Search results for: 4-region isotherm model

Authors: Ruchi Pathania, Ahmad Ahmad, Shireesh Srivastava

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Cyanobacteria is a promising microbe that can capture and convert atmospheric CO₂ and light into valuable industrial bio-products like biofuels, biodegradable plastics, etc. Among their most attractive traits are faster autotrophic growth, whole year cultivation using non-arable land, high photosynthetic activity, much greater biomass and productivity and easy for genetic manipulations. Cyanobacteria store carbon in the form of glycogen which can be hydrolyzed to release glucose and fermented to form bioethanol or other valuable products. Marine cyanobacterial species are especially attractive for countries with scarcity of freshwater. We recently identified a marine native cyanobacterium Synechococcus sp. BDU 130192 which has good growth rate and high level of polyglucans accumulation compared to Synechococcus PCC 7002. In this study, firstly we sequenced the whole genome and the sequences were annotated using the RAST server. Genome scale metabolic model (GSMM) was reconstructed through COBRA toolbox. GSMM is a computational representation of the metabolic reactions and metabolites of the target strain. GSMMs construction through the application of Flux Balance Analysis (FBA), which uses external nutrient uptake rates and estimate steady state intracellular and extracellular reaction fluxes, including maximization of cell growth. The model, which we have named isyn942, includes 942 reactions and 913 metabolites having 831 metabolic, 78 transport and 33 exchange reactions. The phylogenetic tree obtained by BLAST search revealed that the strain was a close relative of Synechococcus PCC 7002. The flux balance analysis (FBA) was applied on the model iSyn942 to predict the theoretical yields (mol product produced/mol CO₂ consumed) for native and non-native products like acetone, butanol, etc. under phototrophic condition by applying metabolic engineering strategies. The reported strain can be a viable strain for biotechnological applications, and the model will be helpful to researchers interested in understanding the metabolism as well as to design metabolic engineering strategies for enhanced production of various bioproducts.

Keywords: cyanobacteria, flux balance analysis, genome scale metabolic model, metabolic engineering

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Authors: Lei Ren, Michael Hartnett, Stephen Nash

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The abundant measured surface current from HF radar system in coastal area is assimilated into model to improve the modeling forecasting ability. A simple sequential data assimilation scheme, Direct Insertion (DI), is applied to update model forecast states. The influence of Direct Insertion data assimilation over time is analyzed at one reference point. Vector maps of surface current from models are compared with HF radar measurements. Root-Mean-Squared-Error (RMSE) between modeling results and HF radar measurements is calculated during the last four days with no data assimilation.

Keywords: data assimilation, CODAR, HF radar, surface current, direct insertion

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Authors: Mauricio E. Cruz, Ilse Cervantes, Manuel J. Fabela

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The evaluation of the maneuverability of road vehicles is generally carried out through the use of specialized computer programs due to the advantages they offer compared to the experimental method. These programs are based on purely geometric considerations of the characteristics of the vehicles, such as main dimensions, the location of the axles, and points of articulation, without considering parameters such as weight distribution and magnitude, tire properties, etc. In this paper, we address the problem of maneuverability in a semi-trailer truck to navigate urban streets, maneuvering yards, and parking lots, using the Ackerman principle to propose a kinematic model that, through geometric considerations, it is possible to determine the space necessary to maneuver safely. The model was experimentally validated by conducting maneuverability tests with an articulated vehicle. The measurements were made through a GPS that allows us to know the position, trajectory, and speed of the vehicle, an inertial motion unit (IMU) that allows measuring the accelerations and angular speeds in the semi-trailer, and an instrumented steering wheel that allows measuring the angle of rotation of the flywheel, the angular velocity and the torque applied to the flywheel. To obtain the steering angle of the tires, a parameterization of the complete travel of the steering wheel and its equivalent in the tires was carried out. For the tests, 3 different angles were selected, and 3 turns were made for each angle in both directions of rotation (left and right turn). The results showed that the proposed kinematic model achieved 95% accuracy for speeds below 5 km / h. The experiments revealed that that tighter maneuvers increased significantly the space required and that the vehicle maneuverability was limited by the size of the semi-trailer. The maneuverability was also tested as a function of the vehicle load and 3 different load levels we used: light, medium, and heavy. It was found that the internal turning radii also increased with the load, probably due to the changes in the tires' adhesion to the pavement since heavier loads had larger contact wheel-road surfaces. The load was found as an important factor affecting the precision of the model (up to 30%), and therefore I should be considered. The model obtained is expected to be used to improve maneuverability through a robust control system.

Keywords: articuled vehicle, experimental validation, kinematic model, maneuverability, semi-trailer truck

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Authors: Zongyan Li, Matt Best

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This paper presents an optimization method for reducing the number of input channels and the complexity of the feed-forward NARX neural network (NN) without compromising the accuracy of the NN model. By utilizing the correlation analysis method, the most significant regressors are selected to form the input layer of the NN structure. An application of vehicle dynamic model identification is also presented in this paper to demonstrate the optimization technique and the optimal input layer structure and the optimal number of neurons for the neural network is investigated.

Keywords: correlation analysis, F-ratio, levenberg-marquardt, MSE, NARX, neural network, optimisation

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Authors: J. P. P. Andrade, V. A. F. Campos

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This work presents an application of Linear Matrix Inequalities (LMI) for the robust control of an F-16 aircraft through an algorithm ensuring the damping factor to the closed loop system. The results show that the zero and gain settings are sufficient to ensure robust performance and stability with respect to various operating points. The technique used is the pole placement, which aims to put the system in closed loop poles in a specific region of the complex plane. Test results using a dynamic model of the F-16 aircraft are presented and discussed.

Keywords: F-16 aircraft, linear matrix inequalities, pole placement, robust control

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Authors: Massy Guillaume

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Introduction: The implementation of creativity in educational policies or curricula raises several issues, including the evaluation of creativity and the means to do so. This doctoral research focuses on the appropriation and transposition of creativity assessment models by future teachers. Our objective is to identify the elements of the models that are most transferable to practice in order to improve their implementation in the students' curriculum while seeking to create a new model for assessing creativity in the school environment. Methods: In order to meet our objective, this preliminary quantitative exploratory study by questionnaire was conducted at two points in the participants' training: at the beginning of the training module and throughout the practical work. The population is composed of 40 people of diverse origins with an average age of 26 (s:8,623) years. In order to be as close as possible to our research objective and to test our questionnaires, we set up a pre-test phase during the spring semester of 2022. Results: The results presented focus on aspects of the OECD Creative Competencies Assessment Model. Overall, 72% of participants support the model's focus on skill levels as appropriate for the school context. More specifically, the data indicate that the separation of production and process in the rubric facilitates observation by the assessor. From the point of view of transposing the grid into teaching practice, the participants emphasised that production is easier to plan and observe in students than in the process. This difference is reinforced by a lack of knowledge about certain concepts such as innovation or risktaking in schools. Finally, the qualitative results indicate that the addition of multiple levels of competencies to the OECD rubric would allow for better implementation in the classroom. Conclusion: The identification by the students of the elements allowing the evaluation of creativity in the school environment generates an innovative approach to the training contents. These first data, from the test phase of our research, demonstrate the difficulty that exists between the implementation of an evaluation model in a training program and its potential transposition by future teachers.

Keywords: creativity, evaluation, schooling, training

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Authors: Xiaodong Li, Peng Gao, Chao-Jung Huang, Shiying Hao, Xuefeng B. Ling, Yongxia Han, Yaqi Zhang, Le Zheng, Chengyin Ye, Modi Liu, Minjie Xia, Changlin Fu, Bo Jin, Karl G. Sylvester, Eric Widen

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Predicting the risk of Pancreatic Adenocarcinoma (PA) in advance can benefit the quality of care and potentially reduce population mortality and morbidity. The aim of this study was to develop and prospectively validate a risk prediction model to identify patients at risk of new incident PA as early as 3 months before the onset of PA in a statewide, general population in Maine. The PA prediction model was developed using Deep Neural Networks, a deep learning algorithm, with a 2-year electronic-health-record (EHR) cohort. Prospective results showed that our model identified 54.35% of all inpatient episodes of PA, and 91.20% of all PA that required subsequent chemoradiotherapy, with a lead-time of up to 3 months and a true alert of 67.62%. The risk assessment tool has attained an improved discriminative ability. It can be immediately deployed to the health system to provide automatic early warnings to adults at risk of PA. It has potential to identify personalized risk factors to facilitate customized PA interventions.

Keywords: cancer prediction, deep learning, electronic health records, pancreatic adenocarcinoma

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Authors: Adebayo J. Julius, Oluremi N. Iluyomade

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Livelihood in Nigeria is a paradox of poverty amidst plenty. The Country is endowed with a good climate for agriculture, naturally growing fruit trees and vegetables, and undomesticated water resources. In spite of all its endowment, Nigeria continues to live in poverty year in year out. Rural communities adopted for this study are Ido, Omi-Adio, Onigambari, Okija and Lambata, 500 questionnaires were administered to solicit information from the respondents. This study focused on comparative analysis of the utilization of community informatics for sustainable livelihoods through agriculture. The idea projected in this study is that small strategic changes in the modus operandi of social informatics can have a significant impact on the sustainability of livelihoods. This paper carefully explored the theories of community informatics and its efficacies in dealing with sustainability issues. This study identified, described and evaluates the roles of community informatics in some sectors of the economy, different analytical tools to benchmark the influence of social informatics in agriculture against what is obtainable in agricultural sectors of the economy were used. It further employed comparative analysis to build a case model for sustainable livelihood in agriculture through community informatics.

Keywords: informatics, model, rural community, livelihood, Nigeria

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Authors: Faidon Kyriakou, William Dempster, David Nash

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Abdominal Aortic Aneurysm (AAA) is a major life-threatening pathology for which modern approaches reduce the need for open surgery through the use of stenting. The success of stenting though is sometimes jeopardized by the final position of the stent graft inside the human artery which may result in migration, endoleaks or blood flow occlusion. Herein, a finite element (FE) model of the commercial medical device AnacondaTM (Vascutek, Terumo) has been developed and validated in order to create a numerical tool able to provide useful clinical insight before the surgical procedure takes place. The AnacondaTM device consists of a series of NiTi rings sewn onto woven polyester fabric, a structure that despite its column stiffness is flexible enough to be used in very tortuous geometries. For the purposes of this study, a FE model of the device was built in Abaqus® (version 6.13-2) with the combination of beam, shell and surface elements; the choice of these building blocks was made to keep the computational cost to a minimum. The validation of the numerical model was performed by comparing the deployed position of a full stent graft device inside a constructed AAA with a duplicate set-up in Abaqus®. Specifically, an AAA geometry was built in CAD software and included regions of both high and low tortuosity. Subsequently, the CAD model was 3D printed into a transparent aneurysm, and a stent was deployed in the lab following the steps of the clinical procedure. Images on the frontal and sagittal planes of the experiment allowed the comparison with the results of the numerical model. By overlapping the experimental and computational images, the mean and maximum distances between the rings of the two models were measured in the longitudinal, and the transverse direction and, a 5mm upper bound was set as a limit commonly used by clinicians when working with simulations. The two models showed very good agreement of their spatial positioning, especially in the less tortuous regions. As a result, and despite the inherent uncertainties of a surgical procedure, the FE model allows confidence that the final position of the stent graft, when deployed in vivo, can also be predicted with significant accuracy. Moreover, the numerical model run in just a few hours, an encouraging result for applications in the clinical routine. In conclusion, the efficient modelling of a complicated structure which combines thin scaffolding and fabric has been demonstrated to be feasible. Furthermore, the prediction capabilities of the location of each stent ring, as well as the global shape of the graft, has been shown. This can allow surgeons to better plan their procedures and medical device manufacturers to optimize their designs. The current model can further be used as a starting point for patient specific CFD analysis.

Keywords: AAA, efficiency, finite element analysis, stent deployment

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Authors: Michael Campbell, Malaz Elsaddig, Kevin Jones

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Background: Despite encouraging progress, global maternal mortality has remained stubbornly high since the declaration of the Millennium development goals. Sub-Saharan Africa accounts for well over half of maternal deaths with Post-Partum Haemorrhage (PPH) being the lead cause. ‘In house’ simulation training delivered by local doctors may be a sustainable approach for improving emergency obstetric care. The aim of this study was to evaluate the use of a Train-the-Trainer (TtT) model in a rural Ugandan hospital to ascertain whether it can feasibly improve practitioners’ management of PPH. Methods: Three Ugandan doctors underwent a training course to enable them to design and deliver simulation training. These doctors used MamaNatalie® models to simulate PPH scenarios for midwives, nurses and medical students. The main outcome was improvement in participants’ knowledge and confidence, assessed using self-reported scores on a 10-point scale. Results: The TtT model produced significant improvements in the confidence and knowledge scores of the ten participants. The mean confidence score rose significantly (p=0.0005) from 6.4 to 8.6 following the simulation training. There was also a significant increase in the mean knowledge score from 7.2 to 9.0 (p=0.04). Medical students demonstrated the greatest overall increase in confidence scores whilst increases in knowledge scores were largest amongst nurses. Conclusions: This study demonstrates that a TtT model can be used in a low resource setting to improve healthcare professionals’ confidence and knowledge in managing obstetric emergencies. This Train-the-Trainer model represents a sustainable approach to addressing skill deficits in low resource settings. We believe that its expansion across healthcare institutions in Sub-Saharan Africa will help to reduce the region’s high maternal mortality rate and step closer to achieving the ambitions of the Millennium development goals.

Keywords: low resource setting, post-partum haemorrhage, simulation training, train the trainer

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Authors: André Augusto Ceballos Melo

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Design to facilitate the recognition of congruent prosthetic movements, context-to-motion translations guided by image, verbal prompt, users nonverbal communication such as facial expressions, gestures, paralinguistics, scene context, and object recognition contributes to this process though it can also be applied to other tasks, such as walking, Prosthetic limbs as assistive technology through gestures, sound codes, signs, facial, body expressions, and scene context The context-to-motion model is a machine learning approach that is designed to improve the control and dexterity of prosthetic limbs. It works by using sensory input from the prosthetic limb to learn about the dynamics of the environment and then using this information to generate smooth, stable movements. This can help to improve the performance of the prosthetic limb and make it easier for the user to perform a wide range of tasks. There are several key benefits to using the context-to-motion model for prosthetic limb control. First, it can help to improve the naturalness and smoothness of prosthetic limb movements, which can make them more comfortable and easier to use for the user. Second, it can help to improve the accuracy and precision of prosthetic limb movements, which can be particularly useful for tasks that require fine motor control. Finally, the context-to-motion model can be trained using a variety of different sensory inputs, which makes it adaptable to a wide range of prosthetic limb designs and environments. Stable diffusion is a machine learning method that can be used to improve the control and stability of movements in robotic and prosthetic systems. It works by using sensory feedback to learn about the dynamics of the environment and then using this information to generate smooth, stable movements. One key aspect of stable diffusion is that it is designed to be robust to noise and uncertainty in the sensory feedback. This means that it can continue to produce stable, smooth movements even when the sensory data is noisy or unreliable. To implement stable diffusion in a robotic or prosthetic system, it is typically necessary to first collect a dataset of examples of the desired movements. This dataset can then be used to train a machine learning model to predict the appropriate control inputs for a given set of sensory observations. Once the model has been trained, it can be used to control the robotic or prosthetic system in real-time. The model receives sensory input from the system and uses it to generate control signals that drive the motors or actuators responsible for moving the system. Overall, the use of the context-to-motion model has the potential to significantly improve the dexterity and performance of prosthetic limbs, making them more useful and effective for a wide range of users Hand Gesture Body Language Influence Communication to social interaction, offering a possibility for users to maximize their quality of life, social interaction, and gesture communication.

Keywords: stable diffusion, neural interface, smart prosthetic, augmenting

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Authors: Shilpa Kapoor, Namrata Yaduvanshi, Pooja Pawar, Sadhna Singh

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A Three Body Interaction Potential (TBIP) model is developed to study the high pressure phase transition of SrS having NaCl (B1) structure at room temperature. This model includes the long range Columbic, three body interaction forces, short range overlap forces operative up to next nearest neighbors and zero point energy effects. We have investigated the phase transition with pressure, volume collapse and second order elastic constants and found results well suited with available experimental data.

Keywords: phase transition, second order elastic constants, three body interaction forces, volume collapses

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Authors: Marzieh Khedmati, Shannon L. Bartelt-Hunt

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Vegetative Filter Strips (VFS) are used for controlling the volume of runoff and decreasing contaminant concentrations in runoff before entering water bodies. Many studies have investigated the role of VFS in sediment and nutrient removal, but little is known about their efficiency for the removal of emerging contaminants such as antimicrobial resistance genes (ARGs). Vegetative Filter Strip Modeling System (VFSMOD) was used to simulate the efficiency of VFS in this regard. Several studies demonstrated the ability of VFSMOD to predict reductions in runoff volume and sediment concentration moving through the filters. The objectives of this study were to calibrate the VFSMOD with experimental data and assess the efficiency of the model in simulating the filter behavior in removing ARGs (ermB) and tylosin. The experimental data were obtained from a prior study conducted at the University of Nebraska (UNL) Rogers Memorial Farm. Three treatment factors were tested in the experiments, including manure amendment, narrow grass hedges and rainfall events. Sediment Delivery Ratio (SDR) was defined as the filter efficiency and the related experimental and model values were compared to each other. The VFS Model generally agreed with the experimental results and as a result, the model was used for predicting filter efficiencies when the runoff data are not available. Narrow Grass Hedges (NGH) were shown to be effective in reducing tylosin and ARGs concentration. The simulation showed that the filter efficiency in removing ARGs is different for different soil types and filter lengths. There is an optimum length for the filter strip that produces minimum runoff volume. Based on the model results increasing the length of the filter by 1-meter leads to higher efficiency but widening beyond that decreases the efficiency. The VFSMOD, which was proved to work well in estimation of VFS trapping efficiency, showed confirming results for ARG removal.

Keywords: antimicrobial resistance genes, emerging contaminants, narrow grass hedges, vegetative filter strips, vegetative filter strip modeling system

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Authors: Davide Gotti, Milan Prodanovic, Sergio Pinilla, David Muñoz-Torrero

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Since its proposal, the Doyle-Fuller-Newman (DFN) lithium-ion battery model has gained popularity in the electrochemical field. In fact, this model provides the user with theoretical support for designing the lithium-ion battery parameters, such as the material particle or the diffusion coefficient adjustment direction. However, the model is mathematically complex as it is composed of several partial differential equations (PDEs) such as Fick’s law of diffusion, the MacInnes and Ohm’s equations, among other phenomena. Thus, to efficiently use the model in a time-domain simulation environment, the selection of the discretization technique is of a pivotal importance. There are several numerical methods available in the literature that can be used to carry out this task. In this study, a comparison between the explicit Euler, Crank-Nicolson, and Chebyshev discretization methods is proposed. These three methods are compared in terms of accuracy, stability, and computational times. Firstly, the explicit Euler discretization technique is analyzed. This method is straightforward to implement and is computationally fast. In this work, the accuracy of the method and its stability properties are shown for the electrolyte diffusion partial differential equation. Subsequently, the Crank-Nicolson method is considered. It represents a combination of the implicit and explicit Euler methods that has the advantage of being of the second order in time and is intrinsically stable, thus overcoming the disadvantages of the simpler Euler explicit method. As shown in the full paper, the Crank-Nicolson method provides accurate results when applied to the DFN model. Its stability does not depend on the integration time step, thus it is feasible for both short- and long-term tests. This last remark is particularly important as this discretization technique would allow the user to implement parameter estimation and optimization techniques such as system or genetic parameter identification methods using this model. Finally, the Chebyshev discretization technique is implemented in the DFN model. This discretization method features swift convergence properties and, as other spectral methods used to solve differential equations, achieves the same accuracy with a smaller number of discretization nodes. However, as shown in the literature, these methods are not suitable for handling sharp gradients, which are common during the first instants of the charge and discharge phases of the battery. The numerical results obtained and presented in this study aim to provide the guidelines on how to select the adequate discretization technique for the DFN model according to the type of application to be performed, highlighting the pros and cons of the three methods. Specifically, the non-eligibility of the simple Euler method for longterm tests will be presented. Afterwards, the Crank-Nicolson and the Chebyshev discretization methods will be compared in terms of accuracy and computational times under a wide range of battery operating scenarios. These include both long-term simulations for aging tests, and short- and mid-term battery charge/discharge cycles, typically relevant in battery applications like grid primary frequency and inertia control and electrical vehicle breaking and acceleration.

Keywords: Doyle-Fuller-Newman battery model, partial differential equations, discretization, numerical methods

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Authors: Merouane Salhi, Toufik Zebbiche, Omar Abada

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When the stagnation pressure of perfect gas increases, the specific heat and their ratio do not remain constant anymore and start to vary with this pressure. The gas does not remain perfect. Its state equation change and it becomes a real gas. In this case, the effects of molecular size and inter molecular attraction forces intervene to correct the state equation. The aim of this work is to show and discuss the effect of stagnation pressure on supersonic thermo dynamical, physical and geometrical flow parameters, to find a general case for real gas. With the assumptions that Berthelot’s state equation accounts for molecular size and inter molecular force effects, expressions are developed for analyzing supersonic flow for thermally and calorically imperfect gas lower than the dissociation molecules threshold. The designs parameters for supersonic nozzle like thrust coefficient depend directly on stagnation parameters of the combustion chamber. The application is for air. A computation of error is made in this case to give a limit of perfect gas model compared to real gas model.

Keywords: supersonic flow, real gas model, Berthelot’s state equation, Simpson’s method, condensation function, stagnation pressure

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Authors: A. Brahma

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Analysis of deferred deformations of concrete under sustained load shows that the creep has a leading role on deferred deformations of concrete structures. Knowledge of the creep characteristics of concrete is a Necessary starting point in the design of structures for crack control. Such knowledge will enable the designer to estimate the probable deformation in pre-stressed concrete or reinforced and the appropriate steps can be taken in design to accommodate this movement. In this study, we propose a prediction model that involves the acting principal parameters on the deferred behaviour of concrete structures. For the estimation of the model parameters Levenberg-Marquardt method has proven very satisfactory. A confrontation between the experimental results and the predictions of models designed shows that it is well suited to describe the evolution of the creep of concrete structures.

Keywords: concrete structure, creep, modelling, prediction

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Authors: Enyia James Diwa, Dodeye Ina Igbong, Archibong Eso Archibong

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The creep life of an industrial gas turbine is determined through a physics-based model used to investigate the high pressure temperature (HPT) of the blade in use. A performance model was carried out via the Cranfield University TURBOMATCH simulation software to size the blade and to determine the corresponding stress. Various effects such as radial temperature distortion factor, turbine entry temperature, ambient temperature, blade metal temperature, and compressor degradation on the blade creep life were investigated. The output results show the difference in creep life and the location of failure along the span of the blade enabling better-informed advice for the gas turbine operator.

Keywords: creep, living, performance, degradation

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Authors: H. Hotait, X. Chiementin, L. Rasolofondraibe

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This paper presents a continuous work to detect the abnormal state in the rolling bearing by studying the vibration signature analysis and calculation of the remaining useful life. To achieve these aims, two methods; the first method is the classification to detect the degradation state by the AOM-OPTICS (Acousto-Optic Modulator) method. The second one is the prediction of the degradation state using least-squares support vector regression and then compared with the linear degradation model. An experimental investigation on ball-bearing was conducted to see the effectiveness of the used method by applying the acquired vibration signals. The proposed model for predicting the state of bearing gives us accurate results with the experimental and numerical data.

Keywords: bearings, automatization, optimization, prognosis, classification, defect detection

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Authors: Shahad Nagoor, Lucy Hederman, Kevin Koidl, Annalina Caputo

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Doctors’ notes reflect their impressions, attitudes, clinical sense, and opinions about patients’ conditions and progress, and other information that is essential for doctors’ daily clinical decisions. Despite their value, clinical notes are insufficiently researched within the language processing community. Automatically extracting information from unstructured text data is known to be a difficult task as opposed to dealing with structured information such as vital physiological signs, images, and laboratory results. The aim of this research is to investigate how Natural Language Processing (NLP) techniques and machine learning techniques applied to clinician notes can assist in doctors’ decision-making in Intensive Care Unit (ICU) for coronavirus disease 2019 (COVID-19) patients. The hypothesis is that clinical outcomes like survival or mortality can be useful in influencing the judgement of clinical sentiment in ICU clinical notes. This paper introduces two contributions: first, we introduce COVID_ICU_BERT, a fine-tuned version of clinical transformer models that can reliably predict clinical sentiment for notes of COVID patients in the ICU. We train the model on clinical notes for COVID-19 patients, a type of notes that were not previously seen by clinicalBERT, and Bio_Discharge_Summary_BERT. The model, which was based on clinicalBERT achieves higher predictive accuracy (Acc 93.33%, AUC 0.98, and precision 0.96 ). Second, we perform data augmentation using clinical contextual word embedding that is based on a pre-trained clinical model to balance the samples in each class in the data (survived vs. deceased patients). Data augmentation improves the accuracy of prediction slightly (Acc 96.67%, AUC 0.98, and precision 0.92 ).

Keywords: BERT fine-tuning, clinical sentiment, COVID-19, data augmentation

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Authors: Carlos V. Gonzalez

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Background: Providers of in-home and community based services strive for the elimination of preventable harm to the people under their care as well as to the employees who support them. Traditional models of safety and protection from harm have assumed that the absence of incidents of harm is a good indicator of safe practices. However, this model creates an illusion of safety that is easily shaken by sudden and inadvertent harmful events. As an alternative, we have developed and implemented an evidence-based resilient model of safety known as C.O.P.E. (Caring, Observing, Predicting and Evaluating). Within this model, safety is not defined by the absence of harmful incidents, but by the presence of continuous monitoring, anticipation, learning, and rapid response to events that may lead to harm. Objective: The objective was to evaluate the effectiveness of the C.O.P.E. model for the reduction of harm to individuals with mental disabilities who receive home and community based services. Methods: Over the course of 2 years we counted the number of incidents of harm and near misses. We trained employees on strategies to eliminate incidents before they fully escalated. We trained employees to track different levels of patient status within a scale from 0 to 10. Additionally, we provided direct support professionals and supervisors with customized smart phone applications to track and notify the team of changes in that status every 30 minutes. Finally, the information that we collected was saved in a private computer network that analyzes and graphs the outcome of each incident. Result and conclusions: The use of the COPE model resulted in: A reduction in incidents of harm. A reduction the use of restraints and other physical interventions. An increase in Direct Support Professional’s ability to detect and respond to health problems. Improvement in employee alertness by decreasing sleeping on duty. Improvement in caring and positive interaction between Direct Support Professionals and the person who is supported. Developing a method to globally measure and assess the effectiveness of prevention from harm plans. Future applications of the COPE model for the reduction of harm to people who receive home and community based services are discussed.

Keywords: harm, patients, resilience, safety, mental illness, disability

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Authors: M. Karimpour, L. Hitihamillage, N. Elkhoury, S. Moridpour, R. Hesami

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Rail transport authorities around the world have been facing a significant challenge when predicting rail infrastructure maintenance work for a long period of time. Generally, maintenance monitoring and prediction is conducted manually. With the restrictions in economy, the rail transport authorities are in pursuit of improved modern methods, which can provide precise prediction of rail maintenance time and location. The expectation from such a method is to develop models to minimize the human error that is strongly related to manual prediction. Such models will help them in understanding how the track degradation occurs overtime under the change in different conditions (e.g. rail load, rail type, rail profile). They need a well-structured technique to identify the precise time that rail tracks fail in order to minimize the maintenance cost/time and secure the vehicles. The rail track characteristics that have been collected over the years will be used in developing rail track degradation prediction models. Since these data have been collected in large volumes and the data collection is done both electronically and manually, it is possible to have some errors. Sometimes these errors make it impossible to use them in prediction model development. This is one of the major drawbacks in rail track degradation prediction. An accurate model can play a key role in the estimation of the long-term behavior of rail tracks. Accurate models increase the track safety and decrease the cost of maintenance in long term. In this research, a short review of rail track degradation prediction models has been discussed before estimating rail track degradation for the curve sections of Melbourne tram track system using Adaptive Network-based Fuzzy Inference System (ANFIS) model.

Keywords: ANFIS, MGT, prediction modeling, rail track degradation

Procedia PDF Downloads 338

Authors: Jamal Fayazi, Farhad Anoosheh, Mohammad R. Ghorbani, Ali R. Paydar

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In this study, to determine the best non-linear regression model describing the growth curve of native poultry, 9657 chicks of generations 18, 19, and 20 raised in Mazandaran breeding center were used. Fowls and roosters of this center distributed in south of Caspian Sea region. To estimate the genetic variability of none linear regression parameter of growth traits, a Gibbs sampling of Bayesian analysis was used. The average body weight traits in the first day (BW1), eighth week (BW8) and twelfth week (BW12) were respectively estimated as 36.05, 763.03, and 1194.98 grams. Based on the coefficient of determination, mean squares of error and Akaike information criteria, Gompertz model was selected as the best growth descriptive function. In Gompertz model, the estimated values for the parameters of maturity weight (A), integration constant (B) and maturity rate (K) were estimated to be 1734.4, 3.986, and 0.282, respectively. The direct heritability of BW1, BW8 and BW12 were respectively reported to be as 0.378, 0.3709, 0.316, 0.389, 0.43, 0.09 and 0.07. With regard to estimated parameters, the results of this study indicated that there is a possibility to improve some property of growth curve using appropriate selection programs.

Keywords: direct heritability, Gompertz, growth traits, maturity weight, native poultry

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Authors: Ashique Mahmud, MD. Ataul Gani Osmani, Shoria Sharmin

Abstract:

In the first quarter of the twenty-first century, the most substantial global concern is environmental contamination, and it has gained the prioritization of both the national and international community. Keeping in mind this crucial fact, this study conducted different statistical and econometrical methods to identify whether the gross national income of the country has a significant impact on electricity production from nonrenewable sources and different air pollutants like carbon dioxide, nitrous oxide, and methane emissions. Besides, the primary objective of this research was to analyze whether the environmental Kuznets curve hypothesis holds for the examined variables. After analyzing different statistical properties of the variables, this study came to the conclusion that the environmental Kuznets curve hypothesis holds for gross national income and carbon dioxide emission in Bangladesh in the short run as well as the long run. This study comes to this conclusion based on the findings of ordinary least square estimations, ARDL bound tests, short-run causality analysis, the Error Correction Model, and other pre-diagnostic and post-diagnostic tests that have been employed in the structural model. Moreover, this study wants to demonstrate that the outline of gross national income and carbon dioxide emissions is in its initial stage of development and will increase up to the optimal peak. The compositional effect will then force the emission to decrease, and the environmental quality will be restored in the long run.

Keywords: environmental Kuznets curve hypothesis, carbon dioxide emission in Bangladesh, gross national income in Bangladesh, autoregressive distributed lag model, granger causality, error correction model

Procedia PDF Downloads 152

Authors: Mohammed Bnebaghdad, Feriel Betouche, Malika Semmani

Abstract:

Image processing become more sophisticated over time due to technological advances, especially artificial intelligence (AI) technology. Currently, AI image processing is used in many areas, including surveillance, industry, science, and medicine. AI in medical image processing can help doctors diagnose diseases faster, with minimal mistakes, and with less effort. Among these diseases is malaria, which remains a major public health challenge in many parts of the world. It affects millions of people every year, particularly in tropical and subtropical regions. Early detection of malaria is essential to prevent serious complications and reduce the burden of the disease. In this paper, we propose and implement a scheme based on AI image processing to enhance malaria disease diagnosis through automated analysis of blood smear images. The scheme is based on the convolutional neural network (CNN) method. So, we have developed a model that classifies infected and uninfected single red cells using images available on Kaggle, as well as real blood smear images obtained from the Central Laboratory of Medical Biology EHS Laadi Flici (formerly El Kettar) in Algeria. The real images were segmented into individual cells using the watershed algorithm in order to match the images from the Kaagle dataset. The model was trained and tested, achieving an accuracy of 99% and 97% accuracy for new real images. This validates that the model performs well with new real images, although with slightly lower accuracy. Additionally, the model has been embedded in a Raspberry Pi4, and a graphical user interface (GUI) was developed to visualize the malaria diagnostic results and facilitate user interaction.

Keywords: medical image processing, malaria parasite, classification, CNN, artificial intelligence

Procedia PDF Downloads 23

Authors: H. Triki, Y. Hamaizi, A. El-Akrmi

Abstract:

We consider the higher order nonlinear Schrödinger equation model with fourth-order dispersion, cubic-quintic terms, and self-steepening. This equation governs the propagation of fem to second pulses in optical fibers. We present new bright and dark solitary wave type solutions for such a model under certain parametric conditions. This kind of solution may be useful to explain some physical phenomena related to wave propagation in a nonlinear optical fiber systems supporting high-order nonlinear and dispersive effects.

Keywords: nonlinear Schrödinger equation, high-order effects, soliton solution

Procedia PDF Downloads 639

Authors: M. F. Slim, A. Alhussein, F. Sanchette, M. François

Abstract:

Thin films are widely used in various applications to enhance the surface properties and characteristics of materials. They are used in many domains such as: biomedical, automotive, aeronautics, military, electronics and energy. Depending on the elaboration technique, the elastic behavior of thin films may be different from this of bulk materials. This dependence on the elaboration techniques and their parameters makes the control of the elasticity constants of coated components necessary. Our work is focused on the characterization of the elasticity constants of isotropic thin films by means of Impulse Excitation Techniques. The tests rely on the measurement of the sample resonance frequency before and after deposition. In this work, a finite element model was performed with ABAQUS software. This model was then compared with the analytical approaches used to determine the Young’s and shear moduli. The best model to determine the film Young’s modulus was identified and a relation allowing the determination of the shear modulus of thin films of any thickness was developed. In order to confirm the model experimentally, Tungsten films were deposited on glass substrates by DC magnetron sputtering of a 99.99% purity tungsten target. The choice of tungsten was done because it is well known that its elastic behavior at crystal scale is ideally isotropic. The macroscopic elasticity constants, Young’s and shear moduli and Poisson’s ratio of the deposited film were determined by means of Impulse Excitation Technique. The Young’s modulus obtained from IET was compared with measurements by the nano-indentation technique. We did not observe any significant difference and the value is in accordance with the one reported in the literature. This work presents a new methodology on the determination of the elasticity constants of thin films using Impulse Excitation Technique. A formulation allowing the determination of the shear modulus of a coating, whatever the thickness, was developed and used to determine the macroscopic elasticity constants of tungsten films. The developed model was validated numerically and experimentally.

Keywords: characterization, coating, dynamical resonant method, Poisson's ratio, PVD, shear modulus, Young's modulus

Procedia PDF Downloads 365

Authors: B. Gültekin, T. Gültekin

Abstract:

Peace diplomacy is the most important international tool to maintain peace all over the World. This study consists of three parts. In the first part, the leadership of Devlet Bahçeli, leader of the Nationalist Movement Party, will be introduced as a tool of peace communication and peace management. Also, in this part, peace communication will be explained by the peace leadership traits of Devlet Bahçeli, who is one of the efficient political leaders representing the concepts of compromise and agreement on different sides of politics. In the second part of study, it is aimed to analyze Devlet Bahçeli’s leadership within the frame of peace communication and the final part of this study is about creating an original public communication model for public diplomacy based on Devlet Bahçeli as an example. As a result, the main purpose of this study is to develop an original peace communication model including peace modules, peace management projects, original dialogue procedures and protocols exhibited in the policies of Devlet Bahçeli. The political leadership represented by Devlet Bahçeli inspires political leaders to provide peace communication. In this study, principles and policies of peace leadership of Devlet Bahçeli will be explained as an original model on a peace communication platform.

Keywords: public diplomacy, dialogue management, peace leadership, peace diplomacy

Procedia PDF Downloads 171

Authors: I .Ait El Harch, K. Outaaoui, Y. El Foutayeni

Abstract:

This study aims to deepen the understanding and optimize fishing activity in Morocco by holistically integrating biological and economic aspects. We develop a biological equilibrium model in which these competing species present their natural growth by logistic equations, taking into account density and competition between them. The integration of human intervention adds a realistic dimension to our model. A company specifically targets the three species, thus influencing population dynamics according to their fishing activities. The aim of this work is to determine the fishing effort that maximizes the company’s profit, taking into account the constraints associated with conserving ecosystem equilibrium.

Keywords: bioeconomical modeling, optimization techniques, linear complementarity problem LCP, biological equilibrium, maximizing profits

Procedia PDF Downloads 28

Authors: Xulu Yao, Moi Hoon Yap, Yanlong Zhang

Abstract:

As a branch of artificial neural network, deep learning is widely used in the field of image recognition, but the lack of its dataset leads to imperfect model learning. By analysing the data scale requirements of deep learning and aiming at the application in GUI generation, it is found that the collection of GUI dataset is a time-consuming and labor-consuming project, which is difficult to meet the needs of current deep learning network. To solve this problem, this paper proposes a semi-supervised deep learning model that relies on the original small-scale datasets to produce a large number of reliable data sets. By combining the cyclic neural network with the generated countermeasure network, the cyclic neural network can learn the sequence relationship and characteristics of data, make the generated countermeasure network generate reasonable data, and then expand the Rico dataset. Relying on the network structure, the characteristics of collected data can be well analysed, and a large number of reasonable data can be generated according to these characteristics. After data processing, a reliable dataset for model training can be formed, which alleviates the problem of dataset shortage in deep learning.

Keywords: GUI, deep learning, GAN, data augmentation

Procedia PDF Downloads 187

Authors: N. Bourayou, A. -H. Meniai, A. Gouaoura

Abstract:

The presence of salt can either raise or lower the distribution coefficient of a solute acetic acid in liquid- liquid equilibria. The coefficient of solute is defined as the ratio of the composition of solute in solvent rich phase to the composition of solute in diluents (water) rich phase. The phenomena are known as salting–out or salting-in, respectively. The effect of monovalent salt, sodium chloride and the bivalent salt, sodium sulfate on the distribution of acetic acid between 1-butanol and water at 298.15K were experimentally shown to be effective in modifying the liquid-liquid equilibrium of water/acetic acid/1-butanol system in favour of the solvent extraction of acetic acid from an aqueous solution with 1-butanol, particularly at high salt concentrations of both salts. All the two salts studied are found to have to salt out effect for acetic acid in varying degrees. The experimentally measured data were well correlated by Eisen-Joffe equation. NRTL model for solvent mixtures containing salts was able to provide good correlation of the present liquid-liquid equilibrium data. Using the regressed salt concentration coefficients for the salt-solvent interaction parameters and the solvent-solvent interaction parameters obtained from the same system without salt. The calculated phase equilibrium was in a quite good agreement with the experimental data, showing the ability of NRTL model to correlate salt effect on the liquid-liquid equilibrium.

Keywords: activity coefficient, Eisen-Joffe, NRTL model, sodium chloride

Procedia PDF Downloads 285