Search results for: small signal model

Authors: Daseul Lee, Jeong Jun Lee, Ok-Sang Jung

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Self-assembly of a series of ZnX2 (X- = Cl-, Br-, and I-) with 2,3-bis(4’-nicotinamidephenoxy)naphthalene (L) as a new bidentate pyridyl-donor ligand yields systematic metallacyclodimeric unit, [ZnX2L]2. The supramolecule constitutes a characteristically stacked forming both 1D suprachannels and cages. Weak C-H⋯π and inter-digitated π⋯π interactions are main driving forces in the formation of both suprachannels and cages. The slightly different features between the suprachannel and cage have been investigated by 1H NMR and TG analysis, which solvent quantitatively exchange within only suprachannels. Photo-unstable CH2I2 molecules are stabilized via capturing within suprachannels, which is monitored by UV-Vis spectroscopy. Furthermore, the photoluminescence intensity, from the chromophore naphthyl moiety of [ZnCl2L]2, gradually decreases with the addition of CH2I2. And washing off the CH2I2 by dichloromethane returned the PL intensity back to its approximately original signal.

Keywords: metallacyclodimer, suprachannel, π⋯π interaction, molecular recognition

Procedia PDF Downloads 308

Authors: S. Lemoussu, J. C. Chaudemar, R. A. Vingerhoeds

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The aeronautics sector is currently living an unprecedented growth largely due to innovative projects. In several cases, such innovative developments are being carried out by Small and Medium sized-Enterprises (SMEs). For instance, in Europe, a handful of SMEs are leading projects like airships, large civil drones, or flying cars. These SMEs have all limited resources, must make strategic decisions, take considerable financial risks and in the same time must take into account the constraints of safety, cost, time and performance as any commercial organization in this industry. Moreover, today, no international regulations fully exist for the development and certification of this kind of projects. The absence of such a precise and sufficiently detailed regulatory framework requires a very close contact with regulatory instances. But, SMEs do not always have sufficient resources and internal knowledge to handle this complexity and to discuss these issues. This poses additional challenges for those SMEs that have system integration responsibilities and that must provide all the necessary means of compliance to demonstrate their ability to design, produce, and operate airships with the expected level of safety and reliability. The final objective of our research is thus to provide a methodological framework supporting SMEs in their development taking into account recent innovation and institutional rules of the sector. We aim to provide a contribution to the problematic by developing a specific Model-Based Systems Engineering (MBSE) approach. Airspace regulation, aeronautics standards and international norms on systems engineering are taken on board to be formalized in a set of models. This paper presents the on-going research project combining Systems Engineering and Project Management process modeling and taking into account the metamodeling problematic.

Keywords: aeronautics, certification, process modeling, project management, SME, systems engineering

Procedia PDF Downloads 154

Authors: Keyvan Kashkouli Nejad, Motoaki Sugiura, Atsushi Sato, Takayuki Nozawa, Hyeonjeong Jeong, Sugiko Hanawa , Yuka Kotozaki, Ryuta Kawashima

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The perception of agency/control is altered with presence of discrepancies in the environment or mismatch of predictions (of possible results) and actual results the sense of agency might become altered. Synofzik et al. proposed a two layer model of agency: In the first layer, the Feeling of Agency (FoA) is not directly available to awareness; a slight mismatch in the environment/outcome might cause alterations in FoA, while the agent still feels in control. If the discrepancy passes a threshold, it becomes available to consciousness and alters Judgment of Agency (JoA), which is directly available in the person’s awareness. Most experiments so far only investigate subjects rather conscious JoA, while FoA has been neglected. In this experiment we target FoA by using subliminal discrepancies that can not be consciously detectable by the subjects. Here, we explore whether we can detect this two level model in the subjects behavior and then try to map this in their brain activity. To do this, in a fMRI study, we incorporated both consciously detectable mismatching between action and result and also subliminal discrepancies in the environment. Also, unlike previous experiments where subjective questions from the participants mainly trigger the rather conscious JoA, we also tried to measure the rather implicit FoA by asking participants to rate their performance. We compared behavioral results and also brain activation when there were conscious discrepancies and when there were subliminal discrepancies against trials with no discrepancies and against each other. In line with our expectations, conditions with consciously detectable incongruencies triggered lower JoA ratings than conditions without. Also, conditions with any type of discrepancies had lower FoA ratings compared to conditions without. Additionally, we found out that TPJ and angular gyrus in particular to have a role in coding of JoA and also FoA.

Keywords: agency, fMRI, TPJ, two layer model

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Authors: Rabia Korkmaz Tan, Şebnem Bora

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The major problem encountered when modeling complex systems with agent-based modeling and simulation techniques is the existence of large parameter spaces. A complex system model cannot be expected to reflect the whole of the real system, but by specifying the most appropriate parameters, the actual system can be represented by the model under certain conditions. When the studies conducted in recent years were reviewed, it has been observed that there are few studies for parameter tuning problem in agent based simulations, and these studies have focused on tuning parameters of a single model. In this study, an approach of parameter tuning is proposed by using metaheuristic algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colonies (ABC), Firefly (FA) algorithms. With this hybrid structured study, the parameter tuning problems of the models in the different fields were solved. The new approach offered was tested in two different models, and its achievements in different problems were compared. The simulations and the results reveal that this proposed study is better than the existing parameter tuning studies.

Keywords: parameter tuning, agent based modeling and simulation, metaheuristic algorithms, complex systems

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Authors: C. Mayr, J. Periya, A. Kariminezhad

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In this paper, we developed a learning framework for the classification of vulnerable road users (VRU) by their range-Doppler signatures. The frequency-modulated continuous-wave (FMCW) radar raw data is first pre-processed to obtain robust object range-Doppler maps per coherent time interval. The complex-valued range-Doppler maps captured from our outdoor measurements are further fed into a convolutional neural network (CNN) to learn the classification. This CNN has gone through a hyperparameter optimization process for improved learning. By learning VRU range-Doppler signatures, the three classes 'pedestrian', 'dog', and 'noise' are classified with an average accuracy of almost 95%. Interestingly, this classification accuracy holds for a combined longitudinal and lateral object trajectories.

Keywords: machine learning, radar, signal processing, autonomous driving

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Authors: Paulina Pergoł, Paulina Pergoł, Tomasz Kryczka

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Aging of societies is a phenomenon that is visible all over the world. The above has social, economic, and medical consequences. Many methods of pro-health stimulation of the elderly, improving cognitive functions and quality of life, are described in the world literature. The most frequently used method of activating the elderly is physical activity, in which, in addition to standard exercise programs, activities such as dance, tai chi, and yoga are often introduced. The introduction of physical activity may be limited due to the disability that often accompanies people aged 65+. Other activating methods mentioned in the literature are therapies with the participation of animals - animal therapy or plant therapy - hortitherapy, as well as music therapy, which is increasingly popular. All of the above-mentioned therapeutic methods require the involvement of a person who would conduct the so-called occupational therapist, which can be a financial barrier for many nursing homes. Researchers all over the world are trying to find solutions that can be applied in any care institution, even in those where financial support for running such centers is small. One of the forms of therapy that is recommended in the group of elderly people and which does not require large financial outlays is the so-called reminiscence therapy based on recalling personal experiences and experiences. Thanks to reminiscence therapy, seniors can return to happy moments in their lives. Research shows that reminiscence therapy increases self-esteem and reduces symptoms of depression. The method of activating older people with the use of correspondence can be an effective and low-cost form of therapy, as shown by the pilot study conducted in 2019 as part of the Queen Silvia Nursing Award competition, consisting in a systematic exchange of correspondence between volunteers and residents of the Nursing Home. This study was conducted with the participation of a small group of participants, therefore, in order to identify and prove the beneficial effect of this form of activation, it should be carried out on a larger group of respondents using standardized scales assessing various psychological parameters.

Keywords: nursing, elderly care, psychiatry, psychology

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Authors: Katerina Kormusheva

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Pricing plays a pivotal role in the marketing discipline as it directly influences consumer perceptions, purchase decisions, and overall market positioning of a product or service. This paper seeks to expand current knowledge in the area of discriminatory and differential pricing, a main area of marketing research. The methodology includes developing a framework and a model for determining how many price points to implement in differential pricing. We focus on choosing the levels of differentiation, derive a function form of the model framework proposed, and lastly, test it empirically with data from a large-scale marketing pricing experiment of services in telecommunications.

Keywords: marketing, differential pricing, price points, optimization

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Authors: Timothée Bligny, Clément Codron, Antoine Estruch, Nicolas Girodet, Clément Ginet

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Collateralized Debt Obligations are not as widely used nowadays as they were before 2007 Subprime crisis. Nonetheless there remains an enthralling challenge to optimize cash flows associated with synthetic CDOs. A Gaussian-based model is used here in which default correlation and unconditional probabilities of default are highlighted. Then numerous simulations are performed based on this model for different scenarios in order to evaluate the associated cash flows given a specific number of defaults at different periods of time. Cash flows are not solely calculated on a single bought or sold tranche but rather on a combination of bought and sold tranches. With some assumptions, the simplex algorithm gives a way to find the maximum cash flow according to correlation of defaults and maturities. The used Gaussian model is not realistic in crisis situations. Besides present system does not handle buying or selling a portion of a tranche but only the whole tranche. However the work provides the investor with relevant elements on how to know what and when to buy and sell.

Keywords: synthetic collateralized debt obligation (CDO), credit default swap (CDS), cash flow optimization, probability of default, default correlation, strategies, simulation, simplex

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Authors: José E. Q. Souza, Marcio Fontana, Antonio C. C. Lima

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This paper presents analysis and characterization of a piezoelectric micro-generator for energy harvesting application. A low-cost experimental prototype was designed to operate as piezoelectric micro-generator in the laboratory. An input acceleration of 9.8m/s2 using a sine signal (peak-to-peak voltage: 1V, offset voltage: 0V) at frequencies ranging from 10Hz to 160Hz generated a maximum average power of 432.4μW (linear mass position = 25mm) and an average power of 543.3μW (angular mass position = 35°). These promising results show that the prototype can be considered for low consumption load application as an energy harvesting micro-generator.

Keywords: piezoelectric, micro-generator, energy harvesting, cantilever beam

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Authors: Abdolreza Memari

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In this article in order to estimate the viscosity of crude oil,a numerical method has been used. We use this method to measure the crude oil's viscosity for 3 states: Saturated oil's viscosity, viscosity above the bubble point and viscosity under the saturation pressure. Then the crude oil's viscosity is estimated by using KHAN model and roller ball method. After that using these data that include efficient conditions in measuring viscosity, the estimated viscosity by the presented method, a radial based neural method, is taught. This network is a kind of two layered artificial neural network that its stimulation function of hidden layer is Gaussian function and teaching algorithms are used to teach them. After teaching radial based neural network, results of experimental method and artificial intelligence are compared all together. Teaching this network, we are able to estimate crude oil's viscosity without using KHAN model and experimental conditions and under any other condition with acceptable accuracy. Results show that radial neural network has high capability of estimating crude oil saving in time and cost is another advantage of this investigation.

Keywords: viscosity, Iranian crude oil, radial based, neural network, roller ball method, KHAN model

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Authors: S. S. Sree Sanker, K. N. Madhusoodanan

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Optical biosensors are dominant alternative for traditional analytical methods, because of their small size, simple design and high sensitivity. Photonic sensing method is one of the recent advancing technology for biosensors. It measures the change in refractive index which is induced by the difference in molecular interactions due to the change in concentration of the analyte. Glucose is an aldosic monosaccharide, which is a metabolic source in many of the organisms. The terahertz waves occupies the space between infrared and microwaves in the electromagnetic spectrum. Terahertz waves are expected to be applied to various types of sensors for detecting harmful substances in blood, cancer cells in skin and micro bacteria in vegetables. We have designed glucose sensors using silicon based 1D and 2D photonic crystal pillar arrays in terahertz frequency range. 1D photonic crystal has rectangular pillars with height 100 µm, length 1600 µm and width 50 µm. The array period of the crystal is 500 µm. 2D photonic crystal has 5×5 cylindrical pillar array with an array period of 75 µm. Height and diameter of the pillar array are 160 µm and 100 µm respectively. Two samples considered in the work are blood and glucose solution, which are labelled as sample 1 and sample 2 respectively. The proposed sensor detects the concentration of glucose in the samples from 0 to 100 mg/dL. For this, the crystal was irradiated with 0.3 to 3 THz waves. By analyzing the obtained S parameter, the refractive index of the crystal corresponding to the particular concentration of glucose was measured using the parameter retrieval method. Refractive indices of the two crystals decreased gradually with the increase in concentration of glucose in the sample. For 1D photonic crystals, a gradual decrease in refractive index was observed at 1 THz. 2D photonic crystal showed this behavior at 2 THz. The proposed sensor was simulated using CST Microwave studio. This will enable us to develop a model which can be used to characterize a glucose sensor. The present study is expected to contribute to blood glucose monitoring.

Keywords: CST microwave studio, glucose sensor, photonic crystal, terahertz waves

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Authors: J. K. Adedeji, M. O. Oyekanmi

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This paper has critically examined the use of Machine Learning procedures in curbing unauthorized access into valuable areas of an organization. The use of passwords, pin codes, user’s identification in recent times has been partially successful in curbing crimes involving identities, hence the need for the design of a system which incorporates biometric characteristics such as DNA and pattern recognition of variations in facial expressions. The facial model used is the OpenCV library which is based on the use of certain physiological features, the Raspberry Pi 3 module is used to compile the OpenCV library, which extracts and stores the detected faces into the datasets directory through the use of camera. The model is trained with 50 epoch run in the database and recognized by the Local Binary Pattern Histogram (LBPH) recognizer contained in the OpenCV. The training algorithm used by the neural network is back propagation coded using python algorithmic language with 200 epoch runs to identify specific resemblance in the exclusive OR (XOR) output neurons. The research however confirmed that physiological parameters are better effective measures to curb crimes relating to identities.

Keywords: biometric characters, facial recognition, neural network, OpenCV

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Authors: Anung Style Bukhori, Ani Dijah Rahajoe

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Poverty poses a significant challenge in Indonesia, requiring an effective analytical approach to understand and address this issue. In this research, we applied the Naïve Bayes classification method to examine and classify poverty data in Indonesia. The main focus is on classifying data using RapidMiner, a powerful data analysis platform. The analysis process involves data splitting to train and test the classification model. First, we collected and prepared a poverty dataset that includes various factors such as education, employment, and health..The experimental results indicate that the Naïve Bayes classification model can provide accurate predictions regarding the risk of poverty. The use of RapidMiner in the analysis process offers flexibility and efficiency in evaluating the model's performance. The classification produces several values to serve as the standard for classifying poverty data in Indonesia using Naive Bayes. The accuracy result obtained is 40.26%, with a moderate recall result of 35.94%, a high recall result of 63.16%, and a low recall result of 38.03%. The precision for the moderate class is 58.97%, for the high class is 17.39%, and for the low class is 58.70%. These results can be seen from the graph below.

Keywords: poverty, classification, naïve bayes, Indonesia

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Authors: Tapas Kumar Sinha, Joseph Mathew

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At the moment earth is in grave danger due to threats of global warming. The temperature of the earth has risen by almost 20C. Glaciers in the Arctic have started to melt. It would be foolhardy to think that this is a small effect and in time it would go away. Global warming is caused by a number of factors. However, one sure and simple way to totally eliminate this problem is to put programmable shields in space. Just as an umbrella blocks sunlight, a programmable shield in space will block sun rays from reaching the earth as in a solar eclipse and cause cooling in the penumbral region just as it happens during an eclipse.

Keywords: glaciers, green house, global warming space, satellites

Procedia PDF Downloads 572

Authors: Anastasios Papathanasiou, George Liontos, Athanasios Katsouras, Vasiliki Liagkou, Euripides Glavas

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Email remains a crucial communication tool due to its efficiency, accessibility and cost-effectiveness, enabling rapid information exchange across global networks. However, the global adoption of email has also made it a prime target for cyber threats, including phishing, malware and Business Email Compromise (BEC) attacks, which exploit its integral role in personal and professional realms in order to perform fraud and data breaches. To combat these threats, this research advocates for a multi-layered defense strategy incorporating advanced technological tools such as anti-spam and anti-malware software, machine learning algorithms and authentication protocols. Moreover, we developed an artificial intelligence model specifically designed to analyze email headers and assess their security status. This AI-driven model examines various components of email headers, such as "From" addresses, ‘Received’ paths and the integrity of SPF, DKIM and DMARC records. Upon analysis, it generates comprehensive reports that indicate whether an email is likely to be malicious or benign. This capability empowers users to identify potentially dangerous emails promptly, enhancing their ability to avoid phishing attacks, malware infections and other cyber threats.

Keywords: email security, artificial intelligence, header analysis, threat detection, phishing, DMARC, DKIM, SPF, ai model

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Authors: J. Van der Walt, N. A. Plastow, M. Unger

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Gross and fine motor skill difficulties among children affect their ability to learn and progress in school. Research indicates that children in low socio-economic areas are at a higher risk of motor skill difficulties, while therapy resources are limited. The Hopscotch motor skill programme is a well-researched accessible in-school intervention developed by occupational and physiotherapists through complex intervention development. The development stage of the complex intervention development model firstly included a prevalence study in a low-resourced area in the West Coast of South Africa, indicating a high prevalence with significant motor skill difficulties among pre-school children at 14.5% with fine motor skill difficulties at 24.6%. A scoping review identifies motor skill interventions for pre-school children and a proposed a framework of fundamental concepts to consider when developing a motor skill intervention. a Delphi-study considered the framework and encouraged collaboration between therapists and educators to make the programme accessible, resource and cost effective, specifically geared towards a rural, low resourced area. The results from the Delphi study, together with the proposed framework from the scoping review was used to develop the Hopscotch programme, adopting a task-shifting approach. The eight-week small-group programme is facilitated by teachers with the support of therapists. The programme aims to improve the motor skills of pre-school aged children with motor skill difficulties to promote academic readiness through obstacle courses, ball skill games and fine motor games and crafts. A randomised controlled trial is planned as a next stage to determine the preliminary effect of the programme on the motor and early academic skills of pre-school children.

Keywords: accesible learning, motor skill intervention, school readiness, task shifting

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Authors: Jayesh Mahitkar, V. K. Singh, Surendra Singh Kachhwaha

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Wire Mesh Heat Pipe (WMHP) as an effective component of thermal control system in the payload of spacecraft, utilizing ammonia to transfer efficient amount of heat. One dimensional generic and robust mathematical model with partial-analytical hydraulic approach (PAHA) is developed to study inside behaviour of WMHP. In this model, inside performance during operation is investigated like mass flow rate, and velocity along the wire mesh as well as vapour core is modeled respectively. This numerical model investigate heat flow along length, pressure drop along wire mesh as well as vapour line in axial direction. Furthermore, WMHP is modeled into equivalent resistance network such that total thermal resistance of heat pipe, temperature drop across evaporator end and condenser end is evaluated. This numerical investigation should be carried out for single layer and double layer wire mesh each with heat input at evaporator section is 10W, 20 W and 30 W at condenser temperature maintained at 20˚C.

Keywords: ammonia, heat transfer, modeling, wire mesh

Procedia PDF Downloads 265

Authors: Adel Khorramrouz, Monireh Ahmadi Bani, Ehsan Norouzi, Morvarid Lalenoor

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Background: Plantar pressure distribution measurement has been used for a long time to assess foot disorders. Plantar pressure is an important component affecting the foot and ankle function and Changes in plantar pressure distribution could indicate various foot and ankle disorders. Morphologic and mechanical properties of the foot may be important factors affecting the plantar pressure distribution. Accurate and early measurement may help to reduce the prevalence of pes planus. With recent developments in technology, new techniques such as machine learning have been used to assist clinicians in predicting patients with foot disorders. Significance of the study: This study proposes a neural network learning-based flat foot classification methodology using static foot pressure distribution. Methodologies: Data were collected from 895 patients who were referred to a foot clinic due to foot disorders. Patients with pes planus were labeled by an experienced physician based on clinical examination. Then all subjects (with and without pes planus) were evaluated for static plantar pressures distribution. Patients who were diagnosed with the flat foot in both feet were included in the study. In the next step, the leg length was normalized and the network was trained for plantar pressure mapping images. Findings: From a total of 895 image data, 581 were labeled as pes planus. A computational neural network (CNN) ran to evaluate the performance of the proposed model. The prediction accuracy of the basic CNN-based model was performed and the prediction model was derived through the proposed methodology. In the basic CNN model, the training accuracy was 79.14%, and the test accuracy was 72.09%. Conclusion: This model can be easily and simply used by patients with pes planus and doctors to predict the classification of pes planus and prescreen for possible musculoskeletal disorders related to this condition. However, more models need to be considered and compared for higher accuracy.

Keywords: foot disorder, machine learning, neural network, pes planus

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Authors: Sun Zhe, Ruggero Micheletto

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The research of neural network is very important for the development of advanced next generation intelligent devices and the medical treatment. The most important part of the neural network research is the learning. The process of learning in our brain is essentially several adjustment processes of connection strength between neurons. It is very difficult to figure out how this mechanism works in the complex network and how the connection strength influences brain functions. For this reason, we made a model with only two coupled neurons and studied the influence of connection strength between them. To emulate the neuronal activity of realistic neurons, we prefer to use the Izhikevich neuron model. This model can simulate the neuron variables accurately and it’s simplicity is very suitable to implement on computers. In this research, the parameter ρ is used to estimate the correlation coefficient between spike train of two coupling neurons.We think the results is very important for figuring out the mechanism between synchronization of coupling neurons and synaptic plasticity. The result also presented the importance of the spike frequency adaptation in complex systems.

Keywords: neural networks, noise, stochastic processes, coupled neurons, correlation coefficient, synchronization, pacemaker, synaptic plasticity

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Authors: Hesham Abd Elkhalek, Sherif M. Hafez, Yasser M. El Fahham

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Allocating limited budget to maintain bridge networks and selecting effective maintenance strategies for each bridge represent challenging tasks for maintenance managers and decision makers. In Egypt, bridges are continuously deteriorating. In many cases, maintenance works are performed due to user complaints. The objective of this paper is to develop a practical and reliable framework to manage the maintenance, repair, and rehabilitation (MR&R) activities of Bridges network considering performance and budget limits. The model solves an optimization problem that maximizes the average condition of the entire network given the limited available budget using Genetic Algorithm (GA). The framework contains bridge inventory, condition assessment, repair cost calculation, deterioration prediction, and maintenance optimization. The developed model takes into account multiple parameters including serviceability requirements, budget allocation, element importance on structural safety and serviceability, bridge impact on network, and traffic. A questionnaire is conducted to complete the research scope. The proposed model is implemented in software, which provides a friendly user interface. The framework provides a multi-year maintenance plan for the entire network for up to five years. A case study of ten bridges is presented to validate and test the proposed model with data collected from Transportation Authorities in Egypt. Different scenarios are presented. The results are reasonable, feasible and within acceptable domain.

Keywords: bridge management systems (BMS), cost optimization condition assessment, fund allocation, Markov chain

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Authors: Kaewkarn Phuangsombat, Arthit Phuangsombat, Anupun Terdwongworakul

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Hard seeds will not grow and can cause mold in sprouting process. Thus, the hard seeds need to be separated from the normal seeds. Near infrared hyperspectral imaging in a range of 900 to 1700 nm was implemented to develop a model by partial least squares discriminant analysis to discriminate the hard seeds from the normal seeds. The orientation of the seeds was also studied to compare the performance of the models. The model based on hilum-up orientation achieved the best result giving the coefficient of determination of 0.98, and root mean square error of prediction of 0.07 with classification accuracy was equal to 100%.

Keywords: mung bean, near infrared, germinatability, hard seed

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Authors: Sneha Thakur, Sanjeev Karmakar

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This paper proposed hybrid Particle Swarm Optimization (PSO) – Long-Short Term Memory (LSTM) model for groundwater level prediction. The evaluation of the performance is realized using the parameters: root mean square error (RMSE) and mean absolute error (MAE). Ground water level forecasting will be very effective for planning water harvesting. Proper calculation of water level forecasting can overcome the problem of drought and flood to some extent. The objective of this work is to develop a ground water level forecasting model using deep learning technique integrated with optimization technique PSO by applying 29 years data of Chhattisgarh state, In-dia. It is important to find the precise forecasting in case of ground water level so that various water resource planning and water harvesting can be managed effectively.

Keywords: long short-term memory, particle swarm optimization, prediction, deep learning, groundwater level

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Authors: Xi Gu, Guan Heng Yeoh, Victoria Timchenko

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In the current work, a three-dimensional geometry of a 75% stenosed blood vessel is analysed. Large eddy simulation (LES) with the help of a dynamic subgrid scale Smagorinsky model is applied to model the turbulent pulsatile flow. The geometry, the transmural pressure and the properties of the blood and the elastic boundary were based on clinical measurement data. For the flexible wall model, a thin solid region is constructed around the 75% stenosed blood vessel. The deformation of this solid region was modelled as a deforming boundary to reduce the computational cost of the solid model. Fluid-structure interaction is realised via a two-way coupling between the blood flow modelled via LES and the deforming vessel. The information of the flow pressure and the wall motion was exchanged continually during the cycle by an arbitrary lagrangian-eulerian method. The boundary condition of current time step depended on previous solutions. The fluctuation of the velocity in the post-stenotic region was analysed in the study. The axial velocity at normalised position Z=0.5 shows a negative value near the vessel wall. The displacement of the elastic boundary was concerned in this study. In particular, the wall displacement at the systole and the diastole were compared. The negative displacement at the stenosis indicates a collapse at the maximum velocity and the deceleration phase.

Keywords: Large Eddy Simulation, Fluid Structural Interaction, constricted artery, Computational Fluid Dynamics

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Authors: Fatemeh Jannesarvatan, Ghazaal Parastooei, Jimmy frerejan, Saedeh Mokhtari, Peter Van Rosmalen

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Medical and dental education increasingly emphasizes the acquisition, integration, and coordination of complex knowledge, skills, and attitudes that can be applied in practical situations. Instructional design approaches have focused on using real-life tasks in order to facilitate complex learning in both real and simulated environments. The Four component instructional design (4C/ID) model has become a useful guideline for designing instructional materials that improve learning transfer, especially in health profession education. The objective of this study was to apply the 4C/ID model in the creation of virtual patients (VPs) that dental students can use to practice their clinical management and clinical reasoning skills. The study first explored the context and concept of complication factors and common errors for novices and how they can affect the design of a virtual patient program. The study then selected key dental information and considered the content needs of dental students. The design of virtual patients was based on the 4C/ID model's fundamental principles, which included: Designing learning tasks that reflect real patient scenarios and applying different levels of task complexity to challenge students to apply their knowledge and skills in different contexts. Creating varied learning materials that support students during the VP program and are closely integrated with the learning tasks and students' curricula. Cognitive feedback was provided at different levels of the program. Providing procedural information where students followed a step-by-step process from history taking to writing a comprehensive treatment plan. Four virtual patients were designed using the 4C/ID model's principles, and an experimental design was used to test the effectiveness of the principles in achieving the intended educational outcomes. The 4C/ID model provides an effective framework for designing engaging and successful virtual patients that support the transfer of knowledge and skills for dental students. However, there are some challenges and pitfalls that instructional designers should take into account when developing these educational tools.

Keywords: 4C/ID model, virtual patients, education, dental, instructional design

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Authors: Kim Jin Sup

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This paper presents a vertical hall effect device using current-mode. Among different geometries that have been studied and simulated using COMSOL Multiphysics, optimized cross-shaped model displayed the best sensitivity. The cross-shaped model emerged as the optimum plate to fit the lowest noise and residual offset and the best sensitivity. The symmetrical cross-shaped hall plate is widely used because of its high sensitivity and immunity to alignment tolerances resulting from the fabrication process. The hall effect device has been designed using a 0.18-μm CMOS technology. The simulation uses the nominal bias current of 12μA. The applied magnetic field is from 0 mT to 20 mT. Simulation results achieved in COMSOL and validated with respect to the electrical behavior of equivalent circuit for Cadence. Simulation results of the one structure over the 13 available samples shows for the best geometry a current-mode sensitivity of 6.6 %/T at 20mT. Acknowledgment: This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. R7117-16-0165, Development of Hall Effect Semiconductor for Smart Car and Device).

Keywords: vertical hall device, current-mode, crossed-shaped model, CMOS technology

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Authors: Reza Zakerinejad

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Gully erosion is a serious problem that threading the sustainability of agricultural area and rangeland and water in a large part of Iran. This type of water erosion is the main source of sedimentation in many catchment areas in the north of Iran. Since in many national assessment approaches just qualitative models were applied the aim of this study is to predict the spatial distribution of gully erosion processes by means of detail terrain analysis and GIS -based logistic regression in the loess deposition in a case study in the Golestan Province. This study the DEM with 25 meter result ion from ASTER data has been used. The Landsat ETM data have been used to mapping of land use. The TreeNet model as a stochastic modeling was applied to prediction the susceptible area for gully erosion. In this model ROC we have set 20 % of data as learning and 20 % as learning data. Therefore, applying the GIS and satellite image analysis techniques has been used to derive the input information for these stochastic models. The result of this study showed a high accurate map of potential for gully erosion.

Keywords: TreeNet model, terrain analysis, Golestan Province, Iran

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Authors: Moustafa Osman Mohammed

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The modeling of a spatial-economic database is crucial in recitation economic network structure to social development. Sustainability within the spatial-economic model gives attention to green businesses to comply with Earth’s Systems. The natural exchange patterns of ecosystems have consistent and periodic cycles to preserve energy and materials flow in systems ecology. When network topology influences formal and informal communication to function in systems ecology, ecosystems are postulated to valence the basic level of spatial sustainable outcome (i.e., project compatibility success). These referred instrumentalities impact various aspects of the second level of spatial sustainable outcomes (i.e., participant social security satisfaction). The sustainability outcomes are modeling composite structure based on a network analysis model to calculate the prosperity of panel databases for efficiency value, from 2005 to 2025. The database is modeling spatial structure to represent state-of-the-art value-orientation impact and corresponding complexity of sustainability issues (e.g., build a consistent database necessary to approach spatial structure; construct the spatial-economic-ecological model; develop a set of sustainability indicators associated with the model; allow quantification of social, economic and environmental impact; use the value-orientation as a set of important sustainability policy measures), and demonstrate spatial structure reliability. The structure of spatial-ecological model is established for management schemes from the perspective pollutants of multiple sources through the input–output criteria. These criteria evaluate the spillover effect to conduct Monte Carlo simulations and sensitivity analysis in a unique spatial structure. The balance within “equilibrium patterns,” such as collective biosphere features, has a composite index of many distributed feedback flows. The following have a dynamic structure related to physical and chemical properties for gradual prolong to incremental patterns. While these spatial structures argue from ecological modeling of resource savings, static loads are not decisive from an artistic/architectural perspective. The model attempts to unify analytic and analogical spatial structure for the development of urban environments in a relational database setting, using optimization software to integrate spatial structure where the process is based on the engineering topology of systems ecology.

Keywords: ecological modeling, spatial structure, orientation impact, composite index, industrial ecology

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Authors: Sven Gehrke, Johannes Ruhland

Abstract:

Data is the oil of our time, without them progress would come to a hold [1]. On the other hand, the mistrust of data mining is increasing [2]. The paper at hand shows different aspects of the concept of trust and describes the information asymmetry of the typical stakeholders of a data mining project using the CRISP-DM phase model. Based on the identified influencing factors in relation to trust, problematic aspects of the current approach are verified using various interviews with the stakeholders. The results of the interviews confirm the theoretically identified weak points of the phase model with regard to trust and show potential research areas.

Keywords: trust, data mining, CRISP DM, stakeholder management

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Authors: Victoria Brennan, Andrea Mallaburn, Linda Seton

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Science outreach programmes connect school pupils with external agencies to provide activities and experiences that enhance their exposure to science. It can be argued that these programmes not only aim to support teachers with curriculum engagement and promote scientific literacy but also provide pivotal opportunities to spark scientific interest in students. In turn, a further objective of these programmes is to increase awareness of career opportunities within this field. Although outreach work is also often described as a fun and satisfying venture, a plethora of researchers express caution to how successful the processes are to increases engagement post-16 in science. When researching the impact of outreach programmes, it is often student feedback regarding the activities or enrolment numbers to particular science courses post-16, which are generated and analysed. Although this is informative, the longevity of the programme’s impact could be better informed by the teacher’s perceptions; the evidence of which is far more limited in the literature. In addition, there are strong suggestions that teachers can have an indirect impact on a student’s own self-concept. These themes shape the focus and importance of this ongoing research project as it presents the rationale that teachers are under-used resources when it comes to considering the design of science outreach programmes. Therefore, the end result of the research will consist of a presentation of an ‘optimum’ model of outreach. The result of which should be of interest to the wider stakeholders such as universities or private or government organisations who design science outreach programmes in the hope to recruit future scientists. During phase one, questionnaires (n=52) and interviews (n=8) have generated both quantitative and qualitative data. These have been analysed using the Wilcoxon non-parametric test to compare teachers’ perceptions of science outreach interventions and thematic analysis for open-ended questions. Both of these research activities provide an opportunity for a cross-section of teacher opinions of science outreach to be obtained across all educational levels. Therefore, an early draft of the ‘optimum’ model of science outreach delivery was generated using both the wealth of literature and primary data. This final (ongoing) phase aims to refine this model using teacher focus groups to provide constructive feedback about the proposed model. The analysis uses principles of modified Grounded Theory to ensure that focus group data is used to further strengthen the model. Therefore, this research uses a pragmatist approach as it aims to focus on the strengths of the different paradigms encountered to ensure the data collected will provide the most suitable information to create an improved model of sustainable outreach. The results discussed will focus on this ‘optimum’ model and teachers’ perceptions of benefits and drawbacks when it comes to engaging with science outreach work. Although the model is still a ‘work in progress’, it provides both insight into how teachers feel outreach delivery can be a sustainable intervention tool within the classroom and what providers of such programmes should consider when designing science outreach activities.

Keywords: educational partnerships, science education, science outreach, teachers

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Authors: Ayesha Sanjana Kawser Parsha

Abstract:

S-acylation is an irreversible bond in which cysteine residues are linked to fatty acids palmitate (74%) or stearate (22%), either at the COOH or NH2 terminal, via a thioester linkage. There are several experimental methods that can be used to identify the S-palmitoylation site; however, since they require a lot of time, computational methods are becoming increasingly necessary. There aren't many predictors, however, that can locate S- palmitoylation sites in Arabidopsis Thaliana with sufficient accuracy. This research is based on the importance of building a better prediction tool. To identify the type of machine learning algorithm that predicts this site more accurately for the experimental dataset, several prediction tools were examined in this research, including the GPS PALM 6.0, pCysMod, GPS LIPID 1.0, CSS PALM 4.0, and NBA PALM. These analyses were conducted by constructing the receiver operating characteristics plot and the area under the curve score. An AI-driven deep learning-based prediction tool has been developed utilizing the analysis and three sequence-based input data, such as the amino acid composition, binary encoding profile, and autocorrelation features. The model was developed using five layers, two activation functions, associated parameters, and hyperparameters. The model was built using various combinations of features, and after training and validation, it performed better when all the features were present while using the experimental dataset for 8 and 10-fold cross-validations. While testing the model with unseen and new data, such as the GPS PALM 6.0 plant and pCysMod mouse, the model performed better, and the area under the curve score was near 1. It can be demonstrated that this model outperforms the prior tools in predicting the S- palmitoylation site in the experimental data set by comparing the area under curve score of 10-fold cross-validation of the new model with the established tools' area under curve score with their respective training sets. The objective of this study is to develop a prediction tool for Arabidopsis Thaliana that is more accurate than current tools, as measured by the area under the curve score. Plant food production and immunological treatment targets can both be managed by utilizing this method to forecast S- palmitoylation sites.

Keywords: S- palmitoylation, ROC PLOT, area under the curve, cross- validation score

Procedia PDF Downloads 59