deviance explained was greater for richness than abundance and may indicate that some drivers of species richness operate independently of any effects on species richness mediated by their effect on abundance. Deviance explained differed most between richness and abundance for bryozoans (23.3% difference) and soft corals (15.2% difference). While sediments were consistently the best predictors across all taxa, the inconsistent influence of all other predictors across taxonomic groups, as well as the low correlation of richness and abundance across taxonomic groups, cautions against predicting regional patterns of species richness and abundance from few taxa.
Influence of Coastal Proximity on Evapotranspiration Rates and Crop Coefficients of Maine Lowbush Blueberry (Vaccinium angustifolium)

Supplemental irrigation can be a viable means of increasing lowbush blueberry (Vaccinium angustifolium) crop yields in humid environments. To best utilize this management technique, Maine growers require accurate knowledge of regional crop water demands. Meteorological data and weighing lysimeters were used to determine Penman–Montieth grass reference evapotranspiration (ETo) rates and actual evapotranspiration rates of the lowbush blueberry at five irrigated and two nonirrigated fields of varying distance from the Maine coast. Mean daily values of air temperature, relative humidity range, and solar flux were all found to increase with increasing distance from the coast. The frequency of growing season fog hours increased from 3.7% of all hours at a distance of 22.5 km from the coast, to 11.1% of all hours at a distance of 0.8 km from the coast. Water equivalent inputs from fog per growing season at these two sites averaged 7.2 cm and 18.3 cm, respectively. These geographic climatic differences were reflected in the calculated evapotranspirationo rates, which also generally increased with distance from the coast. Four-year mean weekly ETo rates for July, the month of greatest potential water demand, were 2.76 cm/week, 3.21 cm/week, 3.52 cm/week, and 4.23 cm/week for sites located 0.8 km, 6.4 km, 22.5 km, and 48 km, respectively, from the coast. Actual ET rates for both crop and prune rotations followed a similar pattern of increasing magnitude with distance from the coast. For July, mean crop and prune year pooled weekly ET rates at 0.8 km, 6.4 km, and 22.5 km from the coast were: 1.69 cm/week (0.85), 2.28 cm/week (0.73), and 2.40 cm/week (0.83), respectively. Standard condition crop coefficient (Kc) values for both rotations during the months of May through September were calculated to be 0.68 for sites 6.4 and 22.5 km from the coast. Thus, it would appear that the same Kc value can be used universally within the typical growing range of the Maine lowbush blueberry. These findings should substantially contribute to improved water use efficiency for the irrigated lowbush blueberry in Maine.
Multiresolution analysis of ranking data

This thesis introduces a multiresolution analysis framework for ranking data. Initiated in the 18th century in the context of elections, the analysis of ranking data has attracted a major interest in many fields of the scientific literature : psychometry, statistics, economics, operations research, machine learning or computational social choice among others. It has been even more revitalized by modern applications such as recommender systems, where the goal is to infer users preferences in order to make them the best personalized suggestions. In these settings, users express their preferences only on small and varying subsets of a large catalog of items. The analysis of such incomplete rankings poses however both a great statistical and computational challenge, leading industrial actors to use methods that only exploit a fraction of available information. This thesis introduces a new representation for the data, which by construction overcomes the two aforementioned challenges. Though it relies on results from combinatorics and algebraic topology, it shares several analogies with multiresolution analysis, offering a natural and efficient framework for the analysis of incomplete rankings. As it does not involve any assumption on the data, it already leads to overperforming estimators in small-scale settings and can be combined with many regularization procedures for large-scale settings. For all those reasons, we believe that this multiresolution representation paves the way for a wide range of future developments and applications
Comparison of Green and White Mesh Trammel Nets and Gill Nets to Assess the Fish Community in a Large River

Abstract Standardized monitoring programs in great rivers need to identify and minimize the bias in the estimates of fish population characteristics to enable fishery managers to make informed decisions. We compared the effectiveness of green and white mesh in drifted trammel and anchored gill nets in capturing fish in the Missouri River from downstream of Fort Randall Dam, South Dakota, to the mouth of the river near St. Louis, Missouri. Sampling occurred from March to November 2006 and from April to May 2007. Paired green and white trammel net drifts (N = 383) caught 28 fish species from 12 families. Pairs of anchored gill nets set overnight (N = 193) caught 24 fish species from 12 families. Chi-square tests indicated that for most species there were no significant differences in occurrence between mesh colors in both the trammel and gill nets. However, occurrence was significantly higher in white mesh nets for goldeye Hiodon alosoides and blue sucker Cycleptus elongatus in trammel nets and for river ca.
The Structure and Formation of Lomasomes

SUMMARY: Saprolegnia ferax and Dictyuchus sterile contain lomasomes and plasma-lemmasomes whose structure and distribution are described. It is suggested that plasmalemmasomes are produced when plasmalemma production is not balanced by cell expansion. When plasmalemmasomes become sequestered in developing cell walls they may be termed lomasomes.
Yolk mass variation in White-Rumped Swallows (Tachycineta leucorrhoa)

Abstract Egg mass production is costly, but hatching from heavier eggs could be beneficial for the nestling's phenotype and fitness. Egg mass could be influenced by proximate causes, like food abundance, female condition, environmental conditions, and/or by ultimate causes, such as females depositing resources differentially within a clutch to increase the biological fitness of some eggs. Yolk mass, although poorly studied, is the source of nutrients for the embryo, so its mass should be more influential for the nestlings than total egg mass. We used a technique that allowed us to measure yolk size without destroying the eggs. We studied yolk mass in 212 eggs of White-rumped Swallows (Tachycineta leucorrhoa) and found that yolk mass was influenced by laying order, with last laid eggs having heavier yolks than first laid eggs, and the pattern was consistent with egg mass variation. Food abundance also affected yolk mass: when insect availability was high the yolks were heavier. We conclude that embryos in the last laid eggs have more resources from which to develop, and excluding food abundance, neither environmental conditions nor female's condition affected yolk mass. We encourage other researchers to study yolk mass given that multiple variables affected total egg mass.