Search results for: score prediction

Authors: Tayeb Aissaoui, Inas M. AlNashef

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In recent years, green solvents named deep eutectic solvents (DESs) have been found to possess significant properties and to be applicable in several technologies. Choline chloride (ChCl) mixed with urea at a ratio of 1:2 and 80 °C was the first discovered DES. In this article, chemical structure and combination mechanism of ChCl: urea based DES were investigated. Moreover, the implementation of this DES in water removal from natural gas was reported. Dehydration of natural gas by ChCl:urea shows significant absorption efficiency compared to triethylene glycol. All above operations were retrieved from COSMOthermX software. This article confirms the potential application of DESs in gas industry.

Keywords: COSMO-RS, deep eutectic solvents, dehydration, natural gas, structure, organic salt

Procedia PDF Downloads 294

Authors: Maryam Amiri, Leyli Mohammad-Khanli

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Cloud computing is becoming increasingly popular and more business applications are moving to cloud. In this regard, services that provide similar functional properties are increasing. So, the ability to select a service with the best non-functional properties, corresponding to the user preference, is necessary for the user. This paper presents an Evaluation Framework of Service Trustworthiness (EFST) that evaluates the trustworthiness of equivalent services without need to additional invocations of them. EFST extracts user preference automatically. Then, it assesses trustworthiness of services in two dimensions of qualitative and quantitative metrics based on the experiences of past usage of services. Finally, EFST determines the overall trustworthiness of services using Fuzzy Inference System (FIS). The results of experiments and simulations show that EFST is able to predict the missing values of Quality of Service (QoS) better than other competing approaches. Also, it propels users to select the most appropriate services.

Keywords: user preference, cloud service, trustworthiness, QoS metrics, prediction

Procedia PDF Downloads 289

Authors: Tarek Litim, Ouahiba Taamallah

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The present paper is an investigation of the effect of burnishing on the surface integrity of a component made of 15NiCr6 steel. This work shows a statistical study based on regression, and Taguchi's design has allowed the development of mathematical models to predict the output responses as a function of the technological parameters studied. The response surface methodology (RSM) showed a simultaneous influence of the burnishing parameters and observe the optimal processing parameters. ANOVA analysis of the results resulted in the validation of the prediction model with a determination coefficient R=90.60% and 92.41% for roughness and hardness, respectively. Furthermore, a multi-objective optimization allowed to identify a regime characterized by P=10kgf, i=3passes, and f=0.074mm/rev, which favours minimum roughness and maximum hardness. The result was validated by the desirability of D= (0.99 and 0.95) for roughness and hardness, respectively.

Keywords: 15NiCr6 steel, burnishing, surface integrity, Taguchi, RSM, ANOVA

Procedia PDF Downloads 194

Authors: Tariq Mansoor, Lubos Hes, Vladimir Bajzik

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Socks comfort has great significance in our daily life. This significance even increased when we have undergone a work of low or high activity. It causes the sweating of our body with different rates. In this study, plain socks with differential fibre composition were wetted to saturated level. Then after successive intervals of conditioning, these socks are characterized by thermal resistance in dry and wet states. Theoretical thermal resistance is predicted by using combined filling coefficients and thermal conductivity of wet polymers instead of dry polymer (fibre) in different models. By this modification, different mathematical models could predict thermal resistance at different moisture levels. Furthermore, predicted thermal resistance by different models has reasonable correlation range between (0.84 -0.98) with experimental results in both dry (lab conditions moisture) and wet states. "This work is supported by Technical University of Liberec under SGC-2019. Project number is 21314".

Keywords: thermal resistance, mathematical model, plain socks, moisture loss rate

Procedia PDF Downloads 200

Authors: Johnson C. Y. Pang, Amy S. N. Fu, Ryan K. L. Lee, Allan C. L. Fu

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Dry needling (DN) is one of the puncturing methods that involves the insertion of needles into the tender spots of the human body without the injection of any substance. DN has long been used to treat the patient with knee pain caused by knee osteoarthritis (KOA) and patellofemoral pain syndrome (PFPS), but the effectiveness is still inconsistent. This study aimed to conduct a systematic review and meta-analysis to assess the intervention methods and effects of DN with and without ultrasound guidance for treating pain and dysfunctions in people with KOA and PFPS. Design: This systematic review adhered to the PRISMA reporting guidelines. The registration number of the study protocol published in the PROSPERO database was CRD42021221419. Six electronic databases were searched manually through CINAHL Complete (1976-2020), Cochrane Library (1996-2020), EMBASE (1947-2020), Medline (1946-2020), PubMed (1966-2020), and Psychinfo (1806-2020) in November 2020. Randomized controlled trials (RCTs) and controlled clinical trials were included to examine the effects of DN on knee pain, including KOA and PFPS. The key concepts included were: DN, acupuncture, ultrasound guidance, KOA, and PFPS. Risk of bias assessment and qualitative analysis were conducted by two independent reviewers using the PEDro score. Results: Fourteen articles met the inclusion criteria, and eight of them were high-quality papers in accordance with the PEDro score. There were variations in the techniques of DN. These included the direction, depth of insertion, number of needles, duration of stay, needle manipulation, and the number of treatment sessions. Meta-analysis was conducted on eight articles. DN group showed positive short-term effects (from immediate after DN to less than 3 months) on pain reduction for both KOA and PFPS with the overall standardized mean difference (SMD) of -1.549 (95% CI=-0.588 to -2.511); with great heterogeneity (P=0.002, I²=96.3%). In subgroup analysis, DN demonstrated significant effects in pain reduction on PFPS (p < 0.001) that could not be found in subjects with KOA (P=0.302). At 3-month post-intervention, DN also induced significant pain reduction in both subjects with KOA and PFPS with an overall SMD of -0.916 (95% CI=-0.133 to -1.699, and great heterogeneity (P=0.022, I²=95.63%). Besides, DN induced significant short-term improvement in function with the overall SMD=6.069; 95% CI=8.595 to 3.544; with great heterogeneity (P<0.001, I²=98.56%) when analyzed was conducted on both KOA and PFPS groups. In subgroup analysis, only PFPS showed a positive result with SMD=6.089, P<0.001; while KOA showed statistically insignificant with P=0.198 in short-term effect. Similarly, at 3-month post-intervention, significant improvement in function after DN was found when the analysis was conducted in both groups with the overall SMD=5.840; 95% CI=9.252 to 2.428; with great heterogeneity (P<0.001, I²=99.1%), but only PFPS showed significant improvement in sub-group analysis (P=0.002, I²=99.1%). Conclusions: The application of DN in KOA and PFPS patients varies among practitioners. DN is effective in reducing pain and dysfunction at short-term and 3-month post-intervention in individuals with PFPS. To our best knowledge, no study has reported the effects of DN with ultrasound guidance on KOA and PFPS. The longer-term effects of DN on KOA and PFPS are waiting for further study.

Keywords: dry needling, knee osteoarthritis, patellofemoral pain syndrome, ultrasound guidance

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Authors: Muna Al-Kalbani, Thuwayba Al Barwani, Otherine Neisler, Hussain Alkharusi, David Clayton, Humaira Al-Sulaimani, Mohammad Khan, Hamad Al-Yahmadi

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This study describes the psychometric properties and factor structure of the University Readiness Survey (URS). Survey data were collected from sample of 2652 students from Sultan Qaboos University. Exploratory factor analysis identified ten significant factors underlining the structure. The results of Confirmatory factor analysis showed a good fit to the data where the indices for the revised model were χ2(df = 1669) = 6093.4; CFI = 0.900; GFI =0.926; PCLOSE = 1.00 and RMSAE = 0.030 where each of these indices were above threshold. The overall value of Cronbach’s alpha was 0.899 indicating that the instrument score was reliable. Results imply that the URS is a valid measure describing the college readiness pattern among Sultan Qaboos University students and the Arabic version could be used by university counselors to identify students’ readiness factors. Nevertheless, further validation of the of the USR is recommended.

Keywords: college readiness, confirmatory factor analysis, reliability, validity

Procedia PDF Downloads 228

Authors: Richard Ren

Abstract:

Air pollution is a serious danger to international well-being and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.

Keywords: air quality prediction, air pollution, artificial intelligence, machine learning algorithms

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Authors: Damiaa Saad Khudor

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The paper describes an investigation for the thermal design of a fluidized bed cooler and prediction of heat transfer rate among the media categories. It is devoted to the thermal design of such equipment and their application in the industrial fields. It outlines the strategy for the fluidization heat transfer mode and its implementation in industry. The thermal design for fluidized bed cooler is used to furnish a complete design for a fluidized bed cooler of Sodium Bicarbonate. The total thermal load distribution between the air-solid and water-solid along the cooler is calculated according to the thermal equilibrium. The step by step technique was used to accomplish the thermal design of the fluidized bed cooler. It predicts the load, air, solid and water temperature along the trough. The thermal design for fluidized bed cooler revealed to the installation of a heat exchanger consists of (65) horizontal tubes with (33.4) mm diameter and (4) m length inside the bed trough.

Keywords: fluidization, powder technology, thermal design, heat exchangers

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Authors: Zhi-Jun Lu, Qi Lu, Meng Wu, Qian Xiang, Jun Gu

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The analysis of perforated steel members is a 3D problem in nature, therefore the traditional analytical expressions for the ultimate load of thin-walled steel sections cannot be used for the perforated steel member design. In this study, finite element method (FEM) and artificial neural network (ANN) were used to simulate the process of stub column tests based on specific codes. Results show that compared with those of the FEM model, the ultimate load predictions obtained from ANN technique were much closer to those obtained from the physical experiments. The ANN model for the solving the hard problem of complex steel perforated sections is very promising.

Keywords: artificial neural network (ANN), finite element method (FEM), perforated sections, thin-walled Steel, ultimate load

Procedia PDF Downloads 353

Authors: Gao Fangting

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With the accelerated pace of industrialization and urbanization, the economic level has been significantly improved, and at the same time, the air quality situation has also become a focus of attention, which not only affects people's health but also has certain impacts on the economy and ecology. As the capital city of China, the air quality situation in Beijing has attracted much attention. In this paper, based on the day-by-day PM2.5, PM10, CO, NO₂, SO₂ and O₃ conditions in Beijing from 2018 to 2022, the characterization of pollutants is launched, and the seasonal decomposition and prediction of the main pollutants, PM2.5, PM10 and O3, are performed in STL. The results of the study show that (1) the overall air quality of Beijing has significantly improved from 2018 to 2022, and the main pollutants are PM2.5, PM10, and O₃; (2) the seasonal intensities of the main pollutants are higher, and they are influenced by seasonal factors; and (3) it is predicted that the O₃ concentration will have a trend of slowly increasing from 2023 to 2026, and the PM10 and PM2.5 pollution situation slowly improves.

Keywords: air pollutants, Beijing, characteristic analysis, STL

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Authors: Warda Khan, Remo Lachmann, Adarsh S. Garakahally

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The increasing complexity of software and its applicability in a wide range of industries, e.g., automotive, call for enhanced quality assurance techniques. Test automation is one option to tackle the prevailing challenges by supporting test engineers with fast, parallel, and repetitive test executions. A high degree of test automation allows for a shift from mundane (manual) testing tasks to a more analytical assessment of the software under test. However, a high initial investment of test resources is required to establish test automation, which is, in most cases, a limitation to the time constraints provided for quality assurance of complex software systems. Hence, a computer-aided creation of automated test cases is crucial to increase the benefit of test automation. This paper proposes the application of machine learning for the generation of automated test cases. It is based on supervised learning to analyze test specifications and existing test implementations. The analysis facilitates the identification of patterns between test steps and their implementation with test automation components. For the test case generation, this approach exploits historical data of test automation projects. The identified patterns are the foundation to predict the implementation of unknown test case specifications. Based on this support, a test engineer solely has to review and parameterize the test automation components instead of writing them manually, resulting in a significant time reduction for establishing test automation. Compared to other generation approaches, this ML-based solution can handle different writing styles, authors, application domains, and even languages. Furthermore, test automation tools require expert knowledge by means of programming skills, whereas this approach only requires historical data to generate test cases. The proposed solution is evaluated using various multi-label evaluation criteria (EC) and two small-sized real-world systems. The most prominent EC is ‘Subset Accuracy’. The promising results show an accuracy of at least 86% for test cases, where a 1:1 relationship (Multi-Class) between test step specification and test automation component exists. For complex multi-label problems, i.e., one test step can be implemented by several components, the prediction accuracy is still at 60%. It is better than the current state-of-the-art results. It is expected the prediction quality to increase for larger systems with respective historical data. Consequently, this technique facilitates the time reduction for establishing test automation and is thereby independent of the application domain and project. As a work in progress, the next steps are to investigate incremental and active learning as additions to increase the usability of this approach, e.g., in case labelled historical data is scarce.

Keywords: machine learning, multi-class, multi-label, supervised learning, test automation

Procedia PDF Downloads 134

Authors: A. Hocine, A. Ghouaoula, S. M. Medjdoub, M. Cherifi

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The present paper proposed a meso–macro model describing the mechanical behaviour composite laminates of staking sequence [+θ/-θ]s under tensil loading. The behaviour of a layer is ex-pressed through elasticity coupled to damage. The elastic strain is due to the elasticity of the layer and can be modeled by using the classical laminate theory, and the laminate is considered as an orthotropic material. This means that no coupling effect between strain and curvature is considered. In the present work, the damage is associated to cracking of the matrix and parallel to the fibers and it being taken into account by the changes in the stiffness of the layers. The anisotropic damage is completely described by a single scalar variable and its evolution law is specified from the principle of maximum dissipation. The stress/strain relationship is investigated in plane stress loading.

Keywords: damage, behavior modeling, meso-macro model, composite laminate, membrane loading

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Authors: Adrien Marque, Daniel Delahaye, Pierre Maréchal, Isabelle Berry

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Wind direction and uncertainty are crucial in aircraft or unmanned aerial vehicle trajectories. By computing wind covariance matrices on each spatial grid point, these spatial grids can be defined as images with symmetric positive definite matrix elements. A data pre-processing step, a specific convolution, a specific max-pooling, and a specific flatten layers are implemented to process such images. Then, the neural network is applied to spatial grids, whose elements are wind covariance matrices, to solve classification problems related to the feasibility of unmanned aerial vehicles based on wind direction and wind uncertainty.

Keywords: wind direction, uncertainty level, unmanned aerial vehicle, convolution neural network, SPD matrices

Procedia PDF Downloads 56

Authors: Shyamani Hettiarachchi, Thilini Lokubalasuriya, Shakeela Saleem, Dinusha Nonis, Isuru Dharmaratne, Lakshika Udugama

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Introduction: Late identification of language difficulties in children could result in long-term negative consequences for communication, literacy and self-esteem. This highlights the need for early identification and intervention for speech, language and communication difficulties. Speech and language therapy is a relatively new profession in Sri Lanka and at present, there are no formal standardized screening tools to assess language skills in Sinhala-speaking children. The development and validation of a short, accurate screening tool to enable the identification of children with syntactic difficulties in Sinhala is a current need. Aims: 1) To develop test items for a Sinhala Syntactic Structures (S3 Short Form) test on children aged between 3;0 to 5;0 years 2) To validate the test of Sinhala Syntactic Structures (S3 Short Form) on children aged between 3; 0 to 5; 0 years Methods: The Sinhala Syntactic Structures (S3 Short Form) was devised based on the Renfrew Action Picture Test. As Sinhala contains post-positions in contrast to English, the principles of the Renfrew Action Picture Test were followed to gain an information score and a grammar score but the test devised reflected the linguistic-specificity and complexity of Sinhala and the pictures were in keeping with the culture of the country. This included the dative case marker ‘to give something to her’ (/ejɑ:ʈə/ meaning ‘to her’), the instrumental case marker ‘to get something from’ (/ejɑ:gən/ meaning ‘from him’ or /gɑhən/ meaning ‘from the tree’), possessive noun (/ɑmmɑge:/ meaning ‘mother’s’ or /gɑhe:/ meaning ‘of the tree’ or /male:/ meaning ‘of the flower’) and plural markers (/bɑllɑ:/ bɑllo:/ meaning ‘dog/dogs’, /mɑlə/mɑl/ meaning ‘flower/flowers’, /gɑsə/gɑs/ meaning ‘tree/trees’ and /wɑlɑ:kulə/wɑlɑ:kulu/ meaning ‘cloud/clouds’). The picture targets included socio-culturally appropriate scenes of the Sri Lankan New Year celebration, elephant procession and the Buddhist ‘Wesak’ ceremony. The test was piloted with a group of 60 participants and necessary changes made. In phase 1, the test was administered to 100 Sinhala-speaking children aged between 3; 0 and 5; 0 years in one district. In this presentation on phase 2, the test was administered to another 100 Sinhala-speaking children aged between 3; 0 to 5; 0 in three districts. In phase 2, the selection of the test items was assessed via measures of content validity, test-retest reliability and inter-rater reliability. The age of acquisition of each syntactic structure was determined using content and grammar scores which were statistically analysed using t-tests and one-way ANOVAs. Results: High percentage agreement was found on test-retest reliability on content validity and Pearson correlation measures and on inter-rater reliability. As predicted, there was a statistically significant influence of age on the production of syntactic structures at p<0.05. Conclusions: As the target test items included generated the information and the syntactic structures expected, the test could be used as a quick syntactic screening tool with preschool children.

Keywords: Sinhala, screening, syntax, language

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Authors: Farzad Sharifi, Raziyeh Shamsi

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In this paper, we obtain the projection of inefficient units in data envelopment analysis (DEA) in the case of stochastic inputs and outputs using the multi-objective programming (MOP) structure. In some problems, the inputs might be stochastic while the outputs are deterministic, and vice versa. In such cases, we propose molti-objective DEA-R model, because in some cases (e.g., when unnecessary and irrational weights by the BCC model reduces the efficiency score), an efficient DMU is introduced as inefficient by the BCC model, whereas the DMU is considered efficient by the DEA-R model. In some other case, only the ratio of stochastic data may be available (e.g; the ratio of stochastic inputs to stochastic outputs). Thus, we provide multi objective DEA model without explicit outputs and prove that in-put oriented MOP DEA-R model in the invariable return to scale case can be replacing by MOP- DEA model without explicit outputs in the variable return to scale and vice versa. Using the interactive methods for solving the proposed model, yields a projection corresponding to the viewpoint of the DM and the analyst, which is nearer to reality and more practical. Finally, an application is provided.

Keywords: DEA, MOLP, STOCHASTIC, DEA-R

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Authors: Kais A. Kadhim

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This paper is a semantic analysis of the English modals in Obama’s speech. The main objective of this study is to analyze selected modal auxiliaries identified in selected speeches of Obama’s campaign based on Coates’ (1983) semantic clusters. A total of fifteen speeches of Obama’s campaign were selected as the primary data and the modal auxiliaries selected for analysis include will, would, can, could, should, must, ought, shall, may and might. All the modal auxiliaries taken from the speeches of Barack Obama were analyzed based on the framework of Coates’ semantic clusters. Such analytical framework was carried out to examine how modal auxiliaries are used in the context of persuading people in Obama’s campaign speeches. The findings reveal that modals of intention, prediction, futurity and modals of possibility, ability, permission are mostly used in Obama’s campaign speeches.

Keywords: modals, meaning, persuasion, speech

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Authors: Mabrouka Morri, Amel Soualmia, Philippe Belleudy

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Vegetation affects the mean and turbulent flow structure. It may increase flood risks and sediment transport. Therefore, it is important to develop analytical approaches for the bed shear stress on vegetated bed, to predict resistance caused by vegetation. In the recent years, experimental and numerical models have both been developed to model the effects of submerged vegetation on open-channel flow. In this paper, different analytic models are compared and tested using the criteria of deviation, to explore their capacity for predicting the mean velocity and select the suitable one that will be applied in real case of rivers. The comparison between the measured data in vegetated flume and simulated mean velocities indicated, a good performance, in the case of rigid vegetation, whereas, Huthoff model shows the best agreement with a high coefficient of determination (R2=80%) and the smallest error in the prediction of the average velocities.

Keywords: analytic models, comparison, mean velocity, vegetation

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Authors: Jafar Kazemzadeh, Soheila Rabiepoor, Saeedeh Alizadeh

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Introduction: Burn, especially in women, can affect the quality of life and their quality of life due to a change in appearance. This study was designed to investigate the relationship between quality of life and sexual satisfaction in women with burn. Methods: This was a descriptive-analytical cross-sectional study conducted on 101 women with severe burns referring to Imam Khomeini Hospital in Urmia in 2016. The data gathering scales were demographic questionnaire, burn specific health scale-brief (BSHS-B) and index of sexual satisfaction (ISS). The data were analyzed using SPSS software version 16. Results: Mean score of quality of life was 102.94 ± 20.88 and sexual satisfaction was 57.03 ± 25.91. Also, there was a significant relationship between quality of life and its subscales with sexual satisfaction and some demographic variables (p < 0.05). Conclusion: According to the results of this study, it should be noted that interventional efforts for improving sexual satisfaction and thus improving the quality of life in these patients are important. The findings of this study appear to be effective in planning for women with a history of burns.

Keywords: burn, quality of life, sexual satisfaction, women

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Authors: Koffi Sodokin

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Financing microstructures are increasingly seen as a means of financial inclusion and improving overall well-being in developing countries. In practice, digital transformation in finance can accelerate the optimal functioning of financing microstructures, such as access by households to microfinance and microinsurance. Large households' access to finance can lead to a reduction in income inequality and an overall improvement in well-being. This paper explores the impact of access to digital finance and financing microstructures on household well-being and the reduction of income inequality. To this end, we use the propensity score matching, the double difference, and the smooth instrumental quantile regression as estimation methods with two periods of survey data. The paper uses the FinScope consumer data (2016) and the Harmonized Living Standards Measurement Study (2018) from Togo in a comparative perspective. The results indicate that access to digital finance, as a cultural game changer, and to financing microstructures improves overall household well-being and contributes significantly to reducing income inequality.

Keywords: financing microstructure, microinsurance, microfinance, digital finance, well-being, income inequality

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Authors: Chariya Laohavich

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Background: Some limitations and complications have been found associated with commercial mouthpiece in bronchoscopic procedure. Therefore, we invented the Rama-chest mouthpiece from plastic normal saline bottle. Objective: The aim of this study was to compare user satisfaction in Rama-chest mouthpiece with the commercial mouthpiece. Methods: A prospective randomized controlled trial between commercial mouthpiece and Rama-chest mouthpiece was conducted on patients who were underwent bronchoscopy and required mouthpiece insertion from May to June 2014. The questionnaire about satisfaction was completed by the bronchoscopists, assistant nurses, and patients. Results: Thirty procedures in both groups were investigated. Mean satisfaction scores filled by the bronchoscopists and assistant nurses were not different between both groups. However, higher satisfaction score filled by the patients was found in Rama-chest mouthpiece than the comparator (p=0.011). Complications such as abrasion, pain, and itching were observed in commercial mouthpiece but not found in Rama-chest mouthpiece. Conclusion: We have introduced Rama-chest mouthpiece and proved its usefulness comparable to the commercial mouthpiece.

Keywords: mouthpiece, bronchoscopist, bronchology, pulmonology and respiratory diseases

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Authors: Kamal Tolani, Sandeep Kumar, Rohit Luthra, Ankit Dadhania, Krishnaprasad K., Ram Gupta, Deepa Joshi

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Background: Post-operative analgesia remains a clinical challenge, with central and peripheral sensitization playing a pivotal role in treatment-related complications and impaired quality of life. Centrally acting opioids offer poor risk benefit profile with increased intensity of gastrointestinal or central side effects and slow onset of clinical analgesia. The objective of this study was to assess the clinical feasibility of induction and maintenance therapy with Tapentadol Nasal Spray (NS) in moderate to severe acute post-operative pain. Methods: Phase III, randomized, active-controlled, non-inferiority clinical trial involving 294 cases who had undergone surgical procedures under general anesthesia or regional anesthesia. Post-surgery patients were randomized to receive either Tapentadol NS 45 mg or Tramadol 100mg IV as a bolus and subsequent 50 mg or 100 mg dose over 2-3 minutes. The frequency of administration of NS was at every 4-6 hours. At the end of 24 hrs, patients in the tramadol group who had a pain intensity score of ≥4 were switched to oral tramadol immediate release 100mg capsule until the pain intensity score reduced to <4. All patients who had achieved pain intensity ≤ 4 were shifted to a lower dose of either Tapentadol NS 22.5 mg or oral Tramadol immediate release 50mg capsule. The statistical analysis plan was envisaged as a non-inferiority trial involving comparison with Tramadol for Pain intensity difference at 60 minutes (PID60min), Sum of Pain intensity difference at 60 minutes (SPID60min), and Physician Global Assessment at 24 hrs (PGA24 hrs). Results: The per-protocol analyses involved 255 hospitalized cases undergoing surgical procedures. The median age of patients was 38.0 years. For the primary efficacy variables, Tapentadol NS was non-inferior to Inj/Oral Tramadol in relief of moderate to severe post-operative pain. On the basis of SPID60min, no clinically significant difference was observed between Tapentadol NS and Tramadol IV (1.73±2.24 vs. 1.64± 1.92, -0.09 [95% CI, -0.43, 0.60]). In the co-primary endpoint PGA24hrs, Tapentadol NS was non–inferior to Tramadol IV (2.12 ± 0.707 vs. 2.02 ±0.704, - 0.11[95% CI, -0.07, 0.28). However, on further assessment at 48hr, 72 hrs, and 120hrs, clinically superior pain relief was observed with the Tapentadol NS formulation that was statistically significant (p <0.05) at each of the time intervals. Secondary efficacy measures, including the onset of clinical analgesia and TOTPAR, showed non-inferiority to Tramadol. The safety profile and need for rescue medication were also similar in both the groups during the treatment period. The most common concomitant medications were anti-bacterial (98.3%). Conclusion: Tapentadol NS is a clinically feasible option for improved compliance as induction and maintenance therapy while offering a sustained and persistent patient response that is clinically meaningful in post-surgical settings.

Keywords: tapentadol nasal spray, acute pain, tramadol, post-operative pain

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Authors: J. L. Andrade-Pineda, J.M. León-Blanco, M. Calle, P. L. González-R

Abstract:

The enrollment in current Master's degree programs usually pursues gaining the expertise required in real-life workplaces. The experience we present here concerns the learning process of "Project Management Methodology (PMM)", around a cooperative/collaborative mechanism aimed at affording students measurable learning goals and providing the teacher with the ability of focusing on the weaknesses detected. We have designed a mixed summative/formative evaluation, which assures curriculum engage while enriches the comprehension of PMM key concepts. In this experience we converted the students into active actors in the evaluation process itself and we endowed ourselves as teachers with a flexible process in which along with qualifications (score), other attitudinal feedback arises. Despite the high level of self-affirmation on their discussion within the interactive assessment sessions, they ultimately have exhibited a great ability to review and correct the wrong reasoning when that was the case.

Keywords: cooperative-collaborative learning, educational management, formative-summative assessment, leadership training

Procedia PDF Downloads 170

Authors: I. Goual, M. S. Goual, M. K. Gueddouda, Taïbi Saïd, Abou-Bekr Nabil, A. Ferhat

Abstract:

This paper presents the study of hydro mechanical behavior of this optimal mixture. A first experimental phase was carried out in order to find the optimal mixture. This showed that the material composed of 80% tuff and 20% calcareous sand provides the maximum mechanical strength. The second experimental phase concerns the study of the drying- wetting behavior of the optimal mixture was carried out on slurry samples and compacted samples at the MPO. Experimental results let to deduce the parameters necessary for the prediction of the hydro-mechanical behavior of pavement formulated from tuff and calcareous sand mixtures, related to moisture. This optimal mixture satisfies the regulation rules and hence constitutes a good local eco-material, abundantly available, for the conception of pavements.

Keywords: tuff, sandy calcareous, road engineering, hydro mechanical behaviour, suction

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Authors: Pei-Chann Chang, Jhen-Fu Liao, Chin-Hung Teng, Meng-Hui Chen

Abstract:

This research combines artificial immune system with user and item based collaborative filtering to create an efficient and accurate recommendation system. By applying the characteristic of antibodies and antigens in the artificial immune system and using Pearson correlation coefficient as the affinity threshold to cluster the data, our collaborative filtering can effectively find useful users and items for rating prediction. This research uses MovieLens dataset as our testing target to evaluate the effectiveness of the algorithm developed in this study. The experimental results show that the algorithm can effectively and accurately predict the movie ratings. Compared to some state of the art collaborative filtering systems, our system outperforms them in terms of the mean absolute error on the MovieLens dataset.

Keywords: artificial immune system, collaborative filtering, recommendation system, similarity

Procedia PDF Downloads 536

Authors: Majid Forghani, Michael Khachay

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In genetics, the impact of neighboring amino acids on a target site is referred as the nearest-neighbor effect or simply neighbor effect. In this paper, a new method called wavelet particle decomposition representing the one-dimensional neighbor effect using wavelet packet decomposition is proposed. The main idea lies in known dependence of wavelet packet sub-bands on location and order of neighboring samples. The method decomposes the value of a signal sample into small values called particles that represent a part of the neighbor effect information. The results have shown that the information obtained from the particle decomposition can be used to create better model variables or features. As an example, the approach has been applied to improve the correlation of test and reference sequence distance with titer in the hemagglutination inhibition assay.

Keywords: antigenic variants, neighbor effect, wavelet packet, wavelet particle decomposition

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Authors: Alberto Alcalá-Alvarez, Pablo Padilla-Longoria

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The present paper describes a framework for constructing mathematical networks encoding relevant musical information from a music score for structural analysis. These graphs englobe statistical information about music elements such as notes, chords, rhythms, intervals, etc., and the relations among them, and so become helpful in visualizing and understanding important stylistic features of a music fragment. In order to build such networks, musical data is parsed out of a digital symbolic music file. This data undergoes different analytical procedures from Graph Theory, such as measuring the centrality of nodes, community detection, and entropy calculation. The resulting networks reflect important structural characteristics of the fragment in question: predominant elements, connectivity between them, and complexity of the information contained in it. Music pieces in different styles are analyzed, and the results are contrasted with the traditional analysis outcome in order to show the consistency and potential utility of this method for music analysis.

Keywords: computational musicology, mathematical music modelling, music analysis, style classification

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Authors: R. S. Remya, U. S. Sethulekshmi

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Detecting the authenticity of a video is an important issue in digital forensics as Video is used as a silent evidence in court such as in child pornography, movie piracy cases, insurance claims, cases involving scientific fraud, traffic monitoring etc. The biggest threat to video data is the availability of modern open video editing tools which enable easy editing of videos without leaving any trace of tampering. In this paper, we propose an efficient passive method for inter-frame video tampering detection, its type and location by estimating the optical flow of wavelet features of adjacent frames and thresholding the variation in the estimated feature. The performance of the algorithm is compared with the z-score thresholding and achieved an efficiency above 95% on all the tested databases. The proposed method works well for videos with dynamic (forensics) as well as static (surveillance) background.

Keywords: discrete wavelet transform, optical flow, optical flow variation, video tampering

Procedia PDF Downloads 360

Authors: Josiah O. Carter, Charlotte Hayden, Elizabeth Arthurs

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Background: During the COVID-19 pandemic, hospital management policies have changed frequently and rapidly. This has created novel challenges in keeping the workforce abreast of these changes to enable them to deliver safe and effective care. Traditional communication methods, e.g. email, do not keep pace with the rapidly changing environment in the hospital, resulting in inaccurate, irrelevant, or outdated information being communicated, resulting in inefficiencies in patient care. Methods: The creation of a WhatsApp messaging group within the medical division at the Bristol Royal Infirmary has enabled senior clinicians and the hospital management team to update the medical workforce in real-time. It has two primary functions: (1) To enable dissemination of a concise, important operational summary. This comprises information on bed status and infection control procedural changes. It is fed directly from a daily critical incident briefing (2) To facilitate a monthly scheduled question and answer (Q&A) session for junior doctors to clarify issues with clinical directors, rota, and management staff. Additional ad-hoc updates are sent out for time-critical information; otherwise, it mainly functions as a broadcast-only group to prevent important information from being lost amongst other communication. All junior doctors within the medical division were invited to join the group. At present, the group comprises 131 participants, of which 10 are administrative staff (rota coordinators, management staff & clinical directors); the remaining 121 are junior clinicians working within the medical division. An electronic survey via Microsoft forms was sent out to junior doctors via the WhatsApp group and via email to assess its utilisation and effectiveness with the aim of quality improvements. Results: Of the 121 group participants, 19 completed the questionnaire (response rate 15.7%). Of these, 16/19 (84.2%) used it regularly, and 12/19 (63.2%) rated it as the most useful source for reliable updates relating to the hospital response to the COVID-19 pandemic, whereas only 2/19 (10.5%) found the trust intranet and the trust COVID-19 operational email update most useful. Respondents rated the WhatsApp group more useful as an information source (mean score 7.7/10) than as a means of providing feedback to management staff (mean score 6.3/10). Qualitative feedback suggested information around ward closures and changes to COVID cohorting, along with updates on staffing issues, were most useful. Respondents also noted the Q&A sessions were an efficient way of relaying feedback about management decisions but that it would be preferable if these sessions could be delivered more frequently. Discussion: During the current global COVID-19 pandemic, there is an increased need for rapid dissemination of critical information within NHS trusts; this includes communication between junior doctors, managers, and senior clinicians. The versatility of WhatsApp permits a variety of functions allowing for regular updates, the dissemination of time-critical information, and enables conversing and feedback. The project has demonstrated that reserved and well-managed use of a WhatsApp group is a welcome, efficient and practical means of communication between the senior management team and the junior medical workforce.

Keywords: communication, COVID-19, hospital management, WhatsApp

Procedia PDF Downloads 114

Authors: Joanne Rieta Miranda

Abstract:

Learning anatomy and physiology among Deaf Non-Science major students is a challenge. They have this mindset that Anatomy and Physiology are difficult and very technical. In this study, nine (9) deaf students who are business majors were considered. Non-conventional teaching strategies and classroom activities were employed such as cooperative learning, virtual lab, Facebook live, big sky, blood typing, mind mapping, reflections, etc. Of all the activities; the deaf students ranked cooperative learning as the best learning activity. This is where they played doctors. They measured the pulse rate, heart rate and blood pressure of their partner classmate. In terms of mindset, 2 out of 9 students have a growth mindset with some fixed ideas while 7 have a fixed mindset with some growth ideas. All the students passed the course. Three out of nine students got a grade of 90% and above. The teacher was evaluated by the deaf students as very satisfactory with a mean score of 3.54. This means that the learner-centered practices in the classroom are manifested to a great extent.

Keywords: deaf students, learning anatomy and physiology, teaching strategies, learner-entered practices

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Authors: Dua Hişam, Serhat İkizoğlu

Abstract:

Identifying the problem behind balance disorder is one of the most interesting topics in the medical literature. This study has considerably enhanced the development of artificial intelligence (AI) algorithms applying multiple machine learning (ML) models to sensory data on gait collected from humans to classify between normal people and those suffering from Vestibular System (VS) problems. Although AI is widely utilized as a diagnostic tool in medicine, AI models have not been used to perform feature extraction and identify VS disorders through training on raw data. In this study, three machine learning (ML) models, the Random Forest Classifier (RF), Extreme Gradient Boosting (XGB), and K-Nearest Neighbor (KNN), have been trained to detect VS disorder, and the performance comparison of the algorithms has been made using accuracy, recall, precision, and f1-score. With an accuracy of 95.28 %, Random Forest Classifier (RF) was the most accurate model.

Keywords: vestibular disorder, machine learning, random forest classifier, k-nearest neighbor, extreme gradient boosting

Procedia PDF Downloads 71