41 research outputs found
Integral Root Labeling of Pm ∪ G Graphs
No full textLet G= V,E be a graph with p vertices and q edges. The distinct vertex labeling induces a distinct edge labeling on the graph. The graph which admits Integral Root labeling is called an Integral Root Graph. In this paper, we investigate the Integral Root labeling of P m G graphs likeP m P n,P m P n K 1 , P m L n,P m P n K 1,2 , P m P n K 1,3 , P m P n K 1 K 1,2 V. L. Stella Arputha Mary | N. Nanthini "Integral Root Labeling of Pm∪G Graphs" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-5 , August 2018, URL: https://www.ijtsrd.com/papers/ijtsrd18233.pd
The determinants of cross-border mergers in four selected ASEAN countries
No full textOne organization purchases a second organization and acquiring ownership rights
over its assets, business lines, operations, stocks and products. Therefore, they need one act to secure what they purchase which is will call its merger and acquisitions (M&As). In this particular matter, our study attempts to investigate the role of financial development on cross-border M&As in 4 ASEAN countries, namely Indonesia, Malaysia, Singapore and Thailand. Based on period of thirteen years
(2000-2012), the data were analyzed by using the panel data econometric technique; fixed-effects and random-effects models. The results of the study indicate that a number of variables such as GDP, trade costs, financial development indicators such as stock which is the market capitalization of equity market, the amount of money in circulation (M2), and the real exchange rate (RER) are significantly influential in determining cross-border M&As from the whole of selected ASEAN countries. The
findings of the study reveal the importance of domestic financial markets in stimulating cross-border M&As. These results also indicate that policy makers should pay more attention to promote cross-border M&As in term of policies and emphasize towards a stable exchange rate and trade cos
Determinants of responsible innovation for sustainability transition in a developing country: Contested narratives for transition in the Sri Lankan power sector
No full textGlobal efforts towards sustainable energy transition remain uneven. Developing countries are embedded in a vulnerable setting requiring rapid but responsible action to meet increasing energy demands due to their specific projected economic and population growth. Consequently, such countries have addressed the challenges of achieving sustainable energy transition differently compared with developed countries with regard to renewable energy development and its governance. Theories of sustainability transition and responsible innovation (RI) have their origin in developed countries, and the application of this Western-centric version has been found incompatible with the contexts of developing countries. The aim of the paper is to explore how contextual understandings of RI are discursively constructed and how such understandings enable or constrain sustainable energy pathways in developing countries. The author draws on empirical evidence relating to the power sector in Sri Lanka and analyses three narratives in play revealed by a qualitative case study. The findings indicate that developing countries must place greater emphasis on aligning technological innovation systems with RI in efforts to achieve sustainability transitions by being vigilant with regard to contextual narratives on RI. The author concludes that prevalent narratives should be regarded as a bridge for linking sustainability transitions to RI.publishedVersio
Fairness in Machine Learning Methods for Surgical Skill Assessment
No full textSurgical skill development is crucial for ensuring high-quality patient care and successful surgical outcomes. Traditional methods of surgical skill assessment, such as direct observation and outcome-based metrics, often suffer from subjectivity and scalability issues. Video-based assessments (VBA) using machine learning (ML) could be a promising alternative, offering the potential for more objective and scalable evaluations. However, a significant challenge in deploying ML models in this context is the potential for bias, which can stem from various sources, including the data. Such biases can skew results, leading to unfair assessments and potentially impacting surgeon careers and patient outcomes.
Utilizing a pairwise comparison framework, we introduce bias of various intensities at a granular level and quantify it. Our findings highlight a significant difference in model evaluation on biased test sets, with an average 8% to 17.1% drop in Area Under the Curve (AUC) scores for each 10% increase in rater bias. We also simulate realistic ratings for a sample of raters based on a study’s data on IAT scores of 131 surgeons, ensuring that the simulated data reflects the variability and distribution seen in real-world data.
Next, we evaluate the performance of models trained on biased and unbiased data sets, demonstrating that models trained on unbiased data outperform biased models in this case. We also propose a pipeline that begins with the quantification of dataset bias, which could be used to train models that compensate for identified biases downstream through reweighting and other corrective techniques. Central to our method is the use of fairness metrics, such as the true positive rate (TPR) and false positive rate (FPR) for equalized odds, to measure bias. These metrics are calculated by comparing rater labels against expert labels within strategically sampled subsets of the data.
Our work underscores the necessity of addressing implicit bias in training and test sets to ensure the fairness and reliability of automated surgical skill assessment models
Fairness in Machine Learning Methods for Surgical Skill Assessment
No full textSurgical skill development is crucial for ensuring high-quality patient care and successful surgical outcomes. Traditional methods of surgical skill assessment, such as direct observation and outcome-based metrics, often suffer from subjectivity and scalability issues. Video-based assessments (VBA) using machine learning (ML) could be a promising alternative, offering the potential for more objective and scalable evaluations. However, a significant challenge in deploying ML models in this context is the potential for bias, which can stem from various sources, including the data. Such biases can skew results, leading to unfair assessments and potentially impacting surgeon careers and patient outcomes.
Utilizing a pairwise comparison framework, we introduce bias of various intensities at a granular level and quantify it. Our findings highlight a significant difference in model evaluation on biased test sets, with an average 8% to 17.1% drop in Area Under the Curve (AUC) scores for each 10% increase in rater bias. We also simulate realistic ratings for a sample of raters based on a study’s data on IAT scores of 131 surgeons, ensuring that the simulated data reflects the variability and distribution seen in real-world data.
Next, we evaluate the performance of models trained on biased and unbiased data sets, demonstrating that models trained on unbiased data outperform biased models in this case. We also propose a pipeline that begins with the quantification of dataset bias, which could be used to train models that compensate for identified biases downstream through reweighting and other corrective techniques. Central to our method is the use of fairness metrics, such as the true positive rate (TPR) and false positive rate (FPR) for equalized odds, to measure bias. These metrics are calculated by comparing rater labels against expert labels within strategically sampled subsets of the data.
Our work underscores the necessity of addressing implicit bias in training and test sets to ensure the fairness and reliability of automated surgical skill assessment models
Optimization of dimple configurations on heat dissipation of aluminium flat surface
No full textIn the car manufacturing industry,countless inventions, improvements, and modifications are continuously being updated to meet customer expectations. Therefore, engineers and inventors always give higher priority to improving every part of a vehicle. However, there are still numerous reports of customer frustration, especially in medium-priced cars parts reliability. One of the main issues are involves engine mounts, which are exposed to high temperatures from the engine heat,leading to a short life span. An engine mount is the part that holds the engine to the body or to the engine cradle (sub-frame) of the car. The engine mount exposed high heat energy from the engine during the combustion process (130°C). This causes the engine mount to lose its mechanical strength, resulting in a short service life. The lifespan of the engine mount depends on the effectiveness of heat dissipation during dynamic state. Therefore, it is essential to improve the heat transfer of the engine mounting. Thus, the aim of this research is to develop and evaluate a spherical dimple profile for a smooth surface to enhance heat transfer rate. It is widely known that introducing a dimple profile results in improved heat transfer over a surface. This research focuses on geometric modification and optimization of cooling paran1eters for a spherical dimpled surface of an aluminium block. The aluminium block is used throughout this experiment because it is one of the best conductors of heat. Thus, in this experiment, the dimpled design is the main focus.In this project, experimental and numerical investigation were carried out to examine the cooling effect and flow structure of the spherical dimple profile during steady laminar flow in a wind tunnel. Seventeen different sets of parameters related to the dimple diameter ( 10-14 mm),dimple orientation ( 60°-90° angle),and airflow velocity (16-18 m/s) were studied. The Box-Behnken of Response Surface Methodology (RSM) was used as a Design of Experiments (DoE) tool to evaluate the effect of these parameters on cooling time. This work applies Analysis of Variance (ANOV A) in order to establish the significant effect of the input parameters.ANSYS Fluent software was used as a simulation tool to analyze the flow structure of the dimpled surface. The optimal cooling time is produced from the experiment is 7.23 minutes with a relative error of 5.24% compared to the prediction results.The optimal parameters are a dimple diameter of 12 mm,a dimple orientation angle of 60°,and an airflow velocity of 18 m/s
Gravimetric, electrochemical and surface study on the good’s buffer ionic liquid as corrosion inhibitor for carbon steel in acidic medium
No full textThis corrosion study assessed the inhibition performance of carbon steel in 1 M of hydrochloric acid (HCl) using Good’s buffer ionic liquids (GBIL) namely 1-Butyl-3-methylimidazolium 2-(N-Morpholino) propane, [BMIM][MOPS] using electrochemical impedance, potentiodynamic polarization, and weight loss (gravimetric) measurements. GBIL are synthesized by the combination of Good's buffer as anion and various organic bases as the cation. The gravimetric measurements exhibit higher reduction in weight for carbon steel exposed to the acidic environment in the absence of corrosion inhibitor (CI) as compared to carbon steel immersed in the presence of inhibitor molecule. Potentiodynamic polarization study indicates that the synthesized inhibitor acted as a mixed type inhibitor. The inhibition efficiency increases with increase in the concentration of [BMIM][MOPS]. Corrosion protection efficiency ranging from 88% to 90% was featured at 800 ppm of CI in the HCl medium. The adsorption of [BMIM][MOPS] on the carbon steel surface was described by the Langmuir's adsorption isotherm. The scanning electron micrographs inspected the morphology of the carbon steel surface exposed to the solution without and with the presence of inhibitor. The result showed that compound effectively suppressed corrosion by the appearance of an improved surface structure of carbon steel with increasing concentration of [BMIM][MOPS]
Occlusion Detection Based on Fractal Texture Analysis in Surveillance Videos Using Tree-Based Classifiers
No full textDimensionality Reduction Using Latent Variable across the Domains in Recommender System
No full textDimensionality reduction plays an important role in big data analytics and machine learning for the past decades. While exploring the large volumes of data, it is necessary to perform the larger computation. In order to overcome this, a novel latent variable based dimensionality reduction across the domains in Recommender System (RS) is proposed. Firstly, we define the latent class corresponding to the attributes from two domains and user profiles. Then many-to-one mapping of attributes to a latent class variable is achieved. Finally, the entire data variables are reduced to five latent class variables and sharing the knowledge across the domains. The overall dimensionality reduction is very useful for easy processing of data and reducing the processing time in various applications. Compared with the traditional dimensionality reduction method, the proposed method discovers the hidden variable from the observed variable without any loss of information
Dimensionality Reduction Using Latent Variable across the Domains in Recommender System
No full textDimensionality reduction plays an important role in big data analytics and machine learning for the past decades. While exploring the large volumes of data, it is necessary to perform the larger computation. In order to overcome this, a novel latent variable based dimensionality reduction across the domains in Recommender System (RS) is proposed. Firstly, we define the latent class corresponding to the attributes from two domains and user profiles. Then many-to-one mapping of attributes to a latent class variable is achieved. Finally, the entire data variables are reduced to five latent class variables and sharing the knowledge across the domains. The overall dimensionality reduction is very useful for easy processing of data and reducing the processing time in various applications. Compared with the traditional dimensionality reduction method, the proposed method discovers the hidden variable from the observed variable without any loss of information.</jats:p
