Search results for: deep wells

Authors: Muhammad Ahnaf Zahin, Yaw Adu-Gyamfi

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Bus transit is a crucial component of transportation networks, especially in urban areas. Any intelligent transportation system must have accurate real-time information on bus travel times since it minimizes waiting times for passengers at different stations along a route, improves service reliability, and significantly optimizes travel patterns. Bus agencies must enhance the quality of their information service to serve their passengers better and draw in more travelers since people waiting at bus stops are frequently anxious about when the bus will arrive at their starting point and when it will reach their destination. For solving this issue, different models have been developed for predicting bus travel times recently, but most of them are focused on smaller road networks due to their relatively subpar performance in high-density urban areas on a vast network. This paper develops a deep learning-based architecture using a single-step multi-station forecasting approach to predict average bus travel times for numerous routes, stops, and trips on a large-scale network using heterogeneous bus transit data collected from the GTFS database. Over one week, data was gathered from multiple bus routes in Saint Louis, Missouri. In this study, Gated Recurrent Unit (GRU) neural network was followed to predict the mean vehicle travel times for different hours of the day for multiple stations along multiple routes. Historical time steps and prediction horizon were set up to 5 and 1, respectively, which means that five hours of historical average travel time data were used to predict average travel time for the following hour. The spatial and temporal information and the historical average travel times were captured from the dataset for model input parameters. As adjacency matrices for the spatial input parameters, the station distances and sequence numbers were used, and the time of day (hour) was considered for the temporal inputs. Other inputs, including volatility information such as standard deviation and variance of journey durations, were also included in the model to make it more robust. The model's performance was evaluated based on a metric called mean absolute percentage error (MAPE). The observed prediction errors for various routes, trips, and stations remained consistent throughout the day. The results showed that the developed model could predict travel times more accurately during peak traffic hours, having a MAPE of around 14%, and performed less accurately during the latter part of the day. In the context of a complicated transportation network in high-density urban areas, the model showed its applicability for real-time travel time prediction of public transportation and ensured the high quality of the predictions generated by the model.

Keywords: gated recurrent unit, mean absolute percentage error, single-step forecasting, travel time prediction.

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Authors: L. Bikulciene, E. Venskaityte, G. Jarusevicius

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Non-stationary and nonlinear signals generated by living complex systems defy traditional mechanistic approaches, which are based on homeostasis. Previous our studies have shown that the evaluation of the interactions of physiological signals by using special analysis methods is suitable for observation of physiological processes. It is demonstrated the possibility of using deep physiological model, based interpretation of the changes of the human body’s functional states combined with an application of the analytical method based on matrix theory for the physiological signals analysis, which was applied on high risk cardiac patients. It is shown that evaluation of cardiac signals interactions show peculiar for each individual functional changes at the onset of hemodynamic restoration procedure. Therefore we suggest that the alterations of functional state of the body, after patients overcome surgery can be complemented by the data received from the suggested approach of the evaluation of functional variables interactions.

Keywords: cardiac diseases, complex systems theory, ECG analysis, matrix analysis

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Authors: Habab Ahmed Hassan Abuzeid

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Organizations are social systems. If one wishes to work in them or to manage them, it is necessary to understand how they operate. Organizations combine science and people–technology and humanity. Unless we have qualified people to design and implement, techniques alone will not produce desirable results. Human behavior in organizations is rather unpredictable. It is unpredictable because it arises from people’s deep-seated needs and value systems. However, it can be partially understood in terms of the framework of behavioral science, management and other disciplines. There is no idealistic solution to organizational problems. All that can be done is to increase our understanding and skills so that human relations at work can be enhanced. In this paper, we consider management of organization behavior utilizing human resources. Study the elements of organization behavior, the effectiveness of mechanism to enhance staff relationships. Many approaches could be applied for healthy organizational environment, it’s highlighted more details in this paper. Organization behavior can raise the employees’ engagement, loyalty and commitment; to accomplish the goal.

Keywords: environment, engagement, human resources, organization behavior

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Authors: Yasir Memon

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The complexity and challenges in the offshore industry are increasing more than the past. The oil and gas industry is expanding every day by accomplishing these challenges. More challenging wells such as longer and deeper are being drilled in today’s environment. Blowout prevention phenomena hold a worthy importance in oil and gas biosphere. In recent, so many past years when the oil and gas industry was growing drilling operation were extremely dangerous. There was none technology to determine the pressure of reservoir and drilling hence was blind operation. A blowout arises when an uncontrolled reservoir pressure enters in wellbore. A potential of blowout in the oil industry is the danger for the both environment and the human life. Environmental damage, state/country regulators, and the capital investment causes in loss. There are many cases of blowout in the oil the gas industry caused damage to both human and the environment. A huge capital investment is being in used to stop happening of blowout through all over the biosphere to bring damage at the lowest level. The objective of this study is to promote safety and good resources to assure safety and environmental integrity in all operations during drilling. This study shows that human errors and management failure is the main cause of blowout therefore proper management with the wise use of precautions, prevention methods or controlling techniques can reduce the probability of blowout to a minimum level. It also discusses basic procedures, concepts and equipment involved in well control methods and various steps using at various conditions. Furthermore, another aim of this study work is to highlight management role in oil gas operations. Moreover, this study analyze the causes of Blowout of Macondo well occurred in the Gulf of Mexico on April 20, 2010, and deliver the recommendations and analysis of various aspect of well control methods and also provides the list of mistakes and compromises that British Petroleum and its partner were making during drilling and well completion methods and also the Macondo well disaster happened due to various safety and development rules violation. This case study concludes that Macondo well blowout disaster could be avoided with proper management of their personnel’s and communication between them and by following safety rules/laws it could be brought to minimum environmental damage.

Keywords: energy, environment, oil and gas industry, Macondo well accident

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Authors: Abdelkader Soumaya, Aymen Arfaoui, Noureddine Ben Ayed, Ali Kadri

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The Chotts belt represents the southernmost folded structure in the Tunisian Atlas domain. It is dominated by inherited deep extensional E-W trending fault zones, which are reactivated as strike-slip faults during the Cenozoic compression. By examining the geological maps at different scales and based on the fieldwork data, we propose new structural interpretations for the geometries and fault kinematics in the Chotts chain. A set of ENE-WSW right-lateral en echelon folds, with curved shapes and steeply inclined southern limbs, is visible in the map view of this belt. These asymmetric tight anticlines are affected by E-W trending fault segments linked by local bends and stepovers. The revealed kinematic indicators along one of these E-W striated faults (Tafferna segment), such as breccias and gently inclined slickenlines (N094, 80N, 15°W pitch angles), show direct evidence of dextral strike-slip movement. The calculated stress tensors from corresponding faults slip data reveal an overall strike-slip tectonic regime with reverse component and NW-trending sub-horizontal σ1 axis ranking between N130 to N150. From west to east, we distinguished several types of structures along the segmented dextral fault system of the Chotts Range. The NE-SW striking fold-thrust belt (~25 km-long) between two continuously linked E-W fault segments (NW of Tozeur town) has been suggested as a local restraining bend. The central part of the Chotts chain is occupied by the ENE-striking Ksar Asker anticlines (Taferna, Torrich, and Sif Laham), which are truncated by a set of E-W strike-slip fault segments. Further east, the fault segments of Hachichina and Sif Laham connected across the NW-verging asymmetric fold-thrust system of Bir Oum Ali, which can be interpreted as a left-stepping contractional bend (~20 km-long). The oriental part of the Chotts belt corresponds to an array of subparallel E-W oriented fault segments (i.e., Beidha, Bouloufa, El Haidoudi-Zemlet El Beidha) with similar lengths (around 10 km). Each of these individual separated segments is associated with curved ENE-trending en echelon right-stepping anticlines. These folds are affected by a set of conjugate R and R′ shear-type faults indicating a dextral strike-lip motion. In addition, the relay zones between these E-W overstepping fault segments define local releasing stepovers dominated by NW-SE subsidiary faults. Finally, the Chotts chain provides well-exposed examples of strike-slip tectonics along E-W distributed fault segments. Each fault zone shows a typical strike-slip architecture, including parallel fault segments connecting via local stepovers or bends. Our new structural interpretations for this region reveal a great influence of the E-W deep fault segments on regional tectonic deformations and stress field during the Cenozoic shortening.

Keywords: chotts belt, tunisian atlas, strike-slip fault, stepovers, fault segments

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Authors: Donya Ashtiani Haghighi, Amirali Baniasadi

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Coronavirus (COVID-19) disease has spread abruptly all over the world since the end of 2019. Computed tomography (CT) scans and X-ray images are used to detect this disease. Different Deep Neural Network (DNN)-based diagnosis solutions have been developed, mainly based on Convolutional Neural Networks (CNNs), to accelerate the identification of COVID-19 cases. However, CNNs lose important information in intermediate layers and require large datasets. In this paper, Capsule Network (CapsNet) is used. Capsule Network performs better than CNNs for small datasets. Accuracy of 0.9885, f1-score of 0.9883, precision of 0.9859, recall of 0.9908, and Area Under the Curve (AUC) of 0.9948 are achieved on the Capsule-based framework with hyperparameter tuning. Moreover, different dropout rates are investigated to decrease overfitting. Accordingly, a dropout rate of 0.1 shows the best results. Finally, we remove one convolution layer and decrease the number of trainable parameters to 146,752, which is a promising result.

Keywords: capsule network, dropout, hyperparameter tuning, classification

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Authors: S. J. Addinell, T. Richard, B. Evans

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The Deep Exploration Technologies Cooperative Research Centre (DET CRC) is researching and developing a new coiled tubing based greenfields mineral exploration drilling system utilising downhole water powered percussive drill tooling. This new drilling system is aimed at significantly reducing the costs associated with identifying mineral resource deposits beneath deep, barron cover. This system has shown superior rates of penetration in water-rich hard rock formations at depths exceeding 500 meters. Several key challenges exist regarding the deployment and use of these bottom hole assemblies for mineral exploration, and this paper discusses some of the key technical challenges. This paper presents experimental results obtained from the research program during laboratory and field testing of the prototype drilling system. A study of the morphological aspects of the cuttings generated during the percussive drilling process is presented and shows a strong power law relationship for particle size distributions. Several percussive drilling parameters such as RPM, applied fluid pressure and weight on bit have been shown to influence the particle size distributions of the cuttings generated. This has direct influence on other drilling parameters such as flow loop performance, cuttings dewatering, and solids control. Real-time, accurate knowledge of percussive system operating parameters will assist the driller in maximising the efficiency of the drilling process. The applied fluid flow, fluid pressure, and rock properties are known to influence the natural oscillating frequency of the percussive hammer, but this paper also shows that drill bit design, drill bit wear and the applied weight on bit can also influence the oscillation frequency. Due to the changing drilling conditions and therefore changing operating parameters, real-time understanding of the natural operating frequency is paramount to achieving system optimisation. Several techniques to understand the oscillating frequency have been investigated and presented. With a conventional top drive drilling rig, spectral analysis of applied fluid pressure, hydraulic feed force pressure, hold back pressure and drill string vibrations have shown the presence of the operating frequency of the bottom hole tooling. Unfortunately, however, with the implementation of a coiled tubing drilling rig, implementing a positive displacement downhole motor to provide drill bit rotation, these signals are not available for interrogation at the surface and therefore another method must be considered. The investigation and analysis of ground vibrations using geophone sensors, similar to seismic-while-drilling techniques have indicated the presence of the natural oscillating frequency of the percussive hammer. This method is shown to provide a robust technique for the determination of the downhole percussive oscillation frequency when used with a coiled tubing drill rig.

Keywords: cuttings characterization, drilling optimization, oscillation frequency, percussive drilling, spectral analysis

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Authors: Aula Khatteb Abu-Liel

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The study investigated the effects of diglossia and orthography on reading in Arabic, manipulating reading in Spoken Arabic (SA), using Arabizi, in which it is written using Latin letters on computers/phones, and the two forms of the conventional written form Modern Standard Arabic (MSA): vowelled (shallow) and unvowelled (deep). 77 skilled readers in 8th grade performed oral reading of single words and narrative and expository texts, and silent reading comprehension of both genres of text. Oral reading and comprehension revealed different patterns. Single words and texts were read faster and more accurately in unvoweled MSA, slowest and least accurately in vowelled MSA, and in-between in Arabizi. Comprehension was highest for vowelled MSA. Narrative texts were better than expository texts in Arabizi with the opposite pattern in MSA. The results suggest that frequency of the type of texts and the way in which phonology is encoded affect skilled reading.

Keywords: Arabic, Arabize, computer mediated communication, diglossia, modern standard Arabic

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Authors: Chungkuk Jin, Moohyun Kim, Woo Chul Chung

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This paper presents global performance and dynamic behaviors of a discrete-pontoon-type floating bridge with mooring lines in time domain under wind and wave excitations. The structure is designed for long-distance and deep-water crossing and consists of the girder, columns, pontoons, and mooring lines. Their functionality and behaviors are investigated by using elastic-floater/mooring fully-coupled dynamic simulation computer program. Dynamic wind, first- and second-order wave forces, and current loads are considered as environmental loads. Girder’s dynamic responses and mooring tensions are analyzed under different analysis methods and environmental conditions. Girder’s lateral responses are highly influenced by the second-order wave and wind loads while the first-order wave load mainly influences its vertical responses.

Keywords: floating bridge, mooring line, pontoon, wave excitation

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Authors: Jiaying Zhang, Xin Mu, Chang Ni, Jeff L. Zhang

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Magnetic resonance elastography (MRE) non-invasively assesses tissue elastic properties, such as shear modulus, by measuring tissue’s displacement in response to mechanical waves. The estimated metrics on tissue elasticity or stiffness have been shown to be valuable for monitoring physiologic or pathophysiologic status of tissue, such as a tumor or fatty liver. To quantify tissue shear modulus from MRE-acquired displacements (essentially an inverse problem), multiple approaches have been proposed, including Local Frequency Estimation (LFE) and Direct Inversion (DI). However, one common problem with these methods is that the estimates are severely noise-sensitive due to either the inverse-problem nature or noise propagation in the pixel-by-pixel process. With the advent of deep learning (DL) and its promise in solving inverse problems, a few groups in the field of MRE have explored the feasibility of using DL methods for quantifying shear modulus from MRE data. Most of the groups chose to use real MRE data for DL model training and to cut training images into smaller patches, which enriches feature characteristics of training data but inevitably increases computation time and results in outcomes with patched patterns. In this study, simulated wave images generated by Finite Differential Time Domain (FDTD) simulation are used for network training, and U-Net is used to extract features from each training image without cutting it into patches. The use of simulated data for model training has the flexibility of customizing training datasets to match specific applications. The proposed method aimed to estimate tissue shear modulus from MRE data with high robustness to noise and high model-training efficiency. Specifically, a set of 3000 maps of shear modulus (with a range of 1 kPa to 15 kPa) containing randomly positioned objects were simulated, and their corresponding wave images were generated. The two types of data were fed into the training of a U-Net model as its output and input, respectively. For an independently simulated set of 1000 images, the performance of the proposed method against DI and LFE was compared by the relative errors (root mean square error or RMSE divided by averaged shear modulus) between the true shear modulus map and the estimated ones. The results showed that the estimated shear modulus by the proposed method achieved a relative error of 4.91%±0.66%, substantially lower than 78.20%±1.11% by LFE. Using simulated data, the proposed method significantly outperformed LFE and DI in resilience to increasing noise levels and in resolving fine changes of shear modulus. The feasibility of the proposed method was also tested on MRE data acquired from phantoms and from human calf muscles, resulting in maps of shear modulus with low noise. In future work, the method’s performance on phantom and its repeatability on human data will be tested in a more quantitative manner. In conclusion, the proposed method showed much promise in quantifying tissue shear modulus from MRE with high robustness and efficiency.

Keywords: deep learning, magnetic resonance elastography, magnetic resonance imaging, shear modulus estimation

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Authors: Chastiti Mediafira Wulolo

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Our Eyes is a cooperation pact in the field of intelligence information exchanges initiated by the Indonesian Ministry of Defense, which has been signed by Indonesia, Philippines, Malaysia, Brunei Darussalam, Thailand, and Singapore. This cooperation mostly engages the military acts as a central role, but this pact still requires the involvement of various parties such as police and other linear institution. This paper will use a qualitative content analysis method by doing some deep analyzing the pattern of cooperation itself. As the implementation of translantic counter-terrorism cooperation, this research will address how the role of Our Eyes can be optimized as a form of government’s response towards the contemporary threat in the Dynamics of Strategic Environmental Security in the Asia Pacific Region. Optimizing the role of this cooperation will also acquire from the previous counter-terrorism cooperation in ASEAN region, so it expects that Our Eyes collaboration can be the most effective cooperation in overcoming terrorism issues in ASEAN, eventually in Asia Pacific.

Keywords: our eyes, Defense Ministry of Indonesia, ASEAN, counter-terrorism

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Authors: David Tena-Gago, Jose M. Alcaraz Calero, Qi Wang

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The evolution and integration of automated vehicles have become more and more tangible in recent years. State-of-the-art technological advances in the field of camera-based Artificial Intelligence (AI) and computer vision greatly favor the performance and reliability of the Advanced Driver Assistance System (ADAS), leading to a greater knowledge of vehicular operation and resembling human behavior. However, the exclusive use of this technology still seems insufficient to control vehicular operation at 100%. To reveal the degree of accuracy of the current camera-based automated driving AI modules, this paper studies the structure and behavior of one of the main solutions in a controlled testing environment. The results obtained clearly outline the lack of reliability when using exclusively the AI model in the perception stage, thereby entailing using additional complementary sensors to improve its safety and performance.

Keywords: accuracy assessment, AI-driven mobility, artificial intelligence, automated vehicles

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Authors: Nour El Houda Arabi, Aicha Iratni, Talaighil Razika, Bruno Capoen, Mohamed Bouazaoui

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In this paper, we report the effect of the calcination temperature and SiO2 addition on structural, optical and hydrophilicity of TiO2 films deposited by deep-coating sol-gel process. XRD investigation of the structural TiO2 films with increasing the temperature calcination, reveals that rutile phase will appear for the high temperature (>1000°C). However, the addition of SiO2 relate the densification of TiO2 films. Ellipsometric and UV-visible measure show that the refractive index grow with increasing temperature, against the film thickness decreases. On the other hand, the addition of SiO2 decreases the refractive index and increases the TiO2 film thickness. Finally, the hydrophilicity is assisted by contact angle measurement. It is found that addition of 50% of SiO2 to TiO2 is most effective for reducing the contact angle of water.

Keywords: physical properties, sol, gel, TiO2/SiO2 composite films

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Authors: Yining Yu, Bingjie Li, Miaolei Jia, Lei Wang

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Ambient color temperature is one of the most ubiquitous factors in retailing. However, there is limited research regarding the effect of cold versus warm ambient color on consumers’ food consumption. This research investigates an unexplored lay belief named the “cold ambient color = healthy” intuition and its impact on food choice. We demonstrate that consumers have built the “cold ambient color = healthy” intuition, such that they infer that a restaurant with a cold-colored ambiance is more likely to sell healthy food than a warm-colored restaurant. This deep-seated intuition also guides consumers’ food choices. We find that using a cold (vs. warm) ambient color increases the choice of healthy food, which offers insights into healthy diet promotion for retailers and policymakers. Theoretically, our work contributes to the literature on color psychology, sensory marketing, and food consumption.

Keywords: ambient color temperature, cold ambient color, food choice, consumer wellbeing

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Authors: Duncan Fraser

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A 3-Dimensional (3D) conceptual site model was developed using the Leapfrog Works® platform utilising a comprehensive historical dataset for a large former Gasworks site in Fitzroy, Melbourne. The gasworks had been constructed across two fractured geological units with varying hydraulic conductivities. A Newer Volcanic (basaltic) outcrop covered approximately half of the site and was overlying a fractured Melbourne formation (Siltstone) bedrock outcropping over the remaining portion. During the investigative phase of works, a dense non-aqueous phase liquid (DNAPL) plume (coal tar) was identified within both geological units in the subsurface originating from multiple sources, including gasholders, tar wells, condensers, and leaking pipework. The first stage of model development was undertaken to determine the horizontal and vertical extents of the coal tar in the subsurface and assess the potential causality between potential sources, plume location, and site geology. Concentrations of key contaminants of interest (COIs) were also interpolated within Leapfrog to refine the distribution of contaminated soils. The model was subsequently used to develop a robust soil remediation strategy and achieve endorsement from an Environmental Auditor. A change in project scope, following the removal and validation of the three former gasholders, necessitated the additional excavation of a significant volume of residual contaminated rock to allow for the future construction of two-story underground basements. To assess financial liabilities associated with the offsite disposal or thermal treatment of material, the 3D model was updated with three years of additional analytical data from the active remediation phase of works. Chemical concentrations and the residual tar plume within the rock fractures were modelled to pre-classify the in-situ material and enhance separation strategies to prevent the unnecessary treatment of material and reduce costs.

Keywords: 3D model, contaminated land, Leapfrog, remediation

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Authors: Dina Salem

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A variety of definitions and interpretations for sustainable development has been offered since the widely known definition of the World Commission on Environment and Development in 1987, the perspectives have ranged from deep ecology to better life quality for people. Sustainable landscape is widely understood as a key contributor to urban sustainability for the fact that all landscapes has a social, economic, cultural and ecological function for the community’s well-being and urban development, that was evident even before the emergence of sustainability concept. In this paper, the concepts of landscape planning and sustainable development are briefly reviewed; visions for landscape sustainability are demonstrated and classified. Challenges facing sustainable landscape planning are discussed. Finally, the paper investigates how our future urban open space could be sustainable and how does this contribute to urban sustainability, by creating urban landscapes that takes into account the social and cultural values of users of urban open space besides the ecological balance of urban open spaces as an integrated network.

Keywords: urban landscape, urban sustainability, resilience, open spaces

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Authors: Obokhai Kess Asikhia

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This work explores design concept generation by understanding user needs through observation and interview. The aim is to examine several principles and guidelines in obtaining evidence from observing how users interact with the targeted product and interviewing them to acquire deep insights of their needs. With the help of Quality Function Deployment (QFD), the identified needs of the users while interacting with the product were ranked using the normalised weighting approach. Furthermore, a low fidelity prototype of the alarm clock is developed with a view of addressing the identified needs of the users. Finally, the low fidelity prototype design was evaluated with two design prototypes already existing in the market through a study involving 30 participants. Preliminary results reveal higher performance ratings by the majority of the participants of the new prototype compared to the other existing alarm clocks in the market used in the study.

Keywords: design concept, low fidelity prototype, normalised weighting approach, quality function deployment, user needs

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Authors: Prince Dacosta Aboagye, Ayyoob Sharifi

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Cities respond to climate concerns mainly through their climate action plans (CAPs). A comprehensive content analysis of the dynamics in existing urban CAPs is not well represented in the literature. This literature void presents a difficulty in appreciating the strengths and weaknesses of urban CAPs. Here, we perform a qualitative content analysis (QCA) on CAPs from 278 cities worldwide and use text-mining tools to map and visualize the relevant data. Our analysis showed a decline in the number of CAPs developed and published following the global COVID-19 lockdown period. Evidently, megacities are leading the deep decarbonisation agenda. We also observed a transition from developing mainly mitigation-focused CAPs pre-COP21 to both mitigation and adaptation CAPs. A lack of inclusiveness in local climate planning was common among European and North American cities. The evidence is a catalyst for understanding the trends in existing urban CAPs to shape future urban climate planning.

Keywords: urban, climate action plans, strengths, weaknesses

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Authors: Hanan Begali, Shahi Dost, Annett Ziegler, Markus Cornberg, Maria-Esther Vidal, Anke R. M. Kraft

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Chronic hepatitis B virus (HBV) infection can be treated with nucleot(s)ide analog (NA), for example, which inhibits HBV replication. However, they have hardly any influence on the functional cure of HBV, which is defined by hepatitis B surface antigen (HBsAg) loss. NA needs to be taken life-long, which is not available for all patients worldwide. Additionally, NA-treated patients are still at risk of developing cirrhosis, liver failure, or hepatocellular carcinoma (HCC). Although each patient has the same components of the immune system, immune responses vary between patients. Therefore, a deeper understanding of the immune response against HBV in different patients is necessary to understand the parameters leading to HBV cure and to use this knowledge to optimize HBV therapies. This requires seamless integration of an enormous amount of diverse and fine-grained data from viral markers, e.g., hepatitis B core-related antigen (HBcrAg) and hepatitis B surface antigen (HBsAg). The data integration system relies on the assumption that profiling human immune systems requires the analysis of various variables (e.g., demographic data, treatments, pre-existing conditions, immune cell response, or HLA-typing) rather than only one. However, the values of these variables are collected independently. They are presented in a myriad of formats, e.g., excel files, textual descriptions, lab book notes, and images of flow cytometry dot plots. Additionally, patients can be identified differently in these analyses. This heterogeneity complicates the integration of variables, as data management techniques are needed to create a unified view in which individual formats and identifiers are transparent when profiling the human immune systems. The proposed study (HBsRE) aims at integrating heterogeneous data sets of 87 chronically HBV-infected patients, e.g., clinical data, immune cell response, and HLA-typing, with knowledge encoded in biomedical ontologies and open-source databases into a knowledge-driven framework. This new technique enables us to harmonize and standardize heterogeneous datasets in the defined modeling of the data integration system, which will be evaluated in the knowledge graph (KG). KGs are data structures that represent the knowledge and data as factual statements using a graph data model. Finally, the analytic data model will be applied on top of KG in order to develop a deeper understanding of the immune profiles among various patients and to evaluate factors playing a role in a holistic profile of patients with HBsAg level loss. Additionally, our objective is to utilize this unified approach to stratify patients for new effective treatments. This study is developed in the context of the project “Transforming big data into knowledge: for deep immune profiling in vaccination, infectious diseases, and transplantation (ImProVIT)”, which is a multidisciplinary team composed of computer scientists, infection biologists, and immunologists.

Keywords: chronic hepatitis B infection, immune response, knowledge graphs, ontology

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Authors: Hana Barak, Alex Sivan, Ariel Kushmaro

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Microorganisms are the most diverse and abundant life forms on Earth and account for a large portion of the Earth’s biomass and biodiversity. To date though, our knowledge regarding microbial life is lacking, as it is based mainly on information from cultivated organisms. Indeed, microbiologists have borrowed from astrophysics and termed the ‘uncultured microbial majority’ as ‘microbial dark matter’. The realization of how diverse and unexplored microorganisms are, actually stems from recent advances in molecular biology, and in particular from novel methods for sequencing microbial small subunit ribosomal RNA genes directly from environmental samples termed next-generation sequencing (NGS). This has led us to use NGS that generates several gigabases of sequencing data in a single experimental run, to identify and classify environmental samples of microorganisms. In metagenomics sequencing analysis (both 16S and shotgun), sequences are compared to reference databases that contain only small part of the existing microorganisms and therefore their taxonomy assignment may reveal groups of unknown microorganisms or origins. These unknowns, or the ‘microbial sequences dark matter’, are usually ignored in spite of their great importance. The goal of this work was to develop an improved bioinformatics method that enables more complete analyses of the microbial communities in numerous environments. Therefore, NGS was used to identify previously unknown microorganisms from three different environments (industrials wastewater, Negev Desert’s rocks and water wells at the Arava valley). 16S rRNA gene metagenome analysis of the microorganisms from those three environments produce about ~4 million reads for 75 samples. Between 0.1-12% of the sequences in each sample were tagged as ‘Unassigned’. Employing relatively simple methodology for resequencing of original gDNA samples through Sanger or MiSeq Illumina with specific primers, this study demonstrates that the mysterious ‘Unassigned’ group apparently contains sequences of candidate phyla. Those unknown sequences can be located on a phylogenetic tree and thus provide a better understanding of the ‘sequences dark matter’ and its role in the research of microbial communities and diversity. Studying this ‘dark matter’ will extend the existing databases and could reveal the hidden potential of the ‘microbial dark matter’.

Keywords: bacteria, bioinformatics, dark matter, Next Generation Sequencing, unknown

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Authors: Sergey Ermolin, Olga Ermolin

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A Deep Convolution Neural Network with Triplet Loss is used to identify duplicate images in real-estate advertisements in the presence of image artifacts such as watermarking, cropping, hue/brightness adjustment, and others. The effects of batch normalization, spatial dropout, and various convergence methodologies on the resulting detection accuracy are discussed. For comparative Return-on-Investment study (per industry request), end-2-end performance is benchmarked on both Nvidia Titan GPUs and Intel’s Xeon CPUs. A new real-estate dataset from San Francisco Bay Area is used for this work. Sufficient duplicate detection accuracy is achieved to supplement other database-grounded methods of duplicate removal. The implemented method is used in a Proof-of-Concept project in the real-estate industry.

Keywords: visual recognition, convolutional neural networks, triplet loss, spatial batch normalization with dropout, duplicate removal, advertisement technologies, performance benchmarking

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Authors: Arnab Banerjee, Deep Sagar Verma

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Tier 3 cities of India is home to one of the fastest growing socio-economic powers in the world and hence is the focus of a lot of business activity as it is almost a blue ocean giving the first mover a huge strategic advantage. Among the various sectors, the retailing is perhaps one of the most promising sectors. The study caries out 129 successfully structured mall-intercept interviews in the town of Shillong, Meghalaya in an attempt to understand the SBO and MBO shoppers. Demographic variables itself does not show any store format preference although discounts do attract the lower income group more while clear difference is observed among genders when it comes to importance of ambience, and it is more pronounced for SBO patrons. SBO patrons are more focused while MBO patrons are more into leisure shopping. Price is the most important predictor of satisfaction especially for MBO shoppers. The market shows three basic segments i.e experiential, relationship and value shoppers.

Keywords: demographic variables, degree of importance, degree of satisfaction, SBO and MBO

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Authors: Subodh Chandra Shakya, Badal Shrestha, Suni Thapa, Ashutosh Chauhan, Saugat Adhikari

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To tackle our problem of clothes identification, we used different architectures of Convolutional Neural Networks. Among different architectures, the outcome from Inception ResNet V2 and MobileNet V2 seemed promising. On comparison of the metrices, we observed that the Inception ResNet V2 slightly outperforms MobileNet V2 for this purpose. So this paper of ours proposes the cloth identifier using Inception ResNet V2 and also contains the comparison between the outcome of ResNet V2 and MobileNet V2. The document here contains the results and findings of the research that we performed on the DeepFashion Dataset. To improve the dataset, we used different image preprocessing techniques like image shearing, image rotation, and denoising. The whole experiment was conducted with the intention of testing the efficiency of convolutional neural networks on cloth identification so that we could develop a reliable system that is good enough in identifying the clothes worn by the users. The whole system can be integrated with some kind of recommendation system.

Keywords: inception ResNet, convolutional neural net, deep learning, confusion matrix, data augmentation, data preprocessing

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Authors: Saba Rahimi, Ozan Oktay, Javier Alvarez-Valle, Sujeeth Bharadwaj

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Successful application of deep learning in medical image analysis necessitates unprecedented amounts of labeled training data. Unlike conventional 2D applications, radiological images can be three-dimensional (e.g., CT, MRI), consisting of many instances within each image. The problem is exacerbated when expert annotations are required for effective pixel-wise labeling, which incurs exorbitant labeling effort and cost. Active learning is an established research domain that aims to reduce labeling workload by prioritizing a subset of informative unlabeled examples to annotate. Our contribution is a cost-effective approach for U-Net 3D models that uses Monte Carlo sampling to analyze pixel-wise uncertainty. Experiments on the AAPM 2017 lung CT segmentation challenge dataset show that our proposed framework can achieve promising segmentation results by using only 42% of the training data.

Keywords: image segmentation, active learning, convolutional neural network, 3D U-Net

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Authors: George Zhou, Yunchan Chen, Candace Chien

Abstract:

Kidney replacement therapy is the current standard of care for end-stage renal diseases. In-center or home hemodialysis remains an integral component of the therapeutic regimen. Arteriovenous fistulas (AVF) make up the vascular circuit through which blood is filtered and returned. Naturally, AVF patency determines whether adequate clearance and filtration can be achieved and directly influences clinical outcomes. Our aim was to build a deep learning model for automated AVF stenosis screening based on the sound of blood flow through the AVF. A total of 311 patients with AVF were enrolled in this study. Blood flow sounds were collected using a digital stethoscope. For each patient, blood flow sounds were collected at 6 different locations along the patient’s AVF. The 6 locations are artery, anastomosis, distal vein, middle vein, proximal vein, and venous arch. A total of 1866 sounds were collected. The blood flow sounds are labeled as “patent” (normal) or “stenotic” (abnormal). The labels are validated from concurrent ultrasound. Our dataset included 1527 “patent” and 339 “stenotic” sounds. We show that blood flow sounds vary significantly along the AVF. For example, the blood flow sound is loudest at the anastomosis site and softest at the cephalic arch. Contextualizing the sound with location metadata significantly improves classification performance. How to encode and incorporate categorical metadata is an active area of research1. Herein, we study ordinal (i.e., integer) encoding schemes. The numerical representation is concatenated to the flattened feature vector. We train a vision transformer (ViT) on spectrogram image representations of the sound and demonstrate that using scalar multiples of our integer encodings improves classification performance. Models are evaluated using a 10-fold cross-validation procedure. The baseline performance of our ViT without any location metadata achieves an AuROC and AuPRC of 0.68 ± 0.05 and 0.28 ± 0.09, respectively. Using the following encodings of Artery:0; Arch: 1; Proximal: 2; Middle: 3; Distal 4: Anastomosis: 5, the ViT achieves an AuROC and AuPRC of 0.69 ± 0.06 and 0.30 ± 0.10, respectively. Using the following encodings of Artery:0; Arch: 10; Proximal: 20; Middle: 30; Distal 40: Anastomosis: 50, the ViT achieves an AuROC and AuPRC of 0.74 ± 0.06 and 0.38 ± 0.10, respectively. Using the following encodings of Artery:0; Arch: 100; Proximal: 200; Middle: 300; Distal 400: Anastomosis: 500, the ViT achieves an AuROC and AuPRC of 0.78 ± 0.06 and 0.43 ± 0.11. respectively. Interestingly, we see that using increasing scalar multiples of our integer encoding scheme (i.e., encoding “venous arch” as 1,10,100) results in progressively improved performance. In theory, the integer values do not matter since we are optimizing the same loss function; the model can learn to increase or decrease the weights associated with location encodings and converge on the same solution. However, in the setting of limited data and computation resources, increasing the importance at initialization either leads to faster convergence or helps the model escape a local minimum.

Keywords: arteriovenous fistula, blood flow sounds, metadata encoding, deep learning

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Authors: Chandan Pramanik, Bikramjit Chanda

Abstract:

Large metal mines operated with open-cast mining methods must transition to underground mining at the conclusion of the operation; however, this requires a period of a difficult time when production convergence due to interference between the two mining methods. A transition model with collaborative mining operations is presented and established in this work, based on the case of the South Kaliapani Underground Project, to address these technical issues of inadequate production security and other mining challenges during the transition phase and beyond. By integrating the technology of the small-scale Drift and Fill method and Highly productive Sub Level Open Stoping at deep section, this hybrid mining concept tries to eliminate major bottlenecks and offers an optimized production profile with the safe and sustainable operation. Considering every geo-mining aspect, this study offers a genuine and precise technical deliberation for the transition from open pit to underground mining.

Keywords: drift and fill, geo-mining aspect, sublevel open stoping, underground mining method

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Authors: Abdelkader Djehiche, Mostefa Gafsi, Henri Bertin, Aziz Omari

Abstract:

The flows of colloidal suspensions in porous media find many applications in fields such as Petroleum, Hydraulic engineering, deep-bed filtration. For each application, the scientific problems can be summarized the flow in porous medium of a colloidal suspension whose particles having characteristic dimension is considerable in comparison with the pores dimension. In certain cases, one can observe a deposit of particles on the surface of the pores which results in a significant modification in the physical properties of the porous medium. The objective of our study is to use a non-destructive experimental method, the attenuation of g-rays, to study the influence of the number of Peclet on the deposit of latex particles in a consolidated porous medium. The first results obtained show a good agreement between local and global measurements of the deposit of the particles in porous medium. The deposit takes place in a progressive way along the porous medium and leads to a monolayer deposit of which the average thickness is of about the size diameter of the colloidal particles.

Keywords: colloid, gamma ray, Peclet number, permeability, porous medium

Procedia PDF Downloads 181

Authors: Sherine El-Menshawy

Abstract:

Death, funerary beliefs and customs form an essential feature of belief systems and practices in many cultures. It is evident that during the pre-historical periods, various techniques of corpses burial and funerary rituals were conducted. Occasionally, corpses were merely buried in the sand, or in a grave where the body is placed in a contracted position- with knees drawn up under the chin and hands normally lying before the face- with mounds of sand, marking the grave or the bodies were burnt. However, common practice, that was demonstrable in the archaeological record, was burial. The earliest graves were very simple consisting of a shallow circular or oval pits in the ground. The current study focuses on the material culture at Qatar during the pre-historical period, specifically their funerary architecture and burial practices. Since information about burial customs and funerary practices in Qatar prehistory is both scarce and fragmentary, the importance of such study is to answer research questions related to funerary believes and burial habits during the early stages of civilization transformations at prehistory Qatar compared with Mesopotamia, since chronologically, the earliest pottery discovered in Qatar belongs to prehistoric Ubaid culture of Mesopotamia, that was collected from the excavations. This will lead to deep understanding of life and social status in pre-historical period at Qatar. The research also explores the relationship between pre-history Qatar funerary traditions and those of neighboring cultures in the Mesopotamia and Ancient Egypt, with the aim of ascertaining the distinctive aspects of pre-history Qatar culture, the reception of classical culture and the role it played in the creation of local cultural identities in the Near East. Methodologies of this study based on published books and articles in addition to unpublished reports of the Danish excavation team that excavated in and around Doha, Qatar archaeological sites from the 50th. The study is also constructed on compared material related to burial customs found in Mesopotamia. Therefore this current research: (i) Advances knowledge of the burial customs of the ancient people who inhabited Qatar, a study which is unknown recently to scholars, the study though will apply deep understanding of the history of ancient Qatar and its culture and values with an aim to share this invaluable human heritage. (ii) The study is of special significance for the field of studies, since evidence derived from the current study has great value for the study of living conditions, social structure, religious beliefs and ritual practices. (iii) Excavations brought to light burials of different categories. The graves date to the bronze and Iron ages. Their structure varies between mounds above the ground or burials below the ground level. Evidence comes from sites such as Al-Da’asa, Ras Abruk, and Al-Khor. Painted Ubaid sherds of Mesopotamian culture have been discovered in Qatar from sites such as Al-Da’asa, Ras Abruk, and Bir Zekrit. In conclusion, there is no comprehensive study which has been done and lack of general synthesis of information about funerary practices is problematic. Therefore, the study will fill in the gaps in the area.

Keywords: archaeological, burial, findings, prehistory, Qatar

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Authors: Khaled A. Madi, Abdulalhakim O. Naji, Hassouna A. Aalaoh, Elmahdi Eldeeb

Abstract:

Solar energy is one of the cleanest energy sources with no environmental impact. Due to rapid increase in industrial as well as domestic needs, solar energy becomes a good candidate for safe and easy to handle energy source, especially after it becomes available due to reduction of manufacturing price. The main part of the solar inverter system is the inverter where the DC is inverted to AC, where we try to minimize the loss of power to the minimum possible level by the use of microcontroller. In this work, a deep investigation is made experimentally as well as theoretically for a microcontroller based variable frequency power inverter. The microcontroller will provide the variable frequency Pulse Width Modulation (PWM) signal that will control the switching of the gate of the Insulating Gate Bipolar Transistor (IGBT) with less harmonics at the output of power inverter which can be fed to the public grid at high quality. The proposed work for single phase as well as three phases is also simulated using Matlab/Simulink where we found a good agreement between the simulated and the practical results, even though the experimental work were done in the laboratory of the academy.

Keywords: solar, inverter, PV, solar inverter system

Procedia PDF Downloads 454

Authors: Mingzhu Lyu, Runlei Ren, Xinyu Dai, Jiaxuan Pi, Kanghua Li

Abstract:

The study of video game policy in China has been divided into two branches: "pedagogy" and "game industry". The binary perspective of policy reveals the "contradictory" side of policy performance. Based on this suspicion, this paper constructs a three-dimensional sequence of time, content and institutions of game policy, and establishes the "contradictory" aspects of policy performance between 1949 and 2019. A central-level database of game policies, clarifying that our game policies follow a shift from reactive response to proactive guidance, stigmatization and de-stigmatization, the evolutionary logic. The study found that the central government has always maintained a strict requirement and prudent guidance for game policy, and the deep contradictions in game policy stem from the essential conflict between the natural amusement of games and the seriousness of the educational system, and the Chinese government's use of the understanding of the public and private domains and the Managing of the conflict.

Keywords: game industry, gaming policy, public domain, private domain

Procedia PDF Downloads 142