Search results for: beta binomial posterior predictive (BBPP) distribution

Authors: Debarati Bhaumik, Diptish Dey

Abstract:

Multilevel models, increasingly deployed in industries such as insurance, food production, and entertainment within functions such as marketing and supply chain management, need to be transparent and ethical. Applications usually result in binary classification within groups or hierarchies based on a set of input features. Using open-source datasets, we demonstrate that popular explainability methods, such as SHAP and LIME, consistently underperform inaccuracy when interpreting these models. They fail to predict the order of feature importance, the magnitudes, and occasionally even the nature of the feature contribution (negative versus positive contribution to the outcome). Besides accuracy, the computational intractability of SHAP for binomial classification is a cause of concern. For transparent and ethical applications of these hierarchical statistical models, sound audit frameworks need to be developed. In this paper, we propose an audit framework for technical assessment of multilevel regression models focusing on three aspects: (i) model assumptions & statistical properties, (ii) model transparency using different explainability methods, and (iii) discrimination assessment. To this end, we undertake a quantitative approach and compare intrinsic model methods with SHAP and LIME. The framework comprises a shortlist of KPIs, such as PoCE (Percentage of Correct Explanations) and MDG (Mean Discriminatory Gap) per feature, for each of these three aspects. A traffic light risk assessment method is furthermore coupled to these KPIs. The audit framework will assist regulatory bodies in performing conformity assessments of AI systems using multilevel binomial classification models at businesses. It will also benefit businesses deploying multilevel models to be future-proof and aligned with the European Commission’s proposed Regulation on Artificial Intelligence.

Keywords: audit, multilevel model, model transparency, model explainability, discrimination, ethics

Procedia PDF Downloads 67

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

Abstract:

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

Procedia PDF Downloads 171

Authors: Kudzanayi Chiteka, Wellington Makondo

Abstract:

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

Procedia PDF Downloads 253

Authors: Alexander N. Savostyanov, Tatiana A. Dolgorukova, Elena A. Esipenko, Mikhail S. Zaleshin, Margherita Malanchini, Anna V. Budakova, Alexander E. Saprygin, Tatiana A. Golovko, Yulia V. Kovas

Abstract:

EEG correlates of mathematical and trait anxiety level were studied in 52 healthy Russian-speakers during execution of error-recognition tasks with lexical, arithmetic and algebraic conditions. Event-related spectral perturbations were used as a measure of brain activity. The ERSP plots revealed alpha/beta desynchronizations within a 500-3000 ms interval after task onset and slow-wave synchronization within an interval of 150-350 ms. Amplitudes of these intervals reflected the accuracy of error recognition, and were differently associated with the three conditions. The correlates of anxiety were found in theta (4-8 Hz) and beta2 (16-20 Hz) frequency bands. In theta band the effects of mathematical anxiety were stronger expressed in lexical, than in arithmetic and algebraic condition. The mathematical anxiety effects in theta band were associated with differences between anterior and posterior cortical areas, whereas the effects of trait anxiety were associated with inter-hemispherical differences. In beta1 and beta2 bands effects of trait and mathematical anxiety were directed oppositely. The trait anxiety was associated with increase of amplitude of desynchronization, whereas the mathematical anxiety was associated with decrease of this amplitude. The effect of mathematical anxiety in beta2 band was insignificant for lexical condition but was the strongest in algebraic condition. EEG correlates of anxiety in theta band could be interpreted as indexes of task emotionality, whereas the reaction in beta2 band is related to tension of intellectual resources.

Keywords: EEG, brain activity, lexical and numerical error-recognition tasks, mathematical and trait anxiety

Procedia PDF Downloads 539

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

Abstract:

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

Procedia PDF Downloads 393

Authors: U. K. Sahin, N. Erdumlu, C. Saricam, I. Gocek, M. H. Arslan, H. Acikgoz-Tufan, B. Kalav

Abstract:

Finishing is an important part of fabric processing with which a wide range of features are imparted to greige or colored fabrics for various end-uses. Especially, by the addition or impartation of nano-scaled particles to the fabric structure composite fabrics, a kind of composite materials can be acquired. Composite materials, generally shortened as composites or in other words composition materials, are engineered or naturally occurring materials made from two or more component materials with significantly different physical, mechanical or chemical characteristics remaining separate and distinctive at the macroscopic or microscopic scale within the end product structure. Therefore, the technique finishing which is one of the fundamental methods to be applied on fabrics for obtainment of composite fabrics with many functionalities was used in the current study with the same purpose. However, regardless of the finishing materials applied, the efficient life of finished product on offering desired feature is low, since the durability of finishes on the material is limited. Any increase in durability of these finishes on textiles would enhance the life of use for textiles, which will result in happier users. Therefore, in this study, since higher durability was desired for the finishing materials fixed on the fabrics, nano-scaled hollow structured cyclodextrins were chemically imparted by grafting to the structure of conventional cotton fabrics by the help of finishing technique in order to be fixed permanently. By this way, a processed and functionalized base fabric having potential to be treated in the subsequent processes with many different finishing agents and nanomaterials could be obtained. Henceforth, this fabric can be used as a multi-functional fabric due to the encapturing ability of cyclodextrins to molecules/particles via physical/chemical means. In this study, scoured and rinsed woven bleached plain weave 100% cotton fabrics were utilized because textiles made of cotton are the most demanded textile products in the textile market by the textile consumers in daily life. Cotton fabric samples were immersed in treating baths containing β-cyclodextrin and 1,2,3,4-butanetetracarboxylic acid and to reduce the curing temperature the catalyst sodium hypophosphite monohydrate was used. All impregnated fabric samples were pre-dried. The reaction of grafting was performed in dry state. The treated and cured fabric samples were rinsed with warm distilled water and dried. The samples were dried for 4 h and weighed before and after finishing and rinsing. Stability and durability of β-cyclodextrins on fabric surface against external factors such as washing as well as strength of functionalized fabric in terms of tensile and tear strength were tested. Presence and homogeneity of distribution of β-cyclodextrins on fabric surface were characterized.

Keywords: cotton fabric, cyclodextrine, improved durability, multifunctional composite textile

Procedia PDF Downloads 271

Authors: I. S. Gandhi, D. N. Patel, M. Johnson, A. R. Hirsch

Abstract:

Although distortion of taste perception following a cerebrovascular event may seem to be a frivolous consequence of a classic stroke presentation, altered taste perception places patients at an increased risk for malnutrition, weight loss, and depression, all of which negatively impact the quality of life. Impaired taste perception can result from a wide variety of cerebrovascular lesions to various locations, including pons, insular cortices, and ventral posteromedial nucleus of the thalamus. Wallenberg syndrome, also known as a lateral medullary syndrome, has been described to impact taste; however, specific sweet to bitter taste dysgeusia from a territory infarction is an infrequent event; as such, a case is presented. One year prior to presentation, this 64-year-old right-handed woman, suffered a right posterior inferior cerebellar artery aneurysm rupture with resultant infarction, culminating in a ventriculoperitoneal shunt placement. One and half months after this event, she noticed the gradual onset of lack of ability to taste sweet, to eventually all sweet food tasting bitter. Since the onset of her chemosensory problems, the patient has lost 60-pounds. Upon gustatory testing, the patient's taste threshold showed ageusia to sucrose and hydrochloric acid, while normogeusia to sodium chloride, urea, and phenylthiocarbamide. The gustatory cortex is made in part by the right insular cortex as well as the right anterior operculum, which are primarily involved in the sensory taste modalities. In this model, sweet is localized in the posterior-most along with the rostral aspect of the right insular cortex, notably adjacent to the region responsible for bitter taste. The sweet to bitter dysgeusia in our patient suggests the presence of a lesion in this localization. Although the primary lesion in this patient was located in the right medulla of the brainstem, neurodegeneration in the rostal and posterior-most aspect, of the right insular cortex may have occurred due to diaschisis. Diaschisis has been described as neurophysiological changes that occur in remote regions to a focal brain lesion. Although hydrocephalus and vasospasm due to aneurysmal rupture may explain the distal foci of impairment, the gradual onset of dysgeusia is more indicative of diaschisis. The perception of sweet, now tasting bitter, suggests that in the absence of sweet taste reception, the intrinsic bitter taste of food is now being stimulated rather than sweet. In the evaluation and treatment of taste parageusia secondary to cerebrovascular injury, prophylactic neuroprotective measures may be worthwhile. Further investigation is warranted.

Keywords: diaschisis, dysgeusia, stroke, taste

Procedia PDF Downloads 154

Authors: M. A. Hasan, M. R. Islam, R. A. Tarefder

Abstract:

This study investigates how the site specific traffic data differs from the Mechanistic Empirical Pavement Design Software default values. Two Weigh-in-Motion (WIM) stations were installed in Interstate-40 (I-40) and Interstate-25 (I-25) to developed site specific data. A computer program named WIM Data Analysis Software (WIMDAS) was developed using Microsoft C-Sharp (.Net) for quality checking and processing of raw WIM data. A complete year data from November 2013 to October 2014 was analyzed using the developed WIM Data Analysis Program. After that, the vehicle class distribution, directional distribution, lane distribution, monthly adjustment factor, hourly distribution, axle load spectra, average number of axle per vehicle, axle spacing, lateral wander distribution, and wheelbase distribution were calculated. Then a comparative study was done between measured data and AASHTOWare default values. It was found that the measured general traffic inputs for I-40 and I-25 significantly differ from the default values.

Keywords: AASHTOWare, traffic, weigh-in-motion, axle load distribution

Procedia PDF Downloads 318

Authors: Bashara Want

Abstract:

One of the key factors limiting the performance of magnetic memory is that the coercivity has a distribution with finite width, and the reversal starts at the weakest link in the distribution. So one must first know the distribution of coercivities in order to learn how to reduce the width of distribution and increase the coercivity field to obtain a system with narrow width. First Order Reversal Curve (FORC) method characterizes a system with hysteresis via the distribution of local coercivities and, in addition, the local interaction field. The method is more versatile than usual conventional major hysteresis loops that give only the statistical behaviour of the magnetic system. The FORC method will be presented and discussed at the conference.

Keywords: magnetic materials, hysteresis, first-order reversal curve method, nanostructures

Procedia PDF Downloads 58

Authors: Lina Paola Orozco Marin, Yuliet Montoya Osorio, John Bustamante Osorno

Abstract:

Ischemic events can culminate in acute myocardial infarction by irreversible cardiac lesions that cannot be restored due to the limited regenerative capacity of the heart. Cell therapy seeks to replace these injured or necrotic cells by transplanting healthy and functional cells. The therapeutic alternatives proposed by tissue engineering and cardiovascular regenerative medicine are the use of biomaterials to mimic the native extracellular medium, which is full of proteins, proteoglycans, and glycoproteins. The selected biomaterials must provide structural support to the encapsulated cells to avoid their migration and death in the host tissue. In this context, the present research work focused on developing a natural thermosensitive hydrogel, its physical and chemical characterization, and the determination of its biocompatibility in vitro. The hydrogel was developed by mixing hydrolyzed bovine and porcine collagen at 2% w/v, chitosan at 2.5% w/v, and beta-glycerolphosphate at 8.5% w/w and 10.5% w/w in magnetic stirring at 4°C. Once obtained, the thermosensitivity and gelation time were determined, incubating the samples at 37°C and evaluating them through the inverted tube method. The morphological characterization of the hydrogels was carried out through scanning electron microscopy. Chemical characterization was carried out employing infrared spectroscopy. The biocompatibility was determined using the MTT cytotoxicity test according to the ISO 10993-5 standard for the hydrogel’s precursors using the fetal human ventricular cardiomyocytes cell line RL-14. The RL-14 cells were also seeded on the top of the hydrogels, and the supernatants were subculture at different periods to their observation under a bright field microscope. Four types of thermosensitive hydrogels were obtained, which differ in their composition and concentration, called A1 (chitosan/bovine collagen/beta-glycerolphosphate 8.5%w/w), A2 (chitosan/porcine collagen/beta-glycerolphosphate 8.5%), B1 (chitosan/bovine collagen/beta-glycerolphosphate 10.5%) and B2 (chitosan/porcine collagen/beta-glycerolphosphate 10.5%). A1 and A2 had a gelation time of 40 minutes, and B1 and B2 had a gelation time of 30 minutes at 37°C. Electron micrographs revealed a three-dimensional internal structure with interconnected pores for the four types of hydrogels. This facilitates the exchange of nutrients, oxygen, and the exit of metabolites, allowing to preserve a microenvironment suitable for cell proliferation. In the infrared spectra, it was possible to observe the interaction that occurs between the amides of polymeric compounds with the phosphate groups of beta-glycerolphosphate. Finally, the biocompatibility tests indicated that cells in contact with the hydrogel or with each of its precursors are not affected in their proliferation capacity for a period of 16 days. These results show the potential of the hydrogel to increase the cell survival rate in the cardiac cell therapies under investigation. Moreover, the results lay the foundations for its characterization and biological evaluation in both in vitro and in vivo models.

Keywords: cardiac cell therapy, cardiac ischemia, natural polymers, thermosensitive hydrogel

Procedia PDF Downloads 164

Authors: Adil Balla Elkrail

Abstract:

Visual MODFLOW computer codes were selected to simulate head distribution, calculate the groundwater budgets of the area, and evaluate the effect of external stresses on the groundwater head and to demonstrate how the groundwater model can be used as a comparative technique in order to optimize utilization of the groundwater resource. A conceptual model of the study area, aquifer parameters, boundary, and initial conditions were used to simulate the flow model. The trial-and-error technique was used to calibrate the model. The most important criteria used to check the calibrated model were Root Mean Square error (RMS), Mean Absolute error (AM), Normalized Root Mean Square error (NRMS) and mass balance. The maps of the simulated heads elaborated acceptable model calibration compared to observed heads map. A time length of eight years and the observed heads of the year 2004 were used for model prediction. The predictive simulation showed that the continuation of pumping will cause relatively high changes in head distribution and components of groundwater budget whereas, the low deficit computed (7122 m3/d) between inflows and outflows cannot create a significant drawdown of the potentiometric level. Hence, the area under consideration may represent a high permeability and productive zone and strongly recommended for further groundwater development.

Keywords: aquifers, model simulation, groundwater, calibrations, trail-and- error, prediction

Procedia PDF Downloads 214

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

Abstract:

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

Procedia PDF Downloads 118

Authors: Nadeen Eltoukhy, Sahar S. Sheta, Walaa Elnaggar, Karim Bakr

Abstract:

Purpose: The aim of this study is to compare the clinical efficacy and side effects of oral versus intralesional propranolol treatment of infantile capillary hemangiomas in infants. Methods: The study enrolled 40 infants diagnosed with infantile capillary hemangiomas. Patients were divided into 2 groups: Group A (Non-invasive group) included 20 infants who received oral propranolol hydrochloride starting at a dose of 1 mg/kg/day BID, then increased to a max of 2 mg/kg/day BID gradually over 2 weeks for 3 months. Group B (Invasive group) included 20 infants who received intralesional propranolol injection at a dose of 1 mg/mL; the volume of the injected drug depended on the size of the lesion (0.2 mL injected per cm of lesion diameter), with a maximum volume of 1 mL for a lesion of 5 cm diameter under complete aseptic conditions in the operating theater. Results: At three months after initiating treatment, the circumferential size of the hemangioma showed a statistically significant decrease in both groups; in Group A from 3.66±2.89 cm to 1.56±1.26 cm with p-value <0.05 and in Group B from 2.99±2.73 cm to 1.32±1.18 cm with p-value <0.05. There is no statistically significant comparative difference between the two groups (p-value = 0.538 = insignificant). Regarding the complications of oral propranolol, one patient (5%) had bradycardia, and one patient (5%) had diarrhea. In the injection group, 20 patients (100%) had local edema, and one patient (5%) had a local infection. Conclusions: Both oral non-invasive and intralesional invasive propranolol are safely used to successfully treat and decrease the size of infantile hemangioma while showing no statistically comparative difference between both treatment techniques.

Keywords: hemangioma, oral beta blocker, intralesional beta blocker, infants

Procedia PDF Downloads 28

Authors: Florge Francis A. Sy

Abstract:

Acute pancreatitis (AP) is an inflammatory condition of the pancreas with local and systemic complications. Severe acute pancreatitis (SAP) has a higher mortality rate. Laboratory parameters like the neutrophil-to-lymphocyte ratio (NLR), red cell distribution width (RDW), and mean platelet volume (MPV) have been associated with SAP but with conflicting results. This study aims to determine the predictive value of these parameters on the severity and in-hospital mortality of AP. This retrospective, cross-sectional study was done in a private hospital in Cebu City, Philippines. One-hundred five patients were classified according to severity based on the modified Marshall scoring. The admitting hemogram, including the NLR, RDW, and MPV, was obtained from the complete blood count (CBC). Cut-off values for severity and in-hospital mortality were derived from the ROC. Association between NLR, RDW, and MPV with SAP and mortality were determined with a p-value of < 0.05 considered significant. The mean age for AP was 47.6 years, with 50.5% being male. Most had an unknown cause (49.5%), followed by a biliary cause (37.1%). Of the 105 patients, 23 patients had SAP, and 4 died. Older age, longer in-hospital duration, congestive heart failure, elevated creatinine, urea nitrogen, and white blood cell count were seen in SAP. The NLR was associated with in-hospital mortality using a cut-off of > 10.6 (OR 1.133, 95% CI, p-value 0.003) with 100% sensitivity, 70.3% specificity, 11.76% PPV and 100% NPV (AUC 0.855). The NLR was not associated with SAP. The RDW and MPV were not associated with SAP and mortality. The admitting NLR is, therefore, an easily accessible parameter that can predict in-hospital mortality in acute pancreatitis. Although the present study did not show an association of NLR with SAP nor RDW and MPV with both SAP and mortality, further studies are suggested to establish their clinical value.

Keywords: acute pancreatitis, mean platelet volume, neutrophil-lymphocyte ratio, red cell distribution width

Procedia PDF Downloads 102

Authors: Thilini Hathurusinghe, Uthpalie Siriwardhana, W. M. Ediri Arachchi, Ranga Thudugala, Indeewari Herath, Gayani Senanayake

Abstract:

The foramen magnum is more prone to protect than other skeletal remains during high impact and severe disruptive injuries. Therefore, it is worthwhile to explore whether these measurements can be used to determine the human gender which is vital in forensic and anthropological studies. The idea was to find out the ability to use quantitative measurements of foramen magnum as an anatomical indicator for human gender estimation and to evaluate the gender-dependent variations of foramen magnum using quantitative measurements. Randomly selected 113 subjects who underwent CT head scans at Sri Jayawardhanapura General Hospital of Sri Lanka within a period of six months, were included in the study. The sample contained 58 males (48.76 ± 14.7 years old) and 55 females (47.04 ±15.9 years old). Maximum length of the foramen magnum (LFM), maximum width of the foramen magnum (WFM), minimum distance between occipital condyles (MnD) and maximum interior distance between occipital condyles (MxID) were measured. Further, AreaT and AreaR were also calculated. The gender was estimated using binomial logistic regression. The mean values of all explanatory variables (LFM, WFM, MnD, MxID, AreaT, and AreaR) were greater among male than female. All explanatory variables except MnD (p=0.669) were statistically significant (p < 0.05). Significant bivariate correlations were demonstrated by AreaT and AreaR with the explanatory variables. The results evidenced that WFM and MxID were the best measurements in predicting gender according to binomial logistic regression. The estimated model was: log (p/1-p) =10.391-0.136×MxID-0.231×WFM, where p is the probability of being a female. The classification accuracy given by the above model was 65.5%. The quantitative measurements of foramen magnum can be used as a reliable anatomical marker for human gender estimation in the Sri Lankan context.

Keywords: foramen magnum, forensic and anthropological studies, gender estimation, logistic regression

Procedia PDF Downloads 130

Authors: Zakir Husain, Neem Sagar, Neeraj Gupta

Abstract:

Due to the increased penetration of renewable energy resources in the distribution system, the system is no longer passive in nature. In this paper, a steady state analysis of the distribution system has been done with the inclusion of wind generation. The modeling of wind turbine generator system and wind generator has been made to obtain the average active and the reactive power injection into the system. The study has been conducted on a IEEE-33 bus system with two wind generators. The present research work is useful not only to utilities but also to customers.

Keywords: distributed generation, distribution network, radial network, wind turbine generating system

Procedia PDF Downloads 375

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

Abstract:

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

Procedia PDF Downloads 538

Authors: Petr Květon, Martin Jelínek

Abstract:

We try to identify the role of various aspects of parenting style in the phenomenon of videogame playing addiction. Relevant self-report questionnaires were part of a wider set of methods focused on the constructs related to videogame playing. The battery of methods was administered in school settings in paper and pencil form. The research sample consisted of 333 (166 males, 167 females) elementary and high school students at the age between 10 and 19 years (m=14.98, sd=1.77). Using stepwise regression analysis, we assessed the influence of demographic variables (gender and age) and parenting styles. Age and gender together explained 26.3% of game addiction variance (F(2,330)=58.81, p<.01). By adding four aspect of parenting styles (inconsistency, involvement, control, and warmth) another 10.2% of variance was explained (∆F(4,326)=13.09, p<.01). The significant predictor was gender of the respondent, where males scored higher on game addiction scale (B=0.70, p<.01), age (β=-0.18, p<.01), where younger children showed higher level of addiction, and parental inconsistency (β=0.30, p<.01), where the higher the inconsistency in upbringing, the more developed game playing addiction.

Keywords: gender, parenting styles, video games, addiction

Procedia PDF Downloads 332

Authors: Muhammad Babar Shahzad

Abstract:

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

Procedia PDF Downloads 485

Authors: Hamdi Beji, Toufik Kanit, Tanguy Messager

Abstract:

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

Procedia PDF Downloads 38

Authors: Levent Taylan Ozgur Yildirim, Qihao Qian, John Yilin Wang

Abstract:

A critical review of assessments of geological carbon dioxide (CO2) storage resources in Pennsylvania and the surrounding region was completed with a focus on the studies of Midwest Regional Carbon Sequestration Partnership (MRCSP), United States Department of Energy (US-DOE), and United States Geological Survey (USGS). Pennsylvania Geological Survey participated in the MRCSP Phase I research to characterize potential storage formations in Pennsylvania. The MRCSP’s volumetric method estimated ~89 gigatonnes (Gt) of total CO2 storage resources in deep saline formations, depleted oil and gas reservoirs, coals, and shales in Pennsylvania. Meanwhile, the US-DOE calculated storage efficiency factors using log-odds normal distribution and Monte Carlo sampling, revealing contingent storage resources of ~18 Gt to ~20 Gt in deep saline formations, depleted oil and gas reservoirs, and coals in Pennsylvania. Additionally, the USGS employed Beta-PERT distribution and Monte Carlo sampling to determine buoyant and residual storage efficiency factors, resulting in 20 Gt of contingent storage resources across four storage assessment units in Appalachian Basin. However, few studies have explored CO2 storage resources in shales in the region, yielding inconclusive findings. This article provides a critical and most up to date review and analysis of geological CO2 storage resources in Pennsylvania and the region.

Keywords: carbon capture and storage, geological CO2 storage, pennsylvania, appalachian basin

Procedia PDF Downloads 20

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

Abstract:

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

Procedia PDF Downloads 144

Authors: Yan Zhang

Abstract:

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

Procedia PDF Downloads 361

Authors: Sonal Sethi, Mukesh Yadav, Abhimanyu Gupta

Abstract:

The upcoming field of radiogenomics has the potential to upgrade the role of imaging in lung cancer management by noninvasive characterization of tumor histology and genetic microenvironment. Receptor positivity like epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK) genotyping are critical in lung adenocarcinoma for treatment. As conventional identification of receptor positivity is an invasive procedure, we analyzed the features on non-invasive computed tomography (CT), which predicts the receptor positivity in lung adenocarcinoma. Retrospectively, we did a comprehensive study from 77 proven lung adenocarcinoma patients with CT images, EGFR and ALK receptor genotyping, and clinical information. Total 22/77 patients were receptor-positive (15 had only EGFR mutation, 6 had ALK mutation, and 1 had both EGFR and ALK mutation). Various morphological characteristics and metastatic distribution on CT were analyzed along with the clinical information. Univariate and multivariable logistic regression analyses were used. On multivariable logistic regression analysis, we found spiculated margin, lymphangitic spread, air bronchogram, pleural effusion, and distant metastasis had a significant predictive value for receptor mutation status. On univariate analysis, air bronchogram and pleural effusion had significant individual predictive value. Conclusions: Receptor positive lung cancer has characteristic imaging features compared with nonreceptor positive lung adenocarcinoma. Since CT is routinely used in lung cancer diagnosis, we can predict the receptor positivity by a noninvasive technique and would follow a more aggressive algorithm for evaluation of distant metastases as well as for the treatment.

Keywords: lung cancer, multidisciplinary cancer care, oncologic imaging, radiobiology

Procedia PDF Downloads 97

Authors: Fatma Zehra Doğru, Olcay Arslan

Abstract:

In this paper, we propose alternative robust estimators for the shape parameters of the Burr XII distribution. We provide a small simulation study and a real data example to illustrate the performance of the proposed estimators over the ML and the LS estimators.

Keywords: burr xii distribution, robust estimator, m-estimator, least squares

Procedia PDF Downloads 407

Authors: Tohru Kawabe

Abstract:

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

Procedia PDF Downloads 538

Authors: Chengming Li, Yong Yin, Peipei Guo, Xiaoli Liu

Abstract:

Without taking account of the attribute richness of POI (point of interest) data and spatial distribution limited by roads, a POI generalization method considering both attribute information and spatial distribution has been proposed against the existing point generalization algorithm merely focusing on overall information of point groups. Hierarchical characteristic of urban POI information expression has been firstly analyzed to point out the measurement feature of the corresponding hierarchy. On this basis, an urban POI generalizing strategy has been put forward: POIs urban road network have been divided into three distribution pattern; corresponding generalization methods have been proposed according to the characteristic of POI data in different distribution patterns. Experimental results showed that the method taking into account both attribute information and spatial distribution characteristics of POI can better implement urban POI generalization in the mapping presentation.

Keywords: POI, road network, selection method, spatial information expression, distribution pattern

Procedia PDF Downloads 382

Authors: Demiraw Bikila, Tadesse Lejisa, Yosef Tolcha, Chala Bashea, Mehari Meles Tigist Getahun Genet Ashebir, Wossene Habtu, Feyissa Challa, Ousman Mohammed, Melkitu Kassaw, Adisu Kebede, Letebrhan G. Egzeabher, Endalkachew Befekadu, Mistire Wolde, Aster Tsegaye

Abstract:

Background: Even though several factors affect reference intervals (RIs), the company-derived values are currently in use in many laboratories worldwide. However, little or no data is available regarding serum protein RIs, mainly in resource-limited setting countries like Ethiopia. Objective: To establish a reference interval for serum protein electrophoresis of apparently healthy adults in Addis Ababa, Ethiopia. Method: A cross-sectional study was conducted on a total of 297 apparently healthy adults from April-October 2019 in four selected sub-cities (Akaki, Kirkos, Arada, Yeka) of Addis Ababa, Ethiopia. Laboratory analysis of collected samples was performed using Capillarys 2 Flex Piercing analyzer, while statistical analysis was done using SPSS version 23 and med-cal software. Mann-Whitney test was used to check Partitions. Non-parametric method of reference range establishment was performed as per CLSI guideline EP28A3C. Result: The established RIs were: Albumin 53.83-64.59%, 52.24-63.55%; Alpha-1 globulin 3.04-5.40%, 3.44-5.60%; Alpha-2 globulin 8.0-12.67%, 8.44-12.87%; and Beta-1 globulin 5.01-7.38%, 5.14-7.86%. Moreover, Albumin to globulin ratio was 1.16-1.8, 1.09-1.74 for males and females, respectively. The combined RIs for Beta-2 globulin and Gamma globulin were 2.54-4.90% and 12.40-21.66%, respectively. Conclusion: The established reference interval for serum protein fractions revealed gender-specific differences except for Beta-2 globulin and Gamma globulin.

Keywords: serum protein electrophoresis, reference interval, Addis Ababa, Ethiopia

Procedia PDF Downloads 203

Authors: Mohammad Javad Mollakazemi, Farhad Asadi, Aref Ghafouri

Abstract:

In this paper, we considered and applied parametric modeling for some experimental data of dynamical system. In this study, we investigated the different distribution of output measurement from some dynamical systems. Also, with variance processing in experimental data we obtained the region of nonlinearity in experimental data and then identification of output section is applied in different situation and data distribution. Finally, the effect of the spanning the measurement such as variance to identification and limitation of this approach is explained.

Keywords: Gaussian process, nonlinearity distribution, particle filter, system identification

Procedia PDF Downloads 488

Authors: S. Sudhharani

Abstract:

Energy distribution networks are the main link between the energy industry and consumers and are subject to the most scrutiny and testing of any category. As a result, it is important to monitor energy levels during the distribution phase. Power distribution networks, on the other hand, remain subject to common problems, including voltage breakdown, power outages, harmonics, and capacitor switching, all of which disrupt sinusoidal waveforms and reduce the quality and power of the network. Using power appliances in the form of custom power appliances is one way to deal with energy quality issues. Dynamic Voltage Restorer (DVR), integrated with network and distribution networks, is one of these devices. At the same time, by injecting voltage into the system, it can adjust the voltage amplitude and phase in the network. In the form of injections and three-phase syncing, it is used to compensate for the difficulty of energy quality. This article examines the recent use of DVR for power compensation and provides data on the control of each DVR in distribution networks.

Keywords: dynamic voltage restorer (DVR), power quality, distribution networks, control systems(PWM)

Procedia PDF Downloads 111