Search results for: score prediction

Authors: Theoktisti Makridou, Konstantinos Tsaprailis, George Arvanitakis, Charalampos Kontoes

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In the literature, the data-driven approaches for mosquito abundance prediction relaying on supervised machine learning models that get trained with historical in-situ measurements. The counterpart of this approach is once the model gets trained on pointlevel (specific x,y coordinates) measurements, the predictions of the model refer again to point-level. These point-level predictions reduce the applicability of those solutions once a lot of early warning and mitigation actions applications need predictions for an area level, such as a municipality, village, etc... In this study, we apply a data-driven predictive model, which relies on public-open satellite Earth Observation and geospatial data and gets trained with historical point-level in-Situ measurements of mosquito abundance. Then we propose a methodology to extract information from a point-level predictive model to a broader area-level prediction. Our methodology relies on the randomly spatial sampling of the area of interest (similar to the Poisson hardcore process), obtaining the EO and geomorphological information for each sample, doing the point-wise prediction for each sample, and aggregating the predictions to represent the average mosquito abundance of the area. We quantify the performance of the transformation from the pointlevel to the area-level predictions, and we analyze it in order to understand which parameters have a positive or negative impact on it. The goal of this study is to propose a methodology that predicts the mosquito abundance of a given area by relying on point-level prediction and to provide qualitative insights regarding the expected performance of the area-level prediction. We applied our methodology to historical data (of Culex pipiens) of two areas of interest (Veneto region of Italy and Central Macedonia of Greece). In both cases, the results were consistent. The mean mosquito abundance of a given area can be estimated with similar accuracy to the point-level predictor, sometimes even better. The density of the samples that we use to represent one area has a positive effect on the performance in contrast to the actual number of sampling points which is not informative at all regarding the performance without the size of the area. Additionally, we saw that the distance between the sampling points and the real in-situ measurements that were used for training did not strongly affect the performance.

Keywords: mosquito abundance, supervised machine learning, culex pipiens, spatial sampling, west nile virus, earth observation data

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Authors: Ben Clapperton, Daniel Stahl, Kimberley Goldsmith, Trudie Chalder

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Chronic fatigue syndrome (CFS) is a condition characterised by chronic disabling fatigue and other symptoms that currently can't be explained by any underlying medical condition. Although clinical trials support the effectiveness of cognitive behaviour therapy (CBT), the success rate for individual patients is modest. Patients vary in their response and little is known which factors predict or moderate treatment outcomes. The aim of the project is to develop a prediction model from baseline characteristics of patients, such as demographics, clinical and psychological variables, which may predict likely treatment outcome and provide guidance for clinical decision making and help clinicians to recommend the best treatment. The project is aimed at identifying subgroups of patients with similar baseline characteristics that are predictive of treatment effects using modern cluster analyses and data mining machine learning algorithms. The characteristics of these groups will then be used to inform the types of individuals who benefit from a specific treatment. In addition, results will provide a better understanding of for whom the treatment works. The suitability of different clustering methods to identify subgroups and their response to different treatments of CFS patients is compared.

Keywords: chronic fatigue syndrome, latent class analysis, prediction modelling, self-organizing maps

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Authors: Novi Prasanty, Mustafa Ma, Elmeida Effendy

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Background: Schizophrenia is a psychotic disorder that most often encountered. Nearly 1% of the world population suffers from schizophrenia during their lifetime. Schizophrenia is a severe form of psychotic disorders, and tend to be chronic. Vitamin D plays crucial roles in neuroprotection and neurodevelopment, and low levels are commonly associated with schizophrenia. Lower vitamin D levels were correlated with more severe positive, negative, and overall symptoms in schizophrenia patient men and women. Methods: 54 schizophrenic patients, male and female, who are diagnosed with semistructured MINI ICD-X. A symptom of schizophrenia was measured by using positive and negative Syndrome Scale (PANSS). Examination of serum vitamin D using ELFA. Analysis to compare the serum levels of vitamin D male and female with Independent T-test, and the relationship between serum level of vitamin D and symptom with correlation. Results: In this study serum levels in male schizophrenic patients 22.12 (4.16), and 16.54 (2.88) in female schizophrenic patients. There are differences in male schizophrenic patients and women (p < 0.001). The negative correlation between serum levels of vitamin D in the PANSS total score in patients with schizophrenic male with r -0.58, p (0,016), and the female schizophrenic patients with r -0.69, p (0.031). Conclusion and Suggestion: There is a negative correlation between serum levels of vitamin D with a total score of PANSS, the lower the serum levels of vitamin D, the higher the total score of the PANSS.

Keywords: PANSS, schizophrenia, serum levels of vitamin D, severity illness

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Authors: Ms Azam Najafkouchak, David Todem, Dorothy Pathak, Pramod Pathak, Joseph Gardiner

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Propensity score (PS) methods have recently become the standard analysis as a tool for the causal inference in the observational studies where exposure is not randomly assigned, thus, confounding can impact the estimation of treatment effect on the outcome. For the binary outcome, the effect of treatment on the outcome can be estimated by odds ratios, relative risks, and risk differences. However, using the different PS methods may give you a different estimation of the treatment effect on the outcome. Several methods of PS analyses have been used mainly, include matching, inverse probability of weighting, stratification, and covariate adjusted on PS. Due to the dangers of discretizing continuous variables (exposure, covariates), the focus of this paper will be on how the variation in cut-points or boundaries will affect the average treatment effect (ATE) utilizing the stratification of PS method. Therefore, we are trying to avoid choosing arbitrary cut-points, instead, we continuously discretize the PS and accumulate information across all cut-points for inferences. We will use Monte Carlo simulation to evaluate ATE, focusing on two PS methods, stratification and covariate adjusted on PS. We will then show how this can be observed based on the analyses of the data from a case-control study of breast cancer, the Polish Women’s Health Study.

Keywords: average treatment effect, propensity score, stratification, covariate adjusted, monte Calro estimation, breast cancer, case_control study

Procedia PDF Downloads 107

Authors: Long-Shan Wu, Ming-Chen Ko, Chien-Chang Ho, Po-Fu Lee, Jenn-Woei Hsieh, Ching-Yu Tseng

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This study aimed to determine the effects of whole-body vibration (WBV) training on lower-extremity muscle strength and balance control performance among community-dwelling middle-aged and older adults in the United States. Twenty-nine participants without any contraindication of performing WBV exercise completed all the study procedures. Participants were randomly assigned to do body weight exercise with either an individualized vibration frequency and amplitude, a fixed vibration frequency and amplitude, or no vibration. Isokinetic knee extensor power, limits of stability, and sit-to-stand tests were performed at the baseline and after 8 weeks of training. Neither the individualized frequency-amplitude WBV training protocol nor the fixed frequency-amplitude WBV training protocol improved isokinetic knee extensor power. The limits of stability endpoint excursion score for the individualized frequency-amplitude group increased by 8.8 (12.9%; p = 0.025) after training. No significant differences were observed in fixed and control group. The maximum excursion score for the individualized frequency-amplitude group at baseline increased by 9.2 (11.5%; p = 0.006) after training. The average weight transfer time score significantly decreased by 0.21 s in the fixed group. The participants in the individualized group showed a significant increase (3.2%) in weight rising index score after 8 weeks of WBV training. These results suggest that 8 weeks of WBV training improved limit of stability and sit-to-stand performance. Future studies need to determine whether WBV training improves other factors that can influence posture control.

Keywords: whole-body vibration training, muscle strength, balance control, middle-aged and older adults

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Authors: Brahim Fares Zaidi

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Our work aims to improve our Automatic Recognition System for Dysarthria Speech based on the Hidden Models of Markov and the Hidden Markov Model Toolkit to help people who are sick. With pronunciation problems, we applied two techniques of speech parameterization based on Mel Frequency Cepstral Coefficients and Perceptual Linear Prediction and concatenated them with JITTER and SHIMMER coefficients in order to increase the recognition rate of a dysarthria speech. For our tests, we used the NEMOURS database that represents speakers with dysarthria and normal speakers.

Keywords: ARSDS, HTK, HMM, MFCC, PLP

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Authors: Saw Thiha, Jay Rajasekera

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A boom in video conferencing generated millions of hours of video data daily to be analyzed. However, such enormous data pose certain scalability issues to be analyzed efficiently, let alone do it in real-time, as online conferences can involve hundreds of people and can last for hours. This paper proposes an efficient online proctoring solution that can analyze the online conferences real-time on edge devices such as Android, iOS, and desktops. Since the computation can be done upfront on the devices where online conferences take place, it can scale well without requiring intensive resources such as GPU servers and complex cloud infrastructure. According to the linear models, face orientation does indeed impact the perceived eye openness. Also, the proposed z score facial landmark standardization was proven to be functional in detecting face orientation and contributed to classifying eye blinks with single eyelid distance computation while achieving a better f1 score and accuracy than the Eye Aspect Ratio (EAR) threshold method. Last but not least, the authors implemented the solution natively in the MediaPipe framework and open-sourced it along with the reproducible experimental results on GitHub. The solution provides face orientation, eye blink, facial activity, and translation detections out of the box and is highly customizable and extensible.

Keywords: android, desktop, edge computing, blink, face orientation, facial activity and translation, MediaPipe, open source, real-time, video conference, web, iOS, Z score facial landmark standardization

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Authors: Jay L. Fu

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Patients with Alzheimer's disease progressively lose their memory and thinking skills and, eventually, the ability to carry out simple daily tasks. The disease is irreversible, but early detection and treatment can slow down the disease progression. In this research, publicly available MRI data and demographic data from 373 MRI imaging sessions were utilized to build models to predict dementia. Various machine learning models, including logistic regression, k-nearest neighbor, support vector machine, random forest, and neural network, were developed. Data were divided into training and testing sets, where training sets were used to build the predictive model, and testing sets were used to assess the accuracy of prediction. Key risk factors were identified, and various models were compared to come forward with the best prediction model. Among these models, the random forest model appeared to be the best model with an accuracy of 90.34%. MMSE, nWBV, and gender were the three most important contributing factors to the detection of Alzheimer’s. Among all the models used, the percent in which at least 4 of the 5 models shared the same diagnosis for a testing input was 90.42%. These machine learning models allow early detection of Alzheimer’s with good accuracy, which ultimately leads to early treatment of these patients.

Keywords: Alzheimer's disease, clinical diagnosis, magnetic resonance imaging, machine learning prediction

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Authors: Allyson Augusta Shrikhande, Alexa Rains, Tayyaba Ahmed, Marjorie Mamsaang, Rakhi Vyas, Janaki Natarajan, Erika Moody, Christian Reutter, Kimberlee Leishear, Yogita Tailor, Sandra Sandhu-Restaino, Lora Liu, Neha James, Rosemarie Filart

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Characterized by consistent difficulty getting and keeping an erection firm enough for intercourse, Erectile Dysfunction may affect up to 15% of adult men. Although awareness and access to treatment have improved in recent years, many patients do not actively seek diagnosis or treatment due to the stigma surrounding this condition. Patients who do seek treatment are often dissatisfied by the efficacy of the medication. The condition inhibits patients’ quality of life by worsening mental health and relationships. The purpose of this study was to test the effectiveness of an outpatient neuromuscular treatment protocol in treating the symptoms of Chronic Pelvic Pain and Erectile Dysfunction, improving pain and function. 56 patients ages 20-79 presented to an outpatient clinic for treatment of pelvic pain and Erectile Dysfunction symptoms. These symptoms had persisted for an average of 4 years. All patients underwent external ultrasound-guided hydro-dissection technique targeted at pelvic peripheral nerves in combination with pelvic floor musculature trigger-point injections. To measure the effects of this treatment, a five question Erectile Dysfunction questionnaire was completed by each patient at their first visit to a clinic and three months after treatment began. Answers were summed for a total score of 5-25, with a higher score indicating optimal function. The average score before treatment was 14.125 (SD 5.411) (a=0.05; CI 12.708-15.542), which increased by 18% to an average of 16.625 (SD 6.423) (a=0.05; CI 14.943-18.307) after treatment (P=0.0004). Secondary outcome variables included a Visual Analogue Scale (VAS) to measure pelvic pain intensity and the Functional Pelvic Pain Scale (FPPS) to measure function across multiple areas. VAS scores reduced by 51% after three months. Before treatment, the mean VAS score was 5.87, and the posttreatment mean VAS score was 2.89. Pelvic pain functionality improved by 34% after three months. Pretreatment FPPS scores averaged at 7.48, decreasing to 4.91 after treatment. These results indicate that this unique treatment was very effective at relieving pain and increasing function for patients with Erectile Dysfunction.

Keywords: chronic pelvic pain, erectile dysfunction, nonsurgical, outpatient, trigger point injections

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Authors: Marimuthu Gurusamy

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In vibration analysis (with accelerometers) of rotating machines with rolling element bearing, the customers are interested to know the failure of the machine well in advance to plan the spare inventory and maintenance. But in real world most of the machines fails before the prediction of vibration analyst or Expert analysis software. Presently the prediction of failure is based on ISO 10816 vibration limits only. But this is not enough to monitor the failure of machines well in advance. Because more than 50% of the machines will fail even the vibration readings are within acceptable zone as per ISO 10816.Hence it requires further detail analysis and different techniques to predict the failure well in advance. In vibration Analysis, the velocity spectrum is used to analyse the root cause of the mechanical problems like unbalance, misalignment and looseness etc. The envelope spectrum are used to analyse the bearing frequency components, hence the failure in inner race, outer race and rolling elements are identified. But so far there is no correlation made between these two concepts. The author used both velocity spectrum and Envelope spectrum to analyse the machine behaviour and bearing condition to correlated the changes in dynamic load (by unbalance, misalignment and looseness etc.) and effect of impact on the bearing. Hence we could able to predict the expected life of the machine and bearings in the rotating equipment (with rolling element bearings). Also we used process parameters like temperature, flow and pressure to correlate with flow induced vibration and load variations, when abnormal vibration occurs due to changes in process parameters. Hence by correlation of velocity spectrum, envelope spectrum and process data with 20 years of experience in vibration analysis, the author could able to predict the rotating Equipment and its component’s deterioration and expected duration for maintenance.

Keywords: vibration analysis, velocity spectrum, envelope spectrum, prediction of deterioration

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Authors: Melodi Mbuyi Mata, Blessing Olamide Taiwo, Afolabi Ayodele David

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The purpose of this research is to create empirical models for assessing the profitability of granite aggregate production in Akure, Ondo state aggregate quarries. In addition, an artificial neural network (ANN) model and multivariate predicting models for granite profitability were developed in the study. A formal survey questionnaire was used to collect data for the study. The data extracted from the case study mine for this study includes granite marketing operations, royalty, production costs, and mine production information. The following methods were used to achieve the goal of this study: descriptive statistics, MATLAB 2017, and SPSS16.0 software in analyzing and modeling the data collected from granite traders in the study areas. The ANN and Multi Variant Regression models' prediction accuracy was compared using a coefficient of determination (R²), Root mean square error (RMSE), and mean square error (MSE). Due to the high prediction error, the model evaluation indices revealed that the ANN model was suitable for predicting generated profit in a typical quarry. More quarries in Nigeria's southwest region and other geopolitical zones should be considered to improve ANN prediction accuracy.

Keywords: national development, granite, profitability assessment, ANN models

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Authors: Mohammad Aziz Shah Mohamed Arip, Aslina Ahmad, Fauziah Mohd Sa'ad, Samsiah Mohd Jais, Syed Sofian Syed Salim

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This experimental study evaluates the effect of using Cognitive-Behavioral Therapy (CBT) and Multidimensional Self-Concept Model (MSCM) in a drug prevention programme to increase resiliency and reduce aggression among at-risk youth in Malaysia. A number of 60 (N=60) university students who were at-risk of taking drugs were involved in this study. Participants were identified with self-rating scales, Adolescent Resilience Attitude Scale (ARAS) and Aggression Questionnaire. Based on the mean score of these instruments, the participants were divided into the treatment group, and the control group. Data were analyzed using t-test. The finding showed that the mean score of resiliency was increased in the treatment group compared to the control group. It also shows that the mean score of aggression was reduced in the treatment group compared to the control group. Drug Prevention Programme was found to help in enhancing resiliency and reducing aggression among participants in the treatment group compared to the controlled group. Implications were given regarding the preventive actions on drug abuse among youth in Malaysia.

Keywords: drug prevention programme, cognitive-behavioral therapy (CBT), multidimensional self concept model (MSCM), resiliency, aggression, at-risk youth

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Authors: Minjuan Sun

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Ever since the late 1990s, China has experienced explosive growth in consumer lending, especially in short-term consumer loans, among which, the growth rate of non-bank lending has surpassed bank lending due to the development in financial technology. On the other hand, China does not have a universal credit scoring and registration system that can guide lenders during the processes of credit evaluation and risk control, for example, an individual’s bank credit records are not available for online lenders to see and vice versa. Given this context, the purpose of this paper is three-fold. First, we explore if and how alternative digital footprint data can be utilized to assess borrower’s creditworthiness. Then, we perform a comparative analysis of machine learning methods for the canonical problem of credit default prediction. Finally, we analyze, from an institutional point of view, the necessity of establishing a viable and nationally universal credit registration and scoring system utilizing online digital footprints, so that more people in China can have better access to the consumption loan market. Two different types of digital footprint data are utilized to match with bank’s loan default records. Each separately captures distinct dimensions of a person’s characteristics, such as his shopping patterns and certain aspects of his personality or inferred demographics revealed by social media features like profile image and nickname. We find both datasets can generate either acceptable or excellent prediction results, and different types of data tend to complement each other to get better performances. Typically, the traditional types of data banks normally use like income, occupation, and credit history, update over longer cycles, hence they can’t reflect more immediate changes, like the financial status changes caused by the business crisis; whereas digital footprints can update daily, weekly, or monthly, thus capable of providing a more comprehensive profile of the borrower’s credit capabilities and risks. From the empirical and quantitative examination, we believe digital footprints can become an alternative information source for creditworthiness assessment, because of their near-universal data coverage, and because they can by and large resolve the "thin-file" issue, due to the fact that digital footprints come in much larger volume and higher frequency.

Keywords: credit score, digital footprint, Fintech, machine learning

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Authors: Elda Maraj, Shkelqim Kuka

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Coronary heart disease causes many deaths in the world. Unfortunately, this problem will continue to increase in the future. In this paper, a fuzzy logic model to predict coronary heart disease is presented. This model has been developed with seven input variables and one output variable that was implemented for 30 patients in Albania. Here fuzzy logic toolbox of MATLAB is used. Fuzzy model inputs are considered as cholesterol, blood pressure, physical activity, age, BMI, smoking, and diabetes, whereas the output is the disease classification. The fuzzy sets and membership functions are chosen in an appropriate manner. Centroid method is used for defuzzification. The database is taken from University Hospital Center "Mother Teresa" in Tirana, Albania.

Keywords: coronary heart disease, fuzzy logic toolbox, membership function, prediction model

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Authors: Abdullah Al Faruque, Ram Balachandar

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Series of laboratory tests were carried out to study the extent of scour caused by a three-dimensional wall jets exiting from a square cross-section nozzle and into a non-cohesive sand beds. Previous observations have indicated that the effect of the tailwater depth was significant for densimetric Froude number greater than ten. However, the present results indicate that the cut off value could be lower depending on the value of grain size-to-nozzle width ratio. Numbers of equations are drawn out for a better scaling of numerous scour parameters. Also suggested the empirical prediction of scour to predict the scour centre line profile and plan view of scour profile at any particular time.

Keywords: densimetric froude number, jets, nozzle, sand, scour, tailwater, time

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Authors: Folajinmi Oluwasina, Magbagbeola Dairo, Ikeoluwapo Ajayi

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Contraception has proven to be an effective way of controlling fertility and spacing births. Studies have shown that contraception can avert the high-risk pregnancies and consequently reduce maternal deaths up to 32%. Uptake of modern contraception is promoted as a mechanism to address the reproductive health needs of men and women, as well as the crucial challenge of rapid population increase. A cross- sectional descriptive study using a two- stage systematic sampling technique was used to select 530 women of reproductive age out of 20,000 households. Respondents were interviewed using a semi-structured questionnaire. Knowledge was assessed on a 5 point score in which a score of ≤ 2 rated poor while perception was scored on 36 points score in which a score of ≤ 18 was rated low. Data were analyzed using descriptive statistics, Chi-square test and logistic regression at p< 0.05. There were 530 respondents. Age of respondents was 30.3 ±7.8 years, and 73.0% were married. About 90% had good knowledge of contraception while 60.8% had used contraceptives. The commonest source of information about contraception was mass media (72.8%). Minority (26.1%) obtained husbands approval before using contraceptive while 20.0% had used modern contraceptives before the first birth. Many (54.5%) of the respondents agreed that contraception helps in improving standard of living and 64.7% had good perception about contraception. Factors that hindered effective uptake of contraception services included poor service provider’s attitude (33.3%) and congestion at the service centers (4.5%). Respondents with nonuse of contraceptive before first birth are less likely to subsequently use contraceptives (OR= 0.324, 95% CI= 0.1-0.5). Husband approval of contraceptives use was the major determinant of women’s contraceptive use (OR = 3.4, 95% CI = 1.3-8.7). Respondents who had family planning centers not more than 5 kilometers walking distance to their residence did not significantly use contraception services (41.5%) more than 21.1% of those who had to take means of transportation to the service venues. This study showed that majority of the respondents were knowledgeable and aware of contraception services, but husband’s agreement on the use of modern contraceptives remains poor. Programmes that enhances husbands approval of modern contraception is thus recommended.

Keywords: contraception services, service provider’s attitude, uptake, husbands approval

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Authors: Kholil Kholil, Aniwidayati

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MDS (Multi-Dimensional Scaling) is one method that has been widely used to evaluate the use of natural resources. By using Raffish software tool, we will able to analyze sustainability level of the natural resources use. This paper will discuss the level of sustainability of the reservoir using MDS (Multi-Dimensional Scaling) based on five dimensions: (1) Ecology & Layout, (2) Economics, (3) Social & Culture, (4) Regulations & Institutional, and (5) Infrastructure and Technology. MDS analysis results show that the dimension of ecological and layout, institutional and the regulation are lack of sustainability due to the low index score of 45.76 and 42.24. While for the economic, social and culture, and infrastructure and technology dimension reach each score of 63.12, 64.42, and 68.64 (only the sufficient sustainability category). It means that the sustainability performance of Cirata Reservoir seriously threatened.

Keywords: MDS, cirata reservoir, carrying capacity, water quality, sustainable development, sedimentation, sustainability index

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Authors: Neal Rupani

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Background: A surgical trainee has limited learning opportunities in the operating room in order to gain an ever-increasing standard of surgical skill, competency, and proficiency. These opportunities continue to decline due to numerous factors such as the European Working Time Directive and increasing requirement for service provision. It is therefore imperative to obtain the highest educational value from each educational opportunity. A measure that has yet to be validated in England on surgical trainees called the Operating Room Educational Environment Measure (OREEM) has been developed to identify and evaluate each component of the educational environment with a view to steer future change in optimising educational events in theatre. Aims: The aims of the study are to assess the reliability of the OREEM within England and to evaluate the surgical trainee’s objective perspective of the current operating room educational environment within one region within England. Methods: Using a quantitative study approach, data was collected over one month from surgical trainees within Health Education Thames Valley (Oxford) using an online questionnaire consisting of demographic data, the OREEM, a global satisfaction score. Results: 140 surgical trainees were invited to the study, with an online response of 54 participants (response rate = 38.6%). The OREEM was shown to have good internal consistency (α = 0.906, variables = 40) and unidimensionality, along with all four of its subgroups. The mean OREEM score was 79.16%. The areas highlighted for improvement predominantly focused on improving learning opportunities (average subscale score = 72.9%) and conducting pre- and post-operative teaching (average score = 70.4%). The trainee perception is most satisfactory for the level of supervision and workload (average subscale score = 82.87%). There was no differences found between gender (U = 191.5, p = 0.535) or type of hospital (U = 258.0, p = 0.099), but the learning environment was favoured towards senior trainees (U = 223.5, p = 0.017). There was strong correlation between OREEM and the global satisfaction score (r = 0.755, p<0.001). Conclusions: The OREEM was shown to be reliable in measuring the educational environment in the operating room. This can be used to identify potentially modifiable components for improvement and as an audit tool to ensure high standards are being met. The current perception of the education environment in Health Education Thames Valley is satisfactory, and modifiable internal and external factors such as reducing service provision requirements, empowering trainees to plan lists, creating a team-working ethic between all personnel, and using tools that maximise learning from each operation have been identified to improve learning in the future. There is a favourable attitude to use of such improvement tools, especially for those currently dissatisfied.

Keywords: education environment, surgery, post-graduate education, OREEM

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Authors: Liang Zhao, Jili Zhang, Chongquan Zhong

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Energy consumption data, in particular those involving public buildings, are impacted by many factors: the building structure, climate/environmental parameters, construction, system operating condition, and user behavior patterns. Traditional methods for data analysis are insufficient. This paper delves into the data mining technology to determine its application in the analysis of building energy consumption data including energy consumption prediction, fault diagnosis, and optimal operation. Recent literature are reviewed and summarized, the problems faced by data mining technology in the area of energy consumption data analysis are enumerated, and research points for future studies are given.

Keywords: data mining, data analysis, prediction, optimization, building operational performance

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Authors: Omar M. Elmabrouk., Roaa Y. Taha., Najat M. Ebrahim, Sabbreen A. Mohammed

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Power transformers are the most crucial part of power electrical system, distribution and transmission grid. This part is maintained using predictive or condition-based maintenance approach. The diagnosis of power transformer condition is performed based on Dissolved Gas Analysis (DGA). There are five main methods utilized for analyzing these gases. These methods are International Electrotechnical Commission (IEC) gas ratio, Key Gas, Roger gas ratio, Doernenburg, and Duval Triangle. Moreover, due to the importance of the transformers, there is a need for an accurate technique to diagnose and hence predict the transformer condition. The main objective of this technique is to avoid the transformer faults and hence to maintain the power electrical system, distribution and transmission grid. In this paper, the DGA was utilized based on the data collected from the transformer records available in the General Electricity Company of Libya (GECOL) which is located in Benghazi-Libya. The Fuzzy Logic (FL) technique was implemented as a diagnostic approach based on IEC gas ratio method. The FL technique gave better results and approved to be used as an accurate prediction technique for power transformer faults. Also, this technique is approved to be a quite interesting for the readers and the concern researchers in the area of FL mathematics and power transformer.

Keywords: dissolved gas-in-oil analysis, fuzzy logic, power transformer, prediction

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Authors: R. Hassan, J. Duncombe, E. Darke, A. Dias, K. Anderson, R. G. Middleton

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NHS England has set itself the task of delivering a “Net Zero” National Health service by 2040. It is incumbent upon all health care practioners to work towards this goal. Orthopaedic surgeons are no exception. Distal radial fractures are the most common fractures sustained by the adult population. However, studies are shortcoming on individual patient experience. The aim of this study was to assess the patient’s satisfaction and outcomes with woodcast used in the conservative management of distal radius fractures. For all patients managed with woodcast in our unit, we undertook a structured questionnaire that included the Patient Rated Wrist Evaluation (PRWE) score, The EQ-5D-5L score and the pain numerical score at the time of injury and six weeks after. 30 patients were initially managed with woodcast. 80% of patients tolerated woodcast for the full duration of their treatment. Of these, 20% didn’t tolerate woodcast and had their casts removed within 48 hours. Of the remaining, 79.1% were satisfied about woodcast comfort, 66% were very satisfied about woodcast weight, 70% were satisfied with temperature and sweatiness, 62.5% were very satisfied about the smell/odour, and 75% were satisfied about the level of support woodcast provided. During their treatment, 83.3% of patients rated their pain as five or less. For those who completed their treatment in woodcast, none required any further intervention or utilised the open appointment because of ongoing wrist problems. In conclusion, when woodcast is tolerated, patients’ satisfaction and outcome levels were good. However, we acknowledged 20% of patients in our series were not able to tolerate woodacst, Therefore, we suggest a comparison between the widely used synthetic plaster of Paris casting and woodcast to come in order.

Keywords: distal radius fractures, ecological cast, sustainability, woodcast

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Authors: Prozorkevitch D., Mishurov A., Sokolov K., Karsakov L., Pestrikova L.

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The current body of knowledge about the marine environment and the dynamics of marine ecosystems includes a huge amount of heterogeneous data collected over decades. It generally includes a wide range of hydrological, biological and fishery data. Marine researchers collect these data and analyze how and why the ecosystem changes from past to present. Based on these historical records and linkages between the processes it is possible to predict future changes. Multivariate analysis of trends and their interconnection in the marine ecosystem may be used as an instrument for predicting further ecosystem evolution. A wide range of information about the components of the marine ecosystem for more than 50 years needs to be used to investigate how these arrays can help to predict the future.

Keywords: barents sea ecosystem, abiotic, biotic, data sets, trends, prediction

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Authors: Polamorn Tamprateep, Warapong Thakanun

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This researchs' objectives are: 1. To study the customer demographics that affects the purchasing behavior; 2. To study the marketing mix that affects the purchasing behavior; 3. To study the relationship between purchasing behavior and customers’ perception of Brand Equity. Population of this research is Starbucks Coffee (Thailand) customers in Bangkok. The tool used in this study was questionnaire created from concepts, theories and related researches. The study showed that, of 400 respondents, overall opinion received high score (xˉ= 3.77). When each item is considered, it was found that ‘Staff are knowledgeable in providing service.’, ‘ Staff are friendly.’, ‘Staff possess good communication skill with customers.’, ‘Staff know all types of coffee well.’, and ‘Staff are enthusiastic in giving service.’, all these items received high score with a mean of 3.92, 3.87, 3.77, 3.71 and 3.63, respectively.

Keywords: mix attitude of the product, consumer, buying behavior, Starbucks

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Authors: Yasir E. Mohieldeen

Abstract:

Qatar is one of the most water scarce countries in the World. In 2014, the average per capita rainfall was less than 29 m3/y/ca, while the global average is 6,000 m3/y/ca. However, the per capita water consumption in Qatar is among the highest in the World: more than 500 liters per person per day, whereas the global average is 160 liters per person per day. Since the early 2000s, Qatar has been relying heavily on desalinated water from the Arabian Gulf as the main source of fresh water. In 2009, about 99.9% of the total potable water produced was desalinated. Reliance on desalinated water makes Qatar very vulnerable to water related natural disasters, such as the red-tide phenomenon. Qatar’s strategic water reserve lasts for only 7 days. In case of red-tide outbreak, the country would not be able to desalinate water for days, let alone the months that this disaster would bring about (as it clogs the desalination equipment). The 2008-09 red-tide outbreak, for instance, lasted for more than eight months and forced the closure of desalination plants in the region for weeks. This study aims at identifying favorite conditions for red-tide outbreaks, using satellite data along with in-situ measurements. This identification would allow the prediction of these outbreaks and their hotspots. Prediction and monitoring of outbreaks are crucial to water security in the country, as different measures could be put in place in advance to prevent an outbreak and mitigate its impact if it happened. Red-tide outbreaks are detected using different algorithms for chlorophyll concentration in the Gulf waters. Vegetation indices, such as Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were used along with Surface Algae Bloom Index (SABI) to detect known outbreaks. MODIS (or Moderate Resolution Imaging Spectroradiometer) bands are used to calculate these indices. A red-tide outbreaks atlas in the Arabian Gulf is being produced. Prediction of red-tide outbreaks ahead of their occurrences would give critical information on possible water-shortage in the country. Detecting known outbreaks in the past few decades and related parameters (e.g. water salinity, water surface temperature, nutrition, sandstorms, … etc) enables the identification of favorite conditions of red-tide outbreak that are key to the prediction of these outbreaks.

Keywords: Arabian Gulf, MODIS, red-tide detection, strategic water reserve, water desalination

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Authors: Dariush Jafari, S. Mostafa Nowee

Abstract:

In this study a ternary system containing sodium chloride as solute, water as primary solvent and ethanol as the antisolvent was considered to investigate the application of artificial neural network (ANN) in prediction of sodium solubility in the mixture of water as the solvent and ethanol as the antisolvent. The system was previously studied using by Extended UNIQUAC model by the authors of this study. The comparison between the results of the two models shows an excellent agreement between them (R2=0.99), and also approves the capability of ANN to predict the thermodynamic behavior of ternary electrolyte systems which are difficult to model.

Keywords: thermodynamic modeling, ANN, solubility, ternary electrolyte system

Procedia PDF Downloads 385

Authors: Yujiang Wu

Abstract:

As electrical machine (e-machine) power density re-querulents become more stringent in vehicle electrification, mounting a temperature sensor for e-machine stator windings becomes increasingly difficult. This can lead to higher manufacturing costs, complicated harnesses, and reduced reliability. In this paper, we propose a deep-learning method for predicting electric machine winding temperature, which can either replace the sensor entirely or serve as a backup to the existing sensor. We compare the performance of our method, the stateful long short-term memory networks (LSTM) with truncated backpropagation through time (TBTT), with that of linear regression, as well as stateless LSTM with/without residual connection. Our results demonstrate the strength of combining stateful LSTM and TBTT in tackling nonlinear time series prediction problems with long sequence lengths. Additionally, in industrial applications, high-temperature region prediction accuracy is more important because winding temperature sensing is typically used for derating machine power when the temperature is high. To evaluate the performance of our algorithm, we developed a temperature-stratified MSE. We propose a simple but effective data preprocessing trick to improve the high-temperature region prediction accuracy. Our experimental results demonstrate the effectiveness of our proposed method in accurately predicting winding temperature, particularly in high-temperature regions, while also reducing manufacturing costs and improving reliability.

Keywords: deep learning, electrical machine, functional safety, long short-term memory networks (LSTM), thermal management, time series prediction

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Authors: Farzad Khajavi

Abstract:

Recognition of the interaction between active pharmaceutical ingredients (API) and excipients is a pivotal factor in the development of all pharmaceutical dosage forms. By predicting the interaction between API and excipients, we will be able to prevent the advent of impurities or at least lessen their amount. In this study, we used principle component analysis (PCA) to predict the interaction between Gliclazide as a secondary amine with Lactose in pharmaceutical solid dosage forms. The infrared spectra of binary mixtures of Gliclazide with Lactose at different mole ratios were recorded, and the obtained matrix was analyzed with PCA. By plotting score columns of the analyzed matrix, the incompatibility between Gliclazide and Lactose was observed. This incompatibility was seen experimentally. We observed the appearance of the impurity originated from the Maillard reaction between Gliclazide and Lactose at the chromatogram of the manufactured tablets in room temperature and under accelerated stability conditions. This impurity increases at the stability months. By changing Lactose to Mannitol and using Calcium Dibasic Phosphate in the tablet formulation, the amount of the impurity decreased and was in the acceptance range defined by British pharmacopeia for Gliclazide Tablets. This method is a fast and simple way to predict the existence of incompatibility between excipients and active pharmaceutical ingredients.

Keywords: PCA, gliclazide, impurity, infrared spectroscopy, interaction

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Authors: A. J. Regan, F. J. Lidgey, M. Betteridge, P. Georgiou, C. Toumazou, K. Hayatleh, J. R. Dibble

Abstract:

Our goal is development of an algorithm capable of predicting the directional trend of the Standard and Poor’s 500 index (S&P 500). Extensive research has been published attempting to predict different financial markets using historical data testing on an in-sample and trend basis, with many authors employing excessively complex mathematical techniques. In reviewing and evaluating these in-sample methodologies, it became evident that this approach was unable to achieve sufficiently reliable prediction performance for commercial exploitation. For these reasons, we moved to an out-of-sample strategy based on linear regression analysis of an extensive set of financial data correlated with historical closing prices of the S&P 500. We are pleased to report a directional trend accuracy of greater than 55% for tomorrow (t+1) in predicting the S&P 500.

Keywords: financial algorithm, GPU, S&P 500, stock market prediction

Procedia PDF Downloads 350

Authors: Amal Shehata

Abstract:

Low back pain related to disc prolapse is localized in the lumbar area and it may be radiated to the lower extremities, starting from neurons near or around the spinal canal. Most of the population may be affected with disc prolapse within their lifetime and leads to lost productivity, disability and loss of function. The study purpose was to examine the effect of rehabilitative nursing program on pain intensity and functional status among patients with discectomy. Design: Aquasi experimental design was utilized. Setting: The study was carried out at neurosurgery department and out patient's clinic of Menoufia University and Teaching hospitals at Menoufia governorate, Egypt. Instrument of the study: Five Instruments were used for data collection: Structured interviewing questionnaire, Functional assessment instrument, Observational check list, Numeric rating Scale and Oswestry low back pain disability questionnaire. Results: There was an improvement in mean total knowledge score about disease process, discectomy and rehabilitation program in study group (25.32%) than control group (7.32%). There was highly statistically significant improvement in lumbar flexibility among study group (80%) than control group (30%) after rehabilitation program than before. Also there was a decrease in pain score in study group (58% no pain) than control group (28% no pain) after rehabilitation program. There was an improvement in total disability score of study group (zero %) regarding effect of pain on the activity of daily living after rehabilitation program than control group (16%). Conclusion: Application of rehabilitative nursing program for patient with discectomy had proven a positive effect in relation to knowledge score, pain reduction, activity of daily living and functional abilities. Recommendation: A continuous rehabilitative nursing program should be carried out for all patients immediately after discectomy surgery on regular basis. Also A colored illustrated booklet about rehabilitation program should be available and distributed for all patients before surgery.

Keywords: discectomy, rehabilitative nursing program, pain intensity, functional status

Procedia PDF Downloads 142

Authors: Jong Won, Park, Sung Hyun, Kim

Abstract:

The kinetic energy to pass subjected to shock and chisel reciprocating piston hydraulic power supplied by the excavator using for the purpose of crushing the rock, and roads, buildings, etc., hydraulic breakers blow. Impact frequency, efficiency measurement of the impact energy, hydraulic breakers, to demonstrate the ability of hydraulic breaker manufacturers and users to a very important item. And difficult in order to confirm the initial performance degradation in the life of the hydraulic breaker has been thought to be a problem.In this study, we measure the efficiency of hydraulic breaker, Impact energy and Impact frequency, the degradation analysis of research to predict the life.

Keywords: impact energy, impact frequency, hydraulic breaker, life prediction

Procedia PDF Downloads 441