Search results for: robust penalized regression

Authors: Aimen Batool Bint-E-Rashid, Huma Irfan

Abstract:

This research aims to investigate the role of social media (Snapchat, Facebook, Twitter, etc.) in our daily lives and its implication on our everyday routine in the form of stressors. The study has been validated by a social media survey with 150 social media users belonging to various age groups. The study explores how social media can make an individual anti-social in his or her life offline. To explain the phenomenon, we have proposed and evaluated a model based on social media usage and stressors including burnout and social overload. Results, through correlation and regression tests, have revealed that with increase in social media usage, social overload and burnout also increases. Evidence for the fact that excessive social media usage causes social overload and burnout has been provided in the study.

Keywords: burnout, emotional exhaustion, fatigue, stressors, social networking, social media, social overload

Procedia PDF Downloads 198

Authors: Muhammad Azeem Qureshi, Hammad Tahir, Fawwad Mahmood Butt

Abstract:

Purpose: This study examines empirically the effect of brand satisfaction, brand trust and brand experience on brand loyalty which can be helpful to retain and increase customer base and satisfying customer needs as well. Methodology: Data has been collected on convenient sampling method and cause and effect among variables has been measured by applying regression analysis technique. Findings: Finding of this study have supported the proposed hypotheses and results show that brand loyalty is significantly explained by brand satisfaction, brand trust and brand experience. Practical Implications: The outcome of this study provides a useful framework and importance of brand loyalty culture in Pakistan. Marketers can be benefited trough the findings of this study.

Keywords: brand experience, brand satisfaction, brand trust, brand loyalty, hair-care products

Procedia PDF Downloads 320

Authors: Soowon Park, Min-Ji Kim, Jun-Young Lee

Abstract:

Motivation: The stigma of mental illness has been studied in many disciplines, including social psychology, counseling psychology, sociology, psychiatry, public health care, and related areas, because individuals labeled as ‘mentally ill’ are often deprived of their rights and their life opportunities. To understand the factors that deepen the stigma of mental illness, it is important to understand the influencing factors of the stigma. Problem statement: Depression is a common disorder in adults, but the incidence of help-seeking is low. Researchers have believed that this poor help-seeking behavior is related to the stigma of mental illness, which results from low mental health literacy. However, it is uncertain that increasing mental health literacy decreases mental health stigmatization. Furthermore, even though decreasing stigmatization is important, the stigma of mental illness is still a stable and long-lasting phenomenon. Thus, factors other than knowledge about mental disorders have the power to maintain the stigma. Investigating the influencing factors that facilitate the stigma of psychiatric disease could help lower the social stigmatization. Approach: Face-to-face interviews were conducted with a multi-clustering sample. A total of 700 Korean participants (38% male), ranging in age from 18 to 78 (M(SD)age= 48.5(15.7)) answered demographical questions, Korean version of Link’s Perceived Devaluation and Discrimination (PDD) scale for the assessment of social stigmatization against depression, and the Korean version of the WHO-Composite International Diagnostic Interview for the assessment of mental disorders. Multiple-regression was conducted to find the predicting factors of social stigmatization against depression. Ages, sex, years of education, income, living location, and experience of mental illness were used as the predictors. Results: Predictors accounted for 14% of the variance in the stigma of depressive disorders (F(6, 693) = 20.27, p < .001). Among those, only age, years of education, and experience of mental illness significantly predicted social stigmatization against depression. The standardized regression coefficient of age had a negative association with stigmatization (β = -.20, p < .001), but years of education (β = .20, p < .001) and experience of mental illness (β = .08, p < .05) positively predicted depression stigmatization. Conclusions: The present study clearly demonstrates the association between personal factors and depressive disorder stigmatization. Younger age, more education, and self-stigma appeared to increase the stigmatization. Young, highly educated, and mentally ill people tend to reject patients with depressive disorder as friends, teachers, or babysitters; they also tend to think that those patients have lower intelligence and abilities. These results suggest the possibility that people from a high social class, or highly educated people, who have the power to make decisions, help maintain the social stigma against mental illness patients. To increase the awareness that people from high social classes have more stigmatization against depressive disorders will help decrease the biased attitudes against mentally ill patients.

Keywords: depressive disorder stigmatization, age, education, self-stigma

Procedia PDF Downloads 396

Authors: Kaushik Sathupadi, Sandesh Achar

Abstract:

Human action recognition modeling is a critical task in machine learning. These systems require better techniques for recognizing body parts and selecting optimal features based on vision sensors to identify complex action patterns efficiently. Still, there is a considerable gap and challenges between images and videos, such as brightness, motion variation, and random clutters. This paper proposes a robust approach for classifying human actions over cloud-based image data. First, we apply pre-processing and detection, human and outer shape detection techniques. Next, we extract valuable information in terms of cues. We extract two distinct features: fuzzy local binary patterns and sequence representation. Then, we applied a greedy, randomized adaptive search procedure for data optimization and dimension reduction, and for classification, we used a random forest. We tested our model on two benchmark datasets, AAMAZ and the KTH Multi-view football datasets. Our HMR framework significantly outperforms the other state-of-the-art approaches and achieves a better recognition rate of 91% and 89.6% over the AAMAZ and KTH multi-view football datasets, respectively.

Keywords: computer vision, human motion analysis, random forest, machine learning

Procedia PDF Downloads 14

Authors: Morteza Aien, Masoud Rashidinejad, Mahmud Fotuhi-Firuzabad

Abstract:

Power systems are innately uncertain systems. To face with such uncertain systems, robust uncertainty assessment tools are appealed. This paper inspects the uncertainty assessment formulation of the load flow (LF) problem considering different kinds of uncertainties, developed in its companion paper through some case studies. The proposed methodology is based on the evidence theory and joint propagation of possibilistic and probabilistic uncertainties. The load and wind power generation are considered as probabilistic uncertain variables and the electric vehicles (EVs) and gas turbine distributed generation (DG) units are considered as possibilistic uncertain variables. The cumulative distribution function (CDF) of the system output parameters obtained by the pure probabilistic method lies within the belief and plausibility functions obtained by the joint propagation approach. Furthermore, the imprecision in the DG parameters is explicitly reflected by the gap between the belief and plausibility functions. This gap, due to the epistemic uncertainty on the DG resources parameters grows as the penetration level increases.

Keywords: electric vehicles, joint possibilistic- probabilistic uncertainty modeling, uncertain load flow, wind turbine generator

Procedia PDF Downloads 425

Authors: Shriya Shukla, Lachin Fernando

Abstract:

Pneumonia, a leading cause of mortality in young children globally, presents significant diagnostic challenges, particularly in resource-limited settings. This study presents an approach to diagnosing pediatric pneumonia using Chest X-Ray (CXR) images, employing a lightweight MobileNetV2 model enhanced with synthetic data augmentation. Addressing the challenge of dataset scarcity and imbalance, the study used a Variational Autoencoder-Generative Adversarial Network (VAE-GAN) to generate synthetic CXR images, improving the representation of normal cases in the pediatric dataset. This approach not only addresses the issues of data imbalance and scarcity prevalent in medical imaging but also provides a more accessible and reliable diagnostic tool for early pneumonia detection. The augmented data improved the model’s accuracy and generalization, achieving an overall accuracy of 95% in pneumonia detection. These findings highlight the efficacy of the MobileNetV2 model, offering a computationally efficient yet robust solution well-suited for resource-constrained environments such as mobile health applications. This study demonstrates the potential of synthetic data augmentation in enhancing medical image analysis for critical conditions like pediatric pneumonia.

Keywords: pneumonia, MobileNetV2, image classification, GAN, VAE, deep learning

Procedia PDF Downloads 90

Authors: R. N. Begum, Ambalika Sharma, G. K. Singh

Abstract:

Identity theft has serious ramifications beyond data and personal information loss. This necessitates the implementation of robust and efficient user identification systems. Therefore, automatic biometric recognition systems are the need of the hour, and ECG-based systems are unquestionably the best choice due to their appealing inherent characteristics. The CNNs are the recent state-of-the-art techniques for ECG-based user identification systems. However, the results obtained are significantly below standards, and the situation worsens as the number of users and types of heartbeats in the dataset grows. As a result, this study proposes a highly accurate and resilient ECG-based person identification system using CNN's dense learning framework. The proposed research explores explicitly the calibre of dense CNNs in the field of ECG-based human recognition. The study tests four different configurations of dense CNN which are trained on a dataset of recordings collected from eight popular ECG databases. With the highest FAR of 0.04 percent and the highest FRR of 5%, the best performing network achieved an identification accuracy of 99.94 percent. The best network is also tested with various train/test split ratios. The findings show that DenseNets are not only extremely reliable but also highly efficient. Thus, they might also be implemented in real-time ECG-based human recognition systems.

Keywords: Biometrics, Dense Networks, Identification Rate, Train/Test split ratio

Procedia PDF Downloads 157

Authors: Keven A. Prusak, William F. Christensen, Zack Beddoes

Abstract:

Background: For more than a decade, the Sport Motivation Scale (SMS) has been used to measure contextual motivation across a variety of sporting and physical education (PE) settings but not without criticism as to its tenability. Consequently, a new version of the sport motivation scale (SMS-II) was created to address certain weakness of the original SMS. Purpose: The purpose of this study is to assess the suitability of the SMS-II in the secondary PE setting. Methods: Three hundred and twenty (204 females, and 116 males; grades 7-9) completed the 18-item, six-subscale SMS-II at the end of a required PE class. Factor means, standard deviations, and correlations were calculated and further examined via confirmatory factor analysis (CFA). Model parameters were estimated maximum likelihood function. Results: Results indicate that participants held generally positive perceptions toward PE as a context (more so for males than females). Reliability analysis yielded adequate alphas (rα = 0.71 to 0.87, Mα = 0.78) with the exception of the AM subscale (αAM = .64). Correlation analysis indicated some support for the SIMPLEX pattern, but distal ends of the motivation continuum displayed no relationship. CFA yielded robust fit indices to the proposed structure of the SMS-II for PE. A small but significant variance across genders was noted and discussed. Conclusions: In all, the SMS-II suitably accesses PE context-specific motivational indices within Jr. High PE.

Keywords: motivation, self-determination theory, physical education, confirmatory factor analysis

Procedia PDF Downloads 327

Authors: Tatjana Pekmezovic, Milica Jecmenica-Lukic, Igor Petrovic, Vladimir Kostic

Abstract:

Background: A group of atypical parkinsonian syndromes (APS) includes a variety of rare neurodegenerative disorders characterized by reduced life expectancy, increasing disability, and considerable impact on health-related quality of life (HRQoL). Aim: In this study we wanted to answer two questions: a) which demographic and clinical factors are main contributors of HRQoL in our cohort of patients with APS, and b) how does quality of life of these patients change over 1-year follow-up period. Patients and Methods: We conducted a prospective cohort study in hospital settings. The initial study comprised all consecutive patients who were referred to the Department of Movement Disorders, Clinic of Neurology, Clinical Centre of Serbia, Faculty of Medicine, University of Belgrade (Serbia), from January 31, 2000 to July 31, 2013, with the initial diagnoses of ‘Parkinson’s disease’, ‘parkinsonism’, ‘atypical parkinsonism’ and ‘parkinsonism plus’ during the first 8 months from the appearance of first symptom(s). The patients were afterwards regularly followed in 4-6 month intervals and eventually the diagnoses were established for 46 patients fulfilling the criteria for clinically probable progressive supranuclear palsy (PSP) and 36 patients for probable multiple system atrophy (MSA). The health-related quality of life was assessed by using the SF-36 questionnaire (Serbian translation). Hierarchical multiple regression analysis was conducted to identify predictors of composite scores of SF-36. The importance of changes in quality of life scores of patients with APS between baseline and follow-up time-point were quantified using Wilcoxon Signed Ranks Test. The magnitude of any differences for the quality of life changes was calculated as an effect size (ES). Results: The final models of hierarchical regression analysis showed that apathy measured by the Apathy evaluation scale (AES) score accounted for 59% of the variance in the Physical Health Composite Score of SF-36 and 14% of the variance in the Mental Health Composite Score of SF-36 (p<0.01). The changes in HRQoL were assessed in 52 patients with APS who completed 1-year follow-up period. The analysis of magnitude for changes in HRQoL during one-year follow-up period have shown sustained medium ES (0.50-0.79) for both Physical and Mental health composite scores, total quality of life as well as for the Physical Health, Vitality, Role Emotional and Social Functioning. Conclusion: This study provides insight into new potential predictors of HRQoL and its changes over time in patients with APS. Additionally, identification of both prognostic markers of a poor HRQoL and magnitude of its changes should be considered when developing comprehensive treatment-related strategies and health care programs aimed at improving HRQoL and well-being in patients with APS.

Keywords: atypical parkinsonian syndromes, follow-up study, quality of life, APS

Procedia PDF Downloads 301

Authors: Zhenming Wang, Jun Zhu, Yuchen Yang, Ning Zhao

Abstract:

In the present paper, an alternative framework is proposed to construct a class of finite difference multi-resolution nested weighted essentially non-oscillatory (WENO) schemes with an increasingly higher order of accuracy for solving inviscid Euler equations. These WENO schemes firstly obtain a set of reconstruction polynomials by a hierarchy of nested central spatial stencils, and then recursively achieve a higher order approximation through the lower-order precision WENO schemes. The linear weights of such WENO schemes can be set as any positive numbers with a requirement that their sum equals one and they will not pollute the optimal order of accuracy in smooth regions and could simultaneously suppress spurious oscillations near discontinuities. Numerical results obtained indicate that these alternative finite-difference multi-resolution nested WENO schemes with different accuracies are very robust with low dissipation and use as few reconstruction stencils as possible while maintaining the same efficiency, achieving the high-resolution property without any equivalent multi-resolution representation. Besides, its finite volume form is easier to implement in unstructured grids.

Keywords: finite-difference, WENO schemes, high order, inviscid Euler equations, multi-resolution

Procedia PDF Downloads 137

Authors: Urmi Khatri

Abstract:

Capital asset pricing model (CAPM) draws a direct relationship between the risk and the expected rate of return. There was a criticism on the beta and the assumptions of CAPM, as they are not applicable in the real world. Fama French Three Factor Model and Fama French Five Factor Model have given different factors, which have an impact on the return of any asset like size, value, investment and profitability. This study proposes to see Capital Asset pricing Model through the lenses of the quality aspect. In the study, the six factors are studied. The Fama French Five Factor Model and addition of the quality dimension are studied. Here, Graham’s seven quality and quantity criteria are measured to determine the score of the sample firms. Thus, this study tries to check the model fit. The beta coefficient of the quality dimension and the R square value is seen to determine validity of the proposed model. The sample is drawn from the firms listed on Indian Stock Exchange (BSE). For the study, only nonfinancial firms are been selected. The time period of the study is from January 1999 to December 2019. Hence, the primary objective of the study is to check how robust the model becomes after giving the quality dimension to the capital asset pricing model in addition to the size, value, profitability and investment.

Keywords: asset pricing model, CAPM, Graham’s score, G-score, multifactor model, quality

Procedia PDF Downloads 151

Authors: Fatimah A. Alhomaid

Abstract:

A total of 40 one day-old LSL chicks (Lohman Selected Loghorn) were used in this study. In 20 days-old chicks, the bursa was formed of mucosa, musculosa and serosa. Its lamina propria was lymphoid in nature. After hatching, the bursa continued to grow and became fully developed at the 30th day post- hatching. It appeared as a blind sac. Its lumen was occupied by 12-13 mucosal folds. Each fold was lined by tall columnar or pseudo- stratified columnar epithelium. Its core was made of lamina propria infiltrated by a large number of lymphoid follicles. Most follicles possessed an outer corona surrounding a germinal center. At the age of 6 weeks physiological regression of the bursa was observed. The lymphoid follicles were decreased in size, the lymphocytes were depleted and the interfollicular stroma became obvious, thicker and more fibrous. Fibrosis of the lymphoid follicles was frequently seen in some sections at the age of 30 weeks.

Keywords: Bursa of fabricius, lymphocytes, cloacal Bursa

Procedia PDF Downloads 463

Authors: Faik Derya Ince, Ugur Topgul, Alp Gunay, Can Bayoglu, Dante J. Dorantes-Gonzalez

Abstract:

Explosive attacks are arguably the most lethal threat that may occur in terrorist attacks. In order to counteract this issue, explosive ordnance disposal operators put their lives on the line to dispose of a possible improvised explosive device. Robots can make the disposal process more accurately and saving human lives. For this purpose, there is a demand for more accurate and dexterous manipulating robotic hands that can be teleoperated from a distance. The aim of this project is to design a robotic hand that contains two active and two passive DOF for each finger, as well as a minimum set of tools for mechanical cutting and screw driving within the same robotic hand. Both hand and toolset, are teleoperated from a distance from a haptic robotic glove in order to manipulate dangerous objects such as improvised explosive devices. SolidWorks® Computer-Aided Design, computerized dynamic simulation, and MATLAB® kinematic and static analysis were used for the robotic hand and toolset design. Novel, dexterous and robust solutions for the fingers were obtained, and six servo motors are used in total to remotely control the multi-tooled robotic hand. This project is still undergoing and presents currents results. Future research steps are also presented.

Keywords: Explosive Manipulation, Robotic Hand, Tele-Operation, Tool Integration

Procedia PDF Downloads 133

Authors: Malul Miki, Rafi Bar-El, Eithan Hourie

Abstract:

In this article, we test the influence of an occupation as a calling on the value of job security and its connection with wage levels. Our sample consists of 495 workers in Israel from 10 occupations in the public sector, who are assumed to have a relatively high level of job security, and the private sector, who are assumed to have less job security or none at all. These 10 occupations are social workers, lecturers, lawyers, administration workers, accountants, high school teachers, bank workers, high-tech worker, nurses and psychologists. Using regression analysis, we find that those who have occupations that the literature has defined as a calling value job security less than those in ordinary employment. In addition, salary level has no effect on this relationship. Finally, those who work in occupations that are regarded as a calling have less status quo bias than those in ordinary employment.

Keywords: calling, loss aversion, job security, status quo bias

Procedia PDF Downloads 98

Authors: Sofia Stoica

Abstract:

In this paper, we use historical data to predict the stock price of a tech company. To this end, we use a dataset consisting of the stock prices in the past five years of ten major tech companies – Adobe, Amazon, Apple, Facebook, Google, Microsoft, Netflix, Oracle, Salesforce, and Tesla. We experimented with a variety of models– a linear regressor model, K nearest Neighbors (KNN), a sequential neural network – and algorithms - Multiplicative Weight Update, and AdaBoost. We found that the sequential neural network performed the best, with a testing error of 0.18%. Interestingly, the linear model performed the second best with a testing error of 0.73%. These results show that using historical data is enough to obtain high accuracies, and a simple algorithm like linear regression has a performance similar to more sophisticated models while taking less time and resources to implement.

Keywords: finance, machine learning, opening price, stock market

Procedia PDF Downloads 181

Authors: Z. Bayat

Abstract:

A quantitative structure–property relationship (QSPR) study was performed to develop models those relate the structures of 65 Kovats retention index (RI) of adamantane derivatives. Molecular descriptors derived solely from 3D structures of the molecular compounds. The usefulness of the quantum chemical descriptors, calculated at the level of the DFT theories using 6-311+G** basis set for QSAR study of adamantane derivatives was examined. The use of descriptors calculated only from molecular structure eliminates the need to experimental determination of properties for use in the correlation and allows for the estimation of RI for molecules not yet synthesized. The prediction results are in good agreement with the experimental value. A multi-parametric equation containing maximum Four descriptors at B3LYP/6-31+G** method with good statistical qualities (R2train=0.913, Ftrain=97.67, R2test=0.770, Ftest=3.21, Q2LOO=0.895, R2adj=0.904, Q2LGO=0.844) was obtained by Multiple Linear Regression using stepwise method.

Keywords: DFT, adamantane, QSAR, Kovat

Procedia PDF Downloads 363

Authors: S. Chami, J. Chauvin, T. Demarest, Stan Ng, M. Straus, W. Jahner

Abstract:

Biometrics tools such as fingerprint and iris are widely used in industry to protect critical assets. However, their vulnerability and lack of robustness raise several worries about the protection of highly critical assets. Biometrics based on Electrocardiographic (ECG) signals is a robust identification tool. However, most of the state-of-the-art techniques have worked on clinical signals, which are of high quality and less noisy, extracted from wearable devices like a smartwatch. In this paper, we are presenting a complete machine learning pipeline that identifies people using ECG extracted from an off-person device. An off-person device is a wearable device that is not used in a medical context such as a smartwatch. In addition, one of the main challenges of ECG biometrics is the variability of the ECG of different persons and different situations. To solve this issue, we proposed two different approaches: per person classifier, and one-for-all classifier. The first approach suggests making binary classifier to distinguish one person from others. The second approach suggests a multi-classifier that distinguishes the selected set of individuals from non-selected individuals (others). The preliminary results, the binary classifier obtained a performance 90% in terms of accuracy within a balanced data. The second approach has reported a log loss of 0.05 as a multi-class score.

Keywords: biometrics, electrocardiographic, machine learning, signals processing

Procedia PDF Downloads 137

Authors: Dalin Si, Azizan Aziz, Bertrand Lasternas

Abstract:

To encourage building owners to purchase electricity at the wholesale market and reduce building peak demand, this study aims to develop models that predict day-ahead hourly electricity consumption and demand using artificial neural network (ANN) and support vector machine (SVM). All prediction models are built in Python, with tool Scikit-learn and Pybrain. The input data for both consumption and demand prediction are time stamp, outdoor dry bulb temperature, relative humidity, air handling unit (AHU), supply air temperature and solar radiation. Solar radiation, which is unavailable a day-ahead, is predicted at first, and then this estimation is used as an input to predict consumption and demand. Models to predict consumption and demand are trained in both SVM and ANN, and depend on cooling or heating, weekdays or weekends. The results show that ANN is the better option for both consumption and demand prediction. It can achieve 15.50% to 20.03% coefficient of variance of root mean square error (CVRMSE) for consumption prediction and 22.89% to 32.42% CVRMSE for demand prediction, respectively. To conclude, the presented models have potential to help building owners to purchase electricity at the wholesale market, but they are not robust when used in demand response control.

Keywords: building energy prediction, data mining, demand response, electricity market

Procedia PDF Downloads 312

Authors: Touraj Hashemi, Zahara Saeidi, Paxshan H. Gader-l-Shateri

Abstract:

The present study aimed to determine the relationship between dimensions of psychological capital and psychological well-being. This research was conducted with a correlatiove method. The study population included the students of Sulaymaniyah, Garmian, and Halabja Universities in the Kurdistan region of Iraq. Therefore, using the one-stage cluster method, 300 subjects were selected and completed Riff's psychological well-being scale, and Luthans' psychological capital questionnaire. Data were analyzed using the multiple regression method. Results showed that self-efficacy, optimism, hope, and resilience had a positive relationship with psychological well-being. Hence, it can be concluded the four dimensions of psychological capital are able, in addition to modulating the effects of stress sources, to set the stage for the motivational use of life's stressors in order to develop new challenges and help the individual to continuous effort in order to develop new goals and expand happiness.

Keywords: psychological well-being, self-efficacy, optimism, hope, resilience

Procedia PDF Downloads 64

Authors: Anocha Rojanapanich

Abstract:

This research aimed to identify important factors that effect customer satisfaction in boutique hotels and the important factors effecting customer loyalty in returning to boutique hotels. Furthermore, this study also aimed to study demographics, which effect variable factors. Four hundred questionnaires were completed by customers of the boutique hotels. The descriptive statistics used in this paper were percentages, means, and standard deviation (S.D.), while hypothesis testing was done using T-test, Anova, Correlation and Regression to analyze the relationship among those factors. In terms of the purpose in staying, it was found that the largest respondent was for ‘leisure purposes’. While the frequency indicated that most of the customers who stayed ‘once’in the last two years in the hotels had less concern in the hotel’s image than other groups. For customer’s perceived value and income levels had an influence on customer perceived values in both functional value price and emotional value.

Keywords: boutique hotels, customer attitude, customer satisfaction, customer loyalty

Procedia PDF Downloads 302

Authors: Attila Vámosi, Tamás Mankovits, Dávid Huri, Imre Kocsis, Tamás Szabó

Abstract:

Non-linear FEM calculations are indispensable when important technical information like operating performance of a rubber component is desired. Rubber bumpers built into air-spring structures may undergo large deformations under load, which in itself shows non-linear behavior. The changing contact range between the parts and the incompressibility of the rubber increases this non-linear behavior further. The material characterization of an elastomeric component is also a demanding engineering task. The shape optimization problem of rubber parts led to the study of FEM based calculation processes. This type of problems was posed and investigated by several authors. In this paper the time demand of certain calculation methods are studied and the possibilities of time reduction is presented.

Keywords: rubber bumper, data acquisition, finite element analysis, support vector regression

Procedia PDF Downloads 467

Authors: Pratyush Agnihotri

Abstract:

The space missions are very costly because the transportation to the space is highly expensive and therefore there is the need to achieve complete re-usability in our launch vehicles to make the missions highly economic by cost cutting of the material recovered. Launcher reusability is the most efficient approach to decreasing admittance to space access economy, however stays an incredible specialized hurdle for the aerospace industry. Major concern of the difficulties lies in guidance and control procedure and calculations, specifically for those of the controlled landing stage, which should empower an exact landing with low fuel edges. Although cutting edge ways for navigation and control are present viz hybrid navigation and robust control. But for powered descent and landing of first stage of launch vehicle the guidance control is need to enable on board optimization. At first the CAD model of the launch vehicle I.e. space x falcon 9 rocket is presented for better understanding of the architecture that needs to be identified for the guidance and control solution for the recovery of the launcher. The focus is on providing the landing phase guidance scheme for recovery and re usability of first stage using retro propulsion. After reviewing various GNC solutions, to achieve accuracy in pre requisite landing online convex and successive optimization are explored as the guidance schemes.

Keywords: guidance, navigation, control, retro propulsion, reusable rockets

Procedia PDF Downloads 83

Authors: Débora V. Busman, Venerando E. Amaro, Mattheus da C. Prudêncio

Abstract:

Prognostic studies of the shoreline are of utmost importance for Ponta Negra Beach, located in Natal, Northeastern Brazil, where the infrastructure recently built along the shoreline is severely affected by flooding and erosion. This study compares shoreline predictions using three linear regression methods (LMS, LRR and WLR) and tries to discern the best method for different shoreline position scenarios. The methods have shown erosion on the beach in each of the scenarios tested, even in less intense dynamic conditions. The WLA_A with confidence interval of 95% was the well-adjusted model and calculated a retreat of -1.25 m/yr to -2.0 m/yr in hot spot areas. The change of the shoreline on Ponta Negra Beach can be measured as a negative exponential curve. Analysis of these methods has shown a correlation with the morphodynamic stage of the beach.

Keywords: coastal erosion, prognostic model, DSAS, environmental safety

Procedia PDF Downloads 324

Authors: Harry Burton, Alexios Dimitrios Iliadis, Neil Jones, Aaron Saini, Nicola Bystrzonowski, Alexandros Vris, Georgios Pafitanis

Abstract:

This article portrays the authors’ experience with a complex lower limb bone and soft tissue defect, following chronic osteomyelitis and pathological fracture, which was managed by the multidisciplinary orthoplastic team. The decision for functional amputation versus limb salvage was deemed necessary, enhanced by the principles of “spares parts” in reconstructive microsurgery. This case describes a successful use of the osteocutaneous distal tibia turn-over fillet flap that allowed ‘lowering the level of the amputation’ from a through knee to the conventional level of a below-knee amputation to preserve the knee joint function. This case demonstrates the value of ‘spare-parts’ surgery principles and how these concepts refine complex orthoplastic approaches when limb salvage is not possible to enhance function. The osteocutaneous distal tibia turn-over fillet flap is a robust technique for modified BKA reconstructions that provides sufficient bone length to achieve a tough, sensate stump and functional knee joint.

Keywords: osteocutaneous flap, fillet flap, spare-parts surgery, Below knee amputation

Procedia PDF Downloads 158

Authors: Prateep Wajeetongratana

Abstract:

This research aims to study the audit quality that affected to the reputation of the certified public accountants in Thailand. The researcher defined the population for this research as a group of the certified public accountants in Thailand who are the member of the federation of accounting professions under the royal patronage of his majesty the king also disclose their information .The total sampling size is 325. The results showed the audit quality factor has influence to the reputation of the certified public accountants in Thailand by accuracy auditing, objectiveness auditing and clearness auditing .These factors show by y1 = 1.381 + .372x1.1 + .309x1.2 + .305x1.3 can be describe as professional standard strictly factor (Y.1.1) and the new clients raised from word of mount of old clients regularly factor (Y.1.2) by regression coefficient (R2) as.242, this shows that such variables could predict the audit quality variable as 24.2 percent.

Keywords: audit quality, certified public accountants in Thailand, reputation

Procedia PDF Downloads 245

Authors: Waleed El-Said Abou El-Amaiem

Abstract:

Sole hemorrhages are considered as a main cause for sub clinical laminitis. In this study we aimed at discussing the most prominent risk factors associated with sole hemorrhages causing lameness in Egyptian water buffaloes and native breed cows. The final multivariate logistic regression model showed, a significant association between sub acute ruminal acidosis (P< 0.05), limb affected (P< 0.05) and weight (P< 0.05) and sole hemorrhages causing lameness in Egyptian water buffaloes and native breed cows. According to our knowledge, this is the first paper to discuss the risk factors associated with sole hemorrhages causing lameness in Egyptian water buffaloes and native breed cows.

Keywords: lameness, buffalo, sole hemorrhages, breed cows

Procedia PDF Downloads 443

Authors: Shirin Jalili, Hadi Shirzad, Samaneh Nabavi, Somayeh Khanjani

Abstract:

Forensic entomology is the science of study and analysis of insects evidences to aid in criminal investigation. Being aware of the distribution, biology, ecology and behavior of insects, which are founded at crime scene can provide information about when, where and how the crime has been committed. It has many application in criminal investigations. Its main use is estimation of the minimum time after death in suspicious death. The close association between insects and corpses and the use of insects in criminal investigations is the subject of forensic entomology. Because insects attack to the decomposing corpse and spawning on it from the initial stages. Forensic scientists can estimate the postmortem index by studying the insects population and the developing larval stages.In addition, toxicological and molecular studies of these insects can reveal the cause of death or even the identity of a victim. It also be used to detect drugs and poisons, and determination of incident location. Gathering robust entomological evidences is made possible for experts by recent Techniques. They can provide vital information about death, corpse movement or burial, submersion interval, time of decapitation, identification of specific sites of trauma, post-mortem artefacts on the body, use of drugs, linking a suspect to the scene of a crime, sexual molestations and the identification of suspects.

Keywords: Forensic entomology, post mortem interval, insects, larvae

Procedia PDF Downloads 498

Authors: Sudheer Gupta

Abstract:

Approximately 4 billion citizens of the world live on the equivalent of less than $8 a day. This segment constitutes a $5 trillion global market that remains under-served. Multinational corporations have historically tended to focus their innovation efforts on the upper segments of the economic pyramid. The academic literature has also been dominated by theories and frameworks of innovation that are valid when applied to the developed markets and consumer segments, but fail to adequately account for the challenges and realities of new product and service creation for the poor. Theories of entrepreneurship developed in the context of developed markets similarly ignore the challenges and realities of operating in developing economies that can be characterized by missing institutions, missing markets, information and infrastructural challenges, and resource constraints. Social entrepreneurs working in such contexts develop solutions differently. In this talk, we summarize lessons learnt from a long-term research project that involves data collection from a broad range of social entrepreneurs in developing countries working towards solutions to alleviate poverty, and grounded theory-building efforts. We aim to develop a better understanding of consumers, producers, and other stakeholder involvement, thus laying the foundation to build a robust theory of innovation and entrepreneurship for the poor.

Keywords: poverty alleviation, social enterprise, social innovation, development

Procedia PDF Downloads 393

Authors: Omran Alharbi

Abstract:

It is difficult to find an aspect of our modern lives that has been untouched by mobile technology. Indeed, the use of mobile learning in Saudi Arabia may enhance students’ learning and increase overall educational standards. However, within tertiary education, the success of e-learning implementation depends on the degree to which students and educators accept mobile learning and are willing to utilise it. Therefore, this research targeted the factors that influence Hail University instructors’ intentions to use mobile learning. An online survey was completed by eighty instructors and it was found that their use of mobile learning was heavily predicted by performance experience, effort expectancy, social influence, and facilitating conditions; the multiple regression analysis revealed that 67% of the variation was accounted for by these variables. From these variables, effort expectancy was shown to be the strongest predictor of intention to use e-learning for instructors.

Keywords: acceptance, faculty member, mobile learning, KSA

Procedia PDF Downloads 148

Authors: Aditya Karade, Sharada Falane, Dhananjay Deshmukh, Vijaykumar Mantri

Abstract:

Brain tumors pose a significant challenge in healthcare due to their complex nature and impact on patient outcomes. The application of deep learning (DL) algorithms in medical imaging have shown promise in accurate and efficient brain tumour detection. This paper explores the performance of various pre-trained DL models ResNet50, Xception, InceptionV3, EfficientNetB0, DenseNet121, NASNetMobile, VGG19, VGG16, and MobileNet on a brain tumour dataset sourced from Figshare. The dataset consists of MRI scans categorizing different types of brain tumours, including meningioma, pituitary, glioma, and no tumour. The study involves a comprehensive evaluation of these models’ accuracy and effectiveness in classifying brain tumour images. Data preprocessing, augmentation, and finetuning techniques are employed to optimize model performance. Among the evaluated deep learning models for brain tumour detection, ResNet50 emerges as the top performer with an accuracy of 98.86%. Following closely is Xception, exhibiting a strong accuracy of 97.33%. These models showcase robust capabilities in accurately classifying brain tumour images. On the other end of the spectrum, VGG16 trails with the lowest accuracy at 89.02%.

Keywords: brain tumour, MRI image, detecting and classifying tumour, pre-trained models, transfer learning, image segmentation, data augmentation

Procedia PDF Downloads 67