Search results for: predictive model

Authors: Sejin Jung, Dongho Kang, Byungsik Kim

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Assessing the impact of climate change requires the use of a multi-model ensemble (MME) to quantify uncertainties between scenarios and produce downscaled outlines for simulation of climate under the influence of different factors, including topography. This study decreases climate change scenarios from the 13 global climate models (GCMs) to assess the impacts of future climate change. Unlike South Korea, North Korea lacks in studies using climate change scenarios of the CoupledModelIntercomparisonProject (CMIP5), and only recently did the country start the projection of extreme precipitation episodes. One of the main purposes of this study is to predict changes in the average climatic conditions of North Korea in the future. The result of comparing downscaled climate change scenarios with observation data for a reference period indicates high applicability of the Multi-Model Ensemble (MME). Furthermore, the study classifies climatic zones by applying the Köppen-Geiger climate classification system to the MME, which is validated for future precipitation and temperature. The result suggests that the continental climate (D) that covers the inland area for the reference climate is expected to shift into the temperate climate (C). The coefficient of variation (CVs) in the temperature ensemble is particularly low for the southern coast of the Korean peninsula, and accordingly, a high possibility of the shifting climatic zone of the coast is predicted. This research was supported by a grant (MOIS-DP-2015-05) of Disaster Prediction and Mitigation Technology Development Program funded by Ministry of Interior and Safety (MOIS, Korea).

Keywords: MME, North Korea, Koppen–Geiger, climatic zones, coefficient of variation, CV

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Authors: Iraklis Argyriou

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The transition to net-zero emission urban bus (ZEB) systems is receiving increased attention in research and policymaking throughout the globe. Most studies in this area tend to address techno-economic aspects and the perspectives of a narrow group of stakeholders, while they largely overlook analysis of current bus system dynamics. This offers limited insight into the types of ZEB governance challenges and opportunities that are encountered in real-world contexts, as well as into some of the immediate actions that need to be taken to set off the transition over the longer term. This research offers a multi-stakeholder perspective into both the technical and non-technical factors that influence ZEB transitions within a particular context, the UK. It does so by drawing from a recent transition visioning stakeholder workshop (June 2023) with key public, private and civic actors of the urban bus transportation system. Using NVivo software to qualitatively analyze the workshop discussions, the research examines the key technological and funding aspects, as well as the short-term actions (over the next five years), that need to be addressed for supporting the ZEB transition in UK cities. It finds that ZEB technology has reached a mature stage (i.e., high efficiency of batteries, motors and inverters), but important improvements can be pursued through greater control and integration of ZEB technological components and systems. In this regard, telemetry, predictive maintenance and adaptive control strategies pertinent to the performance and operation of ZEB vehicles have a key role to play in the techno-economic advancement of the transition. Yet, more pressing gaps were identified in the current ZEB funding regime. Whereas the UK central government supports greater ZEB adoption through a series of grants and subsidies, the scale of the funding and its fragmented nature do not match the needs for a UK-wide transition. Funding devolution arrangements (i.e., stable funding settlement deals between the central government and the devolved administrations/local authorities), as well as locally-driven schemes (i.e., congestion charging/workplace parking levy), could then enhance the financial prospects of the transition. As for short-term action, three areas were identified as critical: (1) the creation of whole value chains around the supply, use and recycling of ZEB components; (2) the ZEB retrofitting of existing fleets; and (3) integrated transportation that prioritizes buses as a first-choice, convenient and reliable mode while it simultaneously reduces car dependency in urban areas. Taken together, the findings point to the need for place-based transition approaches that create a viable techno-economic ecosystem for ZEB development but at the same time adopt a broader governance perspective beyond a ‘net-zero’ and ‘bus sectoral’ focus. As such, multi-actor collaborations and the coordination of wider resources and agency, both vertically across institutional scales and horizontally across transport, energy and urban planning, become fundamental features of comprehensive ZEB responses. The lessons from the UK case can inform a broader body of empirical contextual knowledge of ZEB transition governance within domestic political economies of public transportation.

Keywords: net-zero emission transition, stakeholders, transition governance, UK, urban bus transportation

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Authors: Sun-Mi Park, Ji-Hyun Jung, Min-Jung Kang

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Software (SW) education is scheduled to be included in the National curriculum in South Korea by 2018. However, Korean national kindergarten curriculum has been excepted from the revision of the entire Korean national school curriculum including software education. Even though the SW education has not been considered a part of current national kindergarten curriculum, there is a growing interest of adopting software education into the ECE practice. Teachers might be a key element in introducing and implementing new educational change such as SW education. In preparation for the adoption of SW education in ECE, it might be necessary to figure out ECE teachers’ perception and attitudes toward early childhood software education. For this study, 219 ECE teachers’ concern level in SW education was surveyed by using the Stages of Concern Questionnaire (SoCQ). As a result, the teachers' concern level in SW education is the highest at stage 0-Unconcerned and is high level in stage 1-informational, stage 2-personal, and stage 3-management concern. Thus, a non-user pattern was mostly indicated. However, compared to a typical non-user pattern, the personal and informative concern level is slightly high. The 'tailing up' phenomenon toward stage 6-refocusing was shown. Therefore, the pattern aspect close to critical non-user ever appeared to some extent. In addition, a significant difference in concern level was shown at all stages depending on the awareness of necessity. Teachers with SW training experience showed higher intensity only at stage 0. There was statistically significant difference in stage 0 and 6 depending on the future implementation decision. These results will be utilized as a resource in building ECE teachers’ support system according to his or her concern level of SW education.

Keywords: concerns-based adoption model (CBAM), early childhood education teachers, software education, Stages of Concern (SoC)

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Authors: Shaibu Baanni Azumah, Ignatius Tindjina, Stella Obanyi, Tara N. Wood

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This study examined the effect of Urea Deep Placement (UDP) technology on the output of irrigated rice farmers in the northern region of Ghana. Multi-stage sampling technique was used to select 142 rice farmers from the Golinga and Bontanga irrigation schemes, around Tamale. A treatment effect model was estimated at two stages; firstly, to determine the factors that influenced farmers’ decision to adopt the UDP technology and secondly, to determine the effect of the adoption of the UDP technology on the output of rice farmers. The significant variables that influenced rice farmers’ adoption of the UPD technology were sex of the farmer, land ownership, off-farm activity, extension service, farmer group participation and training. The results also revealed that farm size and the adoption of UDP technology significantly influenced the output of rice farmers in the northern region of Ghana. In addition to the potential of the technology to improve yields, it also presents an employment opportunity for women and youth, who are engaged in the deep placement of Urea Super Granules (USG), as well as in the transplantation of rice. It is recommended that the government of Ghana work closely with the IFDC to embed the UDP technology in the national agricultural programmes and policies. The study also recommends an effective collaboration between the government, through the Ministry of Food and Agriculture (MoFA) and the International Fertilizer Development Center (IFDC) to train agricultural extension agents on UDP technology in the rice producing areas of the country.

Keywords: Northern Ghana, output , irrigation rice farmers, treatment effect model, urea deep placement

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Authors: Aayushi Lyngwa, Bimal Kishore Sahoo

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The study discusses the relationship between cognitive ability and the level of education attained by the tribal street entrepreneurs on their financial literacy. It is driven by the objective of examining the effect of cognitive ability on financial ability on the one hand and determining the effect of the same on financial literacy on the other. A field experiment was conducted on 203 tribal street vendors in the north-eastern Indian state of Mizoram. This experiment's calculations are conditioned by providing each question scores like math score (cognitive ability), financial score and debt score (financial ability). After that, categories for each of the variables, like math category (math score), financial category (financial score) and debt category (debt score), are generated to run the regression model. Since the dependent variable is ordinal, an ordered logit regression model was applied. The study shows that street vendors' cognitive and financial abilities are highly correlated. It, therefore, confirms that cognitive ability positively affects the financial literacy of street vendors through the increase in attainment of educational levels. It is also found that concerning the type of street vendors, regular street vendors are more likely to have better cognitive abilities than temporary street vendors. Additionally, street vendors with more cognitive and financial abilities gained better monthly profits and performed habits of bookkeeping. The study attempts to draw a particular focus on a set-up which is economically and socially marginalized in the Indian economy. Its finding contributes to understanding financial literacy in an understudied area and provides policy implications through inclusive financial systems solutions in an economy limited to tribal street vendors.

Keywords: financial literacy, education, street entrepreneurs, tribals, cognitive ability, financial ability, ordered logit regression.

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Authors: Lin-Han Chiang Hsieh, Hsiao-Yi Lin

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This study aims to determine the impact of the disclosure of flood potential map on housing prices. The disclosure is supposed to mitigate the market failure by reducing information asymmetry. On the other hand, opponents argue that the official disclosure of simulated results will only create unnecessary disturbances on the housing market. This study identifies the impact of the disclosure of the flood potential map by comparing the hedonic price of flood potential before and after the disclosure. The flood potential map used in this study is published by Taipei municipal government in 2015, which is a result of a comprehensive simulation based on geographical, hydrological, and meteorological factors. The residential property sales data of 2013 to 2016 is used in this study, which is collected from the actual sales price registration system by the Department of Land Administration (DLA). The result shows that the impact of flood potential on residential real estate market is statistically significant both before and after the disclosure. But the trend is clearer after the disclosure, suggesting that the disclosure does have an impact on the market. Also, the result shows that the impact of flood potential differs by the severity and frequency of precipitation. The negative impact for a relatively mild, high frequency flood potential is stronger than that for a heavy, low possibility flood potential. The result indicates that home buyers are of more concern to the frequency, than the intensity of flood. Another contribution of this study is in the methodological perspective. The classic hedonic price analysis with OLS regression suffers from two spatial problems: the endogeneity problem caused by omitted spatial-related variables, and the heterogeneity concern to the presumption that regression coefficients are spatially constant. These two problems are seldom considered in a single model. This study tries to deal with the endogeneity and heterogeneity problem together by combining the spatial fixed-effect model and geographically weighted regression (GWR). A series of literature indicates that the hedonic price of certain environmental assets varies spatially by applying GWR. Since the endogeneity problem is usually not considered in typical GWR models, it is arguable that the omitted spatial-related variables might bias the result of GWR models. By combing the spatial fixed-effect model and GWR, this study concludes that the effect of flood potential map is highly sensitive by location, even after controlling for the spatial autocorrelation at the same time. The main policy application of this result is that it is improper to determine the potential benefit of flood prevention policy by simply multiplying the hedonic price of flood risk by the number of houses. The effect of flood prevention might vary dramatically by location.

Keywords: flood potential, hedonic price analysis, endogeneity, heterogeneity, geographically-weighted regression

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Authors: Tahani Aljohani, Jialin Yu, Alexandra. I. Cristea

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The more an educational system knows about a learner, the more personalised interaction it can provide, which leads to better learning. However, asking a learner directly is potentially disruptive, and often ignored by learners. Especially in the booming realm of MOOC Massive Online Learning platforms, only a very low percentage of users disclose demographic information about themselves. Thus, in this paper, we aim to predict learners’ demographic characteristics, by proposing an approach using linguistically motivated Deep Learning Architectures for Learner Profiling, particularly targeting gender prediction on a FutureLearn MOOC platform. Additionally, we tackle here the difficult problem of predicting the gender of learners based on their comments only – which are often available across MOOCs. The most common current approaches to text classification use the Long Short-Term Memory (LSTM) model, considering sentences as sequences. However, human language also has structures. In this research, rather than considering sentences as plain sequences, we hypothesise that higher semantic - and syntactic level sentence processing based on linguistics will render a richer representation. We thus evaluate, the traditional LSTM versus other bleeding edge models, which take into account syntactic structure, such as tree-structured LSTM, Stack-augmented Parser-Interpreter Neural Network (SPINN) and the Structure-Aware Tag Augmented model (SATA). Additionally, we explore using different word-level encoding functions. We have implemented these methods on Our MOOC dataset, which is the most performant one comparing with a public dataset on sentiment analysis that is further used as a cross-examining for the models' results.

Keywords: deep learning, data mining, gender predication, MOOCs

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Authors: Harshit Mittal, Neeraj Garg

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Hand symbol recognition is a pivotal component in the domain of computer vision, with far-reaching applications spanning sign language interpretation, human-computer interaction, and accessibility. This research paper discusses the approach with the integration of the Canny Edge algorithm and convolutional neural network. The significance of this study lies in its potential to enhance communication and accessibility for individuals with hearing impairments or those engaged in gesture-based interactions with technology. In the experiment mentioned, the data is manually collected by the authors from the webcam using Python codes, to increase the dataset augmentation, is applied to original images, which makes the model more compatible and advanced. Further, the dataset of about 6000 coloured images distributed equally in 5 classes (i.e., 1, 2, 3, 4, 5) are pre-processed first to gray images and then by the Canny Edge algorithm with threshold 1 and 2 as 150 each. After successful data building, this data is trained on the Convolutional Neural Network model, giving accuracy: 0.97834, precision: 0.97841, recall: 0.9783, and F1 score: 0.97832. For user purposes, a block of codes is built in Python to enable a window for hand symbol recognition. This research, at its core, seeks to advance the field of computer vision by providing an advanced perspective on hand sign recognition. By leveraging the capabilities of the Canny Edge algorithm and convolutional neural network, this study contributes to the ongoing efforts to create more accurate, efficient, and accessible solutions for individuals with diverse communication needs.

Keywords: hand symbol recognition, computer vision, Canny edge algorithm, convolutional neural network

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Authors: Aaron Liggett

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This research focuses on the visionary proposal made by the Olmsted Brothers Landscape Architecture firm in the 1920s for a network of interconnected parkways in Los Angeles. Their envisioned parkways aimed to address environmental and cultural strains by providing green space for recreation, wildlife habitat, and stormwater management while serving as multimodal transportation routes. Although the parkways were never constructed, through an evidence-based approach, this research presents a framework for evaluating the potential functionality and success of the parkways by modeling and visualizing their quantitative and qualitative landscape performance and benefits. Historical documents and innovative digital modeling tools produce detailed analysis, modeling, and visualization of the parkway designs. A set of 1928 construction documents are used to analyze and interpret the design intent of the parkways. Grading plans are digitized in CAD and modeled in Sketchup to produce 3D visualizations of the parkway. Drainage plans are digitized to model stormwater performance. Planting plans are analyzed to model urban forestry and biodiversity. The EPA's Storm Water Management Model (SWMM) predicts runoff quantity and quality. The USDA Forests Service tools evaluate carbon sequestration and air quality. Spatial and overlay analysis techniques are employed to assess urban connectivity and the spatial impacts of the parkway designs. The study reveals how the integration of blue infrastructure, green infrastructure, and transportation infrastructure within the parkway design creates a multifunctional landscape capable of offering alternative spatial and temporal uses. The analysis demonstrates the potential for multiple functional, ecological, aesthetic, and social benefits to be derived from the proposed parkways. The analysis of the Olmsted Brothers' proposed Los Angeles parkways, which predated contemporary ecological design and resiliency practices, demonstrates the potential for providing multiple functional, ecological, aesthetic, and social benefits within urban designs. The findings highlight the importance of integrated blue, green, and transportation infrastructure in creating a multifunctional landscape that simultaneously serves multiple purposes. The research contributes new methods for modeling and visualizing landscape performance benefits, providing insights and techniques for informing future designs and sustainable development strategies.

Keywords: landscape architecture, ecological urban design, greenway, landscape performance

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Authors: Myung-Soo Lee, Sun-Jin Jo, Kyoung-Sae Na, Joo-Eon Park

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Early intervention after a disaster is important for recovery of disaster victims and each country has its own professional mental health service system such as Disaster Psychiatric Assistant Team in Japan and Crisis Counseling Program in the USA. The purpose of this study was to determine key prior components of the Korean Disaster Psychiatric Assistant Team (K-DPAT) for building up Korean disaster mental health service system. We conducted an Analytic Hierarchy Process(AHP) with disaster mental health experts using pairwise comparison questionnaire which compares the relative importance of the key components of Korean disaster mental health service system. Forty-one experts answered the first online survey, and among them, 36 responded to the second. Ten experts were participated in panel meeting and discussed the results of the survey and AHP process. Participants decided the relative importance of the Korean disaster mental health service system regarding initial professional intervention as follows. K-DPAT could be organized at a national level (43.0%) or regional level (40.0%). K-DPAT members should be managed (59.0%) and educated (52.1%) by national level than regional or local level. K-DPAT should be organized independent of the preexisting mental health system (70.1%). Funding for K-DPAT should be from the Ministry of Public Safety and the system could be managed by Ministry of Health (65.8%). Experts agreed K-DPAT leader is suitable for key decision maker for most types of disaster except infectious disease. We expect new model for disaster mental health services can improve insufficiency of the system such as fragmentation and decrease the unmet needs of early professional intervention for the disaster victims.

Keywords: analytic hierarchy process, decision making, disaster, DPAT, mental health services

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Authors: Linda D. F. Bambara, Marie Sawadogo

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Jatropha curcas (jatropha) is the plant that has been the most studied for biofuel production in West Africa. There exist however other plants such as Balanites aegyptiaca (balanites) that have been targeted as a potential feedstock for biofuel production. This biomass could be an alternative feedstock for the production of straight vegetable oil (SVO) at costs lower than jatropha-based SVO production costs. This study aims firstly to determine, through an MILP model, the optimal organization that minimizes the costs of the oilseeds supply of two biomass conversion units (BCU) exploiting respectively jatropha seeds and the balanitès seeds. Secondly, the study aims to carry out a comparative study of these costs obtained for each BCU. The model was then implemented on two theoretical cases studies built on the basis of the common practices in Burkina Faso and two scenarios were carried out for each case study. In Scenario 1, 3 pre-processing locations ("at the harvesting area", "at the gathering points", "at the BCU") are possible. In scenario 2, only one location ("at the BCU") is possible. For each biomass, the system studied is the upstream supply chain (harvesting, transport and pre-processing (drying, dehulling, depulping)), including cultivation (for jatropha). The model optimizes the area of land to be exploited based on the productivity of the studied plants and material losses that may occur during the harvesting and the supply of the BCU. It then defines the configuration of the logistics network allowing an optimal supply of the BCU taking into account the most common means of transport in West African rural areas. For the two scenarios, the results of the implementation showed that the total area exploited for balanites (1807 ha) is 4.7 times greater than the total area exploited for Jatropha (381 ha). In both case studies, the location of pre-processing “at the harvesting area” was always chosen for scenario1. As the balanites trees were not planted and because the first harvest of the jatropha seeds took place 4 years after planting, the cost price of the seeds at the BCU without the pre-processing costs was about 430 XOF/kg. This cost is 3 times higher than the balanites's one, which is 140 XOF/kg. After the first year of harvest, i.e. 5 years after planting, and assuming that the yield remains constant, the same cost price is about 200 XOF/kg for Jatropha. This cost is still 1.4 times greater than the balanites's one. The transport cost of the balanites seeds is about 120 XOF/kg. This cost is similar for the jatropha seeds. However, when the pre-processing is located at the BCU, i.e. for scenario2, the transport costs of the balanites seeds is 1200 XOF/kg. These costs are 6 times greater than the transport costs of jatropha which is 200 XOF/kg. These results show that the cost price of the balanites seeds at the BCU can be competitive compared to the jatropha's one if the pre-processing is located at the harvesting area.

Keywords: Balanites aegyptiaca, biomass conversion, Jatropha curcas, optimization, post-harvest operations

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Authors: Nathalie Baeza-Kallee, Raphaël Bergès, Victoria Hein, Stéphanie Cabaret, Jeremy Garcia, Abigaëlle Gros, Emeline Tabouret, Aurélie Tchoghandjian, Carole Colin, Dominique Figarella-Branger

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Glioblastoma (GBM) contains cancer stem cells that are resistant to treatment. GBM cancer stem cell expresses glycolipids recognized by the A2B5 antibody. A2B5, induced by the enzyme ST8 alpha-N-acetyl-neuraminide alpha-2,8-sialyl transferase 3 (ST8Sia3), plays a crucial role in the proliferation, migration, clonogenicity, and tumorigenesis of GBM cancer stem cells. Our aim was to characterize the resulting effects of neuraminidase that remove A2B5 in order to target GBM cancer stem cells. To this end, we set up a GBM organotypic slice model; quantified A2B5 expression by flow cytometry in U87-MG, U87-ST8Sia3, and GBM cancer stem cell lines, treated or not by neuraminidase; performed RNAseq and DNA methylation profiling; and analyzed the ganglioside expression by liquid chromatography-mass spectrometry in these cell lines, treated or not with neuraminidase. Results demonstrated that neuraminidase decreased A2B5 expression, tumor size, and regrowth after surgical removal in the organotypic slice model but did not induce a distinct transcriptomic or epigenetic signature in GBM CSC lines. RNAseq analysis revealed that OLIG2, CHI3L1, TIMP3, TNFAIP2, and TNFAIP6 transcripts were significantly overexpressed in U87-ST8Sia3 compared to U87-MG. RT-qPCR confirmed these results and demonstrated that neuraminidase decreased gene expression in GBM cancer stem cell lines. Moreover, neuraminidase drastically reduced ganglioside expression in GBM cancer stem cell lines. Neuraminidase, by its pleiotropic action, is an attractive local treatment against GBM.

Keywords: cancer stem cell, ganglioside, glioblastoma, targeted treatment

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Authors: Modi M. Ahmed, Noor Al-Dousari, Ali M. Al-Dousari

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Cornulaca aucheri is an annual herb consider as disturbance indicator currently visible and widely distributed in disturbed lands in Liyah area. Such area is suffered from severe land degradation due to multiple interacting factors such as, overgrazing, gravel and sand quarrying, military activities and natural process. The restoration program is applied after refilled quarries sites and levelled the surface irregularities in order to rehabilitate the natural vegetation and wildlife to its original shape. During the past 10 years of rehabilitation, noticeable greenery healthy cover of Cornulaca sp. are shown specially around artificial lake and playas. The existence of such species in high density it means that restoration program has succeeded and transit from bare ground state to Cornulaca and annual forb state. This state is lower state of Range State Transition Succession model, but it is better than bare soil. Cornulaca spp is native desert plant grows in arid conditions on sandy, stony ground, near oasis, on sand dunes and in sandy depressions. The sheep and goats are repulsive of it. Despite its spiny leaves, it provides good grazing for camels and is said to increase the milk supply produced by lactating females. It is about 80 cm tall and has stems that branched from the base with new faster greenery growth in the summer. It shows good environmental potential to be managed as natural types used for the restoration of degraded lands in desert areas.

Keywords: land degradation, range state transition succession model, rehabilitation, restoration program

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Authors: Arfanara Najnin, Michael W. Roach, Jr., Dr. Jianhong Cecilia Xia

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The SkyCar Rapid Transit System (SRT) is an innovative intelligent transport system for the sustainable urban transport system. This system will increase the urban area network connectivity and decrease urban area traffic congestion. The SRT system is designed as a suspended Personal Rapid Transit (PRT) system that travels under a guideway 5m above the ground. A driver-less passenger is via pod-cars that hang from slender beams supported by columns that replace existing lamp posts. The beams are setup in a series of interconnecting loops providing non-stop travel from beginning to end to assure journey time. The SRT forward movement is effected by magnetic motors built into the guideway. Passenger stops are at either at line level 5m above the ground or ground level via a spur guideway that curves off the main thoroughfare. The main objective of this paper is to propose an integrated Automated Transit Network (ATN) technology for the future intelligent transport system in the urban built environment. To fulfil the objective a 4D simulated model in the urban built environment has been proposed by using the concept of SRT-ATN system. The methodology for the design, construction and testing parameters of a Technology Demonstrator (TD) for proof of concept and a Simulator (S) has been demonstrated. The completed TD and S will provide an excellent proving ground for the next development stage, the SRT Prototype (PT) and Pilot System (PS). This paper covered by a 4D simulated model in the virtual built environment is to effectively show how the SRT-ATN system works. OpenSim software has been used to develop the model in a virtual environment, and the scenario has been simulated to understand and visualize the proposed SkyCar Rapid Transit Network model. The SkyCar system will be fabricated in a modular form which is easily transported. The system would be installed in increasingly congested city centers throughout the world, as well as in airports, tourist resorts, race tracks and other special purpose for the urban community. This paper shares the lessons learnt from the proposed innovation and provides recommendations on how to improve the future transport system in urban built environment. Safety and security of passengers are prime factors to be considered for this transit system. Design requirements to meet the safety needs to be part of the research and development phase of the project. Operational safety aspects would also be developed during this period. The vehicles, the track and beam systems and stations are the main components that need to be examined in detail for safety and security of patrons. Measures will also be required to protect columns adjoining intersections from errant vehicles in vehicular traffic collisions. The SkyCar Rapid Transit takes advantage of all current disruptive technologies; batteries, sensors and 4G/5G communication and solar energy technologies which will continue to reduce the costs and make the systems more profitable. SkyCar's energy consumption is extremely low compared to other transport systems.

Keywords: SkyCar, rapid transit, Intelligent Transport System (ITS), Automated Transit Network (ATN), urban built environment, 4D Visualization, smart city

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Authors: Chunhua Liao, Jinfei Wang, Bo Shan, Yang Song, Yongjun He, Taifeng Dong

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Winter wheat is one of the main crops in Canada. Forecasting of within-field variability of yield in winter wheat at the early stages is essential for precision farming. However, the crop yield modelling based on high spatial resolution satellite data is generally affected by the lack of continuous satellite observations, resulting in reducing the generalization ability of the models and increasing the difficulty of crop yield forecasting at the early stages. In this study, the correlations between Sentinel-2 data (vegetation indices and reflectance) and yield data collected by combine harvester were investigated and a generalized multivariate linear regression (MLR) model was built and tested with data acquired in different years. It was found that the four-band reflectance (blue, green, red, near-infrared) performed better than their vegetation indices (NDVI, EVI, WDRVI and OSAVI) in wheat yield prediction. The optimum phenological stage for wheat yield prediction with highest accuracy was at the growing stages from the end of the flowering to the beginning of the filling stage. The best MLR model was therefore built to predict wheat yield before harvest using Sentinel-2 data acquired at the end of the flowering stage. Further, to improve the ability of the yield prediction at the early stages, three simple unsupervised domain adaptation (DA) methods were adopted to transform the reflectance data at the early stages to the optimum phenological stage. The winter wheat yield prediction using multiple vegetation indices showed higher accuracy than using single vegetation index. The optimum stage for winter wheat yield forecasting varied with different fields when using vegetation indices, while it was consistent when using multispectral reflectance and the optimum stage for winter wheat yield prediction was at the end of flowering stage. The average testing RMSE of the MLR model at the end of the flowering stage was 604.48 kg/ha. Near the booting stage, the average testing RMSE of yield prediction using the best MLR was reduced to 799.18 kg/ha when applying the mean matching domain adaptation approach to transform the data to the target domain (at the end of the flowering) compared to that using the original data based on the models developed at the booting stage directly (“MLR at the early stage”) (RMSE =1140.64 kg/ha). This study demonstrated that the simple mean matching (MM) performed better than other DA methods and it was found that “DA then MLR at the optimum stage” performed better than “MLR directly at the early stages” for winter wheat yield forecasting at the early stages. The results indicated that the DA had a great potential in near real-time crop yield forecasting at the early stages. This study indicated that the simple domain adaptation methods had a great potential in crop yield prediction at the early stages using remote sensing data.

Keywords: wheat yield prediction, domain adaptation, Sentinel-2, within-field scale

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Authors: Henry Munoz, Muhammad Mohsan, Takashi Kiyota

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Strength and deformability characteristics of a liquefiable sand deposit including the development of earthquake-induced shear stress and shear strain as well as soil softening via the progressive degradation of shear modulus were studied via shaking table experiments. To do so, a model of a liquefiable sand deposit was constructed and densely instrumented where accelerations, pressures, and displacements at different locations were continuously monitored. Furthermore, the confinement effects on the strength and deformation characteristics of the liquefiable sand deposit due to an external surcharge by placing a heavy concrete slab (i.e. the model of an actual structural rigid pavement) on the ground surface were examined. The results indicate that as the number of seismic-loading cycles increases, the sand deposit softens progressively as large shear strains take place in different sand elements. Liquefaction state is reached after the combined effects of the progressive degradation of the initial shear modulus associated with the continuous decrease in the mean principal stress, and the buildup of the excess of pore pressure takes place in the sand deposit. Finally, the confinement effects given by a concrete slab placed on the surface of the sand deposit resulted in a favorable increasing in the initial shear modulus, an increase in the mean principal stress and a decrease in the softening rate (i.e. the decreasing rate in shear modulus) of the sand, thus making the onset of liquefaction to take place at a later stage. This is, only after the sand deposit having a concrete slab experienced a higher number of seismic loading cycles liquefaction took place, in contrast to an ordinary sand deposit having no concrete slab.

Keywords: liquefaction, shear modulus degradation, shaking table, earthquake

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Authors: Navneet Singh Deora, Aastha Deswal, H. N. Mishra

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Pasta is one of the most widely consumed food products around the world. Rapid determination of the moisture content in pasta will assist food processors to provide online quality control of pasta during large scale production. Rapid Fourier transform near-infrared method (FT-NIR) was developed for determining moisture content in pasta. A calibration set of 150 samples, a validation set of 30 samples and a prediction set of 25 samples of pasta were used. The diffuse reflection spectra of different types of pastas were measured by FT-NIR analyzer in the 4,000-12,000 cm-1 spectral range. Calibration and validation sets were designed for the conception and evaluation of the method adequacy in the range of moisture content 10 to 15 percent (w.b) of the pasta. The prediction models based on partial least squares (PLS) regression, were developed in the near-infrared. Conventional criteria such as the R2, the root mean square errors of cross validation (RMSECV), root mean square errors of estimation (RMSEE) as well as the number of PLS factors were considered for the selection of three pre-processing (vector normalization, minimum-maximum normalization and multiplicative scatter correction) methods. Spectra of pasta sample were treated with different mathematic pre-treatments before being used to build models between the spectral information and moisture content. The moisture content in pasta predicted by FT-NIR methods had very good correlation with their values determined via traditional methods (R2 = 0.983), which clearly indicated that FT-NIR methods could be used as an effective tool for rapid determination of moisture content in pasta. The best calibration model was developed with min-max normalization (MMN) spectral pre-processing (R2 = 0.9775). The MMN pre-processing method was found most suitable and the maximum coefficient of determination (R2) value of 0.9875 was obtained for the calibration model developed.

Keywords: FT-NIR, pasta, moisture determination, food engineering

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Authors: Nizar Ghali, Bernard Fortin, Guy Lacroix

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In Quebec, colonoscopies volumes have continued to rise in recent years in the absence of effective monitoring mechanism for the appropriateness and the quality of these exams. In 2010, November, Quebec Government introduced the colorectal cancer-screening program in the objective to control for volume and cost imperfection. This program is based on clinical standards and was initiated for first group of institutions. One year later, Government adds financial incentives for participants institutions. In this analysis, we want to assess for the causal effect of the two components of this program: clinical pathways and financial incentives. Especially we assess for the reform effect on healthcare quality and population health in the context that medical remuneration is not directly dependent on this additional funding offered by the program. We have data on admissions episodes and deaths for 8 years. We use multistate model analog to difference in difference approach to estimate reform effect on the transition probability between different states for each patient. Our results show that the reform reduced length of stay without deterioration in hospital mortality or readmission rate. In the other hand, the program contributed to decrease the hospitalization rate and a less invasive treatment approach for colorectal surgeries. This is a sign of healthcare quality and population health improvement. We demonstrate in this analysis that physicians’ behavior can be affected by both clinical standards and financial incentives even if offered to facilities.

Keywords: multi-state and multi-episode transition model, healthcare quality, length of stay, transition probability, difference in difference

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Authors: Amando A. Radomes Jr., LLoyd Jun Benjamin T. Embernatre, Cherssy Kaye F. Eviota, Krizia Allyn L. Nunez, Jose Thaddeus B. Roble III

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Sitio Kantaling is one of the 34 villages in Danao City, Cebu, in the central Philippines. As of 2015, the eight households in the mountainous village extending over 40 hectares of land area, including 12 hectares of arable land, are the source of over a fifth of the agricultural products that go into the city. Over the years, however, the local government had been concerned with the decline in agricultural productivity because increasing number of residents are migrating into the urban areas of the region to look for better employment opportunities. One of the major reasons for the agricultural productivity decline is underdeveloped irrigation infrastructure. The local government had partnered with the University of San Carlos to conduct research on developing an irrigation system that could sustainably meet both agricultural and household consumption needs. From a macro-perspective, a dynamic simulation model was developed to understand the long-term behavior of the status quo and proposed the system. Data on population, water supply and demand, household income, and urban migration were incorporated in the 20-year horizon model. The study also developed a smart irrigation system design. Instead of using electricity to pump water, a network of aqueducts with three main nodes had been designed and strategically located to take advantage of gravity to transport water from a spring. Simulation results showed that implementing a sustainable irrigation system would be able to significantly contribute to the socio-economic progress of the local community.

Keywords: agriculture, aqueduct, simulation, sustainable irrigation system

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Authors: Ahmed Borg, Elsa Aristodemou, Attia Attia

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Well control involves managing the circulating drilling fluid within the wells and avoiding kicks and blowouts as these can lead to losses in human life and drilling facilities. Current practices for good control incorporate predictions of pressure losses through computational models. Developing a realistic hydraulic model for a good control problem is a very complicated process due to the existence of a complex multiphase region, which usually contains a non-Newtonian drilling fluid and the miscibility of formation gas in drilling fluid. The current approaches assume an inaccurate flow fluid model within the well, which leads to incorrect pressure loss calculations. To overcome this problem, researchers have been considering the more complex two-phase fluid flow models. However, even these more sophisticated two-phase models are unsuitable for applications where pressure dynamics are important, such as in managed pressure drilling. This study aims to develop and implement new fluid flow models that take into consideration the miscibility of fluids as well as their non-Newtonian properties for enabling realistic kick treatment. furthermore, a corresponding numerical solution method is built with an enriched data bank. The research work considers and implements models that take into consideration the effect of two phases in kick treatment for well control in conventional drilling. In this work, a corresponding numerical solution method is built with an enriched data bank. Software STARCCM+ for the computational studies to study the important parameters to describe wellbore multiphase flow, the mass flow rate, volumetric fraction, and velocity of each phase. Results showed that based on the analysis of these simulation studies, a coarser full-scale model of the wellbore, including chemical modeling established. The focus of the investigations was put on the near drill bit section. This inflow area shows certain characteristics that are dominated by the inflow conditions of the gas as well as by the configuration of the mud stream entering the annulus. Without considering the gas solubility effect, the bottom hole pressure could be underestimated by 4.2%, while the bottom hole temperature is overestimated by 3.2%. and without considering the heat transfer effect, the bottom hole pressure could be overestimated by 11.4% under steady flow conditions. Besides, larger reservoir pressure leads to a larger gas fraction in the wellbore. However, reservoir pressure has a minor effect on the steady wellbore temperature. Also as choke pressure increases, less gas will exist in the annulus in the form of free gas.

Keywords: multiphase flow, well- control, STARCCM+, petroleum engineering and gas technology, computational fluid dynamic

Procedia PDF Downloads 103

Authors: Christian J. Kennett, Mark A. Baldwin

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As several companies are working towards returning American astronauts back to space on US-made spacecraft, NASA developed a human flight certification-by-test and analysis approach due to the cost-prohibitive nature of extensive testing. This process relies heavily on the quality of analytical models to accurately predict crew injury potential specific to each spacecraft and under dynamic environments not tested. As the prime contractor on the Orion spacecraft, Lockheed Martin was tasked with quantifying the correlation of analytical anthropometric test devices (ATDs), also known as crash test dummies, against test measurements under representative impact conditions. Multiple dynamic impact sled tests were conducted to characterize Hybrid III 5th ATD lumbar, head, and neck responses with and without a modified shuttle-era advanced crew escape suit (ACES) under simulated Orion landing and abort conditions. Each ATD was restrained via a 5-point harness in a mockup Orion seat fixed to a dynamic impact sled at the Wright Patterson Air Force Base (WPAFB) Biodynamics Laboratory in the horizontal impact accelerator (HIA). ATDs were subject to multiple impact magnitudes, half-sine pulse rise times, and XZ - ‘eyeballs out/down’ or Z-axis ‘eyeballs down’ orientations for landing or an X-axis ‘eyeballs in’ orientation for abort. Several helmet constraint devices were evaluated during suited testing. Unique finite element models (FEMs) were developed of the unsuited and suited sled test configurations using an analytical 5th ATD model developed by LSTC (Livermore, CA) and deformable representations of the seat, suit, helmet constraint countermeasures, and body restraints. Explicit FE analyses were conducted using the non-linear solver LS-DYNA. Head linear and rotational acceleration, head rotational velocity, upper neck force and moment, and lumbar force time histories were compared between test and analysis using the enhanced error assessment of response time histories (EEARTH) composite score index. The EEARTH rating paired with the correlation and analysis (CORA) corridor rating provided a composite ISO score that was used to asses model correlation accuracy. NASA occupant protection subject matter experts established an ISO score of 0.5 or greater as the minimum expectation for correlating analytical and experimental ATD responses. Unsuited 5th ATD head X, Z, and resultant linear accelerations, head Y rotational accelerations and velocities, neck X and Z forces, and lumbar Z forces all showed consistent ISO scores above 0.5 in the XZ impact orientation, regardless of peak g-level or rise time. Upper neck Y moments were near or above the 0.5 score for most of the XZ cases. Similar trends were found in the XZ and Z-axis suited tests despite the addition of several different countermeasures for restraining the helmet. For the X-axis ‘eyeballs in’ loading direction, only resultant head linear acceleration and lumbar Z-axis force produced ISO scores above 0.5 whether unsuited or suited. The analytical LSTC 5th ATD model showed good correlation across multiple head, neck, and lumbar responses in both the unsuited and suited configurations when loaded in the XZ ‘eyeballs out/down’ direction. Upper neck moments were consistently the most difficult to predict, regardless of impact direction or test configuration.

Keywords: impact biomechanics, manned spaceflight, model correlation, multi-axial loading

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Authors: Ebtisam Alqahtani

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The purpose of this study is to investigate academic staff perspectives on using Augmented Reality in teaching practice to support students learning remotely during the COVID pandemic. the study adopted the DTPB theoretical model to guide the identification of key potential factors that could motivate academic staff to use or not use AR in teaching practices. A mixing method design was adopted for a better understanding of the study problem. A survey was completed by 851 academic staff, and this was followed by interviews with 20 academic staff. Statistical analyses were used to assess the survey data, and thematic analysis was used to assess the interview data. The study finding indicates that 75% of academic staff were aware of AR as a pedagogical tool, and they agreed on the potential benefits of AR in teaching and learning practices. However, 36% of academic staff use it in teaching and learning practice, and most of them agree with most of the potential barriers to adopting AR in educational environments. In addition, the study results indicate that 91% of them are planning to use it in the future. The most important factors that motivated them to use it in the future are the COVID pandemic factor, hedonic motivation factor, and academic staff attitude factor. The perceptions of academic staff differed according to the universities they attended, the faculties they worked in, and their gender. This study offers further empirical support for the DTPB model, as well as recommendations to help higher education implement technology in its educational environment based on the findings of the study. It is unprecedented the study the necessity of the use of AR technologies in the time of Covid-19. Therefore, the contribution is both theoretical and practice

Keywords: higher education, academic staff, AR technology as pedological tools, teaching and learning practice, benefits of AR, barriers of adopting AR, and motivating factors to adopt AR

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Authors: Bashir Ahmad Amiri, Nsenda Lukumwena

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The developing economies are experiencing an unprecedented rate of urbanization, mainly the urbanization of poverty which is leading to sprawling of slums and informal settlement. Kabul, being the capital and primate city of Afghanistan is grossly encountered to the informal settlement where the majority of the people consider to be informal. Despite all efforts to upgrade and minimize the growth of these settlements, they are growing rapidly. Various interventions have been taken by the government and some international organizations from physical upgrading to urban renewal, but none of them have succeeded to solve the issue of informal settlement. The magnitude of the urbanization and the complexity of informal settlement in Kabul city, and the institutional and capital constraint of the government calls for integration and optimization of currently practiced strategies. This paper provides an overview of informal settlement formation and the conventional upgrading strategies in Kabul city to identify the dominant/successful practices and rationalize the conventional upgrading modes. For this purpose, Hothkhel has been selected as the case study, since it represents the same situation of major informal settlements of the city. Considering the existing potential and features of the Hothkhel and proposed land use by master plan this paper intends to find a suitable upgrading mode for the study area and finally to scale up the model for the city level upgrading. The result highlights that the informal settlements of Kabul city have high (re)development capacity for accepting the additional room without converting the available agricultural area to built-up. The result also indicates that the integrated multi-sector upgrading has the scale-up potential to increase the reach of beneficiaries and to ensure an inclusive and efficient urbanization.

Keywords: informal settlement, upgrading strategies, Kabul city, urban expansion, integrated multi-sector, scale-up

Procedia PDF Downloads 157

Authors: Pui Hong Chung, Wing Chi Ho, Jun Li, Cyrus Leung, Ek Yeoh

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Background: Cigarette smoking is associated with poor tuberculosis (TB) outcomes in terms of progression of active TB, relapse of TB and TB-related mortality, but the association with latent tuberculosis infection (LTBI) is unclear. The systematic review conducted aimed at studying the association between active smoking and LTBI, and likelihood of dose-response relationship. Methods: Two independent reviewers searched three electronic databases comprising PudMed, Medline by EBSCOHOST, ExcerptaMedica Database (EMBASE), from inception up to 31st Dec 2015 for studies reporting data on current smoking and the LTBI with tuberculin skin test (TST) or interferon-γ release assays (IGRAs) results, comparing the odds ratios (ORs) of outcome measure of TST or IGRAs among current smokers with 95% confidence intervals (CI). Results: Seven studies were identified, including six cross-sectional studies and one longitudinal cohort study. The outcome measures from three studies were in TST, three studies in IGRAs and one for both tests. For TST, OR ranging from 1.39 to 3.40 (95% CI) with all studies shown positive association between cigarette smoking and LTBI. For IGRAs, OR ranging from 0.47 to 1.89 (95% CI) with one study shown the negative association that might be related to impaired interferon-gamma production in immunosuppressive persons. One identified study demonstrated positive dose-response relationship in TST result. Conclusions: Cigarette smoking is likely to be a risk factor of LTBI. There is the important implication for TB and tobacco control program to halt TB by empowering public health policy. Further study is also needed to provide more evidence of the dose-response model/relationship.

Keywords: latent tuberculosis infection, systematic review, active smoking, model

Procedia PDF Downloads 242

Authors: Yuning Guan

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Under the background of rapid urbanization, mountainous counties need to break through mountain barriers for urban expansion due to undulating topography, resulting in ecological problems such as landscape fragmentation and reduced biodiversity. Green infrastructure networks are constructed to alleviate the contradiction between urban expansion and ecological protection, promoting the healthy and sustainable development of urban ecosystems. This study applies the MSPA model, the MCR model and Linkage Mapper Tools to identify eco-sources and eco-corridors in the Shapingba District of Chongqing and combined with landscape connectivity assessment and circuit theory to delineate the importance levels to extract ecological pinch point areas on the corridors. The results show that: (1) 20 ecological sources are identified, with a total area of 126.47 km², accounting for 31.88% of the study area, and showing a pattern of ‘one core, three corridors, multi-point distribution’. (2) 37 ecological corridors are formed in the area, with a total length of 62.52km, with a ‘more in the west, less in the east’ pattern. (3) 42 ecological pinch points are extracted, accounting for 25.85% of the length of the corridors, which are mainly distributed in the eastern new area. Accordingly, this study proposes optimization strategies for sub-area protection of ecological sources, grade-level construction of ecological corridors, and precise restoration of ecological pinch points.

Keywords: green infrastructure network, morphological spatial pattern, minimal cumulative resistance, mountainous counties, circuit theory, shapingba district

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Authors: Professor Jo Angouri, Polina Mesinioti, Chris Turner

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In order for a group of ad-hoc professional to perform as a team, a shared understanding of the problem at hand and an agreed action plan are necessary components. This is particularly significant in complex, time sensitive professional settings such as in trauma emergencies. In this context, team briefings prior to the patient arrival (pre-briefings) constitute a critical event for the performance of the team; they provide the necessary space for co-constructing a shared understanding of the situation through summarising information available to the team: yet the act of summarising is widely assumed in medical practice but not systematically researched. In the vast teamwork literature, terms such as ‘shared mental model’, ‘mental space’ and ‘cognate labelling’ are used extensively, and loosely, to denote the outcome of the summarising process, but how exactly this is done interactionally remains under researched. This paper reports on the forms and functions of pre-briefings in a major trauma centre in the UK. Taking an interactional approach, we draw on 30 simulated and real-life trauma emergencies (15 from each dataset) and zoom in on the use of pre-briefings, which we consider focal points in the management of trauma emergencies. We show how ad hoc teams negotiate sharedness of future orientation through summarising, synthesising information, and establishing common understanding of the situation. We illustrate the role, characteristics, and structure of pre-briefing sequences that have been evaluated as ‘efficient’ in our data and the impact (in)effective pre-briefings have on teamwork. Our work shows that the key roles in the event own the act of summarising and we problematise the implications for leadership in trauma emergencies. We close the paper with a model for pre-briefing and provide recommendations for clinical practice, arguing that effective pre-briefing practice is teachable.

Keywords: summarising, medical emergencies, interaction analysis, shared/mental models

Procedia PDF Downloads 77

Authors: Panagiota Katsikouli, William Hamilton Byrne, Thomas Gammeltoft-Hansen, Tijs Slaats

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An individual who demonstrates a well-founded fear of persecution or faces real risk of being subjected to torture is eligible for asylum. In Danish law, the exact legal thresholds reflect those established by international conventions, notably the 1951 Refugee Convention and the 1950 European Convention for Human Rights. These international treaties, however, remain largely silent when it comes to how states should assess asylum claims. As a result, national authorities are typically left to determine an individual’s legal eligibility on a narrow basis consisting of an oral testimony, which may itself be hampered by several factors, including imprecise language interpretation, insecurity or lacking trust towards the authorities among applicants. The leaky ground, on which authorities must assess their subjective perceptions of asylum applicants' credibility, questions whether, in all cases, adjudicators make the correct decision. Moreover, the subjective element in these assessments raises questions on whether individual asylum cases could be afflicted by implicit biases or stereotyping amongst adjudicators. In fact, recent studies have uncovered significant correlations between decision outcomes and the experience and gender of the assigned judge, as well as correlations between asylum outcomes and entirely external events such as weather and political elections. In this study, we analyze a publicly available dataset containing approximately 8,000 summaries of asylum cases, initially rejected, and re-tried by the Refugee Appeals Board (RAB) in Denmark. First, we look for variations in the recognition rates, with regards to a number of applicants’ features: their country of origin/nationality, their identified gender, their identified religion, their ethnicity, whether torture was mentioned in their case and if so, whether it was supported or not, and the year the applicant entered Denmark. In order to extract those features from the text summaries, as well as the final decision of the RAB, we applied natural language processing and regular expressions, adjusting for the Danish language. We observed interesting variations in recognition rates related to the applicants’ country of origin, ethnicity, year of entry and the support or not of torture claims, whenever those were made in the case. The appearance (or not) of significant variations in the recognition rates, does not necessarily imply (or not) bias in the decision-making progress. None of the considered features, with the exception maybe of the torture claims, should be decisive factors for an asylum seeker’s fate. We therefore investigate whether the decision can be predicted on the basis of these features, and consequently, whether biases are likely to exist in the decisionmaking progress. We employed a number of machine learning classifiers, and found that when using the applicant’s country of origin, religion, ethnicity and year of entry with a random forest classifier, or a decision tree, the prediction accuracy is as high as 82% and 85% respectively. tentially predictive properties with regards to the outcome of an asylum case. Our analysis and findings call for further investigation on the predictability of the outcome, on a larger dataset of 17,000 cases, which is undergoing.

Keywords: asylum adjudications, automated decision-making, machine learning, text mining

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Authors: Maria Del Carmen Millan-Linares, Ana Lemus Conejo, Rocio Toscano, Alvaro Villanueva, Francisco Millan, Justo Pedroche, Sergio Montserrat-De La Paz

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GPETAFLR (Glycine-Proline-Glutamine-Threonine-Alanine-Phenylalanine-Leucine-Arginine) is a peptide isolated from Lupinus angustifolius L. protein hydrolysate (LPH). Herein, the effect of this peptide was investigated in two different models of neuroinflammation: in the immortalized murine microglia cell line BV-2 and in a high-fat-diet-induced obesity mouse model. Methods and Results: Effects of GPETAFLR on neuroinflammation were evaluated by RT-qPCR, flow cytometry, and ELISA techniques. In BV-2 microglial cells, Lipopolysaccharides (LPS) enhanced the release of pro-inflammatory cytokines (TNF-α, IL-1β, and IL-6) whereas GPETAFLR decreased pro-inflammatory cytokine levels and increased the release of the anti-inflammatory cytokine IL-10 in BV2 microglial cells. M1 (CCR7 and iNOS) and M2 (Arg-1 and Ym-1) polarization markers results showed how the GPETAFLR octapeptide was able to decrease M1 polarization marker expression and increase the M2 polarization marker expression compared to LPS. Animal model results indicate that GPETAFLR has an immunomodulatory capacity, both decreasing pro-inflammatory cytokine IL-6 and increasing the anti-inflammatory cytokine IL-10 in brain tissue. Polarization markers in the brain tissue were also modulated by GPETAFLR that decreased the pro-inflammatory expression (M1) and increased the anti-inflammatory expression (M2). Conclusion: Our results suggest that GPETAFLR isolated from LPH has significant potential for management of neuroinflammatory conditions and offer benefits derived from the consumption of Lupinus angustifolius L. in the prevention of neuroinflammatory-related diseases.

Keywords: GPETAFLR peptide, BV-2 cell line, neuroinflammation, cytokines, high-fat-diet

Procedia PDF Downloads 131

Authors: Panagiotis Gkekas, Christos Sougles, Dionysios Kehagias, Dimitrios Tzovaras

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This paper presents the architecture of the “Intelligent cooperative integrated system for road safety and road infrastructure maintenance towards 2020” (ODOS2020) advanced infrastructure, which implements a number of cooperative ITS applications based on Internet of Things and Infrastructure-to-Vehicle (V2I) technologies with the purpose to enhance the active road safety level of vehicles through the provision of a fully automated V2I environment. The primary objective of the ODOS2020 project is to contribute to increased road safety but also to the optimization of time for maintenance of road infrastructure. The integrated technological solution presented in this paper addresses all types of vehicles and requires minimum vehicle equipment. Thus, the ODOS2020 comprises a low-cost solution, which is one of its main benefits. The system architecture includes an integrated notification system to transmit personalized information on road, traffic, and environmental conditions, in order for the drivers to receive real-time and reliable alerts concerning upcoming critical situations. The latter include potential dangers on the road, such as obstacles or road works ahead, extreme environmental conditions, etc., but also informative messages, such as information on upcoming tolls and their charging policies. At the core of the system architecture lies an integrated sensorial network embedded in special road infrastructures (strips) that constantly collect and transmit wirelessly information about passing vehicles’ identification, type, speed, moving direction and other traffic information in combination with environmental conditions and road wear monitoring and predictive maintenance data. Data collected from sensors is transmitted by roadside infrastructure, which supports a variety of communication technologies such as ITS-G5 (IEEE-802.11p) wireless network and Internet connectivity through cellular networks (3G, LTE). All information could be forwarded to both vehicles and Traffic Management Centers (TMC) operators, either directly through the ITS-G5 network, or to smart devices with Internet connectivity, through cloud-based services. Therefore, through its functionality, the system could send personalized notifications/information/warnings and recommendations for upcoming events to both road users and TMC operators. In the course of the ODOS2020 project pilot operation has been conducted to allow drivers of both C-ITS equipped and non-equipped vehicles to experience the provided added value services. For non-equipped vehicles, the provided information is transmitted to a smartphone application. Finally, the ODOS2020 system and infrastructure is appropriate for installation on both urban, rural, and highway environments. The paper presents the various parts of the system architecture and concludes by outlining the various challenges that had to be overcome during its design, development, and deployment in a real operational environment. Acknowledgments: Work presented in this paper was co-financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation (call RESEARCH–CREATE–INNOVATE) under contract no. Τ1EDK-03081 (project ODOS2020).

Keywords: infrastructure to vehicle, intelligent transportation systems, internet of things, road safety

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Authors: Yuxuan Tang

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Cognitive diagnostic assessment has received much attention in second language education, and assessment for it can provide pedagogically useful feedback for language learners. However, there is a lack of research on how students interpret and use cognitive diagnostic feedback. Thus the present study aims to adopt a mixed-method approach mainly to explore the relationship between the goal-orientation and students' response to cognitive diagnostic feedback. Almost 200 Chinese undergraduates from two universities in Xi'an, China, will be invited to do a cognitive diagnostic reading test, and each student will receive specialized cognitive diagnostic feedback, comprising of students' reading attributes mastery level generated by applying a well-selected cognitive diagnostic model, students' perceived reading ability assessed by a self-assessing questionnaire and students’ level position in the whole class. And a goal-orientation questionnaire and a self-generated questionnaire on the perception of feedback will be given to students the moment they receive feedback. In addition, interviews of students will be conducted on their future plans to see whether they have awareness of carrying out studying plans. The study aims to find a new perspective towards how students use and interpret cognitive diagnostic feedback in terms of their different goal-orientation (self-based, task-based, and other-based goals) by applying the newest goal orientation model, which is an important construct of motivation in psychology, seldom researched under language learning area. And the study is expected to provide evidence on how diagnostic feedback promotes students' learning under the educational belief of assessment for learning. Practically speaking, according to the personalized diagnostic feedback, students can take remedial self-learning more purposefully, and teachers can target students' weaknesses to adjust teaching methods and carry out tailored teaching.

Keywords: assessment for learning, cognitive diagnostic assessment, goal-orientation, personalized feedback

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