Search results for: mathematical algorithms of targeting and persecution

Authors: Cristina Maria Ribeiro Martins Pereira Caridade, Ana Rita Ferreira Morais

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The use of computational tools has become essential in the context of interactive learning, especially in engineering education. In the medical industry, teaching medical image processing techniques is a crucial part of training biomedical engineers, as it has integrated applications with healthcare facilities and hospitals. The aim of this article is to present a teaching-learning simulation tool developed in MATLAB using a graphical user interface for medical image fusion that explores different image fusion methodologies and processes in combination with image pre-processing techniques. The application uses different algorithms and medical fusion techniques in real time, allowing you to view original images and fusion images, compare processed and original images, adjust parameters, and save images. The tool proposed in an innovative teaching and learning environment consists of a dynamic and motivating teaching simulation for biomedical engineering students to acquire knowledge about medical image fusion techniques and necessary skills for the training of biomedical engineers. In conclusion, the developed simulation tool provides real-time visualization of the original and fusion images and the possibility to test, evaluate and progress the student’s knowledge about the fusion of medical images. It also facilitates the exploration of medical imaging applications, specifically image fusion, which is critical in the medical industry. Teachers and students can make adjustments and/or create new functions, making the simulation environment adaptable to new techniques and methodologies.

Keywords: image fusion, image processing, teaching-learning simulation tool, biomedical engineering education

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Authors: Alexander Blokhin, Ekaterina Kruglova, Boris Semisalov

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Mathematical model based on the mesoscopic theory of polymer dynamics is developed for numerical simulation of the flows of polymeric liquid between two coaxial cylinders. This model is a system of nonlinear partial differential equations written in the cylindrical coordinate system and coupled with the heat conduction equation including a specific dissipation term. The stationary flows similar to classical Poiseuille ones are considered, and the resolving equations for the velocity of flow and for the temperature are obtained. For solving them, a fast pseudospectral method is designed based on Chebyshev approximations, that enables one to simulate the flows through the channels with extremely small relative values of the radius of inner cylinder. The numerical analysis of the dependance of flow on this radius and on the values of dissipation constant is done.

Keywords: dynamics of polymeric liquid, heat dissipation, singularly perturbed problem, pseudospectral method, Chebyshev polynomials, stabilization technique

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Authors: Devadrita Dey Sarkar

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Regardless of the many technologic advances in the past decade, increased training and experience, and the obvious benefits of uniform standards, the false-negative rate in screening mammography remains unacceptably high .A computer aided neural network classification of regions of suspicion (ROS) on digitized mammograms is presented in this abstract which employs features extracted by a new technique based on independent component analysis. CAD is a concept established by taking into account equally the roles of physicians and computers, whereas automated computer diagnosis is a concept based on computer algorithms only. With CAD, the performance by computers does not have to be comparable to or better than that by physicians, but needs to be complementary to that by physicians. In fact, a large number of CAD systems have been employed for assisting physicians in the early detection of breast cancers on mammograms. A CAD scheme that makes use of lateral breast images has the potential to improve the overall performance in the detection of breast lumps. Because breast lumps can be detected reliably by computer on lateral breast mammographs, radiologists’ accuracy in the detection of breast lumps would be improved by the use of CAD, and thus early diagnosis of breast cancer would become possible. In the future, many CAD schemes could be assembled as packages and implemented as a part of PACS. For example, the package for breast CAD may include the computerized detection of breast nodules, as well as the computerized classification of benign and malignant nodules. In order to assist in the differential diagnosis, it would be possible to search for and retrieve images (or lesions) with these CAD systems, which would be reliable and useful method for quantifying the similarity of a pair of images for visual comparison by radiologists.

Keywords: CAD(computer-aided design), lesions, neural network, ROS(region of suspicion)

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Authors: Seyed Sadegh Naseralavi, Sadegh Balaghi, Ehsan Khojastehfar

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Time history seismic analysis is supposed to be the most accurate method to predict the seismic demand of structures. On the other hand, the required computational time of this method toward achieving the result is its main deficiency. While being applied in optimization process, in which the structure must be analyzed thousands of time, reducing the required computational time of seismic analysis of structures makes the optimization algorithms more practical. Apparently, the invented approximate methods produce some amount of errors in comparison with exact time history analysis but the recently proposed method namely, Complete Quadratic Combination (CQC) and Sum Root of the Sum of Squares (SRSS) drastically reduces the computational time by combination of peak responses in each mode. In the present research, the Basic Modal Displacement (BMD) method is introduced and applied towards estimation of seismic demand of main structure. Seismic demand of sampled structure is estimated by calculation of modal displacement of basic structure (in which the modal displacement has been calculated). Shear steel sampled structures are selected as case studies. The error applying the introduced method is calculated by comparison of the estimated seismic demands with exact time history dynamic analysis. The efficiency of the proposed method is demonstrated by application of three types of earthquakes (in view of time of peak ground acceleration).

Keywords: time history dynamic analysis, basic modal displacement, earthquake-induced demands, shear steel structures

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Authors: Verónica Díaz Quezada

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Despite the wide variety of educational resources on the market and the advances produced in the technological field, the practice of teaching continues to be supported mainly by textbooks. This article reports on descriptive research with qualitative methodology carried out on secondary school mathematics teachers in a region of Chile, in order to describe the use and the indicators of appreciation that teachers have on the textbooks distributed by the official body to public educational establishments. Data were collected through an open response opinion questionnaire. According to the results, among the texts available for the annual performance of their teaching work, the expository and technological books predominate, to the detriment of comprehensive books. The exhibition structure favors master expositions and repetitive exercises, while, with the technological structure, a productive exercise is attempted, proposing numerous applications with the intention of giving meaning to the different mathematical rules and procedures. In relation to the indicators of appreciation that teachers have regarding the use of mathematics textbooks, the suitability and quality of the teaching resources are verified as the most satisfying characteristic.

Keywords: mathematics, secondary school, teachers, textbooks

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Authors: Silvia Arrate, Waldo Salud, Eloy París

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The wear of cutting tools is one of the most decisive elements when planning tunneling works, programming the maintenance stops and saving the optimum stock of spare parts during the evolution of the excavation. Being able to predict the behavior of cutting tools can give a very competitive advantage in terms of costs and excavation performance, optimized to the needs of the TBM itself. The incredible evolution of data science in recent years gives the option to implement it at the time of analyzing the key and most critical parameters related to machinery with the purpose of knowing how the cutting head is performing in front of the excavated ground. Taking this as a case study, Metro Line 3 of Guadalajara in Mexico will develop the feasibility of using Specific Energy versus data science applied over parameters of Torque, Penetration, and Contact Force, among others, to predict the behavior and status of cutting tools. The results obtained through both techniques are analyzed and verified in the function of the wear and the field situations observed in the excavation in order to determine its effectiveness regarding its predictive capacity. In conclusion, the possibilities and improvements offered by the application of digital tools and the programming of calculation algorithms for the analysis of wear of cutting head elements compared to purely empirical methods allow early detection of possible damage to cutting tools, which is reflected in optimization of excavation performance and a significant improvement in costs and deadlines.

Keywords: cutting tools, data science, prediction, TBM, wear

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Authors: T. Chinyoka

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We present the Johnson-Segalman constitutive model to capture certain fluid flow phenomena that has been experimentally observed in the flow of complex polymeric fluids. In particular, experimentally observed phenomena such as shear banding, spurt and slip are explored and/or explained in terms of the non-monotonic shear-stress versus shear-rate relationships. We also explore the effects of the inclusion of physical flow aspects such as wall porosity on shear banding. We similarly also explore the effects of the inclusion of mathematical modelling aspects such as stress diffusion into the stress constitutive models in order to predict shear-stress (or shear-rate) paths. We employ semi-implicit finite difference methods for all the computational solution procedures.

Keywords: Johnson-Segalman model, diffusive Johnson-Segalman model, shear banding, finite difference methods, complex fluid flow

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Authors: Ivana Knezevic, Kenneth D. Candido, N. Nick Knezevic

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Background: While pain intensity scales remain generally accepted assessment tool, and the numeric pain rating score is highly subjective, we nevertheless rely on them to make a judgment about treatment effects. Misinterpretation of pain can lead practitioners to underestimate or overestimate the patient’s medical condition. The purpose of this study was to analyze how the numeric rating pain scores given by patients with low back pain correlate with their functional activity levels. Methods: We included 100 consecutive patients with radicular low back pain (LBP) after the Institutional Review Board (IRB) approval. Pain scores, numeric rating scale (NRS) responses at rest and in the movement,Oswestry Disability Index (ODI) questionnaire answers were collected 10 times through 12 months. The ODI questionnaire is targeting a patient’s activities and physical limitations as well as a patient’s ability to manage stationary everyday duties. Statistical analysis was performed by using SPSS Software version 20. Results: The average duration of LBP was 14±22 months at the beginning of the study. All patients included in the study were between 24 and 78 years old (average 48.85±14); 56% women and 44% men. Differences between ODI and pain scores in the range from -10% to +10% were considered “normal”. Discrepancies in pain scores were graded as mild between -30% and -11% or +11% and +30%; moderate between -50% and -31% and +31% and +50% and severe if differences were more than -50% or +50%. Our data showed that pain scores at rest correlate well with ODI in 65% of patients. In 30% of patients mild discrepancies were present (negative in 21% and positive in 9%), 4% of patients had moderate and 1% severe discrepancies. “Negative discrepancy” means that patients graded their pain scores much higher than their functional ability, and most likely exaggerated their pain. “Positive discrepancy” means that patients graded their pain scores much lower than their functional ability, and most likely underrated their pain. Comparisons between ODI and pain scores during movement showed normal correlation in only 39% of patients. Mild discrepancies were present in 42% (negative in 39% and positive in 3%); moderate in 14% (all negative), and severe in 5% (all negative) of patients. A 58% unknowingly exaggerated their pain during movement. Inconsistencies were equal in male and female patients (p=0.606 and p=0.928).Our results showed that there was a negative correlation between patients’ satisfaction and the degree of reporting pain inconsistency. Furthermore, patients talking opioids showed more discrepancies in reporting pain intensity scores than did patients taking non-opioid analgesics or not taking medications for LBP (p=0.038). There was a highly statistically significant correlation between morphine equivalents doses and the level of discrepancy (p<0.0001). Conclusion: We have put emphasis on the patient education in pain evaluation as a vital step in accurate pain level reporting. We have showed a direct correlation with patients’ satisfaction. Furthermore, we must identify other parameters in defining our patients’ chronic pain conditions, such as functionality scales, quality of life questionnaires, etc., and should move away from an overly simplistic subjective rating scale.

Keywords: pain score, functionality scales, low back pain, lumbar

Procedia PDF Downloads 218

Authors: Mahdi Shamsi, Tohid Yousefi Rezaii, Siavash Eftekharifar

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Compressed sensing (CS) is a new powerful mathematical theory concentrating on sparse signals which is widely used in signal processing. The main idea is to sense sparse signals by far fewer measurements than the Nyquist sampling rate, but the reconstruction process becomes nonlinear and more complicated. Common dilemma in sparse signal recovery in CS is the lack of knowledge about sparsity order of the signal, which can be viewed as model order selection procedure. In this paper, we address the problem of sparsity order estimation in sparse signal recovery. This is of main interest in situations where the signal sparsity is unknown or the signal to be recovered is approximately sparse. It is shown that the proposed method also leads to some kind of signal denoising, where the observations are contaminated with noise. Finally, the performance of the proposed approach is evaluated in different scenarios and compared to an existing method, which shows the effectiveness of the proposed method in terms of order selection as well as denoising.

Keywords: compressed sensing, data denoising, model order selection, sparse representation

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Authors: Md. Aminul Islam, Adel Merabet, Rachid Beguenane, Hussein Ibrahim

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This paper presents a simulation and mathematical model of stand-alone solar-wind-diesel based hybrid energy system (HES). A power management system is designed for multiple energy resources in a stand-alone hybrid energy system. Both Solar photovoltaic and wind energy conversion system consists of maximum power point tracking (MPPT), voltage regulation, and basic power electronic interfaces. An additional diesel generator is included to support and improve the reliability of stand-alone system when renewable energy sources are not available. A power management strategy is introduced to distribute the generated power among resistive load banks. The frequency regulation is developed with conventional phase locked loop (PLL) system. The power management algorithm was applied in Matlab®/Simulink® to simulate the results.

Keywords: solar photovoltaic, wind energy, diesel engine, hybrid energy system, power management, frequency and voltage regulation

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Authors: Andreas Hirseland, Lukas Kerschbaumer

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In Germany, during the past years there constantly are about one million long term unemployed who did not benefit from the prospering labor market while most short term unemployed did. Instead, they are continuously dependent on welfare and sometimes precarious short-term employment, experiencing work poverty. Long term unemployment thus turns into a main obstacle to regular employment, especially if accompanied by other impediments such as low level education (school/vocational), poor health (especially chronical illness), advanced age (older than fifty), immigrant status, motherhood or engagement in care for other relatives. Almost two thirds of all welfare recipients have multiple impediments which hinder a successful transition from welfare back to sustainable and sufficient employment. Hiring them is often considered as an investment too risky for employers. Therefore formal application schemes based on formal qualification certificates and vocational biographies might reduce employers’ risks but at the same time are not helpful for long-term unemployed and welfare recipients. The panel survey ‘Labor market and social security’ (PASS; ~15,000 respondents in ~10,000 households), carried out by the Institute of Employment Research (the research institute of the German Federal Labor Agency), shows that their chance to get back to work tends to fall to nil. Only 66 cases of such unlikely transitions could be observed. In a sequential explanatory mixed-method study, the very scarce ‘success stories’ of unlikely transitions from long term unemployment to work were explored by qualitative inquiry – in-depth interviews with a focus on biography accompanied by qualitative network techniques in order to get a more detailed insight of relevant actors involved in the processes which promote the transition from being a welfare recipient to work. There is strong evidence that sustainable transitions are influenced by biographical resources like habits of network use, a set of informal skills and particularly a resilient way of dealing with obstacles, combined with contextual factors rather than by job-placement procedures promoted by Job-Centers according to activation rules or by following formal paths of application. On the employer’s side small and medium-sized enterprises are often found to give job opportunities to a wider variety of applicants, often based on a slow but steadily increasing relationship leading to employment. According to these results it is possible to show and discuss some limitations of (German) activation policies targeting welfare dependency and long-term unemployment. Based on these findings, indications for more supportive small scale measures in the field of labor-market policies are suggested to help long-term unemployed with multiple impediments to overcome their situation.

Keywords: against-all-odds, economic sociology, long-term unemployment, mixed-methods

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Authors: Areej Salaymeh, Loren Schwiebert, Stephen Remias, Jonathan Waddell

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Extracting information from new data sources has emerged as a crucial task in many traffic planning processes, such as identifying traffic patterns, route planning, traffic forecasting, and locating infrastructure improvements. Given the advanced technologies used to collect Global Positioning System (GPS) data from dedicated GPS devices, GPS equipped phones, and navigation tools, intelligent data analysis methodologies are necessary to mine this raw data. In this research, an automatic detection framework is proposed to help identify and classify the locations of stopped GPS waypoints into two main categories: signalized intersections or highway congestion. The Delaunay triangulation is used to perform this assessment in the clustering phase. While most of the existing clustering algorithms need assumptions about the data distribution, the effectiveness of the Delaunay triangulation relies on triangulating geographical data points without such assumptions. Our proposed method starts by cleaning noise from the data and normalizing it. Next, the framework will identify stoppage points by calculating the traveled distance. The last step is to use clustering to form groups of waypoints for signalized traffic and highway congestion. Next, a binary classifier was applied to find distinguish highway congestion from signalized stop points. The binary classifier uses the length of the cluster to find congestion. The proposed framework shows high accuracy for identifying the stop positions and congestion points in around 99.2% of trials. We show that it is possible, using limited GPS data, to distinguish with high accuracy.

Keywords: Delaunay triangulation, clustering, intelligent transportation systems, GPS data

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Authors: T. S. Terence Ma, Irene T. Ho

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Conducting adequate assessment of the challenges students with ASD (Autism Spectrum Disorder) face and the support they need is imperative for promoting their school adjustment. Students with ASD often show deficits in communication, social interaction, emotional regulation, and self-management in learning. While targeting these areas in intervention is often helpful, we argue that not enough attention has been paid to weak self-regulation being a key factor underlying their manifest difficulty in all these areas. Self-regulation refers to one’s ability to moderate their behavioral or affective responses without assistance from others. Especially for students with high functioning autism, who often show problems not so much in acquiring the needed skills but rather in applying those skills appropriately in everyday problem-solving, self-regulation becomes a key to successful adjustment in daily life. Therefore, a greater understanding of the construct of self-regulation, its relationship with other daily skills, and its role in school functioning for students with ASD would generate insights on how students’ school adjustment could be promoted more effectively. There were two focuses in this study. Firstly, we examined the extent to which self-regulation is a distinct construct that is differentiable from other daily skills and the most salient indicators of this construct. Then we tested a model of relationships between self-regulation and other daily school skills as well as their relative and combined effects on school adjustment. A total of 1,345 Grade1 to Grade 6 students with ASD attending mainstream schools in Hong Kong participated in the research. In the first stage of the study, teachers filled out a questionnaire consisting of 136 items assessing a wide range of student skills in social, emotional and learning areas. Results from exploratory factor analysis (EFA) with 673 participants and subsequent confirmatory factor analysis (CFA) with another group of 672 participants showed that there were five distinct factors of school skills, namely (1) communication skills, (2) pro-social behavior, (3) emotional skills, (4) learning management, and (5) self-regulation. Five scales representing these skill dimensions were generated. In the second stage of the study, a model postulating the mediating role of self-regulation for the effects of the other four types of skills on school adjustment was tested with structural equation modeling (SEM). School adjustment was defined in terms of the extent to which the student is accepted well in school, with high engagement in school life and self-esteem as well as good interpersonal relationships. A 5-item scale was used to assess these aspects of school adjustment. Results showed that communication skills, pro-social behavior, emotional skills and learning management had significant effects on school adjustment only indirectly through self-regulation, and their total effects were found to be not high. The results indicate that support rendered to students with ASD focusing only on the training of well-defined skills is not adequate for promoting their inclusion in school. More attention should be paid to the training of self-management with an emphasis on the application of skills backed by self-regulation. Also, other non-skill factors are important in promoting inclusive education.

Keywords: autism, assessment, factor analysis, self-regulation, school adjustment

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Authors: Wei Zhang

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With the rapid development of deep learning, neural network and deep learning algorithms play a significant role in various practical applications. Due to the high accuracy and good performance, Convolutional Neural Networks (CNNs) especially have become a research hot spot in the past few years. However, the size of the networks becomes increasingly large scale due to the demands of the practical applications, which poses a significant challenge to construct a high-performance implementation of deep learning neural networks. Meanwhile, many of these application scenarios also have strict requirements on the performance and low-power consumption of hardware devices. Therefore, it is particularly critical to choose a moderate computing platform for hardware acceleration of CNNs. This article aimed to survey the recent advance in Field Programmable Gate Array (FPGA)-based acceleration of CNNs. Various designs and implementations of the accelerator based on FPGA under different devices and network models are overviewed, and the versions of Graphic Processing Units (GPUs), Application Specific Integrated Circuits (ASICs) and Digital Signal Processors (DSPs) are compared to present our own critical analysis and comments. Finally, we give a discussion on different perspectives of these acceleration and optimization methods on FPGA platforms to further explore the opportunities and challenges for future research. More helpfully, we give a prospect for future development of the FPGA-based accelerator.

Keywords: deep learning, field programmable gate array, FPGA, hardware accelerator, convolutional neural networks, CNN

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Authors: Tarek M. Alguhane, Ayman H. Khalil, Nour M. Fayed, Ayman M. Ismail

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Early, pre-code, reinforced concrete structures present undetermined resistance to earthquakes. This situation is particularly unacceptable in the case of essential structures, such as healthcare structures and pilgrims' houses. Among these, existing old RC building in Madinah is seismically evaluated with and without infill wall and their dynamic characteristics are compared with measured values in the field using ambient vibration measurements (AVM). After, updating the mathematical models for this building with the experimental results, three dimensional pushover analysis (Nonlinear static analysis) was carried out using SAP 2000 software incorporating inelastic material properties for concrete, infill and steel. The purpose of this analysis is to evaluate the expected performance of structural systems by estimating, strength and deformation demands in design, and comparing these demands to available capacities at the performance levels of interest. The results are summarized and discussed.

Keywords: seismic assessment, pushover analysis ambient vibration, modal update

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Authors: Natalia Alvarado, Myldred Hernandez-Gonzalez, Mary Laird, Madeline Phillips, Elizabeth Miller, Luis Mendez, Teresa Satterfield Linares

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Objectives: Focusing on the reported levels of depressive symptoms from Hispanic individuals in the U.S. during the ongoing COVID-19 pandemic, we analyze the psychological effects of being labeled an ‘essential worker/trabajador(a) esencial.’ We situate this attribute within the complex context of how an individual’s mental health is linked to work status and his/her community’s attitude toward such a status. Method: 336 Spanish-speaking adults (Mage = 34.90; SD = 11.00; 46% female) living in the U.S. participated in a mixed-method study. Participants completed a self-report Spanish-language survey consisting of COVID-19 prompts (e.g., Soy un trabajador esencial durante la pandemia. I am an ‘essential worker’ during the pandemic), civic engagement scale (CES) attitudes (e.g., Me siento responsable de mi comunidad. I feel responsible for my community) and behaviors (e.g., Ayudo a los miembros de mi comunidad. I help members of my community), and the Center for Epidemiological Studies Depression Scale (e.g., Me sentía deprimido/a. I felt depressed). The survey was conducted several months into the pandemic and before the vaccine distribution. Results: Regression analyses show that being labeled an essential worker was correlated to CES attitudes (b= .28, p < .001) and higher CES behaviors (b= .32, p < .001). Essential worker status also reported higher levels of depressive symptoms (b= .17, p < .05). In addition, we found that CES attitudes and CES behaviors were related to higher levels of depressive symptoms (b= .11, p <.05, b = .22, p < .001, respectively). These findings suggest that those who are on the frontlines during the COVID-19 pandemic suffer higher levels of depressive symptoms, despite their affirming community attitudes and behaviors. Discussion: Hispanics/Latinxs make up 53% of the high-proximity employees who must work in person and in close contact with others; this is the highest rate of any racial or ethnic category. Moreover, 31% of Hispanics are classified as essential workers. Our outcomes show that those labeled as trabajadores esenciales convey attitudes of remaining strong and resilient for COVID-19 victims. They also express community attitudes and behaviors reflecting a sense of responsibility to continue working to help others during these unprecedented times. However, we also find that the pressure of maintaining basic needs for others exacerbates mental health challenges and stressors, as many essential workers are anxious and stressed about their physical and economic security. As a result, community attitudes do not protect from depressive symptoms as Hispanic essential workers are failing to balance everyone’s needs, including their own (e.g., physical exhaustion and psychological distress). We conclude with a discussion on alternatives to the phrase ‘essential worker’ and of incremental steps that can be taken to address pandemic-related mental health issues targeting US Hispanic workers.

Keywords: COVID-19, essential worker, mental health, race and ethnicity

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Authors: Abdelhafid Zenati, Mohamed Tadjine

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The mathematical formulation of biomedical problems is an important phase to understand and predict the dynamic of the controlled population. In this paper we perform a stability analysis of a coupled model for healthy and cancerous cells dynamics in Acute Myeloid Leukemia, this represents our first aim. Second, we illustrate the effect of the interconnection between healthy and cancer cells. The PDE-based model is transformed to a nonlinear distributed state space model (delay system). For an equilibrium point of interest, necessary and sufficient conditions of global asymptotic stability are given. Thus, we came up to give necessary and sufficient conditions of global asymptotic stability of the origin and the healthy situation and control of the dynamics of normal hematopoietic stem cells and cancerous during myelode Acute leukemia. Simulation studies are given to illustrate the developed results.

Keywords: distributed delay, global stability, modelling, nonlinear models, PDE, state space

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Authors: Sang Kompiang Wirawan, Chandra Purnomo

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Carbon dioxide (CO2) adsorption on the activated Klaten-Indonesian natural zeolite (AKINZ) in a packed bed adsorber has been studied. Experiment works consisted of acid activation and adsorption experiments. The natural zeolite sample was activated using 0.3 M HCl at the temperature of 353 K. In the adsorption experiments the feed gas concentrations were 40 and 80 % CO2 in helium within various temperatures of 303; 323 and 373 K. The experiments were conducted by using transient step change adsorption and 20 % Ar/He tracer experiment was conducted to measure dispersion and time lag effect of the packed bed system. A mathematical model of CO2 adsorption had been set up by assuming plug flow;isothermal;isobaric and no gas film mass transport resistance. Single site Langmuir physisorption and Maxwell Stefan mass transport in micropore were applied. All the data were then optimized to get the best value of modified fitted parameter. The model was in a good agreement with the experiment data. Diffusivity tended to increase by increasing temperatures.

Keywords: adsorption, Langmuir, Maxwell-Stefan, natural zeolite, surface diffusion

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Authors: David J. Foran, Nhan Do, Samuel Ajjarapu, Wenjin Chen, Tahsin Kurc, Joel H. Saltz

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The large-scale data and computational requirements of investigators throughout the clinical and research communities demand an informatics infrastructure that supports both existing and new investigative and translational projects in a robust, secure environment. In some subspecialties of medicine and research, the capacity to generate data has outpaced the methods and technology used to aggregate, organize, access, and reliably retrieve this information. Leading health care centers now recognize the utility of establishing an enterprise-wide, clinical data warehouse. The primary benefits that can be realized through such efforts include cost savings, efficient tracking of outcomes, advanced clinical decision support, improved prognostic accuracy, and more reliable clinical trials matching. The overarching objective of the work presented here is the development and implementation of a flexible Intelligent Retrieval and Interrogation System (IRIS) that exploits the combined use of computational imaging, genomics, and data-mining capabilities to facilitate clinical assessments and translational research in oncology. The proposed System includes a multi-modal, Clinical & Research Data Warehouse (CRDW) that is tightly integrated with a suite of computational and machine-learning tools to provide insight into the underlying tumor characteristics that are not be apparent by human inspection alone. A key distinguishing feature of the System is a configurable Extract, Transform and Load (ETL) interface that enables it to adapt to different clinical and research data environments. This project is motivated by the growing emphasis on establishing Learning Health Systems in which cyclical hypothesis generation and evidence evaluation become integral to improving the quality of patient care. To facilitate iterative prototyping and optimization of the algorithms and workflows for the System, the team has already implemented a fully functional Warehouse that can reliably aggregate information originating from multiple data sources including EHR’s, Clinical Trial Management Systems, Tumor Registries, Biospecimen Repositories, Radiology PAC systems, Digital Pathology archives, Unstructured Clinical Documents, and Next Generation Sequencing services. The System enables physicians to systematically mine and review the molecular, genomic, image-based, and correlated clinical information about patient tumors individually or as part of large cohorts to identify patterns that may influence treatment decisions and outcomes. The CRDW core system has facilitated peer-reviewed publications and funded projects, including an NIH-sponsored collaboration to enhance the cancer registries in Georgia, Kentucky, New Jersey, and New York, with machine-learning based classifications and quantitative pathomics, feature sets. The CRDW has also resulted in a collaboration with the Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC) at the U.S. Department of Veterans Affairs to develop algorithms and workflows to automate the analysis of lung adenocarcinoma. Those studies showed that combining computational nuclear signatures with traditional WHO criteria through the use of deep convolutional neural networks (CNNs) led to improved discrimination among tumor growth patterns. The team has also leveraged the Warehouse to support studies to investigate the potential of utilizing a combination of genomic and computational imaging signatures to characterize prostate cancer. The results of those studies show that integrating image biomarkers with genomic pathway scores is more strongly correlated with disease recurrence than using standard clinical markers.

Keywords: clinical data warehouse, decision support, data-mining, intelligent databases, machine-learning.

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Authors: H. W. Meerveld, R. H. A. Lindelauf

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In contemporary warfare, data science is crucial for the military in achieving information superiority. Yet, to the authors’ knowledge, no extensive literature survey on data science in military decision-making has been conducted so far. In this study, 156 peer-reviewed articles were analysed through an integrative, semi-systematic literature review to gain an overview of the topic. The study examined to what extent literature is focussed on the opportunities or risks of data science in military decision-making, differentiated per level of war (i.e. strategic, operational, and tactical level). A relatively large focus on the risks of data science was observed in social science literature, implying that political and military policymakers are disproportionally influenced by a pessimistic view on the application of data science in the military domain. The perceived risks of data science are, however, hardly addressed in formal science literature. This means that the concerns on the military application of data science are not addressed to the audience that can actually develop and enhance data science models and algorithms. Cross-disciplinary research on both the opportunities and risks of military data science can address the observed research gaps. Considering the levels of war, relatively low attention for the operational level compared to the other two levels was observed, suggesting a research gap with reference to military operational data science. Opportunities for military data science mostly arise at the tactical level. On the contrary, studies examining strategic issues mostly emphasise the risks of military data science. Consequently, domain-specific requirements for military strategic data science applications are hardly expressed. Lacking such applications may ultimately lead to a suboptimal strategic decision in today’s warfare.

Keywords: data science, decision-making, information superiority, literature review, military

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Authors: Susan Thurstans, Natalie Sessions, Carmel Dolan, Kate Sadler, Bernardette Cichon, Shelia Isanaka, Dominique Roberfroid, Heather Stobagh, Patrick Webb, Tanya Khara

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For many years, wasting and stunting have been viewed as separate conditions without clear evidence supporting this distinction. In 2014, the Emergency Nutrition Network (ENN) examined the relationship between wasting and stunting and published a report highlighting the evidence for linkages between the two forms of undernutrition. This systematic review aimed to update the evidence generated since this 2014 report to better understand the implications for improving child nutrition, health and survival. Following PRISMA guidelines, this review was conducted using search terms to describe the relationship between wasting and stunting. Studies related to children under five from low- and middle-income countries that assessed both ponderal growth/wasting and linear growth/stunting, as well as the association between the two, were included. Risk of bias was assessed in all included studies using SIGN checklists. 45 studies met the inclusion criteria- 39 peer reviewed studies, 1 manual chapter, 3 pre-print publications and 2 published reports. The review found that there is a strong association between the two conditions whereby episodes of wasting contribute to stunting and, to a lesser extent, stunting leads to wasting. Possible interconnected physiological processes and common risk factors drive an accumulation of vulnerabilities. Peak incidence of both wasting and stunting was found to be between birth and three months. A significant proportion of children experience concurrent wasting and stunting- Country level data suggests that up to 8% of children under 5 may be both wasted and stunted at the same time, global estimates translate to around 16 million children. Children with concurrent wasting and stunting have an elevated risk of mortality when compared to children with one deficit alone. These children should therefore be considered a high-risk group in the targeting of treatment. Wasting, stunting and concurrent wasting and stunting appear to be more prevalent in boys than girls and it appears that concurrent wasting and stunting peaks between 12- 30 months of age with younger children being the most affected. Seasonal patterns in prevalence of both wasting and stunting are seen in longitudinal and cross sectional data and in particular season of birth has been shown to have an impact on a child’s subsequent experience of wasting and stunting. Evidence suggests that the use of mid-upper-arm circumference combined with weight-for-age Z-score might effectively identify children most at risk of near-term mortality, including those concurrently wasted and stunted. Wasting and stunting frequently occur in the same child, either simultaneously or at different moments through their life course. Evidence suggests there is a process of accumulation of nutritional deficits and therefore risk over the life course of a child demonstrates the need for a more integrated approach to prevention and treatment strategies to interrupt this process. To achieve this, undernutrition policies, programmes, financing and research must become more unified.

Keywords: Concurrent wasting and stunting, Review, Risk factors, Undernutrition

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Authors: Vijaya K. Srivastava, Davide Spinello

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This paper presents performance of two robust gradient-based heuristic optimization procedures based on 3ⁿ enumeration and tunneling approach to seek global optimum of constrained integer problems. Both these procedures consist of two distinct phases for locating the global optimum of integer problems with a linear or non-linear objective function subject to linear or non-linear constraints. In both procedures, in the first phase, a local minimum of the function is found using the gradient approach coupled with hemstitching moves when a constraint is violated in order to return the search to the feasible region. In the second phase, in one optimization procedure, the second sub-procedure examines 3ⁿ integer combinations on the boundary and within hypercube volume encompassing the result neighboring the result from the first phase and in the second optimization procedure a tunneling function is constructed at the local minimum of the first phase so as to find another point on the other side of the barrier where the function value is approximately the same. In the next cycle, the search for the global optimum commences in both optimization procedures again using this new-found point as the starting vector. The search continues and repeated for various step sizes along the function gradient as well as that along the vector normal to the violated constraints until no improvement in optimum value is found. The results from both these proposed optimization methods are presented and compared with one provided by popular MS Excel solver that is provided within MS Office suite and other published results.

Keywords: constrained integer problems, enumerative search algorithm, Heuristic algorithm, Tunneling algorithm

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Authors: Youssef Mazloum, Haytham Sayah, Maroun Nemer

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The penetration of renewable energy sources into the electric grid is significantly increasing. However, the intermittence of these sources breaks the balance between supply and demand for electricity. Hence, the importance of the energy storage technologies, they permit restoring the balance and reducing the drawbacks of intermittence of the renewable energies. This paper discusses the modeling and the cost-effectiveness of an isobaric adiabatic compressed air energy storage (IA-CAES) system. The proposed system is a combination among a compressed air energy storage (CAES) system with pumped hydro storage system and thermal energy storage system. The aim of this combination is to overcome the disadvantages of the conventional CAES system such as the losses due to the storage pressure variation, the loss of the compression heat and the use of fossil fuel sources. A steady state model is developed to perform an energy and exergy analyses of the IA-CAES system and calculate the distribution of the exergy losses in the latter system. A sensitivity analysis is also carried out to estimate the effects of some key parameters on the system’s efficiency, such as the pinch of the heat exchangers, the isentropic efficiency of the rotating machinery and the pressure losses. The conducted sensitivity analysis is a local analysis since the sensibility of each parameter changes with the variation of the other parameters. Therefore, an exergoeconomic study is achieved as well as a cost optimization in order to reduce the electricity cost produced during the production phase. The optimizer used is OmOptim which is a genetic algorithms based optimizer.

Keywords: cost-effectiveness, Exergoeconomic analysis, isobaric adiabatic compressed air energy storage (IA-CAES) system, thermodynamic modeling

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Authors: Aris R. Terzopoulos, Georgia Z. Niolaki, Lynne G. Duncan, Mark A. J. Wilson, Antonios Kyparissiadis, Jackie Masterson

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As word databases for bilingual children are not available, researchers, educators and textbook writers must rely on monolingual databases. The aim of this study is thus to develop a bilingual word database, BiLex-kids, an online open access developmental word database for 5-13 year old bilingual children who learn Greek as a second language and have English as their dominant one. BiLex-kids is compiled from 120 Greek textbooks used in Greek-English bilingual education in the UK, USA and Australia, and provides word translations in the two languages, pronunciations in Greek, and psycholinguistic variables (e.g. Zipf, Frequency per million, Dispersion, Contextual Diversity, Neighbourhood size). After clearing the textbooks of non-relevant items (e.g. punctuation), algorithms were applied to extract the psycholinguistic indices for all words. As well as one total lexicon, the database produces values for all ages (one lexicon for each age) and for three age bands (one lexicon per age band: 5-8, 9-11, 12-13 years). BiLex-kids provides researchers with accurate figures for a wide range of psycholinguistic variables, making it a useful and reliable research tool for selecting stimuli to examine lexical processing among bilingual children. In addition, it offers children the opportunity to study word spelling, learn translations and listen to pronunciations in their second language. It further benefits educators in selecting age-appropriate words for teaching reading and spelling, while special educational needs teachers will have a resource to control the content of word lists when designing interventions for bilinguals with literacy difficulties.

Keywords: bilingual children, psycholinguistics, vocabulary development, word databases

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Authors: S. Njoumi, L. Ben Haj Said, M. J. Amiot, S. Bellagha

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The main objective of this research was to establish the kinetics of variation of total polyphenol content (TPC) and total flavonoid content (TFC) in Tunisian Corchorus olitorius powder and in a traditional home cooked-meal (Molokheiya) when using stewing and stir-frying as cooking methods, but also to compare the effect of these two common cooking practices on water content, TPC, TFC and color. The L*, a* and b* coordinates values of the Molokheiya varied from 24.955±0.039 to 21.301±0.036, from -1.556±0.048 to 0.23±0.026 and from 5.675±0.052 to 6.313±0.103 when using stewing and from 21.328±0.025 to 20.56±0.021, from -1.093± 0.011to 0.121±0.007 and from 5.708±0.020 to 6.263±0.007 when using stir-frying, respectively. TPC and TFC increased during cooking. TPC of Molokheiya varied from 29.852±0.866 mg GAE/100 g to 220.416±0.519 mg GAE/100 g after 150 min of stewing and from 25.257±0.259 mg GAE/100 g to 208.897 ±0.173 mg GAE/100 g using stir-frying method during 150 min. TFC of Molokheiya varied from 48.229±1.47 mg QE/100 g to 843.802±1.841 mg QE/100 g when using stewing and from 37.031± 0.368 mg QE/100 g to 775.312±0.736 mg QE/100 g when using stir-frying. Kinetics followed similar curves in all cases but resulted in different final TPC and TFC. The shape of the kinetics curves suggests zero-order kinetics. The mathematical relations and the numerical approach used to model the kinetics of polyphenol and flavonoid contents in Molokheiya are described.

Keywords: Corchorus olitorius, Molokheiya, phenolic compounds, kinetic

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Authors: M. Louise Humbert, Karen E. Chad, Natalie E. Houser, Marta E. Erlandson

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Physical literacy is the motivation, confidence, physical competence, knowledge, and understanding to value and takes responsibility for engagement in physical activities for life. In recent years, physical literacy has emerged as a determinant of health, promoting a positive lifelong physical activity trajectory. Physical literacy’s holistic approach and emphasis on the intrinsic valuation of movement provide an encouraging avenue for intervention among children to develop competent and confident movers. Although there is research on physical literacy interventions, no evidence exists on the outcomes of multi-sectoral interventions involving a combination of home, school, and community contexts. Since children interact with and in a wide range of contexts (home, school, community) daily, interventions designed to address a combination of these contexts are critical to the development of physical literacy. Working with school administrators and teachers, sports and recreation leaders, and community members, our team of university and community researchers conducted and evaluated one of the first multi-contextual and multi-sectoral physical literacy interventions in Canada. Schools played a critical role in this multi-sector intervention, and in this project, teachers and administrators focused their actions on developing physical literacy in students 10 to 14 years of age through the instruction of physical literacy-focused physical education lessons. Little is known about the experiences of educators when they work alongside an array of community representatives to develop physical literacy in school-aged children. Given the uniqueness of this intervention, we sought to answer the question, ‘What were the experiences of school-based educators involved in a multi-sectoral partnership focused on creating a physical literacy enriched community intervention?’ A thematic analysis approach was used to analyze data collected from interviews with educators and administrators, informal conversations, documents, and observations at workshops and meetings. Results indicated that schools and educators played the largest role in this multi-sector intervention. Educators initially reported a limited understanding of physical literacy and expressed a need for resources linked to the physical education curriculum. Some anxiety was expressed by the teachers as their students were measured, and educators noted they wanted to increase their understanding and become more involved in the assessment of physical literacy. Teachers reported that the intervention’s focus on physical literacy positively impacted the scheduling and their instruction of physical education. Administrators shared their desire for school and division-level actions targeting physical literacy development like the current focus on numeracy and literacy, treaty education, and safe schools. As this was one of the first multi-contextual and multi-sectoral physical literacy interventions, it was important to document creation and delivery experiences to encourage future growth in the area and develop suggested best practices.

Keywords: physical literacy, multi sector intervention, physical education, teachers

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Authors: Ahmed ElAzab, Amira M. Idrees, Mahmoud A. Mahmoud, Hesham Hefny

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Social networking sites such as Twitter and Facebook attracts over 500 million users across the world, for those users, their social life, even their practical life, has become interrelated. Their interaction with social networking has affected their life forever. Accordingly, social networking sites have become among the main channels that are responsible for vast dissemination of different kinds of information during real time events. This popularity in Social networking has led to different problems including the possibility of exposing incorrect information to their users through fake accounts which results to the spread of malicious content during life events. This situation can result to a huge damage in the real world to the society in general including citizens, business entities, and others. In this paper, we present a classification method for detecting fake accounts on Twitter. The study determines the minimized set of the main factors that influence the detection of the fake accounts on Twitter, then the determined factors have been applied using different classification techniques, a comparison of the results for these techniques has been performed and the most accurate algorithm is selected according to the accuracy of the results. The study has been compared with different recent research in the same area, this comparison has proved the accuracy of the proposed study. We claim that this study can be continuously applied on Twitter social network to automatically detect the fake accounts, moreover, the study can be applied on different Social network sites such as Facebook with minor changes according to the nature of the social network which are discussed in this paper.

Keywords: fake accounts detection, classification algorithms, twitter accounts analysis, features based techniques

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Authors: George Abu-Aqil, Leah Tsror, Elad Shufan, Shaul Mordechai, Mahmoud Huleihel, Ahmad Salman

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Pectobacterium and Dickeya spp which negatively affect a wide range of crops are the main causes of the aggressive diseases of agricultural crops. These aggressive diseases are responsible for a huge economic loss in agriculture including a severe decrease in the quality of the stored vegetables and fruits. Therefore, it is important to detect these pathogenic bacteria at their early stages of infection to control their spread and consequently reduce the economic losses. In addition, early detection is vital for producing non-infected propagative material for future generations. The currently used molecular techniques for the identification of these bacteria at the strain level are expensive and laborious. Other techniques require a long time of ~48 h for detection. Thus, there is a clear need for rapid, non-expensive, accurate and reliable techniques for early detection of these bacteria. In this study, infrared spectroscopy, which is a well-known technique with all its features, was used for rapid detection of Pectobacterium and Dickeya spp. at the strain level. The bacteria were isolated from potato plants and tubers with soft rot symptoms and measured by infrared spectroscopy. The obtained spectra were analyzed using different machine learning algorithms. The performances of our approach for taxonomic classification among the bacterial samples were evaluated in terms of success rates. The success rates for the correct classification of the genus, species and strain levels were ~100%, 95.2% and 92.6% respectively.

Keywords: soft rot enterobacteriaceae (SRE), pectobacterium, dickeya, plant infections, potato, solanum tuberosum, infrared spectroscopy, machine learning

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Authors: Sheetla Prasad

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The objective of this study is ‘discovering the real psyche of human beings for prediction of mode, direction and strength of the potential of actions of the individual. The human face was taken as a source of central point to search for the route of real psyche. Analysis of the face architecture (shape and salient features of face) was done by three directional photographs ( 600 left and right and camera facing) of human beings. The shapes and features of the human face were scaled in 177 units on the basis of face–features locations (FFL). The mathematical analysis was done of FFLs by self developed and standardized formula. At this phase, 800 samples were taken from the population of students, teachers, advocates, administrative officers, and common persons. The finding shows that real psyche has two external rings (ER). These ER are itself generator of two independent psyches (manifested and manipulated). Prima-facie, it was proved that micro differences in FFLs have potential to predict the state of art of the human psyche. The potential of psyches was determined by the saving and distribution of mental energy. It was also mathematically proved.

Keywords: face architecture, psyche, potential, face functional ratio, external rings

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Authors: Bokkasam Sasidhar, Ibrahim Aljasser

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The problem of finding optimal schedules for each equipment in a production process is considered, which consists of a single stage of manufacturing and which can handle different types of products, where changeover for handling one type of product to the other type incurs certain costs. The machine capacity is determined by the upper limit for the quantity that can be processed for each of the products in a set up. The changeover costs increase with the number of set ups and hence to minimize the costs associated with the product changeover, the planning should be such that similar types of products should be processed successively so that the total number of changeovers and in turn the associated set up costs are minimized. The problem of cost minimization is equivalent to the problem of minimizing the number of set ups or equivalently maximizing the capacity utilization in between every set up or maximizing the total capacity utilization. Further, the production is usually planned against customers’ orders, and generally different customers’ orders are assigned one of the two priorities – “normal” or “priority” order. The problem of production planning in such a situation can be formulated into a Multiple Arc Network (MAN) model and can be solved sequentially using the algorithm for maximizing flow along a MAN and the algorithm for maximizing flow along a MAN with priority arcs. The model aims to provide optimal production schedule with an objective of maximizing capacity utilization, so that the customer-wise delivery schedules are fulfilled, keeping in view the customer priorities. Algorithms have been presented for solving the MAN formulation of the production planning with customer priorities. The application of the model is demonstrated through numerical examples.

Keywords: scheduling, maximal flow problem, multiple arc network model, optimization

Procedia PDF Downloads 389