Search results for: prognosis prediction

Authors: Marcelo Müller Beuren, José Luis Duarte Ribeiro

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Studies point that Brazilian large airports are not managing their assets efficiently. Therefore, organizations seek improvements to raise their asset’s productivity. Hence, identification of assets useful life in airports becomes an important subject, since its accuracy leads to better maintenance plans and technological substitution, contribution to airport services management. However, current useful life prediction models do not converge in terms of determinant elements used, as they are particular to the studied situation. For that reason, the main objective of this paper is to identify the determinant elements for a useful life of major assets in airports. With that purpose, a case study was held in the key airport of the south of Brazil trough historical data analysis and specialist interview. This paper concluded that most of the assets useful life are determined by technical elements, maintenance cost, and operational costs, while few presented influence of technological obsolescence. As a highlight, it was possible to identify the determinant elements to be considered by a model which objective is to identify the useful life of airport’s major assets.

Keywords: airports, asset management, asset useful life

Procedia PDF Downloads 522

Authors: Mohamed Mamdouh Al-Banna, Warda Youssef Mohamed Morsy, Hanaa Ali El-Feky, Ashraf Hussein Abdelmohsen

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Background: The mechanically ventilated patients need a special nursing care with continuous closed observation. The patients’ body mass index may affect their prognosis or outcomes. Aim of the study: to investigate the relationship between BMI and selected outcomes of critically ill mechanically ventilated patients. Research Design: A descriptive correlational research design was utilized Research questions: a) what is the BMI profile of mechanically ventilated patients admitted to critical care units over a period of six months? b) What is the relationship between body mass index and frequency of organ dysfunction, length of ICU stay, weaning from mechanical ventilation, and the mortality rate among adult critically ill mechanically ventilated patients? Setting: different intensive care units of Cairo University Hospitals. Sample: A convenience sample of 30 mechanically ventilated patients for at least 72 hours. Tools of data collection: Three tools were utilized to collect data pertinent to the current study: tool 1: patients’ sociodemographic and medical data sheet, tool 2: BURNS Wean Assessment Program (BWAP) checklist, tool 3: Sequential organ failure assessment (SOFA score) sheet. Results: The majority of the studied sample (77%) was males, and (26.7 %) of the studied sample were in the age group of 18-28 years old, and (26.7 %) were in the age group of 40-50 years old. Moreover, two thirds (66.7%) of the studied sample were within normal BMI. No significant statistical relationship between BMI category and ICU length of stay or the mortality rate among the studied sample, (X² = 11.31, P value = 0.79), (X² = 0.15, P value = 0.928) respectively. No significant statistical relationship between BMI category and the weaning trials from mechanical ventilation among the studied sample, (X² = 0.15, P value = 0.928). No significant statistical relationship was found between BMI category and the occurrence of organ dysfunction among the studied sample, (X² = 2.54, P value = 0.637). Conclusion: No relationship between the BMI categories and the selected patients’ outcomes (weaning from MV, length of ICU stay, occurrence of organ dysfunction, mortality rate). Recommendations: Replication of this study on a larger sample from different geographical locations in Arab Republic of Egypt, conducting farther studies to assess the effect of the quality of nursing care on the mechanically ventilated patients’ outcomes.

Keywords: mechanical ventilation, body mass index, outcomes of mechanically ventilated patient, organ failure

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Authors: An-Jui. Li, Kelvin Lim, Chien-Kuo Chiu, Benson Hsiung

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This paper details the utilization of artificial intelligence (AI) in the field of slope stability whereby quick and convenient solutions can be obtained using the developed tool. The AI tool used in this study is the artificial neural network (ANN), while the slope stability analysis methods are the finite element limit analysis methods. The developed tool allows for the prompt prediction of the safety factors of fill slopes and their corresponding probability of failure (depending on the degree of variation of the soil parameters), which can give the practicing engineer a reasonable basis in their decision making. In fact, the successful use of the Extreme Learning Machine (ELM) algorithm shows that slope stability analysis is no longer confined to the conventional methods of modeling, which at times may be tedious and repetitive during the preliminary design stage where the focus is more on cost saving options rather than detailed design. Therefore, similar ANN-based tools can be further developed to assist engineers in this aspect.

Keywords: landslide, limit analysis, artificial neural network, soil properties

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Authors: Redouane Lombarkia

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To apply a new material model developed and validated for plain weave fabric CFRP composites usually used in stanchions in sub-cargo section in aircrafts. This work deals with the development of a numerical model of the fuselage section of commercial aircraft based on the pure explicit finite element method FEM within Abaqus/Explicit commercial code. The aim of this work is the evaluation of the energy absorption capabilities of a full-scale composite fuselage section, including sub-cargo stanchions, Drop tests were carried out from a free fall height of about 5 m and impact velocity of about 6 m∕s. To asses, the prediction efficiency of the proposed numerical modeling procedure, a comparison with literature existed experimental results was performed. We demonstrate the efficiency of the proposed methodology to well capture crash damage mechanisms compared to experimental results

Keywords: crashworthiness, fuselage section, finite elements method (FEM), stanchions, specific energy absorption SEA

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Authors: Yulia V. Krasavtseva, Tatiana V. Kornilova

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Risk-taking behaviors are not only dictated by cognitive components but also involve emotional aspects. Anticipatory emotions, involving both cognitive and affective mechanisms, are involved in decision-making in general, and risk-taking in particular. Affective reactions are prompted when an expectation or prediction is either validated or invalidated in the achieved result. This study aimed to combine predictions, anticipatory emotions, affective reactions, and personality traits in the context of risk-taking behaviors. An experimental online method Emotion and Prediction In a Casino (EPIC) was used, based on a casino-like roulette game. In a series of choices, the participant is presented with progressively riskier roulette combinations, where the potential sums of wins and losses increase with each choice and the participant is given a choice: to 'walk away' with the current sum of money or to 'play' the displayed roulette, thus accepting the implicit risk. Before and after the result is displayed, participants also rate their emotions, using the Self-Assessment Mannequin [Bradley, Lang, 1994], picking a picture, representing the intensity of pleasure, arousal, and dominance. The following personality measures were used: 1) Personal Decision-Making Factors [Kornilova, 2003] assessing risk and rationality; 2) I7 – Impulsivity Questionnaire [Kornilova, 1995] assessing impulsiveness, risk readiness, and empathy and 3) Subjective Risk Intelligence Scale [Craparo et al., 2018] assessing negative attitude toward uncertainty, emotional stress vulnerability, imaginative capability, and problem-solving self-efficacy. Two groups of participants took part in the study: 1) 98 university students (Mage=19.71, SD=3.25; 72% female) and 2) 94 online participants (Mage=28.25, SD=8.25; 89% female). Online participants were recruited via social media. Students with high rationality rated their pleasure and dominance before and after choices as lower (ρ from -2.6 to -2.7, p < 0.05). Those with high levels of impulsivity rated their arousal lower before finding out their result (ρ from 2.5 - 3.7, p < 0.05), while also rating their dominance as low (ρ from -3 to -3.7, p < 0.05). Students prone to risk-rated their pleasure and arousal before and after higher (ρ from 2.5 - 3.6, p < 0.05). High empathy was positively correlated with arousal after learning the result. High emotional stress vulnerability positively correlates with arousal and pleasure after the choice (ρ from 3.9 - 5.7, p < 0.05). Negative attitude to uncertainty is correlated with high anticipatory and reactive arousal (ρ from 2.7 - 5.7, p < 0.05). High imaginative capability correlates negatively with anticipatory and reactive dominance (ρ from - 3.4 to - 4.3, p < 0.05). Pleasure (.492), arousal (.590), and dominance (.551) before and after the result were positively correlated. Higher predictions positively correlated with reactive pleasure and arousal. In a riskier scenario (6/8 chances to win), anticipatory arousal was negatively correlated with the pleasure emotion (-.326) and vice versa (-.265). Correlations occur regardless of the roulette outcome. In conclusion, risk-taking behaviors are linked not only to personality traits but also to anticipatory emotions and affect in a modeled casino setting. Acknowledgment: The study was supported by the Russian Foundation for Basic Research, project 19-29-07069.

Keywords: anticipatory emotions, casino game, risk taking, impulsiveness

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Authors: Bharatendra Rai

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Predictive data analysis and modeling involving machine learning techniques become challenging in presence of too many explanatory variables or features. Presence of too many features in machine learning is known to not only cause algorithms to slow down, but they can also lead to decrease in model prediction accuracy. This study involves housing dataset with 79 quantitative and qualitative features that describe various aspects people consider while buying a new house. Boruta algorithm that supports feature selection using a wrapper approach build around random forest is used in this study. This feature selection process leads to 49 confirmed features which are then used for developing predictive random forest models. The study also explores five different data partitioning ratios and their impact on model accuracy are captured using coefficient of determination (r-square) and root mean square error (rsme).

Keywords: housing data, feature selection, random forest, Boruta algorithm, root mean square error

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Authors: Eun Kyung Kim, Kyehan Rhee

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Viscoelastic wall properties of the arterial plaques change as the disease progresses, and estimation of wall viscoelasticity can provide a valuable assessment tool for plaque rupture prediction. Cross section of the stenotic coronary artery was modeled based on the IVUS image, and the finite element analysis was performed to get wall deformation under pulsatile pressure. The effects of viscoelastic parameters of the plaque on luminal diameter variations were explored. The result showed that decrease of viscous effect reduced the phase angle between the pressure and displacement waveforms, and phase angle was dependent on the viscoelastic properties of the wall. Because viscous effect of tissue components could be identified using the phase angle difference, wall deformation waveform analysis may be applied to predict plaque wall composition change and vascular wall disease progression.

Keywords: atherosclerotic plaque, diameter variation, finite element method, viscoelasticity

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Authors: Ju-young Hwang, Hyo-Gyoung Kwak, Hong Jae Yim

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This paper introduces a numerical analysis method for reinforced-concrete (RC) structures exposed to fire and compares the result with experimental results. The proposed analysis method for RC structure under the high temperature consists of two procedures. First step is to decide the temperature distribution across the section through the heat transfer analysis by using the time-temperature curve. After determination of the temperature distribution, the nonlinear analysis is followed. By considering material and geometrical non-linearity with the temperature distribution, nonlinear analysis predicts the behavior of RC structure under the fire by the exposed time. The proposed method is validated by the comparison with the experimental results. Finally, Prediction model to describe the status of after-cooling concrete can also be introduced based on the results of additional experiment. The product of this study is expected to be embedded for smart structure monitoring system against fire in u-City.

Keywords: RC structures, heat transfer analysis, nonlinear analysis, after-cooling concrete model

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Authors: Li Zhang, Yuehong Su

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Lightpipe as a mature solar light tube technique has been employed worldwide. Accurately assessing the performance of lightpipe and evaluate daylighting available has been a challenging topic. Previous research had used regression model and computational simulation methods to estimate the performance of lightpipe. However, due to the nonlinear nature of solar light transferring in lightpipe, the methods mentioned above express inaccurate and time-costing issues. In the present study, a neural network model as an alternative method is investigated to predict the transmittance of lightpipe. Four types of commercial lightpipe with bended angle 0°, 30°, 45° and 60° are discussed under clear, intermediate and overcast sky conditions respectively. The neural network is generated in MATLAB by using the outcomes of an optical software Photopia simulations as targets for networks training and testing. The coefficient of determination (R²) for each model is higher than 0.98, and the mean square error (MSE) is less than 0.0019, which indicate the neural network strong predictive ability and the use of the neural network method could be an efficient technique for determining the performance of lightpipe.

Keywords: neural network, bended lightpipe, transmittance, Photopia

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Authors: Benjamin Leiby, Darryl Ahner

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This study demonstrates an alternative stochastic imputation approach for large datasets when preferred commercial packages struggle to iterate due to numerical problems. A large country conflict dataset motivates the search to impute missing values well over a common threshold of 20% missingness. The methodology capitalizes on correlation while using model residuals to provide the uncertainty in estimating unknown values. Examination of the methodology provides insight toward choosing linear or nonlinear modeling terms. Static tolerances common in most packages are replaced with tailorable tolerances that exploit residuals to fit each data element. The methodology evaluation includes observing computation time, model fit, and the comparison of known values to replaced values created through imputation. Overall, the country conflict dataset illustrates promise with modeling first-order interactions while presenting a need for further refinement that mimics predictive mean matching.

Keywords: correlation, country conflict, imputation, stochastic regression

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Authors: Lars Laußat, Manfred Helmus, Kamil Szczesny, Markus König

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As one result of the project “Reactive Construction Project Scheduling using Real Time Construction Logistic Data and Simulation”, a procedure for using data about uncertain resource availability assumptions in reactive scheduling processes has been developed. Prediction data about resource availability is generated in a formalized way using real-time monitoring data e.g. from auto-ID systems on the construction site and in the supply chains. The paper focuses on the formalization of the procedure for monitoring construction logistic processes, for the detection of disturbance and for generating of new and uncertain scheduling assumptions for the reactive resource constrained simulation procedure that is and will be further described in other papers.

Keywords: auto-ID, construction logistic, fuzzy, monitoring, RFID, scheduling

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Authors: Bothinah Altaf, Gary Montague, Elaine B. Martin

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This study involves the modeling and monitoring of an ammonia synthesis fixed-bed reactor using partial least squares (PLS) and its variants. The process exhibits complex dynamic behavior due to the presence of heat recycling and feed quench. One limitation of static PLS model in this situation is that it does not take account of the process dynamics and hence dynamic PLS was used. Although it showed, superior performance to static PLS in terms of prediction, the monitoring scheme was inappropriate hence adaptive PLS was considered. A limitation of adaptive PLS is that non-conforming observations also contribute to the model, therefore, a new adaptive approach was developed, robust adaptive dynamic PLS. This approach updates a dynamic PLS model and is robust to non-representative data. The developed methodology showed a clear improvement over existing approaches in terms of the modeling of the reactor and the detection of faults.

Keywords: ammonia synthesis fixed-bed reactor, dynamic partial least squares modeling, recursive partial least squares, robust modeling

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Authors: Belkacem Laimouche

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With the field of artificial intelligence (AI) experiencing exponential growth, fueled by technological advancements that pave the way for increasingly innovative and promising applications, there is an escalating need to develop rigorous methods for assessing their performance in pursuit of transparency and equity. This article proposes a metrology-inspired statistical framework for evaluating bias and explainability in AI systems. Drawing from the principles of metrology, we propose a pioneering approach, using a concrete example, to evaluate the accuracy and precision of AI models, as well as to quantify the sources of measurement uncertainty that can lead to bias in their predictions. Furthermore, we explore a statistical approach for evaluating the explainability of AI systems based on their ability to provide interpretable and transparent explanations of their predictions.

Keywords: artificial intelligence, metrology, measurement uncertainty, prediction error, bias, machine learning algorithms, probabilistic models, interlaboratory comparison, data analysis, data reliability, measurement of bias impact on predictions, improvement of model accuracy and reliability

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Authors: Debesh R. Roy

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The fundamental issue in understanding the origin and growth mechanism of nanomaterials, from a fundamental unit is a big challenging problem to the scientists. Recently, an immense attention is generated to the researchers for prediction of exceptionally stable atomic cluster units as the building units for future smart materials. The present study is a systematic investigation on the stability and electronic properties of a series of bimetallic (semiconductor-alkaline earth) clusters, viz., BxMg3 (x=1-5) is performed, in search for exceptional and/ or unusual stable motifs. A very popular hybrid exchange-correlation functional, B3LYP as proposed by A. D. Becke along with a higher basis set, viz., 6-31+G[d,p] is employed for this purpose under the density functional formalism. The magic stability among the concerned clusters is explained using the jellium model. It is evident from the present study that the magic stability of B4Mg3 cluster arises due to the jellium shell closure.

Keywords: atomic clusters, density functional theory, jellium model, magic clusters, smart nanomaterials

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Authors: M. Khalfa, H. Khachai, F. Chiker, K. Bougherara, R. Khenata, G. Murtaza, M. Harmel

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A theoretical study of structural, elastic, electronic and thermodynamic properties of Fe2VX, (with X = Al and Ga), were studied by means of the full-relativistic version of the full-potential augmented plane wave plus local orbitals method. For exchange and correlation potential we used both generalized-gradient approximation (GGA) and local-density approximation (LDA). Our calculated ground state properties like as lattice constants, bulk modulus and elastic constants appear more accurate when we employed the GGA rather than the LDA approximation, and these results agree very well with the available experimental and theoretical data. Further, prediction of the thermal effects on some macroscopic properties of Fe2VAl and Fe2VGa are given in this paper using the quasi-harmonic Debye model in which the lattice vibrations are taken into account. We have obtained successfully the variations of the primitive cell volume, volume expansion coefficient, heat capacities and Debye temperature with pressure and temperature in the ranges of 0–40 GPa and 0–1500 K.

Keywords: full Heusler, FP-LAPW, electronic properties, thermal properties

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Authors: Radoslav Židek, Veronika Šidlová, Radovan Kasarda, Birgit Fuerst-Waltl

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Machine learning represents a set of topics dealing with the creation and evaluation of algorithms that facilitate pattern recognition, classification, and prediction, based on models derived from existing data. The data can present identification patterns which are used to classify into groups. The result of the analysis is the pattern which can be used for identification of data set without the need to obtain input data used for creation of this pattern. An important requirement in this process is careful data preparation validation of model used and its suitable interpretation. For breeders, it is important to know the origin of animals from the point of the genetic diversity. In case of missing pedigree information, other methods can be used for traceability of animal´s origin. Genetic diversity written in genetic data is holding relatively useful information to identify animals originated from individual countries. We can conclude that the application of data mining for molecular genetic data using supervised learning is an appropriate tool for hypothesis testing and identifying an individual.

Keywords: genetic data, Pinzgau cattle, supervised learning, machine learning

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Authors: Kasra Afsahi, Maryam Soheilifar, S. Hossein Hosseini

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Background: There is growing evidence that Cerebral Vascular Accident (CVA) can be a consequence of Covid-19 infection. Understanding novel treatment approaches are important in optimizing patient outcomes. Case: This case explores the use of Virtual Reality (VR) in the treatment of a 23-year-old COVID-positive female presenting with left hemiparesis in August 2020. Imaging showed right globus pallidus, thalamus, and internal capsule ischemic stroke. Conventional rehabilitation was started two weeks later, with virtual reality (VR) included. This game-based virtual reality (VR) technology developed for stroke patients was based on upper extremity exercises and functions for stroke. Physical examination showed left hemiparesis with muscle strength 3/5 in the upper extremity and 4/5 in the lower extremity. The range of motion of the shoulder was 90-100 degrees. The speech exam showed a mild decrease in fluency. Mild lower lip dynamic asymmetry was seen. Babinski was positive on the left. Gait speed was decreased (75 steps per minute). Intervention: Our game-based VR system was developed based on upper extremity physiotherapy exercises for post-stroke patients to increase the active, voluntary movement of the upper extremity joints and improve the function. The conventional program was initiated with active exercises, shoulder sanding for joint ROMs, walking shoulder, shoulder wheel, and combination movements of the shoulder, elbow, and wrist joints, alternative flexion-extension, pronation-supination movements, Pegboard and Purdo pegboard exercises. Also, fine movements included smart gloves, biofeedback, finger ladder, and writing. The difficulty of the game increased at each stage of the practice with progress in patient performances. Outcome: After 6 weeks of treatment, gait and speech were normal and upper extremity strength was improved to near normal status. No adverse effects were noted. Conclusion: This case suggests that VR is a useful tool in the treatment of a patient with covid-19 related CVA. The safety of newly developed instruments for such cases provides new approaches to improve the therapeutic outcomes and prognosis as well as increased satisfaction rate among patients.

Keywords: covid-19, stroke, virtual reality, rehabilitation

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Authors: Nikolaos Simantiris, Markos Avlonitis

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This work builds a simulation platform, modeling the spatial diffusion of the invasive species Callinectes sapidus (blue crab) as a random walk, incorporating also generation, fatality, and fishing rates modeling the time evolution of its population. Antinioti lagoon in West Greece was used as a testbed for applying the simulation model. Field measurements from June 2020 to June 2021 on the lagoon’s setting, bathymetry, and blue crab juveniles provided the initial population simulation of blue crabs, as well as biological parameters from the current literature were used to calibrate simulation parameters. The scope of this study is to render the authors able to predict the evolution of the blue crab population in confined environments of the Ionian Islands region in West Greece. The first result of the simulation experiments shows the possibility for a robust prediction for blue crab population evolution in the Antinioti lagoon.

Keywords: antinioti lagoon, blue crab, stochastic simulation, random walk

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Authors: Umit Duru

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The Soil and Water Assessment Tool (SWAT) was tested for prediction of water balance and sediment yield in the Ankara gauged basin, Turkey. The overall objective of this study was to evaluate the performance and applicability of the SWAT in this region of Turkey. Thirteen years of monthly stream flow, and suspended sediment, data were used for calibration and validation. This research assessed model performance based on differences between observed and predicted suspended sediment yield during calibration (1987-1996) and validation (1982-1984) periods. Statistical comparisons of suspended sediment produced values for NSE (Nash Sutcliffe efficiency), RE (relative error), and R² (coefficient of determination), of 0.81, -1.55, and 0.93, respectively, during the calibration period, and NSE, RE (%), and R² of 0.77, -2.61, and 0.87, respectively, during the validation period. Based on the analyses, SWAT satisfactorily simulated observed hydrology and sediment yields and can be used as a tool in decision making for water resources planning and management in the basin.

Keywords: calibration, GIS, sediment yield, SWAT, validation

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Authors: Omar Hussein El Ayadi, EmadY. El-Mansouri, Mohamed B. Dozan

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Production oil process require specific details i.e. oil composition. Generally, types of oil or differentiation between reservoir fluids depend specifically on composition. The main purpose of this study is to correlate and predict the Libyan oil (reservoir fluid and residual) composition utilizing tri-angle-coordinate plots discovered and tasked with Excel. The reservoir fluid data (61 old + 47 new), the residual oil data (33 new) collected from most of Libyan reservoirs were correlated with each others. Moreover, find a relation between stock tank molecular weight and stock tank oil gravity (oAPI), the molecular weight oh (C7+) versus residual oil gravity (oAPI). The average value of every oil composition was estimated including non-hydrocarbon (H2S, CO2, and N2). Nevertheless, the isomers (i-…) and normal (n-…) structure of (C4) and (C5) were also obtained. The summary of the conclusion is; utilizing excel Microsoft office to draw triangle coordinates to find two unknown component if only one is known. However, it is recommended to use the obtained oil composition plots and equations for any oil composition dependents i.e. optimum separator pressure.

Keywords: PVT, phase behavior, petroleum, chemical engineering

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Authors: Sasitharan Ambicapathy, J. Vignesh, P. Sivaraj, Godfrey Derek Sams, K. Sabarinath, V. R. Sanal Kumar

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The external flow simulation of a flying car at take off phase is a daunting task owing to the fact that the prediction of the transient unsteady flow features during its deployment phase is very complex. In this paper 3D numerical simulations of external flow of Ferrari F430 proposed flying car with different NACA 9618 rectangular wings have been carried. Additionally, the aerodynamics characteristics have been generated for optimizing its geometry for achieving the minimum take off velocity with better overall performance in both road and air. The three-dimensional standard k-omega turbulence model has been used for capturing the intrinsic flow physics during the take off phase. In the numerical study, a fully implicit finite volume scheme of the compressible, Reynolds-Averaged, Navier-Stokes equations is employed. Through the detailed parametric analytical studies we have conjectured that Ferrari F430 flying car facilitated with high wings having three different deployment histories during the take off phase is the best choice for accomplishing its better performance for the commercial applications.

Keywords: aerodynamics of flying car, air taxi, negative lift, roadable airplane

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Authors: Lan Nguyen, Lam Cao Van

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Thuan Phuoc is a suspension bridge built in Danang city, Vietnam. Because this bridge locates near the estuary, its structure has degraded rapidly. Many cracks have currently occurred on most of the concrete piers of the curved approach spans. This paper aims to present the results of diagnostic analysis of causes for cracks as well as some calculations for strengthening piers by carbon fiber reinforced polymer (CFRP). Besides, it describes how to use concrete nonlinear analysis software ATENA to diagnostically analyze cracks, strengthening designs. Basing on the results of studying the map of distributing crack on Thuan Phuoc bridge’s concrete piers is analyzed by the software ATENA is suitable for the real conditions and CFRP would be the best solution to strengthen piers in a sound and fast way.

Keywords: ATENA, bridge pier strengthening, carbon fiber reinforced polymer (CFRP), crack prediction analysis

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Authors: Samah Laalej, Abdelfattah Bouatem

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In the field of wind turbine blades, it is complicated to evaluate the aerodynamic performances through experimental measurements as it requires a lot of computing time and resources. Therefore, in this paper, a hybrid BEM-CFD numerical technique is developed to predict power and aerodynamic forces acting on the blades. Computational fluid dynamics (CFD) simulation was conducted to calculate the drag and lift forces through Ansys software using the K-w model. Then an enhanced BEM code was created to predict the power outputs generated by the wind turbine using the aerodynamic properties extracted from the CFD approach. The numerical approach was compared and validated with experimental data. The power curves calculated from this hybrid method were in good agreement with experimental measurements for all velocity ranges.

Keywords: blade element momentum, aerodynamic forces, wind turbine blades, computational fluid dynamics approach

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Authors: M. G. Talasbayen, N. N. Dyussenbayev, Y. D. Kali, R. A. Zholbarysov, Y. N. Duissenbayev, I. Z. Mammadinova, S. M. Nuradilov

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Background. Hemorrhagic stroke is an acute cerebrovascular accident resulting from rupture of a cerebral vessel or increased permeability of the wall and imbibition of blood into the brain parenchyma. Arterial hypertension is a common cause of hemorrhagic stroke. Male gender and age over 55 years is a risk factor for intracerebral hemorrhage. Treatment of intracerebral hemorrhage is aimed at the primary pathophysiological link: the relief of coagulopathy and the control of arterial hypertension. Early surgical treatment can limit cerebral compression; prevent toxic effects of blood to the brain parenchyma. Despite progress in the development of neuroimaging data, the use of minimally invasive techniques, and navigation system, mortality from intracerebral hemorrhage remains high. Materials and methods. The study included 78 patients (62.82% male and 37.18% female) with a verified diagnosis of hemorrhagic stroke in the period from 2019 to 2021. The age of patients ranged from 25 to 80 years, the average age was 54.66±11.9 years. Demographic, brain CT data (localization, volume of hematomas), methods of treatment, and disease outcome were analyzed. Results. The retrospective analyze demonstrate that 78.2% of all patients underwent surgical treatment: decompressive craniectomy in 37.7%, craniotomy with hematoma evacuation in 29.5%, and hematoma draining in 24.59% cases. The study of the proportion of deaths, depending on the volume of intracerebral hemorrhage, shows that the number of deaths was higher in the group with a hematoma volume of more than 60 ml. Evaluation of the relationship between the time before surgery and mortality demonstrates that the most favorable outcome is observed during surgical treatment in the interval from 3 to 24 hours. Mortality depending on age did not reveal a significant difference between age groups. An analysis of the impact of the surgery type on mortality reveals that decompressive craniectomy with or without hematoma evacuation led to an unfavorable outcome in 73.9% of cases, while craniotomy with hematoma evacuation and drainage led to mortality only in 28.82% cases. Conclusion. Even though the multimodal approaches, the development of surgical techniques and equipment, and the selection of optimal conservative therapy, the question of determining the tactics of managing and treating hemorrhagic strokes is still controversial. Nevertheless, our experience shows that surgical intervention within 24 hours from the moment of admission and craniotomy with hematoma evacuation improves the prognosis of treatment outcomes.

Keywords: hemorragic stroke, Intracerebral hemorrhage, surgical treatment, stroke mortality

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Authors: Ngozi Claribelle Nwogu, Mohammed Nasir Kajama, Oyoh Kechinyere, Edward Gobina

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Atmospheric carbon dioxide emissions are considered as the greatest environmental challenge the world is facing today. The challenges to control the emissions include the recovery of CO2 from flue gas. This concern has been improved due to recent advances in materials process engineering resulting in the development of inorganic gas separation membranes with excellent thermal and mechanical stability required for most gas separations. This paper therefore evaluates the performance of a highly selective inorganic membrane for CO2 recovery applications. Analysis of results obtained is in agreement with experimental literature data. Further results show the prediction performance of the membranes for gas separation and the future direction of research. The materials selection and the membrane preparation techniques are discussed. Method of improving the interface defects in the membrane and its effect on the separation performance has also been reviewed and in addition advances to totally exploit the potential usage of this innovative membrane.

Keywords: carbon dioxide, gas separation, inorganic ceramic membrane, permselectivity

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Authors: Sadia Ilyas, Hyunjung Kim, Rajiv R. Srivastava

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Current work has attempted to establish the linkages between circular bio-economy and recycling of copper and gold from urban mine by applying microbial activities instead of the smelter and chemical technologies. Thereafter, based on the potential of microbial approaches and research hypothesis, the structural model has been tested for a significance level of 99%, which is supported by the corresponding standardization co-efficient values. A prediction model applied to determine the recycling impact on circular bio-economy indicates to re-circulate 51,833 tons of copper and 58 tons of gold by 2030 for the production of virgin metals/raw-materials, while recycling rate of the accumulated e-waste remains to be 20%. This restoration volume of copper and gold through the microbial activities corresponds to mitigate 174 million kg CO₂ emissions and 24 million m³ water consumption if compared with the primary production activities. The study potentially opens a new window for environmentally-friendly biotechnological recycling of e-waste urban mine under the umbrella concept of circular bio-economy.

Keywords: urban mining, biobleaching, circular bio-economy, environmental impact

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Authors: J. K. Kavitha, D. Manjula, U. Kanimozhi

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Mining frequent and high utility item sets have gained much significance in the recent years. When the data arrives sporadically, incremental and interactive rule mining and utility mining approaches can be adopted to handle user’s dynamic environmental needs and avoid redundancies, using previous data structures, and mining results. The dependence on recommendation systems has exponentially risen since the advent of search engines. This paper proposes a model for building a recommendation system that suggests frequent and high utility item sets over dynamic datasets for a cluster based location prediction strategy to predict user’s trajectories using the Efficient Incremental Rule Mining (EIRM) algorithm and the Fast Update Utility Pattern Tree (FUUP) algorithm. Through comprehensive evaluations by experiments, this scheme has shown to deliver excellent performance.

Keywords: data sets, recommendation system, utility item sets, frequent item sets mining

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Authors: Tamphasana Wairokpam

Abstract:

Inflammatory myopathies (IMs) have long been recognised as a heterogenous family of myopathies with acute, subacute, and sometimes chronic presentation and are potentially treatable. Necrotizing autoimmune myopathies (NAM) are a relatively new subset of myopathies. Patients generally present with subacute onset of proximal myopathy and significantly elevated creatinine kinase (CK) levels. It is being increasingly recognised that there are limitations to the independent diagnostic utility of muscle biopsy. Immunohistochemistry tests may reveal important information in these cases. The traditional classification of IMs failed to recognise NAM as a separate entity and did not adequately emphasize the diversity of IMs. This review and case report on NAM aims to highlight the heterogeneity of this entity and focus on the distinct clinical presentation, biopsy findings, specific auto-antibodies implicated, and available treatment options with prognosis. This article is a meta-analysis of literatures on NAM and a case report illustrating the clinical course, investigation and biopsy findings, antibodies implicated, and management of a patient with NAM. The main databases used for the search were Pubmed, Google Scholar, and Cochrane Library. Altogether, 67 publications have been taken as references. Two biomarkers, anti-signal recognition protein (SRP) and anti- hydroxyl methylglutaryl-coenzyme A reductase (HMGCR) Abs, have been found to have an association with NAM in about 2/3rd of cases. Interestingly, anti-SRP associated NAM appears to be more aggressive in its clinical course when compared to its anti-HMGCR associated counterpart. Biopsy shows muscle fibre necrosis without inflammation. There are reports of statin-induced NAM where progression of myopathy has been seen even after discontinuation of statins, pointing towards an underlying immune mechanism. Diagnosisng NAM is essential as it requires more aggressive immunotherapy than other types of IMs. Most cases are refractory to corticosteroid monotherapy. Immunosuppressive therapy with other immunotherapeutic agents such as IVIg, rituximab, mycophenolate mofetil, azathioprine has been explored and found to have a role in the treatment of NAM. In conclusion,given the heterogeneity of NAM, it appears that NAM is not just a single entity but consists of many different forms, despite the similarities in presentation and its classification remains an evolving field. A thorough understanding of underlying mechanism and the clinical correlation with antibodies associated with NAM is essential for efficacious management and disease prognostication.

Keywords: inflammatory myopathies, necrotising autoimmune myopathies, anti-SRP antibody, anti-HMGCR antibody, statin induced myopathy

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Authors: Lu Si, Jie Yu, Shasha Li, Jun Ma, Lei Luo, Qingbo Wu, Yongqi Ma, Zhengji Liu

Abstract:

Instance selection (IS) technique is used to reduce the data size to improve the performance of data mining methods. Recently, to process very large data set, several proposed methods divide the training set into some disjoint subsets and apply IS algorithms independently to each subset. In this paper, we analyze the limitation of these methods and give our viewpoint about how to divide and conquer in IS procedure. Then, based on fast condensed nearest neighbor (FCNN) rule, we propose a large data sets instance selection method with MapReduce framework. Besides ensuring the prediction accuracy and reduction rate, it has two desirable properties: First, it reduces the work load in the aggregation node; Second and most important, it produces the same result with the sequential version, which other parallel methods cannot achieve. We evaluate the performance of FCNN-MR on one small data set and two large data sets. The experimental results show that it is effective and practical.

Keywords: instance selection, data reduction, MapReduce, kNN

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Authors: Abhimanyu Kumar, Chirag Gupta

Abstract:

This work presents a comprehensive study on the employment of Model Predictive Control (MPC) for a three-phase voltage-source inverter to regulate the output voltage efficiently. The inverter is modeled via the Clarke Transformation, considering a scenario where the load is unknown. An LC filter model is developed, demonstrating its efficacy in Total Harmonic Distortion (THD) reduction. The system, when implemented with fault-tolerant multilevel inverter topologies, ensures reliable operation even under fault conditions, a requirement that is paramount with the increasing dependence on renewable energy sources. The research also integrates a Fuzzy Logic based fault tolerance system which identifies and manages faults, ensuring consistent inverter performance. The efficacy of the proposed methodology is substantiated through rigorous simulations and comparative results, shedding light on the voltage prediction efficiency and the robustness of the model even under fault conditions.

Keywords: total harmonic distortion, fuzzy logic, renewable energy sources, MLI

Procedia PDF Downloads 131