Search results for: deep groundwater potential

Authors: Seok Woo Chang

Abstract:

Aim: Capseal I and Capseal II are calcium silicate and calcium phosphate based experimental root canal sealer. The aim of this study was to evaluate the biocompatibility and mineralization potential of Capseal I and Capseal II. Materials and Methods: The biocompatibility and mineralization-related gene expression (alkaline phosphatase (ALP), bone sialoprotein (BSP), and osteocalcin (OCN)) of Capseal I and Capseal II were compared using methylthiazol tetrazolium assay and reverse transcription-polymerization chain reaction analysis, respectively. The results were analyzed by Kruskal-Wallis test. P-value of < 0.05 was considered significant. Result: Both Capseal I and Capseal II were favorable in biocompatibility and influenced the messenger RNA expression of ALP and BSP. Conclusion: Within the limitation of this study, Capseal is biocompatible and have mineralization promoting potential, and thus could be a promising root canal sealer.

Keywords: biocompatibility, mineralization-related gene expression, Capseal I, Capseal II

Procedia PDF Downloads 260

Authors: K. Raghuwaiya, S. Singh, B. Sharma, J. Vanualailai

Abstract:

This paper presents a set of artificial potential field functions that improves upon; in general, the motion planning and posture control, with theoretically guaranteed point and posture stabilities, convergence and collision avoidance properties of 3-trailer systems in a priori known environment. We basically design and inject two new concepts; ghost walls and the Distance Optimization Technique (DOT) to strengthen point and posture stabilities, in the sense of Lyapunov, of our dynamical model. This new combination of techniques emerges as a convenient mechanism for obtaining feasible orientations at the target positions with an overall reduction in the complexity of the navigation laws. The effectiveness of the proposed control laws were demonstrated via simulations of two traffic scenarios.

Keywords: artificial potential fields, 3-trailer systems, motion planning, posture, parking and collision, free trajectories

Procedia PDF Downloads 367

Authors: Manel Boulakoud, Abdelkader Chouaih, Fodil Hamzaoui

Abstract:

In the present work we are interested in the determination of the Molecular electrostatic potential (MEP) in Z-3N(2-Ethoxyphenyl), 2-N’(2-Ethoxyphenyl) imino thiazolidin-4-one molecule by ab initio and Density Functional Theory (DFT) in the ground state. The MEP is related to the electronic density and is a very useful descriptor in understanding sites for electrophilic attack and nucleophilic reactions as well as hydrogen bonding interactions. First, geometry optimization was carried out using Hartree–Fock (HF) and DFT methods with 6-311G(d,p) basis set. In order to get more information on the molecule, its stability has been analyzed by natural bond orbital (NBO) analysis. Mulliken population analyses have been calculated. Finally, the molecular electrostatic potential (MEP) and HOMO-LUMO energy levels have been performed. The calculated HOMO and LUMO energies show also the charge transfer within the molecule. The energy gap obtained is about 4 eV which explain the stability of the studied compound. The obtained molecular electrostatic potential from the two methods confirms the nature of the electron charge transfer at the molecular shell and locate the electropositive part and the electronegative part in molecular scale of the title compound.

Keywords: DFT, ab initio, HOMO-LUMO, organic compounds

Procedia PDF Downloads 518

Authors: Ozgu Hafizoglu

Abstract:

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

Procedia PDF Downloads 169

Authors: Xu Jiaqiao

Abstract:

Text sentiment analysis is an important branch of natural language processing. This technology is widely used in public opinion analysis and web surfing recommendations. At present, the mainstream sentiment analysis methods include three parts: sentiment analysis based on a sentiment dictionary, based on traditional machine learning, and based on deep learning. This paper mainly analyzes and compares the advantages and disadvantages of the SVM method of traditional machine learning and the Long Short-term Memory (LSTM) method of deep learning in the field of Chinese sentiment analysis, using Chinese comments on Sina Microblog as the data set. Firstly, this paper classifies and adds labels to the original comment dataset obtained by the web crawler, and then uses Jieba word segmentation to classify the original dataset and remove stop words. After that, this paper extracts text feature vectors and builds document word vectors to facilitate the training of the model. Finally, SVM and LSTM models are trained respectively. After accuracy calculation, it can be obtained that the accuracy of the LSTM model is 85.80%, while the accuracy of SVM is 91.07%. But at the same time, LSTM operation only needs 2.57 seconds, SVM model needs 6.06 seconds. Therefore, this paper concludes that: compared with the SVM model, the LSTM model is worse in accuracy but faster in processing speed.

Keywords: sentiment analysis, support vector machine, long short-term memory, Chinese microblog comments

Procedia PDF Downloads 75

Authors: Johnstone Walubengo Wangusi

Abstract:

Geotechnical engineering is a multidisciplinary field that encompasses the study of soil, rock, and groundwater properties and their interactions with civil engineering structures. This research paper provides an in-depth overview of geotechnical engineering, covering its fundamental principles, applications in civil infrastructure projects, and the challenges faced by practitioners in the field. Through a comprehensive examination of soil mechanics, foundation design, slope stability analysis, and geotechnical site investigation techniques, this paper aims to highlight the importance of geotechnical engineering in ensuring the safety, stability, and sustainability of infrastructure development. Additionally, it discusses emerging trends, innovative technologies, and future directions in geotechnical engineering research and practice.

Keywords: sustainable geotechnical engineering solutions, education and training for future generations geotechnical engineers, integration of geotechnical engineering and structural engineering, use of AI in geotechnical engineering modelling

Procedia PDF Downloads 31

Authors: R. A. Mandour

Abstract:

The physical, chemical, and microbiological analyses of fifty Quaternary water samples representing the different types of drinking water (surface and wells) in the governorate were carried-out. This paper aims to evaluate the drinking water in Dakahlia governorate in comparison with the national and international standards as a step to handle water pollutants affecting human health in this governorate. All investigated water samples were chemically considered suitable for drinking except two samples for iron, two samples for lead and one water sample for manganese having values higher than the permissible limit of EMH and WHO. Also microbiologically there were five water samples having a high total count of bacteria and three samples having high coli form than the permissible limit of EMH. Obviously, groundwater samples from Mit-Ghamr, El-Sinbillawin and Aga districts of Dakahlia governorate should have special attention for treatment.

Keywords: major ions, minor elements, microbiology, EMH, WHO

Procedia PDF Downloads 361

Authors: Hichem Sassi

Abstract:

Pneumonia, being a respiratory infection, has garnered global attention due to its rapid transmission and relatively high mortality rates. Timely detection and treatment play a crucial role in significantly reducing mortality associated with pneumonia. Presently, X-ray diagnosis stands out as a reasonably effective method. However, the manual scrutiny of a patient's X-ray chest radiograph by a proficient practitioner usually requires 5 to 15 minutes. In situations where cases are concentrated, this places immense pressure on clinicians for timely diagnosis. Relying solely on the visual acumen of imaging doctors proves to be inefficient, particularly given the low speed of manual analysis. Therefore, the integration of artificial intelligence into the clinical image diagnosis of pneumonia becomes imperative. Additionally, AI recognition is notably rapid, with convolutional neural networks (CNNs) demonstrating superior performance compared to human counterparts in image identification tasks. To conduct our study, we utilized a dataset comprising chest X-ray images obtained from Kaggle, encompassing a total of 5216 training images and 624 test images, categorized into two classes: normal and pneumonia. Employing five mainstream network algorithms, we undertook a comprehensive analysis to classify these diseases within the dataset, subsequently comparing the results. The integration of artificial intelligence, particularly through improved network architectures, stands as a transformative step towards more efficient and accurate clinical diagnoses across various medical domains.

Keywords: deep learning, computer vision, pneumonia, models, comparative study

Procedia PDF Downloads 41

Authors: Yongjian Gu

Abstract:

Ocean thermal energy is one of the ocean energy sources. It is a renewable, sustainable, and green energy source. Ocean thermal energy conversion (OTEC) applies the ocean temperature gradient between the warmer surface seawater and the cooler deep seawater to run a heat engine and produce a useful power output. Unfortunately, the ocean temperature gradient is not big. Even in the tropical and equatorial regions, the surface water temperature can only reach up to 28oC and the deep water temperature can be as low as 4oC. The thermal efficiency of the OTEC plants, therefore, is low. In order to improve the plant thermal efficiency by using the limited ocean temperature gradient, some OTEC plants use the method of adding more equipment for better heat recovery, such as heat exchangers, pumps, etc. Obviously, the method will increase the plant's complexity and cost. The more important impact of the method is the additional equipment needs to consume power too, which may have an adverse effect on the plant net power output, in turn, the plant thermal efficiency. In the paper, the author first describes varied OTEC plants and the practice of using the method of adding more equipment for improving the plant's thermal efficiency. Then the author proposes a parameter, plant back works ratio ϕ, for measuring if the added equipment is appropriate for the plant thermal efficiency improvement. Finally, in the paper, the author presents examples to illustrate the application of the back work ratio ϕ as a key parameter in the OTEC plant design and operation.

Keywords: ocean thermal energy, ocean thermal energy conversion (OTEC), OTEC plant, plant back work ratio ϕ

Procedia PDF Downloads 178

Authors: S. Shamohammadi, L. Razavi

Abstract:

This paper based on the principle concepts of SCS-CN model, a new mathematical model for computation of retention potential (S) presented. In the mathematical model, not only precipitation-runoff concepts in SCS-CN model are precisely represented in a mathematical form, but also new concepts, called “maximum retention” and “total retention” is introduced, and concepts of potential retention capacity, maximum retention, and total retention have been separated from each other. In the proposed model, actual retention (F), maximum actual retention (Fmax), total retention (S), maximum retention (Smax), and potential retention (Sp), for the first time clearly defined, so that Sp is not variable, but a function of morphological characteristics of the watershed. Indeed, based on the mathematical relation of the conceptual curve of SCS-CN model, the proposed model provides a new method for the computation of actual retention in watershed and it simply determined runoff based on. In the corresponding relations, in addition to Precipitation (P), Initial retention (Ia), cumulative values of actual retention capacity (F), total retention (S), runoff (Q), antecedent moisture (M), potential retention (Sp), total retention (S), we introduced Fmax and Fmin referring to maximum and minimum actual retention, respectively. As well as, ksh is a coefficient which depends on morphological characteristics of the watershed. Advantages of the modified version versus the original model include a better precision, higher performance, easier calibration and speed computing.

Keywords: model, mathematical, retention, watershed, SCS

Procedia PDF Downloads 438

Authors: Jian Li

Abstract:

The uncertainty quantification and inversion problems of subsurface oil-water phase flow usually require extensive repeated forward calculations for new runs with changed conditions. To reduce the computational time, various forms of surrogate models have been built. Related research shows that deep learning has emerged as an effective surrogate model, while most surrogate models with deep learning are purely data-driven, which always leads to poor robustness and abnormal results. To guarantee the model more consistent with the physical laws, a coupled theory-guided convolutional neural network (TgCNN) based surrogate model is built to facilitate computation efficiency under the premise of satisfactory accuracy. The model is a convolutional neural network based on multi-well reservoir simulation. The core notion of this proposed method is to bridge two separate blocks on top of an overall network. They underlie the TgCNN model in a coupled form, which reflects the coupling nature of pressure and water saturation in the two-phase flow equation. The model is driven by not only labeled data but also scientific theories, including governing equations, stochastic parameterization, boundary, and initial conditions, well conditions, and expert knowledge. The results show that the TgCNN-based surrogate model exhibits satisfactory accuracy and efficiency in subsurface oil-water phase flow under multi-well conditions.

Keywords: coupled theory-guided convolutional neural network, multi-well conditions, surrogate model, subsurface oil-water phase

Procedia PDF Downloads 67

Authors: Parsa Mahdavi, M. Amin Hariri-Ardebili

Abstract:

The development of accurate machine learning and deep learning models traditionally demands hands-on expertise and a solid background to fine-tune hyperparameters. With the continuous expansion of datasets in various scientific and engineering domains, researchers increasingly turn to machine learning methods to unveil hidden insights that may elude classic regression techniques. This surge in adoption raises concerns about the adequacy of the resultant meta-models and, consequently, the interpretation of the findings. In response to these challenges, automated machine learning (AutoML) emerges as a promising solution, aiming to construct machine learning models with minimal intervention or guidance from human experts. AutoML encompasses crucial stages such as data preparation, feature engineering, hyperparameter optimization, and neural architecture search. This paper provides a comprehensive overview of the principles underpinning AutoML, surveying several widely-used AutoML platforms. Additionally, the paper offers a glimpse into the application of AutoML on various engineering datasets. By comparing these results with those obtained through classical machine learning methods, the paper quantifies the uncertainties inherent in the application of a single ML model versus the holistic approach provided by AutoML. These examples showcase the efficacy of AutoML in extracting meaningful patterns and insights, emphasizing its potential to revolutionize the way we approach and analyze complex datasets.

Keywords: automated machine learning, uncertainty, engineering dataset, regression

Procedia PDF Downloads 43

Authors: Yazan Al-Kofahi, Jamal Alqawasmi.

Abstract:

In this study, a comprehensive literature review (SLR) was conducted, with the main goal of assessing the existing literature about how artificial intelligence (AI), machine learning (ML), deep learning (DL) models are used in sustainable architecture applications and issues including thermal comfort satisfaction, energy efficiency, cost prediction and many others issues. For this reason, the search strategy was initiated by using different databases, including Scopus, Springer and Google Scholar. The inclusion criteria were used by two research strings related to DL, ML and sustainable architecture. Moreover, the timeframe for the inclusion of the papers was open, even though most of the papers were conducted in the previous four years. As a paper filtration strategy, conferences and books were excluded from database search results. Using these inclusion and exclusion criteria, the search was conducted, and a sample of 59 papers was selected as the final included papers in the analysis. The data extraction phase was basically to extract the needed data from these papers, which were analyzed and correlated. The results of this SLR showed that there are many applications of ML and DL in Sustainable buildings, and that this topic is currently trendy. It was found that most of the papers focused their discussions on addressing Environmental Sustainability issues and factors using machine learning predictive models, with a particular emphasis on the use of Decision Tree algorithms. Moreover, it was found that the Random Forest repressor demonstrates strong performance across all feature selection groups in terms of cost prediction of the building as a machine-learning predictive model.

Keywords: machine learning, deep learning, artificial intelligence, sustainable building

Procedia PDF Downloads 46

Authors: Mirjana Ruppel, Rajendra Persad, Amit Bahl, Sanja Dogramadzi, Chris Melhuish, Lyndon Smith

Abstract:

Multimodal image registration is a profoundly complex task which is why deep learning has been used widely to address it in recent years. However, two main challenges remain: Firstly, the lack of ground truth data calls for an unsupervised learning approach, which leads to the second challenge of defining a feasible loss function that can compare two images of different modalities to judge their level of alignment. To avoid this issue altogether we implement a generative adversarial network consisting of two registration networks GAB, GBA and two discrimination networks DA, DB connected by spatial transformation layers. GAB learns to generate a deformation field which registers an image of the modality B to an image of the modality A. To do that, it uses the feedback of the discriminator DB which is learning to judge the quality of alignment of the registered image B. GBA and DA learn a mapping from modality A to modality B. Additionally, a cycle-consistency loss is implemented. For this, both registration networks are employed twice, therefore resulting in images ˆA, ˆB which were registered to ˜B, ˜A which were registered to the initial image pair A, B. Thus the resulting and initial images of the same modality can be easily compared. A dataset of liver CT and MRI was used to evaluate the quality of our approach and to compare it against learning and non-learning based registration algorithms. Our approach leads to dice scores of up to 0.80 ± 0.01 and is therefore comparable to and slightly more successful than algorithms like SimpleElastix and VoxelMorph.

Keywords: cycle consistency, deformable multimodal image registration, deep learning, GAN

Procedia PDF Downloads 111

Authors: B. Lokuketagoda, M. Weerakoon, U. Madushan, A. N. Senaratne, K. Y. Abeywardena

Abstract:

Ransomware has become a common threat in past few years and the recent threat reports show an increase of growth in Ransomware infections. Researchers have identified different variants of Ransomware families since 2015. Lack of knowledge of the user about the threat is a major concern. Ransomware detection methodologies are still growing through the industry. Email is the easiest method to send Ransomware to its victims. Uninformed users tend to click on links and attachments without much consideration assuming the emails are genuine. As a solution to this in this paper R-Killer Ransomware detection tool is introduced. Tool can be integrated with existing email services. The core detection Engine (CDE) discussed in the paper focuses on separating suspicious samples from emails and handling them until a decision is made regarding the suspicious mail. It has the capability of preventing execution of identified ransomware processes. On the other hand, Sandboxing and URL analyzing system has the capability of communication with public threat intelligence services to gather known threat intelligence. The R-Killer has its own mechanism developed in its Proactive Monitoring System (PMS) which can monitor the processes created by downloaded email attachments and identify potential Ransomware activities. R-killer is capable of gathering threat intelligence without exposing the user’s data to public threat intelligence services, hence protecting the confidentiality of user data.

Keywords: ransomware, deep learning, recurrent neural networks, email, core detection engine

Procedia PDF Downloads 190

Authors: Ahmad Rouhani

Abstract:

Iran has several potentials for using renewable energies, so use them could significantly contribute to energy supply. The purpose of this paper is to identify the potential of the country and select the appropriate DG technologies with consideration the potential and primary energy resources in the regions. In this context, hybrid energy systems proportionate with the potential of different regions will be determined based on technical, economic, and environmental aspect. In the following, the proposed structure will be optimized in terms of size and cost. DG technologies used in this project include the photovoltaic system, wind turbine, diesel generator, and battery bank. The HOMER software is applied for choosing the appropriate structure and the optimization of system sizing. The results have been analyzed in terms of technical and economic. The performance and the cost of each project demonstrate the appropriate structure of hybrid energy system in that region.

Keywords: feasibility, hybrid energy system, Iran, renewable energy

Procedia PDF Downloads 466

Authors: Andrej Pilipović, Branislav Kovačević, Marina Milović, Lazar Kesić, Saša Pekeč, Leopold Poljaković-Pajnik, Saša Orlović

Abstract:

The effect of landfills on the environment reflects in the dispersion of the contaminants on surrounding soils by the groundwater plume. Such negative effect can be mitigated with the establishment of vegetative buffers surrounding landfills. The “TreeRemEnergy” project funded by the Science Fund of Republic of Serbia – Green program focuses on development of phytobuffers for landfill phytoremediation with the use of Short Rotation Woody Crops (SRWC) plantations that can be further used for the biomass for energy. One of the goals of the project is to select most appropriate poplar (Populus sp.) and willow (Salix sp.) clones through phytorecurrent selection that involves testing of various breeding traits. Physiological parameters serve as a significant contribution to the breeding process aimed to early detection of potential candidates. This study involved testing of the effect of the landfill soils on the photosynthetic processes of the selected poplar and willow candidates. For this purpose, measurements of the gas exchange, chlorophyll content and chlorophyll fluorescence were measured on the tested plants. Obtained results showed that there were differences in the influence of the controlled sources of variation on examined physiological parameters. The effect of clone was significant in all parameters, while the effect of the substrate was not statistically significant in any of measured parameters. However, the effect of interaction Clone×Substrate was significant in intercellular CO2 concentration(ci), stomatal conductance (gs) and transpiration rate (E), suggesting that water regime of the tested clones showed different response to the tested soils. Some clones showed more “generalist” behavior (380, 107/65/9, and PE19/66), while “specialist” behavior was recorded in clones PE4/68, S1-8, and 79/64/2. On the other hand, there was no significant effect of the tested substrate on the pigments content measured with SPAD meter. Results of this study allowed us to narrow the group of clones for further trails in field conditions.

Keywords: clones, net photosynthesis, WUE, transpiration, stomatal conductance, SPAD

Procedia PDF Downloads 37

Authors: A. V. Popa, C. Barna, V. Mihalache

Abstract:

Being the largest port at the Black Sea and functioning as a civil and military nodal point between Europe and Asia, Constantza Port has become a potential target on the terrorist international agenda. The authors use qualitative research based on both face-to-face and online semi-structured interviews with relevant stakeholders (top decision-makers in the Romanian Naval Authority, Romanian Maritime Training Centre, National Company "Maritime Ports Administration" and military staff) in order to detect potential vulnerabilities which might be exploited by terrorists in the case of Constantza Port. Likewise, this will enable bringing together the experts’ opinions on potential mitigation measures. Subsequently, this paper formulates various counter-terrorism policies to enhance the robustness of Constantza Port under potential terror attacks and connects them with the attributions in the field of critical infrastructure protection conferred by the law to the lead national authority for preventing and countering terrorism, namely the Romanian Intelligence Service. Extending the national counterterrorism efforts to an international level, the authors propose the establishment – among the experts of the NATO member states of the Wider Black Sea Region – of a platform for the exchange of know-how and best practices in the field of critical infrastructure protection.

Keywords: Constantza Port, counter-terrorism policies, critical infrastructure protection, security, Wider Black Sea Region

Procedia PDF Downloads 282

Authors: Seokbeen Lim, KyeongSeok Sim, DaKyeong Shin, Gilwon Yoon

Abstract:

Event-related potential (ERP) is one of the useful tools for investigating cognitive reactions. In this study, the potential of ERP components detected after auditory and visual stimuli was examined. Subjects were asked to respond upon stimuli that were of three categories; Target, Non-Target and Standard stimuli. The ERP after stimulus was measured. In the experiment of visual evoked potentials (VEPs), the subjects were asked to gaze at a center point on the monitor screen where the stimuli were provided by the reversal pattern of the checkerboard. In consequence of the VEP experiments, we observed consistent reactions. Each peak voltage could be measured when the ensemble average was applied. Visual stimuli had smaller amplitude and a longer latency compared to that of auditory stimuli. The amplitude was the highest with Target and the smallest with Standard in both stimuli.

Keywords: auditory stimulus, EEG, event related potential, oddball task, visual stimulus

Procedia PDF Downloads 272

Authors: Xiaoyun Zhao, Rami Darwish, Anna Pernestål

Abstract:

Automated vehicles (AVs) have the potential to enhance road capacity, improving road safety and traffic efficiency. Research and development on AVs have been going on for many years. However, when the complicated traffic rules and real situations interacted, AVs fail to make decisions on contradicting situations, and are not able to have control in all conditions due to highly dynamic driving scenarios. This limits AVs’ usage and restricts the full potential benefits that they can bring. Furthermore, regulations, infrastructure development, and public acceptance cannot keep up at the same pace as technology breakthroughs. Facing these challenges, this paper proposes automated vehicle traffic control tower (AVTCT) acting as a safe, efficient and integrated solution for AV control. It introduces a concept of AVTCT for control, management, decision-making, communication and interaction with various aspects in transportation. With the prototype demonstrations and simulations, AVTCT has the potential to overcome the control challenges with AVs and can facilitate AV reaching their full potential. Possible functionalities, benefits as well as challenges of AVTCT are discussed, which set the foundation for the conceptual model, simulation and real application of AVTCT.

Keywords: automated vehicle, connectivity and automation, intelligent transport system, traffic control, traffic safety

Procedia PDF Downloads 123

Authors: Tahani H. Alluhaybi, Leila Mejrissi

Abstract:

In the present theoretical study, we investigated adiabatically the electronic structure of the (BaNa)+ by the use of the ab initio calculation. We optimized a large atomic GTO basis set for Na and Ba atoms. The (BaNa)+ molecular ion is considered a two-electron thank to a non-empirical pseudo-potentials approach applied to Ba and Na cores with the Core Polarization Potentials operator (CPP). Then, we performed the Full Configuration Interaction (FCI) method. Accordingly, we calculated the adiabatic Potential Energy Curves (PECs) and their spectroscopic constants (well depth De, transition energies Te, the equilibrium distances Re, vibrational constant ⍵e, and anharmonic constant ⍵exe) for 10 electronic states in Σ+ symmetry. Then we determined the vibrational level energies and their spacing, and the electric Permanent Dipole Moments (PDM).

Keywords: Ab initio, dipole moment, non-empirical pseudo-potential, potential energy curves, spectroscopic constants, vibrational energy

Procedia PDF Downloads 93

Authors: Ruxandra Barbulescu, Daniel Ioan, Gabriela Ciuprina

Abstract:

The saltatory conduction is the way the action potential is transmitted along a myelinated axon. The potential diffuses along the myelinated compartments and it is regenerated in the Ranvier nodes due to the ion channels allowing the flow across the membrane. For an efficient simulation of populations of neurons, it is important to use reduced order models both for myelinated compartments and for Ranvier nodes and to have control over their accuracy and inner parameters. The paper presents a reduced order model of this neural system which allows an efficient simulation method for the saltatory conduction in myelinated axons. This model is obtained by concatenating reduced order linear models of 1D myelinated compartments and nonlinear 0D models of Ranvier nodes. The models for the myelinated compartments are selected from a series of spatially distributed models developed and hierarchized according to their modeling errors. The extracted model described by a nonlinear PDE of hyperbolic type is able to reproduce the saltatory conduction with acceptable accuracy and takes into account the finite propagation speed of potential. Finally, this model is again reduced in order to make it suitable for the inclusion in large-scale neural circuits.

Keywords: action potential, myelinated segments, nonlinear models, Ranvier nodes, reduced order models, saltatory conduction

Procedia PDF Downloads 140

Authors: Mehran Naghizaderokni, Asscar Janalizadechobbasty

Abstract:

There are more probabilistic and deterministic liquefaction evaluation procedures in order to judge whether liquefaction will occur or not. A review of this approach reveals that there is a need for a comprehensive procedure that accounts for different sources of uncertainty in liquefaction evaluation. In fact, for the same set of input parameters, different methods provide different factors of safety and/or probabilities of liquefaction. To account for the different uncertainties, including both the model and measurement uncertainties, reliability analysis is necessary. This paper has obtained information from Standard Penetration Test (SPT) and some empirical approaches such as: Seed et al, Highway bridge of Japan approach to soil liquefaction, The Overseas Coastal Area Development Institute of Japan (OCDI) and reliability method to studying potential of liquefaction in soil of Babol city in the north of Iran are compared. Evaluation potential of liquefaction in soil of Babol city is an important issue since the soil of some area contains sand, seismic area, increasing level of underground waters and consequently saturation of soil; therefore, one of the most important goals of this paper is to gain suitable recognition of liquefaction potential and find the most appropriate procedure of evaluation liquefaction potential to decrease related damages.

Keywords: reliability analysis, liquefaction, Babol, civil, construction and geological engineering

Procedia PDF Downloads 487

Authors: G. Petruzzelli, F. Pedron, M. Grifoni, M. Barbafieri, I. Rosellini, B. Pezzarossa

Abstract:

As a result of diverse industrial activities, pollution from numerous contaminant affects both groundwater and soils. Many contaminated sites have been discovered in industrialized countries and their remediation is a priority in environmental legislations. The aim of this paper is to provide the evolution of remediation from consolidated invasive technologies to environmental friendly green strategies. Many clean-up technologies have been used. Nowadays the technologies selection is no longer exclusively based on eliminating the source of pollution, but the aim of remediation includes also the recovery of soil quality. “Green remediation”, a strategy based on “soft technologies”, appears the key to tackle the issue of remediation of contaminated sites with the greatest attention to environmental quality, including the preservation of soil functionality.

Keywords: bioremediation, Green Remediation, phytoremediation, remediation technologies, soil

Procedia PDF Downloads 214

Authors: Fazıl Gökgöz, Fahrettin Filiz

Abstract:

Electricity prices have sophisticated features such as high volatility, nonlinearity and high frequency that make forecasting quite difficult. Electricity price has a volatile and non-random character so that, it is possible to identify the patterns based on the historical data. Intelligent decision-making requires accurate price forecasting for market traders, retailers, and generation companies. So far, many shallow-ANN (artificial neural networks) models have been published in the literature and showed adequate forecasting results. During the last years, neural networks with many hidden layers, which are referred to as DNN (deep neural networks) have been using in the machine learning community. The goal of this study is to investigate electricity price forecasting performance of the shallow-ANN and DNN models for the Turkish day-ahead electricity market. The forecasting accuracy of the models has been evaluated with publicly available data from the Turkish day-ahead electricity market. Both shallow-ANN and DNN approach would give successful result in forecasting problems. Historical load, price and weather temperature data are used as the input variables for the models. The data set includes power consumption measurements gathered between January 2016 and December 2017 with one-hour resolution. In this regard, forecasting studies have been carried out comparatively with shallow-ANN and DNN models for Turkish electricity markets in the related time period. The main contribution of this study is the investigation of different shallow-ANN and DNN models in the field of electricity price forecast. All models are compared regarding their MAE (Mean Absolute Error) and MSE (Mean Square) results. DNN models give better forecasting performance compare to shallow-ANN. Best five MAE results for DNN models are 0.346, 0.372, 0.392, 0,402 and 0.409.

Keywords: deep learning, artificial neural networks, energy price forecasting, turkey

Procedia PDF Downloads 276

Authors: Soujanya Pasumarthi

Abstract:

Inverse docking is a relatively new technique that has been used to identify potential receptor targets of small molecules. Our docking software package MDock is well suited for such an application as it is both computationally efficient, yet simultaneously shows adequate results in binding affinity predictions and enrichment tests. As a validation study, we present the first stage results of an inverse-docking study which seeks to identify potential direct targets of PRIMA-1. PRIMA-1 is well known for its ability to restore mutant p53's tumor suppressor function, leading to apoptosis in several types of cancer cells. For this reason, we believe that potential direct targets of PRIMA-1 identified in silico should be experimentally screened for their ability to inhibitcancer cell growth. The highest-ranked human protein of our PRIMA-1 docking results is oxidosqualene cyclase (OSC), which is part of the cholesterol synthetic pathway. The results of two followup experiments which treat OSC as a possible anti-cancer target are promising. We show that both PRIMA-1 and Ro 48-8071, a known potent OSC inhibitor, significantly reduce theviability of BT-474 breast cancer cells relative to normal mammary cells. In addition, like PRIMA-1, we find that Ro 48-8071 results in increased binding of mutant p53 to DNA in BT- 474cells (which highly express p53). For the first time, Ro 48-8071 is shown as a potent agent in killing human breast cancer cells. The potential of OSC as a new target for developing anticancer therapies is worth further investigation.

Keywords: inverse docking, in silico screening, protein-ligand interactions, molecular docking

Procedia PDF Downloads 427

Authors: Bolanle Deborah Motilewa, E.K. Rowland Worlu, Gbenga Mayowa Agboola, Marvellous Aghogho Chidinma Gberevbie

Abstract:

Businesses operating in the modern business world are faced with varying challenges; amongst which is the need to ensure that they are performing their societal function of being responsible in the society in which they operate. This responsibility to society is generally termed as corporate social responsibility. For many years, the practice of corporate social responsibility (CSR) was solely philanthropic, where organizations gave ‘charity’ or ‘alms’ to society, without any link to the organization’s mission and objectives. However, there has arisen a shift in the application of CSR from an act of philanthropy to a strategy with a business model engaged in by organizations to create a win-win situation of performing their societal obligation, whilst simultaneously performing their economic obligation. In more recent times, the term has moved from CSR to creating shared value, which is simply corporate policies and practices that enhance the competitiveness of a business organization while simultaneously advancing social and economic conditions in the communities in which the company operates. Creating shared value has in more recent light found more meaning in underdeveloped countries, faced with deep societal challenges that businesses can solve whilst creating economic value. This study thus reviews literature on CSR, conceptualizing the shift to creating shared value and finally viewing its potential significance in Africa’s development.

Keywords: africapitalism, corporate social responsibility, development, shared value

Procedia PDF Downloads 265

Authors: Wei Xiao, Gangzhi Cai, Xingliang Qin, Hongyan Ren, Zaidong Hua, Zhe Zhu, Hongwei Xiao, Ximin Zheng, Jie Yao, Yanzhen Bi

Abstract:

Myostatin (MSTN) is the master regulator of double muscling phenotype with overgrown muscle and decreased fatness in animals, but its action mode to regulate fat deposition remains to be elucidated. In this study a swin-specific pathway through which MSTN acts to regulate the fat deposition was deciphered. Deep sequenincing of the mRNA and miRNA of fat tissues of MSTN knockout (KO) and wildtype (WT) pigs discovered the positive correlation of myocyte enhancer factor 2C (MEF2C) and fat-inhibiting miR222 expression, and the inverse correlation of miR222 and stearoyl-CoA desaturase 5 (SCD5) expression. SCD5 is rodent-absent and expressed only in pig, sheep and cattle. Fatty acid spectrum of fat tissues revealed a lower percentage of oleoyl-CoA (18:1) and palmitoleyl CoA (16:1) in MSTN KO pigs, which are the catalyzing products of SCD5-mediated desaturation of steroyl CoA (18:0) and palmitoyl CoA (16:0). Blood metrics demonstrated a 45% decline of triglyceride (TG) content in MSTN KO pigs. In light of these observations we hypothesized that MSTN might act through MEF2C/miR222/SCD5 pathway to regulate desaturation of fatty acid as well as triglyceride synthesis in pigs. To this end, real-time PCR and Western blotting were carried out to detect the expression of the three genes stated above. These experiments showed that MEF2C expression was up-regulated by nearly 2-fold, miR222 up-regulated by nearly 3-fold and SCD5 down-regulated by nearly 50% in MSTN KO pigs. These data were consistent with the expression change in deep sequencing analysis. Dual luciferase reporter was then used to confirm the regulation of MEF2C upon the promoter of miR222. Ecotopic expression of MEF2C in preadipocyte cells enhanced miR222 expression by 3.48-fold. CHIP-PCR identified a putative binding site of MEF2C on -2077 to -2066 region of miR222 promoter. Electrophoretic mobility shift assay (EMSA) demonstrated the interaction of MEF2C and miR222 promoter in vitro. These data indicated that MEF2C transcriptionally regulates the expression of miR222. Next, the regulation of miR222 on SCD5 mRNA as well as its physiological consequences were examined. Dual luciferase reporter testing revealed the translational inhibition of miR222 upon the 3´ UTR (untranslated region) of SCD5 in preadipocyte cells. Transfection of miR222 mimics and inhibitors resulted in the down-regulation and up-regulation of SCD5 in preadipocyte cells respectively, consistent with the results from reporter testing. RNA interference of SCD5 in preadipocyte cells caused 26.2% reduction of TG, in agreement with the results of TG content in MSTN KO pigs. In summary, the results above supported the existence of a molecular pathway that MSTN signals through MEF2C/miR222/SCD5 to regulate the fat deposition in pigs. This swine-specific pathway offers potential molecular markers for the development and breeding of a new pig line with optimised fatty acid composition. This would benefit human health by decreasing the takeup of saturated fatty acid.

Keywords: fat deposition, MEF2C, miR222, myostatin, SCD5, pig

Procedia PDF Downloads 115

Authors: Dongping Fang, Lian Duan, Xiaojing Yuan, Mike Xu, Allyn Klunder, Kevin Tan, Suiting Cao, Yeqing Ji

Abstract:

Accurate prediction of a medical condition with straight clinical evidence is a long-sought topic in the medical management and health insurance field. Although great progress has been made with machine learning algorithms, the medical community is still, to a certain degree, suspicious about the model's accuracy and interpretability. This paper presents an innovative hierarchical attention deep learning model to achieve good prediction and clear interpretability that can be easily understood by medical professionals. This deep learning model uses a hierarchical attention structure that matches naturally with the medical history data structure and reflects the member’s encounter (date of service) sequence. The model attention structure consists of 3 levels: (1) attention on the medical code types (diagnosis codes, procedure codes, lab test results, and prescription drugs), (2) attention on the sequential medical encounters within a type, (3) attention on the medical codes within an encounter and type. This model is applied to predict the occurrence of stage 3 chronic kidney disease (CKD3), using three years’ medical history of Medicare Advantage (MA) members from a top health insurance company. The model takes members’ medical events, both claims and electronic medical record (EMR) data, as input, makes a prediction of CKD3 and calculates the contribution from individual events to the predicted outcome. The model outcome can be easily explained with the clinical evidence identified by the model algorithm. Here are examples: Member A had 36 medical encounters in the past three years: multiple office visits, lab tests and medications. The model predicts member A has a high risk of CKD3 with the following well-contributed clinical events - multiple high ‘Creatinine in Serum or Plasma’ tests and multiple low kidneys functioning ‘Glomerular filtration rate’ tests. Among the abnormal lab tests, more recent results contributed more to the prediction. The model also indicates regular office visits, no abnormal findings of medical examinations, and taking proper medications decreased the CKD3 risk. Member B had 104 medical encounters in the past 3 years and was predicted to have a low risk of CKD3, because the model didn’t identify diagnoses, procedures, or medications related to kidney disease, and many lab test results, including ‘Glomerular filtration rate’ were within the normal range. The model accurately predicts members A and B and provides interpretable clinical evidence that is validated by clinicians. Without extra effort, the interpretation is generated directly from the model and presented together with the occurrence date. Our model uses the medical data in its most raw format without any further data aggregation, transformation, or mapping. This greatly simplifies the data preparation process, mitigates the chance for error and eliminates post-modeling work needed for traditional model explanation. To our knowledge, this is the first paper on an interpretable deep-learning model using a 3-level attention structure, sourcing both EMR and claim data, including all 4 types of medical data, on the entire Medicare population of a big insurance company, and more importantly, directly generating model interpretation to support user decision. In the future, we plan to enrich the model input by adding patients’ demographics and information from free-texted physician notes.

Keywords: deep learning, interpretability, attention, big data, medical conditions

Procedia PDF Downloads 80

Authors: Bruno Cossu, Elio Carlo

Abstract:

The invention (patent pending) relates to the field of devices aimed to harness wave energy (WEC) especially for artificial upwelling, forced downwelling, production of compressed air. In its basic form, the pump consists of a hydro-pneumatic machine, driven by wave energy, characterised by the fact that it has no moving mechanical parts, and is made up of only two structural components: an hollow body, which is open at the bottom to the sea and partially immersed in sea water, and a tube, both joined together to form a single body. The shape of the hollow body is like a mushroom whose cap and stem are hollow; the stem is open at both ends and the lower part of its surface is crossed by holes; the tube is external and coaxial to the stem and is joined to it so as to form a single body. This shape of the hollow body and the type of connection to the tube allows the pump to operate simultaneously as an air compressor (OWC) on the cap side, and as an airlift on the stem side. The pump can be implemented in four versions, each of which provides different variants and methods of implementation: 1) firstly, for the artificial upwelling of cold, deep ocean water; 2) secondly, for the lifting and transfer of these waters to the place of use (above all, fish farming plants), even if kilometres away; 3) thirdly, for the forced downwelling of surface sea water; 4) fourthly, for the forced downwelling of surface water, its oxygenation, and the simultaneous production of compressed air. The transfer of the deep water or the downwelling of the raised surface water (as for pump versions indicated in points 2 and 3 above), is obtained by making the water raised by the airlift flow into the upper inlet of another pipe, internal or adjoined to the airlift; the downwelling of raised surface water, oxygenation, and the simultaneous production of compressed air (as for the pump version indicated in point 4), is obtained by installing a venturi tube on the upper end of the pipe, whose restricted section is connected to the external atmosphere, so that it also operates like a hydraulic air compressor (trompe). Furthermore, by combining one or more pumps for the upwelling of cold, deep water, with one or more pumps for the downwelling of the warm surface water, the system can be used in an Ocean Thermal Energy Conversion plant to supply the cold and the warm water required for the operation of the same, thus allowing to use, without increased costs, in addition to the mechanical energy of the waves, for the purposes indicated in points 1 to 4, the thermal one of the marine water treated in the process.

Keywords: air lifted upwelling, fish farming plant, hydraulic air compressor, wave energy converter

Procedia PDF Downloads 132