Search results for: deep neural models

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: Chirag Raval, Jimmy Toussaint, Tieuvi Nguyen, Hadi Fadaifard, George Wolberg, Steven Quarfordt, Kung-ming Jan, David S. Rumschitzki

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Numerous studies examine aquaporins’ role in osmotic water transport in various systems but virtually none focus on aquaporins’ role in hydrostatically-driven water transport involving mammalian cells save for our laboratory’s recent study of aortic endothelial cells. Here we investigate aquaporin-1 expression and function in the aortic endothelium in two high-renin rat models of hypertension, the spontaneously hypertensive genomically altered Wystar-Kyoto rat variant and Sprague-Dawley rats made hypertensive by two kidney, one clip Goldblatt surgery. We measured aquaporin-1 expression in aortic endothelial cells from whole rat aortas by quantitative immunohistochemistry, and function by measuring the pressure driven hydraulic conductivities of excised rat aortas with both intact and denuded endothelia on the same vessel. We use them to calculate the effective intimal hydraulic conductivity, which is a combination of endothelial and subendothelial components. We observed well-correlated enhancements in aquaporin-1 expression and function in both hypertensive rat models as well as in aortas from normotensive rats whose expression was upregulated by 2h forskolin treatment. Upregulated aquaporin-1 expression and function may be a response to hypertension that critically determines conduit artery vessel wall viability and long-term susceptibility to atherosclerosis. Numerous studies examine aquaporins’ role in osmotic water transport in various systems but virtually none focus on aquaporins’ role in hydrostatically-driven water transport involving mammalian cells save for our laboratory’s recent study of aortic endothelial cells. Here we investigate aquaporin-1 expression and function in the aortic endothelium in two high-renin rat models of hypertension, the spontaneously hypertensive genomically altered Wystar-Kyoto rat variant and Sprague-Dawley rats made hypertensive by two kidney, one clip Goldblatt surgery. We measured aquaporin-1 expression in aortic endothelial cells from whole rat aortas by quantitative immunohistochemistry, and function by measuring the pressure driven hydraulic conductivities of excised rat aortas with both intact and denuded endothelia on the same vessel. We use them to calculate the effective intimal hydraulic conductivity, which is a combination of endothelial and subendothelial components. We observed well-correlated enhancements in aquaporin-1 expression and function in both hypertensive rat models as well as in aortas from normotensive rats whose expression was upregulated by 2h forskolin treatment. Upregulated aquaporin-1 expression and function may be a response to hypertension that critically determines conduit artery vessel wall viability and long-term susceptibility to atherosclerosis.

Keywords: acute hypertension, aquaporin-1, hydraulic conductivity, hydrostatic pressure, aortic endothelial cells, transcellular flow

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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: Seydou Sinde

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The aims of this paper are to formulate mathematical expressions that can be used to estimate the standpipe pressure (SPP). The developed formulas take into account the main factors that, directly or indirectly, affect the behavior of SPP values. Fluid rheology and well hydraulics are some of these essential factors. Mud Plastic viscosity, yield point, flow power, consistency index, flow rate, drillstring, and annular geometries are represented by the frictional pressure (Pf), which is one of the input independent parameters and is calculated, in this paper, using Herschel-Bulkley rheological model. Other input independent parameters include the rate of penetration (ROP), applied load or weight on the bit (WOB), bit revolutions per minute (RPM), bit torque (TRQ), and hole inclination and direction coupled in the hole curvature or dogleg (DL). The technique of repeating parameters and Buckingham PI theorem are used to reduce the number of the input independent parameters into the dimensionless revolutions per minute (RPMd), the dimensionless torque (TRQd), and the dogleg, which is already in the dimensionless form of radians. Multivariable linear and polynomial regression technique using PTC Mathcad Prime 4.0 is used to analyze and determine the exact relationships between the dependent parameter, which is SPP, and the remaining three dimensionless groups. Three models proved sufficiently satisfactory to estimate the standpipe pressure: multivariable linear regression model 1 containing three regression coefficients for vertical wells; multivariable linear regression model 2 containing four regression coefficients for deviated wells; and multivariable polynomial quadratic regression model containing six regression coefficients for both vertical and deviated wells. Although that the linear regression model 2 (with four coefficients) is relatively more complex and contains an additional term over the linear regression model 1 (with three coefficients), the former did not really add significant improvements to the later except for some minor values. Thus, the effect of the hole curvature or dogleg is insignificant and can be omitted from the input independent parameters without significant losses of accuracy. The polynomial quadratic regression model is considered the most accurate model due to its relatively higher accuracy for most of the cases. Data of nine wells from the Middle East were used to run the developed models with satisfactory results provided by all of them, even if the multivariable polynomial quadratic regression model gave the best and most accurate results. Development of these models is useful not only to monitor and predict, with accuracy, the values of SPP but also to early control and check for the integrity of the well hydraulics as well as to take the corrective actions should any unexpected problems appear, such as pipe washouts, jet plugging, excessive mud losses, fluid gains, kicks, etc.

Keywords: standpipe, pressure, hydraulics, nondimensionalization, parameters, regression

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Authors: Keyur Raval, Steffen Krohn, Bruno Moerschbacher

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Chitin, the biopolymer of the N-acetylglucosamine, is the most abundant biopolymer on the planet after cellulose. Industrially, chitin is isolated and purified from the shell residues of shrimps. A deacetylated derivative of chitin i.e. chitosan has more market value and applications owing to it solubility and overall cationic charge compared to the parent polymer. This deacetylation on an industrial scale is performed chemically using alkalis like sodium hydroxide. This reaction not only is hazardous to the environment owing to negative impact on the marine ecosystem. A greener option to this process is the enzymatic process. In nature, the naïve chitin is converted to chitosan by chitin deacetylase (CDA). This enzymatic conversion on the industrial scale is however hampered by the crystallinity of chitin. Thus, this enzymatic action requires the substrate i.e. chitin to be soluble which is technically difficult and an energy consuming process. We in this project wanted to address this shortcoming of CDA. In lieu of this, we have modeled a fusion protein with CDA and an auxiliary protein. The main interest being to increase the accessibility of the enzyme towards crystalline chitin. A similar fusion work with chitinases had improved the catalytic ability towards insoluble chitin. In the first step, suitable partners were searched through the protein data bank (PDB) wherein the domain architecture were sought. The next step was to create the models of the fused product using various in silico techniques. The models were created by MODELLER and evaluated for properties such as the energy or the impairment of the binding sites. A fusion PCR has been designed based on the linker sequences generated by MODELLER and would be tested for its activity towards insoluble chitin.

Keywords: chitin deacetylase, modeling, chitin binding domain, chitinases

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Authors: Rafid A. Al-Khannak, Fawzi M. Al-Naima

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Power load is one of the most important controlling keys which decide power demands and illustrate power usage to shape power market. Hence, power load forecasting is the parameter which facilitates understanding and analyzing all these aspects. In this paper, power load forecasting is solved under MATLAB environment by constructing a neural network for the power load to find an accurate simulated solution with the minimum error. A developed algorithm to achieve load forecasting application with faster technique is the aim for this paper. The algorithm is used to enable MATLAB power application to be implemented by multi machines in the Grid computing system, and to accomplish it within much less time, cost and with high accuracy and quality. Grid Computing, the modern computational distributing technology, has been used to enhance the performance of power applications by utilizing idle and desired Grid contributor(s) by sharing computational power resources.

Keywords: DeskGrid, Grid Server, idle contributor(s), grid computing, load forecasting

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Authors: Ciro Cursio, Pio Luigi Cursio, Giulia Cursio, Isabella Chiardi, Luigi Cursio

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Introduction: Full-face aesthetic treatments often present a difficult task: since different patients possess different anatomical and tissue characteristics, there is no guarantee that the same treatment will have the same effect on multiple patients; additionally, full-face rejuvenation and beautification treatments require not only a high degree of technical skill but also the ability to choose the right product for each area and a keen artistic eye. Method: We present an artificial intelligence-based algorithm that can generate realistic post-treatment 3D models based on the patient’s requests together with the doctor’s input. These 3-dimensional predictions can be used by the practitioner for two purposes: firstly, they help ensure that the patient and the doctor are completely aligned on the expectations of the treatment; secondly, the doctor can use them as a visual guide, obtaining a natural result that would normally stem from the practitioner's artistic skills. To this end, the algorithm is able to predict injection zones, the type and quantity of hyaluronic acid, the injection depth, and the technique to use. Results: Our innovation consists in providing an objective visual representation of the patient that is helpful in the patient-doctor dialogue. The patient, based on this information, can express her desire to undergo a specific treatment or make changes to the therapeutic plan. In short, the patient becomes an active agent in the choices made before the treatment. Conclusion: We believe that this algorithm will reveal itself as a useful tool in the pre-treatment decision-making process to prevent both the patient and the doctor from making a leap into the dark.

Keywords: hyaluronic acid, fillers, full face, artificial intelligence, 3D

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Authors: Sharifah Sumayyah Engku Alwi, Masrah Azrifah Azmi Murad

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The development of the information technology and Internet has been transforming the healthcare industry. The internet is continuously accessed to seek for health information and there are variety of sources, including search engines, health websites, and social networking sites. Providing more and better information on health may empower individuals, however, ensuring a high quality and trusted health information could pose a challenge. Moreover, there is an ever-increasing amount of information available, but they are not necessarily accurate and up to date. Thus, this paper aims to provide an insight of the models and frameworks related to online health information seeking of consumers. It begins by exploring the definition of information behavior and information seeking to provide a better understanding of the concept of information seeking. In this study, critical factors such as performance expectancy, effort expectancy, and social influence will be studied in relation to the value of seeking health information. It also aims to analyze the effect of age, gender, and health status as the moderator on the factors that influence online health information seeking, i.e. trust and information quality. A preliminary survey will be carried out among the health professionals to clarify the research problems which exist in the real world, at the same time producing a conceptual framework. A final survey will be distributed to five states of Malaysia, to solicit the feedback on the framework. Data will be analyzed using SPSS and SmartPLS 3.0 analysis tools. It is hoped that at the end of this study, a novel framework that can improve online health information seeking is developed. Finally, this paper concludes with some suggestions on the models and frameworks that could improve online health information seeking.

Keywords: information behavior, information seeking, online health information, technology acceptance model, the theory of planned behavior, UTAUT

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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: Trevor C. Brown, David J. Miron

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Gas-solid physical adsorption methods are central to the characterization and optimization of the effective surface area, pore size and porosity for applications such as heterogeneous catalysis, and gas separation and storage. Properties such as adsorption uptake, capacity, equilibrium constants and Gibbs free energy are dependent on the composition and structure of both the gas and the adsorbent. However, challenges remain, in accurately calculating these properties from experimental data. Gas adsorption experiments involve measuring the amounts of gas adsorbed over a range of pressures under isothermal conditions. Various constant-parameter models, such as Langmuir and Brunauer-Emmett-Teller (BET) theories are used to provide information on adsorbate and adsorbent properties from the isotherm data. These models typically do not provide accurate interpretations across the full range of pressures and temperatures. The Langmuir adsorption isotherm is a simple approximation for modelling equilibrium adsorption data and has been effective in estimating surface areas and catalytic rate laws, particularly for high surface area solids. The Langmuir isotherm assumes the systematic filling of identical adsorption sites to a monolayer coverage. The BET model is based on the Langmuir isotherm and allows for the formation of multiple layers. These additional layers do not interact with the first layer and the energetics are equal to the adsorbate as a bulk liquid. This BET method is widely used to measure the specific surface area of materials. Both Langmuir and BET models assume that the affinity of the gas for all adsorption sites are identical and so the calculated adsorbent uptake at the monolayer and equilibrium constant are independent of coverage and pressure. Accurate representations of adsorption data have been achieved by extending the Langmuir and BET models to include pressure-varying uptake capacities and equilibrium constants. These parameters are determined using a novel regression technique called flexible least squares for time-varying linear regression. For isothermal adsorption the adsorption parameters are assumed to vary slowly and smoothly with increasing pressure. The flexible least squares for pressure-varying linear regression (FLS-PVLR) approach assumes two distinct types of discrepancy terms, dynamic and measurement for all parameters in the linear equation used to simulate the data. Dynamic terms account for pressure variation in successive parameter vectors, and measurement terms account for differences between observed and theoretically predicted outcomes via linear regression. The resultant pressure-varying parameters are optimized by minimizing both dynamic and measurement residual squared errors. Validation of this methodology has been achieved by simulating adsorption data for n-butane and isobutane on activated carbon at 298 K, 323 K and 348 K and for nitrogen on mesoporous alumina at 77 K with pressure-varying Langmuir and BET adsorption parameters (equilibrium constants and uptake capacities). This modeling provides information on the adsorbent (accessible surface area and micropore volume), adsorbate (molecular areas and volumes) and thermodynamic (Gibbs free energies) variations of the adsorption sites.

Keywords: Langmuir adsorption isotherm, BET adsorption isotherm, pressure-varying adsorption parameters, adsorbate and adsorbent properties and energetics

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Authors: Shih-Jie Ma, Jui-Hsu Hsiao, Ming-Ying Hsieh, Shin-Yan Yang, Chun-Han Yeh, Kuo-Chun Su

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In Taiwan, social enterprises and non-profit organizations (NPOs) are not familiar with innovative business models, such as live streaming. In 2019, a brand new course called internet celebrity training project is introduced to them by the Social Innovation Lab. The Goal of this paper is to evaluate the effect of this project, to explore the role of new technology (internet live stream) in business process management (BPM), and to analyze how live stream programs can assist social enterprises in creating new business models. Social Innovation, with the purpose to solve social issues in innovative ways, is one of the most popular topics in the world. Social Innovation Lab was established in 2017 by Executive Yuan in Taiwan. The vision of Social Innovation Lab is to exploit technology, innovation and experimental methods to solve social issues, and to maximize the benefits from government investment. Social Innovation Lab aims at creating a platform for both supply and demand sides of social issues, to make social enterprises and start-ups communicate with each other, and to build an eco-system in which stakeholders can make a social impact. Social Innovation Lab keeps helping social enterprises and NPOs to gain better publicity and to enhance competitiveness by facilitating digital transformation. In this project, Social Innovation Lab exerted the influence of social media such as YouTube and Facebook, to make social enterprises and start-ups adjust their business models by using the live stream of social media, which becomes one of the tools to expand their market and diversify their sales channels. Internet live stream training courses were delivered in different regions of Taiwan in 2019, including Taitung, Taichung, Kaohsiung and Hualien. Through these courses, potential groups and enterprises were cultivated to become so-called internet celebrities. With their concern about social issues in mind, these internet celebrities know how to manipulate social media to make a social impact in different fields, such as aboriginal people, food and agriculture, LOHAS (Lifestyles of Health and Sustainability), environmental protection and senior citizens. Participants of live stream training courses in Taiwan are selected to take in-depth interviews and questionnaire surveys. Results indicate that the digital transformation process of social enterprises and NPOs can be successful by implementing business process reengineering, a significant change made by social innovation internet celebrities. Therefore, this project can be the new driving force to facilitate the business model transformation in Taiwan.

Keywords: business process management, digital transformation, live stream, social innovation

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Authors: Dijiang Lyu, Shaoying Li, Zhangzhi Tan, Zhifeng Wu, Feng Gao

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Nowadays, China is experiencing rapidly urban rail transit expansions in the world. The purpose of this study is to finely model factors influencing transit ridership at multi-time dimensions within transit stations’ pedestrian catchment area (PCA) in Guangzhou, China. This study was based on multi-sources spatial data, including smart card data, high spatial resolution images, points of interest (POIs), real-estate online data and building height data. Eight multiple linear regression models using backward stepwise method and Geographic Information System (GIS) were created at station-level. According to Chinese code for classification of urban land use and planning standards of development land, residential land-use were divided into three categories: first-level (e.g. villa), second-level (e.g. community) and third-level (e.g. urban villages). Finally, it concluded that: (1) four factors (CBD dummy, number of feeder bus route, number of entrance or exit and the years of station operation) were proved to be positively correlated with transit ridership, but the area of green land-use and water land-use negative correlated instead. (2) The area of education land-use, the second-level and third-level residential land-use were found to be highly connected to the average value of morning peak boarding and evening peak alighting ridership. But the area of commercial land-use and the average height of buildings, were significantly positive associated with the average value of morning peak alighting and evening peak boarding ridership. (3) The area of the second-level residential land-use was rarely correlated with ridership in other regression models. Because private car ownership is still large in Guangzhou now, and some residents living in the community around the stations go to work by transit at peak time, but others are much more willing to drive their own car at non-peak time. The area of the third-level residential land-use, like urban villages, was highly positive correlated with ridership in all models, indicating that residents who live in the third-level residential land-use are the main passenger source of the Guangzhou Metro. (4) The diversity of land-use was found to have a significant impact on the passenger flow on the weekend, but was non-related to weekday. The findings can be useful for station planning, management and policymaking.

Keywords: fine-scale modeling, Guangzhou city, multi-time dimensions, multi-sources spatial data, transit ridership

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Authors: Deepika Verma, Runa Sarkar

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One of the prominent debates in the social entrepreneurship literature has been to establish whether entrepreneurial work for social well-being by for-profit organizations can be classified as social entrepreneurship or not. Of late, the scholarship has reached a consensus. It concludes that there seems little sense in confining social entrepreneurship to just non-profit organizations. Boosted by this research, increasingly a lot of businesses engaged in filling the social infrastructure gaps in developing countries are calling themselves social enterprise. These organizations are diverse in their ownership, size, objectives, operations and business models. The lack of a comprehensive definition of social enterprise leads to three issues. Firstly, researchers may face difficulty in creating a database for social enterprises because the choice of an entity as a social enterprise becomes subjective or based on some pre-defined parameters by the researcher which is not replicable. Secondly, practitioners who use ‘social enterprise’ in their vision/mission statement(s) may find it difficult to adjust their business models accordingly especially during the times when they face the dilemma of choosing social well-being over business viability. Thirdly, social enterprise and social entrepreneurship attract a lot of donor funding and venture capital. In the paucity of a comprehensive definitional guide, the donors or investors may find assigning grants and investments difficult. It becomes necessary to develop an exhaustive and objective definition of social enterprise and examine whether the understanding of the academicians and practitioners about social enterprise match. This paper develops a dictionary of words often associated with social enterprise or (and) social entrepreneurship. It further compares two lexicographic definitions of social enterprise imputed from the abstracts of academic journal papers and trade publications extracted from the EBSCO database using the ‘tm’ package in R software.

Keywords: EBSCO database, lexicographic definition, social enterprise, text mining

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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: Dominique Arrouays, Songchao Chen, Anne C. Richer-De-Forges

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Soils are at the crossing of many issues such as food and water security, sustainable energy, climate change mitigation and adaptation, biodiversity protection, human health and well-being. They deliver many ecosystem services that are essential to life on Earth. Therefore, there is a growing demand for soil information on a national and global scale. Unfortunately, many countries do not have detailed soil maps, and, when existing, these maps are generally based on more or less complex and often non-harmonized soil classifications. An estimate of their uncertainty is also often missing. Thus, there are not easy to understand and often not properly used by end-users. Therefore, there is an urgent need to provide end-users with spatially exhaustive grids of essential soil properties, together with an estimate of their uncertainty. One way to achieve this is digital soil mapping (DSM). The concept of DSM relies on the hypothesis that soils and their properties are not randomly distributed, but that they depend on the main soil-forming factors that are climate, organisms, relief, parent material, time (age), and position in space. All these forming factors can be approximated using several exhaustive spatial products such as climatic grids, remote sensing products or vegetation maps, digital elevation models, geological or lithological maps, spatial coordinates of soil information, etc. Thus, DSM generally relies on models calibrated with existing observed soil data (point observations or maps) and so-called “ancillary co-variates” that come from other available spatial products. Then the model is generalized on grids where soil parameters are unknown in order to predict them, and the prediction performances are validated using various methods. With the growing demand for soil information at a national and global scale and the increase of available spatial co-variates national and continental DSM initiatives are continuously increasing. This short review illustrates the main national and continental advances in Europe, the diversity of the approaches and the databases that are used, the validation techniques and the main scientific and other issues. Examples from several countries illustrate the variety of products that were delivered during the last ten years. The scientific production on this topic is continuously increasing and new models and approaches are developed at an incredible speed. Most of the digital soil mapping (DSM) products rely mainly on machine learning (ML) prediction models and/or the use or pedotransfer functions (PTF) in which calibration data come from soil analyses performed in labs or for existing conventional maps. However, some scientific issues remain to be solved and also political and legal ones related, for instance, to data sharing and to different laws in different countries. Other issues related to communication to end-users and education, especially on the use of uncertainty. Overall, the progress is very important and the willingness of institutes and countries to join their efforts is increasing. Harmonization issues are still remaining, mainly due to differences in classifications or in laboratory standards between countries. However numerous initiatives are ongoing at the EU level and also at the global level. All these progress are scientifically stimulating and also promissing to provide tools to improve and monitor soil quality in countries, EU and at the global level.

Keywords: digital soil mapping, global soil mapping, national and European initiatives, global soil mapping products, mini-review

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Authors: Xiao Wei, Dandan Sheng, Xiaoyan Jiang, Lili Gui, Huizhu Wang, Xi Yu, Hailiang Liu, Min Hong

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Objective: Mice Type2 allergic contact dermatitis was utilized in this study to explore the effect of AS-IV on Type 2 allergic inflammatory. Methods: The mice were topically sensitized on the shaved abdomens with 1.5% FITC solution on abdominal skin in the day 1 and day 2 and elicited on the right ear with 0.5% FITC solution at day 6. Mice were treated with either AS-IV or normal saline from day 1 to day 5 (induction phase). Auricle swelling was measured 24 h after the elicitation. Ear pathohistological examination was carried out by HE staining. IL-4\IL-13, and IL-9 levels of ear tissue were detected by ELISA. Mice were treated with AS-IV at the initial stage of induction phase, ear tissue was taked at day 3.TSLP level of ear tissue was detected by ELISA and TSLPmRNA\NF-kBmRNA\TLRs（TLR2\TLR3\TLR8\TLR9）mRNA were detected by PCR. Results: AS-IV induction phase evidently inhibited the auricle inflam-mation of the models; pathohistological results indicated that AS-IV induction phase alleviated local edema and angiectasis of mice models and reduced lymphocytic infiltration. AS-IV induction phase markedly decreased IL-4\IL-13, and IL-9 levels in ear tissue. Moreover, at the initial stage of induction pha-se, AS-IV significantly reduced TSLP\TSLPmRNA\NF-kBmRNA\TLR2mRNA\TLR8 mRNA levels in ear tissue. Conclusion: Administration with AS-IV in induction phase could inhibit Type 2 allergic contact dermatitis in mice significantly, and the mechanism may be related with regulating TSLP through TLRs-NF-kB pathway.

Keywords: Astragaioside IV, allergic contact dermatitis, TSLP, interleukin-4, interleukin-13, interleukin-9

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Authors: Ahmed Elaksher, Islam Omar

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Accurate geometric relation algorithms are imperative in Earth and planetary satellite and aerial image processing, particularly for high-resolution images that are used for topographic mapping. Most of these satellites carry push-broom sensors. These sensors are optical scanners equipped with linear arrays of CCDs. These sensors have been deployed on most EOSs. In addition, the LROC is equipped with two push NACs that provide 0.5 meter-scale panchromatic images over a 5 km swath of the Moon. The HiRISE carried by the MRO and the HRSC carried by MEX are examples of push-broom sensor that produces images of the surface of Mars. Sensor models developed in photogrammetry relate image space coordinates in two or more images with the 3D coordinates of ground features. Rigorous sensor models use the actual interior orientation parameters and exterior orientation parameters of the camera, unlike approximate models. In this research, we generate a generic push-broom sensor model to process imageries acquired through linear array cameras and investigate its performance, advantages, and disadvantages in generating topographic models for the Earth, Mars, and the Moon. We also compare and contrast the utilization, effectiveness, and applicability of available photogrammetric techniques and softcopies with the developed model. We start by defining an image reference coordinate system to unify image coordinates from all three arrays. The transformation from an image coordinate system to a reference coordinate system involves a translation and three rotations. For any image point within the linear array, its image reference coordinates, the coordinates of the exposure center of the array in the ground coordinate system at the imaging epoch (t), and the corresponding ground point coordinates are related through the collinearity condition that states that all these three points must be on the same line. The rotation angles for each CCD array at the epoch t are defined and included in the transformation model. The exterior orientation parameters of an image line, i.e., coordinates of exposure station and rotation angles, are computed by a polynomial interpolation function in time (t). The parameter (t) is the time at a certain epoch from a certain orbit position. Depending on the types of observations, coordinates, and parameters may be treated as knowns or unknowns differently in various situations. The unknown coefficients are determined in a bundle adjustment. The orientation process starts by extracting the sensor position and, orientation and raw images from the PDS. The parameters of each image line are then estimated and imported into the push-broom sensor model. We also define tie points between image pairs to aid the bundle adjustment model, determine the refined camera parameters, and generate highly accurate topographic maps. The model was tested on different satellite images such as IKONOS, QuickBird, and WorldView-2, HiRISE. It was found that the accuracy of our model is comparable to those of commercial and open-source software, the computational efficiency of the developed model is high, the model could be used in different environments with various sensors, and the implementation process is much more cost-and effort-consuming.

Keywords: photogrammetry, push-broom sensors, IKONOS, HiRISE, collinearity condition

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Authors: Yang Zhao, Feng Zhang, Xuanchu Duan

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Trans-differentiation of human Tenon fibroblasts (HTFs) to myo-fibroblasts and fibrosis of episcleral tissue are the most common reasons for the failure of glaucoma filtration surgery, with limited treatment options like antimetabolites which always have side-effects such as leakage of filter bulb, infection, hypotony, and endophthalmitis. Rosiglitazone, a specific thiazolidinedione is a synthetic high-affinity ligand for PPAR-r, which has been used in the treatment of type2 diabetes, and found to have pleiotropic functions against inflammatory response, cell proliferation and tissue fibrosis and to benefit to a variety of diseases in animal myocardium models, steatohepatitis models, etc. Here, in vitro we cultured primary HTFs and stimulated with TGF- β to induced myofibrogenic, then treated cells with Rosiglitazone to assess for fibrogenic response. In vivo, we used rabbit glaucoma model to establish the formation of post- trabeculectomy scarring. Then we administered subconjunctival injection with Rosiglitazone beside the filtering bleb, later protein, mRNA and immunofluorescence of fibrogenic markers are checked, and filtering bleb condition was measured. In vitro, we found Rosiglitazone could suppressed proliferation and migration of fibroblasts through macroautophagy via TGF- β /Smad signaling pathway. In vivo, on postoperative day 28, the mean number of fibroblasts in Rosiglitazone injection group was significantly the lowest and had the least collagen content and connective tissue growth factor. Rosiglitazone effectively controlled human and rabbit fibroblasts in vivo and in vitro. Its subconjunctiiva application may represent an effective, new avenue for the prevention of scarring after glaucoma surgery.

Keywords: fibrosis, glaucoma, macroautophagy, rosiglitazone

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Authors: Aymen Laadhari

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The cardiovascular system is centered on the heart and is characterized by a very complex structure with different physical scales in space (e.g. micrometers for erythrocytes and centimeters for organs) and time (e.g. milliseconds for human brain activity and several years for development of some pathologies). The development and numerical implementation of mathematical models of the cardiovascular system is a tremendously challenging topic at the theoretical and computational levels, inducing consequently a growing interest over the past decade. The accurate computational investigations in both healthy and pathological cases of processes related to the functioning of the human cardiovascular system can be of great potential in tackling several problems of clinical relevance and in improving the diagnosis of specific diseases. In this talk, we focus on the specific task of simulating three particular phenomena related to the cardiovascular system on the macroscopic, mesoscopic and microscopic scales, respectively. Namely, we develop numerical methodologies tailored for the simulation of (i) the haemodynamics (i.e., fluid mechanics of blood) in the aorta and sinus of Valsalva interacting with highly deformable thin leaflets, (ii) the hyperelastic anisotropic behaviour of cardiomyocytes and the influence of calcium concentrations on the contraction of single cells, and (iii) the dynamics of red blood cells in microvasculature. For each problem, we present an appropriate fully Eulerian finite element methodology. We report several numerical examples to address in detail the relevance of the mathematical models in terms of physiological meaning and to illustrate the accuracy and efficiency of the numerical methods.

Keywords: finite element method, cardiovascular system, Eulerian framework, haemodynamics, heart valve, cardiomyocyte, red blood cell

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Authors: Cristina Schreiner, Romeo Ciobanu, Marius Pislaru

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This paper aims to build a system based on fuzzy models that can be implemented in the assessment of ecological systems, to determine appropriate methods of action for reducing adverse effects on environmental and implicit the population. The model proposed provides new perspective for environmental assessment, and it can be used as a practical instrument for decision-making.

Keywords: ecosystem model, environmental security, fuzzy logic, sustainability of habitable regions

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Authors: Amir Gharavi, Mohamed Hassan, Amjad Shah

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The main characteristics of unconventional reservoirs include low-to ultra low permeability and low-to-moderate porosity. As a result, hydrocarbon production from these reservoirs requires different extraction technologies than from conventional resources. An unconventional reservoir must be stimulated to produce hydrocarbons at an acceptable flow rate to recover commercial quantities of hydrocarbons. Permeability for unconventional reservoirs is mostly below 0.1 mD, and reservoirs with permeability above 0.1 mD are generally considered to be conventional. The hydrocarbon held in these formations naturally will not move towards producing wells at economic rates without aid from hydraulic fracturing which is the only technique to assess these tight reservoir productions. Horizontal well with multi-stage fracking is the key technique to maximize stimulated reservoir volume and achieve commercial production. The main objective of this research paper is to investigate development options for a tight mature oilfield. This includes multistage hydraulic fracturing and spacing by building of reservoir models in the Reveal simulator to model potential development options based on sidetracking the existing vertical well. To simulate potential options, reservoir models have been built in the Reveal. An existing Petrel geological model was used to build the static parts of these models. A FBHP limit of 40bars was assumed to take into account pump operating limits and to maintain the reservoir pressure above the bubble point. 300m, 600m and 900m lateral length wells were modelled, in conjunction with 4, 6 and 8 stages of fracs. Simulation results indicate that higher initial recoveries and peak oil rates are obtained with longer well lengths and also with more fracs and spacing. For a 25year forecast, the ultimate recovery ranging from 0.4% to 2.56% for 300m and 1000m laterals respectively. The 900m lateral with 8 fracs 100m spacing gave the highest peak rate of 120m3/day, with the 600m and 300m cases giving initial peak rates of 110m3/day. Similarly, recovery factor for the 900m lateral with 8 fracs and 100m spacing was the highest at 2.65% after 25 years. The corresponding values for the 300m and 600m laterals were 2.37% and 2.42%. Therefore, the study suggests that longer laterals with 8 fracs and 100m spacing provided the optimal recovery, and this design is recommended as the basis for further study.

Keywords: unconventional, resource, hydraulic, fracturing

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Authors: Bidzina Matsaberidze

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It is known that, in emergency situations, multi-attribute group decision-making (MAGDM) models are characterized by insufficient objective data and a lack of time to respond to the task. Evidence theory is an effective tool for describing such incomplete information in decision-making models when the expert and his knowledge are involved in the estimations of the MAGDM parameters. We consider an emergency decision-making model, where expert assessments on humanitarian aid from distribution centers (HADC) are represented in q-rung ortho-pair fuzzy numbers, and the data structure is described within the data body theory. Based on focal probability construction and experts’ evaluations, an objective function-distribution centers’ selection ranking index is constructed. Our approach for solving the constructed bicriteria partitioning problem consists of two phases. In the first phase, based on the covering’s matrix, we generate a matrix, the columns of which allow us to find all possible partitionings of the HADCs with the service centers. Some constraints are also taken into consideration while generating the matrix. In the second phase, based on the matrix and using our exact algorithm, we find the partitionings -allocations of the HADCs to the centers- which correspond to the Pareto-optimal solutions. For an illustration of the obtained results, a numerical example is given for the facility location-selection problem.

Keywords: emergency MAGDM, q-rung orthopair fuzzy sets, evidence theory, HADC, facility location problem, multi-objective combinatorial optimization problem, Pareto-optimal solutions

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Authors: Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa

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A generalized log-logistic distribution with variable shapes of the hazard rate was introduced and studied, extending the log-logistic distribution by adding an extra parameter to the classical distribution, leading to greater flexibility in analysing and modeling various data types. The proposed distribution has a large number of well-known lifetime special sub-models such as; Weibull, log-logistic, exponential, and Burr XII distributions. Its basic mathematical and statistical properties were derived. The method of maximum likelihood was adopted for estimating the unknown parameters of the proposed distribution, and a Monte Carlo simulation study is carried out to assess the behavior of the estimators. The importance of this distribution is that its tendency to model both monotone (increasing and decreasing) and non-monotone (unimodal and bathtub shape) or reversed “bathtub” shape hazard rate functions which are quite common in survival and reliability data analysis. Furthermore, the flexibility and usefulness of the proposed distribution are illustrated in a real-life data set and compared to its sub-models; Weibull, log-logistic, and BurrXII distributions and other parametric survival distributions with 3-parmaeters; like the exponentiated Weibull distribution, the 3-parameter lognormal distribution, the 3- parameter gamma distribution, the 3-parameter Weibull distribution, and the 3-parameter log-logistic (also known as shifted log-logistic) distribution. The proposed distribution provided a better fit than all of the competitive distributions based on the goodness-of-fit tests, the log-likelihood, and information criterion values. Finally, Bayesian analysis and performance of Gibbs sampling for the data set are also carried out.

Keywords: hazard rate function, log-logistic distribution, maximum likelihood estimation, generalized log-logistic distribution, survival data, Monte Carlo simulation

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Authors: Michael Williams

Abstract:

The ability to control a flowing well is of the utmost important. During the kill phase, heavy weight kill mud is circulated around the well. While increasing bottom hole pressure near wellbore formation, the damage is increased. The addition of high density spherical objects has the potential to minimise this near wellbore damage, increase bottom hole pressure and reduce operational time to kill the well. This operational time saving is seen in the rapid deployment of high density spherical objects instead of building high density drilling fluid. The research aims to model the well kill process using a Computational Fluid Dynamics software. A model has been created as a proof of concept to analyse the flow of micron sized spherical objects in the drilling fluid. Initial results show that this new methodology of spherical objects in drilling fluid agrees with traditional stream lines seen in non-particle flow. Additional models have been created to demonstrate that areas of higher flow rate around the bit can lead to increased probability of wash out of formations but do not affect the flow of micron sized spherical objects. Interestingly, areas that experience dimensional changes such as tool joints and various BHA components do not appear at this initial stage to experience increased velocity or create areas of turbulent flow, which could lead to further borehole stability. In conclusion, the initial models of this novel well control methodology have not demonstrated any adverse flow patterns, which would conclude that this model may be viable under field conditions.

Keywords: well control, fluid mechanics, safety, environment

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Authors: Rezaei Hachesu Peyman, Oliyaee Azadeh, Salahzadeh Zahra, Alizadeh Somayyeh, Safaei Naser

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Coronary Artery Disease (CAD) is one major cause of disability in adults and one main cause of death in developed. In this study, data mining techniques including Decision Trees, Artificial neural networks (ANNs), and Support Vector Machine (SVM) analyze CAD data. Data of 4948 patients who had suffered from heart diseases were included in the analysis. CAD is the target variable, and 24 inputs or predictor variables are used for the classification. The performance of these techniques is compared in terms of sensitivity, specificity, and accuracy. The most significant factor influencing CAD is chest pain. Elderly males (age > 53) have a high probability to be diagnosed with CAD. SVM algorithm is the most useful way for evaluation and prediction of CAD patients as compared to non-CAD ones. Application of data mining techniques in analyzing coronary artery diseases is a good method for investigating the existing relationships between variables.

Keywords: classification, coronary artery disease, data-mining, knowledge discovery, extract

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Authors: William Albeiro Alvarez, Gloria Patricia Jaramillo, Ivan Reinaldo Sarmiento

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Understanding the behavior of individuals and the different human factors that influence the choice when faced with a complex system such as transportation is one of the most complicated aspects of measuring in the components that constitute the modeling of route choice due to that various behaviors and driving mode directly or indirectly affect the choice. During the last two decades, with the development of information and communications technologies, new data collection techniques have emerged such as GPS, geolocation with mobile phones, apps for choosing the route between origin and destination, individual service transport applications among others, where an interest has been generated to improve discrete choice models when considering the incorporation of these developments as well as psychological factors that affect decision making. This paper implements a discrete choice model that proposes and estimates a hybrid model that integrates route choice models and latent variables based on the observation on the route of a sample of public taxi drivers from the city of Medellín, Colombia in relation to its behavior, personality, socioeconomic characteristics, and driving mode. The set of choice options includes the routes generated by the individual service transport applications versus the driver's choice. The hybrid model consists of measurement equations that relate latent variables with measurement indicators and utilities with choice indicators along with structural equations that link the observable characteristics of drivers with latent variables and explanatory variables with utilities.

Keywords: behavior choice model, human factors, hybrid model, real time data

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Authors: M. Govindarajan

Abstract:

One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. In this research work, new ensemble classification methods are proposed with homogeneous ensemble classifier using bagging and heterogeneous ensemble classifier using arcing and their performances are analyzed in terms of accuracy. A Classifier ensemble is designed using Radial Basis Function (RBF) and Support Vector Machine (SVM) as base classifiers. The feasibility and the benefits of the proposed approaches are demonstrated by the means of standard datasets of intrusion detection. The main originality of the proposed approach is based on three main parts: preprocessing phase, classification phase, and combining phase. A wide range of comparative experiments is conducted for standard datasets of intrusion detection. The performance of the proposed homogeneous and heterogeneous ensemble classifiers are compared to the performance of other standard homogeneous and heterogeneous ensemble methods. The standard homogeneous ensemble methods include Error correcting output codes, Dagging and heterogeneous ensemble methods include majority voting, stacking. The proposed ensemble methods provide significant improvement of accuracy compared to individual classifiers and the proposed bagged RBF and SVM performs significantly better than ECOC and Dagging and the proposed hybrid RBF-SVM performs significantly better than voting and stacking. Also heterogeneous models exhibit better results than homogeneous models for standard datasets of intrusion detection. 

Keywords: data mining, ensemble, radial basis function, support vector machine, accuracy

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Authors: Mario Perez-Won, Roberto Lemus-Mondaca, Fernanda Marin, Constanza Olivares

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This study presents the simultaneous application of high hydrostatic pressure (HHP) and osmotic dehydration of jumbo squid (Dosidicus gigas) slice. Diffusion coefficients for both components water and solids were improved by the process pressure, being influenced by pressure level. The working conditions were different pressures such as 100, 250, 400 MPa and pressure atmospheric (0.1 MPa) for time intervals from 30 to 300 seconds and a 15% NaCl concentration. The mathematical expressions used for mass transfer simulations both water and salt were those corresponding to Newton, Henderson and Pabis, Page and Weibull models, where the Weibull and Henderson-Pabis models presented the best fitted to the water and salt experimental data, respectively. The values for water diffusivity coefficients varied from 1.62 to 8.10x10⁻⁹ m²/s whereas that for salt varied among 14.18 to 36.07x10⁻⁹ m²/s for selected conditions. Finally, as to quality parameters studied under the range of experimental conditions studied, the treatment at 250 MPa yielded on the samples a minimum hardness, whereas springiness, cohesiveness and chewiness at 100, 250 and 400 MPa treatments presented statistical differences regarding to unpressurized samples. The colour parameters L* (lightness) increased, however, but b* (yellowish) and a* (reddish) parameters decreased when increasing pressure level. This way, samples presented a brighter aspect and a mildly cooked appearance. The results presented in this study can support the enormous potential of hydrostatic pressure application as a technique important for compounds impregnation under high pressure.

Keywords: colour, diffusivity, high pressure, jumbo squid, modelling, texture

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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: Majid Saaly, Shahriar Tavousi Tafreshi, Mehdi Nazari Afshar

Abstract:

Most reinforced concrete structures are designed only under heavy loads have large transverse reinforcement spacing values, and therefore suffer severe failure after intense ground movements. The main goal of this paper is to compare the shear- and axial failure of concrete bending frames available in Tehran using incremental dynamic analysis under near- and far-field records. For this purpose, IDA analyses of 5, 10, and 15-story concrete structures were done under seven far-fault records and five near-faults records. The results show that in two-dimensional models of short-rise, mid-rise and high-rise reinforced concrete frames located on Type-3 soil, increasing the distance of the transverse reinforcement can increase the maximum inter-story drift ratio values up to 37%. According to the existing results on 5, 10, and 15-story reinforced concrete models located on Type-3 soil, records with characteristics such as fling-step and directivity create maximum drift values between floors more than far-fault earthquakes. The results indicated that in the case of seismic excitation modes under earthquake encompassing directivity or fling-step, the probability values of failure and failure possibility increasing rate values are much smaller than the corresponding values of far-fault earthquakes. However, in near-fault frame records, the probability of exceedance occurs at lower seismic intensities compared to far-fault records.

Keywords: IDA, failure curve, directivity, maximum floor drift, fling step, evaluating probable bending of frames, near-field and far-field earthquake records

Procedia PDF Downloads 90