Search results for: models error comparison

Authors: Sh. Hamada

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The current work aims to study the rainbow like-structure observed in the elastic scattering of alpha particles on both ¹²C and ¹⁶O nuclei. We reanalyzed the experimental elastic scattering angular distributions data for α+¹²C and α+¹⁶O nuclear systems at different energies using both optical model and double folding potential of different interaction models such as: CDM3Y1, DDM3Y1, CDM3Y6 and BDM3Y1. Potential created by BDM3Y1 interaction model has the shallowest depth which reflects the necessity to use higher renormalization factor (N_r). Both optical model and double folding potential of different interaction models fairly reproduce the experimental data.

Keywords: density distribution, double folding, elastic scattering, nuclear rainbow, optical model

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Authors: Farzad Danaei, Yilmaz Akkaya

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In many civil engineering applications, especially in the construction of large concrete structures, the early age behavior of concrete has shown to be a crucial problem. The uneven rise in temperature within the concrete in these constructions is the fundamental issue for quality control. Therefore, developing accurate and fast temperature prediction models is essential. The thermal properties of concrete fluctuate over time as it hardens, but taking into account all of these fluctuations makes numerical models more complex. Experimental measurement of the thermal properties at the laboratory conditions also can not accurately predict the variance of these properties at site conditions. Therefore, specific heat capacity and the heat conductivity coefficient are two variables that are considered constant values in many of the models previously recommended. The proposed equations demonstrate that these two quantities are linearly decreasing as cement hydrates, and their value are related to the degree of hydration. The effects of changing the thermal conductivity and specific heat capacity values on the maximum temperature and the time it takes for concrete to reach that temperature are examined in this study using numerical sensibility analysis, and the results are compared to models that take a fixed value for these two thermal properties. The current study is conducted in 7 different mix designs of concrete with varying amounts of supplementary cementitious materials (fly ash and ground granulated blast furnace slag). It is concluded that the maximum temperature will not change as a result of the constant conductivity coefficient, but variable specific heat capacity must be taken into account, also about duration when a concrete's central node reaches its max value again variable specific heat capacity can have a considerable effect on the final result. Also, the usage of GGBFS has more influence compared to fly ash.

Keywords: early-age concrete, mass concrete, specific heat capacity, thermal conductivity coefficient

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Authors: Suhani Srivastava

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This research delves into the classification of orbital debris through machine learning (ML): it will categorize the intensity of the threat orbital debris poses through multiple ML models to gain an insight into effectively estimating the danger specific orbital debris can pose to future space missions. As the space industry expands, orbital debris becomes a growing concern in Low Earth Orbit (LEO) because it can potentially obfuscate space missions due to the increased orbital debris pollution. Moreover, detecting orbital debris and identifying its characteristics has become a major concern in Space Situational Awareness (SSA), and prior methods of solely utilizing physics can become inconvenient in the face of the growing issue. Thus, this research focuses on approaching orbital debris concerns through machine learning, an efficient and more convenient alternative, in detecting the potential threat certain orbital debris pose. Our findings found that the Logistic regression machine worked the best with a 98% accuracy and this research has provided insight into the accuracies of specific machine learning models when classifying orbital debris. Our work would help provide space shuttle manufacturers with guidelines about mitigating risks, and it would help in providing Aerospace Engineers facilities to identify the kinds of protection that should be incorporated into objects traveling in the LEO through the predictions our models provide.

Keywords: aerospace, orbital debris, machine learning, space, space situational awareness, nasa

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Authors: Saed Khawaldeh, Mohamed Elsharnouby, Alaa Eddin Alchalabi, Usama Pervaiz, Tajwar Aleef, Vu Hoang Minh

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Taxonomic classification has a wide-range of applications such as finding out more about the evolutionary history of organisms that can be done by making a comparison between species living now and species that lived in the past. This comparison can be made using different kinds of extracted species’ data which include DNA sequences. Compared to the estimated number of the organisms that nature harbours, humanity does not have a thorough comprehension of which specific species they all belong to, in spite of the significant development of science and scientific knowledge over many years. One of the methods that can be applied to extract information out of the study of organisms in this regard is to use the DNA sequence of a living organism as a marker, thus making it available to classify it into a taxonomy. The classification of living organisms can be done in many machine learning techniques including Neural Networks (NNs). In this study, DNA sequences classification is performed using Convolutional Neural Networks (CNNs) which is a special type of NNs.

Keywords: deep networks, convolutional neural networks, taxonomic classification, DNA sequences classification

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Authors: Sheena Christabel Pravin, M. Palanivelan

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Speech disfluencies are common in spontaneous speech. The primary purpose of this study was to distinguish linguistic disfluencies from stuttering disfluencies in bilingual Tamil–English (TE) speaking children. The secondary purpose was to determine whether their disfluencies are mediated by native language dominance and/or on an early onset of developmental stuttering at childhood. A detailed study was carried out to identify the prosodic and acoustic features that uniquely represent the disfluent regions of speech. This paper focuses on statistical modeling of repetitions, prolongations, pauses and interjections in the speech corpus encompassing bilingual spontaneous utterances from school going children – English and Tamil. Two classifiers including Hidden Markov Models (HMM) and the Multilayer Perceptron (MLP), which is a class of feed-forward artificial neural network, were compared in the classification of disfluencies. The results of the classifiers document the patterns of disfluency in spontaneous speech samples of school-aged children to distinguish between Children Who Stutter (CWS) and Children with Language Impairment CLI). The ability of the models in classifying the disfluencies was measured in terms of F-measure, Recall, and Precision.

Keywords: bi-lingual, children who stutter, children with language impairment, hidden markov models, multi-layer perceptron, linguistic disfluencies, stuttering disfluencies

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Authors: U. Waqas, M. F. Ahmed, A. Mehmood, M. A. Rashid

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In geophysical exploration surveys, the quality of acquired data holds significant importance before executing the data processing and interpretation phases. In this study, 2D seismic reflection survey data of Fort Abbas area, Cholistan Desert, Pakistan was taken as test case in order to assess its quality on statistical bases by using normalized root mean square error (NRMSE), Cronbach’s alpha test (α) and null hypothesis tests (t-test and F-test). The analysis challenged the quality of the acquired data and highlighted the significant errors in the acquired database. It is proven that the study area is plain, tectonically least affected and rich in oil and gas reserves. However, subsurface 3D modeling and contouring by using acquired database revealed high degrees of structural complexities and intense folding. The NRMSE had highest percentage of residuals between the estimated and predicted cases. The outcomes of hypothesis testing also proved the biasness and erraticness of the acquired database. Low estimated value of alpha (α) in Cronbach’s alpha test confirmed poor reliability of acquired database. A very low quality of acquired database needs excessive static correction or in some cases, reacquisition of data is also suggested which is most of the time not feasible on economic grounds. The outcomes of this study could be used to assess the quality of large databases and to further utilize as a guideline to establish database quality assessment models to make much more informed decisions in hydrocarbon exploration field.

Keywords: Data quality, Null hypothesis, Seismic lines, Seismic reflection survey

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Authors: M. Zareh, H. Mohammadi, B. Naser

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Atherosclerotic plaque rupture is the main trigger of heart attack and brain stroke which are the leading cause of death in developed countries. Better understanding of rupture-prone plaque can help clinicians detect vulnerable plaques- rupture prone or instable plaques- and apply immediate medical treatment to prevent these life-threatening cardiovascular events. Therefore, there are plenty of studies addressing disclosure of vulnerable plaques properties. Necrotic core and fibrous tissue are two major tissues constituting atherosclerotic plaque; using histopathological and numerical approaches, many studies have demonstrated that plaque rupture is strongly associated with a large necrotic core and a thin fibrous cap, two morphological characteristic which can be acquired by two-dimensional imaging of atherosclerotic plaque present in coronary and carotid arteries. Plaque rupture is widely considered as a mechanical failure inside plaque tissue; this failure occurs when the stress within plaque excesses the strength of tissue material; hence, finite element method, a strong numerical approach, has been extensively applied to estimate stress distribution within plaques with different compositions which is then used for assessment of various vulnerability characteristics including plaque morphology, material properties and blood pressure. This study aims to evaluate significance of three-dimensional morphology on vulnerability degree of atherosclerotic plaque. To reach this end, different two-dimensional geometrical models of atherosclerotic plaques are considered based on available data and named Main 2D Models (M2M). Then, for each of these M2Ms, two three-dimensional idealistic models are created. These two 3D models represent two possible three-dimensional morphologies which might exist for a plaque with similar 2D morphology to one of M2Ms. Finite element method is employed to estimate stress, von-Mises stress, within each 3D models. Results indicate that for each M2Ms stress can significantly varies due to possible 3D morphological changes in that plaque. Also, our results show that an atherosclerotic plaque with thick cap may experience rupture if it has a critical 3D morphology. This study highlights the effect of 3D geometry of plaque on its instability degree and suggests that 3D morphology of plaque might be necessary to more effectively and accurately assess atherosclerotic plaque vulnerability.

Keywords: atherosclerotic plaque, plaque rupture, finite element method, 3D model

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Authors: Nandini Mohan, Thiruvengadam R. B.

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Mahatma Gandhi advocated that the true indicator of modern India lay in the development of its villages. This has been proven with the recent outbreak of the Coronavirus pandemic and the surfacing predicament of our urban centers. Developed on the Industrialization model, the current state of the metropolis is of rampant overcrowding, high rates of unemployment, inadequate infrastructure, and resources to cater to the growing population. A majority of each city’s strength composes of the migrant population, demonstrated through the migrant crisis, a direct repercussion of COVID-19. This paper explores the ideology of how rural development can act as a tactic to counter the high rates of rural-urban migration. It establishes the need for a rural push, as India is predominantly an agrarian economy, with a vast disparity between the urban and rural centers due to its urban bias. It seeks to define development in holistic terms. It studies the models of ‘cluster’ as conceptualized by V.K.R.V. Rao, and detailed by Architect Charles Correa in his book, The New Landscape. The paper reexamines the theory of cluster development through existing models proposed by the government of India. Namely, PURA (Provision of Urban Amenities in Rural Areas), DRI (Deendayal Research Institute), and Rurban under Shyama Prasad Mukharjee Rurban Mission. It analyses the models, their strengths, weaknesses, and reasons for their failure and success to derive parameters for the ideation of an archetype model. A model of rural development that talks of the simultaneous development of existing adjacent villages, by the introduction of set unique functions, that may turn into self-sustaining clusters or agglomerations in the future, which could serve as the next step for Indian village development based on the cluster ideology.

Keywords: counter migration, models of rural development, cluster development theory, India

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Authors: O. I. Nkwonta, B. Dzwairo, J. Adeyemo, A. Jaiyola, N. Sawyerr, F. Otieno

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The effective management of water resources is of great importance to ensure the supply of water resources to support changing water requirements over a selected planning horizon and in a sustainable and cost-effective way. Essentially, the purpose of the water resources planning process is to balance the available water resources in a system with the water requirements and losses to which the system is subjected. In such situations, Water resources yield and planning model can be used to solve those difficulties. It has an advantage over other models by managing model runs, developing a representative system network, modelling incremental sub-catchments, creating a variety of standard system features, special modelling features, and run result output options.

Keywords: complex, water resources, planning, cost effective and management

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Authors: Kevin Giraldo, Juan A. Gallego, Uriel Zapata, Fanny L. Casado

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Compliant mechanisms are designed to deform in a controlled manner in response to external forces, utilizing the flexibility of their components to store potential elastic energy during deformation, gradually releasing it upon returning to its original form. This article explores the design of a knee orthosis intended to assist users during stand-up motion. The orthosis makes use of a compliant mechanism to balance the user’s weight, thereby minimizing the strain on leg muscles during standup motion. The primary function of the compliant mechanism is to store and exchange potential energy, so when coupled with the gravitational potential of the user, the total potential energy variation is minimized. The design process for the semi-rigid knee orthosis involved material selection and the development of a numerical model for the compliant mechanism seen as a spring. Geometric properties are obtained through the numerical modeling of the spring once the desired stiffness and safety factor values have been attained. Subsequently, a 3D finite element analysis was conducted. The study demonstrates a strong correlation between the maximum stress in the mathematical model (250.22 MPa) and the simulation (239.8 MPa), with a 4.16% error. Both analyses safety factors: 1.02 for the mathematical approach and 1.1 for the simulation, with a consistent 7.84% margin of error. The spring’s stiffness, calculated at 90.82 Nm/rad analytically and 85.71 Nm/rad in the simulation, exhibits a 5.62% difference. These results suggest significant potential for the proposed device in assisting patients with knee orthopedic restrictions, contributing to ongoing efforts in advancing the understanding and treatment of knee osteoarthritis.

Keywords: biomechanics, complaint mechanisms, gonarthrosis, orthoses

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Authors: Rui Teixeira, Alan O’Connor, Maria Nogal

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The statistical theory of extreme events is progressively a topic of growing interest in all the fields of science and engineering. The changes currently experienced by the world, economic and environmental, emphasized the importance of dealing with extreme occurrences with improved accuracy. When it comes to the design of offshore structures, particularly offshore wind turbines, the importance of efficiently characterizing extreme events is of major relevance. Extreme events are commonly characterized by extreme values theory. As an alternative, the accurate modeling of the tails of statistical distributions and the characterization of the low occurrence events can be achieved with the application of the Peak-Over-Threshold (POT) methodology. The POT methodology allows for a more refined fit of the statistical distribution by truncating the data with a minimum value of a predefined threshold u. For mathematically approximating the tail of the empirical statistical distribution the Generalised Pareto is widely used. Although, in the case of the exceedances of significant wave data (H_s) the 2 parameters Weibull and the Exponential distribution, which is a specific case of the Generalised Pareto distribution, are frequently used as an alternative. The Generalized Pareto, despite the existence of practical cases where it is applied, is not completely recognized as the adequate solution to model exceedances over a certain threshold u. References that set the Generalised Pareto distribution as a secondary solution in the case of significant wave data can be identified in the literature. In this framework, the current study intends to tackle the discussion of the application of statistical models to characterize exceedances of wave data. Comparison of the application of the Generalised Pareto, the 2 parameters Weibull and the Exponential distribution are presented for different values of the threshold u. Real wave data obtained in four buoys along the Irish coast was used in the comparative analysis. Results show that the application of the statistical distributions to characterize significant wave data needs to be addressed carefully and in each particular case one of the statistical models mentioned fits better the data than the others. Depending on the value of the threshold u different results are obtained. Other variables of the fit, as the number of points and the estimation of the model parameters, are analyzed and the respective conclusions were drawn. Some guidelines on the application of the POT method are presented. Modeling the tail of the distributions shows to be, for the present case, a highly non-linear task and, due to its growing importance, should be addressed carefully for an efficient estimation of very low occurrence events.

Keywords: extreme events, offshore structures, peak-over-threshold, significant wave data

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

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This paper deals with different modeling aspects of masonry infill: no infill model, Layered shell infill model, and strut infill model. These models consider the complicated behavior of the in-filled plane frames under lateral load similar to an earthquake load. Three strut infill models are used: NBCC (2005) strut infill model, ASCE/SEI 41-06 strut infill model and proposed strut infill model based on modification to Canadian, NBCC (2005) strut infill model. Pushover and modal analyses of a masonry infill concrete frame with a single storey and an existing 5-storey RC building have been carried out by using different models for masonry infill. The corresponding hinge status, the value of base shear at target displacement as well as their dynamic characteristics have been determined and compared. A validation of the structural numerical models for the existing 5-storey RC building has been achieved by comparing the experimentally measured and the analytically estimated natural frequencies and their mode shapes. This study shows that ASCE/SEI 41-06 equation underestimates the values for the equivalent properties of the diagonal strut while Canadian, NBCC (2005) equation gives realistic values for the equivalent properties. The results indicate that both ASCE/SEI 41-06 and Canadian, NBCC (2005) equations for strut infill model give over estimated values for dynamic characteristic of the building. Proposed modification to Canadian, NBCC (2005) equation shows that the fundamental dynamic characteristic values of the building are nearly similar to the corresponding values using layered shell elements as well as measured field results.

Keywords: masonry infill, framed structures, RC buildings, non-structural elements

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Authors: Danilo Habermann, Aleksander Medella, Carla Cremon, Yusef Caceres

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This paper aims to analyze the ability of 2d point clouds features to classify different models of helicopters using radars. This method does not need to estimate the blade length, the number of blades of helicopters, and the period of their micro-Doppler signatures. It is also not necessary to generate spectrograms (or any other image based on time and frequency domain). This work transforms a radar return signal into a 2D point cloud and extracts features of it. Three classifiers are used to distinguish 9 different helicopter models in order to analyze the performance of the features used in this work. The high accuracy obtained with each of the classifiers demonstrates that the 2D point clouds features are very useful for classifying helicopters from radar signal.

Keywords: helicopter classification, point clouds features, radar, supervised classifiers

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Authors: Surajit Chakrabarty, Piyush Chauhan, Subhasis Panda, Sujoy Bhattacharya

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A large proportion of roads in India are not well maintained as per the laid down public safety guidelines leading to loss of direction control and fatal accidents. We propose a technique to detect speed breakers and potholes using mobile sensor data captured from multiple vehicles and provide a profile of the road. This would, in turn, help in monitoring roads and revolutionize digital maps. Incorporating randomness in the model formulation for detection of speed breakers and potholes is crucial due to substantial heterogeneity observed in data obtained using a mobile application from multiple vehicles driven by different drivers. This is accomplished with Hidden Markov Models, whose hidden state sequence is found for each time step given the observables sequence, and are then fed as input to LSTM network with peephole connections. A precision score of 0.96 and 0.63 is obtained for classifying bumps and potholes, respectively, a significant improvement from the machine learning based models. Further visualization of bumps/potholes is done by converting time series to images using Markov Transition Fields where a significant demarcation among bump/potholes is observed.

Keywords: deep learning, hidden Markov model, pothole, speed breaker

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Authors: T. Alghamdi, G. Alaghband

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In this paper we develop a model that couples Two Concurrent Convolution Neural Network with different filters (TC*CNN) for face recognition and compare its performance to an existing sequential CNN (base model). We also test and compare the quality and performance of the models on three datasets with various levels of complexity (easy, moderate, and difficult) and show that for the most complex datasets, edges will produce the most accurate and efficient results. We further show that in such cases while Support Vector Machine (SVM) models are fast, they do not produce accurate results.

Keywords: Convolution Neural Network, Edges, Face Recognition , Support Vector Machine.

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Authors: Biki Sarmah, Priyanko Raj Mudiar

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In the modern world of remotely interactive virtual reality-based learning and teaching, including professional skill-building training and acquisition practices, as well as data acquisition and robotic systems, the revolutionary application or implementation of field-programmable neurostimulator aids and first-hand interactive sensitisation techniques into 3D holographic audio-visual platforms have been a coveted dream of many scholars, professionals, scientists, and students. Integration of 'kinaesthetic vibrotactile haptic perception' along with an actuated step magnitude contact profiloscopy in augmented reality-based learning platforms and professional training can be implemented by using an extremely calculated and well-coordinated image telemetry including remote data mining and control technique. A real-time, computer-aided (PLC-SCADA) field calibration based algorithm must be designed for the purpose. But most importantly, in order to actually realise, as well as to 'interact' with some 3D holographic models displayed over a remote screen using remote laser image telemetry and control, all spatio-physical parameters like cardinal alignment, gyroscopic compensation, as well as surface profile and thermal compositions, must be implemented using zero-order type 1 actuators (or transducers) because they provide zero hystereses, zero backlashes, low deadtime as well as providing a linear, absolutely controllable, intrinsically observable and smooth performance with the least amount of error compensation while ensuring the best ergonomic comfort ever possible for the users.

Keywords: haptic feedback, kinaesthetic vibrotactile 3D design, medical simulation training, piezo diaphragm based actuator

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Authors: Angela U. Makolo

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Protein-coding and Non-coding regions determine the biology of a sequenced transcriptome. Research advances have shown that Non-coding regions are important in disease progression and clinical diagnosis. Existing bioinformatics tools have been targeted towards Protein-coding regions alone. Therefore, there are challenges associated with gaining biological insights from transcriptome sequence data. These tools are also limited to computationally intensive sequence alignment, which is inadequate and less accurate to identify both Protein-coding and Non-coding regions. Alignment-free techniques can overcome the limitation of identifying both regions. Therefore, this study was designed to develop an efficient sequence alignment-free model for identifying both Protein-coding and Non-coding regions in sequenced transcriptomes. Feature grouping and randomization procedures were applied to the input transcriptomes (37,503 data points). Successive iterations were carried out to compute the gradient vector that converged the developed Protein-coding and Non-coding Region Identifier (PNRI) model to the approximate coefficient vector. The logistic regression algorithm was used with a sigmoid activation function. A parameter vector was estimated for every sample in 37,503 data points in a bid to reduce the generalization error and cost. Maximum Likelihood Estimation (MLE) was used for parameter estimation by taking the log-likelihood of six features and combining them into a summation function. Dynamic thresholding was used to classify the Protein-coding and Non-coding regions, and the Receiver Operating Characteristic (ROC) curve was determined. The generalization performance of PNRI was determined in terms of F1 score, accuracy, sensitivity, and specificity. The average generalization performance of PNRI was determined using a benchmark of multi-species organisms. The generalization error for identifying Protein-coding and Non-coding regions decreased from 0.514 to 0.508 and to 0.378, respectively, after three iterations. The cost (difference between the predicted and the actual outcome) also decreased from 1.446 to 0.842 and to 0.718, respectively, for the first, second and third iterations. The iterations terminated at the 390th epoch, having an error of 0.036 and a cost of 0.316. The computed elements of the parameter vector that maximized the objective function were 0.043, 0.519, 0.715, 0.878, 1.157, and 2.575. The PNRI gave an ROC of 0.97, indicating an improved predictive ability. The PNRI identified both Protein-coding and Non-coding regions with an F1 score of 0.970, accuracy (0.969), sensitivity (0.966), and specificity of 0.973. Using 13 non-human multi-species model organisms, the average generalization performance of the traditional method was 74.4%, while that of the developed model was 85.2%, thereby making the developed model better in the identification of Protein-coding and Non-coding regions in transcriptomes. The developed Protein-coding and Non-coding region identifier model efficiently identified the Protein-coding and Non-coding transcriptomic regions. It could be used in genome annotation and in the analysis of transcriptomes.

Keywords: sequence alignment-free model, dynamic thresholding classification, input randomization, genome annotation

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Authors: Beverley Gallimore

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The Individuals with Disabilities Education Act of 2004 (IDEA) has helped to improve outcomes for students with special education needs. Through IDEA, students with Special Education Needs (SEN) have opportunities for more equitable education within the General Education classroom. However, students with disabilities lack access to instructions that can help them to maximize their fullest learning potential. Recently, educational stakeholders have emphasized Integrated Co-teaching as a tool to increase engagement and learning outcomes for students with disabilities in general education classrooms. As a result of this new approach, general and special education teachers are working collaboratively to teach students with disabilities. However, co-teaching models are not properly designed and structured to effectively benefit students with disabilities. Teachers must be oriented correctly in the co-teaching models if it is to be beneficial for students.

Keywords: CO-teaching, differentiation, equitable, collaborative

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Authors: Aprajeeta Jha, Punyadarshini P. Tripathy

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Drying technologies for various food processing operations shares an inevitable linkage with energy, cost and environmental sustainability. Hence, solar drying of food grains has become imperative choice to combat duo challenges of meeting high energy demand for drying and to address climate change scenario. But performance and reliability of solar dryers depend hugely on sunshine period, climatic conditions, therefore, offer a limited control over drying conditions and have lower efficiencies. Solar drying technology, supported by Photovoltaic (PV) power plant and hybrid type solar air collector can potentially overpower the disadvantages of solar dryers. For development of such robust hybrid dryers; to ensure quality and shelf-life of paddy grains the optimization of process parameter becomes extremely critical. Investigation of the moisture distribution profile within the grains becomes necessary in order to avoid over drying or under drying of food grains in hybrid solar dryer. Computational simulations based on finite element modeling can serve as potential tool in providing a better insight of moisture migration during drying process. Hence, present work aims at optimizing the process parameters and to develop a 3-dimensional (3D) finite element model (FEM) for predicting moisture profile in paddy during solar drying. COMSOL Multiphysics was employed to develop a 3D finite element model for predicting moisture profile. Furthermore, optimization of process parameters (power level, air velocity and moisture content) was done using response surface methodology in design expert software. 3D finite element model (FEM) for predicting moisture migration in single kernel for every time step has been developed and validated with experimental data. The mean absolute error (MAE), mean relative error (MRE) and standard error (SE) were found to be 0.003, 0.0531 and 0.0007, respectively, indicating close agreement of model with experimental results. Furthermore, optimized process parameters for drying paddy were found to be 700 W, 2.75 m/s at 13% (wb) with optimum temperature, milling yield and drying time of 42˚C, 62%, 86 min respectively, having desirability of 0.905. Above optimized conditions can be successfully used to dry paddy in PV integrated solar dryer in order to attain maximum uniformity, quality and yield of product. PV-integrated hybrid solar dryers can be employed as potential and cutting edge drying technology alternative for sustainable energy and food security.

Keywords: finite element modeling, moisture migration, paddy grain, process optimization, PV integrated hybrid solar dryer

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Authors: Ryotaro Ishikawa

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Obsessive-Compulsive Disorder (OCD) is a common, chronic and long-lasting disorder in which a person has uncontrollable, reoccurring thoughts (obsessions) and behaviors (compulsions) that he or she feels the urge to repeat over and over. Inflated responsibility attitude and interpretation are central beliefs in a cognitive model of OCD. This study aimed to develop a Japanese version of the Responsibility Attitude Scale (RAS-J) and Responsibility Interpretation Questionnaire (RIQ-J). 98 participants (OCD group = 37; anxiety control group = 24; healthy control group = 37) completed the RAS-J, RIQ-J and other measures to assess the validity of the RAS-J and RIQ-J. As a result of analysis, both scales had adequate concurrent validity, demonstrated by significant correlations with other measures of OCD, anxiety, and depression. Group comparison data using ANOVA with Bonferroni method indicated that RAS-J and RIQ-J scores for the OCD group not only differed from the nonclinical group, but also from the clinically anxious comparison group. In conclusion, this study indicated that the developed RAS-J and RIQ-J effectively measure responsibility attitude and responsibility interpretation in the Japanese population.

Keywords: obsessive-compulsive disorder, responsibility, cognitive theory, anxiety disorder

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Authors: Getaneh Berie Tarekegn

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A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 39 cm, and more than 90% of the errors are less than 82 cm. That is, numerical results proved that, in comparison to traditional methods, the proposed SRCLoc method can significantly improve positioning performance and reduce radio map construction costs.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Habib Khodaverdiloo, Fatemeh Afrasiabi, Farrokh Asadzadeh, Martinus Th. Van Genuchten

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The soil particle-size distribution (PSD) is known to affect a broad range of soil physical, mechanical and hydraulic properties. Complete descriptions of a PSD curve should provide more information about these properties as opposed to having only information about soil textural class or the soil sand, silt and clay (SSC) fractions. We compared the accuracy of 19 different models of the cumulative PSD in terms of fitting observed data from a large number of Iranian soils. Parameters of the six most promising models were correlated with measured values of the field saturated hydraulic conductivity (Kfs), the mean weight diameter of soil aggregates (MWD), bulk density (ρb), and porosity (∅). These same soil properties were correlated also with conventional PSD parameters (SSC fractions), selected geometric PSD parameters (notably the mean diameter dg and its standard deviation σg), and several other PSD parameters (D50 and D60). The objective was to find the best predictions of several soil physical quality indices and the soil hydraulic properties. Neither SSC nor dg, σg, D50 and D60 were found to have a significant correlation with both Kfs or logKfs, However, the parameters of several cumulative PSD models showed statistically significant correlation with Kfs and/or logKfs (|r| = 0.42 to 0.65; p ≤ 0.05). The correlation between MWD and the model parameters was generally also higher than either with SSC fraction and dg, or with D50 and D60. Porosity (∅) and the bulk density (ρb) also showed significant correlation with several PSD model parameters, with ρb additionally correlating significantly with various geometric (dg), mechanical (D50 and D60), and agronomic (clay and sand) representations of the PSD. The fitted parameters of selected PSD models furthermore showed statistically significant correlations with Kfs,, MWD and soil porosity, which may be viewed as soil quality indices. Results of this study are promising for developing more accurate pedotransfer functions.

Keywords: particle size distribution, soil texture, hydraulic conductivity, pedotransfer functions

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Authors: Joel Podgorski, Manouchehr Amini, Annette Johnson, Michael Berg

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One-third of the world’s population relies on groundwater for its drinking water. Natural geogenic arsenic and fluoride contaminate ~10% of wells. Prolonged exposure to high levels of arsenic can result in various internal cancers, while high levels of fluoride are responsible for the development of dental and crippling skeletal fluorosis. In poor urban and rural settings, the provision of drinking water free of geogenic contamination can be a major challenge. In order to efficiently apply limited resources in the testing of wells, water resource managers need to know where geogenically contaminated groundwater is likely to occur. The Groundwater Assessment Platform (GAP) fulfills this need by providing state-of-the-art global arsenic and fluoride contamination hazard maps as well as enabling users to create their own groundwater quality models. The global risk models were produced by logistic regression of arsenic and fluoride measurements using predictor variables of various soil, geological and climate parameters. The maps display the probability of encountering concentrations of arsenic or fluoride exceeding the World Health Organization’s (WHO) stipulated concentration limits of 10 µg/L or 1.5 mg/L, respectively. In addition to a reconsideration of the relevant geochemical settings, these second-generation maps represent a great improvement over the previous risk maps due to a significant increase in data quantity and resolution. For example, there is a 10-fold increase in the number of measured data points, and the resolution of predictor variables is generally 60 times greater. These same predictor variable datasets are available on the GAP platform for visualization as well as for use with a modeling tool. The latter requires that users upload their own concentration measurements and select the predictor variables that they wish to incorporate in their models. In addition, users can upload additional predictor variable datasets either as features or coverages. Such models can represent an improvement over the global models already supplied, since (a) users may be able to use their own, more detailed datasets of measured concentrations and (b) the various processes leading to arsenic and fluoride groundwater contamination can be isolated more effectively on a smaller scale, thereby resulting in a more accurate model. All maps, including user-created risk models, can be downloaded as PDFs. There is also the option to share data in a secure environment as well as the possibility to collaborate in a secure environment through the creation of communities. In summary, GAP provides users with the means to reliably and efficiently produce models specific to their region of interest by making available the latest datasets of predictor variables along with the necessary modeling infrastructure.

Keywords: arsenic, fluoride, groundwater contamination, logistic regression

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Authors: Zakaria Aketouane, Mustapha Malha, Abdellah Bah, Omar Ansari, Mohamed Asbik

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The present paper deals with the thermal performance of a hollow-brick filled with Phase Change Material (PCM). The main objective is to study the effect of percentage and melting temperature of the PCM on the thermal inertia and internal surface temperature of the hollow-brick. A numerical model based on the heat transfer equation and the apparent heat capacity method has been validated using experimental study from the literature. The results show that increasing the percentage of the PCM has a significant effect on time lag and decrement factor that define the thermal inertia; the internal temperature is reduced by 1.36°C to 5.39°C for a percentage from 11% to 71% in comparison to a brick without PCM. In addition, an appropriate melting temperature of 37°C has been deduced for the horizontal wall orientation in Rabat in comparison to 27°C and 47°C.

Keywords: appropriate melting temperature, decrement factor, phase change material, thermal inertia, time lag

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Authors: Nina Miklavčič, Urška Kugovnik, Natalia Galkina, Primož Ribarič, Rudi Vončina

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Today, the need for weather predictions is deeply rooted in the everyday life of people as well as it is in industry. The forecasts influence final decision-making processes in multiple areas, from agriculture and prevention of natural disasters to air traffic regulations and solutions on a national level for health, security, and economic problems. Namely, in Slovenia, alongside other existing forms of application, weather forecasts are adopted for the prognosis of electrical current transmission through powerlines. Meteorological parameters are one of the key factors which need to be considered in estimations of the reliable supply of electrical energy to consumers. And like for any other measured value, the knowledge about measurement uncertainty is also critical for the secure and reliable supply of energy. The estimation of measurement uncertainty grants us a more accurate interpretation of data, a better quality of the end results, and even a possibility of improvement of weather forecast models. In the article, we focused on the estimation of measurement uncertainty of the diagnostic microscale meteorological model CALMET. For the purposes of our research, we used a network of meteorological stations spread in the area of our interest, which enables a side-by-side comparison of measured meteorological values with the values calculated with the help of CALMET and the measurement uncertainty estimation as a final result.

Keywords: uncertancy, meteorological model, meteorological measurment, CALMET

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Authors: Ali Safdari-Vaighani

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Pricing financial contracts on several underlying assets received more and more interest as a demand for complex derivatives. The option pricing under asset price involving jump diffusion processes leads to the partial integral differential equation (PIDEs), which is an extension of the Black-Scholes PDE with a new integral term. The aim of this paper is to show how basket option prices in the jump diffusion models, mainly on the Merton model, can be computed using RBF based approximation methods. For a test problem, the RBF-PU method is applied for numerical solution of partial integral differential equation arising from the two-asset European vanilla put options. The numerical result shows the accuracy and efficiency of the presented method.

Keywords: basket option, jump diffusion, ‎radial basis function, RBF-PUM

Procedia PDF Downloads 354

Authors: Asim Majeed, Rehan Bhana, Mak Sharma, Rebecca Goode, Nizam Bolia, Mike Lloyd-Williams

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This study aims to investigate and explore the underlying causes of security concerns of customers emerged when WHSmith transformed its physical system to virtualized business model through NetSuite. NetSuite is essentially fully integrated software which helps transforming the physical system to virtualized business model. Modern organisations are moving away from traditional business models to cloud based models and consequently it is expected to have a better, secure and innovative environment for customers. The vital issue of the modern age race is the security when transforming virtualized through cloud based models and designers of interactive systems often misunderstand privacy and even often ignore it, thus causing concerns for users. The content analysis approach is being used to collect the qualitative data from 120 online bloggers including TRUSTPILOT. The results and finding provide useful new insights into the nature and form of security concerns of online users after they have used the WHSmith services offered online through their website. Findings have theoretical as well as practical implications for the successful adoption of cloud computing Business-to-Business model and similar systems.

Keywords: innovation, virtualization, cloud computing, organizational flexibility

Procedia PDF Downloads 384

Authors: Elisa Pautasso, Matteo Castagno, Michele Osella

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In the last decade, the emergence of new social needs as an effect of the economic crisis has stimulated the flourishing of business endeavours characterised by explicit social goals. Social start-ups, social enterprises or Corporate Social Responsibility operations carried out by traditional companies are quintessential examples in this regard. This paper analyses these kinds of initiatives in order to discover the main characteristics of social business models and to provide insights to social entrepreneurs for developing or improving their strategies. The research is conducted through the integration of literature review and case study analysis and, thanks to the recognition of the importance of both profits and social impacts as the key success factors for a social business model, proposes a framework for identifying indicators suitable for measuring the social impacts generated.

Keywords: business model, case study, impacts, social business

Procedia PDF Downloads 349

Authors: Simon Espigolé, Igor Shalyminov, Helen Hastie

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Deep machine learning techniques including deep neural networks (DNN) have been used to model language and dialogue for conversational agents to perform tasks, such as giving technical support and also for general chit-chat. They have been shown to be capable of generating long, diverse and coherent sentences in end-to-end dialogue systems and natural language generation. However, these systems tend to imitate the training data and will only generate the concepts and language within the scope of what they have been trained on. This work explores how deep neural networks can be used in a task that would normally require human creativity, whereby the human would read the movie description and/or watch the movie and come up with a compelling, interesting movie title. This task differs from simple summarization in that the movie title may not necessarily be derivable from the content or semantics of the movie description. Here, we train a type of DNN called a sequence-to-sequence model (seq2seq) that takes as input a short textual movie description and some information on e.g. genre of the movie. It then learns to output a movie title. The idea is that the DNN will learn certain techniques and approaches that the human movie titler may deploy that may not be immediately obvious to the human-eye. To give an example of a generated movie title, for the movie synopsis: ‘A hitman concludes his legacy with one more job, only to discover he may be the one getting hit.’; the original, true title is ‘The Driver’ and the one generated by the model is ‘The Masquerade’. A human evaluation was conducted where the DNN output was compared to the true human-generated title, as well as a number of baselines, on three 5-point Likert scales: ‘creativity’, ‘naturalness’ and ‘suitability’. Subjects were also asked which of the two systems they preferred. The scores of the DNN model were comparable to the scores of the human-generated movie title, with means m=3.11, m=3.12, respectively. There is room for improvement in these models as they were rated significantly less ‘natural’ and ‘suitable’ when compared to the human title. In addition, the human-generated title was preferred overall 58% of the time when pitted against the DNN model. These results, however, are encouraging given the comparison with a highly-considered, well-crafted human-generated movie title. Movie titles go through a rigorous process of assessment by experts and focus groups, who have watched the movie. This process is in place due to the large amount of money at stake and the importance of creating an effective title that captures the audiences’ attention. Our work shows progress towards automating this process, which in turn may lead to a better understanding of creativity itself.

Keywords: creativity, deep machine learning, natural language generation, movies

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Authors: P. Meethum, C. Suvanjumrat

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Many aluminum motorcycle parts produced by a high pressure die casting. Some parts such as fuel caps were a thin and complex shape. This part risked for porosities and blisters on surface if it only depended on an experience of mold makers for mold design. This research attempted to use CAST-DESIGNER software simulated the high pressure die casting process with the same process parameters of a motorcycle fuel cap production. The simulated results were compared with fuel cap products and expressed the same porosity and blister locations on cap surface. An average of absolute difference of simulated results was obtained 0.094 mm when compared the simulated porosity and blister defect sizes on the fuel cap surfaces with the experimental micro photography. This comparison confirmed an accuracy of software and will use the setting parameters to improve fuel cap molds in the further work.

Keywords: aluminum, die casting, fuel cap, motorcycle

Procedia PDF Downloads 366