Search results for: perceptual metrics

Authors: Mayandi Ramanathan

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Gas turbine blades see the most aggressive thermal stress conditions within the engine, due to high Turbine Entry Temperatures in the range of 1500 to 1600°C. The blades rotate at very high rotation rates and remove a significant amount of thermal power from the gas stream. At high temperatures, the major component failure mechanism is a creep. During its service over time under high thermal loads, the blade will deform, lengthen and rupture. High strength and stiffness in the longitudinal direction up to elevated service temperatures are certainly the most needed properties of turbine blades and gas turbine components. The proposed advanced Ti alloy material needs a process that provides a strategic orientation of metallic ordering, uniformity in composition and high metallic strength. The chemical composition of the proposed Ti alloy material (25% Ta/(Al+Ta) ratio), unlike Ti-47Al-2Cr-2Nb, has less excess Al that could limit the service life of turbine blades. Properties and performance of Ti-47Al-2Cr-2Nb and Ti-6Al-4V materials will be compared with that of the proposed Ti alloy material to generalize the performance metrics of various gas turbine components. This paper will involve the summary of the effects of additive manufacturing and heat treatment process conditions on the changes in the phase composition, grain structure, lattice structure of the material, tensile strength, creep strain rate, thermal expansion coefficient and fracture toughness at different temperatures. Based on these results, additive manufacturing and heat treatment process conditions will be optimized to fabricate turbine blade with Ti-43Al matrix alloyed with an optimized amount of refractory Ta metal. Improvement in service temperature of the turbine blades and corrosion resistance dependence on the coercivity of the alloy material will be reported. A correlation of phase composition and creep strain rate will also be discussed.

Keywords: high temperature materials, aerospace, specific strength, creep strain, phase composition

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Authors: James Ladzekpo

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Background: The urgent need to identify new pharmacological targets for diabetes treatment and prevention has been amplified by the disease's extensive impact on individuals and healthcare systems. A deeper insight into the biological underpinnings of diabetes is crucial for the creation of therapeutic strategies aimed at these biological processes. Current predictive models based on genetic variations fall short of accurately forecasting diabetes. Objectives: Our study aims to pinpoint key epigenetic factors that predispose individuals to diabetes. These factors will inform the development of an advanced predictive model that estimates diabetes risk from genetic profiles, utilizing state-of-the-art statistical and data mining methods. Methodology: We have implemented a recursive feature elimination with cross-validation using the support vector machine (SVM) approach for refined feature selection. Building on this, we developed six machine learning models, including logistic regression, k-Nearest Neighbors (k-NN), Naive Bayes, Random Forest, Gradient Boosting, and Multilayer Perceptron Neural Network, to evaluate their performance. Findings: The Gradient Boosting Classifier excelled, achieving a median recall of 92.17% and outstanding metrics such as area under the receiver operating characteristics curve (AUC) with a median of 68%, alongside median accuracy and precision scores of 76%. Through our machine learning analysis, we identified 31 genes significantly associated with diabetes traits, highlighting their potential as biomarkers and targets for diabetes management strategies. Conclusion: Particularly noteworthy were the Gradient Boosting Classifier and Multilayer Perceptron Neural Network, which demonstrated potential in diabetes outcome prediction. We recommend future investigations to incorporate larger cohorts and a wider array of predictive variables to enhance the models' predictive capabilities.

Keywords: diabetes, machine learning, prediction, biomarkers

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Authors: M. Ahmadi, Sh. Damanabi, F. Sadoughi

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National Health Information System plays an important role in ensuring timely and reliable access to Health information which is essential for strategic and operational decisions that improve health, quality and effectiveness of health care. In other words, by using the National Health information system you can improve the quality of health data, information and knowledge used to support decision making at all levels and areas of the health sector. Since full identification of the components of this system for better planning and management influential factors of performance seems necessary, therefore, in this study, different attitudes towards components of this system are explored comparatively. Methods: This is a descriptive and comparative kind of study. The society includes printed and electronic documents containing components of the national health information system in three parts: input, process, and output. In this context, search for information using library resources and internet search were conducted and data analysis was expressed using comparative tables and qualitative data. Results: The findings showed that there are three different perspectives presenting the components of national health information system, Lippeveld, Sauerborn, and Bodart Model in 2000, Health Metrics Network (HMN) model from World Health Organization in 2008 and Gattini’s 2009 model. All three models outlined above in the input (resources and structure) require components of management and leadership, planning and design programs, supply of staff, software and hardware facilities, and equipment. In addition, in the ‘process’ section from three models, we pointed up the actions ensuring the quality of health information system and in output section, except Lippeveld Model, two other models consider information products, usage and distribution of information as components of the national health information system. Conclusion: The results showed that all the three models have had a brief discussion about the components of health information in input section. However, Lippeveld model has overlooked the components of national health information in process and output sections. Therefore, it seems that the health measurement model of network has a comprehensive presentation for the components of health system in all three sections-input, process, and output.

Keywords: National Health Information System, components of the NHIS, Lippeveld Model

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Authors: Yixin Zhu, Lishengsa Yue, Jian Sun, Lanyue Tang

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Modeling driving distraction in microscopic traffic simulation is crucial for enhancing simulation accuracy. Current driving distraction models are mainly derived from physical motion constraints under distracted states, in which distraction-related error terms are added to existing microscopic driver models. However, the model accuracy is not very satisfying, due to a lack of modeling the cognitive mechanism underlying the distraction. This study models driving distraction based on the Queueing Network Human Processor model (QN-MHP). This study utilizes the queuing structure of the model to perform task invocation and switching for distracted operation and control of the vehicle under driver distraction. Based on the assumption of the QN-MHP model about the cognitive sub-network, server F is a structural bottleneck. The latter information must wait for the previous information to leave server F before it can be processed in server F. Therefore, the waiting time for task switching needs to be calculated. Since the QN-MHP model has different information processing paths for auditory information and visual information, this study divides driving distraction into two types: auditory distraction and visual distraction. For visual distraction, both the visual distraction task and the driving task need to go through the visual perception sub-network, and the stimuli of the two are asynchronous, which is called stimulus on asynchrony (SOA), so when calculating the waiting time for switching tasks, it is necessary to consider it. In the case of auditory distraction, the auditory distraction task and the driving task do not need to compete for the server resources of the perceptual sub-network, and their stimuli can be synchronized without considering the time difference in receiving the stimuli. According to the Theory of Planned Behavior for drivers (TPB), this study uses risk entropy as the decision criterion for driver task switching. A logistic regression model is used with risk entropy as the independent variable to determine whether the driver performs a distraction task, to explain the relationship between perceived risk and distraction. Furthermore, to model a driver’s perception characteristics, a neurophysiological model of visual distraction tasks is incorporated into the QN-MHP, and executes the classical Intelligent Driver Model. The proposed driving distraction model integrates the psychological cognitive process of a driver with the physical motion characteristics, resulting in both high accuracy and interpretability. This paper uses 773 segments of distracted car-following in Shanghai Naturalistic Driving Study data (SH-NDS) to classify the patterns of distracted behavior on different road facilities and obtains three types of distraction patterns: numbness, delay, and aggressiveness. The model was calibrated and verified by simulation. The results indicate that the model can effectively simulate the distracted car-following behavior of different patterns on various roadway facilities, and its performance is better than the traditional IDM model with distraction-related error terms. The proposed model overcomes the limitations of physical-constraints-based models in replicating dangerous driving behaviors, and internal characteristics of an individual. Moreover, the model is demonstrated to effectively generate more dangerous distracted driving scenarios, which can be used to construct high-value automated driving test scenarios.

Keywords: computational cognitive model, driving distraction, microscopic traffic simulation, psychological-physical constraints

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Authors: Chuyu Huang, Itsuki Minemi, Kuanlin Chen, Yuki Hirose

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It remains unclear how language speakers are able to perceive phonological contrasts that do not exist on their own. This experiment uses the vowel-length distinction in Japanese, which is phonologically contrastive and co-occurs with tonal change in some cases. For speakers whose first language does not distinguish vowel length, contrastive duration is usually misperceived, e.g., Mandarin speakers. Two alternative hypotheses for how Mandarin speakers would perceive a phonological contrast that does not exist in their language make different predictions. The stress parameter model does not have a clear prediction about the impact of tonal type. Mandarin speakers will likely be not able to perceive vowel length as well as Japanese native speakers do, but the performance might not correlate to tonal type because the prosody of their language is distinctive, which requires users to encode lexical prosody and notice subtle differences in word prosody. By contrast, cue-based phonetic models predict that Mandarin speakers may rely on pitch differences, a secondary cue, to perceive vowel length. Two groups of Mandarin speakers, including naive non-Japanese speakers and beginner learners, were recruited to participate in an AX discrimination task involving two Japanese sound stimuli that contain a phonologically contrastive environment. Participants were asked to indicate whether the two stimuli containing a vowel-length contrast (e.g., maapero vs. mapero) sound the same. The experiment was bifactorial. The first factor contrasted three syllabic positions (syllable position; initial/medial/final), as it would be likely to affect the perceptual difficulty, as seen in previous studies, and the second factor contrasted two pitch types (accent type): one with accentual change that could be distinguished with the lexical tones in Mandarin (the different condition), with the other group having no tonal distinction but only differing in vowel length (the same condition). The overall results showed that a significant main effect of accent type by applying a linear mixed-effects model (β = 1.48, SE = 0.35, p < 0.05), which implies that Mandarin speakers tend to more successfully recognize vowel-length differences when the long vowel counterpart takes on a tone that exists in Mandarin. The interaction between the accent type and the syllabic position is also significant (β = 2.30, SE = 0.91, p < 0.05), showing that vowel lengths in the different conditions are more difficult to recognize in the word-final case relative to the initial condition. The second statistical model, which compares naive speakers to beginners, was conducted with logistic regression to test the effects of the participant group. A significant difference was found between the two groups (β = 1.06, 95% CI = [0.36, 2.03], p < 0.05). This study shows that: (1) Mandarin speakers are likely to use pitch cues to perceive vowel length in a non-native language, which is consistent with the cue-based approaches; (2) an exposure effect was observed: the beginner group achieved a higher accuracy for long vowel perception, which implied the exposure effect despite the short period of language learning experience.

Keywords: cue-based perception, exposure effect, prosodic perception, vowel duration

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Authors: Bliss Singhal

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Epilepsy is a prevalent neurological disorder affecting approximately 50 million individuals worldwide and 1.2 million Americans. There exist millions of pediatric patients with intractable epilepsy, a condition in which seizures fail to come under control. The occurrence of seizures can result in physical injury, disorientation, unconsciousness, and additional symptoms that could impede children's ability to participate in everyday tasks. Predicting seizures can help parents and healthcare providers take precautions, prevent risky situations, and mentally prepare children to minimize anxiety and nervousness associated with the uncertainty of a seizure. This research proposes a comprehensive framework to predict seizures in pediatric patients by evaluating machine learning algorithms on unimodal neuroimaging data consisting of electroencephalogram signals. The bandpass filtering and independent component analysis proved to be effective in reducing the noise and artifacts from the dataset. Various machine learning algorithms’ performance is evaluated on important metrics such as accuracy, precision, specificity, sensitivity, F1 score and MCC. The results show that the deep learning algorithms are more successful in predicting seizures than logistic Regression, and k nearest neighbors. The recurrent neural network (RNN) gave the highest precision and F1 Score, long short-term memory (LSTM) outperformed RNN in accuracy and convolutional neural network (CNN) resulted in the highest Specificity. This research has significant implications for healthcare providers in proactively managing seizure occurrence in pediatric patients, potentially transforming clinical practices, and improving pediatric care.

Keywords: intractable epilepsy, seizure, deep learning, prediction, electroencephalogram channels

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Authors: S. Raghibdoust, E. Kamari

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Research show that reduced functional hearing has a detrimental influence on the ability of an individual to establish proper phonological representations of words, since the phonological representations are claimed to mediate the conceptual processing of written words. Word processing efficiency is expected to decrease with a decrease in functional hearing. In other words, it is predicted that hearing individuals would be more capable of word processing than individuals with hearing loss, as their functional hearing works normally. Studies also demonstrate that the quality of the functional hearing affects reading comprehension via its effect on their word processing skills. In other words, better hearing facilitates the development of phonological knowledge, and can promote enhanced strategies for the recognition of written words, which in turn positively affect higher-order processes underlying reading comprehension. The aims of this study were to investigate and compare the effect of deafness on the participants’ abilities to process written words at the lexical and sentence levels through using two online and one offline reading comprehension tests. The performance of a group of 8 deaf male students (ages 8-12) was compared with that of a control group of normal hearing male students. All the participants had normal IQ and visual status, and came from an average socioeconomic background. None were diagnosed with a particular learning or motor disability. The language spoken in the homes of all participants was Persian. Two tests of word processing were developed and presented to the participants using OpenSesame software, in order to measure the speed and accuracy of their performance at the two perceptual and conceptual levels. In the third offline test of reading comprehension which comprised of semantically plausible and semantically implausible subject relative clauses, the participants had to select the correct answer out of two choices. The data derived from the statistical analysis using SPSS software indicated that hearing and deaf participants had a similar word processing performance both in terms of speed and accuracy of their responses. The results also showed that there was no significant difference between the performance of the deaf and hearing participants in comprehending semantically plausible sentences (p > 0/05). However, a significant difference between the performances of the two groups was observed with respect to their comprehension of semantically implausible sentences (p < 0/05). In sum, the findings revealed that the seriously impoverished sentence reading ability characterizing the profound deaf subjects of the present research, exhibited their reliance on reading strategies that are based on insufficient or deviant structural knowledge, in particular in processing semantically implausible sentences, rather than a failure to efficiently process written words at the lexical level. This conclusion, of course, does not mean to say that deaf individuals may never experience deficits at the word processing level, deficits that impede their understanding of written texts. However, as stated in previous researches, it sounds reasonable to assume that the more deaf individuals get familiar with written words, the better they can recognize them, despite having a profound phonological weakness.

Keywords: deafness, reading comprehension, reading strategy, word processing, subject and object relative sentences

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Authors: Kehinde Agbele, Longe Olumide, Daniel Ekong, Dele Seluwa, Akintoye Onamade

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One major problem of most existing information retrieval systems (IRS) is that they provide even access and retrieval results to individual users specially based on the query terms user issued to the system. When using IRS, users often present search queries made of ad-hoc keywords. It is then up to IRS to obtain a precise representation of user’s information need, and the context of the information. In effect, the volume and range of the Internet documents is growing exponentially and consequently causes difficulties for a user to obtain information that precisely matches the user interest. Diverse combination techniques are used to achieve the specific goal. This is due, firstly, to the fact that users often do not present queries to IRS that optimally represent the information they want, and secondly, the measure of a document's relevance is highly subjective between diverse users. In this paper, we address the problem by investigating the optimization of IRS to individual information needs in order of relevance. The paper addressed the development of algorithms that optimize the ranking of documents retrieved from IRS. This paper addresses this problem with a two-fold approach in order to retrieve domain-specific documents. Firstly, the design of context of information. The context of a query determines retrieved information relevance using personalization and context-awareness. Thus, executing the same query in diverse contexts often leads to diverse result rankings based on the user preferences. Secondly, the relevant context aspects should be incorporated in a way that supports the knowledge domain representing users’ interests. In this paper, the use of evolutionary algorithms is incorporated to improve the effectiveness of IRS. A context-based information retrieval system that learns individual needs from user-provided relevance feedback is developed whose retrieval effectiveness is evaluated using precision and recall metrics. The results demonstrate how to use attributes from user interaction behavior to improve the IR effectiveness.

Keywords: context, document relevance, information retrieval, personalization, user search behaviors

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Authors: Adam M. Yates, Julie E. Byles

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Hypertension has been identified as a prominent co-morbidity of obesity. To improve clinical intervention of hypertension, it is critical to identify metrics that most accurately reflect risk for increased morbidity. Two of the most relevant and accurate measures for increased risk of hypertension due to excess adipose tissue are Body Mass Index (BMI) and Waist-Height Ratio (WHtR). Previous research has examined these measures in cross-national and cross-ethnic studies, but has most often relied on secondary means such as meta-analysis to identify and evaluate the efficacy of individual body mass measures. In this study, we instead use cross-sectional analysis to assess the cross-ethnic discriminative power of BMI and WHtR to predict risk of hypertension. Using the WHO SAGE survey, which collected anthropometric and biometric data from respondents in six middle-income countries (China, Ghana, India, Mexico, Russia, South Africa), we implement logistic regression to examine the discriminative power of measured BMI and WHtR with a known population of hypertensive and non-hypertensive respondents. We control for gender and age to identify whether optimum cut-off points that are adequately sensitive as tests for risk of hypertension may be different between groups. We report results for OR, RR, and ROC curves for each of the six SAGE countries. As seen in existing literature, results demonstrate that both WHtR and BMI are significant predictors of hypertension (p < .01). For these six countries, we find that cut-off points for WHtR may be dependent upon gender, age and ethnicity. While an optimum omnibus cut-point for WHtR may be 0.55, results also suggest that the gender and age relationship with WHtR may warrant the development of individual cut-offs to optimize health outcomes. Trends through multiple countries show that the optimum cut-point for WHtR increases with age while the area under the curve (AUROC) decreases for both men and women. Comparison between BMI and WHtR indicate that BMI may remain more robust than WHtR. Implications for public health policy are discussed.

Keywords: hypertension, obesity, Waist-Height ratio, SAGE

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Authors: Taha Benarbia

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The advent of automated and connected vehicles has revolutionized the transportation industry, presenting new opportunities for enhancing the efficiency, safety, and sustainability of our transportation networks. This paper explores the integration of automated and connected vehicles into a smart transportation framework, leveraging the power of deep learning techniques to optimize the overall network performance. The first aspect addressed in this paper is the deployment of automated vehicles (AVs) within the transportation system. AVs offer numerous advantages, such as reduced congestion, improved fuel efficiency, and increased safety through advanced sensing and decisionmaking capabilities. The paper delves into the technical aspects of AVs, including their perception, planning, and control systems, highlighting the role of deep learning algorithms in enabling intelligent and reliable AV operations. Furthermore, the paper investigates the potential of connected vehicles (CVs) in creating a seamless communication network between vehicles, infrastructure, and traffic management systems. By harnessing real-time data exchange, CVs enable proactive traffic management, adaptive signal control, and effective route planning. Deep learning techniques play a pivotal role in extracting meaningful insights from the vast amount of data generated by CVs, empowering transportation authorities to make informed decisions for optimizing network performance. The integration of deep learning with automated and connected vehicles paves the way for advanced transportation network optimization. Deep learning algorithms can analyze complex transportation data, including traffic patterns, demand forecasting, and dynamic congestion scenarios, to optimize routing, reduce travel times, and enhance overall system efficiency. The paper presents case studies and simulations demonstrating the effectiveness of deep learning-based approaches in achieving significant improvements in network performance metrics

Keywords: automated vehicles, connected vehicles, deep learning, smart transportation network

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Authors: Farhan A. Alenizi

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Digital watermarking has evolved in the past years as an important means for data authentication and ownership protection. The images and video watermarking was well known in the field of multimedia processing; however, 3D objects' watermarking techniques have emerged as an important means for the same purposes, as 3D mesh models are in increasing use in different areas of scientific, industrial, and medical applications. Like the image watermarking techniques, 3D watermarking can take place in either space or transform domains. Unlike images and video watermarking, where the frames have regular structures in both space and temporal domains, 3D objects are represented in different ways as meshes that are basically irregular samplings of surfaces; moreover, meshes can undergo a large variety of alterations which may be hard to tackle. This makes the watermarking process more challenging. While the transform domain watermarking is preferable in images and videos, they are still difficult to implement in 3d meshes due to the huge number of vertices involved and the complicated topology and geometry, and hence the difficulty to perform the spectral decomposition, even though significant work was done in the field. Spatial domain watermarking has attracted significant attention in the past years; they can either act on the topology or on the geometry of the model. Exploiting the statistical characteristics in the 3D mesh models from both geometrical and topological aspects was useful in hiding data. However, doing that with minimal surface distortions to the mesh attracted significant research in the field. A 3D mesh blind watermarking technique is proposed in this research. The watermarking method depends on modifying the vertices' positions with respect to the center of the object. An optimal method will be developed to reduce the errors, minimizing the distortions that the 3d object may experience due to the watermarking process, and reducing the computational complexity due to the iterations and other factors. The technique relies on the displacement process of the vertices' locations depending on the modification of the variances of the vertices’ norms. Statistical analyses were performed to establish the proper distributions that best fit each mesh, and hence establishing the bins sizes. Several optimizing approaches were introduced in the realms of mesh local roughness, the statistical distributions of the norms, and the displacements in the mesh centers. To evaluate the algorithm's robustness against other common geometry and connectivity attacks, the watermarked objects were subjected to uniform noise, Laplacian smoothing, vertices quantization, simplification, and cropping. Experimental results showed that the approach is robust in terms of both perceptual and quantitative qualities. It was also robust against both geometry and connectivity attacks. Moreover, the probability of true positive detection versus the probability of false-positive detection was evaluated. To validate the accuracy of the test cases, the receiver operating characteristics (ROC) curves were drawn, and they’ve shown robustness from this aspect. 3D watermarking is still a new field but still a promising one.

Keywords: watermarking, mesh objects, local roughness, Laplacian Smoothing

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Authors: Mst Shapna Akter, Hossain Shahriar

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One of the most important challenges in the field of software code audit is the presence of vulnerabilities in software source code. Every year, more and more software flaws are found, either internally in proprietary code or revealed publicly. These flaws are highly likely exploited and lead to system compromise, data leakage, or denial of service. C and C++ open-source code are now available in order to create a largescale, machine-learning system for function-level vulnerability identification. We assembled a sizable dataset of millions of opensource functions that point to potential exploits. We developed an efficient and scalable vulnerability detection method based on deep neural network models that learn features extracted from the source codes. The source code is first converted into a minimal intermediate representation to remove the pointless components and shorten the dependency. Moreover, we keep the semantic and syntactic information using state-of-the-art word embedding algorithms such as glove and fastText. The embedded vectors are subsequently fed into deep learning networks such as LSTM, BilSTM, LSTM-Autoencoder, word2vec, BERT, and GPT-2 to classify the possible vulnerabilities. Furthermore, we proposed a neural network model which can overcome issues associated with traditional neural networks. Evaluation metrics such as f1 score, precision, recall, accuracy, and total execution time have been used to measure the performance. We made a comparative analysis between results derived from features containing a minimal text representation and semantic and syntactic information. We found that all of the deep learning models provide comparatively higher accuracy when we use semantic and syntactic information as the features but require higher execution time as the word embedding the algorithm puts on a bit of complexity to the overall system.

Keywords: cyber security, vulnerability detection, neural networks, feature extraction

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Authors: Nwachukwu Ifeanyi

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Wind energy is a vital component of the global renewable energy portfolio, with wind turbines serving as the primary means of harnessing this abundant resource. However, the efficiency and stability of wind turbines remain critical challenges in maximizing energy output and ensuring long-term operational viability. This study proposes a comprehensive approach utilizing computational aerodynamics and aeromechanics to optimize wind turbine performance across multiple objectives. The proposed research aims to integrate advanced computational fluid dynamics (CFD) simulations with structural analysis techniques to enhance the aerodynamic efficiency and mechanical stability of wind turbine blades. By leveraging multi-objective optimization algorithms, the study seeks to simultaneously optimize aerodynamic performance metrics such as lift-to-drag ratio and power coefficient while ensuring structural integrity and minimizing fatigue loads on the turbine components. Furthermore, the investigation will explore the influence of various design parameters, including blade geometry, airfoil profiles, and turbine operating conditions, on the overall performance and stability of wind turbines. Through detailed parametric studies and sensitivity analyses, valuable insights into the complex interplay between aerodynamics and structural dynamics will be gained, facilitating the development of next-generation wind turbine designs. Ultimately, this research endeavours to contribute to the advancement of sustainable energy technologies by providing innovative solutions to enhance the efficiency, reliability, and economic viability of wind power generation systems. The findings have the potential to inform the design and optimization of wind turbines, leading to increased energy output, reduced maintenance costs, and greater environmental benefits in the transition towards a cleaner and more sustainable energy future.

Keywords: computation, robotics, mathematics, simulation

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Authors: Abdelhalim. N. Mohammed, Mohammed. Y. Esmail

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In the course of recent decades, medical imaging has been dominated by the use of costly film media for review and archival of medical investigation, however due to developments in networks technologies and common acceptance of a standard digital imaging and communication in medicine (DICOM) another approach in light of World Wide Web was produced. Web technologies successfully used in telemedicine applications, the combination of web technologies together with DICOM used to design a web-based and open source DICOM viewer. The Web server allowance to inquiry and recovery of images and the images viewed/manipulated inside a Web browser without need for any preinstalling software. The dynamic site page for medical images visualization and processing created by using JavaScript and HTML5 advancements. The XAMPP ‘apache server’ is used to create a local web server for testing and deployment of the dynamic site. The web-based viewer connected to multiples devices through local area network (LAN) to distribute the images inside healthcare facilities. The system offers a few focal points over ordinary picture archiving and communication systems (PACS): easy to introduce, maintain and independently platforms that allow images to display and manipulated efficiently, the system also user-friendly and easy to integrate with an existing system that have already been making use of web technologies. The wavelet-based image compression technique on which 2-D discrete wavelet transform used to decompose the image then wavelet coefficients are transmitted by entropy encoding after threshold to decrease transmission time, stockpiling cost and capacity. The performance of compression was estimated by using images quality metrics such as mean square error ‘MSE’, peak signal to noise ratio ‘PSNR’ and compression ratio ‘CR’ that achieved (83.86%) when ‘coif3’ wavelet filter is used.

Keywords: DICOM, discrete wavelet transform, PACS, HIS, LAN

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Authors: N. Šarlija, S. Pfeifer, M. Jeger, A. Bilandžić

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Enterprise growth is generally considered as a key driver of competitiveness, employment, economic development and social inclusion. As such, it is perceived to be a highly desirable outcome of entrepreneurship for scholars and decision makers. The huge academic debate resulted in the multitude of theoretical frameworks focused on explaining growth stages, determinants and future prospects. It has been widely accepted that enterprise growth is most likely nonlinear, temporal and related to the variety of factors which reflect the individual, firm, organizational, industry or environmental determinants of growth. However, factors that affect growth are not easily captured, instruments to measure those factors are often arbitrary, causality between variables and growth is elusive, indicating that growth is not easily modeled. Furthermore, in line with heterogeneous nature of the growth phenomenon, there is a vast number of measurement constructs assessing growth which are used interchangeably. Differences among various growth measures, at conceptual as well as at operationalization level, can hinder theory development which emphasizes the need for more empirically robust studies. In line with these highlights, the main purpose of this paper is twofold. Firstly, to compare structure and performance of three growth prediction models based on the main growth measures: Revenues, employment and assets growth. Secondly, to explore the prospects of financial indicators, set as exact, visible, standardized and accessible variables, to serve as determinants of enterprise growth. Finally, to contribute to the understanding of the implications on research results and recommendations for growth caused by different growth measures. The models include a range of financial indicators as lag determinants of the enterprises’ performances during the 2008-2013, extracted from the national register of the financial statements of SMEs in Croatia. The design and testing stage of the modeling used the logistic regression procedures. Findings confirm that growth prediction models based on different measures of growth have different set of predictors. Moreover, the relationship between particular predictors and growth measure is inconsistent, namely the same predictor positively related to one growth measure may exert negative effect on a different growth measure. Overall, financial indicators alone can serve as good proxy of growth and yield adequate predictive power of the models. The paper sheds light on both methodology and conceptual framework of enterprise growth by using a range of variables which serve as a proxy for the multitude of internal and external determinants, but are unlike them, accessible, available, exact and free of perceptual nuances in building up the model. Selection of the growth measure seems to have significant impact on the implications and recommendations related to growth. Furthermore, the paper points out to potential pitfalls of measuring and predicting growth. Overall, the results and the implications of the study are relevant for advancing academic debates on growth-related methodology, and can contribute to evidence-based decisions of policy makers.

Keywords: growth measurement constructs, logistic regression, prediction of growth potential, small and medium-sized enterprises

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Authors: M. Miljkovic, A. Urakpo, M. Simic-Koumoutsaris

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Benefit-risk assessment (BRA) is a valuable tool that takes place in multiple stages during a medicine's lifecycle, and this assessment can be conducted in a variety of ways. The aim was to summarize current BRA methods used during approval decisions and in post-approval settings and to see possible future directions. Relevant reviews, recommendations, and guidelines published in medical literature and through regulatory agencies over the past five years have been examined. BRA implies the review of two dimensions: the dimension of benefits (determined mainly by the therapeutic efficacy) and the dimension of risks (comprises the safety profile of a drug). Regulators, industry, and academia have developed various approaches, ranging from descriptive textual (qualitative) to decision-analytic (quantitative) models, to facilitate the BRA of medicines during the product lifecycle (from Phase I trials, to authorization procedure, post-marketing surveillance and health technology assessment for inclusion in public formularies). These approaches can be classified into the following categories: stepwise structured approaches (frameworks); measures for benefits and risks that are usually endpoint specific (metrics), simulation techniques and meta-analysis (estimation techniques), and utility survey techniques to elicit stakeholders’ preferences (utilities). All these approaches share the following two common goals: to assist this analysis and to improve the communication of decisions, but each is subject to its own specific strengths and limitations. Before using any method, its utility, complexity, the extent to which it is established, and the ease of results interpretation should be considered. Despite widespread and long-time use, BRA is subject to debate, suffers from a number of limitations, and currently is still under development. The use of formal, systematic structured approaches to BRA for regulatory decision-making and quantitative methods to support BRA during the product lifecycle is a standard practice in medicine that is subject to continuous improvement and modernization, not only in methodology but also in cooperation between organizations.

Keywords: benefit-risk assessment, benefit-risk profile, product lifecycle, quantitative methods, structured approaches

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Authors: Raluca Ana Maria Viziteu, Anna Prudnikova

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Industrial control systems (ICS) have received much attention in recent years due to the convergence of information technology (IT) and operational technology (OT) that has increased the interdependence of safety and security issues to be considered. These issues require ICS-tailored solutions. That led to the need to creation of a methodology for supporting ICS device manufacturers and system integrators in carrying out threat modeling of embedded ICS devices in a way that guarantees the quality of the identified threats and minimizes subjectivity in the threat identification process. To research, the possibility of creating such a methodology, a set of existing standards, regulations, papers, and publications related to threat modeling in the ICS sector and other sectors was reviewed to identify various existing methodologies and methods used in threat modeling. Furthermore, the most popular ones were tested in an exploratory phase on a specific PLC device. The outcome of this exploratory phase has been used as a basis for defining specific characteristics of ICS embedded devices and their deployment scenarios, identifying the factors that introduce subjectivity in the threat modeling process of such devices, and defining metrics for evaluating the minimum quality requirements of identified threats associated to the deployment of the devices in existing infrastructures. Furthermore, the threat modeling methodology was created based on the previous steps' results. The usability of the methodology was evaluated through a set of standardized threat modeling requirements and a standardized comparison method for threat modeling methodologies. The outcomes of these verification methods confirm that the methodology is effective. The full paper includes the outcome of research on different threat modeling methodologies that can be used in OT, their comparison, and the results of implementing each of them in practice on a PLC device. This research is further used to build a threat modeling methodology tailored to OT environments; a detailed description is included. Moreover, the paper includes results of the evaluation of created methodology based on a set of parameters specifically created to rate threat modeling methodologies.

Keywords: device manufacturers, embedded devices, industrial control systems, threat modeling

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Authors: Annika Stechemesser, Rachel Pounds, Emma Lucas, Chris Dawson, Julia Lipecki, Pavle Vrljicak, Jan Brosens, Sean Kehoe, Jason Yap, Lawrence Young, Sascha Ott

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Advances in technology make it now possible to retrieve the genetic information of thousands of single cancerous cells. One of the key challenges in single cell analysis of cancerous tissue is to determine the number of different cell types and their characteristic genes within the sample to better understand the tumors and their reaction to different treatments. For this analysis to be possible, it is crucial to filter out background noise as it can severely blur the downstream analysis and give misleading results. In-depth analysis of the state-of-the-art filtering methods for single cell data showed that they do, in some cases, not separate noisy and normal cells sufficiently. We introduced an algorithm that filters and clusters single cell data simultaneously without relying on certain genes or thresholds chosen by eye. It detects communities in a Shared Nearest Neighbor similarity network, which captures the similarities and dissimilarities of the cells by optimizing the modularity and then identifies and removes vertices with a weak clustering belonging. This strategy is based on the fact that noisy data instances are very likely to be similar to true cell types but do not match any of these wells. Once the clustering is complete, we apply a set of evaluation metrics on the cluster level and accept or reject clusters based on the outcome. The performance of our algorithm was tested on three datasets and led to convincing results. We were able to replicate the results on a Peripheral Blood Mononuclear Cells dataset. Furthermore, we applied the algorithm to two samples of ovarian cancer from the same patient before and after chemotherapy. Comparing the standard approach to our algorithm, we found a hidden cell type in the ovarian postchemotherapy data with interesting marker genes that are potentially relevant for medical research.

Keywords: cancer research, graph theory, machine learning, single cell analysis

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Authors: Gayani Ranawake, Kate Wilson

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Underrepresentation of women in academia, particularly in science, has been discussed by many scholars for decades. The causes of this underrepresentation are debated to this day. Publication is an important aspect of success in academia, and publication and citation rates are significant metrics in performance review, promotion, and employment. It has been established that men’s and women’s language use in general, both spoken and written, is different. However, no one, to our knowledge, has looked at whether men’s and women’s writing in science is different. If there are significant differences in the writing of men and women, then these differences may affect women’s ability to succeed in science. This study is part of a larger project to explore whether differences can be recognized in the academic science writing of men and women. Mono authored articles from high ranking physics, biology and psychology journals by men and women authors were compared in terms of readability statistics. In particular, the abstract and introduction sections were compared, as these are the first sections encountered by a reviewer, and so may have an important effect on their impression of the work. The Flesch Reading Ease, the percentage of passive sentences and the Flesch-Kincaid Reading Grade Level were calculated for each section of each article, along with counts of numbers of sentences, words per sentence and sentences per paragraph. Significance of differences was tested using the Behrens statistic. It was found that for both physics and biology papers there were no significant differences in the complexity or verbosity of the writing of men and women authors. However, there was a significant difference between the two disciplines, with physics articles being generally more readable (higher readability score) while also more passive (higher number of passive sentences). In contrast, the psychology articles showed a difference between men and women authors which may be significant. The average readability for introductions in women’s articles was 28 which was higher than for men’s articles, which was 19 (higher values indicate more readable). Women’s articles in psychology also had a greater proportion of passive sentences. It can be concluded that, at least in the more traditional sciences, men and women have adopted similar ways of writing, and that disciplinary differences are greater than gender differences. This may not be the case in psychology, which many consider to be more closely aligned with the humanities. Whether the lack of differences is because women have adapted to a masculine way of writing, or whether the genre itself is gender neutral needs further investigation.

Keywords: academic writing, gender differences, readability, science

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Authors: Sarah Lister, Pauline Palmer

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Research findings presented within this paper form part of a larger scale collaboration between academics at Manchester Metropolitan University and a technology company. The overarching aims of this project focus on developing a series of game-based resources to promote the teaching of aspects of mathematics through a second language (L2) in primary schools. This study explores the potential of game-based learning (GBL) as a dynamic way to engage and motivate learners, making learning fun and purposeful. The research examines the capacity of GBL resources to provide a meaningful and purposeful context for CLIL. GBL is a powerful learning environment and acts as an effective vehicle to promote the learning of mathematics through an L2. The fun element of GBL can minimise stress and anxiety associated with mathematics and L2 learning that can create barriers. GBL provides one of the few safe domains where it is acceptable for learners to fail. Games can provide a life-enhancing experience for learners, revolutionizing the routinized ways of learning through fusing learning and play. This study argues that playing games requires learners to think creatively to solve mathematical problems, using the L2 in order to progress, which can be associated with the development of higher-order thinking skills and independent learning. GBL requires learners to engage appropriate cognitive processes with increased speed of processing, sensitivity to environmental inputs, or flexibility in allocating cognitive and perceptual resources. At surface level, GBL resources provide opportunities for learners to learn to do things. Games that fuse subject content and appropriate learning objectives have the potential to make learning academic subjects more learner-centered, promote learner autonomy, easier, more enjoyable, more stimulating and engaging and therefore, more effective. Data includes observations of the children playing the games and follow up group interviews. Given that learning as a cognitive event cannot be directly observed or measured. A Cognitive Discourse Functions (CDF) construct was used to frame the research, to map the development of learners’ conceptual understanding in an L2 context and as a framework to observe the discursive interactions that occur learner to learner and between learner and teacher. Cognitively, the children were required to engage with mathematical content, concepts and language to make decisions quickly, to engage with the gameplay to reason, solve and overcome problems and learn through experimentation. The visual elements of the games supported the learning of new concepts. Children recognised the value of the games to consolidate their mathematical thinking and develop their understanding of new ideas. The games afforded them time to think and reflect. The teachers affirmed that the games provided meaningful opportunities for the learners to practise the language. The findings of this research support the view that using the game-based resources supported children’s grasp of mathematical ideas and their confidence and ability to use the L2. Engaging with the content and language through the games led to deeper learning.

Keywords: CLIL, gaming, language, mathematics

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Authors: Isabel Lopez-Oliva, Akshay Paropkari, Shweta Saraswat, Stefan Serban, Paola de Pablo, Karim Raza, Andrew Filer, Iain Chapple, Thomas Dietrich, Melissa Grant, Purnima Kumar

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Objective: We aimed to explicate the role of the subgingival microbiome in the causal link between rheumatoid arthritis (RA) and periodontitis (PD). Methods: Subjects with/without RA and with/without PD were randomized for treatment with scaling and root planing (SRP) or oral hygiene instructions. Subgingival biofilm, gingival crevicular fluid, and serum were collected at baseline and at 3- and 6-months post-operatively. Correlations were generated between 72 million 16S rDNA sequences, immuno-inflammatory mediators, circulating antibodies to oral microbial antigens, serum inflammatory molecules, and clinical metrics of RA. The dynamics of inter-microbial and host-microbial interactions were modeled using differential network analysis. Results: RA superseded periodontitis as a determinant of microbial composition, and DAS28 score superseded the severity of periodontitis as a driver of microbial assemblages (p=0.001, ANOSIM). RA subjects evidenced higher serum anti-PPAD (p=0.0013), anti-Pg-enolase (p=0.0031), anti-RPP3, anti- Pg-OMP and anti- Pi-OMP (p=0.001) antibodies than non-RA controls (with and without periodontitis). Following SRP, bacterial networks anchored by IL-1b, IL-4, IL-6, IL-10, IL-13, MIP-1b, and PDGF-b underwent ≥5-fold higher rewiring; and serum antibodies to microbial antigens decreased significantly. Conclusions: Our data suggest a circular relationship between RA and PD, beginning with an RA-influenced dysbiosis within the healthy subgingival microbiome that leads to exaggerated local inflammation in periodontitis and circulating antibodies to periodontal pathogens and positive correlation between severity of periodontitis and RA activity. Periodontal therapy restores host-microbial homeostasis, reduces local inflammation, and decreases circulating microbial antigens. Our data highlights the importance of integrating periodontal care into the management of RA patients.

Keywords: rheumatoid arthritis, periodontal, subgingival, DNA sequence analysis, oral microbiome

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Authors: Daniel Faraj, Arabella Raupach, Charlotte Hespe, Helen Wilcox, Kristie-Lee Anning

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Aim: International child sponsorship programs comprise a considerable proportion of global aid accessible to the general population. Team Philippines (TP), a healthcare and welfare initiative run in association with the University of Notre Dame Sydney since 2013, leads a holistic sponsorship program for thirty children from Calauan, Philippines. To date, empirical research has not been performed on the overall success and impact of the TP child sponsorship program. As such, this study aims to evaluate its effectiveness in improving pediatric outcomes. Methods: Study cohorts comprised thirty sponsored and twenty-nine age- and gender-matched non-sponsored children. Data were extracted from the TP Medical Director database and lifestyle questionnaires for July-November 2019. Outcome measures included anthropometry, markers of medical health, dental health, exercise, and diet. Statistical analyses were performed in SPSS. Results: Sponsorship resulted in fewer medical diagnoses and prescription medications, superior dental health, and improved diet. Further, sponsored children may show a clinically significant trend toward improved physical health. Sponsorship did not affect growth and development metrics or levels of physical activity. Conclusions: The TP child sponsorship program significantly impacts positive pediatric health outcomes in the Calauan community. The strength of the program lies in its holistic, sustainable, and community-based model, which is enabled by effective international child sponsorship. This study further supports the relationship between supporting early livelihood and improved health in the pediatric population.

Keywords: child health, public health, health status disparities, healthcare disparities, social determinants of health, morbidity, community health services, culturally competent care, medically underserved areas, population health management, Philippines

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Authors: Heeba A. Gurku

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Introduction: Cone Beam CT(CBCT) images play an integral part in proper patient positioning in cancer patients undergoing radiation therapy treatment. But these images are low in quality. The purpose of this study is to generate high-quality synthetic CT images from CBCT using generative models. Material and Methods: This study utilized two datasets from The Cancer Imaging Archive (TCIA) 1) Lung cancer dataset of 20 patients (with full view CBCT images) and 2) Pancreatic cancer dataset of 40 patients (only 27 patients having limited view images were included in the study). Cycle Generative Adversarial Networks (GAN) and its variant Attention Guided Generative Adversarial Networks (AGGAN) models were used to generate the synthetic CTs. Models were evaluated by visual evaluation and on four metrics, Structural Similarity Index Measure (SSIM), Peak Signal Noise Ratio (PSNR) Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), to compare the synthetic CT and original CT images. Results: For pancreatic dataset with limited view CBCT images, our study showed that in Cycle GAN model, MAE, RMSE, PSNR improved from 12.57to 8.49, 20.94 to 15.29 and 21.85 to 24.63, respectively but structural similarity only marginally increased from 0.78 to 0.79. Similar, results were achieved with AGGAN with no improvement over Cycle GAN. However, for lung dataset with full view CBCT images Cycle GAN was able to reduce MAE significantly from 89.44 to 15.11 and AGGAN was able to reduce it to 19.77. Similarly, RMSE was also decreased from 92.68 to 23.50 in Cycle GAN and to 29.02 in AGGAN. SSIM and PSNR also improved significantly from 0.17 to 0.59 and from 8.81 to 21.06 in Cycle GAN respectively while in AGGAN SSIM increased to 0.52 and PSNR increased to 19.31. In both datasets, GAN models were able to reduce artifacts, reduce noise, have better resolution, and better contrast enhancement. Conclusion and Recommendation: Both Cycle GAN and AGGAN were significantly able to reduce MAE, RMSE and PSNR in both datasets. However, full view lung dataset showed more improvement in SSIM and image quality than limited view pancreatic dataset.

Keywords: CT images, CBCT images, cycle GAN, AGGAN

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Authors: Cassandra A. Skinner, Linda R. Chamberlain

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The nonprofit and public sectors are increasingly turning to Geographic Information Systems for data visualizations which can better inform programmatic and policy decisions. Additionally, the private and nonprofit sectors are turning to systems mapping to better understand the ecosystems within which they operate. This study explores the potential which combining these data visualization methods—a method which is called geographic systems mapping—to create an exhaustive and comprehensive understanding of a social problem’s ecosystem may have in social innovation efforts. Researchers with Grand Valley State University collaborated with Talent 2025 of West Michigan to conduct a mixed-methods research study to paint a comprehensive picture of the workforce development ecosystem in West Michigan. Using semi-structured interviewing, observation, secondary research, and quantitative analysis, data were compiled on workforce development organizations’ locations, programming, metrics for success, partnerships, funding sources, and service language. To best visualize and disseminate the data, a geographic system map was created which identifies programmatic, operational, and geographic gaps in workforce development services of West Michigan. By combining geographic and systems mapping methods, the geographic system map provides insight into the cross-sector relationships, collaboration, and competition which exists among and between workforce development organizations. These insights identify opportunities for and constraints around cross-sectoral social innovation in the West Michigan workforce development ecosystem. This paper will discuss the process utilized to prepare the geographic systems map, explain the results and outcomes, and demonstrate how geographic systems mapping illuminated the needs of the community and opportunities for social innovation. As complicated social problems like unemployment often require cross-sectoral and multi-stakeholder solutions, there is potential for geographic systems mapping to be a tool which informs, enables, and inspires these solutions.

Keywords: cross-sector collaboration, data visualization, geographic systems mapping, social innovation, workforce development

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Authors: Hossam Ali-Hassan, Michael Bliemel

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This poster reflects upon our experiences using INNOV8, IBM’s Business Process Management (BPM) simulation game, in online MBA and undergraduate MIS classes over a period of 2 years. The game is designed to gives both business and information technology players a better understanding of how effective BPM impacts an entire business ecosystem. The game includes three different scenarios: Smarter Traffic, which is used to evaluate existing traffic patterns and re-route traffic based on incoming metrics; Smarter Customer Service where players develop more efficient ways to respond to customers in a call centre environment; and Smarter Supply Chains where players balance supply and demand and reduce environmental impact in a traditional supply chain model. We use the game as an experiential learning tool, where students have to act as managers making real time changes to business processes to meet changing business demands and environments. The students learn how information technology (IT) and information systems (IS) can be used to intelligently solve different problems and how computer simulations can be used to test different scenarios or models based on business decisions without having to actually make the potentially costly and/or disruptive changes to business processes. Moreover, when students play the three different scenarios, they quickly see how practical process improvements can help meet profitability, customer satisfaction and environmental goals while addressing real problems faced by municipalities and businesses today. After spending approximately two hours in the game, students reflect on their experience from it to apply several BPM principles that were presented in their textbook through the use of a structured set of assignment questions. For each final scenario students submit a screenshot of their solution followed by one paragraph explaining what criteria you were trying to optimize, and why they picked their input variables. In this poster we outline the course and the module’s learning objectives where we used the game to place this into context. We illustrate key features of the INNOV8 Simulation Game, and describe how we used them to reinforce theoretical concepts. The poster will also illustrate examples from the simulation, assignment, and learning outcomes.

Keywords: experiential learning, business process management, BPM, INNOV8, simulation, game

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Authors: Robert Cazar, Nathaly Maza

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Energy drinks and sports drinks are quite popular among young adults and teenagers worldwide. Some concerns regarding their health effects – particularly those of the energy drinks - have been raised based on scientific findings. Differentiating between these two types of drinks by means of their chemical properties seems to be an instructive task. Chemometrics provides the most appropriate strategy to do so. In this study, a discrimination analysis of the energy and sports drinks has been carried out applying chemometric methods. A set of eleven samples of available commercial brands of drinks – seven energy drinks and four sports drinks – were collected. Each sample was characterized by eight chemical variables (carbohydrates, energy, sugar, sodium, pH, degrees Brix, density, and citric acid). The data set was standardized and examined by exploratory chemometric techniques such as clustering and principal component analysis. As a preliminary step, a variable selection was carried out by inspecting the variable correlation matrix. It was detected that some variables are redundant, so they can be safely removed, leaving only five variables that are sufficient for this analysis. They are sugar, sodium, pH, density, and citric acid. Then, a hierarchical clustering `employing the average – linkage criterion and using the Euclidian distance metrics was performed. It perfectly separates the two types of drinks since the resultant dendogram, cut at the 25% similarity level, assorts the samples in two well defined groups, one of them containing the energy drinks and the other one the sports drinks. Further assurance of the complete discrimination is provided by the principal component analysis. The projection of the data set on the first two principal components – which retain the 71% of the data information – permits to visualize the distribution of the samples in the two groups identified in the clustering stage. Since the first principal component is the discriminating one, the inspection of its loadings consents to characterize such groups. The energy drinks group possesses medium to high values of density, citric acid, and sugar. The sports drinks group, on the other hand, exhibits low values of those variables. In conclusion, the application of chemometric methods on a data set that features some chemical properties of a number of energy and sports drinks provides an accurate, dependable way to discriminate between these two types of beverages.

Keywords: chemometrics, clustering, energy drinks, principal component analysis, sports drinks

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Authors: Lukas Vierus, Thomas Schuster

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A general framework is examined for dynamic tensor field tomography within an inhomogeneous medium characterized by refraction and absorption, treated as an inverse source problem concerning the associated transport equation. Guided by Fermat’s principle, the Riemannian metric within the specified domain is determined by the medium's refractive index. While considerable literature exists on the inverse problem of reconstructing a tensor field from its longitudinal ray transform within a static Euclidean environment, limited inversion formulas and algorithms are available for general Riemannian metrics and time-varying tensor fields. It is established that tensor field tomography, akin to an inverse source problem for a transport equation, persists in dynamic scenarios. Framing dynamic tensor tomography as an inverse source problem embodies a comprehensive perspective within this domain. Ensuring well-defined forward mappings necessitates establishing existence and uniqueness for the underlying transport equations. However, the bilinear forms of the associated weak formulations fail to meet the coercivity condition. Consequently, recourse to viscosity solutions is taken, demonstrating their unique existence within suitable Sobolev spaces (in the static case) and Sobolev-Bochner spaces (in the dynamic case), under a specific assumption restricting variations in the refractive index. Notably, the adjoint problem can also be reformulated as a transport equation, with analogous results regarding uniqueness. Analytical solutions are expressed as integrals over geodesics, facilitating more efficient evaluation of forward and adjoint operators compared to solving partial differential equations. Certainly, here's the revised sentence in English: Numerical experiments are conducted using a Nesterov-accelerated Landweber method, encompassing various fields, absorption coefficients, and refractive indices, thereby illustrating the enhanced reconstruction achieved through this holistic modeling approach.

Keywords: attenuated refractive dynamic ray transform of tensor fields, geodesics, transport equation, viscosity solutions

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Authors: Selim M. Khan

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Indoor air quality is strongly influenced by the presence of radioactive radon (222Rn) gas. Indeed, exposure to high 222Rn concentrations is unequivocally linked to DNA damage and lung cancer and is a worsening issue in North American and European built environments, having increased over time within newer housing stocks as a function of as yet unclear variables. Indoor air radon concentration can be influenced by a wide range of environmental, structural, and behavioral factors. As some of these factors are quantitative while others are qualitative, no single statistical model can determine indoor radon level precisely while simultaneously considering all these variables across a complex and highly diverse dataset. The ability of AI- machine (deep) learning to simultaneously analyze multiple quantitative and qualitative features makes it suitable to predict radon with a high degree of precision. Using Canadian and Swedish long-term indoor air radon exposure data, we are using artificial deep neural network models with random weights and polynomial statistical models in MATLAB to assess and predict radon health risk to human as a function of geospatial, human behavioral, and built environmental metrics. Our initial artificial neural network with random weights model run by sigmoid activation tested different combinations of variables and showed the highest prediction accuracy (>96%) within the reasonable iterations. Here, we present details of these emerging methods and discuss strengths and weaknesses compared to the traditional artificial neural network and statistical methods commonly used to predict indoor air quality in different countries. We propose an artificial deep neural network with random weights as a highly effective method for assessing and predicting indoor radon.

Keywords: radon, radiation protection, lung cancer, aI-machine deep learnng, risk assessment, risk prediction, Europe, North America

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Authors: Kaustav Mukherjee

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In this paper we propose a Neural Style Transfer solution whereby we have created a Lightweight Separable Convolution Kernel Based GAN Architecture (SC-GAN) which will very useful for designing filter for Mobile Phone Cameras and also Edge Devices which will convert any image to its 2D ANIMATED COMIC STYLE Movies like HEMAN, SUPERMAN, JUNGLE-BOOK. This will help the 2D animation artist by relieving to create new characters from real life person's images without having to go for endless hours of manual labour drawing each and every pose of a cartoon. It can even be used to create scenes from real life images.This will reduce a huge amount of turn around time to make 2D animated movies and decrease cost in terms of manpower and time. In addition to that being extreme light-weight it can be used as camera filters capable of taking Comic Style Shots using mobile phone camera or edge device cameras like Raspberry Pi 4,NVIDIA Jetson NANO etc. Existing Methods like CartoonGAN with the model size close to 170 MB is too heavy weight for mobile phones and edge devices due to their scarcity in resources. Compared to the current state of the art our proposed method which has a total model size of 31 MB which clearly makes it ideal and ultra-efficient for designing of camera filters on low resource devices like mobile phones, tablets and edge devices running OS or RTOS. .Owing to use of high resolution input and usage of bigger convolution kernel size it produces richer resolution Comic-Style Pictures implementation with 6 times lesser number of parameters and with just 25 extra epoch trained on a dataset of less than 1000 which breaks the myth that all GAN need mammoth amount of data. Our network reduces the density of the Gan architecture by using Depthwise Separable Convolution which does the convolution operation on each of the RGB channels separately then we use a Point-Wise Convolution to bring back the network into required channel number using 1 by 1 kernel.This reduces the number of parameters substantially and makes it extreme light-weight and suitable for mobile phones and edge devices. The architecture mentioned in the present paper make use of Parameterised Batch Normalization Goodfellow etc al. (Deep Learning OPTIMIZATION FOR TRAINING DEEP MODELS page 320) which makes the network to use the advantage of Batch Norm for easier training while maintaining the non-linear feature capture by inducing the learnable parameters

Keywords: comic stylisation from camera image using GAN, creating 2D animated movie style custom stickers from images, depth-wise separable convolutional neural network for light-weight GAN architecture for EDGE devices, GAN architecture for 2D animated cartoonizing neural style, neural style transfer for edge, model distilation, perceptual loss

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Authors: A. Raj Kumar, S. Bilaloglu

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Stroke is a leading cause of adult disability worldwide. We depend on our hands for our activities of daily living(ADL). Although many patients regain the ability to walk, they continue to experience long-term hand motor impairments. As the number of individuals with young stroke is increasing, there is a critical need for effective approaches for rehabilitation of hand function post-stroke. Motor relearning for dexterity requires task-specific kinesthetic, tactile and visual feedback. However, when a stroke results in both sensory and motor impairment, it becomes difficult to ascertain when and what type of sensory substitutions can facilitate motor relearning. In an ideal situation, real-time task-specific data on the ability to learn and data-driven feedback to assist such learning will greatly assist rehabilitation for dexterity. We have found that kinesthetic and tactile information from the unaffected hand can assist patients re-learn the use of optimal fingertip forces during a grasp and lift task. Measurement of fingertip grip force (GF), load forces (LF), their corresponding rates (GFR and LFR), and other metrics can be used to gauge the impairment level and progress during learning. Currently ATI mini force-torque sensors are used in research settings to measure and compute the LF, GF, and their rates while grasping objects of different weights and textures. Use of the ATI sensor is cost prohibitive for deployment in clinical or at-home rehabilitation. A cost effective mechatronic device is developed to quantify GF, LF, and their rates for stroke rehabilitation purposes using off-the-shelf components such as load cells, flexi-force sensors, and an Arduino UNO microcontroller. A salient feature of the device is its integration with an interactive gaming environment to render a highly engaging user experience. This paper elaborates the integration of kinesthetic and tactile sensing through computation of LF, GF and their corresponding rates in real time, information processing, and interactive interfacing through augmented reality for visual feedback.

Keywords: feedback, gaming, kinesthetic, rehabilitation, tactile

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