Search results for: neural tube defect
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 2803

Search results for: neural tube defect

1603 Neural Networks Based Prediction of Long Term Rainfall: Nine Pilot Study Zones over the Mediterranean Basin

Authors: Racha El Kadiri, Mohamed Sultan, Henrique Momm, Zachary Blair, Rachel Schultz, Tamer Al-Bayoumi

Abstract:

The Mediterranean Basin is a very diverse region of nationalities and climate zones, with a strong dependence on agricultural activities. Predicting long term (with a lead of 1 to 12 months) rainfall, and future droughts could contribute in a sustainable management of water resources and economical activities. In this study, an integrated approach was adopted to construct predictive tools with lead times of 0 to 12 months to forecast rainfall amounts over nine subzones of the Mediterranean Basin region. The following steps were conducted: (1) acquire, assess and intercorrelate temporal remote sensing-based rainfall products (e.g. The CPC Merged Analysis of Precipitation [CMAP]) throughout the investigation period (1979 to 2016), (2) acquire and assess monthly values for all of the climatic indices influencing the regional and global climatic patterns (e.g., Northern Atlantic Oscillation [NOI], Southern Oscillation Index [SOI], and Tropical North Atlantic Index [TNA]); (3) delineate homogenous climatic regions and select nine pilot study zones, (4) apply data mining methods (e.g. neural networks, principal component analyses) to extract relationships between the observed rainfall and the controlling factors (i.e. climatic indices with multiple lead-time periods) and (5) use the constructed predictive tools to forecast monthly rainfall and dry and wet periods. Preliminary results indicate that rainfall and dry/wet periods were successfully predicted with lead zones of 0 to 12 months using the adopted methodology, and that the approach is more accurately applicable in the southern Mediterranean region.

Keywords: rainfall, neural networks, climatic indices, Mediterranean

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1602 A Medical Resource Forecasting Model for Emergency Room Patients with Acute Hepatitis

Authors: R. J. Kuo, W. C. Cheng, W. C. Lien, T. J. Yang

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Taiwan is a hyper endemic area for the Hepatitis B virus (HBV). The estimated total number of HBsAg carriers in the general population who are more than 20 years old is more than 3 million. Therefore, a case record review is conducted from January 2003 to June 2007 for all patients with a diagnosis of acute hepatitis who were admitted to the Emergency Department (ED) of a well-known teaching hospital. The cost for the use of medical resources is defined as the total medical fee. In this study, principal component analysis (PCA) is firstly employed to reduce the number of dimensions. Support vector regression (SVR) and artificial neural network (ANN) are then used to develop the forecasting model. A total of 117 patients meet the inclusion criteria. 61% patients involved in this study are hepatitis B related. The computational result shows that the proposed PCA-SVR model has superior performance than other compared algorithms. In conclusion, the Child-Pugh score and echogram can both be used to predict the cost of medical resources for patients with acute hepatitis in the ED.

Keywords: acute hepatitis, medical resource cost, artificial neural network, support vector regression

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1601 Comparison of Support Vector Machines and Artificial Neural Network Classifiers in Characterizing Threatened Tree Species Using Eight Bands of WorldView-2 Imagery in Dukuduku Landscape, South Africa

Authors: Galal Omer, Onisimo Mutanga, Elfatih M. Abdel-Rahman, Elhadi Adam

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Threatened tree species (TTS) play a significant role in ecosystem functioning and services, land use dynamics, and other socio-economic aspects. Such aspects include ecological, economic, livelihood, security-based, and well-being benefits. The development of techniques for mapping and monitoring TTS is thus critical for understanding the functioning of ecosystems. The advent of advanced imaging systems and supervised learning algorithms has provided an opportunity to classify TTS over fragmenting landscape. Recently, vegetation maps have been produced using advanced imaging systems such as WorldView-2 (WV-2) and robust classification algorithms such as support vectors machines (SVM) and artificial neural network (ANN). However, delineation of TTS in a fragmenting landscape using high resolution imagery has widely remained elusive due to the complexity of the species structure and their distribution. Therefore, the objective of the current study was to examine the utility of the advanced WV-2 data for mapping TTS in the fragmenting Dukuduku indigenous forest of South Africa using SVM and ANN classification algorithms. The results showed the robustness of the two machine learning algorithms with an overall accuracy (OA) of 77.00% (total disagreement = 23.00%) for SVM and 75.00% (total disagreement = 25.00%) for ANN using all eight bands of WV-2 (8B). This study concludes that SVM and ANN classification algorithms with WV-2 8B have the potential to classify TTS in the Dukuduku indigenous forest. This study offers relatively accurate information that is important for forest managers to make informed decisions regarding management and conservation protocols of TTS.

Keywords: artificial neural network, threatened tree species, indigenous forest, support vector machines

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1600 Recurrent Neural Networks with Deep Hierarchical Mixed Structures for Chinese Document Classification

Authors: Zhaoxin Luo, Michael Zhu

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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

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1599 Assessing the Survival Time of Hospitalized Patients in Eastern Ethiopia During 2019–2020 Using the Bayesian Approach: A Retrospective Cohort Study

Authors: Chalachew Gashu, Yoseph Kassa, Habtamu Geremew, Mengestie Mulugeta

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Background and Aims: Severe acute malnutrition remains a significant health challenge, particularly in low‐ and middle‐income countries. The aim of this study was to determine the survival time of under‐five children with severe acute malnutrition. Methods: A retrospective cohort study was conducted at a hospital, focusing on under‐five children with severe acute malnutrition. The study included 322 inpatients admitted to the Chiro hospital in Chiro, Ethiopia, between September 2019 and August 2020, whose data was obtained from medical records. Survival functions were analyzed using Kaplan‒Meier plots and log‐rank tests. The survival time of severe acute malnutrition was further analyzed using the Cox proportional hazards model and Bayesian parametric survival models, employing integrated nested Laplace approximation methods. Results: Among the 322 patients, 118 (36.6%) died as a result of severe acute malnutrition. The estimated median survival time for inpatients was found to be 2 weeks. Model selection criteria favored the Bayesian Weibull accelerated failure time model, which demonstrated that age, body temperature, pulse rate, nasogastric (NG) tube usage, hypoglycemia, anemia, diarrhea, dehydration, malaria, and pneumonia significantly influenced the survival time of severe acute malnutrition. Conclusions: This study revealed that children below 24 months, those with altered body temperature and pulse rate, NG tube usage, hypoglycemia, and comorbidities such as anemia, diarrhea, dehydration, malaria, and pneumonia had a shorter survival time when affected by severe acute malnutrition under the age of five. To reduce the death rate of children under 5 years of age, it is necessary to design community management for acute malnutrition to ensure early detection and improve access to and coverage for children who are malnourished.

Keywords: Bayesian analysis, severe acute malnutrition, survival data analysis, survival time

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1598 The Role of Planning and Memory in the Navigational Ability

Authors: Greeshma Sharma, Sushil Chandra, Vijander Singh, Alok Prakash Mittal

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Navigational ability requires spatial representation, planning, and memory. It covers three interdependent domains, i.e. cognitive and perceptual factors, neural information processing, and variability in brain microstructure. Many attempts have been made to see the role of spatial representation in the navigational ability, and the individual differences have been identified in the neural substrate. But, there is also a need to address the influence of planning, memory on navigational ability. The present study aims to evaluate relations of aforementioned factors in the navigational ability. Total 30 participants volunteered in the study of a virtual shopping complex and subsequently were classified into good and bad navigators based on their performances. The result showed that planning ability was the most correlated factor for the navigational ability and also the discriminating factor between the good and bad navigators. There was also found the correlations between spatial memory recall and navigational ability. However, non-verbal episodic memory and spatial memory recall were also found to be correlated with the learning variable. This study attempts to identify differences between people with more and less navigational ability on the basis of planning and memory.

Keywords: memory, planning navigational ability, virtual reality

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1597 Artificial Neural Network Based Approach in Prediction of Potential Water Pollution Across Different Land-Use Patterns

Authors: M.Rüştü Karaman, İsmail İşeri, Kadir Saltalı, A.Reşit Brohi, Ayhan Horuz, Mümin Dizman

Abstract:

Considerable relations has recently been given to the environmental hazardous caused by agricultural chemicals such as excess fertilizers. In this study, a neural network approach was investigated in the prediction of potential nitrate pollution across different land-use patterns by using a feedforward multilayered computer model of artificial neural network (ANN) with proper training. Periodical concentrations of some anions, especially nitrate (NO3-), and cations were also detected in drainage waters collected from the drain pipes placed in irrigated tomato field, unirrigated wheat field, fallow and pasture lands. The soil samples were collected from the irrigated tomato field and unirrigated wheat field on a grid system with 20 m x 20 m intervals. Site specific nitrate concentrations in the soil samples were measured for ANN based simulation of nitrate leaching potential from the land profiles. In the application of ANN model, a multi layered feedforward was evaluated, and data sets regarding with training, validation and testing containing the measured soil nitrate values were estimated based on spatial variability. As a result of the testing values, while the optimal structures of 2-15-1 was obtained (R2= 0.96, P < 0.01) for unirrigated field, the optimal structures of 2-10-1 was obtained (R2= 0.96, P < 0.01) for irrigated field. The results showed that the ANN model could be successfully used in prediction of the potential leaching levels of nitrate, based on different land use patterns. However, for the most suitable results, the model should be calibrated by training according to different NN structures depending on site specific soil parameters and varied agricultural managements.

Keywords: artificial intelligence, ANN, drainage water, nitrate pollution

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1596 An Intelligent Thermal-Aware Task Scheduler in Multiprocessor System on a Chip

Authors: Sina Saadati

Abstract:

Multiprocessors Systems-On-Chips (MPSOCs) are used widely on modern computers to execute sophisticated software and applications. These systems include different processors for distinct aims. Most of the proposed task schedulers attempt to improve energy consumption. In some schedulers, the processor's temperature is considered to increase the system's reliability and performance. In this research, we have proposed a new method for thermal-aware task scheduling which is based on an artificial neural network (ANN). This method enables us to consider a variety of factors in the scheduling process. Some factors like ambient temperature, season (which is important for some embedded systems), speed of the processor, computing type of tasks and have a complex relationship with the final temperature of the system. This Issue can be solved using a machine learning algorithm. Another point is that our solution makes the system intelligent So that It can be adaptive. We have also shown that the computational complexity of the proposed method is cheap. As a consequence, It is also suitable for battery-powered systems.

Keywords: task scheduling, MOSOC, artificial neural network, machine learning, architecture of computers, artificial intelligence

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1595 Exploring the Prebiotic Potential of Glucosamine

Authors: Shilpi Malik, Ramneek Kaur, Archita Gupta, Deepshikha Yadav, Ashwani Mathur, Manisha Singh

Abstract:

Glucosamine (GS) is the most abundant naturally occurring amino monosaccharide and is normally produced in human body via cellular glucose metabolism. It is regarded as the building block of cartilage matrix and is also an essential component of cartilage matrix repair mechanism. Besides that, it can also be explored for its prebiotic potential as many bacterial species are known to utilize the amino sugar by acquiring them to form peptidoglycans and lipopolysaccharides in the bacterial cell wall. Glucosamine can therefore be considered for its fermentation by bacterial species present in the gut. Current study is focused on exploring the potential of glucosamine as prebiotic. The studies were done to optimize considerable concentration of GS to reach GI tract and being fermented by the complex gut microbiota and food grade GS was added to various Simulated Fluids of Gastro-Intestinal Tract (GIT) such as Simulated Saliva, Gastric Fluid (Fast and Fed State), Colonic fluid, etc. to detect its degradation. Since it was showing increase in microbial growth (CFU) with time, GS was Further, encapsulated to increase its residential time in the gut, which exhibited improved resistance to the simulated Gut conditions. Moreover, prepared microspehres were optimized and characterized for their encapsulation efficiency and toxicity. To further substantiate the prebiotic activity of Glucosamine, studies were also performed to determine the effect of Glucosamine on the known probiotic bacterial species, i.e. Lactobacillus delbrueckii (MTCC 911) and Bifidobacteriumbifidum (MTCC 5398). Culture conditions for glucosamine will be added in MRS media in anaerobic tube at 0.20%, 0.40%, 0.60%, 0.80%, and 1.0%, respectively. MRS media without GS was included in this experiment as the control. All samples were autoclaved at 118° C for 15 min. Active culture was added at 5% (v/v) to each anaerobic tube after cooling to room temperature and incubated at 37° C then determined biomass and pH and viable count at incubation 18h. The experiment was completed in triplicate and the results were presented as Mean ± SE (Standard error).The experimental results are conclusive and suggest Glucosamine to hold prebiotic properties.

Keywords: gastro intestinal tract, microspheres, peptidoglycans, simulated fluid

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1594 Neural Network Approach for Solving Integral Equations

Authors: Bhavini Pandya

Abstract:

This paper considers Hη: T2 → T2 the Perturbed Cerbelli-Giona map. That is a family of 2-dimensional nonlinear area-preserving transformations on the torus T2=[0,1]×[0,1]= ℝ2/ ℤ2. A single parameter η varies between 0 and 1, taking the transformation from a hyperbolic toral automorphism to the “Cerbelli-Giona” map, a system known to exhibit multifractal properties. Here we study the multifractal properties of the family of maps. We apply a box-counting method by defining a grid of boxes Bi(δ), where i is the index and δ is the size of the boxes, to quantify the distribution of stable and unstable manifolds of the map. When the parameter is in the range 0.51< η <0.58 and 0.68< η <1 the map is ergodic; i.e., the unstable and stable manifolds eventually cover the whole torus, although not in a uniform distribution. For accurate numerical results we require correspondingly accurate construction of the stable and unstable manifolds. Here we use the piecewise linearity of the map to achieve this, by computing the endpoints of line segments which define the global stable and unstable manifolds. This allows the generalized fractal dimension Dq, and spectrum of dimensions f(α), to be computed with accuracy. Finally, the intersection of the unstable and stable manifold of the map will be investigated, and compared with the distribution of periodic points of the system.

Keywords: feed forward, gradient descent, neural network, integral equation

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1593 Efficient Video Compression Technique Using Convolutional Neural Networks and Generative Adversarial Network

Authors: P. Karthick, K. Mahesh

Abstract:

Video has become an increasingly significant component of our digital everyday contact. With the advancement of greater contents and shows of the resolution, its significant volume poses serious obstacles to the objective of receiving, distributing, compressing, and revealing video content of high quality. In this paper, we propose the primary beginning to complete a deep video compression model that jointly upgrades all video compression components. The video compression method involves splitting the video into frames, comparing the images using convolutional neural networks (CNN) to remove duplicates, repeating the single image instead of the duplicate images by recognizing and detecting minute changes using generative adversarial network (GAN) and recorded with long short-term memory (LSTM). Instead of the complete image, the small changes generated using GAN are substituted, which helps in frame level compression. Pixel wise comparison is performed using K-nearest neighbours (KNN) over the frame, clustered with K-means, and singular value decomposition (SVD) is applied for each and every frame in the video for all three color channels [Red, Green, Blue] to decrease the dimension of the utility matrix [R, G, B] by extracting its latent factors. Video frames are packed with parameters with the aid of a codec and converted to video format, and the results are compared with the original video. Repeated experiments on several videos with different sizes, duration, frames per second (FPS), and quality results demonstrate a significant resampling rate. On average, the result produced had approximately a 10% deviation in quality and more than 50% in size when compared with the original video.

Keywords: video compression, K-means clustering, convolutional neural network, generative adversarial network, singular value decomposition, pixel visualization, stochastic gradient descent, frame per second extraction, RGB channel extraction, self-detection and deciding system

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1592 The Convolution Recurrent Network of Using Residual LSTM to Process the Output of the Downsampling for Monaural Speech Enhancement

Authors: Shibo Wei, Ting Jiang

Abstract:

Convolutional-recurrent neural networks (CRN) have achieved much success recently in the speech enhancement field. The common processing method is to use the convolution layer to compress the feature space by multiple upsampling and then model the compressed features with the LSTM layer. At last, the enhanced speech is obtained by deconvolution operation to integrate the global information of the speech sequence. However, the feature space compression process may cause the loss of information, so we propose to model the upsampling result of each step with the residual LSTM layer, then join it with the output of the deconvolution layer and input them to the next deconvolution layer, by this way, we want to integrate the global information of speech sequence better. The experimental results show the network model (RES-CRN) we introduce can achieve better performance than LSTM without residual and overlaying LSTM simply in the original CRN in terms of scale-invariant signal-to-distortion ratio (SI-SNR), speech quality (PESQ), and intelligibility (STOI).

Keywords: convolutional-recurrent neural networks, speech enhancement, residual LSTM, SI-SNR

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1591 Ground Surface Temperature History Prediction Using Long-Short Term Memory Neural Network Architecture

Authors: Venkat S. Somayajula

Abstract:

Ground surface temperature history prediction model plays a vital role in determining standards for international nuclear waste management. International standards for borehole based nuclear waste disposal require paleoclimate cycle predictions on scale of a million forward years for the place of waste disposal. This research focuses on developing a paleoclimate cycle prediction model using Bayesian long-short term memory (LSTM) neural architecture operated on accumulated borehole temperature history data. Bayesian models have been previously used for paleoclimate cycle prediction based on Monte-Carlo weight method, but due to limitations pertaining model coupling with certain other prediction networks, Bayesian models in past couldn’t accommodate prediction cycle’s over 1000 years. LSTM has provided frontier to couple developed models with other prediction networks with ease. Paleoclimate cycle developed using this process will be trained on existing borehole data and then will be coupled to surface temperature history prediction networks which give endpoints for backpropagation of LSTM network and optimize the cycle of prediction for larger prediction time scales. Trained LSTM will be tested on past data for validation and then propagated for forward prediction of temperatures at borehole locations. This research will be beneficial for study pertaining to nuclear waste management, anthropological cycle predictions and geophysical features

Keywords: Bayesian long-short term memory neural network, borehole temperature, ground surface temperature history, paleoclimate cycle

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1590 Vehicle Detection and Tracking Using Deep Learning Techniques in Surveillance Image

Authors: Abe D. Desta

Abstract:

This study suggests a deep learning-based method for identifying and following moving objects in surveillance video. The proposed method uses a fast regional convolution neural network (F-RCNN) trained on a substantial dataset of vehicle images to first detect vehicles. A Kalman filter and a data association technique based on a Hungarian algorithm are then used to monitor the observed vehicles throughout time. However, in general, F-RCNN algorithms have been shown to be effective in achieving high detection accuracy and robustness in this research study. For example, in one study The study has shown that the vehicle detection and tracking, the system was able to achieve an accuracy of 97.4%. In this study, the F-RCNN algorithm was compared to other popular object detection algorithms and was found to outperform them in terms of both detection accuracy and speed. The presented system, which has application potential in actual surveillance systems, shows the usefulness of deep learning approaches in vehicle detection and tracking.

Keywords: artificial intelligence, computer vision, deep learning, fast-regional convolutional neural networks, feature extraction, vehicle tracking

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1589 Applying Renowned Energy Simulation Engines to Neural Control System of Double Skin Façade

Authors: Zdravko Eškinja, Lovre Miljanić, Ognjen Kuljača

Abstract:

This paper is an overview of simulation tools used to model specific thermal dynamics that occurs while controlling double skin façade. Research has been conducted on simplified construction with single zone where one side is glazed. Heat flow and temperature responses are simulated in three different simulation tools: IDA-ICE, EnergyPlus and HAMBASE. The excitation of observed system, used in all simulations, was a temperature step of exterior environment. Air infiltration, insulation and other disturbances are excluded from this research. Although such isolated behaviour is not possible in reality, experiments are carried out to gain novel information about heat flow transients which are not observable under regular conditions. Results revealed new possibilities for adapting the parameters of the neural network regulator. Along numerical simulations, the same set-up has been also tested in a real-time experiment with a 1:18 scaled model and thermal chamber. The comparison analysis brings out interesting conclusion about simulation accuracy in this particular case.

Keywords: double skin façade, experimental tests, heat control, heat flow, simulated tests, simulation tools

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1588 Effect of Monotonically Decreasing Parameters on Margin Softmax for Deep Face Recognition

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

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1587 Spatiotemporal Neural Network for Video-Based Pose Estimation

Authors: Bin Ji, Kai Xu, Shunyu Yao, Jingjing Liu, Ye Pan

Abstract:

Human pose estimation is a popular research area in computer vision for its important application in human-machine interface. In recent years, 2D human pose estimation based on convolution neural network has got great progress and development. However, in more and more practical applications, people often need to deal with tasks based on video. It’s not far-fetched for us to consider how to combine the spatial and temporal information together to achieve a balance between computing cost and accuracy. To address this issue, this study proposes a new spatiotemporal model, namely Spatiotemporal Net (STNet) to combine both temporal and spatial information more rationally. As a result, the predicted keypoints heatmap is potentially more accurate and spatially more precise. Under the condition of ensuring the recognition accuracy, the algorithm deal with spatiotemporal series in a decoupled way, which greatly reduces the computation of the model, thus reducing the resource consumption. This study demonstrate the effectiveness of our network over the Penn Action Dataset, and the results indicate superior performance of our network over the existing methods.

Keywords: convolutional long short-term memory, deep learning, human pose estimation, spatiotemporal series

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1586 Convergence Analysis of Training Two-Hidden-Layer Partially Over-Parameterized ReLU Networks via Gradient Descent

Authors: Zhifeng Kong

Abstract:

Over-parameterized neural networks have attracted a great deal of attention in recent deep learning theory research, as they challenge the classic perspective of over-fitting when the model has excessive parameters and have gained empirical success in various settings. While a number of theoretical works have been presented to demystify properties of such models, the convergence properties of such models are still far from being thoroughly understood. In this work, we study the convergence properties of training two-hidden-layer partially over-parameterized fully connected networks with the Rectified Linear Unit activation via gradient descent. To our knowledge, this is the first theoretical work to understand convergence properties of deep over-parameterized networks without the equally-wide-hidden-layer assumption and other unrealistic assumptions. We provide a probabilistic lower bound of the widths of hidden layers and proved linear convergence rate of gradient descent. We also conducted experiments on synthetic and real-world datasets to validate our theory.

Keywords: over-parameterization, rectified linear units ReLU, convergence, gradient descent, neural networks

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1585 Bearing Condition Monitoring with Acoustic Emission Techniques

Authors: Faisal AlShammari, Abdulmajid Addali

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Monitoring the conditions of rotating machinery as bearing is important in order to improve its stability of works. Acoustic emission (AE) and vibration analysis are some of the most accomplished techniques used for this purpose. Acoustic emission has the ability to detect the initial phase of component degradation. Moreover, it has been observed that the success of vibration analysis does not take place below 100 rpm rotational speed. This because the energy generated below 100 rpm rotational speed is not detectable using conventional vibration. From this pint, this paper has presented a focused review of using acoustic emission techniques for monitoring bearings condition.

Keywords: condition monitoring, stress wave analysis, low-speed bearings, bearing defect diagnosis

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1584 Comparison of Cardiomyogenic Potential of Amniotic Fluid Mesenchymal Stromal Cells Derived from Normal and Isolated Congenital Heart Defective Fetuses

Authors: Manali Jain, Neeta Singh, Raunaq Fatima, Soniya Nityanand, Mandakini Pradhan, Chandra Prakash Chaturvedi

Abstract:

Isolated Congenital Heart Defect (ICHD) is the major cause of neonatal death worldwide among all forms of CHDs. A significant proportion of fetuses with ICHD die in the neonatal period if no treatment is provided. Recently, stem cell therapies have emerged as a potential approach to ameliorate ICHD in children. ICHD is characterized by cardiac structural abnormalities during embryogenesis due to alterations in the cardiomyogenic properties of a pool of cardiac progenitors/ stem cells associated with fetal heart development. The stem cells present in the amniotic fluid (AF) are of fetal origin and may reflect the physiological and pathological changes in the fetus during embryogenesis. Therefore, in the present study, the cardiomyogenic potential of AF-MSCs derived from fetuses with ICHD (ICHD AF-MSCs) has been evaluated and compared with that of AF-MSCs of structurally normal fetuses (normal AF-MSCs). Normal and ICHD AF-MSC were analyzed for the expression of cardiac progenitor markers viz., stage-specific embryonic antigen-1 (SSEA-1), vascular endothelial growth factor 2 (VEGFR-2) and platelet-derived growth factor receptor-alpha (PDGFR-α) by flow cytometry. The immunophenotypic characterization revealed that ICHD AF-MSCs have significantly lower expression of cardiac progenitor markers VEGFR-2 (0.14% ± 0.6 vs.48.80% ± 0.9; p <0.01), SSEA-1 (70.86% ± 2.4 vs. 88.36% ±2.7; p <0.01), and PDGFR-α (3.92% ± 1.8 vs. 47.59% ± 3.09; p <0.01) in comparison to normal AF-MSCs. Upon induction with 5’-azacytidine for 21 days, ICHD AF-MSCs showed a significantly down-regulated expression of cardiac transcription factors such as GATA-4 (0.4 ± 0.1 vs. 6.8 ± 1.2; p<0.01), ISL-1 (2.3± 0.6 vs. 14.3 ± 1.12; p<0.01), NK-x 2-5 (1.1 ± 0.3 vs. 14.1 ±2.8; p<0.01), TBX-5 (0.4 ± 0.07 vs. 4.4 ± 0.3; p<0.001), and TBX-18 (1.3 ± 0.2 vs. 4.19 ± 0.3; p<0.01) when compared with the normal AF-MSCs. Furthermore, immunocytochemical staining revealed that both types of AF-MSCs could differentiate into cardiovascular lineages and express cardiomyogenic, endothelial, and smooth muscle actin markers, viz., cardiac troponin (cTNT), CD31, and alpha-smooth muscle actin (α-SMA). However, normal AF-MSCs showed an enhanced expression of cTNT (p<0.001), CD31 (p<0.01), and α-SMA (p<0.05), compared to ICHD AF-MSCs. Overall, these results suggest that the ICHD-AF-MSCs have a defective cardiomyogenic differentiation potential and that the defects in these stem cells may have a role in the pathogenesis of ICHD.

Keywords: amniotic fluid, cardiomyogenic potential, isolated congenital heart defect, mesenchymal stem cells

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1583 Application of a Model-Free Artificial Neural Networks Approach for Structural Health Monitoring of the Old Lidingö Bridge

Authors: Ana Neves, John Leander, Ignacio Gonzalez, Raid Karoumi

Abstract:

Systematic monitoring and inspection are needed to assess the present state of a structure and predict its future condition. If an irregularity is noticed, repair actions may take place and the adequate intervention will most probably reduce the future costs with maintenance, minimize downtime and increase safety by avoiding the failure of the structure as a whole or of one of its structural parts. For this to be possible decisions must be made at the right time, which implies using systems that can detect abnormalities in their early stage. In this sense, Structural Health Monitoring (SHM) is seen as an effective tool for improving the safety and reliability of infrastructures. This paper explores the decision-making problem in SHM regarding the maintenance of civil engineering structures. The aim is to assess the present condition of a bridge based exclusively on measurements using the suggested method in this paper, such that action is taken coherently with the information made available by the monitoring system. Artificial Neural Networks are trained and their ability to predict structural behavior is evaluated in the light of a case study where acceleration measurements are acquired from a bridge located in Stockholm, Sweden. This relatively old bridge is presently still in operation despite experiencing obvious problems already reported in previous inspections. The prediction errors provide a measure of the accuracy of the algorithm and are subjected to further investigation, which comprises concepts like clustering analysis and statistical hypothesis testing. These enable to interpret the obtained prediction errors, draw conclusions about the state of the structure and thus support decision making regarding its maintenance.

Keywords: artificial neural networks, clustering analysis, model-free damage detection, statistical hypothesis testing, structural health monitoring

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1582 O2 Saturation Comparison Between Breast Milk Feeding and Tube Feeding in Very Low Birth Weight Neonates

Authors: Ashraf Mohammadzadeh, Ahmad Shah Farhat, Azin Vaezi, Aradokht Vaezi

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Background & Aim: Preterm infants born at less than 34 weeks postconceptional age are not as neurologically mature as their term counterparts and thus have difficulty coordinating sucking, swallowing and breathing. As a result, they are traditionally gavage fed until they are able to oral feed successfully. The aim of study was to evaluate comparative effect of orogastric and breast feeding on oxygen saturation in very low birth weight infant (<1500gm). Patients and Methods: In this clinical trial all babies admitted in the Neonatal Research Center of Imamreza Hospital, Mashhad during a 4 months period were elected. Criteria for entrance to study included birth weight ≤ 1500 grams, exclusive breastfeeding, having no special problem after 48 hours, receivinge only routine care and intake of milk was 100cc/kg/day. Each neonate received two rounds of orogastric and breast feeding in the morning and in the afternoon, during which mean oxygen saturation was measured by pulse-oxymetry. During the study the heart rate and temperature of the neonates were monitored, and in case of hypothermia, bradycardia(less than 100 per minute) or apnea the feeding was discontinued and the study was repeated the following day. Data analysis was carried out using SPSS. Results: Fifty neonates were studied. The average birth weight was 1267.20±165.42 grams and average gestational age was 31.81±1.92 and female/male ratio was 1.2. There was no significant statistical difference in arterial oxygen saturation in orogastric and breast feeding in the morning and in the afternoon. (p=0.16 in the morning and p=0.6 in the afternoon). There was no complication of apnea, hypothermia or bradycardia. Conclusion: There was no significant statistical difference between the two methods in arterial oxygen saturation. It seems that oral feeding (which is a natural route) and skin contact between the mother and neonate causes a strong emotional bonding between the two and brings about better social adaptation for the neonate. Also shorter period of stay in hospital is more preferred, and breast feeding should be started at the earliest possible time after birth.

Keywords: Very low birth weight (V.L.B.W), O2 Saturation, Breast Feeding, Tube Feeding

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1581 Design and Developing the Infrared Sensor for Detection and Measuring Mass Flow Rate in Seed Drills

Authors: Bahram Besharti, Hossein Navid, Hadi Karimi, Hossein Behfar, Iraj Eskandari

Abstract:

Multiple or miss sowing by seed drills is a common problem on the farm. This problem causes overuse of seeds, wasting energy, rising crop treatment cost and reducing crop yield in harvesting. To be informed of mentioned faults and monitoring the performance of seed drills during sowing, developing a seed sensor for detecting seed mass flow rate and monitoring in a delivery tube is essential. In this research, an infrared seed sensor was developed to estimate seed mass flow rate in seed drills. The developed sensor comprised of a pair of spaced apart circuits one acting as an IR transmitter and the other acting as an IR receiver. Optical coverage in the sensing section was obtained by setting IR LEDs and photo-diodes directly on opposite sides. Passing seeds made interruption in radiation beams to the photo-diode which caused output voltages to change. The voltage difference of sensing units summed by a microcontroller and were converted to an analog value by DAC chip. The sensor was tested by using a roller seed metering device with three types of seeds consist of chickpea, wheat, and alfalfa (representing large, medium and fine seed, respectively). The results revealed a good fitting between voltage received from seed sensor and mass flow of seeds in the delivery tube. A linear trend line was set for three seeds collected data as a model of the mass flow of seeds. A final mass flow model was developed for various size seeds based on receiving voltages from the seed sensor, thousand seed weight and equivalent diameter of seeds. The developed infrared seed sensor, besides monitoring mass flow of seeds in field operations, can be used for the assessment of mechanical planter seed metering unit performance in the laboratory and provide an easy calibrating method for seed drills before planting in the field.

Keywords: seed flow, infrared, seed sensor, seed drills

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1580 The Buccal Fat Pad for Closure of Oroantral Communication

Authors: Stefano A. Denes, Riccardo Tieghi, Giovanni Elia

Abstract:

The buccal fat pad is a well-established tool in oral and maxillofacial surgery and its use has proved of value for the closure of oroantral communications. Oroantral communication may be a common complication after sequestrectomy in "Bisphosphonate-related osteonecrosis of the jaws". We report a clinical case of a 70-year-old female patient in bisphosphonate therapy presented with right maxillary sinusitis and oroantral communication after implants insertion. The buccal fat pad was used to close the defect. The case had an uneventful postoperative healing without dehiscence, infection and necrosis. We postulate that the primary closure of the site with buccal fat pad may ensure a sufficient blood supply and adequate protection for an effective bone-healing response to occur.

Keywords: buccal fat pad, oroantral communication, oral surgery, dehiscence

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1579 The Using of Liquefied Petroleum Gas (LPG) on a Low Heat Loss Si Engine

Authors: Hanbey Hazar, Hakan Gul

Abstract:

In this study, Thermal Barrier Coating (TBC) application is performed in order to reduce the engine emissions. Piston, exhaust, and intake valves of a single-cylinder four-cycle gasoline engine were coated with chromium carbide (Cr3C2) at a thickness of 300 µm by using the Plasma Spray coating method which is a TBC method. Gasoline engine was converted into an LPG system. The study was conducted in 4 stages. In the first stage, the piston, exhaust, and intake valves of the gasoline engine were coated with Cr3C2. In the second stage, gasoline engine was converted into the LPG system and the emission values in this engine were recorded. In the third stage, the experiments were repeated under the same conditions with a standard (uncoated) engine and the results were recorded. In the fourth stage, data obtained from both engines were loaded on Artificial Neural Networks (ANN) and estimated values were produced for every revolution. Thus, mathematical modeling of coated and uncoated engines was performed by using ANN. While there was a slight increase in exhaust gas temperature (EGT) of LPG engine due to TBC, carbon monoxide (CO) values decreased.

Keywords: LPG fuel, thermal barrier coating, artificial neural network, mathematical modelling

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1578 Ultrasonic Atomizer for Turbojet Engines

Authors: Aman Johri, Sidhant Sood, Pooja Suresh

Abstract:

This paper suggests a new and more efficient method of atomization of fuel in a combustor nozzle of a high bypass turbofan engine, using ultrasonic vibrations. Since atomization of fuel just before the fuel spray is injected into the combustion chamber is an important and crucial aspect related to functioning of a propulsion system, the technology suggested by this paper and the experimental analysis on the system components eventually proves to assist in complete and rapid combustion of the fuel in the combustor module of the engine. Current propulsion systems use carburetors, atomization nozzles and apertures in air intake pipes for atomization. The idea of this paper is to deploy new age hybrid technology, namely the Ultrasound Field Effect (UFE) to effectively atomize fuel before it enters the combustion chamber, as a viable and effective method to increase efficiency and improve upon existing designs. The Ultrasound Field Effect is applied axially, on diametrically opposite ends of an atomizer tube that gloves onto the combustor nozzle, where the fuel enters and exits under a pre-defined pressure. The Ultrasound energy vibrates the fuel particles to a breakup frequency. At reaching this frequency, the fuel particles start disintegrating into smaller diameter particles perpendicular to the axis of application of the field from the parent boundary layer of fuel flow over the baseplate. These broken up fuel droplets then undergo swirling effect as per the original nozzle design, with a higher breakup ratio than before. A significant reduction of the size of fuel particles eventually results in an increment in the propulsive efficiency of the engine. Moreover, the Ultrasound atomizer operates within a control frequency such that effects of overheating and induced vibrations are least felt on the overall performance of the engine. The design of an electrical manifold for the multiple-nozzle system over a typical can-annular combustor is developed along with this study, such that the product can be installed and removed easily for maintenance and repairing, can allow for easy access for inspections and transmits least amount of vibrational energy to the surface of the combustor. Since near-field ultrasound is used, the vibrations are easily controlled, thereby successfully reducing vibrations on the outer shell of the combustor. Experimental analysis is carried out on the effect of ultrasonic vibrations on flowing jet turbine fuel using an ultrasound generator probe and results of an effective decrease in droplet size across a constant diameter, away from the boundary layer of flow is noted using visual aid by observing under ultraviolet light. The choice of material for the Ultrasound inducer tube and crystal along with the operating range of temperatures, pressures, and frequencies of the Ultrasound field effect are also studied in this paper, while taking into account the losses incurred due to constant vibrations and thermal loads on the tube surface.

Keywords: atomization, ultrasound field effect, titanium mesh, breakup frequency, parent boundary layer, baseplate, propulsive efficiency, jet turbine fuel, induced vibrations

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1577 Graph Clustering Unveiled: ClusterSyn - A Machine Learning Framework for Predicting Anti-Cancer Drug Synergy Scores

Authors: Babak Bahri, Fatemeh Yassaee Meybodi, Changiz Eslahchi

Abstract:

In the pursuit of effective cancer therapies, the exploration of combinatorial drug regimens is crucial to leverage synergistic interactions between drugs, thereby improving treatment efficacy and overcoming drug resistance. However, identifying synergistic drug pairs poses challenges due to the vast combinatorial space and limitations of experimental approaches. This study introduces ClusterSyn, a machine learning (ML)-powered framework for classifying anti-cancer drug synergy scores. ClusterSyn employs a two-step approach involving drug clustering and synergy score prediction using a fully connected deep neural network. For each cell line in the training dataset, a drug graph is constructed, with nodes representing drugs and edge weights denoting synergy scores between drug pairs. Drugs are clustered using the Markov clustering (MCL) algorithm, and vectors representing the similarity of drug pairs to each cluster are input into the deep neural network for synergy score prediction (synergy or antagonism). Clustering results demonstrate effective grouping of drugs based on synergy scores, aligning similar synergy profiles. Subsequently, neural network predictions and synergy scores of the two drugs on others within their clusters are used to predict the synergy score of the considered drug pair. This approach facilitates comparative analysis with clustering and regression-based methods, revealing the superior performance of ClusterSyn over state-of-the-art methods like DeepSynergy and DeepDDS on diverse datasets such as Oniel and Almanac. The results highlight the remarkable potential of ClusterSyn as a versatile tool for predicting anti-cancer drug synergy scores.

Keywords: drug synergy, clustering, prediction, machine learning., deep learning

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1576 Intelligent Computing with Bayesian Regularization Artificial Neural Networks for a Nonlinear System of COVID-19 Epidemic Model for Future Generation Disease Control

Authors: Tahir Nawaz Cheema, Dumitru Baleanu, Ali Raza

Abstract:

In this research work, we design intelligent computing through Bayesian Regularization artificial neural networks (BRANNs) introduced to solve the mathematical modeling of infectious diseases (Covid-19). The dynamical transmission is due to the interaction of people and its mathematical representation based on the system's nonlinear differential equations. The generation of the dataset of the Covid-19 model is exploited by the power of the explicit Runge Kutta method for different countries of the world like India, Pakistan, Italy, and many more. The generated dataset is approximately used for training, testing, and validation processes for every frequent update in Bayesian Regularization backpropagation for numerical behavior of the dynamics of the Covid-19 model. The performance and effectiveness of designed methodology BRANNs are checked through mean squared error, error histograms, numerical solutions, absolute error, and regression analysis.

Keywords: mathematical models, beysian regularization, bayesian-regularization backpropagation networks, regression analysis, numerical computing

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1575 Simulation of Flow through Dam Foundation by FEM and ANN Methods Case Study: Shahid Abbaspour Dam

Authors: Mehrdad Shahrbanozadeh, Gholam Abbas Barani, Saeed Shojaee

Abstract:

In this study, a finite element (Seep3D model) and an artificial neural network (ANN) model were developed to simulate flow through dam foundation. Seep3D model is capable of simulating three-dimensional flow through a heterogeneous and anisotropic, saturated and unsaturated porous media. Flow through the Shahid Abbaspour dam foundation has been used as a case study. The FEM with 24960 triangular elements and 28707 nodes applied to model flow through foundation of this dam. The FEM being made denser in the neighborhood of the curtain screen. The ANN model developed for Shahid Abbaspour dam is a feedforward four layer network employing the sigmoid function as an activator and the back-propagation algorithm for the network learning. The water level elevations of the upstream and downstream of the dam have been used as input variables and the piezometric heads as the target outputs in the ANN model. The two models are calibrated and verified using the Shahid Abbaspour’s dam piezometric data. Results of the models were compared with those measured by the piezometers which are in good agreement. The model results also revealed that the ANN model performed as good as and in some cases better than the FEM.

Keywords: seepage, dam foundation, finite element method, neural network, seep 3D model

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1574 A Machine Learning Approach for Efficient Resource Management in Construction Projects

Authors: Soheila Sadeghi

Abstract:

Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: resource allocation, machine learning, optimization, data-driven decision-making, project management

Procedia PDF Downloads 40