Search results for: domain decomposition

Authors: Cristina Costa-Lobo, Carla B. Vestena, Filomena E. Ponte

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Creativity and intelligence are concepts that have aroused very expressive interest in the field of educational sciences and the field of psychological science since the middle of the last century since they have a great impact on the potential and well-being of individuals. However, due to progress in cognitive and positive psychology, there has been a growing interest in the psychoeducational domain of intelligence and creativity in the last decade. In this theoretical work, are analyzed comparatively the theoretical models that relate the intelligence and the creativity, are analyzed several psychoeducational intervention programs that have been implemented with a view to the promotion of creativity and signal possibilities, realities and ironies around the psychological evaluation of intelligence and creativity. In order to reach a broad perspective on creativity, the evidence is presented that points the need to evaluate different psychological domains. The psychoeducational intervention programs addressed have, with a common characteristic, the full stimulation of the creative potential of the participants, assumed as a highly valued capacity at the present time. The results point to the systematize that all interventions in the ambit of creativity have two guiding principles: all individuals can be creative, and creativity is a capacity that can be stimulated. This work refers to the importance of stimulus creativity in educational contexts, to the usefulness and pertinence of the creation, the implementation, and monitoring of flexible curricula, adapted to the educational needs of students, promoting a collaborative work among teachers, parents, students, psychologists, managers and educational administrators.

Keywords: creativity, intelligence, psychoeducational intervention programs, psychological evaluation, educational contexts

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Authors: Cory Callahan

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Here the author synthesizes findings and implications from two research studies that comprise a continuing line of inquiry into the potential of an innovative professional development program to help in-service teachers understand and implement a complex model of social studies instruction. The paper specifically explores the question: To what degree can a collaborative professional development program centered around innovative curriculum materials help social studies teachers understand and implement a powerful social studies approach? Findings suggest the teachers increasingly incorporated substantive thinking (i.e., second-order historical domain knowledge) into their respective practice and they facilitated students’ use of historical photographs as evidence to begin to answer a compelling question. The teachers also began to effectively support students’ abilities to make claims about the past. Implications include the foregrounding of high-quality questions during planning and the need for explicit guidance in the form of structures and procedures (i.e., scaffolds) to help teachers systematically review students’ work products. The work shared here may contribute to scholarship that posits explanations for why teacher-support is routinely ineffectual and suggests ways to provide substantive collaborative support for in-service social studies teachers.

Keywords: educative curriculum, social studies, professional development, lesson study

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Authors: Proficiency Munsaka, Peter Baricholo, Erich Rohwer

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Pulse evolutions along the 5 cm long, 6.0 ×4.2 μm² cross-section silicon germanium (SiGe) photonic waveguides were simulated and compared with experiments. Simulations were carried out by solving a generalized nonlinear Schrodinger equation (GNLSE) for an optical pulse evolution along the length of the SiGe photonic waveguides by the split-step Fourier method (SSFM). The solution obtained from the SSFM gave the pulse envelope in both time and spectral domain calculated at each distance step along the propagation direction. The SiGe photonic waveguides were pumped in an anomalous group velocity dispersion (GVD) regime using a 4.7 μm, 210 fs femtosecond laser to produce a significant supercontinuum (SC). The simulated propagation of ultrafast pulse along the SiGe photonic waveguides produced an SC covering the atmospheric window (2.5-8.5 μm) containing the molecular fingerprints for important gases. Thus, the mid-infrared supercontinuum generation in SiGe photonic waveguides system can be commercialized for gas spectroscopy for detecting gases that include CO₂, CH₄, H₂O, SO₂, SO₃, NO₂, H₂S, CO, and NO at trace level using absorption spectroscopy technique. The simulated profile evolutions are spectrally and temporally similar to those obtained by other researchers. Obtained evolution profiles are characterized by pulse compression, Soliton fission, dispersive wave generation, stimulated Raman Scattering, and Four Wave mixing.

Keywords: silicon germanium photonic waveguide, supercontinuum generation, spectroscopy, mid infrared

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Authors: Dorota Miroslaw-Swiatek, Mateusz Grygoruk, Sylwia Szporak

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We applied a two-dimensional surface water flow model with irregular wet boundaries. In this model, flow equations are in the form of a 2-D, non-linear diffusion equations which allows to account spatial variations in flow resistance and topography. Calculation domain to simulate the flow pattern in the floodplain is congruent with a Digital Elevation Model (DEM) grid. The rate and direction of sheet flow in wetlands is affected by vegetation type and density, therefore the developed model take into account spatial distribution vegetation resistance to the water flow. The model was tested in a part of the Biebrza Valley, of an outstanding heterogeneity in the elevation and flow resistance distributions due to various ecohydrological conditions and management measures. In our approach we used the highest-possible quality of the DEM in order to obtain hydraulic slopes and vegetation distribution parameters for the modelling. The DEM was created from the cloud of points measured in the LiDAR technology. The LiDAR reflects both the land surface as well as all objects on top of it such as vegetation. Depending on the density of vegetation cover the ability of laser penetration is variable. Therefore to obtain accurate land surface model the “vegetation effect” was corrected using data collected in the field (mostly the vegetation height) and satellite imagery such as Ikonos (to distinguish different vegetation types of the floodplain and represent them spatially). Model simulation was performed for the spring thaw flood in 2009.

Keywords: floodplain flow, Biebrza valley, model simulation, 2D surface flow model

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Authors: Jitendra Pratap, Jonathan Sivyer

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Introduction: Dual energy/Spectral CT scanning permits simultaneous acquisition of two x-ray spectra datasets and can complement radiological diagnosis by allowing tissue characterisation (e.g., uric acid vs. non-uric acid renal stones), enhancing structures (e.g. boost iodine signal to improve contrast resolution), and quantifying substances (e.g. iodine density). However, the latter showed inconsistent results between the 2 main modes of dual energy scanning (i.e. dual source vs. dual layer). Therefore, the present study aimed to determine which technology is more accurate in quantifying iodine density. Methods: Twenty vials with known concentrations of iodine solutions were made using Optiray 350 contrast media diluted in sterile water. The concentration of iodine utilised ranged from 0.1 mg/ml to 1.0mg/ml in 0.1mg/ml increments, 1.5 mg/ml to 4.5 mg/ml in 0.5mg/ml increments followed by further concentrations at 5.0 mg/ml, 7mg/ml, 10 mg/ml and 15mg/ml. The vials were scanned using Dual Energy scan mode on a Siemens Somatom Force at 80kV/Sn150kV and 100kV/Sn150kV kilovoltage pairing. The same vials were scanned using Spectral scan mode on a Philips iQon at 120kVp and 140kVp. The images were reconstructed at 5mm thickness and 5mm increment using Br40 kernel on the Siemens Force and B Filter on Philips iQon. Post-processing of the Dual Energy data was performed on vendor-specific Siemens Syngo VIA (VB40) and Philips Intellispace Portal (Ver. 12) for the Spectral data. For each vial and scan mode, the iodine concentration was measured by placing an ROI in the coronal plane. Intraclass correlation analysis was performed on both datasets. Results: The iodine concentrations were reproduced with a high degree of accuracy for Dual Layer CT scanner. Although the Dual Source images showed a greater degree of deviation in measured iodine density for all vials, the dataset acquired at 80kV/Sn150kV had a higher accuracy. Conclusion: Spectral CT scanning by the dual layer technique has higher accuracy for quantitative measurements of iodine density compared to the dual source technique.

Keywords: CT, iodine density, spectral, dual-energy

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Authors: Usha Kiran, M. Z. Abdin

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Osmotin is an abundant cationic multifunctional protein discovered in cells of tobacco (Nicotiana tabacum L. var Wisconsin 38) adapted to an environment of low osmotic potential. It provides plants protection from pathogens, hence placed in the PRP family of proteins. The osmotin induced proline accumulation has been reported in plants including transgenic tomato and strawberry conferring tolerance against both biotic and abiotic stresses. The exact mechanism of induction of proline by osmotin is however, not known till date. These observations have led us to hypothesize that osmotin induced proline accumulation could be due to its involvement as transcription factor and/or cell signal pathway modulator in proline biosynthesis. The present investigation was therefore, undertaken to analyze the osmotin protein as transcription factor /cell signalling modulator using bioinformatics tools. The results of available online DNA binding motif search programs revealed that osmotin does not contain DNA-binding motifs. The alignment results of osmotin protein with the protein sequence from DATF showed the homology in the range of 0-20%, suggesting that it might not contain a DNA binding motif. Further to find unique DNA-binding domain, the superimposition of osmotin 3D structure on modeled Arabidopsis transcription factors using Chimera also suggested absence of the same. We, however, found evidence implicating osmotin in cell signaling. With these results, we concluded that osmotin is not a transcription factor but regulating proline biosynthesis and accumulation through cell signaling during abiotic stresses.

Keywords: osmotin, cell signaling modulator, bioinformatic tools, protein

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Authors: Andrea Tundis, Carlos García Cordero, Rolf Egert, Alfredo Garro, Max Mühlhäuser

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Resilience is an important system property that relies on the ability of a system to automatically recover from a degraded state so as to continue providing its services. Resilient systems have the means of detecting faults and failures with the added capability of automatically restoring their normal operations. Mastering resilience in the domain of Cyber-Physical Systems is challenging due to the interdependence of hybrid hardware and software components, along with physical limitations, laws, regulations and standards, among others. In order to overcome these challenges, this paper presents a modeling approach, based on the concept of Dynamic Cells, tailored to the management of Smart Grids. Additionally, a heuristic algorithm that works on top of the proposed modeling approach, to find resilient configurations, has been defined and implemented. More specifically, the model supports a flexible representation of Smart Grids and the algorithm is able to manage, at different abstraction levels, the resource consumption of individual grid elements on the presence of failures and faults. Finally, the proposal is evaluated in a test scenario where the effectiveness of such approach, when dealing with complex scenarios where adequate solutions are difficult to find, is shown.

Keywords: cyber-physical systems, energy management, optimization, smart grids, self-healing, resilience, security

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Authors: Bouthiba Amina

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The estimation of the statistics bound to the precipitation represents a vast domain, which puts numerous challenges to meteorologists and hydrologists. Sometimes, it is necessary, to approach in value the extreme events for sites where there is little, or no datum, as well as their periods of return. The search for a model of the frequency of the heights of daily rains dresses a big importance in operational hydrology: It establishes a basis for predicting the frequency and intensity of floods by estimating the amount of precipitation in past years. The most known and the most common approach is the statistical approach, It consists in looking for a law of probability that fits best the values observed by the random variable " daily maximal rain " after a comparison of various laws of probability and methods of estimation by means of tests of adequacy. Therefore, a frequent analysis of the annual series of daily maximal rains was realized on the data of 54 pluviometric stations of the pond of high and average. This choice was concerned with five laws usually applied to the study and the analysis of frequent maximal daily rains. The chosen period is from 1970 to 2013. It was of use to the forecast of quantiles. The used laws are the law generalized by extremes to three components, those of the extreme values to two components (Gumbel and log-normal) in two parameters, the law Pearson typifies III and Log-Pearson III in three parameters. In Algeria, Gumbel's law has been used for a long time to estimate the quantiles of maximum flows. However, and we will check and choose the most reliable law.

Keywords: return period, extreme flow, statistics laws, Gumbel, estimation

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Authors: Nayara Torres B. Acioli, José Fernando T. Jucá

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The uncontrolled emission of gases in urban residues' embankment located near urban areas is a social and environmental problem, common in Brazilian cities. Several environmental impacts in the local and global scope may be generated by atmospheric air contamination by the biogas resulted from the decomposition of solid urban materials. In Brazil, the cities of small size figure mostly with 90% of all cities, with the population smaller than 50,000 inhabitants, according to the 2011 IBGE' census, most of the landfill covering layer is composed of clayey, pure soil. The embankments undertaken with pure soil may reach up to 60% of retention of methane, for the other 40% it may be dispersed into the atmosphere. In face of this figures the oxidative covering layer is granted some space of study, envisaging to reduce this perceptual available in the atmosphere, releasing, in spite of methane, carbonic gas which is almost 20 times as less polluting than Methane. This paper exposes the results of studies on the characteristics of the soil used for the oxidative coverage layer of the experimental embankment of Solid Urban Residues (SUR), built in Muribeca-PE, Brazil, supported of the Group of Solid Residues (GSR), located at Federal University of Pernambuco, through laboratory vacuum experiments (determining the characteristics curve), granularity, and permeability, that in soil with saturation over 85% offers dramatic drops in the test of permeability to the air, by little increments of water, based in the existing Brazilian norm for this procedure. The suction was studied, as in the other tests, from the division of prospection of an oxidative coverage layer of 60cm, in the upper half (0.1 m to 0.3 m) and lower half (0.4 m to 0.6 m). Therefore, the consequences to be presented from the lixiviation of the fine materials after 5 years of finalization of the embankment, what made its permeability increase. Concerning its humidity, it is most retained in the upper part, that comprises the compound, with a difference in the order of 8 percent the superior half to inferior half, retaining the least suction from the surface. These results reveal the efficiency of the oxidative coverage layer in retaining the rain water, it has a lower cost when compared to the other types of layer, offering larger availability of this layer as an alternative for a solution for the appropriate disposal of residues.

Keywords: oxidative coverage layer, permeability, suction, saturation

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Authors: John G. Vongas, Raghid Al Hajj

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In recent years, there has been a surge in research on empathy across disciplines ranging from management and psychology to philosophy and neuroscience. In organizational behavior, in particular, scholars have become interested in leader empathy given the rise of workplace diversity and the growing perception of leaders as managers of group emotions. It would appear that the current zeitgeist in behavioral and philosophical science is that empathy is a cornerstone of morality and that our world would be better off if only more people – and by extension, more leaders – were empathic. In spite of these claims, however, researchers have used different terminologies to explore empathy, confusing it at times with other related constructs such as emotional intelligence and compassion. Second, extant research that specifies what empathic leaders do and how their behavior affects organizational stakeholders, including themselves, does not devolve from a unifying theoretical framework. These problems plague knowledge development in this important research domain. Therefore, to the authors' best knowledge, this paper provides the first comprehensive review and synthesis of the literature on leader empathy by drawing on disparate yet complementary fields of inquiry. It clarifies empathy from other constructs and presents a theoretical model that elucidates the mechanisms by which a leader’s empathy translates into behaviors that could be either beneficial or harmful to the leaders themselves, as well as to their followers and groups. And third, it specifies the boundary conditions under which a leader’s empathy will become manifest. Finally, it suggests ways in which training could be implemented to improve empathy in practice while also remaining skeptical of its conceptualization as a moral or even effective guide in human affairs.

Keywords: compassion, empathy, leadership, group outcomes

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Authors: Fadi Al-Badour, Nesar Merah, Abdelrahman Shuaib, Abdelaziz Bazoune

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In the current work, a Coupled Eulerian Lagrangian (CEL) model is developed to simulate the friction stir welding (FSW) process of dissimilar Aluminum alloys (Al 6061-T6 with Al 5083-O). The model predicts volumetric defects, material flow, developed temperatures, and stresses in addition to tool reaction loads. Simulation of welding phase is performed by employing a control volume approach, whereas the welding speed is defined as inflow and outflow over Eulerian domain boundaries. Only material softening due to inelastic heat generation is considered and material behavior is assumed to obey Johnson-Cook’s Model. The model was validated using published experimentally measured temperatures, at similar welding conditions, and by qualitative comparison of dissimilar weld microstructure. The FE results showed that most of developed temperatures were below melting and that the bulk of the deformed material in solid state. The temperature gradient on AL6061-T6 side was found to be less than that of Al 5083-O. Changing the position Al 6061-T6 from retreating (Ret.) side to advancing (Adv.) side led to a decrease in maximum process temperature and strain rate. This could be due to the higher resistance of Al 6061-T6 to flow as compared to Al 5083-O.

Keywords: friction stir welding, dissimilar metals, finite element modeling, coupled Eulerian Lagrangian Analysis

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Authors: Sakina Fatima, Joseph-Patrick W. E. Clarke, Patricia A. Thibault, Subha Kalyaanamoorthy, Michael Levin, Aravindhan Ganesan

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Heteronuclear ribonucleoprotein (hnRNPA1 or A1) is associated with the pathology of different diseases, including neurological disorders and cancers. In particular, the aggregation and dysfunction of A1 have been identified as a critical driver for neurodegeneration (NDG) in Multiple Sclerosis (MS). Structurally, A1 includes a low-complexity domain (LCD) and two RNA-recognition motifs (RRMs), and their interdomain coordination may play a crucial role in A1 aggregation. Previous studies propose that RNA-inhibitors or nucleoside analogs that bind to RRMs can potentially prevent A1 self-association. Therefore, molecular-level understanding of the structures, dynamics, and nucleotide interactions with A1 RRMs can be useful for developing therapeutics for NDG in MS. In this work, a combination of computational modelling and biochemical experiments were employed to analyze a set of RNA-A1 RRM complexes. Initially, the atomistic models of RNA-RRM complexes were constructed by modifying known crystal structures (e.g., PDBs: 4YOE and 5MPG), and through molecular docking calculations. The complexes were optimized using molecular dynamics simulations (200-400 ns), and their binding free energies were computed. The binding affinities of the selected complexes were validated using a thermal shift assay. Further, the most important molecular interactions that contributed to the overall stability of the RNA-A1 RRM complexes were deduced. The results highlight that adenine and guanine are the most suitable nucleotides for high-affinity binding with A1. These insights will be useful in the rational design of nucleotide-analogs for targeting A1 RRMs.

Keywords: hnRNPA1, molecular docking, molecular dynamics, RNA-binding proteins

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Authors: Khaled Suleiman Mohammed Al-Mashrafi

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A compositional plume is a fluid flow in a directional channel of finite width in another fluid of different material composition. The study of the dynamics of compositional plumes plays an essential role in many real-life applications like industrial applications (e.g., iron casting), environmental applications (e.g., salt fingers and sea ice), and geophysical applications (e.g., solidification at the inner core boundary (ICB) of the Earth, and mantle plumes). The dynamics of compositional plumes have been investigated experimentally and theoretically. The experimental works observed that the plume flow seems to be stable, although some experiments showed that it can be unstable. At the same time, the theoretical investigations showed that the plume flow is unstable. This is found to be true even if the plume is subject to rotation or/and in the presence of a magnetic field and even if another plume of different composition is also present. It is noticeable that all the theoretical studies on the dynamics of compositional plumes are conducted in unbounded domains. The present work is to investigate theoretically the influence of vertical walls (boundaries) on the dynamics of compositional plumes in the absence/presence of a rotation field. The mathematical model of the dynamics of compositional plumes used the equations of continuity, motion, heat, concentration of light material, and state. It is found that the presence of boundaries has a strong influence on the basic state solution as well as the stability of the plume, particularly when the plume is close to the boundary, but the compositional plume remains unstable.

Keywords: compositional plumes, stability, bounded domain, vertical boundaries

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Authors: Laura Ulitzky, Dougbeh-Chris Nyan, Manuel M. Lafer, Erica Silberstein, Nicoleta Cehan, Deborah R. Taylor

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Hepatitis C virus (HCV)-associated hepatocellular carcinoma, fibrosis and cirrhosis are more frequent in men and postmenopausal women than in premenopausal women and women receiving hormone replacement therapy, suggesting that β-estradiol (estrogen) plays an innate role in preventing viral infection and liver disease. Estrogen classically acts through nuclear estrogen receptors or, alternatively, through the membrane-bound G-protein-coupled estrogen receptor (GPR30 or GPER). We observed a marked decrease in detectable virus when HCV-infected human hepatoma cells were treated with estrogen. The effect was mimicked by both Tamoxifen (Tam) and G1, a GPR30-specific agonist, and was reversed by the GPR30-specific antagonist, G15. Through GPR30, estrogen-mediated the down-regulation of occludin; a tight junction protein and HCV receptor, by promoting activation of matrix metalloproteinases (MMPs). Activated MMP-9 was secreted in response to estrogen, cleaving occludin in the extracellular Domain D, the motif required for HCV entry and spread. This pathway gives new insight into a novel innate immune pathway and the disparate host-virus responses to HCV demonstrated by the two sexes. Moreover, these data suggest that hormone replacement therapy may have beneficial antiviral properties for HCV-infected postmenopausal women and show promise for new antiviral treatments for both men and women.

Keywords: HCV, estrogen, occludin, MMPs

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Authors: Arth Bohra, Marwa Mahmoud

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In this paper, we utilize computer vision to develop a tool for youth coaches to formulate set-piece tactics for their players. We used the Soccernet database to extract the ResNet features and camera calibration data for over 3000 corner kick across 500 professional matches in the top 6 European leagues (English Premier League, UEFA Champions League, Ligue 1, La Liga, Serie A, Bundesliga). Leveraging the provided homography matrix, we construct a feature vector representing the formation of players on these corner kicks. Additionally, labeling the videos manually, we obtained the pass-trajectory of each of the 3000+ corner kicks by segmenting the field into four zones. Next, after determining the localization of the players and ball, we used event data to give the corner kicks a rating on a 1-4 scale. By employing a Convolutional Neural Network, our model managed to predict the success of a corner kick given the formations of players. This suggests that with the right formations, teams can optimize the way they approach corner kicks. By understanding this, we can help coaches formulate set-piece tactics for their own teams in order to maximize the success of their play. The proposed model can be easily extended; our method could be applied to even more game situations, from free kicks to counterattacks. This research project also gives insight into the myriad of possibilities that artificial intelligence possesses in transforming the domain of sports.

Keywords: soccer, corner kicks, AI, computer vision

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Authors: Neslihan Genckal, Reha Gursoy, Vedat Z. Dogan

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In this study, dynamic responses of composite plates on elastic foundations subjected to impact and moving loads are investigated. The first order shear deformation (FSDT) theory is used for moderately thick plates. Pasternak-type (two-parameter) elastic foundation is assumed. Elastic foundation effects are integrated into the governing equations. It is assumed that plate is first hit by a mass as an impact type loading then the mass continues to move on the composite plate as a distributed moving loading, which resembles the aircraft landing on airport pavements. Impact and moving loadings are modeled by a mass-spring-damper system with a wheel. The wheel is assumed to be continuously in contact with the plate after impact. The governing partial differential equations of motion for displacements are converted into the ordinary differential equations in the time domain by using Galerkin’s method. Then, these sets of equations are solved by using the Runge-Kutta method. Several parameters such as vertical and horizontal velocities of the aircraft, volume fractions of the steel rebar in the reinforced concrete layer, and the different touchdown locations of the aircraft tire on the runway are considered in the numerical simulation. The results are compared with those of the ABAQUS, which is a commercial finite element code.

Keywords: elastic foundation, impact, moving load, thick plate

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Authors: Karan Sharma, Ajay Kumar

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Epileptic seizure is a type of disease due to which electrical charge in the brain flows abruptly resulting in abnormal activity by the subject. One percent of total world population gets epileptic seizure attacks.Due to abrupt flow of charge, EEG (Electroencephalogram) waveforms change. On the display appear a lot of spikes and sharp waves in the EEG signals. Detection of epileptic seizure by using conventional methods is time-consuming. Many methods have been evolved that detect it automatically. The initial part of this paper provides the review of techniques used to detect epileptic seizure automatically. The automatic detection is based on the feature extraction and classification patterns. For better accuracy decomposition of the signal is required before feature extraction. A number of parameters are calculated by the researchers using different techniques e.g. approximate entropy, sample entropy, Fuzzy approximate entropy, intrinsic mode function, cross-correlation etc. to discriminate between a normal signal & an epileptic seizure signal.The main objective of this review paper is to present the variations in the EEG signals at both stages (i) Interictal (recording between the epileptic seizure attacks). (ii) Ictal (recording during the epileptic seizure), using most appropriate methods of analysis to provide better healthcare diagnosis. This research paper then investigates the effects of a noninvasive healing therapy on the subjects by studying the EEG signals using latest signal processing techniques. The study has been conducted with Reiki as a healing technique, beneficial for restoring balance in cases of body mind alterations associated with an epileptic seizure. Reiki is practiced around the world and is recommended for different health services as a treatment approach. Reiki is an energy medicine, specifically a biofield therapy developed in Japan in the early 20th century. It is a system involving the laying on of hands, to stimulate the body’s natural energetic system. Earlier studies have shown an apparent connection between Reiki and the autonomous nervous system. The Reiki sessions are applied by an experienced therapist. EEG signals are measured at baseline, during session and post intervention to bring about effective epileptic seizure control or its elimination altogether.

Keywords: EEG signal, Reiki, time consuming, epileptic seizure

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Authors: Yassine Messaoud, Gordon Nigh, Faouzi Messaoud, Han Chen

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The effect of climate change on tree growth in boreal and temperate forests has received increased interest in the context of global warming. However, most studies were conducted in small areas and with a limited number of tree species. Here, we examined the height growth responses of seventeen tree species to climate change in Western North America. 37009 stands from forest inventory databases in Canada and USA with varying establishment date were selected. Dominant and co-dominant trees from each stand were sampled to determine top tree height at 50 years breast height age. Height was related to historical mean annual and summer temperatures, annual and summer Palmer Drought Severity Index, tree establishment date, slope, aspect, soil fertility as determined by the rate of carbon organic matter decomposition (carbon/nitrogen), geographic locations (latitude, longitude, and elevation), species range (coastal, interior, and both ranges), shade tolerance and leaf form (needle leaves, deciduous needle leaves, and broadleaves). Climate change had mostly a positive effect on tree height growth. The results explained 62.4% of the height growth variance. Since 1880, height growth increase was greater for coastal, high shade tolerant, and broadleaf species. Height growth increased more on steep slopes and high soil fertility soils. Greater height growth was mostly observed at the leading range and upward. Conversely, some species showed the opposite pattern probably due to the increase of drought (coastal Mediterranean area), precipitation and cloudiness (Alaska and British Columbia) and peculiarity (higher latitudes-lower elevations and vice versa) of western North America topography. This study highlights the role of the species ecological amplitude and traits, and geographic locations as the main factors determining the growth response and its magnitude to the recent global climate change.

Keywords: Height growth, global climate change, species range, species characteristics, species ecological amplitude, geographic locations, western North America

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Authors: Jummalla Aashrith, N. L. Shiva Sai, K. Bhavya Sri

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The swift progress of web technology has not only amassed a vast reservoir of internet data but also triggered a substantial surge in data generation. The internet has metamorphosed into one of the dynamic hubs for online education, idea dissemination, as well as opinion-sharing. Notably, the widely utilized social networking platform Twitter is experiencing considerable expansion, providing users with the ability to share viewpoints, participate in discussions spanning diverse communities, and broadcast messages on a global scale. The upswing in online engagement has sparked a significant curiosity in subjective analysis, particularly when it comes to Twitter data. This research is committed to delving into sentiment analysis, focusing specifically on the realm of Twitter. It aims to offer valuable insights into deciphering information within tweets, where opinions manifest in a highly unstructured and diverse manner, spanning a spectrum from positivity to negativity, occasionally punctuated by neutrality expressions. Within this document, we offer a comprehensive exploration and comparative assessment of modern approaches to opinion mining. Employing a range of machine learning algorithms such as Naive Bayes and Logistic Regression, our investigation plunges into the domain of Twitter data streams. We delve into overarching challenges and applications inherent in the realm of subjectivity analysis over Twitter.

Keywords: machine learning, sentiment analysis, visualisation, python

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Authors: Zahow Muoftah

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Traditional manual visual grading of fruits has been one of the agricultural industry’s major challenges due to its laborious nature as well as inconsistency in the inspection and classification process. The main requirements for computer vision and visual processing are some effective techniques for identifying defects and estimating defect areas. Automated defect detection using computer vision and machine learning has emerged as a promising area of research with a high and direct impact on the visual inspection domain. Grading, sorting, and disease detection are important factors in determining the quality of fruits after harvest. Many studies have used computer vision to evaluate the quality level of fruits during post-harvest. Many studies have used computer vision to evaluate the quality level of fruits during post-harvest. Many studies have been conducted to identify diseases and pests that affect the fruits of agricultural crops. However, most previous studies concentrated solely on the diagnosis of a lesion or disease. This study focused on a comprehensive study to identify pests and diseases of apple and banana fruits using detection and classification defects on Banana and Apples by Computer Vision. As a result, the current article includes research from these domains as well. Finally, various pattern recognition techniques for detecting apple and banana defects are discussed.

Keywords: computer vision, banana, apple, detection, classification

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Authors: Parker Townes, Prabdeep Panesar, Chunlin Liu, Soo Youn Lee, Dan Devoe, Paul D. Arnold, Jennifer Crosbie, Russell Schachar

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Attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are impairing childhood neurodevelopmental disorders with problems in executive functions. Executive functions are higher-level mental processes essential for daily functioning and goal attainment. There is genetic and neural overlap between ADHD and ASD. The aim of this meta-analysis was to evaluate if pediatric ASD and ADHD have distinct executive function profiles. This review was completed following Cochrane guidelines. Fifty-eight articles were identified through database searching, followed by a blinded screening in duplicate. A meta-analysis was performed for all task performance metrics evaluated by at least two articles. Forty-five metrics from 24 individual tasks underwent analysis. No differences were found between youth with ASD and ADHD in any domain under direct comparison. However, individuals with ASD and ADHD exhibited deficient attention, flexibility, visuospatial abilities, working memory, processing speed, and response inhibition compared to controls. No deficits in planning were noted in either disorder. Only 11 studies included a group with comorbid ASD+ADHD, making it difficult to determine whether common executive function deficits are a function of comorbidity. Further research is needed to determine if comorbidity accounts for the apparent commonality in executive function between ASD and ADHD.

Keywords: autism spectrum disorder, ADHD, neurocognition, executive function, youth

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Authors: Muhammad Ali

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Cyber-attacks and anomaly detection on the Internet of Things (IoT) infrastructure is emerging concern in the domain of data-driven intrusion. Rapidly increasing IoT risk is now making headlines around the world. denial of service, malicious control, data type probing, malicious operation, DDos, scan, spying, and wrong setup are attacks and anomalies that can affect an IoT system failure. Everyone talks about cyber security, connectivity, smart devices, and real-time data extraction. IoT devices expose a wide variety of new cyber security attack vectors in network traffic. For further than IoT development, and mainly for smart and IoT applications, there is a necessity for intelligent processing and analysis of data. So, our approach is too secure. We train several machine learning models that have been compared to accurately predicting attacks and anomalies on IoT systems, considering IoT applications, with ANOVA-based feature selection with fewer prediction models to evaluate network traffic to help prevent IoT devices. The machine learning (ML) algorithms that have been used here are KNN, SVM, NB, D.T., and R.F., with the most satisfactory test accuracy with fast detection. The evaluation of ML metrics includes precision, recall, F1 score, FPR, NPV, G.M., MCC, and AUC & ROC. The Random Forest algorithm achieved the best results with less prediction time, with an accuracy of 99.98%.

Keywords: machine learning, analysis of variance, Internet of Thing, network security, intrusion detection

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Authors: Gabriel Cucato, Marilia Correia, Wagner Domingues, Aline Palmeira, Paulo Longano, Nelson Wolosker, Raphael Ritti-Dias

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Women with peripheral arterial disease (PAD) present lower walking capacity in comparison with men. However, whether cardiovascular regulation is also different between genders is unknown. Thus, the aim of this study was to compare walking capacity and cardiovascular regulation between men and women with PAD. A total of 23 women (66±7 yrs) and 31 men (64±9 yrs) were recruited. Patients performed a 6-minute test and the onset claudication distance and total walking distance were measured. Additionally, cardiovascular regulation was assessed by arterial stiffness (pulse wave velocity and augmentation index) and heart rate variability (frequency domain). Independent T test or Mann-Whitney U test were performed. In comparison with men, women present lower onset claudication distance (108±66m vs. 143±50m; P=0.032) and total walking distance (286±83m vs. 361±91 m, P=0.007). Regarding cardiovascular regulation, there were no differences in heart rate variability SDNN (72±160ms vs. 32±22ms, P=0.587); RMSSD (75±209 vs. 25±22ms, P=0.726); pNN50 (11±17ms vs. 8±14ms, P=0.836) in women and men, respectively. Moreover, there were no difference in augmentation index (39±10% vs. 34±11%, P=0.103); pulse pressure (59±17mmHg vs. 56±19mmHg, P=0.593) and pulse wave velocity (8.6±2.6m\s vs. 9.0±2.7m/s, P=0.580). In conclusion, women have impaired walking capacity compared to men. However, sex differences were not observed on cardiovascular regulation in patients with PAD.

Keywords: exercise, intermittent claudication, cardiovascular load, arterial stiffness

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Authors: Zhihao Zheng, Zhilin Wang, Linxin Liu

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In the research of automated knowledge question-answering systems, accuracy and efficiency are critical challenges. This paper proposes a knowledge graph-enhanced Retrieval-Augmented Generation (RAG) method, combined with a Graph Neural Network (GNN) structure, to automatically determine the correctness of knowledge competition questions. First, a domain-specific knowledge graph was constructed from a large corpus of academic journal literature, with key entities and relationships extracted using Natural Language Processing (NLP) techniques. Then, the RAG method's retrieval module was expanded to simultaneously query both text databases and the knowledge graph, leveraging the GNN to further extract structured information from the knowledge graph. During answer generation, contextual information provided by the knowledge graph and GNN is incorporated to improve the accuracy and consistency of the answers. Experimental results demonstrate that the knowledge graph and GNN-enhanced RAG method perform excellently in determining the correctness of questions, achieving an accuracy rate of 95%. Particularly in cases involving ambiguity or requiring contextual information, the structured knowledge provided by the knowledge graph and GNN significantly enhances the RAG method's performance. This approach not only demonstrates significant advantages in improving the accuracy and efficiency of automated knowledge question-answering systems but also offers new directions and ideas for future research and practical applications.

Keywords: knowledge graph, graph neural network, retrieval-augmented generation, NLP

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Authors: Tomer Gans-Or, Shmulik Pinkert

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The determination of lateral pile capacity per unit length is a key aspect in geotechnical engineering. Traditional approaches for assessing piles lateral capacity in cohesive soils involve the application of upper-bound and lower-bound plasticity theorems. However, a comprehensive solution encompassing the entire spectrum of soil strength parameters, particularly in frictional soils with or without cohesion, is still lacking. This research introduces an innovative implementation of the slice method limit equilibrium solution for lateral capacity assessment. For any given numerical discretization of the soil's domain around the pile, the lateral capacity evaluation is based on mobilized strength concept. The critical failure geometry is then found by a unique optimization procedure which includes both factor of safety minimization and geometrical optimization. The robustness of this suggested methodology is that the solution is independent of any predefined assumptions. Validation of the solution is accomplished through a comparison with established plasticity solutions for cohesive soils. Furthermore, the study demonstrates the applicability of the limit equilibrium method to address unresolved cases related to frictional and cohesive-frictional soils. Beyond providing capacity values, the method enables the utilization of the mobilized strength concept to generate safety-factor distributions for scenarios representing pre-failure states.

Keywords: lateral pile capacity, slice method, limit equilibrium, mobilized strength

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Authors: Baohua Yu

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In line with wider tends in the expansion of international student mobility, the number of international higher degree research students has grown at a significant rate in recent years. In particular, Australia has become a hub for attracting international higher degree research students from around the world. However, research has identified that international higher degree research students often encounter a wide range of academic and socio-cultural challenges in adapting to their new environment. Moreover, this can have a significant bearing on their levels of satisfaction with their studies. This paper outlines the findings of a mixed method study exploring the experiences and perceptions of international higher degree research students in Australia. Findings revealed that IRHD students’ overall and academic satisfaction in Australia were highly related to each other, and they were strongly influenced by their learning and research, moderately influenced by co-national support and intercultural contact ability. Socio-cultural satisfaction seemed to belong to a different domain from academic satisfaction because it was explained by a different set of variables such as living and adaptation and intercultural contact ability. In addition, the most important issues in terms of satisfaction were not directly related to academic studies. Instead, factors such as integration into the community, interacting with other students, relationships with supervisors, and the provision of adequate desk space were often given the greatest weight. Implications for how university policy can better support international doctoral students are discussed.

Keywords: international higher degree research students, academic adaptation, socio-cultural adaptation, student satisfaction

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Authors: Zhou Yang, Heli Sun, Jianbin Huang, Jizhong Zhao, Shaojie Qiao

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Traffic prediction is essential in a multitude of ways in modern urban life. The researchers of earlier work in this domain carry out the investigation chiefly with two major focuses: (1) the accurate forecast of future values in multiple time series and (2) knowledge extraction from spatial-temporal correlations. However, two key considerations for traffic prediction are often missed: the completeness of raw data and the full context of the prediction timestamp. Concentrating on the two drawbacks of earlier work, we devise an approach that can address these issues in a two-phase framework. First, we utilize the raw trajectories to a greater extent through building a VLA table and data compression. We obtain the intra-trajectory features with graph-based encoding and the intertrajectory ones with a grid-based model and the technique of back projection that restore their surrounding high-resolution spatial-temporal environment. To the best of our knowledge, we are the first to study direct feature extraction from raw trajectories for traffic prediction and attempt the use of raw data with the least degree of reduction. In the prediction phase, we provide a broader context for the prediction timestamp by taking into account the information that are around it in the training dataset. Extensive experiments on several well-known datasets have verified the effectiveness of our solution that combines the strength of raw trajectory data and prediction context. In terms of performance, our approach surpasses several state-of-the-art methods for traffic prediction.

Keywords: traffic prediction, raw data utilization, context building, data reduction

Procedia PDF Downloads 127

Authors: Loubna Hamami, Bouchaib Nassereddine

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Due to the evolution of technologies, the need to observe and manage hostile environments, and reduction in size, wireless sensor networks (WSNs) are becoming essential and implicated in the most fields of life. WSNs enable us to change the style of living, working and interacting with the physical environment. The agricultural sector is one of such sectors where WSNs are successfully used to get various benefits. For successful agricultural production, the irrigation system is one of the most important factors, and it plays a tactical role in the process of agriculture domain. However, it is considered as the largest consumer of freshwater. Besides, the scarcity of water, the drought, the waste of the limited available water resources are among the critical issues that touch the almost sectors, notably agricultural services. These facts are leading all governments around the world to rethink about saving water and reducing the volume of water used; this requires the development of irrigation practices in order to have a complete and independent system that is more efficient in the management of irrigation. Consequently, the selection of WSNs in irrigation system has been a benefit for developing the agriculture sector. In this work, we propose a prototype for a complete and intelligent irrigation system based on wireless sensor networks and we present and discuss the design of this prototype. This latter aims at saving water, energy and time. The proposed prototype controls water system for irrigation by monitoring the soil temperature, soil moisture and weather conditions for estimation of water requirements of each plant.

Keywords: precision irrigation, sensor, wireless sensor networks, water resources

Procedia PDF Downloads 153

Authors: Toktam Khatibi

Abstract:

Self-supervised learning (SSL) includes machine learning models that are trained on one aspect and/or one part of the input to learn other aspects and/or part of it. SSL models are divided into two different categories, including pre-text task-based models and contrastive learning ones. Pre-text tasks are some auxiliary tasks learning pseudo-labels, and the trained models are further fine-tuned for downstream tasks. However, one important disadvantage of SSL using pre-text task solving is defining an appropriate pre-text task for each image dataset with a variety of image modalities. Therefore, it is required to design an appropriate pretext task automatically for each dataset and each downstream task. To the best of our knowledge, the automatic designing of pretext tasks for image analysis has not been considered yet. In this paper, we present a framework based on In-context learning that describes each task based on its input and output data using a pre-trained image transformer. Our proposed method combines the input image and its learned description for optimizing the pre-text task design and its hyper-parameters using Meta-learning models. The representations learned from the pre-text tasks are fine-tuned for solving the downstream tasks. We demonstrate that our proposed framework outperforms the compared ones on unseen tasks and image modalities in addition to its superior performance for previously known tasks and datasets.

Keywords: in-context learning (ICL), meta learning, self-supervised learning (SSL), vision-language domain, transformers

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Authors: Naziema B. Jappie

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The objective of this study is to examine the relationship between college readiness characteristics and learning outcome assessment scores. About this, it is important to examine the first-year retention and success rate. In order to undertake this study, it will be necessary to look at proficiency levels on general and domain-specific knowledge and skills reflected on national benchmark test scores (NBT), in-college interventions and course-taking patterns. Preliminary results based on data from more than 1000 students suggest that there is a positive association between NBT scores and students’ 1st-year college GPA and their retention status. For example, 63% of students with a proficient level of math skills in the NBT had the highest level of GPA at the end of 1st-year of college in comparison to 56% of those who started with a primary or intermediate level, respectively. The retention rates among those with proficiency levels were also higher than those with basic or intermediate levels (98% vs. 93% and 88%, respectively). By the end of 3rd year in college, students with intermediate or proficient entering NBT math skills had 7% and 8% of dropout rate, compared to 14% for those started at primary level; a greater percentage of students qualified by the end of 3rd-year qualified among proficient students than that among intermediate or basic level students (50% vs. 44% and 27% respectively). The findings of this study added knowledge to the field in South Africa and are expected to help stakeholders and policymakers to better understand college learning and challenges for students with disadvantaged backgrounds and provide empirical evidence in support of related practices and policies.

Keywords: assessment, data analysis, performance, proficiency, policy, student success

Procedia PDF Downloads 132