Search results for: Grey prediction model

Authors: Faruk Ortes, Derya Karabulut, Yunus Ziya Arslan

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Myoelectric features gathering from musculature environment are considered on a preferential basis to perceive muscle activation and control human arm prostheses according to recent experimental researches. EMG (electromyography) signal based human arm prostheses have shown a promising performance in terms of providing basic functional requirements of motions for the amputated people in recent years. However, these assistive devices for neurorehabilitation still have important limitations in enabling amputated people to perform rather sophisticated or functional movements. Surface electromyogram (EMG) is used as the control signal to command such devices. This kind of control consists of activating a motion in prosthetic arm using muscle activation for the same particular motion. Extraction of clear and certain neural information from EMG signals plays a major role especially in fine control of hand prosthesis movements. Many signal processing methods have been utilized for feature extraction from EMG signals. The specific objective of this study was to compare widely used time domain features of EMG signal including integrated EMG(IEMG), root mean square (RMS) and waveform length(WL) for prediction of externally applied forces to human hands. Obtained features were classified using artificial neural networks (ANN) to predict the forces. EMG signals supplied to process were recorded during only type of muscle contraction which is isometric and isotonic one. Experiments were performed by three healthy subjects who are right-handed and in a range of 25-35 year-old aging. EMG signals were collected from muscles of the proximal part of the upper body consisting of: biceps brachii, triceps brachii, pectorialis major and trapezius. The force prediction results obtained from the ANN were statistically analyzed and merits and pitfalls of the extracted features were discussed with detail. The obtained results are anticipated to contribute classification process of EMG signal and motion control of powered human arm prosthetics control.

Keywords: assistive devices for neurorehabilitation, electromyography, feature extraction, force estimation, human arm prosthesis

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Authors: Hong Lu, Yongquan Zhang, Wei Fan, Xiangang Su

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Bent mandrel has been widely used as precise mould in automobile industry, shipping industry and aviation industry. To improve the versatility and efficiency of turning method of bent mandrel with fixed rotational center, an instantaneous machining model based on cutting parameters and machine dimension is prospered in this paper. The spiral-like tool path generation approach in non-axisymmetric turning process of bent mandrel is developed as well to deal with the error of part-to-part repeatability in existed turning model. The actual cutter-location points are calculated by cutter-contact points, which are obtained from the approach of spiral sweep process using equal-arc-length segment principle in polar coordinate system. The tool offset is set to avoid the interference between tool and work piece is also considered in the machining model. Depend on the spindle rotational angle, synchronization control of X-axis, Z-axis and C-axis is adopted to generate the tool-path of the turning process. The simulation method is developed to generate NC program according to the presented model, which includes calculation of cutter-location points and generation of tool-path of cutting process. With the approach of a bent mandrel taken as an example, the maximum offset of center axis is 4mm in the 3D space. Experiment results verify that the machining model and turning method are appropriate for the characteristics of bent mandrel.

Keywords: bent mandrel, instantaneous machining model, simulation method, tool-path generation

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Authors: Annalakshmi G., Sakthivel Murugan S.

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This paper presents a local directional encoded derivative binary pattern (LDEDBP) feature extraction method that can be applied for the classification of submarine coral reef images. The classification of coral reef images using texture features is difficult due to the dissimilarities in class samples. In coral reef image classification, texture features are extracted using the proposed method called local directional encoded derivative binary pattern (LDEDBP). The proposed approach extracts the complete structural arrangement of the local region using local binary batten (LBP) and also extracts the edge information using local directional pattern (LDP) from the edge response available in a particular region, thereby achieving extra discriminative feature value. Typically the LDP extracts the edge details in all eight directions. The process of integrating edge responses along with the local binary pattern achieves a more robust texture descriptor than the other descriptors used in texture feature extraction methods. Finally, the proposed technique is applied to an extreme learning machine (ELM) method with a meta-heuristic algorithm known as weighted distance grey wolf optimizer (GWO) to optimize the input weight and biases of single-hidden-layer feed-forward neural networks (SLFN). In the empirical results, ELM-WDGWO demonstrated their better performance in terms of accuracy on all coral datasets, namely RSMAS, EILAT, EILAT2, and MLC, compared with other state-of-the-art algorithms. The proposed method achieves the highest overall classification accuracy of 94% compared to the other state of art methods.

Keywords: feature extraction, local directional pattern, ELM classifier, GWO optimization

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Authors: Jonathan Gong

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deep learning, image processing, machine learning

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Authors: Hasan A Al-Asadi, Ali Samir, Afrah Turki Awad, Ali Basem

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Integrating the phase change material (PCM) with photovoltaic (PV) panels is one of the effective techniques to minimize the PV panel temperature and increase their electric output. In order to investigate the impact of the PCM on the electric output of the PV panels for a whole year, a lumped-distributed parameter model for the PV-PCM module has been developed. This development has considered the impact of the PCM density variation between the solid phase and liquid phase. This contribution will increase the assessment accuracy of the electric output of the PV-PCM module. The second contribution is to assess the impact of the expanded composite graphite-PCM on the PV electric output in Milan for a whole year. The novel one-dimensional model has been solved using MATLAB software. The results of this model have been validated against literature experiment work. The weather and the solar radiation data have been collected. The impact of expanded graphite-PCM on the electric output of the PV panel for a whole year has been investigated. The results indicate this impact has an enhancement rate of 2.39% for the electric output of the PV panel in Milan for a whole year.

Keywords: PV panel efficiency, PCM, numerical model, solar energy

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Authors: Sunday Oladele, Joseph Oluwagbeja Simeon

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Subsurface exploration, integrating methods of geotechnics and geophysics, of a planned construction site in the coastal city of Lagos, Nigeria has been carried out with the aim of characterizing the soil properties and their implication for the proposed infrastructural development. Six Standard Penetration Tests (SPT), fourteen Dutch Cone Penetrometer Tests (DCPT) and 2D Electrical Resistivity Imaging employing Dipole-dipole and Pole-dipole arrays were implemented on the site. The topsoil (0 - 4m) consists of highly compacted sandy lateritic clay(10 to 5595Ωm) to 1.25m in some parts and dense sand in other parts to 5.50m depth. This topsoil was characterized as a material of very high shear strength (≤ 150kg/m2) and allowable bearing pressure value of 54kN/m2 to 85kN/m2 and a safety factor of 2.5. Soft amorphous peat/peaty clay (0.1 to 11.4Ωm), 3-6m thick, underlays the lateritic clay to about 18m depth. Grey, medium dense to very dense sand (0.37 to 2387Ωm) with occasional gravels underlies the peaty clay down to 30m depth. Within this layer, the freshwater bearing zones are characterized by high resistivity response (83 to 2387Ωm), while the clayey sand/saline water intruded sand produced subdued resistivity output (0.37 to 40Ωm). The overall ground-bearing pressure for the proposed structure would be 225kN/m2. Bored/cast-in-place pile at 18.00m depth with any of these diameters and respective safe working loads 600mm/1,140KN, 800mm/2,010KN and 1000mm/3,150KN is recommended for the proposed multi-story structure.

Keywords: subsurface exploration, Geotechnical properties, resistivity imaging, pile

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Authors: Mariyah Poonawala, Selvan Ravindran, Anuradha Vaidya

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Despite much progress in understanding the regulatory factors and cytokines that support the maturation of the various cell lineages of the hematopoietic system, factors that govern the self-renewal and proliferation of hematopoietic stem cells (HSCs) is still a grey area of research. Hematopoietic stem cell transplantation (HSCT) has evolved over the years and gained tremendous importance in the treatment of both malignant and non-malignant diseases. However, factors such as graft rejection and multiple organ failure have challenged HSCT from time to time, underscoring the urgent need for development of milder processes for successful hematopoietic transplantation. An emerging concept in the field of stem cell biology states that the interactions between the bone-marrow micro-environment and the hematopoietic stem and progenitor cells is essential for regulation, maintenance, commitment and proliferation of stem cells. Understanding the role of mesenchymal stromal cells in modulating the functionality of HSCs is, therefore, an important area of research. Trans-resveratrol has been extensively studied for its various properties to combat and prevent cancer, diabetes and cardiovascular diseases etc. The aim of the present study was to understand the effect of trans-resveratrol on HSCs using single and co-culture systems. We have used KG1a cells since it is a well accepted hematopoietic stem cell model system. Our preliminary experiments showed that low concentrations of trans-resveratrol stimulated the HSCs to undergo proliferation whereas high concentrations of trans-resveratrol did not stimulate the cells to proliferate. We used a murine fibroblast cell line, M210B4, as a stromal feeder layer. On culturing the KG1a cells with M210B4 cells, we observed that the stimulatory as well as inhibitory effects of trans-resveratrol at low and high concentrations respectively, were enhanced. Our further experiments showed that low concentration of trans-resveratrol reduced the generation of reactive oxygen species (ROS) and nitric oxide (NO) whereas high concentrations increased the oxidative stress in KG1a cells. We speculated that perhaps the oxidative stress was imposing inhibitory effects at high concentration and the same was confirmed by performing an apoptotic assay. Furthermore, cell cycle analysis and growth kinetic experiments provided evidence that low concentration of trans-resveratrol reduced the doubling time of the cells. Our hypothesis is that perhaps at low concentration of trans-resveratrol the cells get pushed into the G0/G1 phase and re-enter the cell cycle resulting in their proliferation, whereas at high concentration the cells are perhaps arrested at G2/M phase or at cytokinesis and therefore undergo apoptosis. Liquid Chromatography-Quantitative-Time of Flight–Mass Spectroscopy (LC-Q-TOF MS) analyses indicated the presence of trans-resveratrol and its metabolite(s) in the supernatant of the co-cultured cells incubated with high concentration of trans-resveratrol. We conjecture that perhaps the metabolites of trans-resveratrol are responsible for the apoptosis observed at the high concentration. Our findings may shed light on the unsolved problems in the in vitro expansion of stem cells and may have implications in the ex vivo manipulation of HSCs for therapeutic purposes.

Keywords: co-culture system, hematopoietic micro-environment, KG1a cell line, M210B4 cell line, trans-resveratrol

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Authors: Xiaoyun Li, Suoang Longzhou

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This paper derives the analytical solution of stellar distance according to its redshift based on the photon-dominated universe expansion model. Firstly, it calculates stellar separation speed and the farthest distance of observable stars via simulation. Then the analytical solution of stellar distance according to its redshift is derived. It shows that when the redshift is large, the stellar distance (and its separation speed) is not proportional to its redshift due to the relativity effect. It also reveals the relationship between stellar age and its redshift. The correctness of the analytical solution is verified by the latest astronomic observations of Ia supernovas in 2020.

Keywords: redshift, cosmic expansion model, analytical solution, stellar distance

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Authors: Laleh Taheri, Noraini C. Pa, Rusli Abdullah, Salfarina Abdullah

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Knowledge plays an important role to the success of any organization. Software development organizations are highly knowledge-intensive organizations especially in their Requirement Elicitation Process (REP). There are several problems regarding communicating and using the knowledge in REP such as misunderstanding, being out of scope, conflicting information and changes of requirements. All of these problems occurred in transmitting the requirements knowledge during REP. Several researches have been done in REP in order to solve the problem towards requirements. Knowledge Audit (KA) approaches were proposed in order to solve managing knowledge in human resources, financial, and manufacturing. There is lack of study applying the KA in requirements elicitation process. Therefore, this paper proposes a KA model for REP in supporting to acquire good requirements.

Keywords: knowledge audit, requirement elicitation process, KA model, knowledge in requirement elicitation

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Authors: Stefanie Jeanette Huber

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This paper explores the relevance and implications of preferences for housing services on house price fluctuations through the lens of an overlapping generation’s model. The model implies that an economy whose agents have lower preferences for housing services is characterized with lower expenditure shares on housing services and will tend to experience more frequent and more volatile housing bubbles. These model predictions are tested empirically in the companion paper Housing Booms and Busts - Convergences and Divergences across OECD countries. Between 1970 - 2013, countries who spend less on housing services as a share of total income experienced significantly more housing cycles and the associated housing boom-bust cycles were more violent. Finally, the model is used to study the impact of rental subsidies and help-to-buy schemes on rational housing bubbles. Rental subsidies are found to contribute to the control of housing bubbles, whereas help-to- buy scheme makes the economy more bubble-prone.

Keywords: housing bubbles, housing booms and busts, preference for housing services, expenditure shares for housing services, rental and purchase subsidies

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Authors: Mohammed A. Zidan, Alaa Sagheer, Nasser Metwally

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Real-time learning is an important goal that most of artificial intelligence researches try to achieve it. There are a lot of problems and applications which require low cost learning such as learn a robot to be able to classify and recognize patterns in real time and real-time recall. In this contribution, we suggest a model of quantum competitive learning based on a series of quantum gates and additional operator. The proposed model enables to recognize any incomplete patterns, where we can increase the probability of recognizing the pattern at the expense of the undesired ones. Moreover, these undesired ones could be utilized as new patterns for the system. The proposed model is much better compared with classical approaches and more powerful than the current quantum competitive learning approaches.

Keywords: competitive learning, quantum gates, quantum gates, winner-take-all

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Authors: Armin Nabaei, M. Omair Ahmad, M. N. S. Swamy

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Many facial expression and emotion recognition methods in the traditional approaches of using LDA, PCA, and EBGM have been proposed. In recent years deep learning models have provided a unique platform addressing by automatically extracting the features for the detection of facial expression and emotions. However, deep networks require large training datasets to extract automatic features effectively. In this work, we propose an efficient emotion detection algorithm using face images when only small datasets are available for training. We design a deep network whose feature extraction capability is enhanced by utilizing several parallel modules between the input and output of the network, each focusing on the extraction of different types of coarse features with fined grained details to break the symmetry of produced information. In fact, we leverage long range dependencies, which is one of the main drawback of CNNs. We develop this work by introducing a Dynamic Soft-Margin SoftMax.The conventional SoftMax suffers from reaching to gold labels very soon, which take the model to over-fitting. Because it’s not able to determine adequately discriminant feature vectors for some variant class labels. We reduced the risk of over-fitting by using a dynamic shape of input tensor instead of static in SoftMax layer with specifying a desired Soft- Margin. In fact, it acts as a controller to how hard the model should work to push dissimilar embedding vectors apart. For the proposed Categorical Loss, by the objective of compacting the same class labels and separating different class labels in the normalized log domain.We select penalty for those predictions with high divergence from ground-truth labels.So, we shorten correct feature vectors and enlarge false prediction tensors, it means we assign more weights for those classes with conjunction to each other (namely, “hard labels to learn”). By doing this work, we constrain the model to generate more discriminate feature vectors for variant class labels. Finally, for the proposed optimizer, our focus is on solving weak convergence of Adam optimizer for a non-convex problem. Our noteworthy optimizer is working by an alternative updating gradient procedure with an exponential weighted moving average function for faster convergence and exploiting a weight decay method to help drastically reducing the learning rate near optima to reach the dominant local minimum. We demonstrate the superiority of our proposed work by surpassing the first rank of three widely used Facial Expression Recognition datasets with 93.30% on FER-2013, and 16% improvement compare to the first rank after 10 years, reaching to 90.73% on RAF-DB, and 100% k-fold average accuracy for CK+ dataset, and shown to provide a top performance to that provided by other networks, which require much larger training datasets.

Keywords: computer vision, facial expression recognition, machine learning, algorithms, depp learning, neural networks

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Authors: Soudabeh Shemehsavar

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In this paper, we consider the situation under a life test, in which the failure time of the test units are not related deterministically to an observable stochastic time varying covariate. In such a case, the joint distribution of failure time and a marker value would be useful for modeling the step stress life test. The problem of accelerating such an experiment is considered as the main aim of this paper. We present a step stress accelerated model based on a bivariate Wiener process with one component as the latent (unobservable) degradation process, which determines the failure times and the other as a marker process, the degradation values of which are recorded at times of failure. Parametric inference based on the proposed model is discussed and the optimization procedure for obtaining the optimal time for changing the stress level is presented. The optimization criterion is to minimize the approximate variance of the maximum likelihood estimator of a percentile of the products’ lifetime distribution.

Keywords: bivariate normal, Fisher information matrix, inverse Gaussian distribution, Wiener process

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Authors: Muhanad Al-Jubouri

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A local scour is the most important of the several scours impacting bridge performance and security. Even though scour is widespread in bridges, especially during flood seasons, the experimental tests could not be applied to many standard highway bridges. A computational fluid dynamics numerical model was used to solve the problem of calculating local scouring and deposition for non-cohesive silt and clear water conditions near single and double cylindrical piers with the effect of floating debris. When FLOW-3D software is employed with the Rang turbulence model, the Nilsson bed-load transfer equation and fine mesh size are considered. The numerical findings of single cylindrical piers correspond pretty well with the physical model's results. Furthermore, after parameter effectiveness investigates the range of outcomes based on predicted user inputs such as the bed-load equation, mesh cell size, and turbulence model, the final numerical predictions are compared to experimental data. When the findings are compared, the error rate for the deepest point of the scour is equivalent to 3.8% for the single pier example.

Keywords: local scouring, non-cohesive, clear water, computational fluid dynamics, turbulence model, bed-load equation, debris

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Authors: Ali D. El-mehdawi, Ibrahim E. Elkanouni

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Thick pre-Upper Cretaceous sandstones, sandstones intercalated with red/black shale or quarzitic sandstones, traditionally known to range in age from Cambrian to Early Cretaceous, mostly overlie the subsurface basement rocks of the Sirt Basin of Libya. These sediments known as Nubian, Sarir, Amal or Cambro-Ordovician sandstones. They are usually barren of any age datable palynomorphs and microfossils and represent the main hydrocarbon reservoirs in the basin. As a part of an ongoing regional project concerned with revision and updating of the stratigraphic nomenclature of the Sirt Basin and adjacent areas, sixteen core and ditch cutting samples from four wells penetrating the known Cambro-Ordovician sediments in the central and eastern parts of the basin were examined palynologicaly to investigate its age and the depositional paleoenvironment. The samples proved to be barren or yielded rare palynomorph assemblage, which dominated by dark grey to black small and large-sized sphaeromorph acritarchs assemblage of leiosphaerid types. The dominated species are Kildinosphaera chagrinata, K. cf. chagrinata, Kildinella ripheica, Kilinella timanica, Leiosphaeridia asperata and Leiosphaeridia spp. These leiosphaerides assemblage are comparable to those have been reported from the Late Precambrian, late Riphean age in Cyrenaica Platform, NE Libya, and would indicated shallow marine depositional environment. The age assignment suggests that this interval most probably equates to Mourizide, Bir Bayai and Wadi alHayt formations known in the Murzuq, Kufrah and Cyrenaica areas, respectively. This study proves the presence of Precambrian sediments in Jaghbub high and Amal Platform in the eastern part of Sirt Basin and probably in Maradah Trough and Aj Jahamah/Zoltun Platform northwestern part of the Sirt Basin.

Keywords: palynology, leiosphaerides, precambrian, sirt basin, libya

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Authors: Pablo J. Valverde, Jaime E. Fernandez

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This paper shows under which conditions public corruption can emerge. The theoretical model includes variables such as the public employee wage (w), a control corruption parameter (c), and the group size of interactions (GS) between clusters of public officers and contractors. The system behavior is analyzed using phase diagrams based on combinations of such parameters (c, w, GS). Numerical simulations are implemented in order to contrast analytic results based on Nash equilibria of the theoretical model. Major findings include the functional relationship between wages and network topology, which attempts to reduce the emergence of corrupt behavior.

Keywords: public corruption, game theory, complex systems, Nash equilibrium.

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Authors: B. Selma

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The aim of this study is to test the feasibility of SIPINA method to predict the harmfulness parameters controlling the seismic response. The approach developed takes into consideration both the focal depth and the peak ground acceleration. The parameter to determine is displacement. The data used for the learning of this method and analysis nonlinear seismic are described and applied to a class of models damaged to some typical structures of the existing urban infrastructure of Jassy, Romania. The results obtained indicate an influence of the focal depth and the peak ground acceleration on the displacement.

Keywords: SIPINA algorithm, seism, focal depth, peak ground acceleration, displacement

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Authors: Azizul Baten, Kamrul Hossain, Abdullah-Al-Zabir

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The objective of this study is to develop an appropriate stochastic frontier secondary schools efficiency model by ICT Intervention and to examine the impact of ICT challenges on secondary schools efficiency in the Sylhet division in Bangladesh using stochastic frontier analysis. The Translog stochastic frontier model was found an appropriate than the Cobb-Douglas model in secondary schools efficiency by ICT Intervention. Based on the results of the Cobb-Douglas model, it is found that the coefficient of the number of teachers, the number of students, and teaching ability had a positive effect on increasing the level of efficiency. It indicated that these are related to technical efficiency. In the case of inefficiency effects for both Cobb-Douglas and Translog models, the coefficient of the ICT lab decreased secondary school inefficiency, but the online class in school was found to increase the level of inefficiency. The coefficients of teacher’s preference for ICT tools like multimedia projectors played a contributor role in decreasing the secondary school inefficiency in the Sylhet division of Bangladesh. The interaction effects of the number of teachers and the classrooms, and the number of students and the number of classrooms, the number of students and teaching ability, and the classrooms and teaching ability of the teachers were recorded with the positive values and these have a positive impact on increasing the secondary school efficiency. The overall mean efficiency of urban secondary schools was found at 84.66% for the Translog model, while it was 83.63% for the Cobb-Douglas model. The overall mean efficiency of rural secondary schools was found at 80.98% for the Translog model, while it was 81.24% for the Cobb-Douglas model. So, the urban secondary schools performed better than the rural secondary schools in the Sylhet division. It is observed from the results of the Tobit model that the teacher-student ratio had a positive influence on secondary school efficiency. The teaching experiences of those who have 1 to 5 years and 10 years above, MPO type school, conventional teaching method have had a negative and significant influence on secondary school efficiency. The estimated value of σ-square (0.0625) was different from Zero, indicating a good fit. The value of γ (0.9872) was recorded as positive and it can be interpreted as follows: 98.72 percent of random variation around in secondary school outcomes due to inefficiency.

Keywords: efficiency, secondary schools, ICT, stochastic frontier analysis

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Authors: Manik Sharma, Ganapathy Krishnamurthi

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The cost of some drugs and medical treatments has risen in recent years, that many patients are having to go without. A classification project could make researchers more efficient. One of the more surprising reasons behind the cost is how long it takes to bring new treatments to market. Despite improvements in technology and science, research and development continues to lag. In fact, finding new treatment takes, on average, more than 10 years and costs hundreds of millions of dollars. If successful, we could dramatically improve the industry's ability to model cellular images according to their relevant biology. In turn, greatly decreasing the cost of treatments and ensure these treatments get to patients faster. This work aims at solving a part of this problem by creating a cellular image classification model which can decipher the genetic perturbations in cell (occurring naturally or artificially). Another interesting question addressed is what makes the deep-learning model decide in a particular fashion, which can further help in demystifying the mechanism of action of certain perturbations and paves a way towards the explainability of the deep-learning model.

Keywords: cellular images, genetic perturbations, deep-learning, explainability

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Authors: Ozgu Hafizoglu

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Analogy is an essential tool of human cognition that enables connecting diffuse and diverse systems with attributional, deep structural, casual relations that are essential to learning, to innovation in artificial worlds, and to discovery in science. Cognitive Model of Analogy (CMA) leads and creates information pattern transfer within and between domains and disciplines in science. This paper demonstrates the Cognitive Model of Analogy (CMA) as an evolutionary approach to scientific research. The model puts forward the challenges of deep uncertainty about the future, emphasizing the need for flexibility of the system in order to enable reasoning methodology to adapt to changing conditions. In this paper, the model of analogical reasoning is created based on brain cells, their fractal, and operational forms within the system itself. Visualization techniques are used to show correspondences. Distinct phases of the problem-solving processes are divided thusly: encoding, mapping, inference, and response. The system is revealed relevant to brain activation considering each of these phases with an emphasis on achieving a better visualization of the brain cells: glial cells, axons, axon terminals, and neurons, relative to matching conditions of analogical reasoning and relational information. It’s found that encoding, mapping, inference, and response processes in four-term analogical reasoning are corresponding with the fractal and operational forms of brain cells: glial, axons, and neurons.

Keywords: analogy, analogical reasoning, cognitive model, brain and glials

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Authors: D. S. Gomes, A. T. Silva

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Analysis of the uncertainty quantification related to nuclear safety margins applied to the nuclear reactor is an important concept to prevent future radioactive accidents. The nuclear fuel performance code may involve the tolerance level determined by traditional deterministic models producing acceptable results at burn cycles under 62 GWd/MTU. The behavior of nuclear fuel can simulate applying a series of material properties under irradiation and physics models to calculate the safety limits. In this study, theoretical predictions of nuclear fuel failure under transient conditions investigate extended radiation cycles at 75 GWd/MTU, considering the behavior of fuel rods in light-water reactors under reactivity accident conditions. The fuel pellet can melt due to the quick increase of reactivity during a transient. Large power excursions in the reactor are the subject of interest bringing to a treatment that is known as the Fuchs-Hansen model. The point kinetic neutron equations show similar characteristics of non-linear differential equations. In this investigation, the multivariate logistic regression is employed to a probabilistic forecast of fuel failure. A comparison of computational simulation and experimental results was acceptable. The experiments carried out use the pre-irradiated fuels rods subjected to a rapid energy pulse which exhibits the same behavior during a nuclear accident. The propagation of uncertainty utilizes the Wilk's formulation. The variables chosen as essential to failure prediction were the fuel burnup, the applied peak power, the pulse width, the oxidation layer thickness, and the cladding type.

Keywords: logistic regression, reactivity-initiated accident, safety margins, uncertainty propagation

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Authors: Yoonsuh Jung, Steven N. MacEachern

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Check loss function is used to define quantile regression. In the prospect of cross validation, it is also employed as a validation function when underlying truth is unknown. However, our empirical study indicates that the validation with check loss often leads to choosing an over estimated fits. In this work, we suggest a modified or L2-adjusted check loss which rounds the sharp corner in the middle of check loss. It has a large effect of guarding against over fitted model in some extent. Through various simulation settings of linear and non-linear regressions, the improvement of check loss by L2 adjustment is empirically examined. This adjustment is devised to shrink to zero as sample size grows.

Keywords: cross-validation, model selection, quantile regression, tuning parameter selection

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Authors: Mueller Jann, Hoffmann Christian Hugo

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Conventional quantitative risk management in banking is a risk factor of its own, because it rests on assumptions such as independence and availability of data which do not hold when rare events of extreme consequences are involved. There is a growing recognition of the need for alternative risk measures that do not make these assumptions. We propose a novel method for modeling the risk associated with investment products, in particular derivatives, by using a formal language for specifying financial contracts. Expressions in this language are interpreted in the category of values annotated with (a formal representation of) uncertainty. The choice of uncertainty formalism thus becomes a parameter of the model, so it can be adapted to the particular application and it is not constrained to classical probabilities. We demonstrate our approach using a simple logic-based uncertainty model and a case study in which we assess the risk of counter party default in a portfolio of collateralized loans.

Keywords: risk model, uncertainty monad, derivatives, contract algebra

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Authors: JoonHo Lee, KyoJin An, JunSu Kim, Young-Chul Park

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In this study, the fluid flow characteristics and performance numerical study through CFD model of the Non-split quick coupling for flow control in hydraulic system equipment for the aerospace business group focused to predict. In this study, we considered turbulence models for the application of Computational Fluid Dynamics for the CFD model of the Non-split Quick Coupling for aerospace business. In addition to this, the adequacy of the CFD model were verified by comparing with standard value. Based on this analysis, accurate the fluid flow characteristics can be predicted. It is, therefore, the design of the fluid flow characteristic contribute the reliability for the Quick Coupling which is required in industries on the basis of research results.

Keywords: CFD, FEM, quick coupling, turbulence

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Authors: Hamed Zolfaghari, Mojtaba Kord

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Project planners and controllers are frequently faced with the challenge of inadequate software for the preparation of automatic project progress reports based on actual project information updates. They usually make dashboards in Microsoft Excel, which is local and not applicable online. Another shortcoming is that it is not linked to planning software such as Microsoft Project, which lacks the database required for data storage. This study aimed to propose a model for the preparation of reports on automatic online project progress based on actual project information updates by the integration of Primavera P6, SQL database, and Power BI for a construction project. The designed model could be applicable to project planners and controller agents by enabling them to prepare project reports automatically and immediately after updating the project schedule using actual information. To develop the model, the data were entered into P6, and the information was stored on the SQL database. The proposed model could prepare a wide range of reports, such as earned value management, HR reports, and financial, physical, and risk reports automatically on the Power BI application. Furthermore, the reports could be published and shared online.

Keywords: primavera P6, SQL, Power BI, EVM, integration management

Procedia PDF Downloads 93

Authors: Badawia Ibrahim, Iman Hussein, Samar El Sheikh, Fatma Abou Elkasem, Hazem Abo Ismael

Abstract:

Background: Response of breast carcinoma to neoadjuvant chemotherapy (NAC) varies regarding many factors including hormonal receptor status. Breast cancer is a heterogenous disease with different outcomes, hence a need arises for new markers predicting the outcome of NAC especially for the triple negative group when estrogen, progesterone receptors and Her2/neu are negative. FOXP3 is a promising target with unclear role. Aim: To examine the value of FOXP3 expression in locally advanced triple negative breast cancer tumoral cells as well as tumor infiltrating lymphocytes (TILs) and to elucidate its relation to the extent of NAC response. Material and Methods: Forty five cases of immunohistochemically confirmed to be triple negative breast carcinoma were evaluated for NAC (Doxorubicin, Cyclophosphamide AC x 4 cycles + Paclitaxel x 12 weeks, patients with ejection fraction less than 60% received Taxotere or Cyclophosphamide, Methotrexate, Fluorouracil CMF) response in both tumour and lymph nodes status according to Miller & Payne's and Sataloff's systems. FOXP3 expression in tumor as well as TILs evaluated in the pretherapy biopsies was correlated with NAC response in breast tumor and lymph nodes as well as other clinicopathological factors. Results: Breast tumour cells showed FOXP3 positive cytoplasmic expression in (42%) of cases. High FOXP3 expression percentage was detected in (47%) of cases. High infiltration by FOXP3+TILs was detected in (49%) of cases. Positive FOXP3 expression was associated with negative lymph node metastasis. High FOXP3 expression percentage and high infiltration by FOXP3+TILs were significantly associated with complete therapy response in axillary lymph nodes. High FOXP3 expression in tumour cells was associated with high infiltration by FOXP3+TILs. Conclusion: This result may provide evidence that FOXP3 marker is a good prognostic and predictive marker for triple negative breast cancer (TNBC) indicated for neoadjuvant chemotherapy and can be used for stratifications of TNBC cases indicated for NAC. As well, this study confirmed the fact that the tumour cells and the surrounding microenvironment interact with each other and the tumour microenvironment can influence the treatment outcomes of TNBC.

Keywords: breast cancer, FOXP3 expression, prediction of neoadjuvant chemotherapy effect, triple negative

Procedia PDF Downloads 261

Authors: Aarthi Sridhar, Henri Gavin, Karah Kelly

Abstract:

Rolling Isolation Systems (RISs) are simple and effective means to mitigate earthquake hazards to equipment in critical and precious facilities, such as hospitals, network collocation facilities, supercomputer centers, and museums. The RIS works by isolating components acceleration the inertial forces felt by the subsystem. The RIS consists of two platforms with counter-facing concave surfaces (dishes) in each corner. Steel balls lie inside the dishes and allow the relative motion between the top and bottom platform. Formerly, a mathematical model for the dynamics of RISs was developed using Lagrange’s equations (LE) and experimentally validated. A new mathematical model was developed using Gauss’s Principle of Least Constraint (GPLC) and verified by comparing impulse response trajectories of the GPLC model and the LE model in terms of the peak displacements and accelerations of the top platform. Mathematical models for the RIS are tedious to derive because of the non-holonomic rolling constraints imposed on the system. However, using Gauss’s Principle of Least constraint to find the equations of motion removes some of the obscurity and yields a system that can be easily extended. Though the GPLC model requires more state variables, the equations of motion are far simpler. The non-holonomic constraint is enforced in terms of accelerations and therefore requires additional constraint stabilization methods in order to avoid the possibility that numerical integration methods can cause the system to go unstable. The GPLC model allows the incorporation of more physical aspects related to the RIS, such as contribution of the vertical velocity of the platform to the kinetic energy and the mass of the balls. This mathematical model for the RIS is a tool to predict the motion of the isolation platform. The ability to statistically quantify the expected responses of the RIS is critical in the implementation of earthquake hazard mitigation.

Keywords: earthquake hazard mitigation, earthquake isolation, Gauss’s Principle of Least Constraint, nonlinear dynamics, rolling isolation system

Procedia PDF Downloads 241

Authors: Marcos Valdez Alexander Junior, Igor Aguilar-Alonso

Abstract:

Malnutrition is the root of different diseases that universally affect everyone, diseases such as obesity and malnutrition. The objective of this research is to predict the calories of the food to be eaten, developing a smart mobile application to show the user if a meal is balanced. Due to the large percentage of obesity and malnutrition in Peru, the present work is carried out. The development of the intelligent application is proposed with a three-layer architecture, and for the prediction of the nutritional value of the food, the use of pre-trained models based on convolutional neural networks is proposed.

Keywords: volume estimation, calorie estimation, artificial vision, food nutrition

Procedia PDF Downloads 83

Authors: Gizachew Mulugeta Manahelohe, Khidmet Safarovich Shikhaliev

Abstract:

There has been a significant surge in interest in the synthesis of heterocyclic compounds that contain hydroquinoline fragments. This surge can be attributed to the broad range of pharmaceutical and industrial applications that these compounds possess. The present study provides a comprehensive account of the synthesis of both linear and fused heterocyclic systems that incorporate hydroquinoline fragments. Furthermore, the pharmacological activity spectra of the synthesized compounds were assessed using the in silico method, employing the prediction of activity spectra of substances (PASS) program. Hydroquinoline nitriles 7 and 8 were prepared through the reaction of the corresponding hydroquinolinecarbaldehyde using a hydroxylammonium chloride/pyridine/toluene system and iodine in aqueous ammonia under ambient conditions, respectively. 2-Phenyl-1,3-oxazol-5(4H)-ones 9a,b and 10a,b were synthesized via the condensation of compounds 5a,b and 6a,b with hippuric acid in acetic acid in 30–60% yield. When activated, 7-methylazolopyrimidines 11a and b were reacted with N-alkyl-2,2,4-trimethyl-1,2,3,4-tetrahydroquinoline-6-carbaldehydes 6a and b, and triazolo/pyrazolo[1,5-a]pyrimidin-6-yl carboxylic acids 12a and b were obtained in 60–70% yield. The condensation of 7-hydroxy-1,2,3,4-tetramethyl-1,2-dihydroquinoline 3 h with dimethylacetylenedicarboxylate (DMAD) and ethyl acetoacetate afforded cyclic products 16 and 17, respectively. The condensation reaction of 6-formyl-7-hydroxy-1,2,2,4-tetramethyl-1,2-dihydroquinoline 5e with methylene-active compounds such as ethyl cyanoacetate/dimethyl-3-oxopentanedioate/ethyl acetoacetate/diethylmalonate/Meldrum’s acid afforded 3-substituted coumarins containing dihydroquinolines 19 and 21. Pentacyclic coumarin 22 was obtained via the random condensation of malononitrile with 5e in the presence of a catalytic amount of piperidine in ethanol. The biological activities of the synthesized compounds were assessed using the PASS program. Based on the prognosis, compounds 13a, b, and 14 exhibited a high likelihood of being active as inhibitors of gluconate 2-dehydrogenase, as well as possessing antiallergic, antiasthmatic, and antiarthritic properties, with a probability value (Pa) ranging from 0.849 to 0.870. Furthermore, it was discovered that hydroquinoline carbonitriles 7 and 8 tended to act as effective progesterone antagonists and displayed antiallergic, antiasthmatic, and antiarthritic effects (Pa = 0.276–0.827). Among the hydroquinolines containing coumarin moieties, compounds 17, 19a, and 19c were predicted to be potent progesterone antagonists, with Pa values of 0.710, 0.630, and 0.615, respectively.

Keywords: heterocyclic compound, hydroquinoline, Vilsmeier–Haack formulation, quinolone

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Authors: Paolo Pietro Biancone

Abstract:

The spread of Islamic financial instruments is an opportunity to offer integration for the immigrant population and to attract, through the specific products, the richness of sovereign funds from the "Arab" countries. However, it is important to consider the possibility of comparing a traditional finance model, which in recent times has given rise to many doubts, with an "alternative" finance model, where the ethical aspect arising from religious principles is very important.

Keywords: banks, Europe, Islamic finance, Italy

Procedia PDF Downloads 254