Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-Organization during Friction of Slide Bearing Antifriction Materials

This article discusses the peculiarities of self-organization behavior and formation of dissipative structures during friction of antifriction alloys for slide bearings against a steel counterbody. It shows that during self-organization, the moment of friction in a tribosystem may be decreasing with the load growth and in the bifurcations of the coefficient of friction with respect to load. Self-organization and the formation of dissipative structures lead to an increase in the seizure load.
Compaction Filter as an Optimum Solution for Multirate Subband Coder of Cyclostationary Signals

A consistent theory of optimum subband coding of zero mean wide-sense cyclostationary signals with N- periodic statistics is presented in this paper. Blocked poly- phase representation of the analysis and synthesis filter banks is introduced as an effective way of multirate sub- band coder description. Optimum energy compaction using Nyquist-M process is presented as a solution for maximi- zing the coding gain of the coder. In two definitions and four theorems the author proves that Nyquist-M filters ful- fill necessary and sufficient conditions imposed on subband signals. Results from Matlab simulations are presented to support theoretical conclusions.
A Novel Approach for Seismic Signal Magnitude Detection Using Haar Wavelet

Earth quakes are among the most damaging event caused by the earth itself. As urbanization progresses universally, earthquakes pose severe risk to lives and properties for urban areas and all the subduction zones. Short term earthquake prediction, months in advance, is an elusive goal of earth sciences, and is of great importance for fundamental science and for disaster preparedness. Till date, many of the researchers applied different techniques like prediction based on radon emissions, EEW algorithm, M8 algorithm, prediction using extraction of instantaneous frequency from underground water and so on, but neither of them could provide an effective and efficient result. In the present research seismic signals are analyzed by using Haar wavelet transform in order to evaluate the parameters such as energy, frequency, magnitude of the signal. Among all the parameters the magnitude which forms the base of analysis is used for the detection of earthquake. The minor quakes are neglected and the surface wave magnitude of the quakes that show impact on earth's surface is calculated and found as 3.0. The obtained results are taken up as datasets and are tested using classification algorithms such as J48, Random Forest, REP tree, LMT, Naïve Bayes and Back propagation model of neural networks to evaluate the accuracy, precision and recall performance measures.
Robust Palm Vein Recognition Using LMKNCN Classification

This paper presents a novel approach to improve the recognition percentage of palm-vascular-based authentication systems presented in the literature. The proposed method efficiently accommodates the rotational, translational changes and potential deformations by encoding the orientation preserving features. The proposed palm-vein approach is compared with other existing methods and obtained an improved performance in both verification and recognition scenarios. The experimental results obtained in this work based on PolyU and CASIA Vein databases conforms the superiority of the proposed method in both the recognition and verification.
Motivic Hopf elements and relations

We use Cayley-Dickson algebras to produce Hopf elements eta, nu and sigma in the motivic stable homotopy groups of spheres, and we prove via geometric arguments that the the products eta*nu and nu*sigma both vanish. Along the way we develop several basic facts about the motivic stable homotopy ring.
Adiposity and hyperglycaemia in pregnancy and related health outcomes in European ethnic minorities of Asian and African origin: a review

Background Ethnic minorities in Europe have high susceptibility to type 2 diabetes (T2DM) and, in some groups, also cardiovascular disease (CVD). Pregnancy can be considered a stress test that predicts future morbidity patterns in women and that affects future health of the child. Objective To review ethnic differences in: 1) adiposity, hyperglycaemia, and pre-eclampsia during pregnancy; 2) future risk in the mother of obesity, T2DM and CVD; and 3) prenatal development and possible influences of maternal obesity, hyperglycaemia, and pre-eclampsia on offspring's future disease risk, as relevant for ethnic minorities in Europe of Asian and African origin. Design Literature review. Results Maternal health among ethnic minorities is still sparsely documented. Higher pre-pregnant body mass index (BMI) is found in women of African and Middle Eastern descent, and lower BMI in women from East and South Asia compared with women from the majority population. Within study populations, risk of gestational diabetes mellitus (GDM) is considerably higher in many minority groups, particularly South Asians, than in the majority population. This increased risk is apparent at lower BMI and younger ages. Women of African origin have higher risk of pre-eclampsia. A GDM pregnancy implies approximately seven-fold higher risk of T2DM than normal pregnancies, and both GDM and pre-eclampsia increase later risk of CVD. Asian neonates have lower birth weights, and mostly also African neonates. This may translate into increased risks of later obesity, T2DM, and CVD. Foetal overgrowth can promote the same conditions. Breastfeeding represents a possible strategy to reduce risk of T2DM in both the mother and the child. Conclusions Ethnic minority women in Europe with Asian and African origin and their offspring seem to be at increased risk of T2DM and CVD, both currently and in the future. Pregnancy is an important window of opportunity for short and long-term disease prevention.
Evolutionary Neural Networks for Value Orderingin Constraint Satisfaction

A new method for developing good value-ordering strategies in constraint satisfaction search is presented. Using an evolutionary technique called SANE, in which individual neurons evolve to cooperate and form a neural network, problem-speciic knowledge can be discovered that results in better value-ordering decisions than those based on problem-general heuristics. A neural network was evolved in a chronological backtrack search to decide the ordering of cars in a resource-limited assembly line. The network required 1/30 of the backtracks of random ordering and 1/3 of the backtracks of the maximization of future options heuristic. The SANE approach should extend well to other domains where heuristic information is either diicult to discover or problem-speciic.
Challenges in coevolutionary learning: arms-race dynamics, open-endedness, and medicocre stable states

Coevolution has been proposed as a way to evolve a learner and a learning environment simultaneously such that open-ended progress arises naturally, via a competitive arms race, with minimal inductive bias. Nevertheless, the conditions necessary to initiate and sustain arms-race dynamics are not well understood; mediocre stable states frequently result from learning through self-play (Angeline & Pollack 1994), while analysis usually requires closed domains with known optima, like sorting-networks (Hillis 1991). While intuitions regarding what enables successful coevolution abound, none have been methodically tested. We present a game that a ords such methodical investigation. A population of deterministic string generators is coevolved with two populations of string predictors, one \friendly" and one \hostile"; generators are rewarded to behave in a manner that is simultaneously predictable to the friendly predictors and unpredictable to the hostile predictors. This game design allows us to employ information theory to provide rigorous characterizations of agent behavior and coevolutionary progress. Further, we can craft agents of known ability and environments of known di culty, and thus precisely frame questions regarding learnability. Our results show that subtle changes to the game determine whether it is open-ended, and profoundly a ect the existence and nature of an arms race.
ADRC based ship tracking controller design and simulations

Because of the strong non-linearity, uncertainty and typical underactuated properties, as well as the restraints of rudder, the design of ship tracking controller is yet a challenge work. In this paper, the problem was investigated by utilizing the active disturbance rejection control (ADRC) method. Firstly, the ADRC was introduced briefly and the characteristics of ship tracking motion were analyzed. And the systempsilas nonlinear mathematical model with restraints and uncertainties was established. Then the system was described as a series structure, and the tracking ADRC controller was designed based on a kind of three order polynomial trajectory planning algorithm. At last, the simulations were performed. The results show that the controller designed can achieve high precision on ship tracking control and has strong robustness to ship parameter perturbations and environment disturbances.
Convolutional networks for kidney segmentation in contrast-enhanced CT scans

Organ segmentation in medical imagery can be used to guide patient diagnosis, treatment and follow ups. In this paper, we present a fully automatic framework for kidney segmentation with convolutional networks (ConvNets) in contrast-enhanced computerised tomography (CT) scans. In our approach, a ConvNet is trained using a patch-wise approach to predict the class membership of the central voxel in 2D patches. The segmentation of the kidneys is then produced by densely running the ConvNet over each slice of a CT scan. Efficient predictions can be achieved by transforming fully connected layers into convolutional operations and by fragmenting the maxpooling layers to segment a whole CT scan volume in a few seconds. We report the segmentation performance of our framework on a highly variable data-set of 79 cases using a variety of evaluation metrics.
Relationships Between Accuracy and Diversity in Heterogeneous Ensemble Classiers

The relationship between ensemble classier performance and the diversity of the predictions made by ensemble base classiers is explored in the context of heterogeneous ensemble classiers. Specically, numerical studies indicate that heterogeneous ensembles can be generated from base classiers of homogeneous ensemble classiers that are both signicantly more accurate and diverse than the base classiers. Results for experiments using several standard diversity measures on a variety of binary and multiclass classication problems are presented to illustrate the improved performance.
A Semantic-based Approach for Facilitating Arbovirus Data Usage

Today’s continuous growth for healthcare information entails an increasing need for using large amounts of data. Particularly, the incidence of arboviruses has been on the rise in some countries, what causes specific needs for studies and definitions of public strategies. In this light, providing a computational platform for usage and reuse on arboviruses related data may help matters. The idea is that different applications and users can make use of that data in diverse ways. In this work, we propose a semantic-based approach for facilitating use and reuse of arboviruses related data. We present the definitions underlying our approach, examples illustrating how it works, and some promising results we have obtained.
A framework for semantics and agent based personalized information retrieval in agriculture

Web has seen a tremendous growth in its size in recent years. It has become a very tedious task to find the relevant information from Web search engines. Agriculture plays an important role in the economy of our country. Farmers, producers, supply-chain managers, extension workers, researchers and academicians are some of the important stakeholders in the field of agriculture. Web has now become an important medium for dissemination of information required by all of these. But, the retrieval of desired information from Web is a difficult task as it requires sifting through all the search results and identifying correct Web pages. A solution to this problem is personalization of search results according to user interests and preferences. Further, semantic Web and multi-agent technology is playing an important role in adding the meaning to the Web contents. At present, very little efforts have seen in applying personalization using semantic Web and agent technology in the area of agriculture. This research proposes a framework based on semantic Web and agent technology for the retrieval of personalized information from Web in agriculture domain. This would help stakeholders in accessing the required information easily and efficiently through agents.
Probability-Based PWM Generator for Large-Size LED Display System

—In this paper, we address a new PWM generation method and its hardware architecture for a large-size LED display system. Since the proposed probability-based PWM generator invokes pulses stochastically, the pulses can vary their positions even if the input PWM value is same. Furthermore, the periodic repeatability of PWM generation can be also removed, and thus the PWM pulses are evenly distributed in all time periods. The probability-based PWM method efficiently reduces the inrush current problem and maintains an image-quality without degradation. Experimental results using a commercial LED panel show that the proposed method is superior to previous ones in all aspects, such as the hardware cost, feasibility, and flexibility.
Trastorno por déficit de atención con hiperactividad: un modelo predictor de comorbilidad con trastornos del comportamiento Attention deficit hyperactivity disorder: a model predicting of comorbidity with behavior disorder

This paper examines the co-morbidity profile of the Attention Deficit Hyperactivity Disorder (ADHD) and Behavior Disorder (BD), exploring the pattern that predicts this psychopathological association. A sample of 90 ADHD subjects aged 6 to 16 is analysed. Using a differential methodology, ADHD+BD is compared with the absence of co-morbidity. ADHD was assessed according to DSM-IV standards and the co-morbidity by means of CSI. Intellectual profile (as measured by WISC-R), academic performance, the relational dimension, psychiatric antecedents and nucleus of coexistence were taken into account. Descriptive and exploratory statistics were used and a logistic regression was implemented. The differential profile implies that ADHD+BD subjects present a significantly higher deterioration in the relational dimension, academic performance and percentage of separated parents. The pattern predicting ADHD+BD includes the relational dimension, whereas ADHD + Conduct Disorder includes the relational dimension and separated parents (p <.05).
Self-avoiding walks and trails on the 3.122 lattice

We find the generating function of self-avoiding walks (SAWs) and trails on a semi-regular lattice called the 3.12 2 lattice in terms of the generating functions of simple graphs, such as SAWs, self-avoiding polygons and tadpole graphs on the hexagonal lattice. Since the growth constant for these graphs is known on the hexagonal lattice we can find the growth constant for both walks and trails on the 3.12 2 lattice. A result of Watson (1970 J. Phys. C: Solid State Phys. 3 L28–30) then allows us to find the generating function and growth constant of neighbour-avoiding walks on the covering lattice of the 3.12 2 lattice which is tetra-valent. A mapping into walks on the covering lattice allows us to obtain improved bounds on the growth constant for a range of lattices.
Comparison of NASA-TLX scale, modified Cooper–Harper scale and mean inter-beat interval as measures of pilot mental workload during simulated flight tasks

Abstract The sensitivity of NASA-TLX scale, modified Cooper–Harper (MCH) scale and the mean inter-beat interval (IBI) of successive heart beats, as measures of pilot mental workload (MWL), were evaluated in a flight training device (FTD). Operational F/A-18C pilots flew instrument approaches with varying task loads. Pilots’ performance, subjective MWL ratings and IBI were measured. Based on the pilots’ performance, three performance categories were formed; high-, medium- and low-performance. Values of the subjective rating scales and IBI were compared between categories. It was found that all measures were able to differentiate most task conditions and there was a strong, positive correlation between NASA-TLX and MCH scale. An explicit link between IBI, NASA-TLX, MCH and performance was demonstrated. While NASA-TLX, MCH and IBI have all been previously used to measure MWL, this study is the first one to investigate their association in a modern FTD, using a realistic flying mission and operational pilots. Practitioner summary: NASA-TLX scale, MCH scale and the IBI were evaluated in a flight training device. All measures were able to differentiate most task conditions and there was a positive correlation between NASA-TLX and MCH scale. An explicit link between IBI, NASA-TLX, MCH and performance was demonstrated. Abbreviations: ANOVA: Analysis of Variance; ECG: Electrocardiograph; F/A: fighter/attack; ft: feet; FTD: flight training device; G: Gravity; km: kilometer; m: meter; m/s: meters per second; MWL: mental workload; MCH: modified cooper-harper; NASA-TLX: NASA Task Load Index; NM: Nautical Mile; NN: normal-to-normal; IBI: inter-beat interval; ILS: Instrument Landing System; RR: R-Wave to R-Wave; SD: standard deviation; TTP: tactics, techniques and procedures; WTSAT: Weapon Tactics and Situation Awareness Trainer
3D shape recovery by image focus using Lorentzian-Cauchy function

In the past recent years, shape recovery using image focus has been investigated a lot. Traditionally, generalized Gaussian models are used to approximate the focus curves obtained after applying the focus measure. In this paper, we have proposed a new approximation model for the focus curve, using Lorentzian-Cauchy function. The focusing of light rays, by the imaging device, are approximated by using a Gaussian beam, which has the intensity profile along the propagation axis defined by Lorentzian-Cauchy function. Experiments performed on the simulated and real objects have shown the effectiveness of the proposed scheme.
Data-driven sequential goal selection model for multi-agent simulation

With recent advances in distributed virtual worlds, online users have access to larger and more immersive virtual environments. Sometimes the number of users in virtual worlds is not large enough to make the virtual world realistic. In our paper, we present a crowd simulation algorithm that allows a large number of virtual agents to navigate around the virtual world autonomously by sequentially selecting the goals. Our approach is based on our sequential goal selection model (SGS) which can learn goal-selection patterns from synthetic sequences. We demonstrate our algorithm's simulation results in complex scenarios containing more than 20 goals.
Collective information processing and computer mediated communication: An overview of major theoretical frameworks

How problem-solving and decision-making groups process information can impact outcome of their efforts. Information processing by groups, or collective information processing (CIP), in turn, is influenced by group communication. With the increasing use of computer-mediated communication (CMC) technologies by organizational workgroups, it makes sense that CIPCMC theoretical frameworks are reexamined and opportunities for research identified. In this paper, we provide an overview of major theories related to CIP and CMC, and develop suggestions for future research.
Soziale Einflüsse in Beratung. Wirkfaktoren professioneller Hilfe und die extratherapeutischen Einflüsse sozialer Netzwerke

Neben verschiedenen Klient/innen- und Helfer/innenvariablen, der professionellen Hilfebeziehung, dem Hilfesetting und den Hilfestrategien scheinen vor allem verschiedene soziale Einflusse aus den personlichen Beziehungsnetzwerken der Klient/innen Zugang, Verlauf und Wirkungen von Beratung und Therapie entscheidend zu moderieren. Netzwerkmitglieder und ihre Einflussnahmen konnen die professionellen Interventionen einerseits unterstutzen und fordern, sie aber andererseits auch behindern und erschweren. Dennoch bleiben diese sozialen Einflusse in der Beratungs-/Therapiewirkungsforschung ebenso wie in der Beratungs-/Therapieentwicklung und -praxis weitgehend unberucksichtigt. Dabei liefern die wenigen vorliegenden Befunde eine Vielzahl an Grunden, gerade die sozialkontextuellen und interaktiven Einflusse in ihrer Wirkung genauer zu studieren und in der Beratungs- und Therapiepraxis nachhaltiger zu berucksichtigen.
Daily Gene Expression Rhythms in Rat White Adipose Tissue Do Not Differ Between Subcutaneous and Intra-Abdominal Depots

White adipose tissue (WAT) is present in different depots throughout the body. Although all depots are exposed to systemic humoral signals, they are not functionally identical. Studies in clock gene knockout animals and in shift workers suggest that daily rhythmicity may play an important role in lipid metabolism. Differences in rhythmicity between fat depots might explain differences in depot function; therefore, we measured mRNA expression of clock genes and metabolic genes on a 3-h interval over a 24-h period in the subcutaneous inguinal depot and in the intra-abdominal perirenal, epididymal, and mesenteric depots of male Wistar rats. We analyzed rhythmicity using CircWave software. Additionally, we measured plasma concentrations of glucose, insulin, corticosterone, and leptin. The clock genes (Bmal1/Per2/Cry1/Cry2/RevErbα/DBP) showed robust daily gene expression rhythms, which did not vary between WAT depots. Metabolic gene expression rhythms (SREBP1c/PPARα/PPARγ/FAS/LPL/Glut4/HSL/CPT1b/leptin/visfatin/resistin) were more variable between depots. However, no distinct differences between intra-abdominal and subcutaneous rhythms were found. Concluding, specific fat depots are not associated with differences in clock gene expression rhythms and, therefore, do not provide a likely explanation for the differences in metabolic function between different fat depots.
CA1-specific N-methyl-d-aspartate receptor knockout mice are deficient in solving a nonspatial transverse patterning task

In both humans and animals, the hippocampus is critical to memory across modalities of information (e.g., spatial and nonspatial memory) and plays a critical role in the organization and flexible expression of memories. Recent studies have advanced our understanding of cellular basis of hippocampal function, showing that N-methyl-d-aspartate (NMDA) receptors in area CA1 are required in both the spatial and nonspatial domains of learning. Here we examined whether CA1 NMDA receptors are specifically required for the acquisition and flexible expression of nonspatial memory. Mice lacking CA1 NMDA receptors were impaired in solving a transverse patterning problem that required the simultaneous acquisition of three overlapping odor discriminations, and their impairment was related to an abnormal strategy by which they failed to adequately sample and compare the critical odor stimuli. By contrast, they performed normally, and used normal stimulus sampling strategies, in the concurrent learning of three nonoverlapping concurrent odor discriminations. These results suggest that CA1 NMDA receptors play a crucial role in the encoding and flexible expression of stimulus relations in nonspatial memory.