Miracle-dz/UniFAR-FTUs huggingface dataset:
Association Rules to Identify Factors Affecting Risk and Severity of Road Accidents

The current increase in automobilization leads to a decrease in road safety. Therefore, the purpose of this research is to analyze and identify the causes that significantly affect the risk and severity of accidents. For this purpose, both histograms plotting and association rules were used. The statistics of traffic accidents in Elabuga town for 2017-2018 were taken as initial information. To identify the most traumatic types of traffic accidents, graphs of the number of accidents by types of accidents and the number of victims for 2017-2018 were constructed. It was found that the greatest number of injured (wounded or dead) is observed in collisions and hitting a pedestrian. Then, road sections of the concentration of accidents were analyzed, the most common types of accidents and the main violations of traffic rules that contribute to the occurrence of accidents were determined. To identify hidden relationships between factors and accidents with severe consequences, association rules were applied. As a result, the influence of weather conditions, quality of road infrastructure and marking was established.
Substrate Plasticity of a Fungal Peptide α-N-Methyltransferase

The methylation of amide nitrogen atoms can improve the stability, oral availability, and cell permeability of peptide therapeutics. Chemical N-methylation of peptides is challenging. Omphalotin A is a ribosomally synthesized, macrocylic dodecapeptide with nine backbone N-methylations. The fungal natural product is derived from the precursor protein, OphMA, harboring both the core peptide and a SAM-dependent peptide α-N-methyltransferase domain. OphMA forms a homodimer and its α-N-methyltransferase domain installs the methyl groups in trans on the hydrophobic core dodecapeptide and some additional C-terminal residues of the protomers. These post-translational backbone N-methylations occur in a processive manner from the N- to the C-terminus of the peptide substrate. We demonstrate that OphMA can methylate polar, aromatic, and charged residues when these are introduced into the core peptide. Some of these amino acids alter the efficiency and pattern of methylation. Proline, depending on its sequence context, can act as a tunable stop signal. Crystal structures of OphMA variants have allowed rationalization of these observations. Our results hint at the potential to control this fungal α-N-methyltransferase for biotechnological applications.
Polymorphic microsatellite loci for the rice stem borer, Chilo suppressalis (Walker) (Lepidoptera: Crambidae)

Four microsatellite loci were isolated and characterized from the rice stem borer moth Chilo suppressalis, which is an important insect pest of rice plants in Japan. These loci were not in linkage disequilibrium; however, in two of four loci, the observed heterozygosities were significantly less than expected, possibly due to the effect of null alleles. Although caution should be taken in analysis, these loci were sufficiently polymorphic to be used for population genetic analysis of this species.
Weed Detection and Removal based on Image Processing

Agriculture, although known as the backbone of the Indian economy, is facing crisisin terms of production. One of the major issues in the agriculture sector is the growth of weeds among the crops. They compete with the desired plants for various resources and hence their growth must be inhibited. At present weeds are removed either manually, which is a time consuming and labour intensive task, or herbicides are being sprayed uniformly all over the field to keep them under check. Spraying of herbicide is very inefficient as the chemical contributes less to weed control and cause contamination of the environment. The main objective of this work is a weed control system that differentiates the weed from crops and restricts weed growth alone by the precise removal of the weed. This is implemented by capturing the images of the field at regular intervals and processing them with a Raspberry Pi board by making use of an image processing algorithm to differentiate the desired plants from the weeds. This is based on various features like colour and size of the crop and weed. Once the weeds are identified and located correctly through image processing, a signal is transmitted from the Raspberry Pi board to turn on the weed cutting system. The selective activation of the weed removal system helps in the precise removal of the weeds and this provides a better environment for the desired plants to grow well.
Evidence for a gamma-interferon receptor that regulates macrophage tumoricidal activity

Gamma-interferon (IFN-gamma) is the macrophage-activating factor (MAF) produced by normal murine splenic cells and the murine T cell hybridoma 24/G1 that induces nonspecific tumoricidal activity in macrophages. Incubation of 24/G1 supernatants diluted to 8.3 IRU IFN-gamma/ml with 6 X 10(6) elicited peritoneal macrophages or bone marrow-derived macrophages for 4 h at 37 degrees C, resulted in removal of 80% of the MAF activity from the lymphokine preparation. Loss of activity appeared to result from absorption and not consumption because (a) 40% of the activity was removed after exposure to macrophage for 30 min at 4 degrees C, (b) no reduction of MAF activity was detected when the 24/G1 supernatant was incubated with macrophage culture supernatants, and (c) macrophage-treated supernatants showed a selective loss of MAF activity but not interleukin 2 (IL-2) activity. Absorption was dependent on the input of either IFN-gamma or macrophages and was time dependent at 37 degrees C but not at 4 degrees C. With four rodent species tested, absorption of murine IFN-gamma displayed species specificity. However, cultured human peripheral blood monocytes and the human histiocytic lymphoma cell line U937 were able to absorb the murine lymphokine. Although the majority of murine cell lines tested absorbed 24/G1 MAF activity, two murine macrophage cell lines, P388D1 and J774, were identified which absorbed significantly reduced amounts of natural IFN- gamma. Purified murine recombinant IFN-gamma was absorbed by elicited macrophages but not by P388D1. Normal macrophages but not P388D1 bound fluoresceinated microspheres coated with recombinant IFN-gamma and binding was inhibited by pretreatment of the normal cells with 24/G1 supernatants. Scatchard plot analysis showed that 12,000 molecules of soluble 125I-recombinant IFN-gamma bound per bone marrow macrophage with a Ka of 0.9 X 10(8) M-1. Binding was quantitatively inhibitable by natural IFN-gamma but not by murine IFN alpha. IFN-beta competed only weakly. Monoclonal antibodies against IFN-gamma either inhibited or enhanced MAF activity by blocking or increasing IFN-gamma binding to macrophages, respectively. These results indicate that IFN-gamma reacts with a receptor on macrophage in a specific and saturable manner and this interaction initiates macrophage activation.
Crisis Management Systems: Big Data and Machine Learning Approach

A crisis is defined as an event that, by its nature or its consequences, poses a threat to the vital national interests or the basic needs of the population, encourages rapid decision making, and requires coordination between the various departments and agencies. Hence the need and importance of crisis and disaster management systems. These crisis and disaster management systems have several phases and techniques, and require many resources and tactics and needs. Among the needs of these systems are useful and necessary information that can be used to improve the making of good decisions, such as data on past and current crises. The application of machine learning and big data technologies in data processing of crises and disasters can yield important results in this area. In this document, we address in the first part the crisis management systems, and the tools of big data and machine learning that can be used. Then in the second part, we have established a literature review that includes a state of the art, and a discussion. Then we established a machine learning and big data approach for crisis management systems, with a description and experimentation, as well as a discussion of results and
Invasion ratcheting in the zebra mussel (Dreissena polymorpha) and the ability of native and invaded ranges to predict its global distribution

In this study, we investigate changes in the environmental niche of an important pest species, the zebra mussel (Dreissena polymorpha), across its world‐wide distribution. The ability of models based on the native (Ponto‐Caspian) and invaded (Western European and North American) ranges to accurately model the potential distribution of zebra mussel elsewhere was also investigated.
Multi Software Product Lines: A Systematic Mapping Study

Even if Software Product Line (SPL) is an established technique in software engineering, there are several limitations of its use. This is mainly caused by the exponential increase of software systems complexity and by the high pace of software and hardware evolution. Many of these limitations have been studied and faced by applying the Multi Software Product Line (MSPL), an extension of the SPL. MSPL is an emerging and novel technique based on using more than one SPL to derive a functional product system. This paper aims to characterize the state of the art of MSPL, the main goal is to highlight the reached achievement in this field and then discuss about the open issues or the not covered aspects of this approach. In order to make an overall analysis inside the research community, a well-defined method of systematic mapping is applied in order to classify, in a proper scheme, every paper strictly related to this topic. These classified results could give a valid hint about lacks which should be investigated further.
Uncertainty Quantification in Multimodal Ensembles of Deep Learners

Uncertainty quantiﬁcation in deep learning is an active area of research that examines two primary types of uncertainty in deep learning: epistemic uncertainty and aleatoric uncertainty. Epistemic uncertainty is caused by not having enough data to adequately learn. This creates volatility in the parameters and predictions and causes uncertainty. High epistemic uncertainty can indicate that the model’s prediction is based on a pattern with which is it not familiar. Aleatoric uncertainty measures the uncertainty due to noise in the data. Two additional active areas of research are multimodal learning and malware analysis. Multimodal learning takes into consideration distinct expressions of features such as different representations (e.g., audio and visual data) or different sampling techniques. Multimodal learning has recently been used in malware analysis to combine multiple types of features. In this work, we present and analyze a novel technique to measure epistemic uncertainty from deep ensembles of modalities. Our results suggest that deep ensembles of modalities provide higher accuracy and lower uncertainty that the constituent single modalities and than the comparable hierarchical multimodal deep learner.
Modeling Procedural State Changes over Time with Probabilistic Soft Logic

Robust natural language understanding involves the automatic extraction and representation of entities, events, and states from unstructured text. However, a signiﬁcant portion of the knowledge required for human-level understanding is implicit in the text and can only be accessed through inference. In this work, we employ Probabilistic Soft Logic (PSL) as a framework for leveraging common-sense knowledge to support natural language understanding over procedural texts. Under this framework, we combine logical consistency constraints with succinct representations of common-sense knowledge to probabilistically model entity-centric sta-tive information over time. We demonstrate the feasibility of using PSL to represent procedural stative knowledge through a scalability assessment over an in-house, multi-domain, synthetic dataset.
On discerning the cause of late Pleistocene megafaunal extinctions

I examine the late Pleistocene megafaunal extinctions by testing the only extinction model with strong a priori predictions, the blitzkrieg model (Martin 1973; Mosimann and Martin 1975). I first test an assumption of the blitzkrieg and other extinction models that the megafaunal extinctions occurred in the terminal Wisconsin (12-10 Ka). This assumption has been disputed by Grayson (1989, 1991), but I find that both a reanalysis of Grayson's data and an analysis of new reliable data support a terminal Wisconsin extinction. The blitzkrieg model predicts that the ranges of megafauna in North America were constricted as the semicircular front of hunters moved southeastward; hence the extinctions should be time-transgressive from northwest to southeast. I test this prediction in three separate analyses that examine (1) the location of terminal sites for each taxon relative to all their other late Wisconsin fossil sites, (2) the location of terminal sites for each taxon relative to all their other reliably dated late Wisconsin fossil sites, and (3) the spatio-temporal pattern of all the reliably dated terminal Wisconsin sites without regard to taxonomy. The geographic distribution of the megafaunal remains does not support the blitzkrieg hypothesis in any of the three analyses. Moreover, all of the patterns in the data are in a direction opposite to that predicted by the blitzkrieg hypothesis. I examine how these conclusions affect both climatic and predation models, particularly in relation to the testability of other extinction hypotheses.
Moisture content and nutrition as selection forces for emerald ash borer larval feeding behaviour

1. The exotic phloem‐feeding emerald ash borer (EAB), Agrilus planipennis, has killed tens of millions of North American ash trees (Fraxinus) since its first detection in the U.S.A. in 2002. Ash trees are killed by larval feeding in the cambial region, which disrupts translocation of photosynthates and nutrients.
Isolation and characterization of short tandem repeats in an invasive swimbladder nematode, parasitic in Atlantic freshwater eels, Anguillicola crassus

We describe the isolation and characterization of seven polymorphic short tandem repeats (STR) for the eel parasite Anguillicola crassus . This invasive swimbladder nematode endemic in East Asia was recently introduced into Europe. The number of alleles for each STR ranged from 13 to 39 per locus with observed heterozygosities between 0.49 and 0.98. The Taiwanese population displayed higher genetic diversity compared to the Irish sample, an observation consistent with the Asian biogeographical origin of the nematode. Availability of the reported STR will facilitate the investigation of the population genetic structure with regard to multiple invasions.
INTERACTION BETWEEN A VESICULAR‐ARBUSCULAR MYCORRHIZA AND RHIZOBIUM AND THEIR EFFECTS ON SOYBEAN IN THE FIELD

Summary Interaction between Glomus fasciculatus and Rhizobium japonicum and their effects on soybean in the field was studied in a phosphorus deficient sandy loam soil with pH 5·6. The number, dry weight and nitrogen content of the root nodules in plants inoculated with Glomus plus Rhizobium were significantly more compared to plants inoculated with only Rhizobium. Rhizobium inoculation did not have any significant effect on sporulation of G. fasciculatus in the rhizosphere. Although soybean plants inoculated with G. fasciculatus recorded increased phosphorus content, dry weight and grain yield than uninoculated plants the differences were not statistically significant. In Rhizobium only inoculation markedly increased the nitrogen content of the plant and grain yield. Dual inoculation with both the symbionts increased significantly the dry weight of the shoot and its nitrogen content over single inoculation with either Glomus or Rhizobium. These results suggest that vesicular–arbuscular (VA) mycorrhiza can greatly assist nodulation and nitrogen fixation in field growth soybean inoculated with rhizobia.
Selfie Drones for 3D Modelling, Geological Mapping and Data Collection: Key Examples from Santorini Volcanic Complex, Greece

: In the present work, we tested the use of selfie drones as a tool for 3D modeling, geological mapping, and data collection. The model we used is a 0.300-kg multirotor quadcopter being equipped with a 1/2.3-inch CMOS sensor capable of capturing 12 Megapixel pictures, attached to a 2-axis mechanical gimble and with approximately 16 minutes of flight time. Test sites are located in Santorini and are characterised by different settings: i) the 1570-1573 AD volcanic crater area, in Nea Kameni island, has a mostly horizontal topography; ii) the outcrop along Vlychada beach, showing layers of the Late Bronze Age (also well-known as Minoan) eruption, has mostly vertical topography. By applying the Structure from Motion techniques to pictures collected using the selfie drone, we were capable of: i) reconstructing the two sites with centimetric to sub-centimetric resolution; ii) recognizing geological features on very high-resolution Digital Surface Models and Ortomosaics; iii) mapping vertical cliffs made up of volcanic deposits on 3D Digital Outcrops Models; iv) collect new quantitative data for both sites.
Deployment of Auction Theoretic Models for Spectrum Allocation in Cognitive Radio Networks

Cognitive Radio (CR) is a novel technology which empowers the unlicensed users to opportunistically avail the free channels left unoccupied by the licensed owners. This apparently resolves the spectrum scarcity problem arising in today’s telecommunication industry. In this technology, once the free bands (called spectrum holes) are detected using sensing techniques, next there is a need to efficiently redistribute the idle spectrum. Such an allocation process of CR has attracted several researchers across the world to contribute in this domain. With an initiative to analyze the functionality of spectrum allocation, this paper carries out a survey on the allocation mechanisms which have been applied to use the spectrum holes. We primarily focus our exploration on auction theoretic allocation models due to their efficacy in channel allocation. Auction formulation takes the unlicensed users as bidders where they bid for the spectrum holes as the auctioned item. Winner determination strategy and pricing strategy are two important aspects of an auction model which determines a fair allocation pattern and along with decides the monetary benefit of sellers and auctioneer. We go through a detailed study on single-sided auction and double-sided auction which are deployed with different CR network constraints to design the allocation model. Finally, we conclude the paper by underlining some future research directions in this area.
Molecular evidence for recognizing Antithamnion sparsum (Ceramiales, Rhodophyta) at the species level

S. -R. Lee, I. K. Lee and Y. Suh. 2005. Molecular evidence for recognizing Antithamnion sparsum (Ceramiales, Rhodophyta) at the species level. Phycologia 44: 530–535. The nucleotide sequences of rbcS and Rubisco spacer in the chloroplast genome, and the internal transcribed spacer (ITS) 1 and the small subunit ribosomal DNA in the nuclear genome were determined to clarify the taxonomic relationships of Antithamnion sparsum with its close allies. The species has been often considered to be conspecific with A. densum, but this taxonomic placement still remains to be confirmed. Phylogenetic analyses based on multiple molecular markers clearly distinguished A. sparsum from A. densum. In addition, the Atlantic A. densum from Kerry, Ireland, was clearly separated from the North Pacific A. defectum from San Juan Island, Washington. Comparative analyses of four DNA regions showed that the rbcS region provided the most useful information with the highest resolution and statistical support among the four DNA regions studied. Molecular evidence strongly suggests that A. sparsum should be treated as an independent species.
Promiscuity, male reproductive success and mate relatedness in a natural population of the common vole

Behavioural ecologists have long been interested in mating systems and variance of reproductive success. Highly variable molecular markers now enable researchers to reassess mating systems from the genetic point of view. We used 10 microsatellite loci to detail the mating pattern and male reproductive success in a natural population of the common vole Microtus arvalis, one of the most numerous species in Europe. By genotyping 32 females and their offspring, we found evidence for multiple paternity in 50% of litters sired by two or three males. This result was confirmed by paternity analysis of candidate fathers caught in the population; it also showed that both males and females mated with several unrelated partners. Comparisons of two sires in a given multiple-sire litter showed their relatedness to be low. The common vole population was characterized by a relatively high standardized variance of male reproductive success, indicating that males competed for mating. While one of the males could sire up to 83% of offspring in a multiple-sire litter, mating with an already mated female gave lower reproductive success than mating with one female exclusively. Our results suggest that the occurrence of multiple paternity in the common vole population can be explained by the inability of males to monopolize and mate with all females of a colony, and also by their tendency to increase their reproductive success by getting access to already mated females.
Efficient immobilization of fructose-6-phosphate aldolase in layered double hydroxide: improved stereoselective synthesis of sugar analogues

Fructose-6-phosphate aldolase immobilized on layered double hydroxide (FSA@LDH) has been characterized and evaluated as a biocatalyst in aldol reactions. The versatile FSA@LDH showed a comparable activity to the free FSA and can be reused several times without a notable loss of activity.
Demand Response of Smart Homes Using State Flow Algorithm

Nowadays power demand is increased by consumers. Usually peak demand is present in different seasons. During peak demand frequency drop and voltage dip are occurred. Due to these problems power system may block out and system reliability decreases. Generation demand and load demand should be maintained equally. Generation demand should be increased to maintain equal load demand and generation demand. Generation demand is increased by increasing generating plants. But generating plants cost is very high compared to DSM.Demand side management is the quickest response to reduce peak loads. So the peak demand can be reduced by load management. Demand side management is facilitated to load management by state flow algorithm. State flow algorithm is made by conditions of system voltage and frequency and peak time or off peak time. By using Demand side management, consumers and energy providers get more benefits. DSM used to reduce expensive imports of fuel, energy prices and harmful emissions to the environment pollution are reduced. Simulation is done in MATLAB SIMULINK.
Efficient and Selective Upload of Data from Connected Vehicles

: Vehicles are evolving into a connected sensing platform, generating enormous amounts data about themselves and their surroundings. In this work, we focus on the efﬁcient data collection for connected vehicles, exploit-ing the fact that the context data of cars on the same road is often redundant. This is for instance relevant for applications which need roadside data for map updating. We propose a vehicular data dissemination architecture with a central coordination scheme to avoid redundant uploads. It also uses roadside WiFi hotspots opportunistically. To evaluate the beneﬁts, we use the SUMO simulator to benchmark our results against a baseline solution, showing improvements of factor 10 up to 20.