Search results for: deep deterministic policy gradient (DDPG)

Authors: Nkechinyere Joy Olawuyi, Babajide Samuel Afolabi, Bola Ibitoye

Abstract:

This study collected a preprocessed dataset of chest radiographs and formulated a deep neural network model for detecting abnormalities. It also evaluated the performance of the formulated model and implemented a prototype of the formulated model. This was with the view to developing a deep neural network model to automatically classify abnormalities in chest radiographs. In order to achieve the overall purpose of this research, a large set of chest x-ray images were sourced for and collected from the CheXpert dataset, which is an online repository of annotated chest radiographs compiled by the Machine Learning Research Group, Stanford University. The chest radiographs were preprocessed into a format that can be fed into a deep neural network. The preprocessing techniques used were standardization and normalization. The classification problem was formulated as a multi-label binary classification model, which used convolutional neural network architecture to make a decision on whether an abnormality was present or not in the chest radiographs. The classification model was evaluated using specificity, sensitivity, and Area Under Curve (AUC) score as the parameter. A prototype of the classification model was implemented using Keras Open source deep learning framework in Python Programming Language. The AUC ROC curve of the model was able to classify Atelestasis, Support devices, Pleural effusion, Pneumonia, A normal CXR (no finding), Pneumothorax, and Consolidation. However, Lung opacity and Cardiomegaly had a probability of less than 0.5 and thus were classified as absent. Precision, recall, and F1 score values were 0.78; this implies that the number of False Positive and False Negative is the same, revealing some measure of label imbalance in the dataset. The study concluded that the developed model is sufficient to classify abnormalities present in chest radiographs into present or absent.

Keywords: transfer learning, convolutional neural network, radiograph, classification, multi-label

Procedia PDF Downloads 129

Authors: Mehmet Izzettin Yilmazer, Emin Cadirli

Abstract:

Zn-7wt.%Al-2.96wt.%Cu eutectic alloy was directionally solidified upwards with different temperature gradients (from 6.70 K/mm to 10.67 K/mm) at a constant growth rate (16.4 Km/s) and also different growth rate (from 8.3 micron/s to 166 micron/s) at a constant temperature gradient (10.67 K/mm) using a Bridgman–type growth apparatus.The values of eutectic spacing were measured from longitudinal and transverse sections of the samples. The dependency of microstructures on the G and V were determined with linear regression analysis and experimental equations were found as λl=8.953xVexp-0.49, λt=5.942xVexp-0.42 and λl=0.008xGexp-1.23, λt=0.024xGexp-0.93. The measurements of microhardness of directionally solidified samples were obtained by using a microhardness test device. The dependence of microhardness HV on temperature gradient and growth rate were analyzed. The dependency of microhardness on the G and V were also determined with linear regression analysis as HVl=110.66xVexp0.02, HVt=111.94xVexp0.02 and HVl=69.66xGexp0.17, HVt=68.86xGexp0.18. The experimental results show that the microhardness of the directionally solidified Zn-Al-Cu alloy increases with increasing the growth rate. The results obtained in this work were compared with the previous similar experimental results.

Keywords: directional solidification, eutectic alloys, microstructure, microhardness

Procedia PDF Downloads 451

Authors: Zhen Cao, Yu Zhu, Junxue Fu

Abstract:

Immunoassay, i.e., antigen-antibody reaction, is crucial for disease diagnostics. To achieve the adequate signal of the antigen protein detection, a large amount of sample and long incubation time is needed. However, the amount of protein is usually small at the early stage, which makes it difficult to detect. Unlike cells and DNAs, no valid chemical method exists for protein amplification. Thus, an alternative way to improve the signal is through particle manipulation techniques to concentrate proteins, among which dielectrophoresis (DEP) is an effective one. DEP is a technique that concentrates particles to the designated region through a force created by the gradient in a non-uniform electric field. Since DEP force is proportional to the cube of particle size and square of electric field gradient, it is relatively easy to capture larger particles such as cells. For smaller ones like proteins, a super high gradient is then required. In this work, three-dimensional Ag/SiO2 nanorods arrays, fabricated by an easy physical vapor deposition technique called as oblique angle deposition, have been integrated with a DEP device and created the field gradient as high as of 2.6×10²⁴ V²/m³. The nanorods based DEP device is able to enrich bovine serum albumin (BSA) protein by 1800-fold and the rate has reached 180-fold/s when only applying 5 V electric potential. Based on the above nanorods integrated DEP platform, an immunoassay of mouse immunoglobulin G (IgG) proteins has been performed. Briefly, specific antibodies are immobilized onto nanorods, then IgG proteins are concentrated and captured, and finally, the signal from fluorescence-labelled antibodies are detected. The limit of detection (LoD) is measured as 275.3 fg/mL (~1.8 fM), which is a 20,000-fold enhancement compared with identical assays performed on blank glass plates. Further, prostate-specific antigen (PSA), which is a cancer biomarker for diagnosis of prostate cancer after radical prostatectomy, is also quantified with a LoD as low as 2.6 pg/mL. The time to signal saturation has been significantly reduced to one minute. In summary, together with an easy nanorod fabrication and integration method, this nanorods based DEP platform has demonstrated highly sensitive immunoassay performance and thus poses great potentials in applications for early point-of-care diagnostics.

Keywords: dielectrophoresis, immunoassay, oblique angle deposition, protein concentration

Procedia PDF Downloads 103

Authors: Kwi Young Kang

Abstract:

Objective: To determine the association between inflammatory and structural lesions on sacroiliac joint (SIJ) MRI and BMD and to identify risk factors for low BMD in patients with axial spondyloarthritis (axSpA). Methods: Seventy-six patients who fulfilled the ASAS axSpA criteria were enrolled. All underwent SIJ MRI and BMD measurement at the lumbar spine, femoral neck, and total hip. Inflammatory and structural lesions on SIJ MRI were scored. Laboratory tests and assessment of radiographic and disease activity were performed at the time of MRI. The association between SIJ MRI findings and BMD was evaluated. Results: Among the 76 patients, 14 (18%) had low BMD. Patients with low BMD showed significantly higher bone marrow edema (BME) and deep BME scores on MRI than those with normal BMD (p<0.047 and 0.007, respectively). Inflammatory lesions on SIJ MRI correlated with BMD at the femoral neck and total hip. Multivariate analysis identified the presence of deep BME on SIJ MRI, increased CRP, and sacroiliitis on X-ray as risk factors for low BMD (OR: 5.6, 14.6, and 2.5, respectively). Conclusion: The presence of deep BME on SIJ MRI, increased CRP levels, and severity of sacroiliitis on X-ray were independent risk factors for low BMD.

Keywords: axial spondyloarthritis, sacroiliac joint MRI, bone mineral density, sacroiliitis

Procedia PDF Downloads 532

Authors: M. Devaki, K. B. Jayanthi

Abstract:

The identification of water bodies from satellite images has recently received a great deal of attention. Different methods have been developed to distinguish water bodies from various satellite images that vary in terms of time and space. Urban water identification issues body manifests in numerous applications with a great deal of certainty. There has been a sharp rise in the usage of satellite images to map natural resources, including urban water bodies and forests, during the past several years. This is because water and forest resources depend on each other so heavily that ongoing monitoring of both is essential to their sustainable management. The relevant elements from satellite pictures have been chosen using a variety of techniques, including machine learning. Then, a convolution neural network (CNN) architecture is created that can identify a superpixel as either one of two classes, one that includes water or doesn't from input data in a complex metropolitan scene. The deep learning technique, CNN, has advanced tremendously in a variety of visual-related tasks. CNN can improve classification performance by reducing the spectral-spatial regularities of the input data and extracting deep features hierarchically from raw pictures. Calculate the water body using the satellite image's resolution. Experimental results demonstrate that the suggested method outperformed conventional approaches in terms of water extraction accuracy from remote-sensing images, with an average overall accuracy of 97%.

Keywords: water body, Deep learning, satellite images, convolution neural network

Procedia PDF Downloads 89

Authors: Thi Phuong Lan Nguyen

Abstract:

The English Language Teacher Education (ELTE) in Vietnam is rapidly changing to address the new requirements of the globalization and socialization era. Although there has been a range of investments and innovation in policy and curriculum, tertiary educators and learners do not engage in the enactment. It is vital to understand the practices at the tertiary education level. The study is to understand the higher education curriculum development policy, both in theory and in practice across four representatives of ELTE institutions in the North of Vietnam. The lecturers’ perceptions about the extent to which the enacted curriculum is aligned with national standards will be explored. Nineteen policy documents, seventy surveys, and twelve interviews with lecturers and instructional leaders across these four Vietnamese Northern ELTE institutions have been analyzed to investigate how the policy shape the practice. The two most significant findings are (i) a low level of alignment between curriculum and soft-skills standards of the graduates required by the Vietnamese Ministry of Education and Training (MOET) and (ii) incoherence between current national policy and these institutions’ implementation. In order to address these gaps, it is strongly recommended that curriculum needs to be further developed, focusing more on the institutional outcomes, MOET’s standards, and the social demands in times of globalization. More importantly, professional development in ELTE is vital for a range of curriculum and educational policy stakeholders. The study helps to develop the English teaching profession in Vietnam in a systematic way, from policymakers to implementers, and from instructors to learners. Its significance lies in its relevance to English teaching careers, particularly within the researcher’s specific context, yet also remains relevant to ELTE in other parts of Vietnam and in other EFL (English as a Foreign Language) countries.

Keywords: curriculum, English language teaching education, policy implementation, standard, teaching practice

Procedia PDF Downloads 239

Authors: Zhaoxin Luo, Michael Zhu

Abstract:

In natural languages, there are always complex semantic hierarchies. Obtaining the feature representation based on these complex semantic hierarchies becomes the key to the success of the model. Several RNN models have recently been proposed to use latent indicators to obtain the hierarchical structure of documents. However, the model that only uses a single-layer latent indicator cannot achieve the true hierarchical structure of the language, especially a complex language like Chinese. In this paper, we propose a deep layered model that stacks arbitrarily many RNN layers equipped with latent indicators. After using EM and training it hierarchically, our model solves the computational problem of stacking RNN layers and makes it possible to stack arbitrarily many RNN layers. Our deep hierarchical model not only achieves comparable results to large pre-trained models on the Chinese short text classification problem but also achieves state of art results on the Chinese long text classification problem.

Keywords: nature language processing, recurrent neural network, hierarchical structure, document classification, Chinese

Procedia PDF Downloads 68

Authors: Farzaneh Sarbandi Farahani

Abstract:

Drought and decrease in the level of lakes in recent years due to global warming and excessive use of water resources feeding lakes are of great importance, and this research has provided a structure to investigate this issue. First, the information required for simulating lake drought is provided with strong references and necessary assumptions. Entity-Component-System (ECS) structure has been used for simulation, which can consider assumptions flexibly in simulation. Three major users (i.e., Industry, agriculture, and Domestic users) consume water from groundwater and surface water (i.e., streams, rivers and lakes). Lake Mead has been considered for simulation, and the information necessary to investigate its drought has also been provided. The results are presented in the form of a scenario-based design and optimal strategy selection. For optimal strategy selection, a deep reinforcement algorithm is developed to select the best set of strategies among all possible projects. These results can provide a better view of how to plan to prevent lake drought.

Keywords: drought simulation, Mead lake, entity component system programming, deep reinforcement learning

Procedia PDF Downloads 90

Authors: Shantosa Yudha Siswanto, Julianto Arief Ismail, Rachmat Harryanto

Abstract:

The research was conducted with the aim to know the effect of slope steepness on organic carbon and soil erodibility as erosion factor. This research was conducted from September to December 2011 in the Raharja and Cinanjung Village, Tanjungsari, Sumedang District, West Java, Indonesia. The study was carried out using physiographic free survey method, which is a survey based on land physiographic appearance. Soil sampling was carried out into transect on the similarity slope without calculating the point of observation range. Soil sampling was carried onto three classes of slope as follows: 8–15%, 15–25% and 25–40%. Each was consisted of three slope position i.e. top slope, middle slope and down slope and four samples of soil were taken from each of them, hence it resulted in 36 points of observation. The results of this study indicate that gradient of slope have some significant contribution in every sample. Middle slope with gradient 26-40% has the highest potential erosion occurrence. It has organic C content (0.84%) and the highest erodibility value (0.1092).

Keywords: slope steepness, erosion, erodibility, erosion factor

Procedia PDF Downloads 403

Authors: Z. Mortezaie, H. Hassanpour, S. Asadi Amiri

Abstract:

Captured images may suffer from Gaussian blur due to poor lens focus or camera motion. Unsharp masking is a simple and effective technique to boost the image contrast and to improve digital images suffering from Gaussian blur. The technique is based on sharpening object edges by appending the scaled high-frequency components of the image to the original. The quality of the enhanced image is highly dependent on the characteristics of both the high-frequency components and the scaling/gain factor. Since the quality of an image may not be the same throughout, we propose an adaptive unsharp masking method in this paper. In this method, the gain factor is computed, considering the gradient variations, for individual pixels of the image. Subjective and objective image quality assessments are used to compare the performance of the proposed method both with the classic and the recently developed unsharp masking methods. The experimental results show that the proposed method has a better performance in comparison to the other existing methods.

Keywords: unsharp masking, blur image, sub-region gradient, image enhancement

Procedia PDF Downloads 214

Authors: Mergen Dyussenov

Abstract:

Food security has become a prominent issue on the global policy agenda. Yet, one particular region that remains understudied is a cohort of Central Asian countries. To shed light onto the issue, the paper looks into a review of existing literature related to food security policies in Central Asia. In so doing, it seeks to systematize the context analyzed, key findings, and recommendations. Furthermore, it analyzes the role of key actors in promoting the food security policies across Central Asian nations. Finally, the paper attempts to set the agenda for further research.

Keywords: food security, central Asia, the role of actors, policy analysis

Procedia PDF Downloads 326

Authors: Fabrice Setephin Atedjio, Jean-Pierre Lienou, Frederica F. Nelson, Sachin S. Shetty, Charles A. Kamhoua

Abstract:

Malicious users exploit vulnerabilities in computer systems, significantly disrupting their performance and revealing the inadequacies of existing protective solutions. Even machine learning-based approaches, designed to ensure reliability, can be compromised by adversarial attacks that undermine their robustness. This paper addresses two critical aspects of enhancing model reliability. First, we focus on improving model performance and robustness against adversarial threats. To achieve this, we propose a strategy by harnessing deep reinforcement learning. Second, we introduce an approach leveraging generative adversarial networks to counter adversarial attacks effectively. Our results demonstrate substantial improvements over previous works in the literature, with classifiers exhibiting enhanced accuracy in classification tasks, even in the presence of adversarial perturbations. These findings underscore the efficacy of the proposed model in mitigating intrusions and adversarial attacks within the machine-learning landscape.

Keywords: machine learning, reliability, adversarial attacks, deep-reinforcement learning, robustness

Procedia PDF Downloads 9

Authors: Yuan Zheng

Abstract:

3D model-based vehicle matching provides a new way for vehicle recognition, localization and tracking. Its key is to construct an evaluation function, also called fitness function, to measure the degree of vehicle matching. The existing fitness functions often poorly perform when the clutter and occlusion exist in traffic scenarios. In this paper, we present a practical and efficient fitness function. Unlike the existing evaluation functions, the proposed fitness function is to study the vehicle matching problem from both local and global perspectives, which exploits the pixel gradient information as well as the silhouette information. In view of the discrepancy between 3D vehicle model and real vehicle, a weighting strategy is introduced to differently treat the fitting of the model’s wireframes. Additionally, a normalization operation for the model’s projection is performed to improve the accuracy of the matching. Experimental results on real traffic videos reveal that the proposed fitness function is efficient and robust to the cluttered background and partial occlusion.

Keywords: 3D-2D matching, fitness function, 3D vehicle model, local image gradient, silhouette information

Procedia PDF Downloads 399

Authors: Nora Yusma Bte Mohamed Yusoff, Hussain Ali Bekhet

Abstract:

The rationalization of a gradual subsidies reforms plan has been set out by the Malaysian government to achieve the high-income nation target. This paper attempts to analyze the impacts of energy subsidy reform policy on fiscal deficit and macroeconomics variables in Malaysia. The Computable General Equilibrium (CGE) Model is employed. Three simulations based on different groups of scenarios have been developed. Importantly, the overall results indicate that removal of fuel subsidy has significantly improved the real GDP and reduced the government fiscal deficit. On the other hand, the removal of the fuel subsidy has increased most of the local commodity prices, especially energy commodities. The findings of the study could provide some imperative inputs for policy makers, especially to identify the right policy mechanism. This is especially ensures the subsidy savings from subsidy removal could be transferred back into the domestic economy in the form of infrastructure development, compensation and increases in others sector output contributions towards a sustainable economic growth.

Keywords: CGE, deficit, energy, reform, subsidy

Procedia PDF Downloads 265

Authors: Yuanhao Gao, Ping Lin, Kees Weijer

Abstract:

An optimal control system model is proposed for the cell flow in the process of chick embryo gastrulation in this paper. The target is to determine the cellular interaction forces which are hard to measure. This paper will take an approach to investigate the forces with the idea of the inverse problem. By choosing the forces as the control variable and regarding the cell flow as Stokes fluid, an objective functional will be established to match the numerical result of cell velocity with the experimental data. So that the forces could be determined by minimizing the objective functional. The Lagrange multiplier method is utilized to derive the state and adjoint equations consisting the optimal control system, which specifies the first-order necessary conditions. Finite element method is used to discretize and approximate equations. A conjugate gradient algorithm is given for solving the minimum solution of the system and determine the forces.

Keywords: optimal control model, Stokes equation, conjugate gradient method, finite element method, chick embryo gastrulation

Procedia PDF Downloads 259

Authors: Sana Eid, Ahmad S. Darwish, Tarek Lemaoui, Maguy Abi Jaoude, Fawzi Banat, Shadi W. Hasan, Inas M. AlNashef

Abstract:

Addressing the growing challenge of per- and polyfluoroalkyl substances (PFAS) pollution in water bodies, this study introduces natural deep eutectic solvents (NADESs) as a pioneering solution for the efficient extraction of perfluorooctanoic acid (PFOA), one of the most persistent and concerning PFAS pollutants. Among the tested NADESs, trioctylphosphine oxide: lauric acid (TOPO:LauA) in a 1:1 molar ratio was distinguished as the most effective, achieving an extraction efficiency of approximately 99.52% at a solvent-to-feed (S:F) ratio of 1:2, room temperature, and neutral pH. This efficiency is achieved within a notably short mixing time of only one min, which is significantly less than the time required by conventional methods, underscoring the potential of TOPO:LauA for rapid and effective PFAS remediation. TOPO:LauA maintained consistent performance across various operational parameters, including a range of initial PFOA concentrations (0.1 ppm to 1000 ppm), temperatures (15 °C to 100 °C), pH values (3 to 9), and S:F ratios (2:3 to 1:7), demonstrating its versatility and robustness. Furthermore, its effectiveness was consistently high over seven consecutive extraction cycles, highlighting TOPO:LauA as a sustainable, environmentally friendly alternative to hazardous organic solvents, with promising applications for reliable, repeatable use in combating persistent water pollutants such as PFOA.

Keywords: deep eutectic solvents, natural deep eutectic solvents, perfluorooctanoic acid, water remediation

Procedia PDF Downloads 61

Authors: Byomakesh Debata, Jitendra Mahakud

Abstract:

The recent financial crisis has been characterized by massive monetary policy interventions by the Central bank, and it has amplified the importance of liquidity for the stability of the stock market. This paper empirically elucidates the actual impact of monetary policy interventions on stock market liquidity covering all National Stock Exchange (NSE) Stocks, which have been traded continuously from 2002 to 2015. The present study employs a multivariate VAR model along with VAR-granger causality test, impulse response functions, block exogeneity test, and variance decomposition to analyze the direction as well as the magnitude of the relationship between monetary policy and market liquidity. Our analysis posits a unidirectional relationship between monetary policy (call money rate, base money growth rate) and aggregate market liquidity (traded value, turnover ratio, Amihud illiquidity ratio, turnover price impact, high-low spread). The impulse response function analysis clearly depicts the influence of monetary policy on stock liquidity for every unit innovation in monetary policy variables. Our results suggest that an expansionary monetary policy increases aggregate stock market liquidity and the reverse is documented during the tightening of monetary policy. To ascertain whether our findings are consistent across all periods, we divided the period of study as pre-crisis (2002 to 2007) and post-crisis period (2007-2015) and ran the same set of models. Interestingly, all liquidity variables are highly significant in the post-crisis period. However, the pre-crisis period has witnessed a moderate predictability of monetary policy. To check the robustness of our results we ran the same set of VAR models with different monetary policy variables and found the similar results. Unlike previous studies, we found most of the liquidity variables are significant throughout the sample period. This reveals the predictability of monetary policy on aggregate market liquidity. This study contributes to the existing body of literature by documenting a strong predictability of monetary policy on stock liquidity in an emerging economy with an order driven market making system like India. Most of the previous studies have been carried out in developing economies with quote driven or hybrid market making system and their results are ambiguous across different periods. From an eclectic sense, this study may be considered as a baseline study to further find out the macroeconomic determinants of liquidity of stocks at individual as well as aggregate level.

Keywords: market liquidity, monetary policy, order driven market, VAR, vector autoregressive model

Procedia PDF Downloads 374

Authors: Katarzyna Zięba, Hajnalka Szentgyörgyi, Paweł Miśkowiec, Agnieszka Moos-Matysik

Abstract:

Bees are effective pollinators of plants using by humans. However, there is a concern about the fate different species due to their recently decline. Pollution of the environment is described in the literature as one of the causes of this phenomenon. Due to human activities, heavy metals and aromatic hydrocarbons can occur in bee organisms in high concentrations. The presented study aims to provide information on how pollution affects bee quality, taking into account, also the biological differences between various groups of bees. Understanding the consequences of environmental pollution on bees can help to create and promote bee friendly habitats and actions. The analyses were carried out using two contamination gradients with 5 sites on each. The first, mainly heavy metal polluted gradient is stretching approx. 30km from the Bukowno Zinc smelter near Olkusz in the Lesser Poland Voivodship, to the north. The second cuts through the agglomeration of Kraków up to the southern borders of the Ojców National Park. The gradient near Olkusz is a well-described pollution gradient contaminated mainly by zinc, lead, and cadmium. The second gradient cut through the agglomeration of Kraków and end below the Ojców National Park. On each gradient, two bee species were installed: red mason bees (Osmia bicornis) and honey bees (Apis mellifera). Red mason bee is a polylectic, solitary bee species, widely distributed in Poland. Honey bees are a highly social species of bees, with clearly defined casts and roles in the colony. Before installing the bees in the field, samples of imagos of red mason bees and samples of pollen and imagos from each honey bee colony were analysed for zinc, lead cadmium, polycyclic and monocyclic hydrocarbons levels. After collecting the bees from the field, samples of bees and pollen samples for each site were prepared for heavy metal, monocyclic hydrocarbon, and polycyclic hydrocarbon analysis. Analyses of aromatic hydrocarbons were performed with gas chromatography coupled with a headspace sampler (HP 7694E) and mass spectrometer (MS) as detector. Monocyclic compounds were injected into column with headspace sampler while polycyclic ones with manual injector (after solid-liquid extraction with hexane). The heavy metal content (zinc, lead and cadmium) was assessed with flame atomic absorption spectroscopy (FAAS AAnalyst 300 Perkin Elmer spectrometer) according to the methods for honey and bee products described in the literature. Pollution levels found in bee bodies and imago body masses in both species, and proportion of sex in case of red mason bees were correlated with pollution levels found in pollen for each site and colony or trap nest. An attempt to pinpoint the most important form of contamination regarding bee health was also be undertaken based on the achieved results.

Keywords: heavy metals, aromatic hydrocarbons, bees, pollution

Procedia PDF Downloads 508

Authors: Reza Khoshnood, Zahra Khoshnood, Ali Hajinajaf, Farzad Fahim, Behdokht Hajinajaf, Farhad Fahim

Abstract:

In 2013, 24 fish samples were taken from two fishery regions in Bandar-Abbas and Bandar-Lengeh, the fishing grounds north of Hormoz Strait (Persian Gulf) near the Iranian coastline. The two flat fishes were oriental sole (Euryglossa orientalis) and deep flounder (Psettodes erumei). Using the ROPME method (MOOPAM) for chemical digestion, Cd concentration was measured with a nonflame atomic absorption spectrophotometry technique. The average concentration of Cd in the edible muscle tissue of deep flounder was measured in Bandar-Abbas and was found to be 0.15±.06 µg g-1. It was 0.1±.05 µg.g-1 in Bandar-Lengeh. The corresponding values for oriental sole were 0.2±0.13 and 0.13±0.11 µg.g-1. The average concentration of Cd in the liver tissue of deep flounder in Bandar-Abbas was 0.22±.05 µg g-1 and that in Bandar-Lengeh was 0.2±0.04 µg.g-1. The values for oriental sole were 0.31±0.09 and 0.24±0.13 µg g-1 in Bandar-Abbas and Bandar-Lengeh, respectively.

Keywords: trace metal, Euryglossa orientalis, Psettodes erumei, Persian Gulf

Procedia PDF Downloads 669

Authors: Rosa Tsegaye Aga, Xuan Jiang, Pavel Vazquez Faci, Siqing Liu, Simon Rayner, Endalkachew Alemu, Markos Abebe

Abstract:

Tuberculosis (TB) is a major cause of disease globally. In most cases, TB is treatable and curable, but only with the proper treatment. There is a time when drug-resistant TB occurs when bacteria become resistant to the drugs that are used to treat TB. Current strategies to identify drug-resistant TB bacteria are laboratory-based, and it takes a longer time to identify the drug-resistant bacteria and treat the patient accordingly. But machine learning (ML) and data science approaches can offer new approaches to the problem. In this study, we propose to develop an ML-based model to predict the antibiotic resistance phenotypes of TB isolates in minutes and give the right treatment to the patient immediately. The study has been using the whole genome sequence (WGS) of TB isolates as training data that have been extracted from the NCBI repository and contain different countries’ samples to build the ML models. The reason that different countries’ samples have been included is to generalize the large group of TB isolates from different regions in the world. This supports the model to train different behaviors of the TB bacteria and makes the model robust. The model training has been considering three pieces of information that have been extracted from the WGS data to train the model. These are all variants that have been found within the candidate genes (F1), predetermined resistance-associated variants (F2), and only resistance-associated gene information for the particular drug. Two major datasets have been constructed using these three information. F1 and F2 information have been considered as two independent datasets, and the third information is used as a class to label the two datasets. Five machine learning algorithms have been considered to train the model. These are Support Vector Machine (SVM), Random forest (RF), Logistic regression (LR), Gradient Boosting, and Ada boost algorithms. The models have been trained on the datasets F1, F2, and F1F2 that is the F1 and the F2 dataset merged. Additionally, an ensemble approach has been used to train the model. The ensemble approach has been considered to run F1 and F2 datasets on gradient boosting algorithm and use the output as one dataset that is called F1F2 ensemble dataset and train a model using this dataset on the five algorithms. As the experiment shows, the ensemble approach model that has been trained on the Gradient Boosting algorithm outperformed the rest of the models. In conclusion, this study suggests the ensemble approach, that is, the RF + Gradient boosting model, to predict the antibiotic resistance phenotypes of TB isolates by outperforming the rest of the models.

Keywords: machine learning, MTB, WGS, drug resistant TB

Procedia PDF Downloads 52

Authors: Gatot Dwi Hendro, Hayyan ul Haq

Abstract:

This work aims to explore the strategy in embodying the ecological validity in creating the sustainability of public policy, particularly in strengthening the green economy in Indonesia. This green economy plays an important role in supporting the national development in Indonesia, as it is a part of the national policy that posits the primary priority in Indonesian governance. The green economy refers to the national development covering strategic natural resources, such as mining, gold, oil, coal, forest, water, marine, and the other supporting infrastructure for products and distribution, such as fabrics, roads, bridges, and so forth. Thus, all activities in those national development should consider the sustainability. This sustainability requires the strong commitment of the national and regional government, as well as the local governments to put the ecology as the main requirement for issuing any policy, such as licence in mining production, and developing and building new production and supporting infrastructures for optimising the national resources. For that reason this work will focus on the strategy how to embody the ecological values and norms in the public policy. In detail, this work will offer the method, i.e. legal techniques, in visualising and embodying the norms and public policy that valid ecologically. This ecological validity is required in order to maintain and sustain our collective life.

Keywords: ecological validity, sustainable development, coherence, Indonesian Pancasila values, environment, marine

Procedia PDF Downloads 485

Authors: Varun Joshi, Manish K. Rathod

Abstract:

The latent heat thermal energy storage system is a thrust area of research due to exuberant thermal energy storage potential. The thermal performance of PCM is significantly augmented by installation of the high thermal conductivity fins. The objective of the present study is to obtain optimum size and location of the fins to enhance diffusion heat transfer without altering overall melting time. Hence, the constructal theory is employed to eliminate, resize, and re-position the fins. A numerical code based on conjugate heat transfer coupled enthalpy porosity approached is developed to solve Navier-Stoke and energy equation.The numerical results show that the constructal fin design has enhanced the thermal performance along with the increase in the overall volume of PCM when compared to conventional. The overall volume of PCM is found to be increased by half of total of volume of fins. The elimination and repositioning the fins at high temperature gradient from low temperature gradient is found to be vital.

Keywords: constructal theory, enthalpy porosity approach, phase change materials, fins

Procedia PDF Downloads 180

Authors: Saba Rahman, Arvind K. Jain, S. D. Bharti, T. K. Datta

Abstract:

The present study compares the semi-empirical wake-oscillator models that are used to predict vortex-induced vibration of structures. These models include those proposed by Facchinetti, Farshidian, and Dolatabadi, and Skop and Griffin. These models combine a wake oscillator model resembling the Van der Pol oscillator model and a single degree of freedom oscillation model. In order to use these models for estimating the top displacement of chimneys, the first mode vibration of the chimneys is only considered. The modal equation of the chimney constitutes the single degree of freedom model (SDOF). The equations of the wake oscillator model and the SDOF are simultaneously solved using an iterative procedure. The empirical parameters used in the wake-oscillator models are estimated using a newly developed approach, and response is compared with experimental data, which appeared comparable. For carrying out the iterative solution, the ode solver of MATLAB is used. To carry out the comparative study, a tall concrete chimney of height 210m has been chosen with the base diameter as 28m, top diameter as 20m, and thickness as 0.3m. The responses of the chimney are also determined using the linear model proposed by E. Simiu and the deterministic model given in Eurocode. It is observed from the comparative study that the responses predicted by the Facchinetti model and the model proposed by Skop and Griffin are nearly the same, while the model proposed by Fashidian and Dolatabadi predicts a higher response. The linear model without considering the aero-elastic phenomenon provides a less response as compared to the non-linear models. Further, for large damping, the prediction of the response by the Euro code is relatively well compared to those of non-linear models.

Keywords: chimney, deterministic model, van der pol, vortex-induced vibration

Procedia PDF Downloads 221

Authors: Adadu Yahaya, Abdullahi Erunke Canice

Abstract:

The study sets out to examine the interface that tends to exist in the relationship between gender policy and Nigeria’s socio-economic development. Despite Nigeria’s ratification of virtually all international instruments on the protection and promotion of gender rights and equality, it appears that the practice is honored in the breach than in observance; hence, these policies have not been adequately domesticated and implemented. The implication of this is that the women folks have generally been isolated from mainstream politics and their political rights and privileges truncated in the scheme of things. The paper observes that gender inequality and marginalization in Nigeria has practically occasioned the unwholesome subjugation of Nigerian women to the background, hence poses more critical questions and challenges to the national question. The consequence of this, to this paper, is that Nigeria’s development process will be adversely affected if this trend is not checked. The paper sums up with appropriate policy options which are believed to have the potentials of giving women the right pride of place in the socio-economic and political dynamics in the 21st century Nigeria and beyond.

Keywords: development, equality, gender, policy

Procedia PDF Downloads 490

Authors: U. Amin, A. Talib, S. A. Qureshi, M. J. Hossain, G. Ahmad

Abstract:

The choice evaluation between oil-immersed and dry-type transformers is often controlled by cost, location, and application. This paper compares the electrical performance of liquid- filled and dry-type transformers, which will assist the customer to choose the right and efficient ones for particular applications. An accurate assessment of the time-average flux density, electric field intensity and voltage distribution in an oil-insulated and a dry-type transformer have been computed and investigated. The detailed transformer modeling and analysis has been carried out to determine electrical parameter distributions. The models of oil-immersed and dry-type transformers are developed and solved by using the finite element method (FEM) to compare the electrical parameters. The effects of non-uniform and non-coherent voltage gradient, flux density and electric field distribution on the power losses and insulation properties of transformers are studied in detail. The results show that, for the same voltage and kilo-volt-ampere (kVA) rating, oil-immersed transformers have better insulation properties and less hysteresis losses than the dry-type.

Keywords: finite element method, flux density, transformer, voltage gradient

Procedia PDF Downloads 292

Authors: Umair Rashid

Abstract:

Normally softmax loss is used as the supervision signal in face recognition (FR) system, and it boosts the separability of features. In the last two years, a number of techniques have been proposed by reformulating the original softmax loss to enhance the discriminating power of Deep Convolutional Neural Networks (DCNNs) for FR system. To learn angularly discriminative features Cosine-Margin based softmax has been adjusted as monotonically decreasing angular function, that is the main challenge for angular based softmax. On that issue, we propose monotonically decreasing element for Cosine-Margin based softmax and also, we discussed the effect of different monotonically decreasing parameters on angular Margin softmax for FR system. We train the model on publicly available dataset CASIA- WebFace via our proposed monotonically decreasing parameters for cosine function and the tests on YouTube Faces (YTF, Labeled Face in the Wild (LFW), VGGFace1 and VGGFace2 attain the state-of-the-art performance.

Keywords: deep convolutional neural networks, cosine margin face recognition, softmax loss, monotonically decreasing parameter

Procedia PDF Downloads 101

Authors: Saumya Srivastava, Rina Maiti

Abstract:

In order to achieve a better performance of vehicle detection in a complex environment, we present an efficient approach for a multi-vehicle detection system using an adaptive sliding window technique. For a given frame, image segmentation is carried out to establish the region of interest. Gradient computation followed by thresholding, denoising, and morphological operations is performed to extract the binary search image. Near-region field and far-region field are defined to generate hypotheses using the adaptive sliding window technique on the resultant binary search image. For each vehicle candidate, features are extracted using a histogram of oriented gradients, and a pre-trained support vector machine is applied for hypothesis verification. Later, the Kalman filter is used for tracking the vanishing point. The experimental results show that the method is robust and effective on various roads and driving scenarios. The algorithm was tested on highways and urban roads in India.

Keywords: gradient, vehicle detection, histograms of oriented gradients, support vector machine

Procedia PDF Downloads 124

Authors: Felipe A. Guth, Shane Ward, Kevin McDonnell

Abstract:

The assessment of disease levels in crop fields is an important and time-consuming task that generally relies on expert knowledge of trained individuals. Image classification in agriculture problems historically has been based on classical machine learning strategies that make use of hand-engineered features in the top of a classification algorithm. This approach tends to not produce results with high accuracy and generalization to the classes classified by the system when the nature of the elements has a significant variability. The advent of deep convolutional neural networks has revolutionized the field of machine learning, especially in computer vision tasks. These networks have great resourcefulness of learning and have been applied successfully to image classification and object detection tasks in the last years. The objective of this work was to propose a new method based on deep learning convolutional neural networks towards the task of disease level monitoring. Common RGB images of winter wheat were obtained during a growing season. Five categories of disease levels presence were produced, in collaboration with agronomists, for the algorithm classification. Disease level tasks performed by experts provided ground truth data for the disease score of the same winter wheat plots were RGB images were acquired. The system had an overall accuracy of 84% on the discrimination of the disease level classes.

Keywords: crop disease assessment, deep learning, precision agriculture, residual neural networks

Procedia PDF Downloads 332

Authors: Yeong-Gyeong Choi, Kyoung-Seok Kim

Abstract:

This study, literature review research, intends to deal with the problem of conceptual ambiguity among research on emotional labor, and to look into the evolutionary trends and changing aspects of defining the concept of emotional labor. In addition, in existing studies, deep acting and surface acting are highly related to a positive outcome variable and a negative outcome variable, respectively. It was confirmed that for employees performing emotional labor, deep acting and surface acting are highly related to OCB and CWB, respectively. While positive emotion that employees come to experience during job performance process can easily trigger a positive non-task behavior such as OCB, negative emotion that employees experience through excessive workload or unfair treatment can easily induce a negative behavior like CWB. The two management behaviors of emotional labor, surface acting and deep acting, can have either a positive or negative effect on non-task behavior of employees, depending on which one they would choose. Thus, the purpose of this review paper is to clarify the relationship between emotional labor and non-task behavior more specifically.

Keywords: emotion labor, non-task behavior, OCB, CWB

Procedia PDF Downloads 351

Authors: Kerryn Krige, Kerrin Myers

Abstract:

Social entrepreneurship offers the opportunity to simultaneously address both social and economic inequality in South Africa. Its appeal across racial groups, its attractiveness to young people, its applicability in rural and peri-urban markets, and its acceleration in middle income, large-business economies suits the South African context. However, the potential to deliver much-needed developmental benefits has not been realised because the social entrepreneurship debate lacks evidence as to who social entrepreneurs are, their goals and operations and the socio-economic results they achieve. As a result, policy development has been stunted, and legislative barriers and red tape remain. Social entrepreneurs are isolated from the mainstream economy, and struggle to access funding because of limitations in legislative and organisational structures. The objective of the study is to strengthen the ecosystem for social entrepreneurship in South Africa by producing robust, policy-rich information from and about social enterprises currently in operation across the country. The study employs a quantitative survey methodology, using online and telephonic data collection methods. A purposive sample of 1000 social enterprises was included in the first large-scale study of social entrepreneurship in South Africa. The results offer deep insight into the characteristics of social enterprises; the activities they undertake and the markets they serve; their modes of operation and funding sources as well as key challenges and support systems. The results contribute towards developing a model of social enterprise in the African context.

Keywords: social enterprise, key characteristics, challenges and enablers, towards an African model

Procedia PDF Downloads 307