Search results for: X-ray computed microtomography

Authors: Tobias Strübing, Fatemeh Ziaeetabar

Abstract:

Humans usually manipulate objects with their hands. To represent these actions in a simple and understandable way, we need to use a semantic framework. For this purpose, the Semantic Event Chain (SEC) method has already been presented which is done by consideration of touching and non-touching relations between manipulated objects in a scene. This method was improved by a computational model, the so-called enriched Semantic Event Chain (eSEC), which incorporates the information of static (e.g. top, bottom) and dynamic spatial relations (e.g. moving apart, getting closer) between objects in an action scene. This leads to a better action prediction as well as the ability to distinguish between more actions. Each eSEC manipulation descriptor is a huge matrix with thirty rows and a massive set of the spatial relations between each pair of manipulated objects. The current eSEC framework has so far only been used in the category of manipulation actions, which eventually involve two hands. Here, we would like to extend this approach to a whole body action descriptor and make a conjoint activity representation structure. For this purpose, we need to do a statistical analysis to modify the current eSEC by summarizing while preserving its features, and introduce a new version called Enhanced eSEC or (e2SEC). This summarization can be done from two points of the view: 1) reducing the number of rows in an eSEC matrix, 2) shrinking the set of possible semantic spatial relations. To achieve these, we computed the importance of each matrix row in an statistical way, to see if it is possible to remove a particular one while all manipulations are still distinguishable from each other. On the other hand, we examined which semantic spatial relations can be merged without compromising the unity of the predefined manipulation actions. Therefore by performing the above analyses, we made the new e2SEC framework which has 20% fewer rows, 16.7% less static spatial and 11.1% less dynamic spatial relations. This simplification, while preserving the salient features of a semantic structure in representing actions, has a tremendous impact on the recognition and prediction of complex actions, as well as the interactions between humans and robots. It also creates a comprehensive platform to integrate with the body limbs descriptors and dramatically increases system performance, especially in complex real time applications such as human-robot interaction prediction.

Keywords: enriched semantic event chain, semantic action representation, spatial relations, statistical analysis

Procedia PDF Downloads 124

Authors: Ahmedou Moulaye Idriss, Tfeil Yahya, Tamas Ungi, Gabor Fichtinger

Abstract:

Image-guided therapy (IGT) plays a crucial role in minimally invasive procedures for liver interventions. Accurate segmentation of the liver and precise needle placement is essential for successful interventions such as needle aspiration. In this study, we propose a deep learning-based liver 3D slicer designed to enhance segmentation accuracy and facilitate needle aspiration procedures. The developed 3D slicer leverages state-of-the-art convolutional neural networks (CNNs) for automatic liver segmentation in medical images. The CNN model is trained on a diverse dataset of liver images obtained from various imaging modalities, including computed tomography (CT) and magnetic resonance imaging (MRI). The trained model demonstrates robust performance in accurately delineating liver boundaries, even in cases with anatomical variations and pathological conditions. Furthermore, the 3D slicer integrates advanced image registration techniques to ensure accurate alignment of preoperative images with real-time interventional imaging. This alignment enhances the precision of needle placement during aspiration procedures, minimizing the risk of complications and improving overall intervention outcomes. To validate the efficacy of the proposed deep learning-based 3D slicer, a comprehensive evaluation is conducted using a dataset of clinical cases. Quantitative metrics, including the Dice similarity coefficient and Hausdorff distance, are employed to assess the accuracy of liver segmentation. Additionally, the performance of the 3D slicer in guiding needle aspiration procedures is evaluated through simulated and clinical interventions. Preliminary results demonstrate the effectiveness of the developed 3D slicer in achieving accurate liver segmentation and guiding needle aspiration procedures with high precision. The integration of deep learning techniques into the IGT workflow shows great promise for enhancing the efficiency and safety of liver interventions, ultimately contributing to improved patient outcomes.

Keywords: deep learning, liver segmentation, 3D slicer, image guided therapy, needle aspiration

Procedia PDF Downloads 46

Authors: Eronini Iheanyi Umez-Eronini

Abstract:

Most studies and existing implementations of compressed air energy storage (CAES) coupled with a wind farm to overcome intermittency and variability of wind power are based on bulk or centralized CAES plants. A dynamic model of a hybrid wind farm with wind turbines and distributed CAES, consisting of air storage tanks and compressor and expander trains at each wind turbine station, is developed and simulated in MATLAB. An ad hoc supervisory controller, in which the wind turbines are simply operated under classical power optimizing region control while scheduling power production by the expanders and air storage by the compressors, including modulation of the compressor power levels within a control range, is used to regulate overall farm power production to track minute-scale (3-minutes sampling period) TSO absolute power reference signal, over an eight-hour period. Simulation results for real wind data input with a simple wake field model applied to a hybrid plant composed of ten 5-MW wind turbines in a row and ten compatibly sized and configured Diabatic CAES stations show the plant controller is able to track the power demand signal within an error band size on the order of the electrical power rating of a single expander. This performance suggests that much improved results should be anticipated when the global D-CAES control is combined with power regulation for the individual wind turbines using available approaches for wind farm active power control. For standalone power plant fuel electrical efficiency estimate of up to 60%, the round trip electrical storage efficiency computed for the distributed CAES wherein heat generated by running compressors is utilized in the preheat stage of running high pressure expanders while fuel is introduced and combusted before the low pressure expanders, was comparable to reported round trip storage electrical efficiencies for bulk Adiabatic CAES.

Keywords: hybrid wind farm, distributed CAES, diabatic CAES, active power control, dynamic modeling and simulation

Procedia PDF Downloads 82

Authors: Pawan Kumar, Dhananjoy Shaw

Abstract:

Heart rate variability (HRV) is the physiological phenomenon of variation in the time interval between heartbeats and is alterable with fitness, age and different medical conditions including withdrawal/retirement from games/sports. Objectives of the study were to develop (a) percentile norms of heart rate variability (HRV) variables derived from time domain analysis of the Indian sportspersons withdrawn from competitive games/sports pertaining to sympathetic and parasympathetic activity (b) percentile norms of heart rate variability (HRV) variables derived from frequency domain analysis of the Indian sportspersons withdrawn from competitive games/sports pertaining to sympathetic and parasympathetic activity. The study was conducted on 430 males. Ages of the sample ranged from 30 to 35 years of same socio-economic status. Date was collected using ECG polygraphs. Data were processed and extracted using frequency domain analysis and time domain analysis. Collected data were computed with percentile from one to hundred. The finding showed that the percentile norms of heart rate variability (HRV) variables derived from time domain analysis of the Indian sportspersons withdrawn from competitive games/sports pertaining to sympathetic and parasympathetic activity namely, NN50 count (ranged from 1 to 189 score as percentile range). pNN50 count (ranged from .24 to 60.80 score as percentile range). SDNN (ranged from 17.34 to 167.29 score as percentile range). SDSD (ranged from 11.14 to 120.46 score as percentile range). RMMSD (ranged from 11.19 to 120.24 score as percentile range) and SDANN (ranged from 4.02 to 88.75 score as percentile range). The percentile norms of heart rate variability (HRV) variables derived from frequency domain analysis of the Indian sportspersons withdrawn from competitive games/sports pertaining to sympathetic and parasympathetic activity namely Low Frequency (Normalized Power) ranged from 20.68 to 90.49 score as percentile range. High Frequency (Normalized Power) ranged from 14.37 to 81.60 score as percentile range. LF/ HF ratio(ranged from 0.26 to 9.52 score as percentile range). LF (Absolute Power) ranged from 146.79 to 5669.33 score as percentile range. HF (Absolute Power) ranged from 102.85 to 10735.71 score as percentile range and Total Power (Absolute Power) ranged from 471.45 to 25879.23 score as percentile range. Conclusion: The analysis documented percentile norms for time domain analysis and frequency domain analysis for versatile use and evaluation.

Keywords: RMSSD, Percentile, SDANN, HF, LF

Procedia PDF Downloads 419

Authors: Preetham Shankapal, Jill King, Kori Murray, Corby Martin, Paula Giselman, Jason Hicks, Owen Carmicheal

Abstract:

Activation of reward and satiety networks in the brain while processing palatable food cues, as well as functional connectivity during rest has been studied using functional Magnetic Resonance Imaging of the brain in various obesity phenotypes. However, functional connectivity within the reward and satiety network during food cue processing is understudied. 14 obese individuals underwent two fMRI scans during viewing of Macronutrient Picture System images. Each scan included two blocks of images of High Sugar/High Fat (HSHF), High Carbohydrate/High Fat (HCHF), Low Sugar/Low Fat (LSLF) and also non-food images. Seed voxels within seven food reward relevant ROIs: Insula, putamen and cingulate, precentral, parahippocampal, medial frontal and superior temporal gyri were isolated based on a prior meta-analysis. Beta series correlation for task-related functional connectivity between these seed voxels and the rest of the brain was computed. Voxel-level differences in functional connectivity were calculated between: first and the second scan; individuals who saw novel (N=7) vs. Repeated (N=7) images in the second scan; and between the HC/HF, HSHF blocks vs LSLF and non-food blocks. Computations and analysis showed that during food image viewing, reward network ROIs showed significant functional connectivity with each other and with other regions responsible for attentional and motor control, including inferior parietal lobe and precentral gyrus. These functional connectivity values were heightened among individuals who viewed novel HS/HF images in the second scan. In the second scan session, functional connectivity was reduced within the reward network but increased within attention, memory and recognition regions, suggesting habituation to reward properties and increased recollection of previously viewed images. In conclusion it can be inferred that Functional Connectivity within reward network and between reward and other brain regions, varies by important experimental conditions during food photography viewing, including habituation to shown foods.

Keywords: fMRI, functional connectivity, task-based, beta series correlation

Procedia PDF Downloads 269

Authors: Janani Selvaraj, M. Prashanthi Devi, P. B. Harathi

Abstract:

Epidemiologic studies conducted over several decades have provided evidence to suggest that long-term exposure to elevated ambient levels of particulate air pollution is associated with increased mortality. Air quality risk management is significant in developing countries and it highlights the need to understand the role of ecologic covariates in the association between air pollution and mortality. Several new methods show promise in exploring the geographical distribution of disease and the identification of high risk areas using epidemiological maps. However, the addition of the temporal attribute would further give us an in depth idea of the disease burden with respect to forecasting measures. In recent years, new methods developed in the reanalysis were useful for exploring the spatial structure of the data and the impact of spatial autocorrelation on estimates of risk associated with exposure to air pollution. Based on this, our present study aims to explore the spatial and temporal distribution of the lung cancer cases in the Coimbatore district of Tamil Nadu in relation to air pollution risk areas. A spatio temporal moving average method was computed using the CrimeStat software and visualized in ArcGIS 10.1 to document the spatio temporal movement of the disease in the study region. The random walk analysis performed showed the progress of the peak cancer incidences in the intersection regions of the Coimbatore North and South taluks that include major commercial and residential regions like Gandhipuram, Peelamedu, Ganapathy, etc. Our study shows evidence that daily exposure to high air pollutant concentration zones may lead to the risk of lung cancer. The observations from the present study will be useful in delineating high risk zones of environmental exposure that contribute to the increase of cancer among daily commuters. Through our study we suggest that spatially resolved exposure models in relevant time frames will produce higher risks zones rather than solely on statistical theory about the impact of measurement error and the empirical findings.

Keywords: air pollution, cancer, spatio-temporal analysis, India

Procedia PDF Downloads 513

Authors: Elyas Palantei, Dewiani, Farid Armin, Ardiansyah

Abstract:

High precision of printed microwave sensor utilized for sensing and monitoring the potential breast cancer existed in women breast tissue was optimally computed. The single element of UWB printed sensor that successfully modeled through several numerical optimizations was multiple fabricated and incorporated with woman bra to form the spherical sensors array. One sample of UWB microwave sensor obtained through the numerical computation and optimization was chosen to be fabricated. In overall, the spherical sensors array consists of twelve stair patch structures, and each element was individually measured to characterize its electrical properties, especially the return loss parameter. The comparison of S11 profiles of all UWB sensor elements is discussed. The constructed UWB sensor is well verified using HFSS programming, CST programming, and experimental measurement. Numerically, both HFSS and CST confirmed the potential operation bandwidth of UWB sensor is more or less 4.5 GHz. However, the measured bandwidth provided is about 1.2 GHz due to the technical difficulties existed during the manufacturing step. The configuration of UWB microwave sensing and monitoring system implemented consists of 12 element UWB printed sensors, vector network analyzer (VNA) to perform as the transceiver and signal processing part, the PC Desktop/Laptop acting as the image processing and displaying unit. In practice, all the reflected power collected from whole surface of artificial breast model are grouped into several numbers of pixel color classes positioned on the corresponding row and column (pixel number). The total number of power pixels applied in 2D-imaging process was specified to 100 pixels (or the power distribution pixels dimension 10x10). This was determined by considering the total area of breast phantom of average Asian women breast size and synchronizing with the single UWB sensor physical dimension. The interesting microwave imaging results were plotted and together with some technical problems arisen on developing the breast sensing and monitoring system are examined in the paper.

Keywords: UWB sensor, UWB microwave imaging, spherical array, breast cancer monitoring, 2D-medical imaging

Procedia PDF Downloads 191

Authors: Chatrabhuti Pipop, Visessri Supattra, Charinpanitkul Tawatchai

Abstract:

Durian is one of the top agricultural products exported by Thailand. There is the massive market potential for the durian industry. While the global demand for Thai durians, especially the demand from China, is very high, Thailand's durian supply is far from satisfying strong demand. Poor agricultural practices result in low yields and poor quality of fruit. Most irrigation systems currently used by the farmers are fixed schedule or fixed rates that ignore actual weather conditions and crop water requirements. In addition, the technologies emerging are too difficult and complex and prices are too high for the farmers to adopt and afford. Many farmers leave the durian trees to grow naturally. With improper irrigation and nutrient management system, durians are vulnerable to a variety of issues, including stunted growth, not flowering, diseases, and death. Technical development or research for durian is much needed to support the wellbeing of the farmers and the economic development of the country. However, there are a limited number of studies or development projects for durian because durian is a perennial crop requiring a long time to obtain the results to report. This study, therefore, aims to address the problem of durian production by developing an autonomous and precision irrigation system. The system is designed and equipped with an industrial programmable controller, a weather station, and a digital flow meter. Daily water requirements are computed based on weather data such as rainfall and evapotranspiration for daily irrigation with variable flow rates. A prediction model is also designed as a part of the system to enhance the irrigation schedule. Before the system was installed in the field, a simulation model was built and tested in a laboratory setting to ensure its accuracy. Water consumption was measured daily before and after the experiment for further analysis. With this system, the crop water requirement is precisely estimated and optimized based on the data from the weather station. Durian will be irrigated at the right amount and at the right time, offering the opportunity for higher yield and higher income to the farmers.

Keywords: Durian, precision irrigation, precision agriculture, smart farm

Procedia PDF Downloads 117

Authors: Mohamed Korany, Azza Gazy, Essam Khamis, Marwa Adel, Miranda Fawzy

Abstract:

Background: Two new, simple and specific methods; First, a Zero-crossing first-derivative technique and second, a chemometric-assisted spectrophotometric artificial neural network (ANN) were developed and validated in accordance with ICH guidelines. Both methods were used for the simultaneous estimation of the two closely related antioxidant nutraceuticals ; Coenzyme Q10 (Q) ; also known as Ubidecarenone or Ubiquinone-10, and Vitamin E (E); alpha-tocopherol acetate, in their pharmaceutical binary mixture. Results: For first method: By applying the first derivative, both Q and E were alternatively determined; each at the zero-crossing of the other. The D1 amplitudes of Q and E, at 285 nm and 235 nm respectively, were recorded and correlated to their concentrations. The calibration curve is linear over the concentration range of 10-60 and 5.6-70 μg mL-1 for Q and E, respectively. For second method: ANN (as a multivariate calibration method) was developed and applied for the simultaneous determination of both analytes. A training set (or a concentration set) of 90 different synthetic mixtures containing Q and E, in wide concentration ranges between 0-100 µg/mL and 0-556 µg/mL respectively, were prepared in ethanol. The absorption spectra of the training sets were recorded in the spectral region of 230–300 nm. A Gradient Descend Back Propagation ANN chemometric calibration was computed by relating the concentration sets (x-block) to their corresponding absorption data (y-block). Another set of 45 synthetic mixtures of the two drugs, in defined range, was used to validate the proposed network. Neither chemical separation, preparation stage nor mathematical graphical treatment were required. Conclusions: The proposed methods were successfully applied for the assay of Q and E in laboratory prepared mixtures and combined pharmaceutical tablet with excellent recoveries. The ANN method was superior over the derivative technique as the former determined both drugs in the non-linear experimental conditions. It also offers rapidity, high accuracy, effort and money saving. Moreover, no need for an analyst for its application. Although the ANN technique needed a large training set, it is the method of choice in the routine analysis of Q and E tablet. No interference was observed from common pharmaceutical additives. The results of the two methods were compared together

Keywords: coenzyme Q10, vitamin E, chemometry, quantitative analysis, first derivative spectrophotometry, artificial neural network

Procedia PDF Downloads 443

Authors: Vijay Goud, Ramasamy Alagirusamy, Apurba Das, Dinesh Kalyanasundaram

Abstract:

Thermoplastic composites render new opportunities as effective processing technology while crafting newer complications into processing. One of the notable challenges is in achieving thorough wettability that is significantly deterred by the high viscosity of the long molecular chains of the thermoplastics. As a result of high viscosity, it is very difficult to impregnate the resin into a tightly interlaced textile structure to fill the voids present in the structure. One potential solution to the above problem, is to pre-deposit resin on the fiber, prior to consolidation. The current study compares DREF spinning, powder coating and film stacking methods of predeposition of resin onto fibers. An investigation into the flexural properties of unidirectional composites (UDC) produced from blending of carbon fiber and polypropylene (PP) matrix in varying forms of fiber, powder and film are reported. Dr. Ernst Fehrer (DREF) yarns or friction spun hybrid yarns were manufactured from PP fibers and carbon tows. The DREF yarns were consolidated to yield unidirectional composites (UDCs) referred to as UDC-D. PP in the form of powder was coated on carbon tows by electrostatic spray coating. The powder-coated towpregs were consolidated to form UDC-P. For the sake of comparison, a third UDC referred as UDC-F was manufactured by the consolidation of PP films stacked between carbon tows. The experiments were designed to yield a matching fiber volume fraction of about 50 % in all the three UDCs. A comparison of mechanical properties of the three composites was studied to understand the efficiency of matrix wetting and impregnation. Approximately 19% and 68% higher flexural strength were obtained for UDC-P than UDC-D and UDC-F respectively. Similarly, 25% and 81% higher modulus were observed in UDC-P than UDC-D and UDC-F respectively. Results from micro-computed tomography, scanning electron microscopy, and short beam tests indicate better impregnation of PP matrix in UDC-P obtained through electrostatic spray coating process and thereby higher flexural strength and modulus.

Keywords: DREF spinning, film stacking, flexural strength, powder coating, thermoplastic composite

Procedia PDF Downloads 221

Authors: Shraddha Acharya, Sharad Kumar Sharma, Ratna Bhattarai, Bhagwan Maharjan, Deepak Dahal, Serpahine Kaminsa

Abstract:

Background: Drug-resistant (DR) tuberculosis (TB) continues to be a threat in Nepal, with an estimated 2800 new cases every year. The treatment of DR-TB with second line TB drugs is complex and takes longer time with comparatively lower treatment success rate than drug-susceptible TB. Delay in treatment initiation for DR-TB patients might further result in unfavorable treatment outcomes and increased transmission. This study thus aims to determine median time taken to initiate second-line treatment among Rifampicin Resistant (RR) diagnosed TB patients and to assess the proportion of treatment delays among various type of DR-TB cases. Method: A retrospective cohort study was done using national routine electronic data (DRTB and TB Laboratory Patient Tracking System-DHIS2) on drug resistant tuberculosis patients between January 2020 and December 2022. The time taken for treatment initiation was computed as– days from first diagnosis as RR TB through Xpert MTB/Rif test to enrollment on second-line treatment. The treatment delay (>7 days after diagnosis) was calculated. Results: Among total RR TB cases (N=954) diagnosed via Xpert nationwide, 61.4% were enrolled under shorter-treatment regimen (STR), 33.0% under longer treatment regimen (LTR), 5.1% for Pre-extensively drug resistant TB (Pre-XDR) and 0.4% for Extensively drug resistant TB (XDR) treatment. Among these cases, it was found that the median time from diagnosis to treatment initiation was 6 days (IQR:2-15.8). The median time was 5 days (IQR:2.0-13.3) among STR, 6 days (IQR:3.0-15.0) among LTR, 30 days (IQR:5.5-66.8) among Pre-XDR and 4 days (IQR:2.5-9.0) among XDR TB cases. The overall treatment delay (>7 days after diagnosis) was observed in 42.4% of the patients, among which, cases enrolled under Pre-XDR contributed substantially to treatment delay (72.0%), followed by LTR (43.6%), STR (39.1%) and XDR (33.3%). Conclusion: Timely diagnosis and prompt treatment initiation remain fundamental focus of the National TB program. The findings of the study, however suggest gaps in timeliness of treatment initiation for the drug-resistant TB patients, which could bring adverse treatment outcomes. Moreover, there is an alarming delay in second line treatment initiation for the Pre-XDR TB patients. Therefore, this study generates evidence to identify existing gaps in treatment initiation and highlights need for formulating specific policies and intervention in creating effective linkage between the RR TB diagnosis and enrollment on second line TB treatment with intensified efforts from health providers for follow-ups and expansion of more decentralized, adequate, and accessible diagnostic and treatment services for DR-TB, especially Pre-XDR TB cases, due to the observed long treatment delays.

Keywords: drug-resistant, tuberculosis, treatment initiation, Nepal, treatment delay

Procedia PDF Downloads 82

Authors: Fama Jallow, Melody Neaves, Professor Mcgregor

Abstract:

The quest for sustainable energy sources has driven significant interest in hydrogen production as a clean and efficient fuel. Alkaline water electrolysis (AWE) has emerged as a prominent method for generating hydrogen, necessitating the development of advanced electrode designs with improved performance characteristics. Additive manufacturing (AM) by laser powder bed fusion (LPBF) method presents an opportunity to tailor electrode microstructures and properties, enhancing their performance. This research proposes investigating the AM of electrodes with different lattice structures to optimize hydrogen production. The primary objective is to employ advanced modeling techniques to identify and select two optimal lattice structures for electrode fabrication. LPBF will be used to fabricate electrodes with precise control over lattice geometry, pore size, and distribution. The performance evaluation will encompass energy consumption and porosity analysis. AWE will assess energy efficiency, aiming to identify lattice structures with enhanced hydrogen production rates and reduced power requirements. Computed tomography (CT) scanning will analyze porosity to determine material integrity and mass transport characteristics. The research aims to bridge the gap between AM and hydrogen production by investigating lattice structures potential in electrode design. By systematically exploring lattice structures and their impact on performance, this study aims to provide valuable insights into the design and fabrication of highly efficient and cost-effective electrodes for AWE. The outcomes hold promise for advancing hydrogen production through AM. The research will have a significant impact on the development of sustainable energy sources. The findings from this study will help to improve the efficiency of AWE, making it a more viable option for hydrogen production. This could lead to a reduction in our reliance on fossil fuels, which would have a positive impact on the environment. The research is also likely to have a commercial impact. The findings could be used to develop new electrode designs that are more efficient and cost-effective. This could lead to the development of new hydrogen production technologies, which could have a significant impact on the energy market.

Keywords: hydrogen production, electrode, lattice structure, Africa

Procedia PDF Downloads 68

Authors: Tawfig Alghbaili

Abstract:

LP3D Field is located near the center of the Sirt Basin in the Marada Trough approximately 215 km south Marsa Al Braga City. The Marada Trough is bounded on the west by a major fault, which forms the edge of the Beda Platform, while on the east, a bounding fault marks the edge of the Zelten Platform. The main reservoir in the LP3D Field is Upper Paleocene Beda Formation. The Beda Formation is mainly limestone interbedded with shale. The reservoir average thickness is 117.5 feet. To develop a better understanding of the characterization and distribution of the Beda reservoir, quantitative seismic data interpretation has been done, and also, well logs data were analyzed. Six reflectors corresponding to the tops of the Beda, Hagfa Shale, Gir, Kheir Shale, Khalifa Shale, and Zelten Formations were picked and mapped. Special work was done on fault interpretation part because of the complexities of the faults at the structure area. Different attribute analyses were done to build up more understanding of structures lateral extension and to view a clear image of the fault blocks. Time to depth conversion was computed using velocity modeling generated from check shot and sonic data. The simplified stratigraphic cross-section was drawn through the wells A1, A2, A3, and A4-LP3D. The distribution and the thickness variations of the Beda reservoir along the study area had been demonstrating. Petrophysical analysis of wireline logging also was done and Cross plots of some petrophysical parameters are generated to evaluate the lithology of reservoir interval. Structure and Stratigraphic Framework was designed and run to generate different model like faults, facies, and petrophysical models and calculate the reservoir volumetric. This study concluded that the depth structure map of the Beda formation shows the main structure in the area of study, which is north to south faulted anticline. Based on the Beda reservoir models, volumetric for the base case has been calculated and it has STOIIP of 41MMSTB and Recoverable oil of 10MMSTB. Seismic attributes confirm the structure trend and build a better understanding of the fault system in the area.

Keywords: LP3D Field, Beda Formation, reservoir models, Seismic attributes

Procedia PDF Downloads 211

Authors: Malik Abid Hussain Khokhar, Muhammad Naveed Tahir, Muhammad Amin

Abstract:

Energy demand has been increased manifold due to increasing population, urban sprawl and rapid socio-economic improvements. Low water capacity in dams for continuation of hydrological power, land cover and land use are the key parameters which are creating problems for more energy production. Overall installed hydropower capacity of Pakistan is more than 35000 MW whereas Pakistan is producing up to 17000 MW and the requirement is more than 22000 that is resulting shortfall of 5000 - 7000 MW. Therefore, there is a dire need to develop small hydropower to fulfill the up-coming requirements. In this regards, excessive rainfall, snow nurtured fast flowing perennial tributaries and streams in northern mountain regions of Pakistan offer a gigantic scope of hydropower potential throughout the year. Rivers flowing in KP (Khyber Pakhtunkhwa) province, GB (Gilgit Baltistan) and AJK (Azad Jammu & Kashmir) possess sufficient water availability for rapid energy growth. In the backdrop of such scenario, small hydropower plants are believed very suitable measures for more green environment and power sustainable option for the development of such regions. Aim of this study is to estimate hydropower potential sites for small hydropower plants and stream distribution as per steam network available in the available basins in the study area. The proposed methodology will focus on features to meet the objectives i.e. site selection of maximum hydropower potential for hydroelectric generation using well emerging GIS tool SWAT as hydrological run-off model on the Neelum, Kunhar and the Dor Rivers’ basins. For validation of the results, NDWI will be computed to show water concentration in the study area while overlaying on geospatial enhanced DEM. This study will represent analysis of basins, watershed, stream links, and flow directions with slope elevation for hydropower potential to produce increasing demand of electricity by installing small hydropower stations. Later on, this study will be benefitted for other adjacent regions for further estimation of site selection for installation of such small power plants as well.

Keywords: energy, stream network, basins, SWAT, evapotranspiration

Procedia PDF Downloads 220

Authors: Angelis P. Barlampas

Abstract:

The purpose of this study is to inform the reader, about the various applications of artificial intelligence (AI), in cardiac imaging. AI grows fast and its role is crucial in medical specialties, which use large amounts of digital data, that are very difficult or even impossible to be managed by human beings and especially doctors.Artificial intelligence (AI) refers to the ability of computers to mimic human cognitive function, performing tasks such as learning, problem-solving, and autonomous decision making based on digital data. Whereas AI describes the concept of using computers to mimic human cognitive tasks, machine learning (ML) describes the category of algorithms that enable most current applications described as AI. Some of the current applications of AI in cardiac imaging are the follows: Ultrasound: Automated segmentation of cardiac chambers across five common views and consequently quantify chamber volumes/mass, ascertain ejection fraction and determine longitudinal strain through speckle tracking. Determine the severity of mitral regurgitation (accuracy > 99% for every degree of severity). Identify myocardial infarction. Distinguish between Athlete’s heart and hypertrophic cardiomyopathy, as well as restrictive cardiomyopathy and constrictive pericarditis. Predict all-cause mortality. CT Reduce radiation doses. Calculate the calcium score. Diagnose coronary artery disease (CAD). Predict all-cause 5-year mortality. Predict major cardiovascular events in patients with suspected CAD. MRI Segment of cardiac structures and infarct tissue. Calculate cardiac mass and function parameters. Distinguish between patients with myocardial infarction and control subjects. It could potentially reduce costs since it would preclude the need for gadolinium-enhanced CMR. Predict 4-year survival in patients with pulmonary hypertension. Nuclear Imaging Classify normal and abnormal myocardium in CAD. Detect locations with abnormal myocardium. Predict cardiac death. ML was comparable to or better than two experienced readers in predicting the need for revascularization. AI emerge as a helpful tool in cardiac imaging and for the doctors who can not manage the overall increasing demand, in examinations such as ultrasound, computed tomography, MRI, or nuclear imaging studies.

Keywords: artificial intelligence, cardiac imaging, ultrasound, MRI, CT, nuclear medicine

Procedia PDF Downloads 77

Authors: Sireetorn Kuharat, Anwar Beg

Abstract:

In this study, multi-mode heat transfer characteristics of spacecraft solar collectors are investigated computationally. Two-dimensional steady-state incompressible laminar Newtonian viscous convection-radiative heat transfer in a rectangular solar collector geometry. The ANSYS FLUENT finite volume code (version 17.2) is employed to simulate the thermo-fluid characteristics. Several radiative transfer models are employed which are available in the ANSYS workbench, including the classical Rosseland flux model and the more elegant P1 flux model. Mesh-independence tests are conducted. Validation of the simulations is conducted with a computational Harlow-Welch MAC (Marker and Cell) finite difference method and excellent correlation. The influence of aspect ratio, Prandtl number (Pr), Rayleigh number (Ra) and radiative flux model on temperature, isotherms, velocity, the pressure is evaluated and visualized in color plots. Additionally, the local convective heat flux is computed and solutions are compared with the MAC solver for various buoyancy effects (e.g. Ra = 10,000,000) achieving excellent agreement. The P1 model is shown to better predict the actual influence of solar radiative flux on thermal fluid behavior compared with the limited Rosseland model. With increasing Rayleigh numbers the hot zone emanating from the base of the collector is found to penetrate deeper into the collector and rises symmetrically dividing into two vortex regions with very high buoyancy effect (Ra >100,000). With increasing Prandtl number (three gas cases are examined respectively hydrogen gas mixture, air and ammonia gas) there is also a progressive incursion of the hot zone at the solar collector base higher into the solar collector space and simultaneously a greater asymmetric behavior of the dual isothermal zones. With increasing aspect ratio (wider base relative to the height of the solar collector geometry) there is a greater thermal convection pattern around the whole geometry, higher temperatures and the elimination of the cold upper zone associated with lower aspect ratio.

Keywords: thermal convection, radiative heat transfer, solar collector, Rayleigh number

Procedia PDF Downloads 117

Authors: Itsuo Yokoyama, Rika Kikuti, Naoko Watabe

Abstract:

Introduction: It has been known that chronic kidney disease (CKD) patients can benefit from physical exercise during dialysis therapy improving aerobic capacity, muscle function, cardiovascular function, and overall health-related quality of life. This study aimed to evaluate the effectiveness of dialysis rehabilitation. Materials and Methods: A total of 55 patients underwent two-hour resistance exercise training during each hemodialysis session for three consecutive months. Various routine clinical data were collected, including the calculation of the planar dimension of the muscle area in both upper legs at the level of the ischial bone. This area calculation was possible in 26 patients who had yearly plain abdominal computed tomography (CT) scans. DICOM files from the CT scans were used with 3D Slicer software for area calculation. An age and sex-matched group of 26 patients without dialysis rehabilitation also had yearly CT scans during the study period for comparison. Clinical data were compared between the two groups: Group A (rehabilitation) and Group B (non-rehabilitation). Results: There were no differences in basic laboratory data between the two groups. The average muscle area before and after rehabilitation in Group A was 212 cm² and 216 cm², respectively. In Group B, the average areas were 230.0 cm² and 225.8 cm². While there was no significant difference in absolute values, the average percentage increase in muscle area was +1.2% (ranging from -7.6% to 6.54%) for Group A and -2.0% (ranging from -12.1% to 4.9%) for Group B, which was statistically significant. In Group A, 9 of 26 were diabetic (DM), and 13 of 26 in Group B were non-DM. The increase in muscle area for DM patients was 4.9% compared to -0.7% for non-DM patients, which was significantly different. There were no significant differences between the two groups in terms of nutritional assessment, Kt/V, or incidence of clinical complications such as cardiovascular events. Considerations: Dialysis rehabilitation has been reported to prevent muscle atrophy by increasing muscle fibers and capillaries. This study demonstrated that muscle volume increased after dialysis exercise, as evidenced by the increased muscle area in the thighs. Notably, diabetic patients seemed to benefit more from dialysis exercise than non-diabetics. Although this study is preliminary due to its relatively small sample size, it suggests that intradialytic physical training may improve insulin utilization in muscle fiber cells, particularly in type II diabetic patients where insulin receptor function and signaling are altered. Further studies are needed to investigate the detailed mechanisms underlying the muscle hypertrophic effects of dialysis exercise.

Keywords: dialysis, excercise, muscle, hypertrophy, diabetes, insulin

Procedia PDF Downloads 16

Authors: Sanjay Kumar Srivastava, Anu Rani Sharma, Kamna Sachdeva

Abstract:

Every year, fog formation over the Indo-Gangetic Plains (IGPs) of Indian region during the winter months of December and January is believed to create numerous hazards, inconvenience, and economic loss to the inhabitants of this densely populated region of Indian subcontinent. The aim of the paper is to analyze the spatial and temporal variability of winter fog over IGPs. Long term ground observations of visibility and other meteorological parameters (1971-2010) have been analyzed to understand the formation of fog phenomena and its relevance during the peak winter months of January and December over IGP of India. In order to examine the temporal variability, time series and trend analysis were carried out by using the Mann-Kendall Statistical test. Trend analysis performed by using the Mann-Kendall test, accepts the alternate hypothesis with 95% confidence level indicating that there exists a trend. Kendall tau’s statistics showed that there exists a positive correlation between time series and fog frequency. Further, the Theil and Sen’s median slope estimate showed that the magnitude of trend is positive. Magnitude is higher during January compared to December for the entire IGP except in December when it is high over the western IGP. Decade wise time series analysis revealed that there has been continuous increase in fog days. The net overall increase of 99 % was observed over IGP in last four decades. Diurnal variability and average daily persistence were computed by using descriptive statistical techniques. Geo-statistical analysis of fog was carried out to understand the spatial variability of fog. Geo-statistical analysis of fog revealed that IGP is a high fog prone zone with fog occurrence frequency of more than 66% days during the study period. Diurnal variability indicates the peak occurrence of fog is between 06:00 and 10:00 local time and average daily fog persistence extends to 5 to 7 hours during the peak winter season. The results would offer a new perspective to take proactive measures in reducing the irreparable damage that could be caused due to changing trends of fog.

Keywords: fog, climatology, Mann-Kendall test, trend analysis, spatial variability, temporal variability, visibility

Procedia PDF Downloads 238

Authors: Rehan Ullah

Abstract:

The primary purpose of the study is to observe the relationship that exists between the organizational information technology (IT) capabilities and the organizational performance in China and Pakistan. Nations like China and Pakistan utilize modern techno-how to enhance their production endeavors. Therefore, making a wide-ranging comparison of the manufacturing services between China and Pakistan was chosen due to numerous reasons. One reason for carrying out this comparison is to determine how IT of the two countries enhances organizational competency on small and medium-sized manufacturing enterprises (SMEs). The study hypothesized that organizational IT capabilities (IT infrastructure, IT competence) have a positive influence on organizational performance and the strategic benefits of IT have a mediating effect on the relationship between IT capability and organizational performance. To investigate the relationship between IT capabilities and organizational performance, surveys were sent to managers of small, medium-sized manufacturing organizations located in the southwestern region, Sichuan province of China, and Pakistani companies, which are located in Islamabad, Lahore, and Karachi. These cities were selected as typical representatives of each country. Organizational performance has been measured in terms of profitability, organizational success, growth, market share, and innovativeness. Out of 400 surveys distributed to different manufacturing organizations, 303 usable and valid responses were received that are analyzed in this research. The data were examined using SPSS and Smart PLS computer software. The results of the study, including the descriptive statistics of each variable, are used. The outer model has been measured with considerations to content validity, discriminant validity, and convergent validity. The path coefficients among the constructs were also computed when analyzing the structural model using the bootstrapping technique. The analysis of data from both China and Pakistan yields an identical but unique result. The results show that IT infrastructure, IT competence, strategic benefits of IT are all correlated to the performance of the organizations. Moreover, strategic benefits of IT have been proved to mediate the relationship between IT capabilities and organization performance. The author, concerning the role of IT on the performance of an organization, highlights the different aspects as well as its benefits in an organization. The overall study concludes several implications for both managers and academicians. It also provides the limitations of the study and offers recommendations for future studies and practice.

Keywords: organizational performance, IT capabilities, IT infrastructure, IT competence, strategic benefits of IT, China, Pakistan

Procedia PDF Downloads 93

Authors: Deepthy Ann Joy, N. Sreedevi

Abstract:

Hearing impairment (HI) at an early age, identified before the onset of language development can reduce the negative effect on speech and language development of children. Early rehabilitation is very important in the improvement of speech production in children with HI. Other than conventional hearing aids, Cochlear Implants are being used in the rehabilitation of children with HI. However, delay in acquisition of speech and language milestones persist in children with Cochlear Implant (CI). Delay in speech milestones are reflected through speech sound errors. These errors reflect the temporal and spectral characteristics of speech. Hence, acoustical analysis of the speech sounds will provide a better representation of speech production skills in children with CI. The present study aimed at investigating the acoustic characteristics of vowels in Malayalam speaking children with a cochlear implant. The participants of the study consisted of 20 Malayalam speaking children in the age range of four and seven years. The experimental group consisted of 10 children with CI, and the control group consisted of 10 typically developing children. Acoustic analysis was carried out for 5 short (/a/, /i/, /u/, /e/, /o/) and 5 long vowels (/a:/, /i:/, /u:/, /e:/, /o:/) in word-initial position. The responses were recorded and analyzed for acoustic parameters such as Vowel duration, Ratio of the duration of a short and long vowel, Formant frequencies (F₁ and F₂) and Formant Centralization Ratio (FCR) computed using the formula (F₂u+F₂a+F₁i+F₁u)/(F₂i+F₁a). Findings of the present study indicated that the values for vowel duration were higher in experimental group compared to the control group for all the vowels except for /u/. Ratio of duration of short and long vowel was also found to be higher in experimental group compared to control group except for /i/. Further F₁ for all vowels was found to be higher in experimental group with variability noticed in F₂ values. FCR was found be higher in experimental group, indicating vowel centralization. Further, the results of independent t-test revealed no significant difference across the parameters in both the groups. It was found that the spectral and temporal measures in children with CI moved towards normal range. The result emphasizes the significance of early rehabilitation in children with hearing impairment. The role of rehabilitation related aspects are also discussed in detail which can be clinically incorporated for the betterment of speech therapeutic services in children with CI.

Keywords: acoustics, cochlear implant, Malayalam, vowels

Procedia PDF Downloads 143

Authors: Rahat Zaman

Abstract:

The present study was conducted to investigate psychological well-being and positive and negative affect among adults. The sample comprised 221 adults; the sample was collected from all over Pakistan. Psychological well-being was measured with the help of the psychological well-being scale developed by Ryff and Keyes (1995). Lifestyle was measured with the help of the Health Promoting Lifestyle Profile Scale developed by Walker et al. (1995). Positive and negative effects were measured by PANAS, developed by Watson, Clark, and Tellegen (1998). To check the properties of scale, the alpha reliability coefficient was calculated. To test the hypotheses of the research, correlation, independent sample t-rest, and ANOVA were computed. It was hypothesized that there would be a positive relationship between psychological well-being and lifestyles and positive affect. The results show that psychological well-being, lifestyle, and positive affect are positively related. This also supports our hypothesis. The research also searched for relationships in the study variables according to the demographics of the sample. The respondents varied according to their dominant affect levels with respect to their psychological well-being and lifestyles. The research found significant differences for the genders in life appreciation, nutrition, and negative affect. Single and married individuals differed significantly on autonomy, environmental mastery, life appreciation, nutrition, and stress management. Individuals showed significant differences with respect to their living situation, joint and nuclear family members showed significant differences in personal growth, autonomy, health responsibilities, social support, physical activities, and stress management. The sample showed significant differences in environmental mastery, personal growth, purpose in life, life appreciation, health responsibilities, physical activities, stress management, and negative affect when divided in socioeconomic status. Age-wise analysis showed significant differences in autonomy, personal growth, purpose in life, life appreciation, nutrition, and stress management. Provincially significant differences were found in life appreciation, nutrition, social support, physical activities, and stress management, and both positive and negative effects were experienced. Implications of the results are discussed.

Keywords: wellbeing, healthy lifestyle, self acceptance, positive

Procedia PDF Downloads 69

Authors: Yen Ling Liu, Shu-Fen Wu, Chen-Fuh Lam, I-Ling Tsai, Chia-Yu Chen

Abstract:

Purpose: Nurse anesthetists are confronting extraordinarily high job stress in their daily practice, deriving from the fast-track anesthesia care, risk of perioperative complications, routine rotating shifts, teaching programs and interactions with the surgical team in the operating room. This study investigated the influence of work stress on the burnout and turnover intention of nurse anesthetists in a regional general hospital in Southern Taiwan. Methods: This was a descriptive correlational study carried out in 66 full-time nurse anesthetists. Data was collected from March 2017 to June 2017 by in-person interview, and a self-administered structured questionnaire was completed by the interviewee. Outcome measurements included the Practice Environment Scale of the Nursing Work Index (PES-NWI), Maslach Burnout Inventory (MBI) and nursing staff turnover intention. Numerical data were analyzed by descriptive statistics, independent t test, or one-way ANOVA. Categorical data were compared using the chi-square test (x²). Datasets were computed with Pearson product-moment correlation and linear regression. Data were analyzed by using SPSS 20.0 software. Results: The average score for job burnout was 68.7916.67 (out of 100). The three major components of burnout, including emotional depletion (mean score of 26.32), depersonalization (mean score of 13.65), and personal(mean score of 24.48). These average scores suggested that these nurse anesthetists were at high risk of burnout and inversely correlated with turnover intention (t = -4.048, P < 0.05). Using linear regression model, emotional exhaustion and depersonalization were the two independent factors that predicted turnover intention in the nurse anesthetists (19.1% in total variance). Conclusion/Implications for Practice: The study identifies that the high risk of job burnout in the nurse anesthetists is not simply derived from physical overload, but most likely resulted from the additional emotional and psychological stress. The occurrence of job burnout may affect the quality of nursing work, and also influence family harmony, in turn, may increase the turnover rate. Multimodal approach is warranted to reduce work stress and job burnout in nurse anesthetists to enhance their willingness to contribute in anesthesia care.

Keywords: anesthesia nurses, burnout, job, turnover intention

Procedia PDF Downloads 293

Authors: Jitendar Kumar Tiwari, Ajay Mandal, N. Sathish, A. K. Srivastava

Abstract:

Since the last decade, graphene achieved great attention toward the progress of multifunction metal matrix composites, which are highly demanded in industries to develop energy-efficient systems. This study covers the two advanced aspects of the latest scientific endeavor, i.e., graphene as reinforcement in metallic materials and additive manufacturing (AM) as a processing technology. Herein, high-quality graphene and AlSi10Mg powder mechanically mixed by very low energy ball milling with 0.1 wt. % and 0.2 wt. % graphene. Mixed powder directly subjected to the powder bed fusion process, i.e., an AM technique to produce composite samples along with bare counterpart. The effects of graphene on porosity, microstructure, and mechanical properties were examined in this study. The volumetric distribution of pores was observed under X-ray computed tomography (CT). On the basis of relative density measurement by X-ray CT, it was observed that porosity increases after graphene addition, and pore morphology also transformed from spherical pores to enlarged flaky pores due to improper melting of composite powder. Furthermore, the microstructure suggests the grain refinement after graphene addition. The columnar grains were able to cross the melt pool boundaries in case of the bare sample, unlike composite samples. The smaller columnar grains were formed in composites due to heterogeneous nucleation by graphene platelets during solidification. The tensile properties get affected due to induced porosity irrespective of graphene reinforcement. The optimized tensile properties were achieved at 0.1 wt. % graphene. The increment in yield strength and ultimate tensile strength was 22% and 10%, respectively, for 0.1 wt. % graphene reinforced sample in comparison to bare counterpart while elongation decreases 20% for the same sample. The hardness indentations were taken mostly on the solid region in order to avoid the collapse of the pores. The hardness of the composite was increased progressively with graphene content. Around 30% of increment in hardness was achieved after the addition of 0.2 wt. % graphene. Therefore, it can be concluded that powder bed fusion can be adopted as a suitable technique to develop graphene reinforced AlSi10Mg composite. Though, some further process modification required to avoid the induced porosity after the addition of graphene, which can be addressed in future work.

Keywords: graphene, hardness, porosity, powder bed fusion, tensile properties

Procedia PDF Downloads 126

Authors: Timothy Quinn, Ronald Croce, Aliaksandr Leuchanka, Justin Walker

Abstract:

Many endurance athletes train at or just below an intensity associated with their lactate threshold (LT) and often the heart rate (HR) that these athletes use for their LT are above their true LT-HR measured in a laboratory. Training above their true LT-HR may lead to overtraining and injury. Few athletes have the capability of measuring their LT in a laboratory and rely on perception to guide them, as accurate field tests to determine LT are limited. Therefore, the purpose of this study was to determine if an 8-minute field test could accurately define the HR associated with LT as measured in the laboratory. On Day 1, fifteen male runners (mean±SD; age, 27.8±4.1 years; height, 177.9±7.1 cm; body mass, 72.3±6.2 kg; body fat, 8.3±3.1%) performed a discontinuous treadmill LT/maximal oxygen consumption (LT/VO2max) test using a portable metabolic gas analyzer (Cosmed K4b2) and a lactate analyzer (Analox GL5). The LT (and associated HR) was determined using the 1/+1 method, where blood lactate increased by 1 mMol•L-1 over baseline followed by an additional 1 mMol•L-1 increase. Days 2 and 3 were randomized, and the athletes performed either an 8-minute run on the treadmill (TM) or on a 160-m indoor track (TR) in an effort to cover as much distance as possible while maintaining a high intensity throughout the entire 8 minutes. VO2, HR, ventilation (VE), and respiratory exchange ratio (RER) were measured using the Cosmed system, and rating of perceived exertion (RPE; 6-20 scale) was recorded every minute. All variables were averaged over the 8 minutes. The total distance covered over the 8 minutes was measured in both conditions. At the completion of the 8-minute runs, blood lactate was measured. Paired sample t-tests and pairwise Pearson correlations were computed to determine the relationship between variables measured in the field tests versus those obtained in the laboratory at LT. An alpha level of <0.05 was required for statistical significance. The HR (mean +SD) during the TM (167+9 bpm) and TR (172+9 bpm) tests were strongly correlated to the HR measured during the laboratory LT (169+11 bpm) test (r=0.68; p<0.03 and r=0.88; p<0.001, respectively). Blood lactate values during the TM and TR tests were not different from each other but were strongly correlated with the laboratory LT (r=0.73; p<0.04 and r=0.66; p<0.05, respectively). VE (Lmin-1) was significantly greater during the TR (134.8+11.4 Lmin-1) as compared to the TM (123.3+16.2 Lmin-1) with moderately strong correlations to the laboratory threshold values (r=0.38; p=0.27 and r=0.58; p=0.06, respectively). VO2 was higher during TR (51.4 mlkg-1min-1) compared to TM (47.4 mlkg-1min-1) with correlations of 0.33 (p=0.35) and 0.48 (p=0.13), respectively to threshold values. Total distance run was significantly greater during the TR (2331.6+180.9 m) as compared to the TM (2177.0+232.6 m), but they were strongly correlated with each other (r=0.82; p<0.002). These results suggest that an 8-minute running field test can accurately predict the HR associated with the LT and may be a simple test that athletes and coaches could implement to aid in training techniques.

Keywords: blood lactate, heart rate, running, training

Procedia PDF Downloads 251

Authors: Adel A. Ghobbar, Varun Raman

Abstract:

The Corrective maintenance of components and systems is a problem plaguing almost every industry in the world today. Train operators’ and the maintenance repair and overhaul subsidiary of the Dutch railway company is also facing this problem. A considerable portion of the maintenance activities carried out by the company are unscheduled. This, in turn, severely stresses and stretches the workforce and resources available. One possible solution is to have a robust preventive maintenance plan. The other possible solution is to plan maintenance based on real-time data obtained from sensor-based ‘Health and Usage Monitoring Systems.’ The former has been investigated in this paper. The preventive maintenance model developed for train operator will subsequently be extended, to tackle the unscheduled maintenance problem also affecting the aerospace industry. The extension of the model to the aerospace sector will be dealt with in the second part of the research, and it would, in turn, validate the soundness of the model developed. Thus, there are distinct areas that will be addressed in this paper, including the mathematical modelling of preventive maintenance and optimization based on cost and system availability. The results of this research will help an organization to choose the right maintenance strategy, allowing it to save considerable sums of money as opposed to overspending under the guise of maintaining high asset availability. The concept of delay time modelling was used to address the practical problem of unscheduled maintenance in this paper. The delay time modelling can be used to help with support planning for a given asset. The model was run using MATLAB, and the results are shown that the ideal inspection intervals computed using the extended from a minimal cost perspective were 29 days, and from a minimum downtime, perspective was 14 days. Risk matrix integration was constructed to represent the risk in terms of the probability of a fault leading to breakdown maintenance and its consequences in terms of maintenance cost. Thus, the choice of an optimal inspection interval of 29 days, resulted in a cost of approximately 50 Euros and the corresponding value of b(T) was 0.011. These values ensure that the risk associated with component X being maintained at an inspection interval of 29 days is more than acceptable. Thus, a switch in maintenance frequency from 90 days to 29 days would be optimal from the point of view of cost, downtime and risk.

Keywords: delay time modelling, unscheduled maintenance, reliability, maintainability, availability

Procedia PDF Downloads 131

Authors: Nassima Noufail, Sara Bouhali

Abstract:

In this work, we develop a semi-supervised solution for the purpose of action detection in videos and propose an efficient algorithm for video segmentation. The approach is divided into video segmentation, feature extraction, and classification. In the first part, a video is segmented into clips, and we used the K-means algorithm for this segmentation; our goal is to find groups based on similarity in the video. The application of k-means clustering into all the frames is time-consuming; therefore, we started by the identification of transition frames where the scene in the video changes significantly, and then we applied K-means clustering into these transition frames. We used two image filters, the gaussian filter and the Laplacian of Gaussian. Each filter extracts a set of features from the frames. The Gaussian filter blurs the image and omits the higher frequencies, and the Laplacian of gaussian detects regions of rapid intensity changes; we then used this vector of filter responses as an input to our k-means algorithm. The output is a set of cluster centers. Each video frame pixel is then mapped to the nearest cluster center and painted with a corresponding color to form a visual map. The resulting visual map had similar pixels grouped. We then computed a cluster score indicating how clusters are near each other and plotted a signal representing frame number vs. clustering score. Our hypothesis was that the evolution of the signal would not change if semantically related events were happening in the scene. We marked the breakpoints at which the root mean square level of the signal changes significantly, and each breakpoint is an indication of the beginning of a new video segment. In the second part, for each segment from part 1, we randomly selected a 16-frame clip, then we extracted spatiotemporal features using convolutional 3D network C3D for every 16 frames using a pre-trained model. The C3D final output is a 512-feature vector dimension; hence we used principal component analysis (PCA) for dimensionality reduction. The final part is the classification. The C3D feature vectors are used as input to a multi-class linear support vector machine (SVM) for the training model, and we used a multi-classifier to detect the action. We evaluated our experiment on the UCF101 dataset, which consists of 101 human action categories, and we achieved an accuracy that outperforms the state of art by 1.2%.

Keywords: video segmentation, action detection, classification, Kmeans, C3D

Procedia PDF Downloads 77

Authors: Anna Tokajuk, Agnieszka Zakrzeska, Ewa Chabielska, Halina Car

Abstract:

Whey is a by-product generated mainly in the production of cheese and casein. Powder forms of whey are used widely in the food industry as well as a high-protein food for infants, for convalescents, by athletes and especially by bodybuilders to increase muscle mass during exercise. Whey protein concentrate-80 (WPC-80) is a source of bioactive peptides with beneficial effects on the cardiovascular system. It is known that whey proteins health beneficial properties include antidiabetic, blood pressure lowering, improving cardiovascular system function, antibacterial, antiviral and other effects. To study its influence on the development of thrombosis, venous thrombosis model was performed according to the protocol featured by Reyers with modification by Chabielska and Gromotowicz. Male Wistar-Crl: WI (Han) rats from researched groups were supplemented with two doses of WPC-80 (0.3 or 0.5 g/kg) for 7, 14 or 21 days and after these periods, one-hour venous thrombosis model was performed. Control group received 0.9 % NaCl solution and was sham operated. The statistical significance of results was computed by Mann – Whitney’s test. We observed that thrombus weight was decreased in animals obtaining WPC-8080 and that was statistically significant in 14 and 21-day supplemented groups. Blood count parameters did not differ significantly in rats with and without thrombosis induction whether they were fed with WPC-80 or not. Moreover, the number of platelets (PLT) was within the normal range in each group. The examined coagulation parameters in rats of the control groups were within normal limits. After WPC-80 supplementation there was the tendency to prolonged activated partial thromboplastin time (aPTT), but in comparison, the results did not turn out significant. In animals that received WPC-80 0.3 g·kg-1 for 21 days with and without induced thrombosis, prothrombin time (PT) and an international normalized ratio (INR) was somewhat decreased, remaining within the normal range, but the nature and significance of this observation are beyond the framework of the current study. Additionally, fibrinogen and thrombin time (TT) did not differ significantly between groups. Therefore the exact effect of WPC-80 on coagulation system is still elusive and requires further thorough research including mechanisms of action. Determining the potential clinical application of WPC-80 requires the selection of the optimal dose and duration of supplementation.

Keywords: antithrombotic, rats, venous thrombosis, WPC-80

Procedia PDF Downloads 117

Authors: Gayathri Devi V, Aravamudan Kannan, Amit Sircar

Abstract:

In the inner fuel cycle system of a nuclear fusion reactor, the Hydrogen Isotopes Removal System (HIRS) plays a pivoted role. It enables the effective extraction of the hydrogen isotopes from the breeder purge gas which helps to maintain the tritium breeding ratio and sustain the fusion reaction. One of the components of HIRS, Cryogenic Molecular Sieve Bed (CMSB) columns with zeolites adsorbents are considered for the physisorption of hydrogen isotopes at 1 bar and 77 K. Even though zeolites have good thermal stability and reduced activation properties making them ideal for use in nuclear reactor applications, their modest capacity for hydrogen isotopes adsorption is a cause of concern. In order to enhance the adsorbent capacity in an informed manner, it is helpful to understand the adsorption phenomena at the quantum electronic structure level. Physicochemical modifications of the adsorbent material enhances the adsorption capacity through the incorporation of active sites. This may be accomplished through the incorporation of suitable metal ions in the zeolite framework. In this work, molecular hydrogen isotopes adsorption on the active sites of functionalized zeolites are investigated in detail using Density Functional Theory (DFT) study. This involves the utilization of hybrid Generalized Gradient Approximation (GGA) with dispersion correction to account for the exchange and correlation functional of DFT. The electronic energies, adsorption enthalpy, adsorption free energy, Highest Occupied Molecular Orbital (HOMO), Lowest Unoccupied Molecular Orbital (LUMO) energies are computed on the stable 8T zeolite clusters as well as the periodic structure functionalized with different active sites. The characteristics of the dihydrogen bond with the active metal sites and the isotopic effects are also studied in detail. Validation studies with DFT will also be presented for adsorption of hydrogen on metal ion functionalized zeolites. The ab-inito screening analysis gave insights regarding the mechanism of hydrogen interaction with the zeolites under study and also the effect of the metal ion on adsorption. This detailed study provides guidelines for selection of the appropriate metal ions that may be incorporated in the zeolites framework for effective adsorption of hydrogen isotopes in the HIRS.

Keywords: adsorption enthalpy, functionalized zeolites, hydrogen isotopes, nuclear fusion, physisorption

Procedia PDF Downloads 179

Authors: Saif Al-Qaisi, Alif Saba

Abstract:

Normalization of electromyography (EMG) data in ergonomics research is a prerequisite for interpreting the data. Normalizing accounts for variability in the data due to differences in participants’ physical characteristics, electrode placement protocols, time of day, and other nuisance factors. Typically, normalized data is reported as a percentage of the muscle’s isometric maximum voluntary contraction (%MVC). Various MVC techniques have been recommended in the literature for normalizing EMG activity of back muscles. This research tests and compares the recommended MVC techniques in the literature for three back muscles commonly used in ergonomics research, which are the lumbar erector spinae (LES), latissimus dorsi (LD), and thoracic erector spinae (TES). Six healthy males from a university population participated in this research. Five different MVC exercises were compared for each muscle using the Tringo wireless EMG system (Delsys Inc.). Since the LES and TES share similar functions in controlling trunk movements, their MVC exercises were the same, which included trunk extension at -60°, trunk extension at 0°, trunk extension while standing, hip extension, and the arch test. The MVC exercises identified in the literature for the LD were chest-supported shoulder extension, prone shoulder extension, lat-pull down, internal shoulder rotation, and abducted shoulder flexion. The maximum EMG signal was recorded during each MVC trial, and then the averages were computed across participants. A one-way analysis of variance (ANOVA) was utilized to determine the effect of MVC technique on muscle activity. Post-hoc analyses were performed using the Tukey test. The MVC technique effect was statistically significant for each of the muscles (p < 0.05); however, a larger sample of participants was needed to detect significant differences in the Tukey tests. The arch test was associated with the highest EMG average at the LES, and also it resulted in the maximum EMG activity more often than the other techniques (three out of six participants). For the TES, trunk extension at 0° was associated with the largest EMG average, and it resulted in the maximum EMG activity the most often (three out of six participants). For the LD, participants obtained their maximum EMG either from chest-supported shoulder extension (three out of six participants) or prone shoulder extension (three out of six participants). Chest-supported shoulder extension, however, had a larger average than prone shoulder extension (0.263 and 0.240, respectively). Although all the aforementioned techniques were superior in their averages, they did not always result in the maximum EMG activity. If an accurate estimate of the true MVC is desired, more than one technique may have to be performed. This research provides additional MVC techniques for each muscle that may elicit the maximum EMG activity.

Keywords: electromyography, maximum voluntary contraction, normalization, physical ergonomics

Procedia PDF Downloads 192

Authors: Vanessa Kummer, Maik Meusel

Abstract:

Real estate demand estimates are typically derived from transaction data. However, in regions with excess demand, transactions are driven by supply and therefore do not indicate what people are actually looking for. To estimate the demand for housing in Switzerland, search subscriptions from all important Swiss real estate platforms are used. These data do, however, suffer from missing information—for example, many users do not specify how many rooms they would like or what price they would be willing to pay. In economic analyses, it is often the case that only complete data is used. Usually, however, the proportion of complete data is rather small which leads to most information being neglected. Also, the data might have a strong distortion if it is complete. In addition, the reason that data is missing might itself also contain information, which is however ignored with that approach. An interesting issue is, therefore, if for economic analyses such as the one at hand, there is an added value by using the whole data set with the imputed missing values compared to using the usually small percentage of complete data (baseline). Also, it is interesting to see how different algorithms affect that result. The imputation of the missing data is done using unsupervised learning. Out of the numerous unsupervised learning approaches, the most common ones, such as clustering, principal component analysis, or neural networks techniques are applied. By training the model iteratively on the imputed data and, thereby, including the information of all data into the model, the distortion of the first training set—the complete data—vanishes. In a next step, the performances of the algorithms are measured. This is done by randomly creating missing values in subsets of the data, estimating those values with the relevant algorithms and several parameter combinations, and comparing the estimates to the actual data. After having found the optimal parameter set for each algorithm, the missing values are being imputed. Using the resulting data sets, the next step is to estimate the willingness to pay for real estate. This is done by fitting price distributions for real estate properties with certain characteristics, such as the region or the number of rooms. Based on these distributions, survival functions are computed to obtain the functional relationship between characteristics and selling probabilities. Comparing the survival functions shows that estimates which are based on imputed data sets do not differ significantly from each other; however, the demand estimate that is derived from the baseline data does. This indicates that the baseline data set does not include all available information and is therefore not representative for the entire sample. Also, demand estimates derived from the whole data set are much more accurate than the baseline estimation. Thus, in order to obtain optimal results, it is important to make use of all available data, even though it involves additional procedures such as data imputation.

Keywords: demand estimate, missing-data imputation, real estate, unsupervised learning

Procedia PDF Downloads 285