Search results for: PREDICT score

Authors: Selvaprakash Ramalingam, Rabi N. Sahoo, Dharmendra Saraswat, A. Kumar, Rajeev Ranjan, Joydeep Mukerjee, Viswanathan Chinnasamy, K. K. Chaturvedi, Sanjeev Kumar

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Rice is one of the most important staple food crops in the world. Among the various diseases that affect rice crops, rice blast is particularly significant, causing crop yield and economic losses. While the plant has defense mechanisms in place, such as chemical indicators (proteins, salicylic acid, jasmonic acid, ethylene, and azelaic acid) and resistance genes in certain varieties that can protect against diseases, susceptible varieties remain vulnerable to these fungal diseases. Early prediction of rice blast (RB) disease is crucial, but conventional techniques for early prediction are time-consuming and labor-intensive. Hyperspectral remote sensing techniques hold the potential to predict RB disease at its asymptomatic stage. In this study, we aimed to demonstrate the prediction of RB disease at the asymptomatic stage using non-imaging hyperspectral ASD spectroradiometer under controlled laboratory conditions. We applied statistical spectral discrimination theory to identify unknown spectra of M. Oryzae, the fungus responsible for rice blast disease. The infrared (IR) region was found to be significantly affected by RB disease. These changes may result in alterations in the absorption, reflection, or emission of infrared radiation by the affected plant tissues. Our research revealed that the protein spectrum in the IR region is impacted by RB disease. In our study, we identified strong correlations in the region (Amide group - I) around X 1064 nm and Y 1300 nm with the Lambda / Lambda derived spectra methods for protein detection. During the stages when the disease is developing, typically from day 3 to day 5, the plant's defense mechanisms are not as effective. This is especially true for the PB-1 variety of rice, which is highly susceptible to rice blast disease. Consequently, the proteins in the plant are adversely affected during this critical time. The spectral contour plot reveals the highly correlated spectral regions 1064 nm and Y 1300 nm associated with RB disease infection. Based on these spectral sensitivities, we developed new spectral disease indices for predicting different stages of disease emergence. The goal of this research is to lay the foundation for future UAV and satellite-based studies aimed at long-term monitoring of RB disease.

Keywords: rice blast, asymptomatic stage, spectral sensitivity, IR

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Authors: Shaleeza Sohail

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Self, and peer evaluations are some of the main components in almost all group assignments and projects in higher education institutes. These evaluations provide students an opportunity to better understand the learning outcomes of the assignment and/or project. A number of online systems have been developed for this purpose that provides automated assessment and feedback of students’ contribution in a group environment based on self and peer evaluations. All these systems lack a progressive aspect of these assessments and feedbacks which is the most crucial factor for ongoing improvement and life-long learning. In addition, a number of assignments and projects are designed in a manner that smaller or initial assessment components lead to a final assignment or project. In such cases, the evaluation and feedback may provide students an insight into their performance as a group member for a particular component after the submission. Ideally, it should also create an opportunity to improve for next assessment component as well. Self and Peer Progressive Assessment and Feedback System encourages students to perform better in the next assessment by providing a comparative analysis of the individual’s contribution score on an ongoing basis. Hence, the student sees the change in their own contribution scores during the complete project based on smaller assessment components. Self-Assessment Factor is calculated as an indicator of how close the self-perception of the student’s own contribution is to the perceived contribution of that student by other members of the group. Peer-Assessment Factor is calculated to compare the perception of one student’s contribution as compared to the average value of the group. Our system also provides a Group Coherence Factor which shows collectively how group members contribute to the final submission. This feedback is provided for students and teachers to visualize the consistency of members’ contribution perceived by its group members. Teachers can use these factors to judge the individual contributions of the group members in the combined tasks and allocate marks/grades accordingly. This factor is shown to students for all groups undertaking same assessment, so the group members can comparatively analyze the efficiency of their group as compared to other groups. Our System provides flexibility to the instructors for generating their own customized criteria for self and peer evaluations based on the requirements of the assignment. Students evaluate their own and other group members’ contributions on the scale from significantly higher to significantly lower. The preliminary testing of the prototype system is done with a set of predefined cases to explicitly show the relation of system feedback factors to the case studies. The results show that such progressive feedback to students can be used to motivate self-improvement and enhanced team skills. The comparative group coherence can promote a better understanding of the group dynamics in order to improve team unity and fair division of team tasks.

Keywords: effective group work, improvement of team skills, progressive feedback, self and peer assessment system

Procedia PDF Downloads 169

Authors: Ella Tyuryumina, Alexey Neznanov

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This study is an attempt to obtain reliable data on the natural history of breast cancer growth. We analyze the opportunities for using classical mathematical models (exponential and logistic tumor growth models, Gompertz and von Bertalanffy tumor growth models) to try to describe growth of the primary tumor and the secondary distant metastases of human breast cancer. The research aim is to improve predicting accuracy of breast cancer progression using an original mathematical model referred to CoMPaS and corresponding software. We are interested in: 1) modelling the whole natural history of the primary tumor and the secondary distant metastases; 2) developing adequate and precise CoMPaS which reflects relations between the primary tumor and the secondary distant metastases; 3) analyzing the CoMPaS scope of application; 4) implementing the model as a software tool. The foundation of the CoMPaS is the exponential tumor growth model, which is described by determinate nonlinear and linear equations. The CoMPaS corresponds to TNM classification. It allows to calculate different growth periods of the primary tumor and the secondary distant metastases: 1) ‘non-visible period’ for the primary tumor; 2) ‘non-visible period’ for the secondary distant metastases; 3) ‘visible period’ for the secondary distant metastases. The CoMPaS is validated on clinical data of 10-years and 15-years survival depending on the tumor stage and diameter of the primary tumor. The new predictive tool: 1) is a solid foundation to develop future studies of breast cancer growth models; 2) does not require any expensive diagnostic tests; 3) is the first predictor which makes forecast using only current patient data, the others are based on the additional statistical data. The CoMPaS model and predictive software: a) fit to clinical trials data; b) detect different growth periods of the primary tumor and the secondary distant metastases; c) make forecast of the period of the secondary distant metastases appearance; d) have higher average prediction accuracy than the other tools; e) can improve forecasts on survival of breast cancer and facilitate optimization of diagnostic tests. The following are calculated by CoMPaS: the number of doublings for ‘non-visible’ and ‘visible’ growth period of the secondary distant metastases; tumor volume doubling time (days) for ‘non-visible’ and ‘visible’ growth period of the secondary distant metastases. The CoMPaS enables, for the first time, to predict ‘whole natural history’ of the primary tumor and the secondary distant metastases growth on each stage (pT1, pT2, pT3, pT4) relying only on the primary tumor sizes. Summarizing: a) CoMPaS describes correctly the primary tumor growth of IA, IIA, IIB, IIIB (T1-4N0M0) stages without metastases in lymph nodes (N0); b) facilitates the understanding of the appearance period and inception of the secondary distant metastases.

Keywords: breast cancer, exponential growth model, mathematical model, metastases in lymph nodes, primary tumor, survival

Procedia PDF Downloads 324

Authors: Madhuka G. P. D. Udantha, Gihan V. Dias, Surangika Ranathunga

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Today Websites contain very interesting applications. But there are only few methodologies to analyze User navigations through the Websites and formulating if the Website is put to correct use. The web logs are only used if some major attack or malfunctioning occurs. Web Logs contain lot interesting dealings on users in the system. Analyzing web logs has become a challenge due to the huge log volume. Finding interesting patterns is not as easy as it is due to size, distribution and importance of minor details of each log. Web logs contain very important data of user and site which are not been put to good use. Retrieving interesting information from logs gives an idea of what the users need, group users according to their various needs and improve site to build an effective and efficient site. The model we built is able to detect attacks or malfunctioning of the system and anomaly detection. Logs will be more complex as volume of traffic and the size and complexity of web site grows. Unsupervised techniques are used in this solution which is fully automated. Expert knowledge is only used in validation. In our approach first clean and purify the logs to bring them to a common platform with a standard format and structure. After cleaning module web session builder is executed. It outputs two files, Web Sessions file and Indexed URLs file. The Indexed URLs file contains the list of URLs accessed and their indices. Web Sessions file lists down the indices of each web session. Then DBSCAN and EM Algorithms are used iteratively and recursively to get the best clustering results of the web sessions. Using homogeneity, completeness, V-measure, intra and inter cluster distance and silhouette coefficient as parameters these algorithms self-evaluate themselves to input better parametric values to run the algorithms. If a cluster is found to be too large then micro-clustering is used. Using Cluster Signature Module the clusters are annotated with a unique signature called finger-print. In this module each cluster is fed to Associative Rule Learning Module. If it outputs confidence and support as value 1 for an access sequence it would be a potential signature for the cluster. Then the access sequence occurrences are checked in other clusters. If it is found to be unique for the cluster considered then the cluster is annotated with the signature. These signatures are used in anomaly detection, prevent cyber attacks, real-time dashboards that visualize users, accessing web pages, predict actions of users and various other applications in Finance, University Websites, News and Media Websites etc.

Keywords: anomaly detection, clustering, pattern recognition, web sessions

Procedia PDF Downloads 262

Authors: Sophia Liang Zhou

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For investors, businesses, consumers, and governments, an accurate assessment of future housing prices is crucial to critical decisions in resource allocation, policy formation, and investment strategies. Previous studies are contradictory about macroeconomic determinants of housing price and largely focused on one or two areas using point prediction. This study aims to develop data-driven models to accurately predict future housing market trends in different markets. This work studied five different metropolitan areas representing different market trends and compared three-time lagging situations: no lag, 6-month lag, and 12-month lag. Linear regression (LR), random forest (RF), and artificial neural network (ANN) were employed to model the real estate price using datasets with S&P/Case-Shiller home price index and 12 demographic and macroeconomic features, such as gross domestic product (GDP), resident population, personal income, etc. in five metropolitan areas: Boston, Dallas, New York, Chicago, and San Francisco. The data from March 2005 to December 2018 were collected from the Federal Reserve Bank, FBI, and Freddie Mac. In the original data, some factors are monthly, some quarterly, and some yearly. Thus, two methods to compensate missing values, backfill or interpolation, were compared. The models were evaluated by accuracy, mean absolute error, and root mean square error. The LR and ANN models outperformed the RF model due to RF’s inherent limitations. Both ANN and LR methods generated predictive models with high accuracy ( > 95%). It was found that personal income, GDP, population, and measures of debt consistently appeared as the most important factors. It also showed that technique to compensate missing values in the dataset and implementation of time lag can have a significant influence on the model performance and require further investigation. The best performing models varied for each area, but the backfilled 12-month lag LR models and the interpolated no lag ANN models showed the best stable performance overall, with accuracies > 95% for each city. This study reveals the influence of input variables in different markets. It also provides evidence to support future studies to identify the optimal time lag and data imputing methods for establishing accurate predictive models.

Keywords: linear regression, random forest, artificial neural network, real estate price prediction

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Authors: Anders Schou Simonsen, Kim Sorensen, Thomas Condra

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Wet scrubbers are used for flue gas desulfurization by injecting water directly into the flue gas stream from a set of sprayers. The water droplets will flow freely inside the scrubber, and flow down along the scrubber walls as a thin wall film while reacting with the gas phase to remove SO₂. This complex multiphase phenomenon can be divided into three main contributions: the continuous gas phase, the liquid droplet phase, and the liquid wall film phase. This study proposes a complete model, where all three main contributions are taken into account and resolved using OpenFOAM for the continuous gas phase, and MATLAB for the liquid droplet and wall film phases. The 3D continuous gas phase is composed of five species: CO₂, H₂O, O₂, SO₂, and N₂, which are resolved along with momentum, energy, and turbulence. Source terms are present for four species, energy and momentum, which are affecting the steady-state solution. The liquid droplet phase experiences breakup, collisions, dynamics, internal chemistry, evaporation and condensation, species mass transfer, energy transfer and wall film interactions. Numerous sub-models have been implemented and coupled to realise the above-mentioned phenomena. The liquid wall film experiences impingement, acceleration, atomization, separation, internal chemistry, evaporation and condensation, species mass transfer, and energy transfer, which have all been resolved using numerous sub-models as well. The continuous gas phase has been coupled with the liquid phases using source terms by an approach, where the two software packages are couples using a link-structure. The complete CFD model has been verified using 16 experimental tests from an existing scrubber installation, where a gradient-based pattern search optimization algorithm has been used to tune numerous model parameters to match the experimental results. The CFD model needed to be fast for evaluation in order to apply this optimization routine, where approximately 1000 simulations were needed. The results show that the complex multiphase phenomena governing wet scrubbers can be resolved in a single model. The optimization routine was able to tune the model to accurately predict the performance of an existing installation. Furthermore, the study shows that a coupling between OpenFOAM and MATLAB is realizable, where the data and source term exchange increases the computational requirements by approximately 5%. This allows for exploiting the benefits of both software programs.

Keywords: desulfurization, discrete phase, scrubber, wall film

Procedia PDF Downloads 233

Authors: Li-hsing Shih, Wei-Jen Hsu

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Recently, the sale of electrical vehicles (EVs) has increased dramatically due to maturing technology development and decreasing cost. Governments of many countries have made regulations and policies in favor of EVs due to their long-term commitment to net zero carbon emissions. However, due to uncertain factors such as the future price of EVs, forecasting the future market share of EVs is a challenging subject for both the auto industry and local government. This study tries to forecast the market share of EVs using conjoint models and Monte Carlo simulation. The research is conducted in three phases. (1) A conjoint model is established to represent the customer preference structure on purchasing vehicles while five product attributes of both EV and internal combustion engine vehicles (ICEV) are selected. A questionnaire survey is conducted to collect responses from Taiwanese consumers and estimate the part-worth utility functions of all respondents. The resulting part-worth utility functions can be used to estimate the market share, assuming each respondent will purchase the product with the highest total utility. For example, attribute values of an ICEV and a competing EV are given respectively, two total utilities of the two vehicles of a respondent are calculated and then knowing his/her choice. Once the choices of all respondents are known, an estimate of market share can be obtained. (2) Among the attributes, future price is the key attribute that dominates consumers’ choice. This study adopts the assumption of a learning curve to predict the future price of EVs. Based on the learning curve method and past price data of EVs, a regression model is established and the probability distribution function of the price of EVs in 2030 is obtained. (3) Since the future price is a random variable from the results of phase 2, a Monte Carlo simulation is then conducted to simulate the choices of all respondents by using their part-worth utility functions. For instance, using one thousand generated future prices of an EV together with other forecasted attribute values of the EV and an ICEV, one thousand market shares can be obtained with a Monte Carlo simulation. The resulting probability distribution of the market share of EVs provides more information than a fixed number forecast, reflecting the uncertain nature of the future development of EVs. The research results can help the auto industry and local government make more appropriate decisions and future action plans.

Keywords: conjoint model, electrical vehicle, learning curve, Monte Carlo simulation

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Authors: Eunice Christabel Chukwu

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Introduction: Diabetes is a chronic medical condition which is characterized by high levels of glucose in the blood and urine; it is usually diagnosed by means of a glucose tolerance test (GTT). Diabetes can cause a range of health problems if left unmanaged, as it can lead to serious complications. It is essential to manage the condition effectively, particularly in the workplace where the impact on work productivity can be significant. This paper discusses the modelling of optimal control of diabetes in the workplace using a control theory approach. Background: Diabetes mellitus is a condition caused by too much glucose in the blood. Insulin, a hormone produced by the pancreas, controls the blood sugar level by regulating the production and storage of glucose. In diabetes, there may be a decrease in the body’s ability to respond to insulin or a decrease in insulin produced by the pancreas which will lead to abnormalities in the metabolism of carbohydrates, proteins, and fats. In addition to the health implications, the condition can also have a significant impact on work productivity, as employees with uncontrolled diabetes are at risk of absenteeism, reduced performance, and increased healthcare costs. While several interventions are available to manage diabetes, the most effective approach is to control blood glucose levels through a combination of lifestyle modifications and medication. Methodology: The control theory approach involves modelling the dynamics of the system and designing a controller that can regulate the system to achieve optimal performance. In the case of diabetes, the system dynamics can be modelled using a mathematical model that describes the relationship between insulin, glucose, and other variables. The controller can then be designed to regulate the glucose levels to maintain them within a healthy range. Results: The modelling of optimal control of diabetes in the workplace using a control theory approach has shown promising results. The model has been able to predict the optimal dose of insulin required to maintain glucose levels within a healthy range, taking into account the individual’s lifestyle, medication regimen, and other relevant factors. The approach has also been used to design interventions that can improve diabetes management in the workplace, such as regular glucose monitoring and education programs. Conclusion: The modelling of optimal control of diabetes in the workplace using a control theory approach has significant potential to improve diabetes management and work productivity. By using a mathematical model and a controller to regulate glucose levels, the approach can help individuals with diabetes to achieve optimal health outcomes while minimizing the impact of the condition on their work performance. Further research is needed to validate the model and develop interventions that can be implemented in the workplace.

Keywords: mathematical model, blood, insulin, pancreas, model, glucose

Procedia PDF Downloads 44

Authors: Sanjeev Sinha, Samir Saurav

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The rapid growth of urban areas in India has led to transportation challenges like traffic congestion and an increase in accidents. Despite efforts by state governments and local administrations to improve urban transport, the surge in private vehicles has worsened the situation. Patna, located in Bihar State, is an example of the trend of increasing reliance on private motor vehicles, resulting in vehicular congestion and emissions. The existing transportation infrastructure is inadequate to meet future travel demands, and there has been a notable increase in the share of private vehicles in the city. Additionally, there has been a surge in economic activities in the region, which has increased the demand for improved travel convenience and connectivity. To address these challenges, a study was conducted to assess the most suitable transit mode for the proposed transit corridor outlined in the Comprehensive Mobility Plan (CMP) for Patna. The study covered four stages: developing screening criteria, evaluating parameters for various alternatives, qualitative and quantitative evaluations of alternatives, and implementation options for the most viable alternative. The study suggests that a mass transit system such as a metro rail is necessary to enhance Patna's urban public transport system. The New Metro Policy 2017 outlines specific prerequisites for submitting a Metro Rail Project Proposal to the Ministry of Housing and Urban Affairs (MoHUA), including the preparation of a CMP, the formation of an Urban Metropolitan Transport Authority (UMTA), the creation of an Alternative Analysis Report, the development of a Detailed Project Report, a Multi-Modal Integration Plan, and a Transit-Oriented Development (TOD) Plan. In 2018, the Comprehensive Mobility Plan for Patna was prepared, setting the stage for the subsequent steps in the metro rail project proposal. The results indicated that from the screening and analysis of qualitative parameters for different alternative modes in Patna, it is inferred that the Metro Rail and Monorail score 82.25 and 70.50, respectively, on a scale of 100. Based on the initial analysis and alternative evaluation in the form of quantitative analysis, the Metro Rail System significantly outperformed the Monorail system. The Metro Rail System has a positive Economic Net Present Value (ENPV) at a 14% internal rate of return, while the Monorail has a negative value. In conclusion, the study recommends choosing metro rail over monorail for the proposed transit corridor in Patna. However, the lack of broad-based technical expertise may result in implementation delays and increased costs for monorail.

Keywords: comprehensive mobility plan, alternative analysis, mobility corridors, mass transit system

Procedia PDF Downloads 84

Authors: Alexander Heckman, Rohan Goswami, Zachi Attia, Paul Friedman, Peter Noseworthy, Demilade Adedinsewo, Pablo Moreno-Franco, Rickey Carter, Tathagat Narula

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Background: Postoperative atrial fibrillation (POAF) is associated with adverse health consequences, higher costs, and longer hospital stays. Utilizing existing predictive models that rely on clinical variables and circulating biomarkers, multiple societies have published recommendations on the treatment and prevention of POAF. Although reasonably practical, there is room for improvement and automation to help individualize treatment strategies and reduce associated complications. Methods and Results: In this retrospective cohort study of solid organ transplant recipients, we evaluated the diagnostic utility of a previously developed AI-based ECG prediction for silent AF on the development of POAF within 30 days of transplant. A total of 2261 non-cardiac transplant patients without a preexisting diagnosis of AF were found to have a 5.8% (133/2261) incidence of POAF. While there were no apparent sex differences in POAF incidence (5.8% males vs. 6.0% females, p=.80), there were differences by race and ethnicity (p<0.001 and 0.035, respectively). The incidence in white transplanted patients was 7.2% (117/1628), whereas the incidence in black patients was 1.4% (6/430). Lung transplant recipients had the highest incidence of postoperative AF (17.4%, 37/213), followed by liver (5.6%, 56/1002) and kidney (3.6%, 32/895) recipients. The AUROC in the sample was 0.62 (95% CI: 0.58-0.67). The relatively low discrimination may result from undiagnosed AF in the sample. In particular, 1,177 patients had at least 1 AI-ECG screen for AF pre-transplant above .10, a value slightly higher than the published threshold of 0.08. The incidence of POAF in the 1104 patients without an elevated prediction pre-transplant was lower (3.7% vs. 8.0%; p<0.001). While this supported the hypothesis that potentially undiagnosed AF may have contributed to the diagnosis of POAF, the utility of the existing AI-ECG screening algorithm remained modest. When the prediction for POAF was made using the first postoperative ECG in the sample without an elevated screen pre-transplant (n=1084 on account of n=20 missing postoperative ECG), the AUROC was 0.66 (95% CI: 0.57-0.75). While this discrimination is relatively low, at a threshold of 0.08, the AI-ECG algorithm had a 98% (95% CI: 97 – 99%) negative predictive value at a sensitivity of 66% (95% CI: 49-80%). Conclusions: This study's principal finding is that the incidence of POAF is rare, and a considerable fraction of the POAF cases may be latent and undiagnosed. The high negative predictive value of AI-ECG screening suggests utility for prioritizing monitoring and evaluation on transplant patients with a positive AI-ECG screening. Further development and refinement of a post-transplant-specific algorithm may be warranted further to enhance the diagnostic yield of the ECG-based screening.

Keywords: artificial intelligence, atrial fibrillation, cardiology, transplant, medicine, ECG, machine learning

Procedia PDF Downloads 102

Authors: Shailaja Thakur

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Managerial literature abounds with discussions on business strategies, success stories as well as cases of failure, which provide an indication of the parameters that should be considered in gauging the dynamism of an entrepreneur. Neoclassical economics has reduced entrepreneurship to a mere factor of production, driven solely by the profit motive, thus stripping him of all creativity and restricting his decision making to mechanical calculations. His ‘dynamism’ is gauged simply by the amount of profits he earns, marginalizing any discussion on the means that he employs to attain this objective. With theoretical backing, we have developed an Index of Entrepreneurial Dynamism (IED) giving weights to the different moves that the entrepreneur makes during his business journey. Strategies such as changes in product lines, markets and technology are gauged as very important (weighting of 4); while adaptations in terms of technology, raw materials used, upgradations in skill set are given a slightly lesser weight of 3. Use of formal market analysis, diversification in related products are considered moderately important (weight of 2) and being a first generation entrepreneur, employing managers and having plans to diversify are taken to be only slightly important business strategies (weight of 1). The maximum that an entrepreneur can score on this index is 53. A semi-structured questionnaire is employed to solicit the responses from the entrepreneurs on the various strategies that have been employed by them during the course of their business. Binary as well as graded responses are obtained, weighted and summed up to give the IED. This index was tested on about 150 tribal entrepreneurs in Mizoram, a state of India and was found to be highly effective in gauging their dynamism. This index has universal acceptability but is devoid of the socio-cultural context, which is very central to the success and performance of the entrepreneurs. We hypothesize that a society that respects risk taking takes failures in its stride, glorifies entrepreneurial role models, promotes merit and achievement is one that has a conducive socio- cultural environment for entrepreneurship. For obtaining an idea about the social acceptability, we are putting forth questions related to the social acceptability of business to another set of respondents from different walks of life- bureaucracy, academia, and other professional fields. Similar weighting technique is employed, and index is generated. This index is used for discounting the IED of the respondent entrepreneurs from that region/ society. This methodology is being tested for a sample of entrepreneurs from two very different socio- cultural milieus- a tribal society and a ‘mainstream’ society- with the hypothesis that the entrepreneurs in the tribal milieu might be showing a higher level of dynamism than their counterparts in other regions. An entrepreneur who scores high on IED and belongs to society and culture that holds entrepreneurship in high esteem, might not be in reality as dynamic as a person who shows similar dynamism in a relatively discouraging or even an outright hostile environment.

Keywords: index of entrepreneurial dynamism, India, social acceptability, tribal entrepreneurs

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Authors: Bessy Thuranira

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Buildings, as they are designed and used, may contribute to serious environmental problems because of excessive consumption of energy and other natural resources. Buildings in the informal settlements particularly, due to their unplanned physical structure and design, have significantly contributed the global energy problematic scenario typified by high-level inefficiencies. Energy used in buildings in Africa is estimated to be the highest of the total national electricity consumption. Over the last decade, assessments of energy consumption and efficiency/inefficiency has focused on formal and modern buildings. This study seeks to go off the beaten path, by focusing on energy use in informal settlements. Operationally, it sought to establish the contribution of informal buildings in the overall energy consumption in the city and the country at large. This study was carried out in Mukuru kwa Reuben informal settlement where there is distinct manifestation of different settlement morphologies within a small locality. The research narrowed down to three villages (Mombasa, Kosovo and Railway villages) within the settlement, that were representative of the different slum housing typologies. Due to the unpredictability nature and informality in slums, this study takes a multi-methodology approach. Detailed energy audits and measurements are carried out to predict total building consumption, and document building design and envelope, typology, materials and occupancy levels. Moreover, the study uses semi-structured interviews and to access energy supply, cost, access and consumption patterns. Observations and photographs are also used to shed more light on these parameters. The study reveals the high energy inefficiencies in slum buildings mainly related to sub-standard equipment and appliances, building design and settlement layout, poor access and utilization/consumption patterns of energy. The impacts of this inefficiency are high economic burden to the poor, high levels of pollution, lack of thermal comfort and emissions to the environment. The study highlights a set of urban planning and building design principles that can be used to retrofit slums into more energy efficient settlements. The study explores principles of responsive settlement layouts/plans and appropriate building designs that use the beneficial elements of nature to achieve natural lighting, natural ventilation, and solar control to create thermally comfortable, energy efficient, and environmentally responsive buildings/settlements. As energy efficiency in informal settlements is a relatively less explored area of efficiency, it requires further research and policy recommendations, for which this paper will set a background.

Keywords: energy efficiency, informal settlements, renewable energy, settlement layout

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Authors: Neeraj Vij, Christian Leber, Kenneth Schmidt

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Introduction: Total knee arthroplasty is a common procedure. It is well known that the biomechanics of the knee do not fully return to their normal state. Motion analysis has been used to study the biomechanics of the knee after total knee arthroplasty. The purpose of this scoping review is to summarize the current use of gait analysis in total knee arthroplasty and to identify the preoperative motion analysis parameters for which a systematic review aimed at determining the reliability and validity may be warranted. Materials and Methods: This IRB-exempt scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist strictly. Five search engines were searched for a total of 279 articles. Articles underwent a title and abstract screening process followed by full-text screening. Included articles were placed in the following sections: the role of gait analysis as a research tool for operative decisions, other research applications for motion analysis in total knee arthroplasty, gait analysis as a tool in predicting radiologic outcomes, gait analysis as a tool in predicting clinical outcomes. Results: Eleven articles studied gait analysis as a research tool in studying operative decisions. Motion analysis is currently used to study surgical approaches, surgical techniques, and implant choice. Five articles studied other research applications for motion analysis in total knee arthroplasty. Other research applications for motion analysis currently include studying the role of the unicompartmental knee arthroplasty and novel physical therapy protocols aimed at optimizing post-operative care. Two articles studied motion analysis as a tool for predicting radiographic outcomes. Preoperative gait analysis has identified parameters than can predict postoperative tibial component migration. 15 articles studied motion analysis in conjunction with clinical scores. Conclusions: There is a broad range of applications within the research domain of total knee arthroplasty. The potential application is likely larger. However, the current literature is limited by vague definitions of ‘gait analysis’ or ‘motion analysis’ and a limited number of articles with preoperative and postoperative functional and clinical measures. Knee adduction moment, knee adduction impulse, total knee range of motion, varus angle, cadence, stride length, and velocity have the potential for integration into composite clinical scores. A systematic review aimed at determining the validity, reliability, sensitivities, and specificities of these variables is warranted.

Keywords: motion analysis, joint replacement, patient-reported outcomes, knee surgery

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Authors: Vinay Kumar Vanjakula, Frank Adam, Nils Goseberg, Christian Windt

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When a structure is installed on a seabed, the presence of the structure will influence the flow field around it. The changes in the flow field include, formation of vortices, turbulence generation, waves or currents flow breaking and pressure differentials around the seabed sediment. These changes allow the local seabed sediment to be carried off and results in Scour (erosion). These are a threat to the structure's stability. In recent decades, rapid developments of research work and the knowledge of scour On fixed structures (bridges and Monopiles) in rivers and oceans has been carried out, and very limited research work on scour and liquefaction for gravity anchors, particularly for floating Tension Leg Platform (TLP) substructures. Due to its importance and need for enhancement of knowledge in scour and liquefaction around marine structures, the MarTERA funded a three-year (2020-2023) research program called NuLIMAS (Numerical Modeling of Liquefaction Around Marine Structures). It’s a group consists of European institutions (Universities, laboratories, and consulting companies). The objective of this study is to build a numerical model that replicates the reality, which indeed helps to simulate (predict) underwater flow conditions and to study different marine scour and Liquefication situations. It helps to design a heavyweight anchor for the TLP substructure and to minimize the time and expenditure on experiments. And also, the achieved results and the numerical model will be a basis for the development of other design and concepts For marine structures. The Computational Fluid Dynamics (CFD) numerical model will build in OpenFOAM. A conceptual design of heavyweight anchor for TLP substructure is designed through taking considerations of available state-of-the-art knowledge on scour and Liquefication concepts and references to Previous existing designs. These conceptual designs are validated with the available similar experimental benchmark data and also with the CFD numerical benchmark standards (CFD quality assurance study). CFD optimization model/tool is designed as to minimize the effect of fluid flow, scour, and Liquefication. A parameterized model is also developed to automate the calculation process to reduce user interactions. The parameters such as anchor Lowering Process, flow optimized outer contours, seabed interaction study, and FSSI (Fluid-Structure-Seabed Interactions) are investigated and used to carve the model as to build an optimized anchor.

Keywords: gravity anchor, liquefaction, scour, computational fluid dynamics

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Authors: Jisun Mo, Paola Boarin

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The impact of common buildings on climate and environment has prompted people to get involved in the green building standards aimed at implementing rating tools or certifications. Thus, green building rating systems were introduced to the construction industry, and the demand for certified green buildings has increased gradually and succeeded considerably in enhancing people’s environmental awareness. However, the existing certification process has been unsatisfactory in attracting stakeholders and/or professionals who are actively engaged in adopting a rating system. It is because they have faced recurring barriers regarding limited information in understanding the rating process, time-consuming procedures and higher costs, which have a direct influence on pursuing green building rating systems. To promote the achievement of green building certifications within the building industry more successfully, this paper aims at designing a Pre-Assessment Tool (PAT) framework that can help stakeholders and/or professionals engaged in the construction industry to clarify their basic knowledge, timeframe and extra costs needed to activate a green building certification. First, taking the first steps towards the rating tool seems to be complicated because of upfront commitment to understanding the overall rating procedure is required. This conceptual PAT framework can increase basic knowledge of the rating tool and the certification process, mainly in terms of all resources or information of each credit requirements. Second, the assessment process of rating tools is generally known as a “lengthy and time-consuming system”, contributing to unenthusiastic reactions concerning green building projects. The proposed framework can predict the timeframe needed to identify how long it will take for a green project to process each credit requirement and the documentation required from the beginning of the certification process to final approval. Finally, most people often have the initial perception that pursuing green building certification costs more than constructing a non-green building, which makes it more difficult to execute rating tools. To overcome this issue, this PAT will help users to estimate the extra expenses such as certification fees and third-party contributions based on the track of the amount of time it takes to implement the rating tool throughout all the related stages. Also, it can prevent unexpected or hidden costs occurring in the process of assessment. Therefore, this proposed PAT framework can be recommended as an effective method to support the decision-making of inexperienced users and play an important role in promoting green building certification.

Keywords: green building rating tools, Pre-Occupancy Evaluation (PrOE), client’s decision-making, certification

Procedia PDF Downloads 223

Authors: Abdulaziz Alakeel, Roaa Alamri, Abdulrahman Alomair, Mohammed Fouda

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Introduction: Ibrutinib is a selective, potent, and irreversible small-molecule inhibitor of Bruton's tyrosine kinase (BTK). It forms a covalent bond with a cysteine residue (CYS-481) at the active site of Btk, leading to inhibition of Btk enzymatic activity. The drug is indicated to treat certain type of cancers such as mantle cell lymphoma (MCL), chronic lymphocytic leukaemia and Waldenström's macroglobulinaemia (WM). Cardiac failure is a condition referred to inability of heart muscle to pump adequate blood to human body organs. There are multiple types of cardiac failure including left and right-sided heart failure, systolic and diastolic heart failures. The aim of this review is to evaluate the risk of cardiac failure associated with the use of ibrutinib and to suggest regulatory recommendations if required. Methodology: Signal Detection team at the National Pharmacovigilance Center (NPC) of Saudi Food and Drug Authority (SFDA) performed a comprehensive signal review using its national database as well as the World Health Organization (WHO) database (VigiBase), to retrieve related information for assessing the causality between cardiac failure and ibrutinib. We used the WHO- Uppsala Monitoring Centre (UMC) criteria as standard for assessing the causality of the reported cases. Results: Case Review: The number of resulted cases for the combined drug/adverse drug reaction are 212 global ICSRs as of July 2020. The reviewers have selected and assessed the causality for the well-documented ICSRs with completeness scores of 0.9 and above (35 ICSRs); the value 1.0 presents the highest score for best-written ICSRs. Among the reviewed cases, more than half of them provides supportive association (four probable and 15 possible cases). Data Mining: The disproportionality of the observed and the expected reporting rate for drug/adverse drug reaction pair is estimated using information component (IC), a tool developed by WHO-UMC to measure the reporting ratio. Positive IC reflects higher statistical association while negative values indicates less statistical association, considering the null value equal to zero. The results of (IC=1.5) revealed a positive statistical association for the drug/ADR combination, which means “Ibrutinib” with “Cardiac Failure” have been observed more than expected when compared to other medications available in WHO database. Conclusion: Health regulators and health care professionals must be aware for the potential risk of cardiac failure associated with ibrutinib and the monitoring of any signs or symptoms in treated patients is essential. The weighted cumulative evidences identified from causality assessment of the reported cases and data mining are sufficient to support a causal association between ibrutinib and cardiac failure.

Keywords: cardiac failure, drug safety, ibrutinib, pharmacovigilance, signal detection

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Authors: Ian Kingsbury

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The rapid expansion of charter schools (public schools that receive government but do not face the same regulations as traditional public schools) over the preceding two decades has raised concerns over the potential for graft and fraud. These concerns are largely justified: Incidents of financial crime and mismanagement are not unheard of, and the charter sector has become a darling of hedge fund managers. In response, several states have strengthened their charter school regulatory regimes. Imposing regulations and attempting to increase accountability seem like sensible measures, and perhaps they are necessary. However, increased regulation may come at the cost of imposing barriers to entry. Specifically, increased regulation often entails evidence for a high likelihood of fiscal solvency. That should theoretically entail access to capital in the short-term, which may systematically preclude Black or Hispanic applicants from opening charter schools. Moreover, increased regulation necessarily entails more red tape. The institutional wherewithal and the number of hours required to complete an application to open a charter school might favor those who have partnered with an education service provider, specifically a charter management organization (CMO) or education management organization (EMO). These potential barriers to entry pose a significant policy concern. Just as policymakers hope to increase the share of minority teachers and principals, they should sensibly care whether individuals who open charter schools look like the students in that school. Moreover, they might be concerned if successful applications in states with stringent regulations are overwhelmingly affiliated with education service providers. One of the original missions of charter schools was to serve as a laboratory of innovation. Approving only those applications affiliated with education service providers (and in effect establishing a parallel network of schools rather than a diverse marketplace of schools) undermines that mission. Data and methods: The analysis examines more than 2,000 charter school applications from 15 states. It compares the outcomes of applications from states with a strong regulatory environment (those with high scores) from NACSA-the National Association of Charter School Authorizers- to applications from states with a weak regulatory environment (those with a low NACSA score). If the hypothesis is correct, applicants not affiliated with an ESP are more likely to be rejected in high-regulation states compared to those affiliated with an ESP, and minority candidates not affiliated with an education service provider (ESP) are particularly likely to be rejected. Initial returns indicate that the hypothesis holds. More applications in low NASCA-scoring Arizona come from individuals not associated with an ESP, and those individuals are as likely to be accepted as those affiliated with an ESP. On the other hand, applicants in high-NACSA scoring Indiana and Ohio are more than 20 percentage points more likely to be accepted if they are affiliated with an ESP, and the effect is particularly pronounced for minority candidates. These findings should spur policymakers to consider the drawbacks of charter school accountability and consider accountability regimes that do not impose barriers to entry.

Keywords: accountability, barriers to entry, charter schools, choice

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Authors: Chandanee Wasana Kalansooriya

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Dietary behaviour is a crucial factor affecting diabetes control. With increasing rates of diabetes prevalence in Asian countries, examining their dietary patterns, which are largely based on rice, is timely required. It has been identified that higher consumption of some rice varieties is associated with increased risk of type 2 diabetes. Although diabetes patients are advised to consume healthier rice varieties, which contains low glycemic, several conditions, one of which food insecurity, make them difficult to preserve those healthy dietary guidelines. Hence this study tries to investigate how food security affects on making right decisions of rice consumption within diabetes affected households using a sample from Sri Lanka, a country which rice considered as the staple food and records the highest diabetes prevalence rate in South Asia. The study uses data from the Household Income and Expenditure Survey 2016, a nationally representative sample conducted by the Department of Census and Statistics, Sri Lanka. The survey used a two-stage stratified sampling method to cover different sectors and districts of the country and collected micro-data on demographics, health, income and expenditures of different categories. The study uses data from 2547 households which consist of one or more diabetes patients, based on the self-recorded health status. The Household Dietary Diversity Score (HDDS), which constructed based on twelve food groups, is used to measure the level of food security. Rice is categorized into three groups according to their Glycemic Index (GI), high GI, medium GI and low GI, and the likelihood and impact made by food security on each rice consumption categories are estimated using a Two-part Model. The shares of each rice categories out of total rice consumption is considered as the dependent variable to exclude the endogeneity issue between rice consumption and the HDDS. The results indicate that the consumption of medium GI rice is likely to increase with the increasing household food security, but low GI varieties are not. Households in rural and estate sectors are less likely and Tamil ethnic group is more likely to consume low GI rice varieties. Further, an increase in food security significantly decreases the consumption share of low GI rice, while it increases the share of medium GI varieties. The consumption share of low GI rice is largely affected by the ethnic variability. The effects of food security on the likelihood of consuming high GI rice varieties and changing its shares are statistically insignificant. Accordingly, the study concludes that a higher level of food security does not ensure diabetes patients are consuming healthy rice varieties or reducing consumption of unhealthy varieties. Hence policy attention must be directed towards educating people for making healthy dietary choices. Further, the study provides a room for further studies as it reveals considerable ethnic and sectorial differences in making healthy dietary decisions.

Keywords: diabetes, food security, glycemic index, rice consumption

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Authors: Pamela K. Coke, Heidi Frederiksen, Ann Sebald

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Over the past three years, the researchers have been tracking a rise in the number of teacher education candidates leaving the field before completing their university’s educator preparation program. At their institution, this rise is most pronounced in English Education. The purpose of this qualitative research study is to understand English Education teacher candidates' expectations in becoming prepared educators at each phase of their four phase teacher education program at one institution of higher education in the United States. Research questions include: To what extent do we find differences in teacher candidates' expectations of their teacher training program and student teaching experiences based upon undergraduate and graduate programs? Why do (or do not) teacher candidates persist in their teacher training program and student teaching experiences? How do dispositions develop through the course of the teacher training program? What supports do teacher candidates self-identify as needing at each phase of the teacher training program? Based upon participant interviews at each phase of the teacher education program, the researchers, all teacher educators, examine the extent to which English Education students feel prepared to student teach, focusing on preparation, persistence, and dispositions. The Colorado State University Center for Educator Preparation (CEP) provides students with information about teaching dispositions, or desired professional behaviors, throughout their education program. CEP focuses these dispositions around nine categories: Professional Behaviors, Initiative and Dependability, Tact and Judgment, Ethical Behavior and Integrity, Collegiality and Responsiveness, Effective Communicator, Desire to Improve Own Performance, Culturally Responsive, and Commitment to the Profession. Currently, in the first phase of a four phase study, initial results indicate participants expect their greatest joys will be working with and learning from students. They anticipate their greatest challenges will involve discipline and confidence. They predict they will persist in their program because they believe the country needs well-prepared teachers and they have a commitment to their professional growth. None of the participants thus far could imagine why they would leave the program. With regard to strongest and weakest dispositions, results are mixed. Some participants see Tact and Judgment as their strongest disposition; others see it as their weakest. All participants stated mentoring is a necessary support at every phase of the teacher preparation process. This study informs the way teacher educators train and evaluate teacher candidates, and has implications for the frequency and types of feedback students receive from mentors and supervisors. This research contributes to existing work on teacher retention, candidate persistence, and dispositional development.

Keywords: English education, dispositions, persistence, teacher preparation

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Authors: Danni Cheng

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Background: Preclinical and epidemiological studies have reported potential protective effects of low-density lipoprotein cholesterol (LDL-C) lowering drugs on head and neck squamous cell cancer (HNSCC) survival, but the causality was not consistent. Genetic variants associated with LDL-C lowering drug targets can predict the effects of their therapeutic inhibition on disease outcomes. Objective: We aimed to evaluate the causal association of genetically proxied cholesterol-lowering drug targets and circulating lipid traits with cancer survival in HNSCC patients stratified by human papillomavirus (HPV) status using two-sample Mendelian randomization (MR) analyses. Method: Single-nucleotide polymorphisms (SNPs) in gene region of LDL-C lowering drug targets (HMGCR, NPC1L1, CETP, PCSK9, and LDLR) associated with LDL-C levels in genome-wide association study (GWAS) from the Global Lipids Genetics Consortium (GLGC) were used to proxy LDL-C lowering drug action. SNPs proxy circulating lipids (LDL-C, HDL-C, total cholesterol, triglycerides, apoprotein A and apoprotein B) were also derived from the GLGC data. Genetic associations of these SNPs and cancer survivals were derived from 1,120 HPV-positive oropharyngeal squamous cell carcinoma (OPSCC) and 2,570 non-HPV-driven HNSCC patients in VOYAGER program. We estimated the causal associations of LDL-C lowering drugs and circulating lipids with HNSCC survival using the inverse-variance weighted method. Results: Genetically proxied HMGCR inhibition was significantly associated with worse overall survival (OS) in non-HPV-drive HNSCC patients (inverse variance-weighted hazard ratio (HR IVW), 2.64[95%CI,1.28-5.43]; P = 0.01) but better OS in HPV-positive OPSCC patients (HR IVW,0.11[95%CI,0.02-0.56]; P = 0.01). Estimates for NPC1L1 were strongly associated with worse OS in both total HNSCC (HR IVW,4.17[95%CI,1.06-16.36]; P = 0.04) and non-HPV-driven HNSCC patients (HR IVW,7.33[95%CI,1.63-32.97]; P = 0.01). A similar result was found that genetically proxied PSCK9 inhibitors were significantly associated with poor OS in non-HPV-driven HNSCC (HR IVW,1.56[95%CI,1.02 to 2.39]). Conclusion: Genetically proxied long-term HMGCR inhibition was significantly associated with decreased OS in non-HPV-driven HNSCC and increased OS in HPV-positive OPSCC. While genetically proxied NPC1L1 and PCSK9 had associations with worse OS in total and non-HPV-driven HNSCC patients. Further research is needed to understand whether these drugs have consistent associations with head and neck tumor outcomes.

Keywords: Mendelian randomization analysis, head and neck cancer, cancer survival, cholesterol, statin

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Authors: Fei Wang, Jie Meng, Qingxiang Meng

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China is rich in straw and by-product resources, whose utilization has always been a hot topic. The objective of this study was to investigate the effect of feeding soybean straw and wine distiller’s grain as a replacement for corn stover on performance of beef cattle. Sixty Simmental×local crossbred bulls averaging 12 months old and 335.7 ± 39.1 kg of body weight (BW) were randomly assigned into four groups (15 animals per group) and allocated to a diet with 40% maize stover (MSD), a diet with 40% wrapping package maize silage (PMSD), a diet with 12% soybean straw plus 28% maize stover (SSD) and a diet with 12% wine distiller’s grain plus 28% maize stover (WDD). Bulls were fed ad libitum an TMR consisting of 36.0% maize, 12.5% of DDGS, 5.0% of cottonseed meal, 4.0% of soybean meal and 40.0% of by-product as described above. Treatment period lasted for 22 weeks, consisting of 1 week of dietary adaptation. The results showed that dry matter intake (DMI) was significantly higher (P < 0.01) for PMSD group than MSD and SSD groups during 0-7 week and 8-14week, and PMSD and WDD groups had higher (P < 0.05) DMI values than MSD and SSD groups during the whole period. Average daily gain (ADG) values were 1.56, 1.72, 1.68 and 1.58 kg for MSD, PMSD, SSD and WDD groups respectively, although the differences were not significant (P > 0.05). The value of blood sugar concentration was significantly higher (P < 0.01) for MSD group than WDD group, and the blood urea nitrogen concentration of SSD group was lower (P < 0.05) than MSD and WDD groups. No significant difference (P > 0.05) of serum total cholesterol, triglycerides or total protein content was observed among the different groups. Ten bulls with similar body weight were selected at the end of feeding trial and slaughtered for measurement of slaughtering performance, carcass quality and meat chemical composition. SSD group had significantly lower (P < 0.05) shear force value and cooking loss than MSD and PMSD groups. The pH values of MSD and SSD groups were lower (P < 0.05) than PMSD and WDD groups. WDD group had a higher fat color brightness (L*) value than PMSD and SSD groups. There were no significant differences in dressing percentage, meat percentage, top grade meat weight, ribeye area, marbling score, meat color and meat chemical compositions among different dietary treatments. Based on these results, the packed maize stover silage showed a potential of improving the average daily gain and feed intake of beef cattle. Soybean straw had a significant effect on improving the tenderness and reducing cooking loss of beef. In general, soybean straw and packed maize stover silage would be beneficial to nitrogen deposition and showed a potential to substitute maize stover in beef cattle diets.

Keywords: beef cattle, by-products, carcass quality, growth performance

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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

Procedia PDF Downloads 62

Authors: Pedro Gomes, Luís Sá Castelo, António Lopes, Marta Maio, Pedro Mota, Adélia Avelar, António Marques Dias

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Introduction: Total Knee Arthroplasty (TKA) has contributed to improvement of patient`s quality of life, although it has been associated with some complications including component loosening and polyethylene wear. To prevent these complications various fixation techniques have been employed. Hybrid TKA with cemented tibial and cementless femoral components have shown favourable outcomes, although it still lack of consensus in the literature. Objectives: To evaluate the clinical and radiographic results of hybrid versus cemented TKA with an average 5 years follow-up and analyse the survival rates. Methods: A retrospective study of 125 TKAs performed in 92 patients at our institution, between 2006 to 2008, with a minimum follow-up of 2 years. The same prosthesis was used in all knees. Hybrid TKA fixation was performed in 96 knees, with a mean follow-up of 4,8±1,7 years (range, 2–8,3 years) and 29 TKAs received fully cemented fixation with a mean follow-up of 4,9±1,9 years (range, 2-8,3 years). Selection for hybrid fixation was nonrandomized and based on femoral component fit. The Oxford Knee Score (OKS 0-48) was evaluated for clinical assessment and Knee Society Roentgenographic Evaluation Scoring System was used for radiographic outcome. The survival rate was calculated using the Kaplan-Meier method, with failures defined as revision of either the tibial or femoral component for aseptic failures and all-causes (aseptic and infection). Analysis of survivorship data was performed using the log-rank test. SPSS (v22) was the computer program used for statistical analysis. Results: The hybrid group consisted of 72 females (75%) and 24 males (25%), with mean age 64±7 years (range, 50-78 years). The preoperative diagnosis was osteoarthritis (OA) in 94 knees (98%), rheumatoid arthritis (RA) in 1 knee (1%) and Posttraumatic arthritis (PTA) in 1 Knee (1%). The fully cemented group consisted of 23 females (79%) and 6 males (21%), with mean age 65±7 years (range, 47-78 years). The preoperative diagnosis was OA in 27 knees (93%), PTA in 2 knees (7%). The Oxford Knee Scores were similar between the 2 groups (hybrid 40,3±2,8 versus cemented 40,2±3). The percentage of radiolucencies seen on the femoral side was slightly higher in the cemented group 20,7% than the hybrid group 11,5% p0.223. In the cemented group there were significantly more Zone 4 radiolucencies compared to the hybrid group (13,8% versus 2,1% p0,026). Revisions for all causes were performed in 4 of the 96 hybrid TKAs (4,2%) and 1 of the 29 cemented TKAs (3,5%). The reason for revision was aseptic loosening in 3 hybrid TKAs and 1 of the cemented TKAs. Revision was performed for infection in 1 hybrid TKA. The hybrid group demonstrated a 7 years survival rate of 93% for all-cause failures and 94% for aseptic loosening. No significant difference in survivorship was seen between the groups for all-cause failures or aseptic failures. Conclusions: Hybrid TKA yields similar intermediate-term results and survival rates as fully cemented total knee arthroplasty and remains a viable option in knee joint replacement surgery.

Keywords: hybrid, survival rate, total knee arthroplasty, orthopaedic surgery

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Authors: Dangut Maren David, Skaf Zakwan

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Adequate monitoring of vehicle component in other to obtain high uptime is the goal of predictive maintenance, the major challenge faced by businesses in industries is the significant cost associated with a delay in service delivery due to system downtime. Most of those businesses are interested in predicting those problems and proactively prevent them in advance before it occurs, which is the core advantage of Prognostic Health Management (PHM) application. The recent emergence of industry 4.0 or industrial internet of things (IIoT) has led to the need for monitoring systems activities and enhancing system-to-system or component-to- component interactions, this has resulted to a large generation of data known as big data. Analysis of big data represents an increasingly important, however, due to complexity inherently in the dataset such as imbalance classification problems, it becomes extremely difficult to build a model with accurate high precision. Data-driven predictive modeling for condition-based maintenance (CBM) has recently drowned research interest with growing attention to both academics and industries. The large data generated from industrial process inherently comes with a different degree of complexity which posed a challenge for analytics. Thus, imbalance classification problem exists perversely in industrial datasets which can affect the performance of learning algorithms yielding to poor classifier accuracy in model development. Misclassification of faults can result in unplanned breakdown leading economic loss. In this paper, an advanced approach for handling imbalance classification problem is proposed and then a prognostic model for predicting aircraft component replacement is developed to predict component replacement in advanced by exploring aircraft historical data, the approached is based on hybrid ensemble-based method which improves the prediction of the minority class during learning, we also investigate the impact of our approach on multiclass imbalance problem. We validate the feasibility and effectiveness in terms of the performance of our approach using real-world aircraft operation and maintenance datasets, which spans over 7 years. Our approach shows better performance compared to other similar approaches. We also validate our approach strength for handling multiclass imbalanced dataset, our results also show good performance compared to other based classifiers.

Keywords: prognostics, data-driven, imbalance classification, deep learning

Procedia PDF Downloads 151

Authors: Regis Pochon, Nicolas Stefaniak, Veronique Baltazart, Pamela Gobin

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Anxiety is known to have an impact on working memory. In reasoning or memory tasks, individuals with anxiety tend to show longer response times and poorer performance. Furthermore, there is a memory bias for negative information in anxiety. Given the crucial role of working memory in lexical learning, anxious students may encounter more difficulties in learning to read and spell. Anxiety could even affect an earlier learning, that is the activation of phonological representations, which are decisive for the learning of reading and writing. The aim of this study is to compare the access to phonological representations of beginning readers and writers according to their level of anxiety, using an auditory lexical decision task. Eighty students of 6- to 9-years-old completed the French version of the Revised Children's Manifest Anxiety Scale and were then divided into four anxiety groups according to their total score (Low, Median-Low, Median-High and High). Two set of eighty-one stimuli (words and non-words) have been auditory presented to these students by means of a laptop computer. Stimuli words were selected according to their emotional valence (positive, negative, neutral). Students had to decide as quickly and accurately as possible whether the presented stimulus was a real word or not (lexical decision). Response times and accuracy were recorded automatically on each trial. It was anticipated a) longer response times for the Median-High and High anxiety groups in comparison with the two others groups, b) faster response times for negative-valence words in comparison with positive and neutral-valence words only for the Median-High and High anxiety groups, c) lower response accuracy for Median-High and High anxiety groups in comparison with the two others groups, d) better response accuracy for negative-valence words in comparison with positive and neutral-valence words only for the Median-High and High anxiety groups. Concerning the response times, our results showed no difference between the four groups. Furthermore, inside each group, the average response times was very close regardless the emotional valence. Otherwise, group differences appear when considering the error rates. Median-High and High anxiety groups made significantly more errors in lexical decision than Median-Low and Low groups. Better response accuracy, however, is not found for negative-valence words in comparison with positive and neutral-valence words in the Median-High and High anxiety groups. Thus, these results showed a lower response accuracy for above-median anxiety groups than below-median groups but without specificity for the negative-valence words. This study suggests that anxiety can negatively impact the lexical processing in young students. Although the lexical processing speed seems preserved, the accuracy of this processing may be altered in students with moderate or high level of anxiety. This finding has important implication for the prevention of reading and spelling difficulties. Indeed, during these learnings, if anxiety affects the access to phonological representations, anxious students could be disturbed when they have to match phonological representations with new orthographic representations, because of less efficient lexical representations. This study should be continued in order to precise the impact of anxiety on basic school learning.

Keywords: anxiety, emotional valence, childhood, lexical access

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Authors: Khatija Bahdur, Duane Dell’Oca

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There is limited information on how altitude impacts performance in a team sport. Most altitude research in football has been conducted at high elevation ( > 2500m), resulting in a chasm of understanding whether low to moderate altitude affects performance. The South African Premier Soccer League (PSL) fixtures entail matches played at altitudes from sea level to 1700m above mean sea level. Despite coaches highlighting the effect of altitude on performance outcomes in matches, further research is needed to establish whether altitude does impact match results. Greater insight into if and how altitude impacts performance in the PSL will assist coaches in deciding if and how to incorporate altitude in their planning. The purpose of this study is to fill in this gap through the use of a retrospective analysis of PSL matches. This quantitative study is based on a descriptive analysis of 181 PSL matches involving one team based at sea-level, taking place over a period of thirteen seasons. The following data were obtained: altitude at which the match was played, match result, the timing of goals, and timing of substitutions. The altitude was classified in 2 ways: inland ( > 500m) and coastal ( < 500m) and also further subdivided into narrower categories ( < 500m, 500-1000m, 1000-1300m; 1300-1500m, > 1500m). The analysis included a 2-sample t-test to determine differences in total goals scored and timing of goals for inland and coastal matches and the chi-square test to identify the significance of altitude on match results. The level of significance was set at the alpha level of 0.05. Match results are significantly affected by the altitude and level of altitude within inland teams most likely to win when playing at inland venues (p=0.000). The proportion of draws was slightly higher at the coast. At altitudes between 500-1000m, 1300-1500m, and 1500-1700m, a greater percentage of matches were won by coastal teams as opposed to draws. The timing of goals varied based on the team’s base altitude and the match elevation. The most significant differences were between 36-40 minutes (p=0.023), 41-45 minutes (p=0.000) and 50-65 minutes (p=0.000). When breaking down inland team’s matches to different altitude categories, greater differences were highlighted. Inland teams scored more goals per minute between 10-20 minute (p=0.009), 41-45 minutes (p=0.003) and 50-65 minutes (p=0.015). The total number of goals scored per match at different altitudes by a) inland teams (p=0.000), b) coastal teams (p=0.006). Coastal teams made significantly more substitutions when playing at altitude (p=0.034), although there were no significant differences when comparing the different altitude categories. The timing of all three changes, however, did vary significantly at the different altitudes. There were no significant differences in timing or number of substitutions for inland teams. Match results and timing of goals are influenced by altitude, with differences between the level of altitude also playing a role. The trends indicate that inland teams win more matches when playing at altitude against coastal teams, and they score more goals just prior to half-time and in the first quarter of the second half.

Keywords: coastal teams, inland teams, timing of goals, results, substitutions

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Authors: Suneet Jagdev

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The development of the original Bauhaus occurred at a time in the beginning of the 20th century when the industrialization of Germany had reached a climax. The cities were a reflection of the new living conditions of an industrialized society. The Bauhaus can be interpreted as an ambitious attempt to find appropriate answers to the challenges by using architecture-urban development and design. The core elements of the conviction of the day were the belief in the necessary crossing of boundaries between the various disciplines and courage to experiment for a better solution. Even after 100 years, the situation in our cities is shaped by similar complexity. The urban consequences of developments are difficult to estimate and to predict. The paper critically reflected on the central aspects of the history of the Bauhaus and its role in bringing the modernism in India by comparative studies of the methodology adopted by the artists and designer in both the countries. The paper talked in detail about how the Bauhaus Exhibition in 1922 offered Indian artists a new weapon of anti-colonial resistance. The original Bauhaus fought its aesthetic and political battles in the context of economic instability and the rise of German fascism. The Indians had access to dominant global languages and in a particular English. The availability of print media and a vibrant indigenous intellectual culture provided Indian people a tool to accept technology while denying both its dominant role in culture and the inevitability of only one form of modernism. The indigenous was thus less an engagement with their culture as in the West than a tool of anti-colonial struggle. We have shown how the Indian people used Bauhaus as a critique of colonialism itself through an undermining of its typical modes of representation and as a means of incorporating the Indian desire for spirituality into art and as providing the cultural basis for a non-materialistic and anti-industrial form of what we might now term development. The paper reflected how through painting the Bauhaus entered the artistic consciousness of the sub-continent not only for its stylistic and technical innovations but as a tool for a critical and even utopian modernism that could challenge both the hegemony of academic and orientalist art and as the bearer of a transnational avant-garde as much political as it was artistic, and as such the basis of a non-Eurocentric but genuinely cosmopolitan alternative to the hierarchies of oppression and domination that had long bound India and were at that moment rising once again to a tragic crescendo in Europe. We have talked about how the Bauhaus of today can offer an innovative orientation towards discourse around architecture and design.

Keywords: anti-colonial struggle, art over architecture, Bauhaus exhibition of 1922, industrialization

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Authors: M. Aboalazayem, Essam A. Gouda, Ahmed M. Moussa, Amr E. Flifl

Abstract:

Field data collecting is considered one of the most difficult work due to the difficulty of accessing large zones such as large lakes. Also, it is well known that the cost of obtaining field data is very expensive. Remotely monitoring of lake water quality (WQ) provides an economically feasible approach comparing to field data collection. Researchers have shown that lake WQ can be properly monitored via Remote sensing (RS) analyses. Using satellite images as a method of WQ detection provides a realistic technique to measure quality parameters across huge areas. Landsat (LS) data provides full free access to often occurring and repeating satellite photos. This enables researchers to undertake large-scale temporal comparisons of parameters related to lake WQ. Satellite measurements have been extensively utilized to develop algorithms for predicting critical water quality parameters (WQPs). The goal of this paper is to use RS to derive WQ indicators in Aswan High Dam Reservoir (AHDR), which is considered Egypt's primary and strategic reservoir of freshwater. This study focuses on using Landsat8 (L-8) band surface reflectance (SR) observations to predict water-quality characteristics which are limited to Turbidity (TUR), total suspended solids (TSS), and chlorophyll-a (Chl-a). ArcGIS pro is used to retrieve L-8 SR data for the study region. Multiple linear regression analysis was used to derive new correlations between observed optical water-quality indicators in April and L-8 SR which were atmospherically corrected by values of various bands, band ratios, and or combinations. Field measurements taken in the month of May were used to validate WQP obtained from SR data of L-8 Operational Land Imager (OLI) satellite. The findings demonstrate a strong correlation between indicators of WQ and L-8 .For TUR, the best validation correlation with OLI SR bands blue, green, and red, were derived with high values of Coefficient of correlation (R2) and Root Mean Square Error (RMSE) equal 0.96 and 3.1 NTU, respectively. For TSS, Two equations were strongly correlated and verified with band ratios and combinations. A logarithm of the ratio of blue and green SR was determined to be the best performing model with values of R2 and RMSE equal to 0.9861 and 1.84 mg/l, respectively. For Chl-a, eight methods were presented for calculating its value within the study area. A mix of blue, red, shortwave infrared 1(SWR1) and panchromatic SR yielded the greatest validation results with values of R2 and RMSE equal 0.98 and 1.4 mg/l, respectively.

Keywords: remote sensing, landsat 8, nasser lake, water quality

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Authors: Veena Easvaradoss, Ebenezer Joseph, Sumathi Chandrasekaran, Sweta Jain, Aparna Anna Mathai, Senta Christy

Abstract:

Working memory refers to a cognitive processing space where information is received, managed, transformed, and briefly stored. It is an operational process of transforming information for the execution of cognitive tasks in different and new ways. Many class room activities require children to remember information and mentally manipulate it. While the impact of chess training on intelligence and academic performance has been unequivocally established, its impact on working memory needs to be studied. This study, funded by the Cognitive Science Research Initiative, Department of Science & Technology, Government of India, analyzed the effect of one-year chess training on the working memory of children. A pretest–posttest with control group design was used, with 52 children in the experimental group and 50 children in the control group. The sample was selected from children studying in school (grades 3 to 9), which included both the genders. The experimental group underwent weekly chess training for one year, while the control group was involved in extracurricular activities. Working memory was measured by two subtests of WISC-IV INDIA. The Digit Span Subtest involves recalling a list of numbers of increasing length presented orally in forward and in reverse order, and the Letter–Number Sequencing Subtest involves rearranging jumbled alphabets and numbers presented orally following a given rule. Both tasks require the child to receive and briefly store information, manipulate it, and present it in a changed format. The Children were trained using Winning Moves curriculum, audio- visual learning method, hands-on- chess training and recording the games using score sheets, analyze their mistakes, thereby increasing their Meta-Analytical abilities. They were also trained in Opening theory, Checkmating techniques, End-game theory and Tactical principles. Pre equivalence of means was established. Analysis revealed that the experimental group had significant gains in working memory compared to the control group. The present study clearly establishes a link between chess training and working memory. The transfer of chess training to the improvement of working memory could be attributed to the fact that while playing chess, children evaluate positions, visualize new positions in their mind, analyze the pros and cons of each move, and choose moves based on the information stored in their mind. If working-memory’s capacity could be expanded or made to function more efficiently, it could result in the improvement of executive functions as well as the scholastic performance of the child.

Keywords: chess training, cognitive development, executive functions, school children, working memory

Procedia PDF Downloads 239

Authors: Tayo P. Ogundunmade, Adedayo A. Adepoju

Abstract:

Terrorist attacks in liberal democracies bring about a few pessimistic results, for example, sabotaged public support in the governments they target, disturbing the peace of a protected environment underwritten by the state, and a limitation of individuals from adding to the advancement of the country, among others. Hence, seeking for techniques to understand the different factors involved in terrorism and how to deal with those factors in order to completely stop or reduce terrorist activities is the topmost priority of the government in every country. This research aim is to develop an efficient deep learning-based predictive model for the prediction of future terrorist activities in Nigeria, addressing low-quality prediction accuracy problems associated with the existing solution methods. The proposed predictive AI-based model as a counterterrorism tool will be useful by governments and law enforcement agencies to protect the lives of individuals in society and to improve the quality of life in general. A Heterogeneous Bayesian Neural Network (HETBNN) model was derived with Gaussian error normal distribution. Three primary transfer functions (HOTTFs), as well as two derived transfer functions (HETTFs) arising from the convolution of the HOTTFs, are namely; Symmetric Saturated Linear transfer function (SATLINS ), Hyperbolic Tangent transfer function (TANH), Hyperbolic Tangent sigmoid transfer function (TANSIG), Symmetric Saturated Linear and Hyperbolic Tangent transfer function (SATLINS-TANH) and Symmetric Saturated Linear and Hyperbolic Tangent Sigmoid transfer function (SATLINS-TANSIG). Data on the Terrorist activities in Nigeria gathered through questionnaires for the purpose of this study were used. Mean Square Error (MSE), Mean Absolute Error (MAE) and Test Error are the forecast prediction criteria. The results showed that the HETFs performed better in terms of prediction and factors associated with terrorist activities in Nigeria were determined. The proposed predictive deep learning-based model will be useful to governments and law enforcement agencies as an effective counterterrorism mechanism to understand the parameters of terrorism and to design strategies to deal with terrorism before an incident actually happens and potentially causes the loss of precious lives. The proposed predictive AI-based model will reduce the chances of terrorist activities and is particularly helpful for security agencies to predict future terrorist activities.

Keywords: activation functions, Bayesian neural network, mean square error, test error, terrorism

Procedia PDF Downloads 139