Search results for: predictive coding

Authors: R. O. Ocaya, J. J. Terblans

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The laws of Newtonian mechanics allow ab-initio molecular dynamics to model and simulate particle trajectories in material science by defining a differentiable potential function. This paper discusses some considerations for the coding of ab-initio programs for simulation on a standalone computer and illustrates the approach by C language codes in the context of embedded metallic atoms in the face-centred cubic structure. The algorithms use velocity-time integration to determine particle parameter evolution for up to several thousands of particles in a thermodynamical ensemble. Such functions are reusable and can be placed in a redistributable header library file. While there are both commercial and free packages available, their heuristic nature prevents dissection. In addition, developing own codes has the obvious advantage of teaching techniques applicable to new problems.

Keywords: C language, molecular dynamics, simulation, embedded atom method

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Authors: Vahid Nademi

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In response to widely used wearable medical devices equipped with a continuous glucose monitor (CGM) and insulin pump, the advanced control methods are still demanding to get the full benefit of these devices. Unlike costly clinical trials, implementing effective insulin-glucose control strategies can provide significant contributions to the patients suffering from chronic diseases such as diabetes. This study deals with a key role of two-layer insulin-glucose regulator based on model-predictive-control (MPC) scheme so that the patient’s predicted glucose profile is in compliance with the insulin level injected through insulin pump automatically. It is achieved by iterative optimization algorithm which is called an integrated perturbation analysis and sequential quadratic programming (IPA-SQP) solver for handling uncertainties due to unexpected variations in glucose-insulin values and body’s characteristics. The feasibility evaluation of the discussed control approach is also studied by means of numerical simulations of two case scenarios via measured data. The obtained results are presented to verify the superior and reliable performance of the proposed control scheme with no negative impact on patient safety.

Keywords: blood glucose monitoring, insulin pump, predictive control, optimization

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Authors: Theresa Marie Miller, Ma. Nympha Joaquin

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The aim of this study was to check the predictive power of social-oriented and individual-oriented achievement motivation on student engagement and collaborative problem-solving skills in mathematics. A sample of 277 fourth year high school students from the Philippines were selected. Surveys and videos of collaborative problem solving activity were used to collect data from respondents. The mathematics teachers of the participants were interviewed to provide qualitative support on the data. Systemaitc correlation and regression analysis were employed. Results of the study showed that achievement motivations−SOAM and IOAM− linearly predicted student engagement but was not significantly associated to the collaborative problem-solving skills in mathematics. Student engagement correlated positively with collaborative problem-solving skills in mathematics. The results contribute to theorizing about the predictive power of achievement motivations, SOAM and IOAM on the realm of academic behaviors and outcomes as well as extend the understanding of collaborative problem-solving skills of 21st century learners.

Keywords: achievement motivation, collaborative problem-solving skills, individual-oriented achievement motivation, social-oriented achievement motivation, student engagement

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Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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The widespread use of machine learning applications in production is significantly accelerated by improved computing power and increasing data availability. Predictive quality enables the assurance of product quality by using machine learning models as a basis for decisions on test results. The use of real Bosch production data based on geometric gauge blocks from machining, mating data from assembly and hydraulic measurement data from final testing of directional valves is a promising approach to classifying the quality characteristics of workpieces.

Keywords: machine learning, classification, predictive quality, hydraulics, supervised learning

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Authors: Kudzanayi Chiteka, Wellington Makondo

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The whole world is on the drive to go green owing to the negative effects of burning fossil fuels. Therefore, there is immediate need to identify and utilise alternative renewable energy sources. Among these energy sources solar energy is one of the most dominant in Zimbabwe. Solar power plants used to generate electricity are entirely dependent on solar radiation. For planning purposes, solar radiation values should be known in advance to make necessary arrangements to minimise the negative effects of the absence of solar radiation due to cloud cover and other naturally occurring phenomena. This research focused on the prediction of Global Horizontal Irradiation values for the sixth day given values for the past five days. Artificial intelligence techniques were used in this research. Three models were developed based on Support Vector Machines, Radial Basis Function, and Feed Forward Back-Propagation Artificial neural network. Results revealed that Support Vector Machines gives the best results compared to the other two with a mean absolute percentage error (MAPE) of 2%, Mean Absolute Error (MAE) of 0.05kWh/m²/day root mean square (RMS) error of 0.15kWh/m²/day and a coefficient of determination of 0.990. The other predictive models had prediction accuracies of MAPEs of 4.5% and 6% respectively for Radial Basis Function and Feed Forward Back-propagation Artificial neural network. These two models also had coefficients of determination of 0.975 and 0.970 respectively. It was found that prediction of GHI values for the future days is possible using artificial intelligence-based predictive models.

Keywords: solar energy, global horizontal irradiation, artificial intelligence, predictive models

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Authors: Mohamed Abo El Nasr, Mohamed Shoeib, Abdelhamid Abdelkhalik, Amro Serag

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Background: Early assessment of severity of chest trauma, either blunt or penetrating is of critical importance in prediction of patient outcome. Different trauma scoring systems are widely available and are based on anatomical or physiological parameters to expect patient morbidity or mortality. Up till now, there is no ideal, universally accepted trauma score that could be applied in all trauma centers and is suitable for assessment of severity of chest trauma patients. Aim: Our aim was to compare various trauma scoring systems regarding their predictability of morbidity and mortality in chest trauma patients. Patients and Methods: This study was a prospective study including 400 patients with chest trauma who were managed at Tanta University Emergency Hospital, Egypt during a period of 2 years (March 2014 until March 2016). The patients were divided into 2 groups according to the mode of trauma: blunt or penetrating. The collected data included age, sex, hemodynamic status on admission, intrathoracic injuries, and associated extra-thoracic injuries. The patients outcome including mortality, need of thoracotomy, need for ICU admission, need for mechanical ventilation, length of hospital stay and the development of acute respiratory distress syndrome were also recorded. The relevant data were used to calculate the following trauma scores: 1. Anatomical scores including abbreviated injury scale (AIS), Injury severity score (ISS), New injury severity score (NISS) and Chest wall injury scale (CWIS). 2. Physiological scores including revised trauma score (RTS), Acute physiology and chronic health evaluation II (APACHE II) score. 3. Combined score including Trauma and injury severity score (TRISS ) and 4. Chest-Specific score Thoracic trauma severity score (TTSS). All these scores were analyzed statistically to detect their sensitivity, specificity and compared regarding their predictive power of mortality and morbidity in blunt and penetrating chest trauma patients. Results: The incidence of mortality was 3.75% (15/400). Eleven patients (11/230) died in blunt chest trauma group, while (4/170) patients died in penetrating trauma group. The mortality rate increased more than three folds to reach 13% (13/100) in patients with severe chest trauma (ISS of >16). The physiological scores APACHE II and RTS had the highest predictive value for mortality in both blunt and penetrating chest injuries. The physiological score APACHE II followed by the combined score TRISS were more predictive for intensive care admission in penetrating injuries while RTS was more predictive in blunt trauma. Also, RTS had a higher predictive value for expectation of need for mechanical ventilation followed by the combined score TRISS. APACHE II score was more predictive for the need of thoracotomy in penetrating injuries and the Chest-Specific score TTSS was higher in blunt injuries. The anatomical score ISS and TTSS score were more predictive for prolonged hospital stay in penetrating and blunt injuries respectively. Conclusion: Trauma scores including physiological parameters have a higher predictive power for mortality in both blunt and penetrating chest trauma. They are more suitable for assessment of injury severity and prediction of patients outcome.

Keywords: chest trauma, trauma scores, blunt injuries, penetrating injuries

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Authors: Garadi Ahmed, Boubakar S. Bouazza

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The Zero Cross-Correlation (C, w) code is a family of binary sequences of length C and constant Hamming-weight, the cross correlation between any two sequences equal zero. In this paper, we evaluate the performance of ZCC code based on Balanced Incomplete Block Design (BIBD) for Spectral Amplitude Coding Optical Code Division Multiple Access (SAC-OCDMA) system using direct detection. The BER obtained is better than 10-9 for five simultaneous users.

Keywords: spectral amplitude coding-optical code-division-multiple-access (SAC-OCDMA), phase induced intensity noise (PIIN), balanced incomplete block design (BIBD), zero cross-correlation (ZCC)

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Authors: M. T. Alodat

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In this paper, we propose a generalization to the Gaussian process regression(GPR) model called the extended skew Gaussian process for regression(ESGPr) model. The ESGPR model works better than the GPR model when the errors are skewed. We derive the predictive distribution for the ESGPR model at a new input. Also we apply the ESGPR model to FOREX data and we find that it fits the Forex data better than the GPR model.

Keywords: extended skew normal distribution, Gaussian process for regression, predictive distribution, ESGPr model

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Authors: Israel Ibarra Solis, Juan Carlos Rodriguez Sierra, Ma. del Carmen Salazar Hernandez, Isis Rodriguez Sanchez, David Perez Guerrero

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At the present paper we try to explain the analysis techniques use for the lubricating oil in a maintenance period of a city bus (Mercedes Benz Boxer 40), which is call ‘R-24 route’, line Coecillo Centro SA de CV in Leon Guanajuato, to estimate the optimal time for the oil change. Using devices such as the rotational viscometer and the atomic absorption spectrometer, they can detect the incipient form when the oil loses its lubricating properties and, therefore, cannot protect the mechanical components of diesel engines such these trucks. Timely detection of lost property in the oil, it allows us taking preventive plan maintenance for the fleet.

Keywords: atomic absorption spectrometry, maintenance, predictive velocity rate, lubricating oils

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Authors: Muhammad Babar Shahzad

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The purpose of our study is to investigate the relationship between perceived job autonomy and turnover intention in sales & marketing staff. Perceived job autonomy is considered one of most studied dimension of Job Characteristic Model. But still there is a confusion in scholars about predictive role of perceived job autonomy in turnover intention. In line of more complex research on this relation, we investigated the relationship between perceived job autonomy and turnover intention. Did nature of job have any impact on this relationship. On the call of different authors we take interactive effect of perceived job autonomy and procedural justice on turnover intention. Predictive role of distributive justice to employee outcomes is not deniable. But predictive role of distributive justice will be prone in different contextual influences. Interactive role of distributive justice and perceived job autonomy is also not tested before. We collected date from 279 marketing and sales managers working in financial institution, FMCG industries, Pharamesutical Industry & Bank. Strong and direct negative relation was found in perceived job autonomy, distributive justice & procedural justice on turnover intention. Distributive and procedural justice is also amplifying the negative relationship of perceived job autonomy and turnover intention. Limitation and future direction for research is also discussed.

Keywords: perceived job autonomy, turnover intention, procedural justice, distributive job

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Authors: Hamdi Beji, Toufik Kanit, Tanguy Messager

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This study aims to construct a predictive model proficient in foreseeing the linear elastic and thermal characteristics of composite materials, drawing on a multitude of influencing parameters. These parameters encompass the shape of inclusions (circular, elliptical, square, triangle), their spatial coordinates within the matrix, orientation, volume fraction (ranging from 0.05 to 0.4), and variations in contrast (spanning from 10 to 200). A variety of machine learning techniques are deployed, including decision trees, random forests, support vector machines, k-nearest neighbors, and an artificial neural network (ANN), to facilitate this predictive model. Moreover, this research goes beyond the predictive aspect by delving into an inverse analysis using genetic algorithms. The intent is to unveil the intrinsic characteristics of composite materials by evaluating their thermomechanical responses. The foundation of this research lies in the establishment of a comprehensive database that accounts for the array of input parameters mentioned earlier. This database, enriched with this diversity of input variables, serves as a bedrock for the creation of machine learning and genetic algorithm-based models. These models are meticulously trained to not only predict but also elucidate the mechanical and thermal conduct of composite materials. Remarkably, the coupling of machine learning and genetic algorithms has proven highly effective, yielding predictions with remarkable accuracy, boasting scores ranging between 0.97 and 0.99. This achievement marks a significant breakthrough, demonstrating the potential of this innovative approach in the field of materials engineering.

Keywords: machine learning, composite materials, genetic algorithms, mechanical and thermal proprieties

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Authors: Ayliana Dharmawan, Heng Yong Sheng, Zhang Xiaojin, Tan Thai Lian

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To facilitate discharge planning, doctors are presently required to assign an Estimated Discharge Date (EDD) for each patient admitted to the hospital. This assignment of the EDD is largely based on the doctor’s judgment. This can be difficult for cases which are complex or relatively new to the doctor. It is hypothesized that a data-driven approach would be able to facilitate the doctors to make accurate estimations of the discharge date. Making use of routinely collected data on inpatient discharges between January 2013 and May 2016, a predictive model was developed using machine learning techniques to predict the Length of Stay (and hence the EDD) of inpatients, at the point of admission. The predictive performance of the model was compared to that of the clinicians using accuracy measures. Overall, the best performing model was found to be able to predict EDD with an accuracy improvement in Average Squared Error (ASE) by -38% as compared to the first EDD determined by the present method. It was found that important predictors of the EDD include the provisional diagnosis code, patient’s age, attending doctor at admission, medical specialty at admission, accommodation type, and the mean length of stay of the patient in the past year. The predictive model can be used as a tool to accurately predict the EDD.

Keywords: inpatient, estimated discharge date, EDD, prediction, data-driven

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Authors: T. Gangadhararao, K. Krishna Kishore

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Motion Estimation (ME) plays a critical role in a video coder, testing such a module is of priority concern. While focusing on the testing of ME in a video coding system, this work presents an error detection and data recovery (EDDR) design, based on the residue-and-quotient (RQ) code, to embed into ME for video coding testing applications. An error in processing Elements (PEs), i.e. key components of a ME, can be detected and recovered effectively by using the proposed EDDR design. The proposed EDDR design for ME testing can detect errors and recover data with an acceptable area overhead and timing penalty.

Keywords: area overhead, data recovery, error detection, motion estimation, reliability, residue-and-quotient (RQ) code

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Authors: Amandeep Sharma, Digvaijay Verma, Ruplal Choudhary

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Food processing is an activity across the globe that help in better handling of agricultural produce, including dairy, meat, and fish. The operations carried out in the food industry includes raw material quality authenticity; sorting and grading; processing into various products using thermal treatments – heating, freezing, and chilling; packaging; and storage at the appropriate temperature to maximize the shelf life of the products. All this is done to safeguard the food products and to ensure the distribution up to the consumer. The approaches to develop predictive models based on mathematical or statistical tools or empirical models’ development has been reported for various milk processing activities, including plant maintenance and wastage. Recently AI is the key factor for the fourth industrial revolution. AI plays a vital role in the food industry, not only in quality and food security but also in different areas such as manufacturing, packaging, and cleaning. A new conceptual model was developed, which shows that smaller sample size as only spectra would be required to predict the other values hence leads to saving on raw materials and chemicals otherwise used for experimentation during the research and new product development activity. It would be a futuristic approach if these tools can be further clubbed with the mobile phones through some software development for their real time application in the field for quality check and traceability of the product.

Keywords: predictive modlleing, ann, ai, food

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Authors: Yan Zhang

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Predictive maintenance is a technique to predict when an in-service machine will fail so that maintenance can be planned in advance. Analytics-driven predictive maintenance is gaining increasing attention in many industries such as manufacturing, utilities, aerospace, etc., along with the emerging demand of Internet of Things (IoT) applications and the maturity of technologies that support Big Data storage and processing. This study aims to build an end-to-end analytics solution that includes both real-time machine condition monitoring and machine learning based predictive analytics capabilities. The goal is to showcase a general predictive maintenance solution architecture, which suggests how the data generated from field machines can be collected, transmitted, stored, and analyzed. We use a publicly available aircraft engine run-to-failure dataset to illustrate the streaming analytics component and the batch failure prediction component. We outline the contributions of this study from four aspects. First, we compare the predictive maintenance problems from the view of the traditional reliability centered maintenance field, and from the view of the IoT applications. When evolving to the IoT era, predictive maintenance has shifted its focus from ensuring reliable machine operations to improve production/maintenance efficiency via any maintenance related tasks. It covers a variety of topics, including but not limited to: failure prediction, fault forecasting, failure detection and diagnosis, and recommendation of maintenance actions after failure. Second, we review the state-of-art technologies that enable a machine/device to transmit data all the way through the Cloud for storage and advanced analytics. These technologies vary drastically mainly based on the power source and functionality of the devices. For example, a consumer machine such as an elevator uses completely different data transmission protocols comparing to the sensor units in an environmental sensor network. The former may transfer data into the Cloud via WiFi directly. The latter usually uses radio communication inherent the network, and the data is stored in a staging data node before it can be transmitted into the Cloud when necessary. Third, we illustrate show to formulate a machine learning problem to predict machine fault/failures. By showing a step-by-step process of data labeling, feature engineering, model construction and evaluation, we share following experiences: (1) what are the specific data quality issues that have crucial impact on predictive maintenance use cases; (2) how to train and evaluate a model when training data contains inter-dependent records. Four, we review the tools available to build such a data pipeline that digests the data and produce insights. We show the tools we use including data injection, streaming data processing, machine learning model training, and the tool that coordinates/schedules different jobs. In addition, we show the visualization tool that creates rich data visualizations for both real-time insights and prediction results. To conclude, there are two key takeaways from this study. (1) It summarizes the landscape and challenges of predictive maintenance applications. (2) It takes an example in aerospace with publicly available data to illustrate each component in the proposed data pipeline and showcases how the solution can be deployed as a live demo.

Keywords: Internet of Things, machine learning, predictive maintenance, streaming data

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Authors: Tohru Kawabe

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In this paper, a new SMC (Sliding Mode Control) method with MP (Model Predictive Control) integral action for the slip suppression of EV (Electric Vehicle) under braking is proposed. The proposed method introduce the integral term with standard SMC gain , where the integral gain is optimized for each control period by the MPC algorithms. The aim of this method is to improve the safety and the stability of EVs under braking by controlling the wheel slip ratio. There also include numerical simulation results to demonstrate the effectiveness of the method.

Keywords: sliding mode control, model predictive control, integral action, electric vehicle, slip suppression

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Authors: Miral Selim, May Haggag, Ibrahim Abotaleb

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The aging of transportation infrastructure presents significant challenges, particularly concerning the monitoring and maintenance of bridges. This study investigates the application of Random Forest algorithms for predictive modeling of bridge conditions, utilizing data from the US National Bridge Inventory (NBI). The research is significant as it aims to improve bridge management through data-driven insights that can enhance maintenance strategies and contribute to overall safety. Random Forest is chosen for its robustness, ability to handle complex, non-linear relationships among variables, and its effectiveness in feature importance evaluation. The study begins with comprehensive data collection and cleaning, followed by the identification of key variables influencing bridge condition ratings, including age, construction materials, environmental factors, and maintenance history. Random Forest is utilized to examine the relationships between these variables and the predicted bridge conditions. The dataset is divided into training and testing subsets to evaluate the model's performance. The findings demonstrate that the Random Forest model effectively enhances the understanding of factors affecting bridge conditions. By identifying bridges at greater risk of deterioration, the model facilitates proactive maintenance strategies, which can help avoid costly repairs and minimize service disruptions. Additionally, this research underscores the value of data-driven decision-making, enabling better resource allocation to prioritize maintenance efforts where they are most necessary. In summary, this study highlights the efficiency and applicability of Random Forest in predictive modeling for bridge management. Ultimately, these findings pave the way for more resilient and proactive management of bridge systems, ensuring their longevity and reliability for future use.

Keywords: data analysis, random forest, predictive modeling, bridge management

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Authors: Michelle R. Sullivan

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Positive romantic relationships and marriages offer a buffer against a host of physical and emotional difficulties. Conversely, poor relationship quality and marital discord can have deleterious consequences for individuals and families. Research has described non-monogamy, infidelity, and consensual non-monogamy, as both consequential and causal of relationship difficulty, or as a unique way a couple strives to make a relationship work. Much research on consensual non-monogamy has built on feminist theory and critique. To the author’s best knowledge, to date, no studies have examined the predictive relationship between individual and relationship characteristics and expectations of non-monogamy. The current longitudinal study: 1) estimated the prevalence of expectations of partner non-monogamy and 2) evaluated whether gender, sexual identity, age, education, how a couple met, and relationship quality were predictive expectations of partner non-monogamy. This study utilized the publically available longitudinal dataset, How Couples Meet and Stay Together. Adults aged 18- to 98-years old (n=4002) were surveyed by phone over 5 waves from 2009-2014. Demographics and how a couple met were gathered through self-report in Wave 1, and relationship quality and expectations of partner non-monogamy were gathered through self-report in Waves 4 and 5 (n=1047). The prevalence of expectations of partner non-monogamy (encompassing both infidelity and consensual non-monogamy) was 4.8%. Logistic regression models indicated that sexual identity, gender, education, and relationship quality were significantly predictive of expectations of partner non-monogamy. Specifically, male gender, lower education, identifying as lesbian, gay, or bisexual, and a lower relationship quality scores were predictive of expectations of partner non-monogamy. Male gender was not predictive of expectations of partner non-monogamy in the follow up logistic regression model. Age and whether a couple met online were not associated with expectations of partner non-monogamy. Clinical implications include awareness of the increased likelihood of lesbian, gay, and bisexual individuals to have an expectation of non-monogamy and the sequelae of relationship dissatisfaction that may be related. Future research directions could differentiate between non-monogamy subtypes and the person and relationship variables that lead to the likelihood of consensual non-monogamy and infidelity as separate constructs, as well as explore the relationship between predicting partner behavior and actual partner behavioral outcomes.

Keywords: open relationship, polyamory, infidelity, relationship satisfaction

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Authors: Wisam Ismail, Sarah Hosgood, Michael Nicholson

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The research hypothesis is that early post-transplant allograft fibrosis will be linked to donor factors and that acute rejection and/or delayed graft function in the recipient will be independent risk factors for the development of fibrosis. This research hypothesis is to explore whether acute rejection/delay graft function has an effect on the renal transplant fibrosis within the first year post live donor kidney transplant between 1998 and 2009. Methods: The study has been designed to identify five time points of the renal transplant biopsies [0 (pre-transplant), 1 month, 3 months, 6 months and 12 months] for 300 live donor renal transplant patients over 12 years period between March 1997 – August 2009. Paraffin fixed slides were collected from Leicester General Hospital and Leicester Royal Infirmary. These were routinely sectioned at a thickness of 4 Micro millimetres for standardization. Conclusions: Fibrosis at 1 month after the transplant was found significantly associated with baseline fibrosis (p<0.001) and HTN in the transplant recipient (p<0.001). Dialysis after the transplant showed a weak association with fibrosis at 1 month (p=0.07). The negative coefficient for HTN (-0.05) suggests a reduction in fibrosis in the absence of HTN. Fibrosis at 1 month was significantly associated with fibrosis at baseline (p 0.01 and 95%CI 0.11 to 0.67). Fibrosis at 3, 6 or 12 months was not found to be associated with fibrosis at baseline (p=0.70. 0.65 and 0.50 respectively). The amount of fibrosis at 1 month is significantly associated with graft survival (p=0.01 and 95%CI 0.02 to 0.14). Rejection and severity of rejection were not found to be associated with fibrosis at 1 month. The amount of fibrosis at 1 month was significantly associated with graft survival (p=0.02) after adjusting for baseline fibrosis (p=0.01). Both baseline fibrosis and graft survival were significant predictive factors. The amount of fibrosis at 1 month was not found to be significantly associated with rejection (p=0.64) after adjusting for baseline fibrosis (p=0.01). The amount of fibrosis at 1 month was not found to be significantly associated with rejection severity (p=0.29) after adjusting for baseline fibrosis (p=0.04). Fibrosis at baseline and HTN in the recipient were found to be predictive factors of fibrosis at 1 month. (p 0.02, p <0.001 respectively). Age of the donor, their relation to the patient, the pre-op Creatinine, artery, kidney weight and warm time were not found to be significantly associated with fibrosis at 1 month. In this complex model baseline fibrosis, HTN in the recipient and cold time were found to be predictive factors of fibrosis at 1 month (p=0.01,<0.001 and 0.03 respectively). Donor age was found to be a predictive factor of fibrosis at 6 months. The above analysis was repeated for 3, 6 and 12 months. No associations were detected between fibrosis and any of the explanatory variables with the exception of the donor age which was found to be a predictive factor of fibrosis at 6 months.

Keywords: fibrosis, transplant, renal, rejection

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Authors: Janet B. Badong-Badilla

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In Jesuit universities, laypersons, who come from the same or different faith backgrounds or traditions, are considered as collaborators in mission. The Jesuits themselves support the contributions of the lay partners in realizing the mission of the Society of Jesus and recognize the important role that they play in education. This study aims to investigate and generate particular notions and understandings of lived experiences of being a lay partner in Jesuit universities in the Philippines, particularly those involved in higher education. Using the qualitative approach as introduced by grounded theorist Barney Glaser, the lay partners’ concept of being a partner, as lived in higher education, is generated systematically from the data collected in the field primarily through in-depth interviews, field notes and observations. Glaser’s constant comparative method of analysis of data is used going through the phases of open coding, theoretical coding, and selective coding from memoing to theoretical sampling to sorting and then writing. In this study, Glaser’s grounded theory as a methodology will provide a substantial insight into and articulation of the layperson’s actual experience of being a partner of the Jesuits in education. Such articulation provides a phenomenological approach or framework to an understanding of the meaning and core characteristics of Jesuit-Lay partnership in Jesuit educational institution of higher learning in the country. This study is expected to provide a framework or model for lay partnership in academic institutions that have the same practice of having lay partners in mission.

Keywords: grounded theory, Jesuit mission in higher education, lay partner, lived experience

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Authors: Yuzhi Cai

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In this paper, we discuss a Bayesian approach to quantile autoregressive (QAR) time series model estimation and forecasting. Together with a combining forecasts technique, we then predict USD to GBP currency exchange rates. Combined forecasts contain all the information captured by the fitted QAR models at different quantile levels and are therefore better than those obtained from individual models. Our results show that an unequally weighted combining method performs better than other forecasting methodology. We found that a median AR model can perform well in point forecasting when the predictive density functions are symmetric. However, in practice, using the median AR model alone may involve the loss of information about the data captured by other QAR models. We recommend that combined forecasts should be used whenever possible.

Keywords: combining forecasts, MCMC, predictive density functions, quantile forecasting, quantile modelling

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Authors: Pankaj Prashar, Bimlesh Kumar, Ankita Sood, Anamika Gautam

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Pharmacovigilance (PV) is a rapidly growing discipline in pharmaceutical industries as an integral part of clinical research and drug development over the past few decades. PV carries a breadth of scope from drug manufacturing to its regulation with safer utilization. The fundamental steps of PV not only includes data collection and verification, coding of drugs with adverse drug reactions, causality assessment and timely reporting to the authorities but also monitoring drug manufacturing, safety issues, product quality and conduction of due diligence. Standardization of adverse event information, collaboration of multiple departments in different companies, preparation of documents in accordance to both governmental as well as non-governmental organizations (FDA, EMA, GVP, ICH) are the advancements in discipline of PV. De-harmonization, lack of predictive drug safety models, improper funding by government, non-reporting, and non-acceptability of ADRs by developing countries and reports directly from patients to the monitoring centres respectively are the major road backs of PV. Mandatory pharmacovigilance reporting, frequent inspections, funding by government, educating and training medical students, pharmacists and nurses in this segment can bring about empowerment in PV. This area needs to be addressed with a sense of urgency for the safe utilization of pharmaceuticals.

Keywords: pharmacovigilance, regulatory, adverse event, drug safety

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Authors: Shivaram P. Arunachalam, Mustafa Y. Sir, Andy Boggust, David M. Nestler, Thomas R. Hellmich, Kalyan S. Pasupathy

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Emergency departments (EDs) provide urgent care to patients throughout the day in a complex and chaotic environment. Real-time location systems (RTLS) are increasingly being utilized in healthcare settings, and have shown to improve safety, reduce cost, and increase patient satisfaction. Radio Frequency Identification Device (RFID) data in an ED has been shown to compute variables such as patient-provider contact time, which is associated with patient outcomes such as 30-day hospitalization. These variables can provide avenues for improving ED operational efficiency. A major challenge with ED financial operations is under-coding of critical care services due to physicians’ difficulty reporting accurate times for critical care provided under Current Procedural Terminology (CPT) codes 99291 and 99292. In this work, the authors propose a framework to optimize ED critical care billing using RFID data. RFID estimated physician-patient contact times could accurately quantify direct critical care services which will help model a data-driven approach for ED critical care billing. This paper will describe the framework and provide insights into opportunities to prevent under coding as well as over coding to avoid insurance audits. Future work will focus on data analytics to demonstrate the feasibility of the framework described.

Keywords: critical care billing, CPT codes, emergency department, RFID

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Authors: Mohd Tariq, Shyamal K. Nandi, Indra D. Bhatt

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Paris polyphylla Smith (Family- Liliaceae; English name-Love apple: Local name- Satuwa) is an important folk medicinal herb of the Indian subcontinent, being a source of number of bioactive compounds for drug formulation. The rhizomes are widely used as antihelmintic, antispasmodic, digestive stomachic, expectorant and vermifuge, antimicrobial, anti-inflammatory, heart and vascular malady, anti-fertility and sedative. Keeping in view of this, the species is being constantly removed from nature for trade and various pharmaceuticals purpose, as a result, the availability of the species in its natural habitat is decreasing. In this context, it would be pertinent to conserve this species and reintroduce them in its natural habitat. Predictive distribution modelling of this species was performed in Western Himalayan Region. One such recent method is Ecological Niche Modelling, also popularly known as Species distribution modelling, which uses computer algorithms to generate predictive maps of species distributions in a geographic space by correlating the point distributional data with a set of environmental raster data. In case of P. polyphylla, and to understand its potential distribution zones and setting up of artificial introductions, or selecting conservation sites, and conservation and management of their native habitat. Among the different districts of Uttarakhand (28°05ˈ-31°25ˈ N and 77°45ˈ-81°45ˈ E) Uttarkashi, Rudraprayag, Chamoli, Pauri Garhwal and some parts of Bageshwar, 'Maximum Entropy' (Maxent) has predicted wider potential distribution of P. polyphylla Smith. Distribution of P. polyphylla is mainly governed by Precipitation of Driest Quarter and Mean Diurnal Range i.e., 27.08% and 18.99% respectively which indicates that humidity (27%) and average temperature (19°C) might be suitable for better growth of Paris polyphylla.

Keywords: biodiversity conservation, Indian Himalayan region, Paris polyphylla, predictive distribution modelling

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Authors: Ruzanna Muradyan

Abstract:

Medical data mining is a crucial field in contemporary healthcare that offers cutting-edge tactics with enormous potential to transform patient care. This abstract examines how sophisticated data mining techniques could transform the healthcare industry, with a special focus on how they might improve patient outcomes. Healthcare data repositories have dynamically evolved, producing a rich tapestry of different, multi-dimensional information that includes genetic profiles, lifestyle markers, electronic health records, and more. By utilizing data mining techniques inside this vast library, a variety of prospects for precision medicine, predictive analytics, and insight production become visible. Predictive modeling for illness prediction, risk stratification, and therapy efficacy evaluations are important points of focus. Healthcare providers may use this abundance of data to tailor treatment plans, identify high-risk patient populations, and forecast disease trajectories by applying machine learning algorithms and predictive analytics. Better patient outcomes, more efficient use of resources, and early treatments are made possible by this proactive strategy. Furthermore, data mining techniques act as catalysts to reveal complex relationships between apparently unrelated data pieces, providing enhanced insights into the cause of disease, genetic susceptibilities, and environmental factors. Healthcare practitioners can get practical insights that guide disease prevention, customized patient counseling, and focused therapies by analyzing these associations. The abstract explores the problems and ethical issues that come with using data mining techniques in the healthcare industry. In order to properly use these approaches, it is essential to find a balance between data privacy, security issues, and the interpretability of complex models. Finally, this abstract demonstrates the revolutionary power of modern data mining methodologies in transforming the healthcare sector. Healthcare practitioners and researchers can uncover unique insights, enhance clinical decision-making, and ultimately elevate patient care to unprecedented levels of precision and efficacy by employing cutting-edge methodologies.

Keywords: data mining, healthcare, patient care, predictive analytics, precision medicine, electronic health records, machine learning, predictive modeling, disease prognosis, risk stratification, treatment efficacy, genetic profiles, precision health

Procedia PDF Downloads 63

Authors: Jeff Dickie

Abstract:

This research paper combined grounded coding and research questions with the objective to investigate conflict resolution in the classroom. The students’ answers concerning teaching were coded according to phrasal meanings which revealed concepts. These concept codes then became input data into theoretical frameworks. The investigation indicated two conflicts: whether the information was valid and whether to make the study effort which was discussed as perceptions of teacher’s competence in helping to learn. The relevant factors in helping to learn were predominately emotional. These factors were important in the negotiation process to develop relationships. Information validity seemed to be the motivator to begin and participate effectively with the learning process. In effect, confidence in the learning negotiation process with the focus towards relationship building with the subject matter seemed to be the motivator to make the study effort.

Keywords: coding, confidence, competence, conflict resolution, risk, trust, relationship building

Procedia PDF Downloads 431

Authors: Farhad Shafiepour Motlagh, Narges Salehi

Abstract:

Purpose: The aim of the present study was a paradigm model of educational policy review strategies for the development of Professional schools in Iran. Research Methodology: The research method was based on Grounded theory. The statistical population included all articles of the ten years 2022-2010 and the method of sampling in a purposeful manner to the extent of theoretical saturation to 31 articles. For data analysis, open coding, axial coding and selective coding were used. Results: The results showed that causal conditions include social requirements (social expectations, educational justice, social justice); technology requirements (use of information and communication technology, use of new learning methods), educational requirements (development of educational territory, Development of educational tools and development of learning methods), contextual conditions including dual dimensions (motivational-psychological context, context of participation and cooperation), strategic conditions including (decentralization, delegation, organizational restructuring), intervention conditions (poor knowledge) Human resources, centralized system governance) and outcomes (school productivity, school professionalism, graduate entry into the labor market) were obtained. Conclusion: A review of educational policy is necessary to develop Iran's Professional schools, and this depends on decentralization, delegation, and, of course, empowerment of school principals.

Keywords: school productivity, professional schools, educational policy, paradigm

Procedia PDF Downloads 207

Authors: Erdinc Ozturk, Gizem Akcan

Abstract:

The aim of this study was to determine some psychosocial variables that predict childhood trauma and dissociation in university students. These psychosocial variables were perceived social support, relationship status, gender and life satisfaction. 250 (125 males, 125 females) university students (bachelor, master and postgraduate degree) were enrolled in this study. They were chosen from universities in Istanbul at the education year of 2016-2017. Dissociative Experiences Scale (DES), Childhood Trauma Questionnaire (CTQ), Multidimensional Perceived Social Support Scale, Life Satisfaction Scale and Relationship Scales Questionnaire were used to assess related variables. Demographic information form was given to students in order to have their demographic information. Frequency distribution, multiple linear regression, and t-test analysis were used for statistical analysis. As together, perceived social support, relationship status and life satisfaction were found to have predictive value on trauma among university students. However, as together, these psychosocial variables did not have predictive value on dissociation. Only, trauma and relationship status had significant predictive value on dissociation. Moreover, there was significant difference between males and females in terms of trauma; however, dissociation scores of participants were not significantly different in terms of gender.

Keywords: childhood trauma, dissociation, perceived social support, relationship status, life satisfaction

Procedia PDF Downloads 275

Authors: Ahmed AlNouss, Salim Ahmed

Abstract:

Process control refers to the techniques to control the variables in a process in order to maintain them at their desired values. Advanced process control (APC) is a broad term within the domain of control where it refers to different kinds of process control and control related tools, for example, model predictive control (MPC), statistical process control (SPC), fault detection and classification (FDC) and performance assessment. APC is often used for solving multivariable control problems and model predictive control (MPC) is one of only a few advanced control methods used successfully in industrial control applications. Advanced control is expected to bring many benefits to the plant operation; however, the extent of the benefits is plant specific and the application needs a large investment. This requires an analysis of the expected benefits before the implementation of the control. In a real plant simulation studies are carried out along with some experimentation to determine the improvement in the performance of the plant due to advanced control. In this research, such an exercise is undertaken to realize the needs of APC application. The main objectives of the paper are as follows: (1) To apply MPC to a number of simulations set up to realize the need of MPC by comparing its performance with that of proportional integral derivatives (PID) controllers. (2) To study the effect of controller parameters on control performance. (3) To develop appropriate performance index (PI) to compare the performance of different controller and develop novel idea to present tuning map of a controller. These objectives were achieved by applying PID controller and a special type of MPC which is dynamic matrix control (DMC) on the multi-tanks process simulated in loop-pro. Then the controller performance has been evaluated by changing the controller parameters. This performance was based on special indices related to the difference between set point and process variable in order to compare the both controllers. The same principle was applied for continuous stirred tank heater (CSTH) and continuous stirred tank reactor (CSTR) processes simulated in Matlab. However, in these processes some developed programs were written to evaluate the performance of the PID and MPC controllers. Finally these performance indices along with their controller parameters were plotted using special program called Sigmaplot. As a result, the improvement in the performance of the control loops was quantified using relevant indices to justify the need and importance of advanced process control. Also, it has been approved that, by using appropriate indices, predictive controller can improve the performance of the control loop significantly.

Keywords: advanced process control (APC), control loop, model predictive control (MPC), proportional integral derivatives (PID), performance indices (PI)

Procedia PDF Downloads 407

Authors: Jagannath Singh, Durga Prasad Mohapatra

Abstract:

Software refactoring is very essential for maintaining the software quality. It is an usual practice that we first design the software and then go for coding. But after coding is completed, if the requirement changes slightly or our expected output is not achieved, then we change the codes. For each small code change, we cannot change the design. In course of time, due to these small changes made to the code, the software design decays. Software refactoring is used to restructure the code in order to improve the design and quality of the software. In this paper, we propose an approach for performing code refactoring. We use slice-based cohesion metrics to identify the target methods which requires refactoring. After identifying the target methods, we use program slicing to divide the target method into two parts. Finally, we have used the concepts of Aspects to adjust the code structure so that the external behaviour of the original module does not change.

Keywords: software refactoring, program slicing, AOP, cohesion metrics, code restructure, AspectJ

Procedia PDF Downloads 513