Miracle-dz/UniFAR-FTUs huggingface dataset:
Nutritional status and physical activity level as risk factor for traumatic dental injuries occurrence: a systematic review

Aim To systematically review epidemiological articles assessing traumatic dental injuries (TDI) rates according to the physical activity habits and nutritional status. Methods A search was conducted using PubMed, ISI, Scopus, SciELO, LILACS, and gray literature in Brazilian Theses Databank. We searched for dental trauma, traumatic dental injuries, tooth injuries, tooth fractures, physical activity, motor activity, exercise, sedentary lifestyle, sports, obesity, body mass index (BMI), overweight, and fatness. Databases were searched in duplicate from their earliest records until 2012. Additional studies were identified by searching bibliographies of the articles. Two reviewers performed data extraction and analyzed study procedural quality using the Newcastle–Ottawa scale. PRISMA guidelines for reporting systematic reviews were followed. Results We found 1159 articles, of whom 14 reports involving 13 studies were selected. One article was a birth cohort, one had a case–control design, and the others were cross-sectional. The quality of evidence varied across the studies and was high (9) in 3. Eleven of the studies included assessed influence of nutritional status: five show a positive association between dental trauma and overweight and six do not show any association. Regarding physical activity level, five studies assessed its effect on trauma occurrence: two detected that physical activity acts as a protective factor and two that physical active increases the risk of dental injuries, and one showed no differences in TDI occurrence. Physical activity estimated from questionnaires and BMI were the most frequently used measures, but methodological differences prevent the comparison of results. Conclusion The results suggest that no truly causal relationship exists between dental trauma and physical activity and nutritional status. Due to the relatively low level of evidence currently present, studies with more robust design, for example, prospective cohort should address this question, especially in view of the epidemic of obesity.
LINK PREDICTION MODEL FOR PAGE RANKING OF BLOGS

Social Network Analysis is mapping and measuring of relationships and flows of information between people, organizations, computers, or other information or knowledge processing entities. Social media systems such as blogs, LinkedIn, you tube are allows users to share content media, etc. Blog is a social network notepad service with consider on user interactions. In this paper study the link prediction and page ranking using MozRank algorithm using blog websites. It finds out how all the websites on the internet link to each other with the largest Link Intelligence database. As link data is also a component of search engine ranking, understanding the link profile of Search Engine positioning. Here the MozRank algorithm is using backlinks from blog websites and linking websites quality. Good websites with many backlinks which linking the corresponding WebPage give highly value of MozRank. MozRank can be improved a web page's by getting lots of links from semi-popular pages or a few links from very popular pages. The algorithm for page ranking must work differently and MozRank is more comprehensive and accurate than Goggle's page rank. Another tool is Open Site Explorer that is ability to compare five URL's against each other. Open Site Explorer's Compare Link Metrics option is how one measures page level metrics, the other domain. This result can help to generate a chart form for the comparative URLs. A comparison chart of the important metrics for these pages is shown which makes it very clear and easy to compare the data between the five URL's.
Study of the spatial distribution of groundwater quality using soft computing and geostatistical models

Abstract Groundwater quality monitoring and simulation is of great importance in arid and semi-arid regions due to water scarcity in such regions. Geostatistical methods are appropriate techniques for interpolating and spatial mapping of groundwater qualitative parameters. In the present study, geostatistic-based Kriging and Co-Kriging methods were used and compared with data-driven artificial neural network (ANN) and ANFIS models for predicting spatial distribution of groundwater electrical conductivity (EC), then the best model was selected for further sampling in the studied region. Data from 24 wells in the Keshit, Bam Normashir, and Rhmtabad plains (Kerman province, Iran) during the period of 2002–2011 were used for analysis. Root mean square error (RMSE), R 2 , and mean absolute error (MAE) statistical indices were used for assessing the applied models. Results showed that for both the models, i.e. triple input model (including longitude, latitude, and the number of months) and the quadruple-input model (including longitude, latitude, number of months, and Cl), ANN model presented the most accurate results with the lowest RMSE and MAE and the highest R 2 values. Attending to the geostatistical methods, Co-Kriging method outperformed the Kriging method. Finally, using the tested geostatistical model, the EC data were produced for the regions without observational values.
Defining the Ichnogenus Arthrophycus Using Numerical Taxonomic Methods

ABSTRACT A century and a half of imprecise or overly brief descriptions and ambiguous or unclear illustrations have rendered the ichnogenus Arthrophycus ripe for reexamination. An abundance of well-preserved, accessible Arthrophycus specimens from museum collections made it possible to use numerical taxonomic methods to distinguish among ichnospecies of Arthrophycus and refine the diagnosis of the ichnogenus using measurable, reproducible characters. We defined 16 characters for the numerical analyses. Cluster analysis sorts the ichnospecies of Arthrophycus into two groups: a cluster of five ichnospecies, comprising A. alleghaniensis, A. brongniartii, A. lateralis, A. minimus, and A. parallelus, which conform to a coherent concept of Arthrophycus, and a second cluster comprising 14 ichnotaxa questionably assigned to Arthrophycus. Principal coordinates analysis supports the separation of these two groups. The numerical analyses provide a rationale for taxonomic revision of the ichnogenus and the ichnospecies that have been assigned to it.
Two-Hop Co-Located Robust Precoding Design in Radio SWIPT Relay Networks

—This paper studies the two-hop co-located transmission architecture that incorporates simultaneous wireless information and power transfer in radio relay networks. Joint robust precoding design of the information and power transfer that can minimize the overall relays’ power consumption while satisfying our predefined quality-of-service and energy-harvest requirements is provided. By using SemiDefinite Relaxation technique, the non-convex initial problem can be converted to be convex. Additionally, the dual problem of the relaxed problem is investigated, and the closed-form optimal solutions of the relaxed problem can be achieved by our proposed method. Finally, approximate solutions show the effectiveness of our proposed robust precoding design.
Design Rules for Selective Binding of Nuclear Localization Signals to Minor Site of Importin α

Selectivity is a critical issue in molecular recognition. However, design rules that underlie selectivity are often not well understood. Here, we studied five classical nuclear localization signals (NLSs) that contain the motif KRx(W/F/Y)xxAF and selectively bind to the minor site of importin α. The selectivity for the minor site is dissected by building structural models for the NLS-importin α complexes and analyzing the positive design and negative design in the NLSs. In our models, the KR residues of the motif occupy the P1’ and P2’ pockets of importin α, respectively, forming hydrogen-bonding and cation-π interactions. The aromatic residue at the P4’ position plays dual roles in the selectivity for the minor site: by forming π-stacking with W357 of importin α to reinforce the minor-site binding; and by clashing with the P5 pocket in the major binding site. The F residue at the P8’ position occupies a deep pocket, providing additional stabilization. The P7’ position sits on a saddle next to the P8’ pocket and hence requires a small residue; the A residue fulfills this requirement. The principal ideas behind these blind predictions turn out to be correct in an evaluation against subsequently available X-ray structures for the NLS-importin α complexes, but some details are incorrect. These results illustrate that the selectivity for the minor site can be achieved via a variety of design rules.
Isolation enhancment of two planar monopole antennas for MIMO wireless applications

This paper presents a planar slotted meander line structure to reduce the mutual coupling between two microstrip fed planar monopole patch antennas. The array provides a relative operating bandwidth of 98 % (1.37-4.00) GHz matched at a reflection coefficient S11 and S22 less than -10 dB with a total realized gain of 3.9, 4.66, and 4 dB at 1.85, 2.4, and 3.5 GHz respectively. The isolation level has been reduced to less than -20 dB for a wide frequency band of 85 % (1.46-3.64) GHz. The design represents a good candidate for modern wireless standards utilizing MIMO techniques including 3GPP LTE, WiMAX, WiFi, as well as systems utilizing ISM spectrum equipped with multiple antennas.
Gray-box extraction of execution graphs for anomaly detection

Many host-based anomaly detection systems monitor a process by observing the system calls it makes, and comparing these calls to a model of behavior for the program that the process should be executing. In this paper we introduce a new model of system call behavior, called an <i>execution graph</i>. The execution graph is the first such model that both requires no static analysis of the program source or binary, and conforms to the control flow graph of the program. When used as the model in an anomaly detection system monitoring system calls, it offers two strong properties: (i) it accepts only system call sequences that are consistent with the control flow graph of the program; (ii) it is maximal given a set of training data, meaning that any extensions to the execution graph could permit some intrusions to go undetected. In this paper, we formalize and prove these claims. We additionally evaluate the performance of our anomaly detection technique.
Tn1331, a novel multiresistance transposon encoding resistance to amikacin and ampicillin in Klebsiella pneumoniae

A 7.5-kilobase-pair multiresistance transposon, Tn1331, harboring amikacin resistance was identified as part of Klebsiella pneumoniae plasmid pJHCMW1. Restriction mapping, hybridization, and transposition complementation experiments demonstrated that Tn1331 belongs to the Tn3 family. Its structure is similar to that of Tn3 with the insertion of a DNA fragment encoding resistance to amikacin, kanamycin, and tobramycin.
Combining Aspects and Modules

We claim that it is not only possible – but also bene£cial – to combine powerful module constructs with aspect-oriented programming. By tempering the power of AOP with encapsulation boundaries maintained by a module system, the desirable qualities of both can be achieved, while tempering their respective weaknesses. Modules and Aspects are different approaches to a common goal of constructing large, complex software quickly. Unfortunately, neither is solely suf£cient. We demonstrate that the differences between modules and aspects are not irreconcilable, and that a carefully crafted module system can co-exist with aspect-oriented features. We show that it is possible to combine the software-engineering features of module-systems with aspects’ abilities to modularize distributed behaviors. An alternate formulation is that we show that by combining aspects and modules makes modules easier to write and aspects easier to understand. An existence proof in the form of a prototype module system—Aspectual Collaborations—is presented, combining separate compilation, modular reasoning, and type safe reuse, with aspectual interception of oblivious behavior. Thus, we present a system capable of separate compilation and type-safe reuse of aspects. Type soundness is shown to hold for a minimal model of the system.
Invariant Feature Learning on a Mobile Robot

Teaching a robot to perceive and navigate in an unstructured natural world is a difficult task. Without learning, navigation systems are short-range and extremely limited. With learning, the robot can be taught to classify terrain at longer distances, but these classifiers can be fragile as well, leading to extremely conservative planning. A robust, high-level learning-based perception system for a mobile robot needs to continually learn and adapt as it explores new environments. To do this, a strong feature representation is necessary that can encode meaningful, discriminative patterns as well as invariance to irrelevant transformations. A simple realtime classifier can then be trained on those features to predict the traversability of the current terrain.
Visual Representation of Multiple Associations in Data using Constrained Graph Layout

This paper presents a new approach for simultaneously visualizing multiple exclusive associations, deﬁned on the same dataset, using constrained graph layout. We work with two different associations at a time, which can be represented as a binary graph operation. Given an initial graph layout which represents an association of the data, another set of constraints is applied to the graph to represent a second association on the same dataset to obtain the ﬁnal layout. Our motivation is to preserve some features of the ﬁrst layout as well as to achieve a simultaneous view of both the associations. We use this approach generically for three applications: for visualization of data with geometric and categorical constraints, respectively. We further propose to extend it to multiple associations, by using the binary operation multiple times.
3D Reconstruction of Urban Areas

Virtual representations of real world areas are increasingly being employed in a variety of different applications such as urban planning, personnel training, simulations, etc. Despite the increasing demand for such realistic 3D representations, it still remains a very hard and often manual process. In this paper, we address the problem of creating photo realistic 3D scene models for large-scale areas and present a complete system. The proposed system comprises of two main components: (1) A reconstruction pipeline which employs a fully automatic technique for extracting and producing high-fidelity geometric models directly from Light Detection and Ranging (LiDAR) data and (2) A flexible texture blending technique for generating high-quality photo realistic textures by fusing information from multiple optical sensor resources. The result is a photo realistic 3D representation of large-scale areas(city-size) of the real-world. We have tested the proposed system extensively with many city-size datasets which confirms the validity and robustness of the approach. The reported results verify that the system is a consistent work flow that allows non-expert and non-artists to rapidly fuse aerial LiDAR and imagery to construct photo realistic 3D scene models.
A maximum entropy based reordering model for Mongolian-Chinese SMT with morphological information

Different order between Mongolian and Chinese and the scarcity of parallel corpus are the main problems in Mongolian-Chinese statistical machine translation (SMT). We propose a method that adopts morphological information as the features of the maximum entropy based phrase reordering model for Mongolian-Chinese SMT. By taking advantage of the Mongolian morphological information, we add Mongolian stem and affix as phrase boundary information and use a maximum entropy model to predict reordering of neighbor blocks. To some extent, our method can alleviate the influence of reordering caused by the data sparseness. In addition, we further add part-of-speech (POS) as the features in the reordering model. Experiments show that the approach outperforms the maximum entropy model using only boundary words information and provides a maximum improvement of 0.8 BLEU score increment over baseline.
Freshwater Macroinvertebrate Responses to Multiple Stressors: Interactions Between Fine Sediment Grain Size and Flow Velocity Reduction

Agricultural development has resulted in the degradation of freshwater ecosystems worldwide. Disentangling the individual and combined effects of agricultural stressors is crucial for future land-use management and restoration efforts. Two key stressors impacting streams and rivers draining agricultural catchments are deposited fine sediment and water abstraction. The effects of fine sediment grain size combined with water abstraction on benthic invertebrate communities have not yet been investigated. I addressed this knowledge gap by conducting a manipulative experiment in 60 outdoor stream mesocosms using four fine sediment treatments (no added sediment, silt: 0-0.125mm, fine sand: 0.125-0.250mm, coarse sand: 1-2mm) combined with three flow velocity treatments, simulating varying levels of water abstraction (fast: 26.5cm/s; medium: 13.9cm/s; slow: 0.0cm/s). In Chapter 2, I determine the effects of fine sediment and reduced flow velocity on the benthic, drifting, and emerging stream macroinvertebrate communities (55 invertebrate response variables). There were 24 significant responses to sediment (44% of all variables; 83% negative responses) and 27 to flow velocity (49%; 85% negative), and 3 complex interactions (5%; and 20% additive multiple-stressor responses). My results imply that deposited fine sediment (of varying sizes) and reduced flow velocity can have pervasive detrimental impacts on stream invertebrate communities, and that the combined effects of these two stressors may be mainly additive. Effects of fine sediment were often negative regardless of grain size, especially for sediment-sensitive invertebrate taxa (e.g. benthic EPT taxon richness). The negative effects of fine sediment were also often worse at the smaller two grain sizes (e.g. benthic taxon richness and evenness). Finer sediments may reduce benthic habitat, food, and oxygen availability to a greater degree than larger sediments, and cause more damage to sensitive individuals through abrasion, smothering, or burial. In Chapter 3, I use biological traits to determine how stream invertebrate communities responded to the two stressors, by analysing the responses of 8 biological traits (consisting of 33 trait modalities). Biological trait modalities showed 25 significant responses to sediment (76% of all variables; 60% negative responses) and 20 to flow velocity (61%; 60% negative). There were 12 complex interactions between stressors (36%; and also 12 additive multiplestressor responses). Results indicated that fine sediment (of varying grain sizes) and reduced flow velocity often have pervasive negative effects on the functional composition of benthic
Finite Element Eigenvalue Enclosures for the Maxwell Operator

We propose employing the extension of the Lehmann--Maehly--Goerisch method, developed by Zimmermann and Mertins, as a highly effective tool for the pollution-free finite element computation of the eigenfrequencies of the resonant cavity problem on a bounded region. This method gives complementary bounds for the eigenfrequencies which are adjacent to a given parameter $t\in \mathbb{R}$. We present a concrete numerical scheme which provides certified enclosures in a suitable asymptotic regime. We illustrate the applicability of this scheme by means of some numerical experiments on benchmark data using Lagrange elements and unstructured meshes.
Decentralized Dynamic Hop Selection and Power Control in Cognitive Multi-Hop Relay Systems

In this paper, we consider a cognitive multi-hop relay secondary user (SU) system sharing the spectrum with some primary users (PU). The transmit power as well as the hop selection of the cognitive relays can be dynamically adapted according to the local (and causal) knowledge of the instantaneous channel state information (CSI) in the multi-hop SU system. We shall determine a low complexity, decentralized algorithm to maximize the average end-to-end throughput of the SU system with dynamic spatial reuse. The problem is challenging due to the decentralized requirement as well as the causality constraint on the knowledge of CSI. Furthermore, the problem belongs to the class of stochastic Network Utility Maximization (NUM) problems which is quite challenging. We exploit the time-scale difference between the PU activity and the CSI fluctuations and decompose the problem into a master problem and subproblems. We derive an asymptotically optimal low complexity solution using divide-and-conquer and illustrate that significant performance gain can be obtained through dynamic hop selection and power control. The worst case complexity and memory requirement of the proposed algorithm is O(M2) and O(M3) respectively, where M is the number of SUs.
P ORTOLAN: a Model-Driven Cartography Framework

Processing large amounts of data to extract useful information is an essential task within companies. To help in this task, visualization techniques have been commonly used due to their capacity to present data in synthesized views, easier to understand and manage. However, achieving the right visualization display for a data set is a complex cartography process that involves several transformation steps to adapt the (domain) data to the (visualization) data format expected by visualization tools. To maximize the benefits of visualization we propose Portolan, a generic model-driven cartography framework that facilitates the discovery of the data to visualize, the specification of view definitions for that data and the transformations to bridge the gap with the visualization tools. Our approach has been implemented on top of the Eclipse EMF modeling framework and validated on three different use cases.
CNNs are Globally Optimal Given Multi-Layer Support

Stochastic Gradient Descent (SGD) is the central workhorse for training modern CNNs. Although giving impressive empirical performance it can be slow to converge. In this paper we explore a novel strategy for training a CNN using an alternation strategy that offers substantial speedups during training. We make the following contributions: (i) replace the ReLU non-linearity within a CNN with positive hard-thresholding, (ii) reinterpret this non-linearity as a binary state vector making the entire CNN linear if the multi-layer support is known, and (iii) demonstrate that under certain conditions a global optima to the CNN can be found through local descent. We then employ a novel alternation strategy (between weights and support) for CNN training that leads to substantially faster convergence rates, nice theoretical properties, and achieving state of the art results across large scale datasets (e.g. ImageNet) as well as other standard benchmarks.