Search results for: Justin Bin

Authors: Najib Khumaidillah

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A conversation needs skills to create the good flow of it. The skills also need to be paid attention by a host like Oprah Winfrey and Justin Bieber as an artist. This study is aimed at describing turn taking strategies and adjacency pairs used by the speakers. The data are from one segment of The Oprah Winfrey Show’s transcription with Justin Bieber. Those are analyzed by Stenstorm’s turn taking theories and adjacency pairs theories. From the analysis, it was found that both speakers use various turn taking strategies and adjacency pairs. These findings are hoped to be an example for non-native English speaker in doing English conversation and advance people’s comprehension of how to organize good conversation structure.

Keywords: adjacency pairs, conversation structure, the Oprah Winfrey show, turn taking

Procedia PDF Downloads 195

Authors: Justin M. Swanson, Anna Nelson, Daniel Handysides, Patti Herring, Christopher Hill

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Not only may social media use have a psychological impact, but increased use may be tied to decreases in physical activity and influencing sedentary behaviors. Social media can be used to share physically active lifestyles and possibly influence others to participate. In contrast, social media use may have adverse effects by decreasing participation in exercise. This study used a qualitative design to examine the relationship between social media use and exercise patterns. Participants were asked questions about their social media habits and how it might impact their physical activity behaviors. Self-reported exercise seemed to increase after viewing others engage in relatable activities or viewing someone that has overcame challenges. To increase the likelihood of engaging in exercise, exercise related posts should be low in difficulty, require few materials, or displayed progress from the individual posting.

Keywords: social media, exercise, physical activity, adults

Procedia PDF Downloads 265

Authors: Yamba Dabone, Tounwendyam Frédéric Ouedraogo, Pengwendé Justin Kouraogo, Oumarou Sie

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Reliable data transport over the Internet is one of the goals of researchers in the field of computer science. Data such as videos and audio files are becoming increasingly large. As a result, transporting them over the Internet is becoming difficult. Therefore, it has been important to establish a method to locally interconnect autonomous systems (AS) with each other to facilitate traffic exchange. It is in this context that Internet Exchange Points (IXPs) are set up to facilitate local and even regional traffic. They are now the lifeblood of the Internet. Therefore, it is important to think about the factors that can characterize IXPs. However, other more quantifiable characteristics can help determine the quality of an IXP. In addition, these characteristics may allow ISPs to have a clearer view of the exchange node and may also convince other networks to connect to an IXP. To that end, we define five new IXP characteristics: the attraction rate (τₐₜₜᵣ); and the peering rate (τₚₑₑᵣ); the target rate of an IXP (Objₐₜₜ); the number of IXP links (Nₗᵢₙₖ); the resistance rate τₑ𝒻𝒻 and the attraction failure rate (τ𝒻).

Keywords: characteristic, autonomous system, internet service provider, internet exchange point, rate

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Authors: Regina Connolly, Justin Connolly

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Despite the benefits which they confer, Information & Communication Technologies (ICT) also have the potential to be used negatively. This paper focuses on one of those negative social effects - adolescent cyberbullying. Although early research in this field has pointed to the fact that the successful intervention and resolution of bullying incidents is to a large degree dependent on such incidents being reported to an adult caregiver, the literature consistently shows that adolescents who have been bullied tend not to inform others of their experiences. However, the reasons underlying such reluctance to seek adult intervention remain undetermined. Similarly, the degree to which gender, age or other variables apply in the case of adolescents’ resistance to report cyberbullying experiences has yet to be established. Understanding the factors that influence this resistance to communicate on the part of adolescents will assist caregivers, teachers and those involved in the formulation of school anti-bullying policies in their attempts to counter the cyberbullying phenomenon.

Keywords: information and Communication technologies, technology-enabled bullying, cyberbullying

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Authors: Robert J. Sutton, Jon Elvins, Sean Casey, Eifion Jewell, Justin R. Searle

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Thermochemical storage utilises chemical salts which store and release energy a fully reversible endo/exothermic chemical reaction. Highly porous vermiculite impregnated with CaCl2, LiNO3 and MgSO4 (SIMs – Salt In Matrices) are proposed as potential materials for long-term thermochemical storage. The behavior of these materials during typical hydration and dehydration cycles is investigated. A simple moisture experiment represents the hydration, whilst thermogravimetric analysis (TGA) represents the dehydration. Further experiments to approximate the energy density and to determine the peak output temperatures of the SIMs are conducted. The CaCl2 SIM is deemed the best performing SIM across most experiments, whilst the results of MgSO4 SIM indicate difficulty associated with energy recovery.

Keywords: hydrated states, inter-seasonal heat storage, moisture sorption, salt in matrix

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Authors: Justin Reverdi, Sixin Zhang, Serge Gratton, Said Aoues, Thomas Pellegrini

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In Vapor Cycle Systems, the flow sensor plays a key role in different monitoring and control purposes. However, physical sensors can be expensive, inaccurate, heavy, cumbersome, or highly sensitive to vibrations, which is especially problematic when embedded into an aircraft. The conception of a virtual sensor based on other standard sensors is a good alternative. In this paper, a data-driven model using a Convolutional Neural Network is proposed to estimate the flow of the compressor. To fit the model to our dataset, we tested different loss functions. We show in our application that a Dynamic Time Warping based loss function called DILATE leads to better dynamical performance than the vanilla mean squared error (MSE) loss function. DILATE allows choosing a trade-off between static and dynamic performance.

Keywords: deep learning, dynamic time warping, vapor cycle system, virtual sensor

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Authors: Eric Huang, Coleman DeLude, Justin Romberg, Saibal Mukhopadhyay, Madhavan Swaminathan

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High performance computing (HPC) based emulators can be used to model the scattering from multiple stationary and moving targets for RADAR applications. These emulators rely on the RADAR Cross Section (RCS) of the targets being available in complex scenarios. Representing the RCS using tables generated from electromagnetic (EM) simulations is often times cumbersome leading to large storage requirement. This paper proposed a spherical harmonic based anisotropic scatterer model to represent the RCS of complex targets. The problem of finding the locations and reflection profiles of all scatterers can be formulated as a linear least square problem with a special sparsity constraint. This paper solves this problem using a modified Orthogonal Matching Pursuit algorithm. The results show that the spherical harmonic based scatterer model can effectively represent the RCS data of complex targets.

Keywords: RADAR, RCS, high performance computing, point scatterer model

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Authors: Anudeep Appe, Bhanu Poluparthi, Lakshmi Kasivajjula, Udai Mv, Sobha Bagadi, Punya Modi, Aditya Singh, Hemanth Gunupudi, Spenser Troiano, Jeff Paul, Justin Stovall, Justin Yamamoto

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The necessity of data-driven decisions in healthcare strategy formulation is rapidly increasing. A reliable framework which helps identify factors impacting a healthcare provider facility or a hospital (from here on termed as facility) market share is of key importance. This pilot study aims at developing a data-driven machine learning-regression framework which aids strategists in formulating key decisions to improve the facility’s market share which in turn impacts in improving the quality of healthcare services. The US (United States) healthcare business is chosen for the study, and the data spanning 60 key facilities in Washington State and about 3 years of historical data is considered. In the current analysis, market share is termed as the ratio of the facility’s encounters to the total encounters among the group of potential competitor facilities. The current study proposes a two-pronged approach of competitor identification and regression approach to evaluate and predict market share, respectively. Leveraged model agnostic technique, SHAP, to quantify the relative importance of features impacting the market share. Typical techniques in literature to quantify the degree of competitiveness among facilities use an empirical method to calculate a competitive factor to interpret the severity of competition. The proposed method identifies a pool of competitors, develops Directed Acyclic Graphs (DAGs) and feature level word vectors, and evaluates the key connected components at the facility level. This technique is robust since its data-driven, which minimizes the bias from empirical techniques. The DAGs factor in partial correlations at various segregations and key demographics of facilities along with a placeholder to factor in various business rules (for ex. quantifying the patient exchanges, provider references, and sister facilities). Identified are the multiple groups of competitors among facilities. Leveraging the competitors' identified developed and fine-tuned Random Forest Regression model to predict the market share. To identify key drivers of market share at an overall level, permutation feature importance of the attributes was calculated. For relative quantification of features at a facility level, incorporated SHAP (SHapley Additive exPlanations), a model agnostic explainer. This helped to identify and rank the attributes at each facility which impacts the market share. This approach proposes an amalgamation of the two popular and efficient modeling practices, viz., machine learning with graphs and tree-based regression techniques to reduce the bias. With these, we helped to drive strategic business decisions.

Keywords: competition, DAGs, facility, healthcare, machine learning, market share, random forest, SHAP

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Authors: Justin D. Olmanson, Katrina S. Kennett, Bill Cope

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In the field of education technology, innovation is often tightly coupled to recent technological inventions and emerging technologies. Despite this, some scholars have argued that using established technologies in new pedagogical or curricular ways recasts them and places them once more under the umbrella of emerging education technologies. In this study, we trace how an innovative education technology design emerged, not from a technological breakthrough, but rather via a techno-pedagogical pivot. We describe the design and impact of a digital writing tool created to scaffold student self-evaluation of academic texts. We theorize about and trace how innovation can also emerge from a pivot, namely how leveraging existing practices in new ways can create pedagogically and experientially innovative learning opportunities. After describing the design of Info Writer, we unpack the results of a study based on an implementation the tool, and then theorize and reflect on the way the design process and study findings suggest that pivoting an existing practice can lead to innovative education technology designs.

Keywords: design, education, revision, technology, writing

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Authors: Justin Zhengjie Tan, Yang Zhao

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Adiabatic Rapid Passage, a popular method of achieving population inversion, is studied in a Rabi dimer model in the presence of noise which acts as a dissipative environment. The integration of the multi-Davydov D2 Ansatz into the time-dependent variational framework enables us to model the intricate quantum system accurately. By influencing the system with a driving field strength resonant with the energy spacing, the probability of adiabatic rapid passage, which is modelled after the Landau Zener model, can be derived along with several other observables, such as the photon population. The effects of a dissipative environment can be reproduced by coupling the system to a common phonon mode. By manipulating the strength and frequency of the driving field, along with the coupling strength of the phonon mode to the qubits, we are able to control the qubits and photon dynamics and subsequently increase the probability of Adiabatic Rapid Passage happening.

Keywords: quantum electrodynamics, adiabatic rapid passage, Landau-Zener transitions, dissipative environment

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Authors: Alec Wells, Aminu Bello Usman, Justin McKeown

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The growing usage of smart speakers raises many privacy and trust concerns compared to other technologies such as smart phones and computers. In this study, a proxy measure of trust is used to gauge users’ opinions on three different technologies based on an empirical study, and to understand which technology most people are most likely to trust. The collected data were analysed using the Kruskal-Wallis H test to determine the statistical differences between the users’ trust level of the three technologies: smart speaker, computer and smart phone. The findings of the study revealed that despite the wide acceptance, ease of use and reputation of smart speakers, people find it difficult to trust smart speakers with their sensitive information via the Direct Voice Input (DVI) and would prefer to use a keyboard or touchscreen offered by computers and smart phones. Findings from this study can inform future work on users’ trust in technology based on perceived ease of use, reputation, perceived credibility and risk of using technologies via DVI.

Keywords: direct voice input, risk, security, technology, trust

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Authors: Justin Reverdi, Sixin Zhang, Saïd Aoues, Fabrice Gamboa, Serge Gratton, Thomas Pellegrini

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In vapor cycle systems, the mass flow sensor plays a key role for different monitoring and control purposes. However, physical sensors can be inaccurate, heavy, cumbersome, expensive, or highly sensitive to vibrations, which is especially problematic when embedded into an aircraft. The conception of a virtual sensor, based on other standard sensors, is a good alternative. This paper has two main objectives. Firstly, a data-driven model using a convolutional neural network is proposed to estimate the mass flow of the compressor. We show that it significantly outperforms the standard polynomial regression model (thermodynamic maps) in terms of the standard MSE metric and engineer performance metrics. Secondly, a semi-automatic segmentation method is proposed to compute the engineer performance metrics for real datasets, as the standard MSE metric may pose risks in analyzing the dynamic behavior of vapor cycle systems.

Keywords: deep learning, convolutional neural network, vapor cycle system, virtual sensor

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Authors: Justin-Herve Noubissi, Jean Claude Kamgang, Eric Ramat, Januarius Asongu, Christophe Cambier

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The goal of our study is to analyse the impact of climatic factors in malaria transmission taking into account migration between Douala and Yaoundé, two cities of Cameroon country. We show how variations of climatic factors such as temperature and relative humidity affect the malaria spread. We propose a meta-population model of the dynamic transmission of malaria that evolves in space and time and that takes into account temperature and relative humidity and the migration between Douala and Yaoundé. We also integrate the variation of environmental factors as events also called mathematical impulsion that can disrupt the model evolution at any time. Our modelling has been done using the Discrete EVents System Specification (DEVS) formalism. Our implementation has been done on Virtual Laboratory Environment (VLE) that uses DEVS formalism and abstract simulators for coupling models by integrating the concept of DEVS.

Keywords: compartmental models, DEVS, discrete events, meta-population model, VLE

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Authors: Justin Pargeter, Metin Eren

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Lithic miniaturization, the systematic production and use of small tools from small cores, was a consequential development in Pleistocene lithic technology. The bipolar reduction is an important, but often overlooked and misidentified, strategy for lithic miniaturization. This experiment addresses the role of axial bipolar reduction in processes of lithic miniaturization. The experiments answer two questions: what benefits does axial bipolar reduction provide, and can we distinguish axial bipolar reduction from freehand reduction? Our experiments demonstrate the numerous advantages of bipolar reduction in contexts of lithic miniaturization. Bipolar reduction produces more cutting edge per gram and is more economical than freehand reduction. Our cutting edge to mass values exceeds even those obtained with pressure blade production on high-quality obsidian. The experimental results show that bipolar reduction produces cutting edge quicker and is more efficient than freehand reduction. We show that bipolar reduction can be distinguished from freehand reduction with a high degree of confidence using the quantitative criteria in these experiments. These observations overturn long-held perceptions about bipolar reduction. We conclude by discussing the role of bipolar reduction in lithic miniaturization and Stone Age economics more broadly.

Keywords: lithic miniaturization, bipolar reduction, late Pleistocene, Southern Africa

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Authors: Aldin Justin Sundararaj, Austin Lord Tennyson, Divya Jose, A. N. Subash

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Blast waves are generated due to the explosions of high energy materials. An explosion yielding a blast wave has the potential to cause severe damage to buildings and its personnel. In order to understand the physics of effects of blast pressure on buildings, studies in the shock tube on generic configurations are carried out at various pressures on discrete models. The strength of shock wave is systematically varied by using different driver gases and diaphragm thickness. The basic material of the diaphragm is Aluminum. To simulate the effect of shock waves on discrete models a shock tube was used. Generic models selected for this study are suitably scaled cylinder, cone and cubical blocks. The experiments were carried out with 2mm diaphragm with burst pressure ranging from 28 to 31 bar. Numerical analysis was carried out over these discrete models. A 3D model of shock-tube with different discrete models inside the tube was used for CFD computation. It was found that cone has dissipated most of the shock pressure compared to cylinder and cubical block. The robustness and the accuracy of the numerical model were validation with the analytical and experimental data.

Keywords: shock wave, blast wave, discrete models, shock tube

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Authors: K. R. Guna, Aldin Justin Sundararaj, B. C. Pillai, A. N. Subash

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A systematic study has been made for ignition delay times measurement behind a reflected shock wave for the low molecular weight hydrocarbon fuel in argon simulated gas mixtures. The low molecular hydrocarbon fuel–oxygen was diluted with argon for desired concentration is taken for the study. The suitability of the shock tube for measuring the ignition delay time is demonstrated by measuring the ignition delay for the liquefied petroleum gas for equivalence ratios (ф=0.5 & 1) in the temperature range 1150-1650 K. The pressure range was fixed from 5-15 bar. The ignition delay was measured by recording the ignition-induced pressure jump and emission from CH radical simultaneously. From conducting experiments, it was found that the ignition delay time for liquefied petroleum gas reduces with increase in temperature. The shock tube was calibrated for ethane-oxygen gas mixture and the results obtained from this study is compared with the earlier reported values and found to be comparably well suited for the measurement of ignition delay times. The above work was carried out using the shock tube facility at propulsion and high enthalpy laboratory, Karunya University.

Keywords: ignition delay, LPG, reflected shock, shock wave

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Authors: Fahad Al Beyahi, Justin Thomas, Md Mamunur Rashid

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Various definitions of digital well-being have emerged in recent years, most of which center on the impacts -beneficial and detrimental- of digital technology on health and well-being (psychological, social, and financial). Other definitions go further, emphasizing the attainment of balance, viewing digital well-being as wholly subjective, the individual’s perception of optimal balance between the benefits and ills associated with online connectivity. Based on this broad conceptualization of digital well-being, we undertook a global survey measuring various dimensions of this emerging construct. The survey was administered across 35 nations and 7 world regions, with 1000 participants within each territory (N= 35000). Along with attitudinal, behavioral, and sociodemographic variables, the survey included measures of depression, anxiety, problematic social media use, gaming disorder, and other relevant metrics. Coupled with nation-level policy audits, these data were used to create a multinational (global) digital well-being index. Nations are ranked based on various dimensions of digital well-being, and predictive models are used to identify resilience and risk factors for problem technology use. In this paper, we will discuss key findings from the survey and the index. This work can inform public policy and shape our responses to the emerging implications of lives increasingly lived online and interconnected with digital technology.

Keywords: technology, health, behavioral addiction, digital wellbeing

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Authors: David R. LaVetter, Justin B. Homatas, Claudia Benavides Espinoza

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An increased awareness of depression rates among collegiate athletes has aided educational institutions to evaluate their mental health resources for athletes. This paper adds to our knowledge of this growing problem among collegiate athletes. National athletic associations and educational institutions are more knowledgeable of the mental health crisis facing hundreds of thousands of athletes each year, and some have implemented resources to improve mental health. However, college athletes continue to experience depressive symptoms at increasing rates. In this paper, depression rates for the vast numbers of collegiate athletes were found to be significantly greater than the general adult population. This paper used the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) method to examine the literature’s findings on depression rates among collegiate athletes. Particularly, this study answers questions related to risk factors of college athletes’ depressive symptoms. Risk factors unique to this population are also discussed. Prevalence rates by sport participant gender and sport are provided. Implementation measures in current practice at educational institutions in the U.S. are discussed to help alleviate depression rates among college athletes.

Keywords: college athletes, depression, risk factors, mental health

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Authors: Justin Tang

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Balance tests are frequently used to diagnose concussions on the sidelines of sporting events. Manual scoring, however, is labor intensive and subjective, and many concussions go undetected. This study institutes a novel approach to conducting the Balance Error Scoring System (BESS) more quantitatively using Microsoft’s gaming system Kinect, which uses a contactless sensor and several cameras to receive data and estimate body limb positions. Using a machine learning approach, Visual Gesture Builder, and a deterministic approach, MATLAB, we tested whether the Kinect can differentiate between “correct” and erroneous stances of the BESS. We created the two separate solutions by recording test videos to teach the Kinect correct stances and by developing a code using Java. Twenty-two subjects were asked to perform a series of BESS tests while the Kinect was collecting data. The Kinect recorded the subjects and mapped key joints onto their bodies to obtain angles and measurements that are interpreted by the software. Through VGB and MATLAB, the videos are analyzed to enumerate the number of errors committed during testing. The resulting statistics demonstrate a high correlation between manual scoring and the Kinect approaches, indicating the viability of the use of remote tracking devices in conducting concussion tests.

Keywords: automated, concussion detection, contactless sensors, microsoft kinect

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Authors: Justin Orimisan Ijigbade

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The study was conducted to assess the effect of climate variability on honeybee’s production in Ondo State, Nigeria. Multistage sampling technique was employed to collect the data from 60 beekeepers across six Local Government Areas in Ondo State. Data collected were subjected to descriptive statistics and multiple regression model analyses. The results showed that 93.33% of the respondents were male with 80% above 40 years of age. Majority of the respondents (96.67%) had formal education and 90% produced honey for commercial purpose. The result revealed that 90% of the respondents admitted that low temperature as a result of long hours/period of rainfall affected the foraging efficiency of the worker bees, 73.33% claimed that long period of low humidity resulted in low level of nectar flow, while 70% submitted that high temperature resulted in improper composition of workers, dunes and queen in the hive colony. The result of multiple regression showed that beekeepers’ experience, educational level, access to climate information, temperature and rainfall were the main factors affecting honey bees production in the study area. Therefore, beekeepers should be given more education on climate variability and its adaptive strategies towards ensuring better honeybees production in the study area.

Keywords: climate variability, honeybees production, humidity, rainfall and temperature

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Authors: Ian M. Grey, Bryan Roche, Anna Dillon, Justin Thomas, Sarah Cassidy, Dylan Colbert, Ian Stewart

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This paper presents findings from a study conducted in two schools in Abu Dhabi. The hypothesis is that teaching young children to derive various relations between stimuli leads to increases in full-scale IQ scores of typically developing children. In the experimental group, sixteen 6-7-year-old children were exposed over six weeks to an intensive training intervention designed specifically for their age group. This training intervention, presented on a tablet, aimed to improve their understanding of the relations Same, Opposite, Different, contextual control over the concept of Sameness and Difference, and purely arbitrary derived relational responding for Sameness and Difference. In the control group, sixteen 6-7-year-old children interacted with KIBO robotics over six weeks. KIBO purports to improve cognitive skills through engagement with STEAM activities. Increases in full-scale IQ were recorded for most children in the experimental group, while no increases in full-scale IQ were recorded for the control group. These findings support the hypothesis that relational skills underlie many aspects of general cognitive ability.

Keywords: early childhood, derived relational responding, intelligence, relational frame theory, relational skills

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Authors: Lauren M. McKinnon, Justin B. Miller, Michael F. Whiting, John S. K. Kauwe, Perry G. Ridge

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Background: Ramp sequences increase translational speed and accuracy when rare, slowly-translated codons are found at the beginnings of genes. Here, the results of the first analysis of ramp sequences in a phylogenetic construct are presented. Methods: Ramp sequences were compared from 211 vertebrates (110 Mammalian and 101 non-mammalian). The presence and absence of ramp sequences were analyzed as a binary character in a parsimony and maximum likelihood framework. Additionally, ramp sequences were mapped to the Open Tree of Life taxonomy to determine the number of parallelisms and reversals that occurred, and these results were compared to what would be expected due to random chance. Lastly, aligned nucleotides in ramp sequences were compared to the rest of the sequence in order to examine possible differences in phylogenetic signal between these regions of the gene. Results: Parsimony and maximum likelihood analyses of the presence/absence of ramp sequences recovered phylogenies that are highly congruent with established phylogenies. Additionally, the retention index of ramp sequences is significantly higher than would be expected due to random chance (p-value = 0). A chi-square analysis of completely orthologous ramp sequences resulted in a p-value of approximately zero as compared to random chance. Discussion: Ramp sequences recover comparable phylogenies as other phylogenomic methods. Although not all ramp sequences appear to have a phylogenetic signal, more ramp sequences track speciation than expected by random chance. Therefore, ramp sequences may be used in conjunction with other phylogenomic approaches.

Keywords: codon usage bias, phylogenetics, phylogenomics, ramp sequence

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Authors: Justin Ntokamunda Kadima, Christian Ahadi Irenge, Patient Birindwa Mulashe, Félicien Mushagalusa Kasali, Patient Wimba

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Background: Bacterial strains carrying multidrug resistance traits are gaining ground worldwide, especially in countries with limited resources. This study aimed to evaluate the spreading of multidrug-resistant bacteria strains in Bukavu city hospitals in the Democratic Republic of Congo. Methods: We analyzed 758 antibiogram data recorded in files of patients consulted between January 2016 and December 2017 at three reference hospitals selected as sentinel sites, namely the Panzi General Reference Hospital (HGP), BIO -PHARM hospital (HBP), and Saint Luc Clinic (CSL). Results: Of 758 isolates tested, the laboratories identified 12 bacterial strains in 712 isolates, of which 223 (29.42%) presented MDR profile, including Escherichia coli (11.48%), Klebsiella pneumonia (6.07%), Enterobacter (5.8%), Staphylococcus aureus and coagulase-negative Staphylococci (1.58%), Proteus mirabilis (1.85%), Salmonella enterica (1.19%), Pseudomonas aeruginosa (0.53%), Streptococcus pneumonia (0.4%)), Citrobacter (0.13%), Neisseria gonorrhea (0.13%), Enterococcus faecalis (0.13%), and Morganella morganii (0.13%). Infected patients were significantly more adults (73.1% vs. 21.5%) compared to children and mainly women (63.7% vs. 30.9%; p = 0.001). Conclusion: The observed expansion requires that hospital therapeutic committees set up an effective clinical management system and define the right combinations of antibiotics.

Keywords: multidrug resistance, bacteria, antibiogram, Bukavu

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Authors: Justin Morton, Mohammad Khavari, Abhinav Priyadarshi, Nicole Grobert, Dmitry G. Eskin, Jiawei Mi, Kriakos Porfyrakis, Paul Prentice

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Ultrasonic cavitation is used for various processes and applications. Recently, ultrasonic assisted liquid phase exfoliation has been implemented to produce two dimensional nanomaterials. Depending on parameters such as input transducer power and the operational frequency used to induce the cavitation, bubble dynamics can be controlled and optimised. Using ultra-high-speed imagining and acoustic pressure measurements, a dual-frequency systemand its effect on bubble dynamics was investigated. A high frequency transducer (1.174 MHz) showed that bubble fragments and satellite bubbles induced from a low frequency transducer (24 kHz) were able to extend their lifecycle. In addition, this combination of ultrasonic frequencies generated higher acoustic emissions (∼24%) than the sum of the individual transducers. The dual-frequency system also produced an increase in cavitation zone size of∼3 times compared to the low frequency sonotrode. Furthermore, the high frequency induced cavitation bubbleswere shown to rapidly oscillate, although remained stable and did not transiently collapse, even in the presence of a low pressure field. Finally, the spatial distribution of satellite and fragment bubbles from the sonotrode were shown to increase, extending the active cavitation zone. These observations elucidated the benefits of using a dual-frequency system for generating nanomaterials with the aid of ultrasound, in deionised water.

Keywords: dual-frequency, cavitation, bubble dynamics, graphene

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Authors: Justin Lee, Siraj Shaikh, Alice Chu MD

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Children with cerebral palsy (CP) commonly present with upper extremity impairment, affecting one or both extremities, and are classified using the Manual Ability Classification Scale (MACS). The MACS defines bimanual hand abilities for children ages 4-18 years in everyday tasks and is a gradient scale, with I being nearly normal and V requiring total assistance. Children with more severe upper extremity impairment (MACS III-V) are often underrepresented, and relatively few effective therapies have been identified for these patients. Current orthoses are static and are only meant to prevent the progression of contractures in these patients. Other limitations include cost, comfort, accessibility, and longevity of the orthoses. Taking advantage of advances in 3D printing technology, we have created a highly customizable upper extremity orthotic that can be produced at a low cost. Iterations in our design have resulted in an orthotic that is custom fit to the patient based on scans of their arm, made of rigid polymer when needed to provide support, flexible material where appropriate to allow for comfort, and designed with a mechanical pulley system to allow for some functional use of the arm while in the orthotic. Preliminary data has shown that our orthotic can be built at a fraction of the cost of current orthoses and provide clinically significant improvement in assisting hand assessment (AHA) and pediatric quality of life scores (PedsQL).

Keywords: upper extremity orthosis, upper extremity, orthosis, 3-D printing, cerebral palsy, occupational therapy, spasticity, customizable

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Authors: Timothy E. Frank, Josh R. Aldred, Sophie B. Boulware, Michelle K. Cabonce, Justin H. White

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Materials degrade at different rates in different environments depending on factors such as temperature, aridity, salinity, and solar radiation. Therefore, predicting asset longevity depends, in part, on the environmental conditions to which the asset is exposed. Heating, ventilation, and air conditioning (HVAC) systems are critical to building operations yet are responsible for a significant proportion of their energy consumption. HVAC energy use increases substantially with slight operational inefficiencies. Understanding the environmental influences on HVAC degradation in detail will inform maintenance schedules and capital investment, reduce energy use, and increase lifecycle management efficiency. HVAC inspection records spanning 14 years from 21 locations across the United States were compiled and associated with the climate conditions to which they were exposed. Three environmental features were explored in this study: average high temperature, average low temperature, and annual precipitation, as well as four non-environmental features. Initial insights showed no correlations between individual features and the rate of HVAC component degradation. Using neighborhood component analysis, however, the most critical features related to degradation were identified. Two models were considered, and results varied between them. However, longitude and latitude emerged as potentially the best predictors of average HVAC component degradation. Further research is needed to evaluate additional environmental features, increase the resolution of the environmental data, and develop more robust models to achieve more conclusive results.

Keywords: climate, degradation, HVAC, neighborhood component analysis

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Authors: Justin Sentian, Franky Herman, Maggie Chel Gee Ooi, Vivian Kong WAN Yee, Teo You Rou, Chin Jia Hui

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Isoprene emission is known to depend heavily on temperature and radiation. Considering these environmental factors together is crucial for a comprehensive understanding of the impact of climate change on isoprene emissions and atmospheric chemistry. Therefore, the aim of this study is to investigate how isoprene emission responds to changing climate scenarios in Southeast Asia (SEA). Two climate change scenarios, RCP4.5 and RCP8.5, were used to simulate climate change using the Weather Research Forecasting (WRF v3.9.1) model in three different time periods: near-future (2030-2039), mid-century (2050-2059), and far future (2090-2099), with 2010 (2005-2014) as the baseline period. The output from WRF was then used to investigate how isoprene emission changes under a changing climate by using the Model Emission of Gases and Aerosol from Nature (MEGAN v2.1). The results show that the overall isoprene emissions during the baseline period are 1.41 tons hr-1 during DJF and 1.64 tons hr-1 during JJA. The overall emissions for both RCPs slightly increase during DJF, ranging from 0.03 to 0.06 tons hr-1 in the near future, 0.11 to 0.19 tons hr-1 in the mid-century, and 0.24 to 0.52 tons hr-1 in the far future. During JJA season, environmental conditions often favour higher emission rates in MEGAN due to their optimal state. Isoprene emissions also show a strong positive correlation (0.81 – 1.00) with temperature and photosynthetic active radiation (PAR). The future emission rate of isoprene is strongly modulated by both temperature and PAR, as indicated by a strong positive correlation (0.81 - 1.00). This relationship underscores the fact that future warming will not be the sole driver impacting isoprene emissions. Therefore, it is essential to consider the multifaceted effect of climate change in shaping the levels of isoprene in the future.

Keywords: isoprene, climate change, Southeast Asia, WRF, MEGAN.

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Authors: Kristopher A. Pruitt, Justin M. Hill

Abstract:

The purpose of this research is to determine the pacing and nutrition strategies which minimize completion time and carbohydrate intake for athletes competing in ultramarathon races. The model formulation consists of a two-phase optimization. The first-phase mixed-integer nonlinear program (MINLP) determines the minimum completion time subject to the altitude, terrain, and distance of the race, as well as the mass and cardiovascular fitness of the athlete. The second-phase MINLP determines the minimum total carbohydrate intake required for the athlete to achieve the completion time prescribed by the first phase, subject to the flow of carbohydrates through the stomach, liver, and muscles. Consequently, the second phase model provides the optimal pacing and nutrition strategies for a particular athlete for each kilometer of a particular race. Validation of the model results over a wide range of athlete parameters against completion times for real competitive events suggests strong agreement. Additionally, the kilometer-by-kilometer pacing and nutrition strategies, the model prescribes for a particular athlete suggest unconventional approaches could result in lower completion times. Thus, the MINLP provides prescriptive guidance that athletes can leverage when developing pacing and nutrition strategies prior to competing in ultramarathon races. Given the highly-variable topographical characteristics common to many ultramarathon courses and the potential inexperience of many athletes with such courses, the model provides valuable insight to competitors who might otherwise fail to complete the event due to exhaustion or carbohydrate depletion.

Keywords: nutrition, optimization, pacing, ultramarathons

Procedia PDF Downloads 189

Authors: Justin Bin

Abstract:

Artificial intelligence (AI) has been rapidly rising in usage recently, already active in the daily lives of millions, from distinguished AIs like the popular ChatGPT or Siri to more obscure, inconspicuous AIs like those used in social media or internet search engines. As upcoming generations grow immersed in emerging technology, AI will play a vital role in their development. Namely, the education sector, an influential portion of a person’s early life as a student, faces a vast ocean of possibilities concerning the implementation of AI. The main purpose of this study is to analyze the effect that AI will have on the future of the educational field. More particularly, this study delves deeper into the following three categories: school admissions, the productivity of students, and ethical concerns (role of human teachers, purpose of schooling itself, and significance of diplomas). This study synthesizes research and data on the current effects of AI on education from various published literature sources and journals, as well as estimates on further AI potential, in order to determine the main, overarching effects it will have on the future of education. For this study, a systematic organization of data in terms of type (quantitative vs. qualitative), the magnitude of effect implicated, and other similar factors were implemented within each area of significance. The results of the study suggest that AI stands to change all the beforementioned subgroups. However, its specific effects vary in magnitude and favorability (beneficial or harmful) and will be further discussed. The results discussed will reveal to those affiliated with the education field, such as teachers, counselors, or even parents of students, valuable information on not just the projected possibilities of AI in education but the effects of those changes moving forward.

Keywords: artificial intelligence, education, schools, teachers

Procedia PDF Downloads 522

Authors: Mary Therese G. Caluna, Mark Justin B. Campanero, Erlin Maris T. Cantiller, Claudine Mae A. Cantillo, Nerissa L. Caño

Abstract:

Objective: The study determined the effectiveness of cold tomato paste in relieving pain caused by exacerbation of arthritis in the elderly, specifically on clients 60 years old and above. The study focused on alternative, cost-effective and non-pharmacological techniques in relieving pain experienced by the older people with osteoarthritis and rheumatoid arthritis. Methods: Using purposive non-probability sampling, the researchers gathered a total number of 40 subjects that passed the inclusion criteria provided by the researchers. The subjects were divided into two groups, experimental group (20 subjects) and control groups (20 subjects). The Numeric Rating 11-point Scale (NRS-11) was utilized to assess the pain level of the subject prior the application of the treatment and after the application of the treatment. Key findings: There is a significant difference in the pain levels of the experimental group before and after the application of cold tomato paste. This indicates that that the application of cold tomato paste alleviates the pain experienced by elderly clients with exacerbation of arthritis. Conclusion: The effectiveness of cold tomato paste in relieving pain experienced by elderly clients who are in exacerbation of arthritis was proven to be evidence-based. The cold tomato paste application has significant impact in the field of nursing and therefore, can be used in both clinical trials and practices. The effectiveness of cold tomato application promotes innovation in the field of nursing, thus encouraging further researches regarding other uses of tomato and other herbal interventions to relieve the pain caused by osteoarthritis and rheumatoid arthritis.

Keywords: alternative therapy, arthritis, cold tomato paste, elderly clients, exacerbation

Procedia PDF Downloads 423