Search results for: modeling accuracy

Authors: Bliss Singhal

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Epilepsy is a prevalent neurological disorder affecting approximately 50 million individuals worldwide and 1.2 million Americans. There exist millions of pediatric patients with intractable epilepsy, a condition in which seizures fail to come under control. The occurrence of seizures can result in physical injury, disorientation, unconsciousness, and additional symptoms that could impede children's ability to participate in everyday tasks. Predicting seizures can help parents and healthcare providers take precautions, prevent risky situations, and mentally prepare children to minimize anxiety and nervousness associated with the uncertainty of a seizure. This research proposes a comprehensive framework to predict seizures in pediatric patients by evaluating machine learning algorithms on unimodal neuroimaging data consisting of electroencephalogram signals. The bandpass filtering and independent component analysis proved to be effective in reducing the noise and artifacts from the dataset. Various machine learning algorithms’ performance is evaluated on important metrics such as accuracy, precision, specificity, sensitivity, F1 score and MCC. The results show that the deep learning algorithms are more successful in predicting seizures than logistic Regression, and k nearest neighbors. The recurrent neural network (RNN) gave the highest precision and F1 Score, long short-term memory (LSTM) outperformed RNN in accuracy and convolutional neural network (CNN) resulted in the highest Specificity. This research has significant implications for healthcare providers in proactively managing seizure occurrence in pediatric patients, potentially transforming clinical practices, and improving pediatric care.

Keywords: intractable epilepsy, seizure, deep learning, prediction, electroencephalogram channels

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Authors: Anderson Braga Mendes, Wallington Felipe de Almeida, Cicero Medeiros da Silva

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This study aimed to assess the lifespan of the fish transposition system of the Itaipu Power Plant (Brazil/Paraguay) by using 3D hydrodynamic modeling and Churchill trapping effiency in order to identify the sedimentological equilibrium configuration in the main pond of the Piracema Channel, which is part of a 10 km hydraulic circuit that enables fish migration from downstream to upstream (and vice-versa) the Itaipu Dam, overcoming a 120 m water drop. For that, bottom data from 2002 (its opening year) and 2015 were collected and analyzed, besides bed material at 12 stations to the purpose of identifying their granulometric profiles. The Shields and Yalin and Karahan diagrams for initiation of motion of bed material were used to determine the critical bed shear stress for the sedimentological equilibrium state based on the sort of sediment (grain size) to be found at the bottom once the balance is reached. Such granulometry was inferred by analyzing the grosser material (fine and medium sands) which inflows the pond and deposits in its backwater zone, being adopted a range of diameters within the upper and lower limits of that sand stratification. The software Delft 3D was used in an attempt to compute the bed shear stress at every station under analysis. By modifying the input bathymetry of the main pond of the Piracema Channel so as to the computed bed shear stress at each station fell within the intervals of acceptable critical stresses simultaneously, it was possible to foresee the bed configuration of the main pond when the sedimentological equilibrium is reached. Under such condition, 97% of the whole pond capacity will be silted, and a shallow water course with depths ranging from 0.2 m to 1.5 m will be formed; in 2002, depths ranged from 2 m to 10 m. Out of that water path, the new bottom will be practically flat and covered by a layer of water 0.05 m thick. Thus, in the future the main pond of the Piracema Channel will lack its purpose of providing a resting place for migrating fish species, added to the fact that it may become an insurmountable barrier for medium and large sized specimens. Everything considered, it was estimated that its lifespan, from the year of its opening to the moment of the sedimentological equilibrium configuration, will be approximately 95 years–almost half of the computed lifespan of Itaipu Power Plant itself. However, it is worth mentioning that drawbacks concerning the silting in the main pond will start being noticed much earlier than such time interval owing to the reasons previously mentioned.

Keywords: 3D hydrodynamic modeling, Churchill trapping efficiency, fish crossing system, Itaipu power plant, lifespan, sedimentological equilibrium

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Authors: Maciej Zaręba, Sławomir Lasota

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The monitoring of manufacturing processes is an important issue in nowadays ERP systems. The identification and analysis of appropriate data for the units that take part in the production process are ones of the most crucial problems. In this paper, the authors introduce a new approach towards modelling the relation between production units, signals, and factors possible to obtain from the production system. The main idea for the system is based on the ontology of production units.

Keywords: manufacturing operation management, OWL, ontology implementation, ontology modeling

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Authors: Joseph Udofia, Isaac Olufadewa

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Everyday, the size of Electronic Health Records data keeps increasing as new patients visit health practitioner and returning patients fulfil their appointments. As these data grow, so is their susceptibility to cyber-attacks from criminals waiting to exploit this data. In the US, the damages for cyberattacks were estimated at $8 billion (2018), $11.5 billion (2019) and $20 billion (2021). These attacks usually involve the exposure of PII. Health data is considered PII, and its exposure carry significant impact. To this end, an enhancement of Health Policy and Standards in relation to data security, especially among patients and their clinical providers, is critical to ensure ethical practices, confidentiality, and trust in the healthcare system. As Clinical accelerators and applications that contain user data are used, it is expedient to have a review and revamp of policies like the Payment Card Industry Data Security Standard (PCI DSS), the Health Insurance Portability and Accountability Act (HIPAA), the Fast Healthcare Interoperability Resources (FHIR), all aimed to ensure data protection and security in healthcare. FHIR caters for healthcare data interoperability, FHIR caters to healthcare data interoperability, as data is being shared across different systems from customers to health insurance and care providers. The astronomical cost of implementation has deterred players in the space from ensuring compliance, leading to susceptibility to data exfiltration and data loss on the security accuracy of protected health information (PHI). Though HIPAA hones in on the security accuracy of protected health information (PHI) and PCI DSS on the security of payment card data, they intersect with the shared goal of protecting sensitive information in line with industry standards. With advancements in tech and the emergence of new technology, it is necessary to revamp these policies to address the complexity and ambiguity, cost barrier, and ever-increasing threats in cyberspace. Healthcare data in the wrong hands is a recipe for disaster, and we must enhance its protection and security to protect the mental health of the current and future generations.

Keywords: cloud security, healthcare, cybersecurity, policy and standard

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Authors: A. Akbarpour, M. R. Adib Ramezani, M. Zhian, N. Ghorbani Amirabad

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One way to improve the performance of structures against of earthquake is passive control which requires no external power source. In this research, tuned liquid column dampers which are among of systems with the capability to transfer energy between various modes of vibration, are used. For the first time, a liquid column damper for vibration control structure is presented. After modeling this structure in design building software and performing the static and dynamic analysis and obtaining the necessary parameters for the design of tuned liquid column damper, the whole structure will be analyzed in finite elements software. The tuned liquid column dampers are installed on the structure and nonlinear time-history analysis is done in two cases of structures; with and without dampers. Finally the seismic behavior of building in the two cases will be examined. In this study the nonlinear time-history analysis on a twelve-story steel structure equipped with damper subject to records of earthquake including Loma Prieta, Northridge, Imperiall Valley, Pertrolia and Landers was performed. The results of comparing between two cases show that these dampers have reduced lateral displacement and acceleration of levels on average of 10%. Roof displacement and acceleration also reduced respectively 5% and 12%. Due to structural asymmetric in the plan, the maximum displacements of surrounding structures as well as twisting were studied. The results show that the dampers lead to a 10% reduction in the maximum response of structure stories surrounding points. At the same time, placing the dampers, caused to reduce twisting on the floor plan of the structure, Base shear of structure in the different earthquakes also has been reduced on the average of 6%.

Keywords: retrofitting, passive control, tuned liquid column damper, finite element analysis

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Authors: Chung Gun Lee

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Objectives: Certain life change events were shown to have strong effects on physical activity-related behavior, but more research is needed to investigate the longer-term effects of different life change events on physical activity-related behaviors. The purpose of this study is to examine the effect of experiencing physically or non-physically forced sexual activity on sports participation from adolescence to young adulthood. Methods: This study used the National Longitudinal Study of Adolescent Health (Add Health) data. Group-based trajectory modeling was utilized to examine the effect of experiencing sexual assault on trajectories of sports participation from adolescence to young adulthood. Results: Male participants were divided into three trajectory groups (i.e., Low-stable, High-decreasing, and High-stable) and female participants were divided into two trajectory groups (i.e., Low-stable and High-decreasing). The main finding of this study is that women who experienced non-physically forced sexual activity significantly decreases sports participation throughout the trajectory in ‘High-decreasing group.’ The effect of non-physically forced sexual activity on women’s sports participation was considerably weakened and became insignificant after including psychological depression in the model as a potential mediator. Discussion: Special attention should be paid to sport participation among women victims of non-physically forced sexual activity. Further studies are needed to examine other potential mediators in addition to psychological depression when examining the effect of non-physically forced sexual activity on sport participation in women.

Keywords: adolescent, group-based trajectory modeling, sexual assault, young adult

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Authors: A. Aboubakr, E. Fehling, S. A. Mourad, M. Omar

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Wind energy is one of the most effective renewable sources especially offshore wind energy although offshore wind technology is more costly to produce. It is well known that offshore wind energy can potentially be very cheap once infrastructure and researches improve. Laterally, the trend is to construct offshore wind energy to generate the electricity form wind. This leads to intensive research in order to improve the infrastructures. Offshore wind energy is the construction of wind farms in bodies of water to generate electricity from wind. The most important part in offshore wind turbine structure is the foundation and its connection with the wind tower. This is the main difference between onshore and offshore structures. Grouted connection between the foundation and the wind tower is the most important part of the building process when constructing wind offshore turbines. Most attention should be paid to the actual grout connection as this transfers the loads safely from tower to foundations and the soil also. In this paper, finite element analyses have been carried out for studying the behaviour of offshore grouted connection for wind turbine structures. ATENA program have been used for non-linear analysis simulation of the real structural behavior thus demonstrating the crushing, cracking, contact between the two materials and steel yielding. A calibration of the material used in the simulation has been carried out assuring an accurate model of the used material by ATENA program. This calibration was performed by comparing the results from the ATENA program with experimental results to validate the material properties used in ATENA program. Three simple patch test models with different properties have been performed. The research is concluded with a result that the calibration showing a good agreement between the ATENA program material behaviors and the experimental results.

Keywords: grouted connection, 3D modeling, finite element analysis, offshore wind energy turbines, stresses

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Authors: Himanshu Singh, Rishi Kant, Shantanu Bhattacharya

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This paper adopts a top-down approach for mathematical modeling to predict the size reduction from micro to nano-scale through persistent etching. The process is simulated using a finite difference approach. Previously, various researchers have simulated the etching process for 1-D and 2-D substrates. It consists of two processes: 1) Convection-Diffusion in the etchant domain; 2) Chemical reaction at the surface of the particle. Since the process requires analysis along moving boundary, partial differential equations involved cannot be solved using conventional methods. In 1-D, this problem is very similar to Stefan's problem of moving ice-water boundary. A fixed grid method using finite volume method is very popular for modelling of etching on a one and two dimensional substrate. Other popular approaches include moving grid method and level set method. In this method, finite difference method was used to discretize the spherical diffusion equation. Due to symmetrical distribution of etchant, the angular terms in the equation can be neglected. Concentration is assumed to be constant at the outer boundary. At the particle boundary, the concentration of the etchant is assumed to be zero since the rate of reaction is much faster than rate of diffusion. The rate of reaction is proportional to the velocity of the moving boundary of the particle. Modelling of the above reaction was carried out using Matlab. The initial particle size was taken to be 50 microns. The density, molecular weight and diffusion coefficient of the substrate were taken as 2.1 gm/cm3, 60 and 10-5 cm2/s respectively. The etch-rate was found to decline initially and it gradually became constant at 0.02µ/s (1.2µ/min). The concentration profile was plotted along with space at different time intervals. Initially, a sudden drop is observed at the particle boundary due to high-etch rate. This change becomes more gradual with time due to declination of etch rate.

Keywords: particle size reduction, micromixer, FDM modelling, wet etching

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Authors: Said Ghalem

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Honey is primarily composed of sugars: glucose and fructose. Depending honey, it's either fructose or glucose predominates. More the fructose concentration and the less the glycemic index (GI) is high. Thus, changes in the insulin response shows a decrease of the amount of insulin secreted at an increased fructose honey. Honey is also a compound that can reduce the lipid in the blood. Several studies on animals, but which remain to be checked in humans, have shown that the honey can have interesting effects when combined with other molecules: associated with Metformin (a medicine taken by diabetics), it shows the benefits and effects of diabetes preserves the tissue; associated ginger, it increases the antioxidant activity and thus avoids neurologic complications, neuropathic. Molecular modeling techniques are widely used in chemistry, biology, and the pharmaceutical industry. Most of the currently existing drugs target enzymes. Inhibition of DPP-4 is an important approach in the treatment of type 2 diabetes. We have chosen for the inhibition of DPP-4 the following molecules: Linagliptin (BI1356), Sitagliptin (Januvia), Vildagliptin, Saxagliptin, Alogliptin, and Metformin (Glucophage), that are involved in the disease management of type 2 diabetes and added to honey. For this, we used software Molecular Operating Environment. A Wistar rat study was initiated in our laboratory with a well-studied protocol; after sacrifice, according to international standards and respect for the animal This theoretical approach predicts the mode of interaction of a ligand with its target. The honey can have interesting effects when combined with other molecules, it shows the benefits and effects of honey preserves the tissue, it increases the antioxidant activity, and thus avoids neurologic complications, neuropathic or macrovascular. The organs, especially the kidneys of Wistar, shows that the parameters to renal function let us conclude that damages caused by diabetes are slightly perceptible than those observed without the addition of a high concentration of fructose honey.

Keywords: honey, molecular modeling, DPP4 enzyme, metformin

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Authors: Paramvir Singh

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The locomotion of aquatic organisms has long fascinated biologists and engineers alike, with fish fins serving as a prime example of nature's remarkable adaptations for efficient underwater propulsion. This paper presents a comprehensive study focused on the hydrodynamic analysis of fish fin kinematics, employing an innovative approach that combines machine learning and image processing techniques. Through high-speed videography and advanced computational tools, we gain insights into the complex and dynamic motion of the fins of a Tilapia (Oreochromis Niloticus) fish. This study was initially done by experimentally capturing videos of the various motions of a Tilapia in a custom-made setup. Using deep learning and image processing on the videos, the motion of the Caudal and Pectoral fin was extracted. This motion included the fin configuration (i.e., the angle of deviation from the mean position) with respect to time. Numerical investigations for the flapping fins are then performed using a Computational Fluid Dynamics (CFD) solver. 3D models of the fins were created, mimicking the real-life geometry of the fins. Thrust Characteristics of separate fins (i.e., Caudal and Pectoral separately) and when the fins are together were studied. The relationship and the phase between caudal and pectoral fin motion were also discussed. The key objectives include mathematical modeling of the motion of a flapping fin at different naturally occurring frequencies and amplitudes. The interactions between both fins (caudal and pectoral) were also an area of keen interest. This work aims to improve on research that has been done in the past on similar topics. Also, these results can help in the better and more efficient design of the propulsion systems for biomimetic underwater vehicles that are used to study aquatic ecosystems, explore uncharted or challenging underwater regions, do ocean bed modeling, etc.

Keywords: biomimetics, fish fin kinematics, image processing, fish tracking, underwater vehicles

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Authors: Georgi Y. Georgiev, Matthew Brouillet

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This research applies the power of agent-based modeling to a pivotal question at the intersection of biology, computer science, physics, and complex systems theory about the self-organization processes in open, complex, non-equilibrium thermodynamic systems. Central to this investigation is the principle of Maximum Entropy Production (MEP). This principle suggests that such systems evolve toward states that optimize entropy production, leading to the formation of structured environments. It is hypothesized that guided by the least action principle, open thermodynamic systems identify and follow the shortest paths to transmit energy and matter, resulting in maximal entropy production, internal structure formation, and a decrease in internal entropy. Concurrently, it is predicted that there will be an increase in system information as more information is required to describe the developing structure. To test this, an agent-based model is developed simulating an ant colony's formation of a path between a food source and its nest. Utilizing the Netlogo software for modeling and Python for data analysis and visualization, self-organization is quantified by calculating the decrease in system entropy based on the potential states and distribution of the ants within the simulated environment. External entropy production is also evaluated for information increase and efficiency improvements in the system's action. Simulations demonstrated that the system begins at maximal entropy, which decreases as the ants form paths over time. A range of system behaviors contingent upon the number of ants are observed. Notably, no path formation occurred with fewer than five ants, whereas clear paths were established by 200 ants, and saturation of path formation and entropy state was reached at populations exceeding 1000 ants. This analytical approach identified the inflection point marking the transition from disorder to order and computed the slope at this point. Combined with extrapolation to the final path entropy, these parameters yield important insights into the eventual entropy state of the system and the timeframe for its establishment, enabling the estimation of the self-organization rate. This study provides a novel perspective on the exploration of self-organization in thermodynamic systems, establishing a correlation between internal entropy decrease rate and external entropy production rate. Moreover, it presents a flexible framework for assessing the impact of external factors like changes in world size, path obstacles, and friction. Overall, this research offers a robust, replicable model for studying self-organization processes in any open thermodynamic system. As such, it provides a foundation for further in-depth exploration of the complex behaviors of these systems and contributes to the development of more efficient self-organizing systems across various scientific fields.

Keywords: complexity, self-organization, agent based modelling, efficiency

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Authors: Aynura Valiyeva, Basil John Thomas

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Intra-firm communication is critical for building synergy amongst internal business units of a firm, where employees from various functional departments and ranks incorporate their decision-making, understanding of organizational objectives, as well as common norms and culture for better organizational effectiveness. This study builds on and assesses a framework of the causes and consequences of effective communication in business interactions between customer and supplier firms, and the path for efficient communication within a firm. The proposed study’s structural equation modeling (SEM) analysis based on 352 sample responses collected from firm representatives at different job positions ranging from marketing to logistics operations, reveals that, in the frame of reference of intra-organizational communication, organization characteristics and shared values, top management support and style of leadership, as well as information technology, are all significantly related to communication effectiveness. Furthermore, the frequency and variety of interactions enhance the outcome of communication, that improves a company’s performance. The results reveal that cultural factors are significantly related to communication effectiveness, as well as the shared beliefs and goals. In terms of organizational factors, leadership style, top management support and information technology are significant determinants of effective communication. Among the contextual factors, interaction frequency and diversity are found to be priority factors. This study also tests the relationship between supplier and supplier firm performance in the context of communication effectiveness, and finds that they are closely related, when trust and commitment is built between business partners. When firms do business in other multicultural contexts, language and shared values with destination country must be considered significant elements of communication process.

Keywords: business performance, intra-firm communication, inter-firm communication, structural equation modeling

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Authors: Harsha Nagar, Vineet Aniya, Alka Kumari, Satyavathi B.

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In the present study, engineered bio-coal was produced from pressed seed cake, which otherwise is non-edible in origin. The production process involves a slow pyrolysis wherein, based on the optimization of process parameters; a substantial reduction in H/C and O/C of 77% was achieved with respect to the original ratio of 1.67 and 0.8, respectively. The bio-coal, so the product was found to have a higher heating value of 29899 kJ/kg with surface area 17 m²/g and pore volume of 0.002 cc/g. The functional characterization of bio-coal and its subsequent modification was carried out to enhance its active sites, which were further used as an adsorbent material for removal of 2,4,6-Trichlorophenol (2,4,6-TCP) herbicide from the aqueous stream. The point of zero charge for the bio-coal was found to be pH < 3 where its surface is positively charged and attracts anions resulting in the maximum 2, 4, 6-TCP adsorption at pH 2.0. The parametric optimization of the adsorption process was studied based on the Box-Behken design with the desirability approach. The results showed optimum values of adsorption efficiency of 74.04% and uptake capacity of 118.336 mg/g for an initial metal concentration of 250 mg/l and particle size of 0.12 mm at pH 2.0 and 1 g/L of bio-coal loading. Negative Gibbs free energy change values indicated the feasibility of 2,4,6-TCP adsorption on biochar. Decreasing the ΔG values with the rise in temperature indicated high favourability at low temperatures. The equilibrium modeling results showed that both isotherms (Langmuir and Freundlich) accurately predicted the equilibrium data, which may be attributed to the different affinity of the functional groups of bio-coal for 2,4,6-TCP removal. The possible mechanism for 2,4,6-TCP adsorption is found to be physisorption (pore diffusion, p*_p electron donor-acceptor interaction, H-bonding, and van der Waals dispersion forces) and chemisorption (phenolic and amine groups chemical bonding) based on the kinetics data modeling.

Keywords: engineered biocoal, 2, 4, 6-trichlorophenol, box behnken design, biosorption

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Authors: Yuechao Lei, Lei Zhang

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The interface shear between asphalt overlay and existing rigid airport pavements occurs due to differences in the mechanical properties of materials subjected to aircraft loading. Interlayer contact influences the mechanical characteristics of the asphalt overlay directly. However, the effective interlayer relative displacement obtained accurately using existing displacement sensors of the loading apparatus remains challenging. This study aims to utilize digital image correlation technology to enhance the accuracy of interfacial contact parameters by obtaining effective interlayer relative displacements. Composite structure specimens were prepared, and fixtures for interlayer shear tests were designed and fabricated. Subsequently, a digital image recognition scheme for required markers was designed and optimized. Effective interlayer relative displacement values were obtained through image recognition and calculation of surface markers on specimens. Finite element simulations validated the mechanical response of composite specimens with interlayer shearing. Results indicated that an optimized marking approach using the wall mending agent for surface application and color coding enhanced the image recognition quality of marking points on the specimen surface. Further image extraction provided effective interlayer relative displacement values during interlayer shear, thereby improving the accuracy of interface contact parameters. For composite structure specimens utilizing Styrene-Butadiene-Styrene (SBS) modified asphalt as the tack coat, the corresponding maximum interlayer shear stress strength was 0.6 MPa, and fracture energy was 2917 J/m2. This research provides valuable insights for investigating the impact of interlayer contact in composite pavement structures on the mechanical characteristics of asphalt overlay.

Keywords: interlayer contact, effective relative displacement, digital image correlation technology, composite pavement structure, asphalt overlay

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Authors: Mst Shapna Akter, Hossain Shahriar

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One of the most important challenges in the field of software code audit is the presence of vulnerabilities in software source code. Every year, more and more software flaws are found, either internally in proprietary code or revealed publicly. These flaws are highly likely exploited and lead to system compromise, data leakage, or denial of service. C and C++ open-source code are now available in order to create a largescale, machine-learning system for function-level vulnerability identification. We assembled a sizable dataset of millions of opensource functions that point to potential exploits. We developed an efficient and scalable vulnerability detection method based on deep neural network models that learn features extracted from the source codes. The source code is first converted into a minimal intermediate representation to remove the pointless components and shorten the dependency. Moreover, we keep the semantic and syntactic information using state-of-the-art word embedding algorithms such as glove and fastText. The embedded vectors are subsequently fed into deep learning networks such as LSTM, BilSTM, LSTM-Autoencoder, word2vec, BERT, and GPT-2 to classify the possible vulnerabilities. Furthermore, we proposed a neural network model which can overcome issues associated with traditional neural networks. Evaluation metrics such as f1 score, precision, recall, accuracy, and total execution time have been used to measure the performance. We made a comparative analysis between results derived from features containing a minimal text representation and semantic and syntactic information. We found that all of the deep learning models provide comparatively higher accuracy when we use semantic and syntactic information as the features but require higher execution time as the word embedding the algorithm puts on a bit of complexity to the overall system.

Keywords: cyber security, vulnerability detection, neural networks, feature extraction

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Authors: Kishor Chandra Kandpal, Amit Kumar

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The Indian Himalaya region harbours approximately 1748 plants of medicinal importance, and as per International Union for Conservation of Nature (IUCN), the 112 plant species among these are threatened and endangered. To ease the pressure on these plants, the government of India is encouraging its in-situ cultivation. The Saussurea costus, Valeriana jatamansi, and Picrorhiza kurroa have also been prioritized for large scale cultivation owing to their market demand, conservation value and medicinal properties. These species are found from 1000 m to 4000 m elevation ranges in the Indian Himalaya. Identification of these plants in the field requires taxonomic skills, which is one of the major bottleneck in the conservation and management of these plants. In recent years, Hyperspectral remote sensing techniques have been precisely used for the discrimination of plant species with the help of their unique spectral signatures. In this background, a spectral library of the above 03 medicinal plants was prepared by collecting the spectral data using a handheld spectroradiometer (325 to 1075 nm) from farmer’s fields of Himachal Pradesh and Uttarakhand states of Indian Himalaya. The Random forest (RF) model was implied on the spectral data for the classification of the medicinal plants. The 80:20 standard split ratio was followed for training and validation of the RF model, which resulted in training accuracy of 84.39 % (kappa coefficient = 0.72) and testing accuracy of 85.29 % (kappa coefficient = 0.77). This RF classifier has identified green (555 to 598 nm), red (605 nm), and near-infrared (725 to 840 nm) wavelength regions suitable for the discrimination of these species. The findings of this study have provided a technique for rapid and onsite identification of the above medicinal plants in the field. This will also be a key input for the classification of hyperspectral remote sensing images for mapping of these species in farmer’s field on a regional scale. This is a pioneer study in the Indian Himalaya region for medicinal plants in which the applicability of hyperspectral remote sensing has been explored.

Keywords: himalaya, hyperspectral remote sensing, machine learning; medicinal plants, random forests

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Authors: Dhekra Khazri, Hakim Gabtni

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Because of Sidi Bouzid water table overexploitation, this study aims at integrating geophysical methods to determinate aquifers geometry assessing their geological situation and geophysical characteristics. However in highly tectonic zones controlled by Atlassic structural features with NE-SW major directions (central Tunisia), Bouguer gravimetric responses of some areas can be as much dominated by the regional structural tendency, as being non-identified or either defectively interpreted such as the case of Sidi Bouzid basin. This issue required a residual gravity anomaly elaboration isolating the Sidi Bouzid basin gravity response ranging between -8 and -14 mGal and crucial for its aquifers geometry characterization. Several gravity techniques helped constructing the Sidi Bouzid basin's residual gravity anomaly, such as Upwards continuation compared to polynomial regression trends and power spectrum analysis detecting deep basement sources at (3km), intermediate (2km) and shallow sources (1km). A 3D Euler Deconvolution was also performed detecting deepest accidents trending NE-SW, N-S and E-W with depth values reaching 5500 m and delineating the main outcropping structures of the study area. Further gravity treatments highlighted the subsurface geometry and structural features of Sidi Bouzid basin over Horizontal and vertical gradient, and also filters based on them such as Tilt angle and Source Edge detector locating rooted edges or peaks from potential field data detecting a new E-W lineament compartmentalizing the Sidi Bouzid gutter into two unequally residual anomaly and subsiding domains. This subsurface morphology is also detected by the used 2D seismic reflection sections defining the Sidi Bouzid basin as a deep gutter within a tectonic set of negative flower structures, and collapsed and tilted blocks. Furthermore, these structural features were confirmed by forward gravity modeling process over several modeled residual gravity profiles crossing the main area. Sidi Bouzid basin (central Tunisia) is also of a big interest cause of the unknown total thickness and the undefined substratum of its siliciclastic Tertiary package, and its aquifers unbounded structural subsurface features and deep accidents. The Combination of geological, hydrogeological and geophysical methods is then of an ultimate need. Therefore, a geophysical methods integration based on gravity survey supporting available seismic data through forward gravity modeling, enhanced lateral and vertical extent definition of the basin's complex sedimentary fill via 3D gravity models, improved depth estimation by a depth to basement modeling approach, and provided 3D isochronous seismic mapping visualization of the basin's Tertiary complex refining its geostructural schema. A subsurface basin geomorphology mapping, over an ultimate matching between the basin's residual gravity map and the calculated theoretical signature map, was also displayed over the modeled residual gravity profiles. An ultimate multidisciplinary geophysical study of the Sidi Bouzid basin aquifers can be accomplished via an aeromagnetic survey and a 4D Microgravity reservoir monitoring offering temporal tracking of the target aquifer's subsurface fluid dynamics enhancing and rationalizing future groundwater exploitation in this arid area of central Tunisia.

Keywords: aquifer geometry, geophysics, 3D gravity modeling, improved depths, source edge detector

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Authors: Hervice Roméo Fogno Fotsoa, Germaine Djuidje Kenmoe, Claude Vidal Aloyem Kazé

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One of the main applications of machine learning is the prediction of time series. But a more accurate prediction requires a more optimal model of machine learning. Several optimization techniques have been developed, but without considering the input variables disposition of the system. Thus, this work aims to present a new machine learning architecture optimization technique based on their optimal input variables disposition. The validations are done on the prediction of wind time series, using data collected in Cameroon. The number of possible dispositions with four input variables is determined, i.e., twenty-four. Each of the dispositions is used to perform the prediction, with the main criteria being the training and prediction performances. The results obtained from a static architecture and a dynamic architecture of neural networks have shown that these performances are a function of the input variable's disposition, and this is in a different way from the architectures. This analysis revealed that it is necessary to take into account the input variable's disposition for the development of a more optimal neural network model. Thus, a new neural network training algorithm is proposed by introducing the search for the optimal input variables disposition in the traditional back-propagation algorithm. The results of the application of this new optimization approach on the two single neural network architectures are compared with the previously obtained results step by step. Moreover, this proposed approach is validated in a collaborative optimization method with a single objective optimization technique, i.e., genetic algorithm back-propagation neural networks. From these comparisons, it is concluded that each proposed model outperforms its traditional model in terms of training and prediction performance of time series. Thus the proposed optimization approach can be useful in improving the accuracy of time series forecasts. This proves that the proposed optimization approach can be useful in improving the accuracy of time series prediction based on machine learning.

Keywords: input variable disposition, machine learning, optimization, performance, time series prediction

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Authors: Hamadi Djamal, Labiodh Bachir, Ounis Abdelhafid, Chaalane Mourad

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The objective of this work is setting numerically operational finite element CAXI_L for the axisymmetric and nonaxisymmetric shells. This element is based on the Reissner-Mindlin theory and mixed model formulation. The MATLAB language is used for the programming. In order to test the elaborated program, some applications are carried out.

Keywords: axisymmetric shells, nonaxisymmetric behaviour, finite element, MATLAB programming

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Authors: Saurabh Sharma, Yong Sun, Bruce Vernham

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Accurate prediction of NOx emission is a continuous challenge in the field of diesel engine-out emission modeling. Performing experiments for each conditions and scenario cost significant amount of money and man hours, therefore model-based development strategy has been implemented in order to solve that issue. NOx formation is highly dependent on the burn gas temperature and the O₂ concentration inside the cylinder. The current empirical models are developed by calibrating the parameters representing the engine operating conditions with respect to the measured NOx. This makes the prediction of purely empirical models limited to the region where it has been calibrated. An alternative solution to that is presented in this paper, which focus on the utilization of in-cylinder combustion parameters to form a predictive semi-empirical NOx model. The result of this work is shown by developing a fast and predictive NOx model by using the physical parameters and empirical correlation. The model is developed based on the steady state data collected at entire operating region of the engine and the predictive combustion model, which is developed in Gamma Technology (GT)-Power by using Direct Injected (DI)-Pulse combustion object. In this approach, temperature in both burned and unburnt zone is considered during the combustion period i.e. from Intake Valve Closing (IVC) to Exhaust Valve Opening (EVO). Also, the oxygen concentration consumed in burnt zone and trapped fuel mass is also considered while developing the reported model.  Several statistical methods are used to construct the model, including individual machine learning methods and ensemble machine learning methods. A detailed validation of the model on multiple diesel engines is reported in this work. Substantial numbers of cases are tested for different engine configurations over a large span of speed and load points. Different sweeps of operating conditions such as Exhaust Gas Recirculation (EGR), injection timing and Variable Valve Timing (VVT) are also considered for the validation. Model shows a very good predictability and robustness at both sea level and altitude condition with different ambient conditions. The various advantages such as high accuracy and robustness at different operating conditions, low computational time and lower number of data points requires for the calibration establishes the platform where the model-based approach can be used for the engine calibration and development process. Moreover, the focus of this work is towards establishing a framework for the future model development for other various targets such as soot, Combustion Noise Level (CNL), NO₂/NOx ratio etc.

Keywords: diesel engine, machine learning, NOₓ emission, semi-empirical

Procedia PDF Downloads 105

Authors: Kumari Sweta, Ajanta Goswami, Abhilasha Dixit

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Landslide is a geomorphic process that plays an essential role in the evolution of the hill-slope and long-term landscape evolution. But its abrupt nature and the associated catastrophic forces of the process can have undesirable socio-economic impacts, like substantial economic losses, fatalities, ecosystem, geomorphologic and infrastructure disturbances. The estimated fatality rate is approximately 1person /100 sq. Km and the average economic loss is more than 550 crores/year in the Himalayan belt due to landslides. This study presents a comparative performance of a statistical bivariate method and a machine learning technique for landslide susceptibility mapping in and around Dharamshala, Himachal Pradesh. The final produced landslide susceptibility maps (LSMs) with better accuracy could be used for land-use planning to prevent future losses. Dharamshala, a part of North-western Himalaya, is one of the fastest-growing tourism hubs with a total population of 30,764 according to the 2011 census and is amongst one of the hundred Indian cities to be developed as a smart city under PM’s Smart Cities Mission. A total of 209 landslide locations were identified in using high-resolution linear imaging self-scanning (LISS IV) data. The thematic maps of parameters influencing landslide occurrence were generated using remote sensing and other ancillary data in the GIS environment. The landslide causative parameters used in the study are slope angle, slope aspect, elevation, curvature, topographic wetness index, relative relief, distance from lineaments, land use land cover, and geology. LSMs were prepared using information value (Info Val), and Fuzzy Expert System (FES) models. Info Val is a statistical bivariate method, in which information values were calculated as the ratio of the landslide pixels per factor class (Si/Ni) to the total landslide pixel per parameter (S/N). Using this information values all parameters were reclassified and then summed in GIS to obtain the landslide susceptibility index (LSI) map. The FES method is a machine learning technique based on ‘mean and neighbour’ strategy for the construction of fuzzifier (input) and defuzzifier (output) membership function (MF) structure, and the FR method is used for formulating if-then rules. Two types of membership structures were utilized for membership function Bell-Gaussian (BG) and Trapezoidal-Triangular (TT). LSI for BG and TT were obtained applying membership function and if-then rules in MATLAB. The final LSMs were spatially and statistically validated. The validation results showed that in terms of accuracy, Info Val (83.4%) is better than BG (83.0%) and TT (82.6%), whereas, in terms of spatial distribution, BG is best. Hence, considering both statistical and spatial accuracy, BG is the most accurate one.

Keywords: bivariate statistical techniques, BG and TT membership structure, fuzzy expert system, information value method, machine learning technique

Procedia PDF Downloads 118

Authors: Majid Yazdandoust

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This study, tries to suggest a design method based on displacement using finite difference numerical modeling in reinforcing soil retaining wall with steel strip. In this case, dynamic loading characteristics such as duration, frequency, peak ground acceleration, geometrical characteristics of reinforced soil structure and type of the site are considered to correct the pseudo static method and finally introduce the pseudo static coefficient as a function of seismic performance level and peak ground acceleration. For this purpose, the influence of dynamic loading characteristics, reinforcement length, height of reinforced system and type of the site are investigated on seismic behavior of reinforcing soil retaining wall with steel strip. Numerical results illustrate that the seismic response of this type of wall is highly dependent to cumulative absolute velocity, maximum acceleration, and height and reinforcement length so that the reinforcement length can be introduced as the main factor in shape of failure. Considering the loading parameters, mechanically stabilized earth wall parameters and type of the site showed that the used method in this study leads to most efficient designs in comparison with other methods which are generally suggested in cods that are usually based on limit-equilibrium concept. The outputs show the over-estimation of equilibrium design methods in comparison with proposed displacement based methods here.

Keywords: pseudo static coefficient, seismic performance design, numerical modeling, steel strip reinforcement, retaining walls, cumulative absolute velocity, failure shape

Procedia PDF Downloads 479

Authors: N. Nivedita, S. Durbha

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Critical Infrastructure (CI) consists of services and technological networks such as healthcare, transport, water supply, electricity supply, information technology etc. These systems are necessary for the well-being and to maintain effective functioning of society. Critical Infrastructures can be represented as nodes in a network where they are connected through a set of links depicting the logical relationship among them; these nodes are interdependent on each other and interact with each at other at various levels, such that the state of each infrastructure influences or is correlated to the state of another. Disruption in the service of one infrastructure nodes of the network during a disaster would lead to cascading and escalating disruptions across other infrastructures nodes in the network. The operation of Healthcare Infrastructure is one such Critical Infrastructure that depends upon a complex interdependent network of other Critical Infrastructure, and during disasters it is very vital for the Healthcare Infrastructure to be protected, accessible and prepared for a mass casualty. To reduce the consequences of a disaster on the Critical Infrastructure and to ensure a resilient Critical Health Infrastructure network, knowledge, understanding, modeling, and analyzing the inter-dependencies between the infrastructures is required. The paper would present inter-dependencies related to Healthcare Critical Infrastructure based on Hierarchical Coloured Petri Nets modeling approach, given a flood scenario as the disaster which would disrupt the infrastructure nodes. The model properties are being analyzed for the various state changes which occur when there is a disruption or damage to any of the Critical Infrastructure. The failure probabilities for the failure risk of interconnected systems are calculated by deriving a reachability graph, which is later mapped to a Markov chain. By analytically solving and analyzing the Markov chain, the overall vulnerability of the Healthcare CI HCPN model is demonstrated. The entire model would be integrated with Geographic information-based decision support system to visualize the dynamic behavior of the interdependency of the Healthcare and related CI network in a geographically based environment.

Keywords: critical infrastructure interdependency, hierarchical coloured petrinet, healthcare critical infrastructure, Petri Nets, Markov chain

Procedia PDF Downloads 515

Authors: N. Gharbi

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Constraint Satisfaction problems are mathematical problems that are often used to model many real-world problems for which we look if there exists a solution satisfying all its constraints. Table constraints are important for modeling parts of many problems since they list all combinations of allowed or forbidden values. However, they admit practical limitations because they are sometimes too large to be represented in a direct way. In this paper, we present a survey of the different categories of the proposed approaches to compress table constraints in order to reduce both space and time complexities.

Keywords: constraint programming, compression, data mining, table constraints

Procedia PDF Downloads 316

Authors: Nikolaos Papadimas, Joanna Bennett, Amir Sakhaei, Timothy Dodwell

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Composite modeling of consolidation processes is playing an important role in the process and part design by indicating the formation of possible unwanted prior to expensive experimental iterative trial and development programs. Composite materials in their uncured state display complex constitutive behavior, which has received much academic interest, and this with different models proposed. Errors from modeling and statistical which arise from this fitting will propagate through any simulation in which the material model is used. A general hyperelastic polynomial representation was proposed, which can be readily implemented in various nonlinear finite element packages. In our case, FEniCS was chosen. The coefficients are assumed uncertain, and therefore the distribution of parameters learned using Markov Chain Monte Carlo (MCMC) methods. In engineering, the approach often followed is to select a single set of model parameters, which on average, best fits a set of experiments. There are good statistical reasons why this is not a rigorous approach to take. To overcome these challenges, A hierarchical Bayesian framework was proposed in which population distribution of model parameters is inferred from an ensemble of experiments tests. The resulting sampled distribution of hyperparameters is approximated using Maximum Entropy methods so that the distribution of samples can be readily sampled when embedded within a stochastic finite element simulation. The methodology is validated and demonstrated on a set of consolidation experiments of AS4/8852 with various stacking sequences. The resulting distributions are then applied to stochastic finite element simulations of the consolidation of curved parts, leading to a distribution of possible model outputs. With this, the paper, as far as the authors are aware, represents the first stochastic finite element implementation in composite process modelling.

Keywords: data-driven , material consolidation, stochastic finite elements, surrogate models

Procedia PDF Downloads 134

Authors: Raphaël Menard, Etienne Fayette, Paul Azzopardi

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This paper presents the method to generate the geometry of an actual glass canopy project in Rennes, France, by architect Bruno Gaudin, with aim to achieve the best structural efficiency possible using only quadrangle meshing. The paper includes equation of the translational surface generated, the level of accuracy in approximating the funicular shape and the method of constructive implementation.

Keywords: funicular shape, glass canopy, glass panels, lowered arches, mathematics, penalization, shell structure

Procedia PDF Downloads 542

Authors: Haoxiang Desmond Lim, Jie Wu, Tze How Daniel New, Shengxian Shi

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This paper outlines the development of a novel experimental technique in quantifying supersonic jet flows, in an attempt to avoid seeding particle problems frequently associated with particle-image velocimetry (PIV) techniques at high Mach numbers. Based on optical flow algorithms, the idea behind the technique involves using high speed cameras to capture Schlieren images of the supersonic jet shear layers, before they are subjected to an adapted optical flow algorithm based on the Horn-Schnuck method to determine the associated flow fields. The proposed method is capable of offering full-field unsteady flow information with potentially higher accuracy and resolution than existing point-measurements or PIV techniques. Preliminary study via numerical simulations of a circular de Laval jet nozzle successfully reveals flow and shock structures typically associated with supersonic jet flows, which serve as useful data for subsequent validation of the optical flow based experimental results. For experimental technique, a Z-type Schlieren setup is proposed with supersonic jet operated in cold mode, stagnation pressure of 8.2 bar and exit velocity of Mach 1.5. High-speed single-frame or double-frame cameras are used to capture successive Schlieren images. As implementation of optical flow technique to supersonic flows remains rare, the current focus revolves around methodology validation through synthetic images. The results of validation test offers valuable insight into how the optical flow algorithm can be further improved to improve robustness and accuracy. Details of the methodology employed and challenges faced will be further elaborated in the final conference paper should the abstract be accepted. Despite these challenges however, this novel supersonic flow measurement technique may potentially offer a simpler way to identify and quantify the fine spatial structures within the shock shear layer.

Keywords: Schlieren, optical flow, supersonic jets, shock shear layer

Procedia PDF Downloads 307

Authors: Sonia Besbes, Mahmoud El Hajem, Habib Ben Aissia, Jean Yves Champagne, Jacques Jay

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In this work, we present a computational study for the characterization of the flow in a rectangular bubble column. To simulate the dynamic characteristics of the flow, a three-dimensional transient numerical simulations based on a coupled discrete phase model (DPM) and Volume of Fluid (VOF) model are performed. Modeling of bubble column reactor is often carried out under the assumption of a flat liquid surface with a degassing boundary condition. However, the dynamic behavior of the top surface surmounting the liquid phase will to some extent influence the meandering oscillations of the bubble plume. Therefore it is important to capture the surface behavior, and the assumption of a flat surface may not be applicable. So, the modeling approach needs to account for a dynamic liquid surface induced by the rising bubble plume. The volume of fluid (VOF) model was applied for the liquid and top gas which both interacts with bubbles implemented with a discrete phase model. This model treats the bubbles as Lagrangian particles and the liquid and the top gas as Eulerian phases with a sharp interface. Two-way coupling between Eulerian phases and Lagrangian bubbles are accounted for in a single set continuous phase momentum equation for the mixture of the two Eulerian phases. The effect of gas flow rate on the dynamic and time-averaged flow properties was studied. The time averaged liquid velocity field predicted from simulations and from our previous PIV measurements shows that the liquid is entrained up flow in the wake of the bubbles and down flow near the walls. The simulated and measured vertical velocity profiles exhibit a reasonable agreement looking at the minimum velocity values near the walls and the maximum values at the column center.

Keywords: bubble column, computational fluid dynamics (CFD), coupled DPM and VOF model, hydrodynamics

Procedia PDF Downloads 379

Authors: Dona Abdulwassi, Dhaee Dahlawi, Yara Zainy, Leen Joharji

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The relationship between Netflix and artificial intelligence is discussed in this paper. Netflix uses the most effective and efficient approaches to apply artificial intelligence, machine learning, and data science. Netflix employs the personalization tool for their users, recommending or suggesting shows based on what those users have already watched. The researchers conducted an experiment to learn more about how Netflix is used and how AI affects the user experience. The main conclusions of this study are that Netflix has a wide range of AI features, most users are happy with their Netflix subscriptions, and the majority prefer Netflix to alternative apps.

Keywords: easy accessibility, recommends, accuracy, privacy

Procedia PDF Downloads 58

Authors: Kiana Zeighami, Morteza Ozlati Moghadam

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Designing a high accuracy and high precision motion controller is one of the important issues in today’s industry. There are effective solutions available in the industry but the real-time performance, smoothness and accuracy of the movement can be further improved. This paper discusses a complete solution to carry out the movement of three stepper motors in three dimensions. The objective is to provide a method to design a fully integrated System-on-Chip (SOC)-based motion controller to reduce the cost and complexity of production by incorporating Field Programmable Gate Array (FPGA) into the design. In the proposed method the FPGA receives its commands from a host computer via wireless internet communication and calculates the motion trajectory for three axes. A profile generator module is designed to realize the interpolation algorithm by translating the position data to the real-time pulses. This paper discusses an approach to implement the linear interpolation algorithm, since it is one of the fundamentals of robots’ movements and it is highly applicable in motion control industries. Along with full profile trajectory, the triangular drive is implemented to eliminate the existence of error at small distances. To integrate the parallelism and real-time performance of FPGA with the power of Central Processing Unit (CPU) in executing complex and sequential algorithms, the NIOS II soft-core processor was added into the design. This paper presents different operating modes such as absolute, relative positioning, reset and velocity modes to fulfill the user requirements. The proposed approach was evaluated by designing a custom-made FPGA board along with a mechanical structure. As a result, a precise and smooth movement of stepper motors was observed which proved the effectiveness of this approach.

Keywords: 3-axis linear interpolation, FPGA, motion controller, micro-stepping

Procedia PDF Downloads 198