Search results for: patient modeling.

Authors: Abobaker Mohammed Qasem Farhan, Shangming Yang, Mohammed Al-Nehari

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Pneumonia is a kind of lung disease that creates congestion in the chest. Such pneumonic conditions lead to loss of life due to the severity of high congestion. Pneumonic lung disease is caused by viral pneumonia, bacterial pneumonia, or COVID-19 induced pneumonia. The early prediction and classification of such lung diseases help reduce the mortality rate. We propose the automatic Computer-Aided Diagnosis (CAD) system in this paper using the deep learning approach. The proposed CAD system takes input from raw computerized tomography (CT) scans of the patient's chest and automatically predicts disease classification. We designed the Hybrid Deep Learning Algorithm (HDLA) to improve accuracy and reduce processing requirements. The raw CT scans are pre-processed first to enhance their quality for further analysis. We then applied a hybrid model that consists of automatic feature extraction and classification. We propose the robust 2D Convolutional Neural Network (CNN) model to extract the automatic features from the pre-processed CT image. This CNN model assures feature learning with extremely effective 1D feature extraction for each input CT image. The outcome of the 2D CNN model is then normalized using the Min-Max technique. The second step of the proposed hybrid model is related to training and classification using different classifiers. The simulation outcomes using the publicly available dataset prove the robustness and efficiency of the proposed model compared to state-of-art algorithms.

Keywords: CT scans, COVID-19, deep learning, image processing, pneumonia, lung disease.

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Authors: William Xiu Shun Wong, Myungsu Lim, Yoonjin Hyun, Chen Liu, Seongi Choi, Dasom Kim, Kee-Young Kwahk, Namgyu Kim

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Recently, many users have begun to frequently share their opinions on diverse issues using various social media. Therefore, numerous governments have attempted to establish or improve national policies according to the public opinions captured from various social media. In this paper, we indicate several limitations of the traditional approaches to analyze public opinion on science and technology and provide an alternative methodology to overcome these limitations. First, we distinguish between the science and technology analysis phase and the social issue analysis phase to reflect the fact that public opinion can be formed only when a certain science and technology is applied to a specific social issue. Next, we successively apply a start list and a stop list to acquire clarified and interesting results. Finally, to identify the most appropriate documents that fit with a given subject, we develop a new logical filter concept that consists of not only mere keywords but also a logical relationship among the keywords. This study then analyzes the possibilities for the practical use of the proposed methodology thorough its application to discover core issues and public opinions from 1,700,886 documents comprising SNS, blogs, news, and discussions.

Keywords: Big data, social network analysis, text mining, topic modeling.

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Authors: H. W. Corley

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A scalar equilibrium (SE) is an alternative type of equilibrium in pure strategies for an n-person normal-form game G. It is defined using optimization techniques to obtain a pure strategy for each player of G by maximizing an appropriate utility function over the acceptable joint actions. The players’ actions are determined by the choice of the utility function. Such a utility function could be agreed upon by the players or chosen by an arbitrator. An SE is an equilibrium since no players of G can increase the value of this utility function by changing their strategies. SEs are formally defined, and examples are given. In a greedy SE, the goal is to assign actions to the players giving them the largest individual payoffs jointly possible. In a weighted SE, each player is assigned weights modeling the degree to which he helps every player, including himself, achieve as large a payoff as jointly possible. In a compromise SE, each player wants a fair payoff for a reasonable interpretation of fairness. In a parity SE, the players want their payoffs to be as nearly equal as jointly possible. Finally, a satisficing SE achieves a personal target payoff value for each player. The vector payoffs associated with each of these SEs are shown to be Pareto optimal among all such acceptable vectors, as well as computationally tractable.

Keywords: Compromise equilibrium, greedy equilibrium, normal-form game, parity equilibrium, pure strategies, satisficing equilibrium, scalar equilibria, utility function, weighted equilibrium.

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Authors: N. A. Zambri, A. Mohamed, H. Shareef, M. Z. C. Wanik

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This paper presents the modeling and simulation of a hybrid proton exchange membrane fuel cell (PEMFC) with an energy storage system for use in a stand-alone distributed generation (DG) system. The simulation model consists of fuel cell DG, lead-acid battery, maximum power point tracking and power conditioning unit which is modeled in the MATLAB/Simulink platform. Poor loadfollowing characteristics and slow response to rapid load changes are some of the weaknesses of PEMFC because of the gas processing reaction and the fuel cell dynamics. To address the load-tracking issues in PEMFC, a hybrid PEMFC and battery storage system is considered and modelled. The model utilizes PEMFC as the main energy source whereas the battery functions as energy storage to compensate for the limitations of PEMFC.Simulation results are given to show the overall system performance under light and heavyloading conditions.

Keywords: Hybrid, Lead–Acid Battery, Maximum Power Point Tracking, Proton Exchange Membrane Fuel Cell.

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Authors: Yi-Shun Wang, Hsin-Hui Lin, Yi-Wen Liao

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With the proliferation of Weblogs (blogs) use in educational contexts, gaining a better understanding of why students are willing to utilize blog systems has become an important topic for practitioners and academics. While perceived enjoyment has been found to have a significant influence on behavioral intentions to use blogs or hedonic systems, few studies have investigated the antecedents of perceived enjoyment in the acceptance of blogging. The main purpose of the present study is to explore the individual difference antecedents of perceived enjoyment and examine how they influence behavioral intention to blog through the mediation of perceived enjoyment. Based on the previous literature, the Big Five personality traits (i.e., extraversion, agreeableness, conscientiousness, neuroticism, and openness to experience), as well as computer self-efficacy and personal innovation in information technology (PIIT), are hypothesized as potential antecedents of perceived enjoyment in the acceptance of blogging. Data collected from 358 respondents in Taiwan are tested against the research model using the structural equation modeling approach. The results indicate that extraversion, agreeableness, conscientiousness, and PIIT have a significant influence on perceived enjoyment, which in turn significantly influences the behavioral intention to blog. These findings lead to several important implications for future research.

Keywords: Individual difference, Big Five personality traits, perceived enjoyment, blogging

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Authors: Zoran Jovanovic, Zoran Masonicic, S. Dragutinovic, Z. Sakota

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In this paper, results concerning flame propagation of various fuels in a particular combustion chamber with four tilted valves were elucidated. Flame propagation was represented by the evolution of spatial distribution of temperature in various cut-planes within combustion chamber while the flame front location was determined by dint of zones with maximum temperature gradient. The results presented are only a small part of broader on-going scrutinizing activity in the field of multidimensional modeling of reactive flows in combustion chambers with complicated geometries encompassing various models of turbulence, different fuels and combustion models. In the case of turbulence two different models were applied i.e. standard k-ε model of turbulence and k-ξ-f model of turbulence. In this paper flame propagation results were analyzed and presented for two different hydrocarbon fuels, such as CH4 and C8H18. In the case of combustion all differences ensuing from different turbulence models, obvious for non-reactive flows are annihilated entirely. Namely the interplay between fluid flow pattern and flame propagation is invariant as regards turbulence models and fuels applied. Namely the interplay between fluid flow pattern and flame propagation is entirely invariant as regards fuel variation indicating that the flame propagation through unburned mixture of CH4 and C8H18 fuels is not chemically controlled.

Keywords: Automotive flows, flame propagation, combustion modelling.

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Authors: Sandu L., Topală F., Porojan S.

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Cast metal inlays can be used on molars requiring a class II restoration instead amalgam and offer a durable alternative. Because it is known that class II inlays may increase the susceptibility to fracture, it is important to ensure optimal performance in selection of the adequate preparation design to reduce stresses in teeth structures and also in the restorations. The aim of the study was to investigate the influence of preparation design on stress distribution in molars with different class II preparations and in cast metal inlays. The first step of the study was to achieve 3D models in order to analyze teeth and cast metal class II inlays. The geometry of the intact tooth was obtained by 3D scanning using a manufactured device. With a NURBS modeling program the preparations and the appropriately inlays were designed. 3D models of first upper molars of the same shape and size were created. Inlay cavities designs were created using literature data. The geometrical model was exported and the mesh structure of the solid 3D model was created for structural simulations. Stresses were located around the occlusal contact areas. For the studied cases, the stress values were not significant influenced by the taper of the preparation. it was demonstrated stresses are higher in the cast metal restorations and therefore the strength of the teeth is not affected.

Keywords: cast metal inlays, class II restoration, molars, 3D models, structural simulations.

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Authors: Mohd Sidek Ahmad, Zainon Mohd Noor, Zaidah Zainal Ariffin

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Fibrin degradation is an important part in prevention or treatment of intravascular thrombosis and cardiovascular diseases. Plasmin like fibrinolytic enzymes has given new hope to patient with cardiovascular diseases by treating fibrin aggregation related diseases with traditional plasminogen activator which have many side effects. Various researches involving wide range of sources for production of fibrinolytic proteases, from bacteria, fungi, insects and fermented foods. But few have looked into endophytic fungi as a potential source. Sixteen (16) endophytic fungi were isolated from Hibiscus sp. leaves from six different locations in Shah Alam, Selangor. Only two endophytic fungi, FH3 and S13 showed positive fibrinolytic protease activities. FH3 produced 5.78cm and S13 produced 4.48cm on Skim Milk Agar after 4 days of incubation at 27°C. Fibrinolytic activity was observed; 3.87cm and 1.82cm diameter clear zone on fibrin plate of FH3 and S13 respectively. 18srRNA was done for identification of the isolated fungi with positive fibrinolytic protease. S13 had the highest similarity (100%) to that of Penicillium citrinum strain TG2 and FH3 had the highest similarity (99%) to that of Fusarium sp. FW2PhC1, Fusarium sp. 13002, Fusarium sp. 08006, Fusarium equiseti strain Salicorn 8 and Fungal sp. FCASAn-2. Media composition variation showed the effects of carbon nitrogen on protein concentration, where the decrement of 50% of media composition caused drastic decrease in protease of FH3 from 1.081 to 0.056 and also S13 from 2.946 to 0.198.

Keywords: Isolation, identification, fibrinolytic protease, endophytic fungi, Hibiscus leaves.

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Authors: Hamid R. S. Mojaveri, Seyed S. Mousavi, Mojtaba Heydar, Ahmad Aminian

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The aim of this paper is to present a methodology in three steps to forecast supply chain demand. In first step, various data mining techniques are applied in order to prepare data for entering into forecasting models. In second step, the modeling step, an artificial neural network and support vector machine is presented after defining Mean Absolute Percentage Error index for measuring error. The structure of artificial neural network is selected based on previous researchers' results and in this article the accuracy of network is increased by using sensitivity analysis. The best forecast for classical forecasting methods (Moving Average, Exponential Smoothing, and Exponential Smoothing with Trend) is resulted based on prepared data and this forecast is compared with result of support vector machine and proposed artificial neural network. The results show that artificial neural network can forecast more precisely in comparison with other methods. Finally, forecasting methods' stability is analyzed by using raw data and even the effectiveness of clustering analysis is measured.

Keywords: Artificial Neural Networks (ANN), bullwhip effect, demand forecasting, Support Vector Machine (SVM).

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Authors: O. Afshar

Abstract:

A solar receiver is designed for operation under extremely uneven heat flux distribution, cyclic weather, and cloud transient cycle conditions, which can include large thermal stress and even receiver failure. In this study, the effect of different oil velocity on convection coefficient factor and impact of wind velocity on local Nusselt number by Finite Volume Method will be analyzed. This study is organized to give an overview of the numerical modeling using a MATLAB software, as an accurate, time efficient and economical way of analyzing the heat transfer trends over stationary receiver tube for different Reynolds number. The results reveal when oil velocity is below 0.33m/s, the value of convection coefficient is negligible at low temperature. The numerical graphs indicate that when oil velocity increases up to 1.2 m/s, heat convection coefficient increases significantly. In fact, a reduction in oil velocity causes a reduction in heat conduction through the glass envelope. In addition, the different local Nusselt number is reduced when the wind blows toward the concave side of the collector and it has a significant effect on heat losses reduction through the glass envelope.

Keywords: Receiver tube, heat convection, heat conduction, Nusselt number.

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Authors: Ramin Vafadary, Maryam Khanbaghi

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Forecasting electricity load is important for various purposes like planning, operation and control. Forecasts can save operating and maintenance costs, increase the reliability of power supply and delivery systems, and correct decisions for future development. This paper compares various time series methods to forecast 24 hours ahead of electricity load. The methods considered are the Holt-Winters smoothing, SARIMA Modeling, LSTM Network, Fbprophet and Tensorflow probability. The performance of each method is evaluated by using the forecasting accuracy criteria namely, the Mean Absolute Error and Root Mean Square Error. The National Renewable Energy Laboratory (NREL) residential energy consumption data are used to train the models. The results of this study show that SARIMA model is superior to the others for 24 hours ahead forecasts. Furthermore, a Bagging technique is used to make the predictions more robust. The obtained results show that by Bagging multiple time-series forecasts we can improve the robustness of the models for 24 hour ahead electricity load forecasting.

Keywords: Bagging, Fbprophet, Holt-Winters, LSTM, Load Forecast, SARIMA, tensorflow probability, time series.

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Authors: M. M. Shokrieh, A. Karamnejad

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This paper deals with a numerical analysis of the transient response of composite beams with strain rate dependent mechanical properties by use of a finite difference method. The equations of motion based on Timoshenko beam theory are derived. The geometric nonlinearity effects are taken into account with von Kármán large deflection theory. The finite difference method in conjunction with Newmark average acceleration method is applied to solve the differential equations. A modified progressive damage model which accounts for strain rate effects is developed based on the material property degradation rules and modified Hashin-type failure criteria and added to the finite difference model. The components of the model are implemented into a computer code in Mathematica 6. Glass/epoxy laminated composite beams with constant and strain rate dependent mechanical properties under dynamic load are analyzed. Effects of strain rate on dynamic response of the beam for various stacking sequences, load and boundary conditions are investigated.

Keywords: Composite beam, Finite difference method, Progressive damage modeling, Strain rate.

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Authors: Jesús Everardo Olguín Tiznado, Rafael García Martínez, Claudia Camargo Wilson, Juan Andrés López Barreras, Everardo Inzunza González, Javier Ordorica Villalvazo

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The response surface methodology (RSM) is a collection of mathematical and statistical techniques useful in the modeling and analysis of problems in which the dependent variable receives the influence of several independent variables, in order to determine which are the conditions under which should operate these variables to optimize a production process. The RSM estimated a regression model of first order, and sets the search direction using the method of maximum / minimum slope up / down MMS U/D. However, this method selects the step size intuitively, which can affect the efficiency of the RSM. This paper assesses how the step size affects the efficiency of this methodology. The numerical examples are carried out through Monte Carlo experiments, evaluating three response variables: efficiency gain function, the optimum distance and the number of iterations. The results in the simulation experiments showed that in response variables efficiency and gain function at the optimum distance were not affected by the step size, while the number of iterations is found that the efficiency if it is affected by the size of the step and function type of test used.

Keywords: RSM, dependent variable, independent variables, efficiency, simulation

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Authors: Jian Shi, Baoguo Yu, Haonan Jia, Meng Liu, Ping Huang

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Adapting to the flexibility of war, a large number of guided weapons launch from aircraft. Therefore, the inertial navigation system loaded in the weapon needs to undergo an alignment process in the air. This article proposes the following methods to the problem of inaccurate modeling of the system under large misalignment angles, the accuracy reduction of filtering caused by outliers, and the noise changes in GPS signals: first, considering the large misalignment errors of Strapdown Inertial Navigation System (SINS)/GPS, a more accurate model is made rather than to make a small-angle approximation, and the Unscented Kalman Filter (UKF) algorithms are used to estimate the state; then, taking into account the impact of GPS noise changes on the fine alignment algorithm, the innovation adaptive filtering algorithm is introduced to estimate the GPS’s noise in real-time; at the same time, in order to improve the anti-interference ability of the air fine alignment algorithm, a robust filtering algorithm based on outlier detection is combined with the air fine alignment algorithm to improve the robustness of the algorithm. The algorithm can improve the alignment accuracy and robustness under interference conditions, which is verified by simulation.

Keywords: Air alignment, fine alignment, inertial navigation system, integrated navigation system, UKF.

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Authors: D. Bheenick, M. Young, M. Elmussareh, A. Ali

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Coronavirus disease 2019 (COVID-19) has had unprecedented effects on the healthcare system in the UK. The pandemic has impacted every service within secondary care, including urology. Our objective is to determine how COVID-19 has influenced acute urology admissions in a busy district general hospital in the UK. To conduct the study, retrospective data of patients presenting acutely to the urology department were collected between January 13 to March 22, 2020 (pre-lockdown period) and March 23 to May 31, 2020 (lockdown period). The nature of referrals, types of admission encountered, and management required in accordance with the new set of protocols established during the lockdown period were analysed and compared to the same data prior to UK lockdown. Included in the study were 1092 patients. The results show that an overall reduction of 32.5% was seen in the total number of admissions. A marked decrease was seen in non-urological pathology as compared to other categories. Urolithiasis showed the highest proportional increase. Treatment varied proportionately to the diagnosis, with conservative management accounting for the most likely treatment during lockdown. However, the proportion of patients requiring interventions during the lockdown period increased overall. No comparative differences were observed during the two periods in terms of source of referral, length of stay and patient age. The results of the study concluded that the admission rate showed a decrease, with no significant difference in the nature and timing of presentation. Our department was able to continue providing effective management to patients presenting acutely during the COVID-19 outbreak.

Keywords: COVID-19, lockdown, admissions, urology

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Authors: Parvinder S. Sandhu, Roma Jaswal, Sandeep Khimta, Shailendra Singh

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As the majority of faults are found in a few of its modules so there is a need to investigate the modules that are affected severely as compared to other modules and proper maintenance need to be done in time especially for the critical applications. As, Neural networks, which have been already applied in software engineering applications to build reliability growth models predict the gross change or reusability metrics. Neural networks are non-linear sophisticated modeling techniques that are able to model complex functions. Neural network techniques are used when exact nature of input and outputs is not known. A key feature is that they learn the relationship between input and output through training. In this present work, various Neural Network Based techniques are explored and comparative analysis is performed for the prediction of level of need of maintenance by predicting level severity of faults present in NASA-s public domain defect dataset. The comparison of different algorithms is made on the basis of Mean Absolute Error, Root Mean Square Error and Accuracy Values. It is concluded that Generalized Regression Networks is the best algorithm for classification of the software components into different level of severity of impact of the faults. The algorithm can be used to develop model that can be used for identifying modules that are heavily affected by the faults.

Keywords: Neural Network, Software faults, Software Metric.

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Authors: M. Bodner, M. Scampicchio

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Fourier Transform Infrared (FT-IR) spectroscopy coupled with chemometrics was used to distinguish between butter samples and non-butter samples. Further, quantification of the content of margarine in adulterated butter samples was investigated. Fingerprinting region (1400-800 cm^–1) was used to develop unsupervised pattern recognition (Principal Component Analysis, PCA), supervised modeling (Soft Independent Modelling by Class Analogy, SIMCA), classification (Partial Least Squares Discriminant Analysis, PLS-DA) and regression (Partial Least Squares Regression, PLS-R) models. PCA of the fingerprinting region shows a clustering of the two sample types. All samples were classified in their rightful class by SIMCA approach; however, nine adulterated samples (between 1% and 30% w/w of margarine) were classified as belonging both at the butter class and at the non-butter one. In the two-class PLS-DA model’s (R² = 0.73, RMSEP, Root Mean Square Error of Prediction = 0.26% w/w) sensitivity was 71.4% and Positive Predictive Value (PPV) 100%. Its threshold was calculated at 7% w/w of margarine in adulterated butter samples. Finally, PLS-R model (R² = 0.84, RMSEP = 16.54%) was developed. PLS-DA was a suitable classification tool and PLS-R a proper quantification approach. Results demonstrate that FT-IR spectroscopy combined with PLS-R can be used as a rapid, simple and safe method to identify pure butter samples from adulterated ones and to determine the grade of adulteration of margarine in butter samples.

Keywords: Adulterated butter, margarine, PCA, PLS-DA, PLS-R, SIMCA.

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Authors: Rakesh Prajapati, Purvik Patel, Avadhoot Rajurkar

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Nowadays, manufacturing industries augment their production lines with modern machining centers backed by CAM software. Several attempts are being made to cut down the programming time for machining complex geometries. Special programs/software have been developed to generate the digital numerical data and to prepare NC programs by using suitable post-processors for different machines. By selecting the tools and manufacturing process then applying tool paths and NC program are generated. More and more complex mechanical parts that earlier were being cast and assembled/manufactured by other processes are now being machined. Majority of these parts require lots of pocketing operations and find their applications in die and mold, turbo machinery, aircraft, nuclear, defense etc. Pocketing operations involve removal of large quantity of material from the metal surface. The modeling of warm cast and clamping a piece of food processing parts which the used of Pro-E and MasterCAM^® software. Pocketing operation has been specifically chosen for toolpath optimization. Then after apply Pocketing toolpath, Multi Tool Selection and Reduce Air Time give the results of software simulation time and experimental machining time.

Keywords: Toolpath, part program, optimization, pocket.

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Authors: Amir Moslemi, Amir Movafeghi, Shahab Moradi

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One of the most important challenging factors in medical images is nominated as noise. Image denoising refers to the improvement of a digital medical image that has been infected by Additive White Gaussian Noise (AWGN). The digital medical image or video can be affected by different types of noises. They are impulse noise, Poisson noise and AWGN. Computed tomography (CT) images are subjects to low quality due to the noise. Quality of CT images is dependent on absorbed dose to patients directly in such a way that increase in absorbed radiation, consequently absorbed dose to patients (ADP), enhances the CT images quality. In this manner, noise reduction techniques on purpose of images quality enhancement exposing no excess radiation to patients is one the challenging problems for CT images processing. In this work, noise reduction in CT images was performed using two different directional 2 dimensional (2D) transformations; i.e., Curvelet and Contourlet and Discrete Wavelet Transform (DWT) thresholding methods of BayesShrink and AdaptShrink, compared to each other and we proposed a new threshold in wavelet domain for not only noise reduction but also edge retaining, consequently the proposed method retains the modified coefficients significantly that result good visual quality. Data evaluations were accomplished by using two criterions; namely, peak signal to noise ratio (PSNR) and Structure similarity (Ssim).

Keywords: Computed Tomography (CT), noise reduction, curve-let, contour-let, Signal to Noise Peak-Peak Ratio (PSNR), Structure Similarity (Ssim), Absorbed Dose to Patient (ADP).

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Authors: Rosarin Rujananon, Poonsuk Prasertsan, Amornrat Phongdara, Tanate Panrat, Jibin Sun, Sugima Rappert, An-Ping Zeng

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In this study, a synthetic pathway was created by assembling genes from Clostridium butyricum and Escherichia coli in different combinations. Among the genes were dhaB1 and dhaB2 from C. butyricum VPI1718 coding for glycerol dehydratase (GDHt) and its activator (GDHtAc), respectively, involved in the conversion of glycerol to 3-hydroxypropionaldehyde (3-HPA). The yqhD gene from E.coli BL21 was also included which codes for an NADPHdependent 1,3-propanediol oxidoreductase isoenzyme (PDORI) reducing 3-HPA to 1,3-propanediol (1,3-PD). Molecular modeling analysis indicated that the conformation of fusion protein of YQHD and DHAB1 was favorable for direct molecular channeling of the intermediate 3-HPA. According to the simulation results, the yqhD and dhaB1 gene were assembled in the upstream of dhaB2 to express a fusion protein, yielding the recombinant strain E. coliBL21 (DE3)//pET22b+::yqhD-dhaB1_dhaB2 (strain BP41Y3). Strain BP41Y3 gave 10-fold higher 1,3-PD concentration than E. coliBL21 (DE3)//pET22b+::yqhD-dhaB1_dhaB2 (strain BP31Y2) expressing the recombinant enzymes simultaneously but in a non-fusion mode. This is the first report using a gene fusion approach to enhance the biological conversion of glycerol to the value added compound 1,3- PD.

Keywords: Recombinant E.coli, 1, 3-propanediol, glycerol, fusion protein.

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Authors: Limin Zhao, Xingfa Gu, C. Tao Yu

Abstract:

one of the significant factors for improving the accuracy of Land Surface Temperature (LST) retrieval is the correct understanding of the directional anisotropy for thermal radiance. In this paper, the multiple scattering effect between heterogeneous non-isothermal surfaces is described rigorously according to the concept of configuration factor, based on which a directional thermal radiance model is built, and the directional radiant character for urban canopy is analyzed. The model is applied to a simple urban canopy with row structure to simulate the change of Directional Brightness Temperature (DBT). The results show that the DBT is aggrandized because of the multiple scattering effects, whereas the change range of DBT is smoothed. The temperature difference, spatial distribution, emissivity of the components can all lead to the change of DBT. The “hot spot" phenomenon occurs when the proportion of high temperature component in the vision field came to a head. On the other hand, the “cool spot" phenomena occur when low temperature proportion came to the head. The “spot" effect disappears only when the proportion of every component keeps invariability. The model built in this paper can be used for the study of directional effect on emissivity, the LST retrieval over urban areas and the adjacency effect of thermal remote sensing pixels.

Keywords: Directional thermal radiance, multiple scattering, configuration factor, urban canopy, hot spot effect

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Authors: Mannan Mridha, Usama Gazay, Kosovare V. Aslani, Hugo Linder, Alice Ravizza, Carmelo de Maria

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In most developing countries, although the vast majority of the people are living in the rural areas, the qualified medical doctors are not available there. Health care workers and paramedics, called village doctors, informal healthcare providers, are largely responsible for the rural medical care. Mishaps due to wrong diagnosis and inappropriate medication have been causing serious suffering that is preventable. While innovators have created many devices, the vast majority of these technologies do not find applications to address the needs and conditions in low-resource settings. The primary motive is to address the acute lack of affordable medical technologies for the poor people in low-resource settings. A low cost smart medical device that is portable, battery operated and can be used at any point of care has been developed to detect breathing rate, electrocardiogram (ECG) and arterial pulse rate to improve diagnosis and monitoring of patients and thus improve care and safety. This simple and easy to use smart medical device can be used, managed and maintained effectively and safely by any health worker with some training. In order to empower the health workers and village doctors, our device is being further developed to integrate with ICT tools like smart phones and connect to the medical experts wherever available, to manage the serious health problems.

Keywords: Healthcare for low resources settings, health awareness education, improve patient care and safety, smart and affordable medical device.

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Authors: A. Hortacsu, A. Tektas

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This paper aims to develop a model that assists the international retailer in selecting the country that maximizes the degree of fit between the retailer-s goals and the country characteristics in his initial internationalization move. A two-stage multi criteria decision model is designed integrating the Analytic Hierarchy Process (AHP) and Goal Programming. Ethical, cultural, geographic and economic proximity are identified as the relevant constructs of the internationalization decision. The constructs are further structured into sub-factors within analytic hierarchy. The model helps the retailer to integrate, rank and weigh a number of hard and soft factors and prioritize the countries accordingly. The model has been implemented on a Turkish luxury goods retailer who was planning to internationalize. Actual entry of the specific retailer in the selected country is a support for the model. Implementation on a single retailer limits the generalizability of the results; however, the emphasis of the paper is on construct identification and model development. The paper enriches the existing literature by proposing a hybrid multi objective decision model which introduces new soft dimensions i.e. perceived distance, ethical proximity, humane orientation to the decision process and facilitates effective decision making.

Keywords: Analytic hierarchy process, culture, ethics, goal programming, retail foreign market selection.

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Authors: Muhammad Naveed, Muhammad Khalid Abdullah, Khalid Rashid, Hafiz Farooq Ahmad

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Aspect Oriented Programming promises many advantages at programming level by incorporating the cross cutting concerns into separate units, called aspects. Join Points are distinguishing features of Aspect Oriented Programming as they define the points where core requirements and crosscutting concerns are (inter)connected. Currently, there is a problem of multiple aspects- composition at the same join point, which introduces the issues like ordering and controlling of these superimposed aspects. Dynamic strategies are required to handle these issues as early as possible. State chart is an effective modeling tool to capture dynamic behavior at high level design. This paper provides methodology to formulate the strategies for multiple aspect composition at high level, which helps to better implement these strategies at coding level. It also highlights the need of designing shared join point at high level, by providing the solutions of these issues using state chart diagrams in UML 2.0. High level design representation of shared join points also helps to implement the designed strategy in systematic way.

Keywords: Aspect Oriented Software Development, Shared Join Points.

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Authors: Hossein Zeinivand, Florimond De Smedt

Abstract:

Snow cover is an important phenomenon in hydrology, hence modeling the snow accumulation and melting is an important issue in places where snowmelt significantly contributes to runoff and has significant effect on water balance. The physics-based models are invariably distributed, with the basin disaggregated into zones or grid cells. Satellites images provide valuable data to verify the accuracy of spatially distributed model outputs. In this study a spatially distributed physically based model (WetSpa) was applied to predict snow cover and melting in the Latyan dam watershed in Iran. Snowmelt is simulated based on an energy balance approach. The model is applied and calibrated with one year of observed daily precipitation, air temperature, windspeed, and daily potential evaporation. The predicted snow-covered area is compared with remotely sensed images (MODIS). The results show that simulated snow cover area SCA has a good agreement with satellite image snow cover area SCA from MODIS images. The model performance is also tested by statistical and graphical comparison of simulated and measured discharges entering the Latyan dam reservoir.

Keywords: Physical based model, Satellite image, Snow covers.

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Authors: Isaac O. Asante, Yushi Jiang, Hailin Tao

Abstract:

Livestreaming marketing, the new electronic commerce element, has become an optional marketing channel following the COVID-19 pandemic, and many sellers are leveraging the features presented by livestreaming to increase sales. This study was conducted to measure real-time observable interactions between consumers and sellers. Based on the affordance theory, this study conceptualized constructs representing the interactive features and examined how they drive consumers’ purchase willingness during livestreaming sessions using 1238 datasets from Amazon Live, following the manual observation of transaction records. Using structural equation modeling, the ordinary least square regression suggests that live viewers, new followers, live chats, and likes positively affect purchase willingness. The Sobel and Monte Carlo tests show that new followers, live chats, and likes significantly mediate the relationship between live viewers and purchase willingness. The study presents a way of measuring interactions in livestreaming commerce and proposes a way to manually gather data on consumer behaviors in livestreaming platforms when the application programming interface (API) of such platforms does not support data mining algorithms.

Keywords: Livestreaming marketing, live chats, live viewers, likes, new followers, purchase willingness.

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Authors: M. Orefice, V. Di Vito

Abstract:

This paper provides a description of a Collision Avoidance algorithm that has been developed starting from the mathematical modeling of the flight of insects, in terms of spirals and conchospirals geometric paths. It is able to calculate a proper avoidance manoeuver aimed to prevent the infringement of a predefined distance threshold between ownship and the considered intruder, while minimizing the ownship trajectory deviation from the original path and in compliance with the aircraft performance limitations and dynamic constraints. The algorithm is designed in order to be suitable for real-time applications, so that it can be considered for the implementation in the most recent airborne automatic collision avoidance systems using the traffic data received through an ADS-B IN device. The presented approach is able to take into account the rules-of-the-air, due to the possibility to select, through specifically designed decision making logic based on the consideration of the encounter geometry, the direction of the calculated collision avoidance manoeuver that allows complying with the rules-of-the-air, as for instance the fundamental right of way rule. In the paper, the proposed collision avoidance algorithm is presented and its preliminary design and software implementation is described. The applicability of this method has been proved through preliminary simulation tests performed in a 2D environment considering single intruder encounter geometries, as reported and discussed in the paper.

Keywords: collision avoidance, RPAS, spiral geometry, ADS-B based application

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Authors: Sheikh Shams Azam, Manoj Raju, Venkatesh Pagidimarri, Vamsi Kasivajjala

Abstract:

Over the past decade, there has been a steep rise in the data-driven analysis in major areas of medicine, such as clinical decision support system, survival analysis, patient similarity analysis, image analytics etc. Most of the data in the field are well-structured and available in numerical or categorical formats which can be used for experiments directly. But on the opposite end of the spectrum, there exists a wide expanse of data that is intractable for direct analysis owing to its unstructured nature which can be found in the form of discharge summaries, clinical notes, procedural notes which are in human written narrative format and neither have any relational model nor any standard grammatical structure. An important step in the utilization of these texts for such studies is to transform and process the data to retrieve structured information from the haystack of irrelevant data using information retrieval and data mining techniques. To address this problem, the authors present Q-Map in this paper, which is a simple yet robust system that can sift through massive datasets with unregulated formats to retrieve structured information aggressively and efficiently. It is backed by an effective mining technique which is based on a string matching algorithm that is indexed on curated knowledge sources, that is both fast and configurable. The authors also briefly examine its comparative performance with MetaMap, one of the most reputed tools for medical concepts retrieval and present the advantages the former displays over the latter.

Keywords: Information retrieval (IR), unified medical language system (UMLS), Syntax Based Analysis, natural language processing (NLP), medical informatics.

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Authors: Ilma Robo, Saimir Heta, Greta Plaka, Vera Ostreni

Abstract:

Purpose: Drug gingival hyperplasia is an uncommon pathology encountered during routine work in dental units. The purpose of this paper is to present the clinical appearance of gingival hyperplasia caused by medications. There are already three classes of medications that cause hyperplasia and based on data from the literature, the clinical cases encountered and included in this study have been compared. Materials and Methods: The study was conducted in a total of 311 patients, out of which 182 patients were included in our study, meeting the inclusion criteria. After each patient's history was recorded and it was found that patients were in their knowledge of chronic illness, undergoing treatment of gingivitis hypertrophic drugs was performed with a clinical examination of oral cavity and assessment by vertical and horizontal evaluation according to the periodontal indexes. Results: Of the data collected during the study, it was observed that 97% of patients with gingival hyperplasia are treated with nifedipine. 84% of patients treated with selected medicines and gingival hyperplasia in the oral cavity has been exposed at time period for more than 1 year and 1 month. According to the GOI, in the first rank of this index are about 21% of patients, in the second rank are 52%, in the third rank are 24% and in the fourth grade are 3%. According to the horizontal growth index of gingival hyperplasia, grade 1 included about 61% of patients and grade 2 included about 39% of patients with gingival hyperplasia. Bacterial index divides patients by degrees: grading 0 - 8.2%, grading 1 - 32.4%, grading 2 - 14% and grading 3 - 45.1%. Conclusions: The highest percentage of gingival hyperplasia caused by drugs is due to dosing of nifedipine for a duration of dosing and application for systemic healing for more than 1 year.

Keywords: Drug gingival hyperplasia, horizontal growth index, vertical growth index.

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Authors: W. Katsarapong, W. Srisang, K. Jaroensutasinee, M. Jaroensutasinee

Abstract:

National Biodiversity Database System (NBIDS) has been developed for collecting Thai biodiversity data. The goal of this project is to provide advanced tools for querying, analyzing, modeling, and visualizing patterns of species distribution for researchers and scientists. NBIDS data record two types of datasets: biodiversity data and environmental data. Biodiversity data are specie presence data and species status. The attributes of biodiversity data can be further classified into two groups: universal and projectspecific attributes. Universal attributes are attributes that are common to all of the records, e.g. X/Y coordinates, year, and collector name. Project-specific attributes are attributes that are unique to one or a few projects, e.g., flowering stage. Environmental data include atmospheric data, hydrology data, soil data, and land cover data collecting by using GLOBE protocols. We have developed webbased tools for data entry. Google Earth KML and ArcGIS were used as tools for map visualization. webMathematica was used for simple data visualization and also for advanced data analysis and visualization, e.g., spatial interpolation, and statistical analysis. NBIDS will be used by park rangers at Khao Nan National Park, and researchers.

Keywords: GLOBE protocol, Biodiversity, Database System, ArcGIS, Google Earth and webMathematica.

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