Search results for: twin delayed deep deterministic policy gradient

Authors: Ahmed Negm, George Aggidis, Xiandong Ma

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With the diverse nature of urban cities, customer demand patterns, landscape topologies or even seasonal weather trends; managing our water distribution networks (WDNs) has proved a complex task. These unpredictable circumstances manifest as pipe failures, intermittent supply and burst events thus adding to water loss, energy waste and increased carbon emissions. Whilst these events are unavoidable, advanced pressure management has proved an effective tool to control and mitigate them. Henceforth, water utilities have struggled with developing a real-time control method that is resilient when confronting the challenges of water distribution. In this paper we use deep reinforcement learning (DRL) algorithms as a novel pressure control strategy to minimise pressure violations and leakage under both burst and background leakage conditions. Agents based on asynchronous actor critic (A2C) and recurrent proximal policy optimisation (Recurrent PPO) were trained and compared to benchmarked optimisation algorithms (differential evolution, particle swarm optimisation. A2C manages to minimise leakage by 32.48% under burst conditions and 67.17% under background conditions which was the highest performance in the DRL algorithms. A2C and Recurrent PPO performed well in comparison to the benchmarks with higher processing speed and lower computational effort.

Keywords: deep reinforcement learning, pressure management, water distribution networks, leakage management

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Authors: Minto Rattan, Tania Bose, Neeraj Chamoli

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The present paper investigates the effect of linear thermal gradient on the steady-state creep behavior of rotating isotropic disc using threshold stress based Sherby’s creep law. The composite discs made of aluminum matrix reinforced with silicon carbide particulate has been taken for analysis. The stress and strain rate distributions have been calculated for discs rotating at linear thermal gradation using von Mises’ yield criterion. The material parameters have been estimated by regression fit of the available experimental data. The results are displayed and compared graphically in designer friendly format for the above said temperature profile with the disc operating under uniform temperature profile. It is observed that radial and tangential stresses show minor variation and the strain rates vary significantly in the presence of thermal gradation as compared to disc having uniform temperature.

Keywords: creep, isotropic, steady-state, thermal gradient

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Authors: Okoro Ikechukwu Ralph, Fuat Kara

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The discoveries of hydrocarbon reserves has clearly drifted offshore, and in deeper waters - areas where the industry still has limited knowledge; and that were hitherto, regarded as being out of reach. This shift presents significant and increased challenges in technology requirements needed to guarantee safety of personnel, environment and equipment; ensure high reliability of installed equipment; and provide high level of confidence in security of investment and company reputation. Nowhere are these challenges more apparent than on subsea well integrity and production performance. The past two decades has witnessed enormous rise in deep and ultra-deep water offshore field developments for the recovery of hydrocarbons. Subsea installed equipment at the seabed has been the technology of choice for these developments. This paper discusses the role of Subsea Control module (SCM) as a vital factor for deep-water well integrity and production performance. A case study for Deep-water well integrity and production performance is analysed.

Keywords: offshore reliability, production performance, subsea control module, well integrity

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Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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The increasing use of deep learning applications in production is becoming a competitive advantage. Predictive quality enables the assurance of product quality by using data-driven forecasts via machine learning models as a basis for decisions on test results. The use of real Bosch production data along the value chain of hydraulic valves is a promising approach to classifying the leakage of directional valves.

Keywords: artificial neural networks, classification, hydraulics, predictive quality, deep learning

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Authors: Rakkrit Duangsoithong, Jermphiphut Jaruenpunyasak, Alba Garcia

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The use of foot recognition can be applied in many medical fields such as the gait pattern analysis and the knee exercises of patients in rehabilitation. Generally, a camera-based foot recognition system is intended to capture a patient image in a controlled room and background to recognize the foot in the limited views. However, this system can be inconvenient to monitor the knee exercises at home. In order to overcome these problems, this paper proposes to use the deep learning method using Convolutional Neural Networks (CNNs) for foot recognition. The results are compared with the traditional classification method using LBP and HOG features with kNN and SVM classifiers. According to the results, deep learning method provides better accuracy but with higher complexity to recognize the foot images from online databases than the traditional classification method.

Keywords: foot recognition, deep learning, knee rehabilitation, convolutional neural network

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Authors: Ahmed I. Raheem

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In this thesis, stratified and stratified wavy flow regimes have been investigated numerically for the oil (1.57 mPa s viscosity and 780 kg/m3 density) and water twophase flow in small and large horizontal steel pipes with a diameter between 0.0254 to 0.508 m by ANSYS Fluent software. Volume of fluid (VOF) with two phases flows using two equations family models (Realizable k-

Keywords: CFD, two-phase flow, pressure gradient, volume of fluid, large diameter, horizontal pipe, oil-water stratified and stratified wavy flow

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Authors: Suriya

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Rapid socio-economic development and urbanization have led to an increasingly serious air pollution problem in Ulaanbaatar (UB), the capital of Mongolia. PM₂.₅ pollution has become the most pressing aspect of UB air pollution. Therefore, monitoring and predicting PM₂.₅ concentration in UB is of great significance for the health of the local people and environmental management. As of yet, very few studies have used models to predict PM₂.₅ concentrations in UB. Using data from 0:00 on June 1, 2018, to 23:00 on April 30, 2020, we proposed two deep learning models based on Bayesian-optimized LSTM (Bayes-LSTM) and CNN-LSTM. We utilized hourly observed data, including Himawari8 (H8) aerosol optical depth (AOD), meteorology, and PM₂.₅ concentration, as input for the prediction of PM₂.₅ concentrations. The correlation strengths between meteorology, AOD, and PM₂.₅ were analyzed using the gray correlation analysis method; the comparison of the performance improvement of the model by using the AOD input value was tested, and the performance of these models was evaluated using mean absolute error (MAE) and root mean square error (RMSE). The prediction accuracies of Bayes-LSTM and CNN-LSTM deep learning models were both improved when AOD was included as an input parameter. Improvement of the prediction accuracy of the CNN-LSTM model was particularly enhanced in the non-heating season; in the heating season, the prediction accuracy of the Bayes-LSTM model slightly improved, while the prediction accuracy of the CNN-LSTM model slightly decreased. We propose two novel deep learning models for PM₂.₅ concentration prediction in UB, Bayes-LSTM, and CNN-LSTM deep learning models. Pioneering the use of AOD data from H8 and demonstrating the inclusion of AOD input data improves the performance of our two proposed deep learning models.

Keywords: deep learning, AOD, PM2.5, prediction, Ulaanbaatar

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Authors: Thibaud Berthomier, Ali Mansour, Luc Bressollette, Frédéric Le Roy, Dominique Mottier, Léo Fréchier, Barthélémy Hermenault

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Deep Venous Thrombosis (DVT) occurs when a thrombus is formed within a deep vein (most often in the legs). This disease can be deadly if a part or the whole thrombus reaches the lung and causes a Pulmonary Embolism (PE). This disorder, often asymptomatic, has multifactorial causes: immobilization, surgery, pregnancy, age, cancers, and genetic variations. Our project aims to relate the thrombus epidemiology (origins, patient predispositions, PE) to its structure using ultrasound images. Ultrasonography and elastography were collected using Toshiba Aplio 500 at Brest Hospital. This manuscript compares two classification approaches: spectral clustering and scattering operator. The former is based on the graph and matrix theories while the latter cascades wavelet convolutions with nonlinear modulus and averaging operators.

Keywords: deep venous thrombosis, ultrasonography, elastography, scattering operator, wavelet, spectral clustering

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Authors: Jisun Lee, Jay Hyoun Kwon

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As an alternative way to compensate the INS (inertial navigation system) error in non-GNSS (Global Navigation Satellite System) environment, geophysical database referenced navigation is being studied. In this study, both gravity gradient and terrain data were combined to complement the weakness of sole geophysical data as well as to improve the stability of the positioning. The main process to compensate the INS error using geophysical database was constructed on the basis of the EKF (Extended Kalman Filter). In detail, two type of combination method, centralized and decentralized filter, were applied to check the pros and cons of its algorithm and to find more robust results. The performance of each navigation algorithm was evaluated based on the simulation by supposing that the aircraft flies with precise geophysical DB and sensors above nine different trajectories. Especially, the results were compared to the ones from sole geophysical database referenced navigation to check the improvement due to a combination of the heterogeneous geophysical database. It was found that the overall navigation performance was improved, but not all trajectories generated better navigation result by the combination of gravity gradient with terrain data. Also, it was found that the centralized filter generally showed more stable results. It is because that the way to allocate the weight for the decentralized filter could not be optimized due to the local inconsistency of geophysical data. In the future, switching of geophysical data or combining different navigation algorithm are necessary to obtain more robust navigation results.

Keywords: Extended Kalman Filter, geophysical database referenced navigation, gravity gradient, terrain

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Authors: Mst. Tuhin Akter, Sharun Akter Khushbu, S. M. Shaqib

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A real-time system architecture is highly effective for monitoring and detecting various damaged products or fruits that may deteriorate over time or become infected with diseases. Deep learning models have proven to be effective in building such architectures. However, building a deep learning model from scratch is a time-consuming and costly process. A more efficient solution is to utilize deep neural network (DNN) based transfer learning models in the real-time monitoring architecture. This study focuses on using a novel strawberry dataset to develop effective transfer learning models for the proposed real-time monitoring system architecture, specifically for evaluating and detecting strawberry freshness. Several state-of-the-art transfer learning models were employed, and the best performing model was found to be Xception, demonstrating higher performance across evaluation metrics such as accuracy, recall, precision, and F1-score.

Keywords: strawberry freshness evaluation, deep neural network, transfer learning, image augmentation

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Authors: S. Khan, A. Khan, Abdul R. Soomrani, Raja F. Zafar, A. Waqas, G. Akbar

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This paper proposes a low-complexity image interpolation method. Image interpolation is used to convert a low dimension video/image to high dimension video/image. The objective of a good interpolation method is to upscale an image in such a way that it provides better edge preservation at the cost of very low complexity so that real-time processing of video frames can be made possible. However, low complexity methods tend to provide real-time interpolation at the cost of blurring, jagging and other artifacts due to errors in slope calculation. Non-linear methods, on the other hand, provide better edge preservation, but at the cost of high complexity and hence they can be considered very far from having real-time interpolation. The proposed method is a linear method that uses gradient orientation for slope calculation, unlike conventional linear methods that uses the contrast of nearby pixels. Prewitt edge detection is applied to separate uniform regions and edges. Simple line averaging is applied to unknown uniform regions, whereas unknown edge pixels are interpolated after calculation of slopes using gradient orientations of neighboring known edge pixels. As a post-processing step, bilateral filter is applied to interpolated edge regions in order to enhance the interpolated edges.

Keywords: edge detection, gradient orientation, image upscaling, linear interpolation, slope tracing

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Authors: Prima Khairunisa, Febriana Tri Kusumawati, Endah Luthfiana

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Background: Hypertension in elderly, caused by cardiovascular system cannot work normally, because the valves thickened and inelastic blood vessels. It causes vasoconstriction of the blood vessels. Hypertension exercise, increase cardiovascular function and the elasticity of the blood vessels. While slow deep breathing helps the body and mind feel relax. Combination both of them will decrease the blood pressure. Objective: To know the effect of hypertension exercise and slow deep breathing combination to blood pressure in elderly. Method: The study conducted with one group pre-post test experimental design. The samples were 10 elderly both male and female in a Village in Semarang, Central Java, Indonesia. The tool was manual sphygmomanometer to measure blood pressure. Result: Based on paired t-test between hypertension exercise and slow deep breathing with systole blood pressure showed sig (2-tailed) was 0.045, while paired t-test between hypertension exercise hypertension exercise and slow deep breathing with diastole blood pressure showed sig (2-tailed) was 0,343. The changes of systole blood pressure were 127.5 mmHg, and diastole blood pressure was 80 mmHg. Systole blood pressure decreases significantly because the average of systole blood pressure before implementation was 135-160 mmHg. While diastole blood pressure was not decreased significantly. It was influenced by the average of diastole blood pressure before implementation of hypertension exercise was not too high. It was between 80- 90 mmHg. Conclusion: There was an effect of hypertension exercise and slow deep breathing combination to the blood pressure in elderly after 6 times implementations.

Keywords: hypertension exercise, slow deep breathing, elderly, blood pressure

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Authors: Ju Peng Poh, Jun Yu Charles Lee, Jonathan Chew Hoe Khoo

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The emergence of digital twin technology, a digital replica of physical world, has improved the real-time access to data from sensors about the performance of buildings. This digital transformation has opened up many opportunities to improve the management of the building by using the data collected to help monitor consumption patterns and energy leakages. One example is the integration of predictive models for anomaly detection. In this paper, we use the GAM (Generalised Additive Model) for the anomaly detection of Air Handling Units (AHU) power consumption pattern. There is ample research work on the use of GAM for the prediction of power consumption at the office building and nation-wide level. However, there is limited illustration of its anomaly detection capabilities, prescriptive analytics case study, and its integration with the latest development of digital twin technology. In this paper, we applied the general GAM modelling framework on the historical data of the AHU power consumption and cooling load of the building between Jan 2018 to Aug 2019 from an education campus in Singapore to train prediction models that, in turn, yield predicted values and ranges. The historical data are seamlessly extracted from the digital twin for modelling purposes. We enhanced the utility of the GAM model by using it to power a real-time anomaly detection system based on the forward predicted ranges. The magnitude of deviation from the upper and lower bounds of the uncertainty intervals is used to inform and identify anomalous data points, all based on historical data, without explicit intervention from domain experts. Notwithstanding, the domain expert fits in through an optional feedback loop through which iterative data cleansing is performed. After an anomalously high or low level of power consumption detected, a set of rule-based conditions are evaluated in real-time to help determine the next course of action for the facilities manager. The performance of GAM is then compared with other approaches to evaluate its effectiveness. Lastly, we discuss the successfully deployment of this approach for the detection of anomalous power consumption pattern and illustrated with real-world use cases.

Keywords: anomaly detection, digital twin, generalised additive model, GAM, power consumption, supervised learning

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Authors: Ahmad Ardillah Rahman

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The decentralisation policy in education has been a trend in some countries in the last two decades. In Indonesia, the implementation of the policy has been introduced since 2003 with the occurrence of School-Based Management policy. The reform has affected the way principals and teachers should involve in school practices in which more autonomies and flexibilities are given to teachers in conducting their teaching practices. Almost 13 years since the policy was firstly introduced, the government and teachers in Indonesia still face some obstacles in maximising the potential benefits of the implementation of the decentralised education system. This study, thus, critically analyses the challenges of decentralised education policy for teachers in Indonesian education context. The purposes of this study are threefold. Firstly, it will explore the history of policy transformation from a centralised to a decentralised education policy. Secondly, it points out the advantages of the decentralised policy implementation. The last, it provides a comprehensive description of challenges faced by Indonesian teachers with the new roles in designing and implementing a curriculum. By using data from existing surveys and research, this study concludes that to successfully implement the transformation in the educational reform of Indonesia, continual and gradual teachers’ training, professional career pathway, and local monitoring for teachers should be developed and strengthened.

Keywords: curriculum design, decentralisation, school-based management, teachers’ autonomy

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Authors: Sait Ozsoy, Asude Gokmen, Mehtap Yondem, Hanife A. Alkan, Gulnaz T. Javan

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Deep neck infection often develops due to upper respiratory tract and odontogenic infections. Gastrointestinal System perforation can occur for many reasons and is in need of the early diagnosis and prompt surgical treatment. In both cases late or incorrect diagnosis may lead to increase morbidity and high mortality. A patient with a diagnosis of deep neck abscess died while under treatment due to sepsis and multiple organ failure. Autopsy finding showed duodenal ulcer and this is reported in the literature.

Keywords: peptic ulcer perforation, peritonitis, retropharyngeal abscess, sepsis

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Authors: Seyed Abolhasan Naeini, Saeideh Mohammadi

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Time dependent settlement due to loading on soft saturated soils produces many problems such as high consolidation settlements and low consolidation rates. Also, long term consolidation settlement of soft soil underlying the embankment leads to unpredicted settlements and cracks on soil surface. Preloading method is an effective improvement method to solve this problem. Using vertical drains in preloading method is an effective method for improving soft soils. Applying deep soil mixing method on soft soils is another effective method for improving soft soils. There are little studies on using two methods of preloading and deep soil mixing simultaneously. In this paper, the concurrent effect of preloading with deep soil mixing by vertical drains is investigated through a finite element code, Plaxis2D. The influence of parameters such as deep soil mixing columns spacing, existence of vertical drains and distance between them, on settlement and stability factor of safety of embankment embedded on soft soil is investigated in this research.

Keywords: preloading, soft soil, vertical drains, deep soil mixing, consolidation settlement

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Authors: Ondrej Slach, Jan Machacek, Jan Zenka, Lucie Hyllova, Petr Rumpel

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The aim of the paper is to identify and asses different trajectories of two fashionable urban policies –creative and smart cities– in specific post-socialistic context. Drawing on the case of Czechia, we employ the concept of policy mobility research. More specifically, we employ a discourse analysis in order to identify the so-called 'infrastructure' of both policies (such as principal actors, journals, conferences, events), with the special focus on 'agents of transfer' in a multiscale perspective. The preliminary results indicate faster and more aggressive spatial penetration of smart cities policy compared to creative cities policy in Czechia. Further, it seems that existed translation and implementation of smart cities policy into the national and urban context resulted in deliberated fragmented policy of smart cities in Czechia (pure technocratic view), which might be a threat for the future development of social sustainability, especially in cities that are facing increasing social polarisation. Last but not least, due to the fast spatial penetration of the concept and policies of smart cities, it seems that creative cities policy has almost been crowded out of the Czech urban agenda.

Keywords: policy mobility, smart cities, creative cities, Czechia

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Authors: Mahsa Namvari, Kari Ullakko

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It is well-known that the Co-doping of ferromagnetic shape memory alloys (FSMAs) is a crucial tool to control their multifunctional properties. The present work investigates the use of small quantities of Co to fine-tune the transformation, structure, microstructure, mechanical and magnetic properties of the polycrystalline Ni₄₉.₈Mn₂₈.₅Ga₂₁.₇ (at.%) alloy, At Co concentrations of 1-1.5 at.%, a microstructure with an average grain size of about 2.00 mm was formed with a twin structure, enabling the experimental observation of magnetic-field-induced twin variant rearrangement. At higher levels of Co-doping, the grain size was essentially reduced, and the crystal structure of the martensitic phase became 2M martensite. The decreasing grain size and changing crystal structure are attributed to the progress of γ-phase precipitates. Alongside the academic aspect, the results of the present work point to the commercial advantage of fabricating 10M Co-doped Ni-Mn-Ga actuating elements made from large grains of polycrystalline ingots obtained by a standard melting facility instead of grown single crystals.

Keywords: Ni-Mn-Ga, ferromagnetic shape memory, martensitic phase transformation, grain growth

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Authors: Komeil Valipourian

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Urban advances and the growing need for developing infrastructures has increased the importance of deep excavations. In this study, after the introducing probability analysis as an important issue, an attempt has been made to apply it for the deep excavation project of Bangkok’s Metro as a case study. For this, the numerical probability model has been developed based on the Finite Difference Method and Monte Carlo sampling approach. The results indicate that disregarding the issue of probability in this project will result in an inappropriate design of the retaining structure. Therefore, probabilistic redesign of the support is proposed and carried out as one of the applications of probability analysis. A 50% reduction in the flexural strength of the structure increases the failure probability just by 8% in the allowable range and helps improve economic conditions, while maintaining mechanical efficiency. With regard to the lack of efficient design in most deep excavations, by considering geometrical and geotechnical variability, an attempt was made to develop an optimum practical design standard for deep excavations based on failure probability. On this basis, a practical relationship is presented for estimating the maximum allowable horizontal displacement, which can help improve design conditions without developing the probability analysis.

Keywords: numerical probability modeling, deep excavation, allowable maximum displacement, finite difference method (FDM)

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Authors: Meysam Mousakhani, Reza Ziaie Moayed

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Considering the population growth and the needs of society, the improvement of problematic soils and the study of the application of different improvement methods have been considered. One of these methods is deep soil mixing, which has been developed in the past decade, especially in soft soils due to economic efficiency, simple implementation, and other benefits. The use of cement is criticized for its cost and the damaging environmental effects, so these factors lead us to use other additives along with cement in the deep soil mixing. Additives that are used today include fly ash, blast-furnace slag, glass powder, and potassium hydroxide. The present study provides a literature review on the application of different additives in deep soil mixing so that the best additives can be introduced from strength, economic, environmental and other perspectives. The results show that by replacing fly ash and slag with about 40 to 50% of cement, not only economic and environmental benefits but also a long-term strength comparable to cement would be achieved. The use of glass powder, especially in 3% mixing, results in desirable strength. In addition to the other benefits of these additives, potassium hydroxide can also be transported over longer distances, leading to wider soil improvement. Finally, this paper suggests further studies in terms of using other additives such as nanomaterials and zeolite, with different ratios, in different conditions and soils (silty sand, clayey sand, carbonate sand, sandy clay and etc.) in the deep mixing method.

Keywords: deep soil mix, soil stabilization, fly ash, ground improvement

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Authors: Frank Jaksoni Mweta, Nozomu Adachi, Yoshikazu Todaka, Hirokazu Sato, Yuta Sato, Hiromi Miura, Masakazu Kobayashi, Chihiro Watanabe, Yoshiteru Aoyagi

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Advances in the consumption of hydrogen fuel increase demands of high strength steel pipes and storage tanks. However, high strength steels are highly sensitive to hydrogen embrittlement. Because the introduction of hydrogen into steel during the fabrication process or from the environment is unavoidable, it is essential to improve hydrogen embrittlement resistance of high strength steels through microstructural control. In the present study, the heterogeneous nanostructure with a tensile strength of about 1.8 GPa and the homogeneous nanostructure with a tensile strength of about 2.0 GPa of 316LN steels were generated after 92% heavy cold rolling and high-pressure torsion straining, respectively. The heterogeneous nanostructure is composed of twin domains, shear bands, and lamellar grains. The homogeneous nanostructure is composed of uniformly distributed ultrafine nanograins. The influence of heterogeneous and homogenous nanostructures on the hydrogen embrittlement resistance was investigated. The specimen for each nanostructure was electrochemically charged with hydrogen for 3, 6, 12, and 24 hours, respectively. Under the same hydrogen charging time, both nanostructures show almost the same concentration of the diffusible hydrogen based on the thermal desorption analysis. The tensile properties of the homogenous nanostructure were severely affected by the diffusible hydrogen. However, the diffusible hydrogen shows less impact on the tensile properties of the heterogeneous nanostructure. The difference in embrittlement behavior between the heterogeneous and homogeneous nanostructures was elucidated based on the mechanism of the cracks' growth observed in the tensile fractography. The hydrogen embrittlement was suppressed in the heterogeneous nanostructure because the twin domain became an obstacle for crack growth. The homogeneous nanostructure was not consisting an obstacle such as a twin domain; thus, the crack growth resistance was low in this nanostructure.

Keywords: diffusible hydrogen, heterogeneous nanostructure, homogeneous nanostructure, hydrogen embrittlement

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Authors: Jonathan Olusegun Famoroti, Mathew Ekundayo Rotimi, Mishelle Doorasamy

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This study offers an empirical investigation into some selected macroeconomic drivers of the monetary policy rate in member countries of the West African Monetary Zone (WAMZ), considering both internal and external variables. We employed Autoregressive Distributed Lag (ARDL) to carry out the investigation between monetary policy and some macroeconomic variables in both the long-run and short-run relationship. The results suggest that the drivers of the policy rate in this zone, in the long run, include, among others, global oil price, exchange rate, inflation rate, and gross domestic product, while in the short run, federal fund rate, trade openness, exchange rate, inflation rate, and gross domestic product are core determinants of the policy rate. Therefore, in order to ensure long-run stability in the policy rate among the members’ states, these drivers should be given closer consideration so that the trajectory for effective structure can be designed and fused into the economic structure and policy frameworks accordingly.

Keywords: monetary policy rate, macroeconomic variables, WAMZ, ARDL

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Authors: Shaik Jilani Basha, Inavolu Avinash, Alla Venu Sai Reddy, Bitragunta Taraka Ramu

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We can use the neural style transfer technique to build a picture with the same "content" as the beginning image but the "style" of the picture we've chosen. Neural style transfer is a technique for merging the style of one image into another while retaining its original information. The only change is how the image is formatted to give it an additional artistic sense. The content image depicts the plan or drawing, as well as the colors of the drawing or paintings used to portray the style. It is a computer vision programme that learns and processes images through deep convolutional neural networks. To implement software, we used to train deep learning models with the train data, and whenever a user takes an image and a styled image, the output will be as the style gets transferred to the original image, and it will be shown as the output.

Keywords: neural networks, computer vision, deep learning, convolutional neural networks

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Authors: Layla Albdr

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Saudi Arabia instituted the policy of Sensitizing and Training Stakeholders for E-learning and witnessed wide adoption in many institutions. However, it is at the infancy stage and needs time to develop to mirror the US and UK. The majority of the higher education institutions in Saudi Arabia have adopted E-learning as an alternative to traditional methods to advance education. Conversely, effective implementation of the policy of sensitization and training of stakeholders for E-learning implementation has not been attained because of various challenges. The objectives included determining the challenges and opportunities of the E-learning policy of sensitization and training of stakeholders in Saudi Arabia's higher education and examining if sensitization and training of stakeholder's policy will help promote the implementation of E-learning in institutions. The study employed a descriptive research design based on qualitative analysis. The researcher recruited 295 students and 60 academic staff from four Saudi Arabian universities to participate in the study. An online questionnaire was used to collect the data. The data was then analyzed and reported both quantitatively and qualitatively. The analysis provided an in-depth understanding of the opportunities and challenges of E-learning policy in Saudi Arabian universities. The main challenges identified as internal challenges were the lack of educators’ interest in adopting the policy, and external challenges entailed lack of ICT infrastructure and Internet connectivity. The study recommends encouraging, sensitizing, and training all stakeholders to address these challenges and adopt the policy.

Keywords: e-learning, educational policy, Saudi Arabia, policy of sensitization and training

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Authors: Mohhamed El Habib Labdouni

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Introduction: Co-infection with VIH and tuberculosis poses one of the major ongoing challenges for global TB and AIDS prevention and control. The objective of this study is to raise the issue of the resurgence of TB, in People living with VIH supported in a referent center in western Algeria. Its epidemiological, clinical, biological and radiological new trends, and to compare the mortality rate between early and delayed ART. Methods: It was a prospective study, during 36 months from the 01st/01/2012 to 31st/12/2014, by identifying and analyzing cases of TB-VIH co-infection. Our population was devised in two groups/ early ART and delayed ART. The primary and secondary endpoints were analyzed with Kaplan-Meier curves and log-rank test the period of follow up, which was fixed at 300 weeks. Results: Sixty cases of co-infection TB -VIH were enrolled in our study: 78.3% had pulmonary tuberculosis associated with extra-pulmonary, 13.3% had only pulmonary tuberculosis and 08.3% presented strictly extra-pulmonary TB. The clinical particularity of this co-infection is the frequency of serious localization such us: pleural 23.3%, peritoneal 31.7%, and meningeal suffusion 13.3%.y-.biologicaly we notice the predominance both of pancytopenia and leucoanemia, hyponatremia in 38,6% and hypokalemia in 19,3%. By analyzing Kaplan-Meier survival curves, we notice that early ART initiation is associated with a significant reduction of all-cause mortality (p = 0,000), and we have identified several prognostic factors such as hypokalemia hyponatremia, leukocytosis thrombopenemia leucothrombopenia (p = 0,005). Conclusion: Our study confirms most of the results reported in the literature. Early ART initiation reduces the rate of all-cause mortality, despite the probability of the occurrence of TB-IRIS.

Keywords: TB-HIV co-infection, early ART, hyponatremia, extrapulmonary tuberculosis

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Authors: Pawan Kumar Mishra, Ganesh Singh Bisht

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Super-resolution is the technique that is being used in computer vision to construct high-resolution images from a single low-resolution image. It is used to increase the frequency component, recover the lost details and removing the down sampling and noises that caused by camera during image acquisition process. High-resolution images or videos are desired part of all image processing tasks and its analysis in most of digital imaging application. The target behind super-resolution is to combine non-repetition information inside single or multiple low-resolution frames to generate a high-resolution image. Many methods have been proposed where multiple images are used as low-resolution images of same scene with different variation in transformation. This is called multi-image super resolution. And another family of methods is single image super-resolution that tries to learn redundancy that presents in image and reconstruction the lost information from a single low-resolution image. Use of deep learning is one of state of art method at present for solving reconstruction high-resolution image. In this research, we proposed Deep Denoising Super Resolution (DDSR) that is a deep neural network for effectively reconstruct the high-resolution image from low-resolution image.

Keywords: resolution, deep-learning, neural network, de-blurring

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Authors: Susan Diamond

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Machine Learning workloads have traditionally been run in high-performance computing (HPC) environments, where users log in to dedicated machines and utilize the attached GPUs to run training jobs on huge datasets. Training of large neural network models is very resource intensive, and even after exploiting parallelism and accelerators such as GPUs, a single training job can still take days. Consequently, the cost of hardware is a barrier to entry. Even when upfront cost is not a concern, the lead time to set up such an HPC environment takes months from acquiring hardware to set up the hardware with the right set of firmware, software installed and configured. Furthermore, scalability is hard to achieve in a rigid traditional lab environment. Therefore, it is slow to react to the dynamic change in the artificial intelligent industry. Watson Deep Learning as a service, a cloud-based deep learning platform that mitigates the long lead time and high upfront investment in hardware. It enables robust and scalable sharing of resources among the teams in an organization. It is designed for on-demand cloud environments. Providing a similar user experience in a multi-tenant cloud environment comes with its own unique challenges regarding fault tolerance, performance, and security. Watson Deep Learning as a service tackles these challenges and present a deep learning stack for the cloud environments in a secure, scalable and fault-tolerant manner. It supports a wide range of deep-learning frameworks such as Tensorflow, PyTorch, Caffe, Torch, Theano, and MXNet etc. These frameworks reduce the effort and skillset required to design, train, and use deep learning models. Deep Learning as a service is used at IBM by AI researchers in areas including machine translation, computer vision, and healthcare. 

Keywords: deep learning, machine learning, cognitive computing, model training

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Authors: Bowen Ding, Yihao Kuang

Abstract:

In recent years, with the development of large-scale pre-trained lan-guage models, building vector representations of text through deep neural network models has become a standard practice for natural language processing tasks. From the performance on downstream tasks, we can know that the text representation constructed by these models contains linguistic information, but its encoding mode and extent are unclear. In this work, a structural probe is proposed to detect whether the vector representation produced by a deep neural network is embedded with a syntax tree. The probe is trained with the deep metric learning method, so that the distance between word vectors in the metric space it defines encodes the distance of words on the syntax tree, and the norm of word vectors encodes the depth of words on the syntax tree. The experiment results on ELMo and BERT show that the syntax tree is encoded in their parameters and the word representations they produce.

Keywords: deep metric learning, syntax tree probing, natural language processing, word representations

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Authors: Byung Ho Jung, Dong Hoon Lim

Abstract:

Logistic regression is a statistical method for analyzing a dataset in which there are one or more independent variables that determine an outcome. Logistic regression is used extensively in numerous disciplines, including the medical and social science fields. In this paper, we address the problem of estimating parameters in the logistic regression based on MapReduce framework with RHadoop that integrates R and Hadoop environment applicable to large scale data. There exist three learning algorithms for logistic regression, namely Gradient descent method, Cost minimization method and Newton-Rhapson's method. The Newton-Rhapson's method does not require a learning rate, while gradient descent and cost minimization methods need to manually pick a learning rate. The experimental results demonstrated that our learning algorithms using RHadoop can scale well and efficiently process large data sets on commodity hardware. We also compared the performance of our Newton-Rhapson's method with gradient descent and cost minimization methods. The results showed that our newton's method appeared to be the most robust to all data tested.

Keywords: big data, logistic regression, MapReduce, RHadoop

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Authors: Florian Kiebert, Hagen Schmidt

Abstract:

Standing surface acoustic waves (sSAWs) have already been used to manipulate particles in a microfluidic channel made of polydimethylsiloxan (PDMS). Usually two identical facing interdigital transducers (IDTs) are exploited to form an sSAW. Further, it has been reported that an sSAW can be generated by a single IDT using a superstrate resonating cavity or a PDMS post. Nevertheless, both setups utilising a traveling surface acoustic wave (tSAW) to create an sSAW for particle manipulation are costly. We present a simplified setup with a tSAW and a PDMS channel to form an sSAW. The incident tSAW is reflected at the rear PDMS channel wall and superimposed with the reflected tSAW. This superpositioned waves generates an sSAW but only at regions where the distance to the rear channel wall is smaller as the attenuation length of the tSAW minus the channel width. Therefore in a channel of 500µm width a tSAW with a wavelength λ = 120 µm causes a sSAW over the whole channel, whereas a tSAW with λ = 60 µm only forms an sSAW next to the rear wall of the channel, taken into account the attenuation length of a tSAW in water. Hence, it is possible to concentrate and trap particles in a defined region of the channel by adjusting the relation between the channel width and tSAW wavelength. Moreover, it is possible to generate a particle gradient over the channel width by picking the right ratio between channel wall and wavelength. The particles are moved towards the rear wall by the acoustic streaming force (ASF) and the acoustic radiation force (ARF) caused by the tSAW generated bulk acoustic wave (BAW). At regions in the channel were the sSAW is dominating the ARF focuses the particles in the pressure nodes formed by the sSAW caused BAW. On the one side the ARF generated by the sSAW traps the particle at the center of the tSAW beam, i. e. of the IDT aperture. On the other side, the ASF leads to two vortices, one on the left and on the right side of the focus region, deflecting the particles out of it. Through variation of the applied power it is possible to vary the number of particles trapped in the focus points, because near to the rear wall the amplitude of the reflected tSAW is higher and, therefore, the ARF of the sSAW is stronger. So in the vicinity of the rear wall the concentration of particles is higher but decreases with increasing distance to the wall, forming a gradient of particles. The particle gradient depends on the applied power as well as on the flow rate. Thus by variation of these two parameters it is possible to change the particle gradient. Furthermore, we show that the particle gradient can be modified by changing the relation between the channel width and tSAW wavelength. Concluding a single IDT generates an sSAW in a PDMS microchannel enables particle gradient generation in a well-defined microfluidic flow system utilising the ARF and ASF of a tSAW and an sSAW.

Keywords: ARF, ASF, particle manipulation, sSAW, tSAW

Procedia PDF Downloads 305