Using Big Data to Enhance the Bosch Production Line Performance: A Kaggle Challenge

This paper describes our approach to the Bosch production line performance challenge run by Kaggle.com. Maximizing the production yield is at the heart of the manufacturing industry. At the Bosch assembly line, data is recorded for products as they progress through each stage. Data science methods are applied to this huge data repository consisting records of tests and measurements made for each component along the assembly line to predict internal failures. We found that it is possible to train a model that predicts which parts are most likely to fail. Thus a smarter failure detection system can be built and the parts tagged likely to fail can be salvaged to decrease operating costs and increase the profit margins.Comment: IEEE Big Data 2016 Conferenc

Understanding the Nobel Laureate ‘Mo Yan’ Through His Fiction

One of the main characteristics of Contemporary Chinese Literature is that it has remained true to the time it represented. Although it has been used extensively to serve the political agenda of the Communist party on occasions, but it has managed to carry forward the idea of realism, which started to flourish during the May Fourth period. After the announcement of the policy of “Reform and Opening up” by Deng Xiaoping in the Post Mao period China, a brilliant story teller emerged from the rural area of Gaomi in Shandong province of China. This paper aims to understand the phenomena created by Mo Yan’s writings in contemporary period of Chinese literature. The paper initially has discussed the major trends in post-Mao period Chinese literature to provide the background for understanding the emergence of Mo Yan. The paper has tried to discuss the major trends in Mo Yan’s writings focusing on the fiction-world created by him in his novels. Then it has further analysed the characteristics of Mo Yan’s writings. Finally, through the analysis of available contents a conclusion has been drawn

Macroscopic model with anisotropy based on micro-macro informations

Physical experiments can characterize the elastic response of granular materials in terms of macroscopic state-variables, namely volume (packing) fraction and stress, while the microstructure is not accessible and thus neglected. Here, by means of numerical simulations, we analyze dense, frictionless, granular assemblies with the final goal to relate the elastic moduli to the fabric state, i.e., to micro-structural averaged contact network features as contact number density and anisotropy. The particle samples are first isotropically compressed and later quasi-statically sheared under constant volume (undrained conditions). From various static, relaxed configurations at different shear strains, now infinitesimal strain steps are applied to "measure" the effective elastic response; we quantify the strain needed so that plasticity in the sample develops as soon as contact and structure rearrangements happen. Because of the anisotropy induced by shear, volumetric and deviatoric stresses and strains are cross-coupled via a single anisotropy modulus, which is proportional to the product of deviatoric fabric and bulk modulus (i.e. the isotropic fabric). Interestingly, the shear modulus of the material depends also on the actual stress state, along with the contact configuration anisotropy. Finally, a constitutive model based on incremental evolution equations for stress and fabric is introduced. By using the previously measured dependence of the stiffness tensor (elastic moduli) on the microstructure, the theory is able to predict with good agreement the evolution of pressure, shear stress and deviatoric fabric (anisotropy) for an independent undrained cyclic shear test, including the response to reversal of strain

Optimizing the cutting parameters using taguchi method to reduce the cutting tool vibration

In any machining operation, minimizing the vibration of the tool is a very important requirement for any turned workpiece. Thus the choice of optimized cutting parameter is very important for minimizing the vibration of the cutting tool. The focus of this study is the collection of tool vibration data generated by the lathe dry turning of SS304 samples of diameter 31mm using ISO 6R 1212 as the cutting tool at different levels of speed (140, 220, 360rpm), feed (0.1, 0.16, 0.25mm/rev) and depth of cut (0.5, 0.6, 0.7mm) and then analyzing the obtained data using taguchi analysis to show how tool vibration varies within a given range of speed, feed & depth of cut. The vibration here is represented by its peak acceleration. The analysis revealed that for the specified range of speed, feed and depth of cut, any change in the depth of cut causes a large change in the tool vibration while change in the cutting speed causes comparatively lowest change in tool vibration

Sentiment Analysis Using Hybrid Machine Learning Technique

It is observed that consumers often share their opinion, views or feeling about any term used on social network in the form of reviews, comments or feedback. Those feedbacks given by end users have a great impact for evolution of new version of any product. Due to this trend in social media in recent years, sentiment analysis has become an important concern for theoreticians and practitioners Moreover reviews are often written in natural language and are mostly unstructured. Thus, to obtain any meaningful information from these reviews, it needs to be processed. Due to large size of data it is impossible to process this information manually. Hence machine learning algorithms are considered for analysis. Since data are unstructured in nature, unsupervised machine learning algorithm can be helpful in solving this problem. But unsupervised methods have less accuracy; hence not acceptable. In this study, a hybrid machine learning approach is adopted to automatically find the requirements for next version of software. Also some reviews neither belong to positive cluster nor to negative. They mixed reaction or feeling about some topics. Those problem associated with NLP is solved using hybrid technique of the fuzzy c-means and ANN. Moreover in this study, different methods of unsupervised machine leaning algorithm are implemented and their results are compared with each other. The best outcome is used to train the neural network. By using this hybridization technique, accuracy gets increased. And in later stage, this technique is applied to find the new requirement of product

Use of thrombolytic therapy beyond current recommendations for acute ischaemic stroke

In Chapter 1, I introduce ischaemic stroke, thrombolytic therapy, thrombolysis trials and then discuss the rationale for exclusion criteria in stroke thrombolysis guidelines.In Chapter 2, I describe methods for examining outcomes in patients that are currently recommended for exclusions from receiving alteplase for acute ischaemic stroke. In Chapter 3, I examine Virtual International Stroke Trials Archive (VISTA) data to test whether current European recommendation suggesting exclusion of elderly patients (older than 80 years) from thrombolysis for acute ischaemic stroke is justified. Employing non-randomised controlled comparison of outcomes, I show better outcomes amongst all patients (P 30 years. Outcomes assessed by National Institutes of Health Scale (NIHSS) score and dichotomised modified Rankin Scale score are consistently similar. In Chapter 4, I compare thrombolysed patients in Safe Implementation of Thrombolysis in Stroke International Stroke Thrombolysis Register (SITS-ISTR) with VISTA non-thrombolysed patients ("comparators" or "controls") and test exactly similar question as in Chapter 3. Distribution of scores on modified Rankin scale are better amongst all thrombolysis patients than controls (odds ratio 1.6, 95% confidence interval 1.5 to 1.7; Cochran-Mantel-Haenszel P80 (OR 1.4, 95% CI 1.3 to 1.6; P<0.001; n=3439). Odds ratios are consistent across all 10 year age ranges above 30, and benefit is significant from age 41 to 90; dichotomised outcomes (score on modified Rankin scale 0-1 v 2-6; 0-2 v 3-6; and 6 (death) versus rest) are consistent with the results of ordinal analysis. These findings are consistent with results from VISTA reported in Chapter 3. Age alone should not be a criterion for excluding patients from receiving thrombolytic therapy.In Chapter 5, I employ VISTA data to examine whether patients having diabetes and previous stroke have improved outcomes from use of alteplase in acute ischaemic stroke. Employing a non-randomised controlled comparison, I show that the functional outcomes are better for thrombolysed patients versus nonthrombolysed comparators amongst non-diabetic (P < 0.0001; OR 1.4 [95% CI 1.3-1.6]) and diabetic (P = 0.1; OR 1.3 [95% CI1.05-1.6]) patients. Similarly, outcomes are better for thrombolysed versus nonthrombolysed patients who have not had a prior stroke (P < 0.0001; OR 1.4 [95% CI1.2-1.6]) and those who have (P = 0.02; OR 1.3 [95% CI1.04-1.6]). There is no interaction of diabetes and prior stroke with treatment (P = 0.8). Neurological outcomes (NIHSS) are consistent with functional outcomes (mRS). In Chapter 6, I undertake a non-randomised controlled comparison of SITS-ISTR data with VISTA controls and examine whether patients having diabetes and previous stroke have improved outcomes from use of alteplase in acute ischaemic stroke. I show that adjusted mRS outcomes are better for thrombolysed versus non-thrombolysed comparators amongst patients with diabetes mellitus (OR 1.45[95% CI1.30-1.62], N=5354), previous stroke (OR 1.55[95% CI1.40-1.72], N=4986), or concomitant diabetes mellitus and previous stroke (OR 1.23 [95% CI 0.996-1.52], P=0.05, N=1136), all CMH p<0.0001. These are comparable to outcomes between thrombolysed and non-thrombolysed comparators amongst patients suffering neither diabetes mellitus nor previous stroke: OR=1.53(95%CI 1.42-1.63), p<0.0001, N=19339. There are no interaction between diabetes mellitus and previous stroke with alteplase treatment (t-PA*DM*PS, p=0.5). Present data supports results obtained from the analyses of VISTA data in chapter 5. There is no statistical evidence to recommend exclusion of patients with diabetes and previous stroke from receiving alteplase.In Chapter 7, I examine VISTA data to test whether exclusion of patients having a mild or severe stroke at baseline would be justified. Stratifying baseline stroke severity for quintiles of NIHSS scores, I observe that there are significant associations of use of alteplase with improved outcomes for baseline NIHSS levels from 5 to 24 (p<0.05). This association lose significance for baseline NIHSS categories 1 to 4 (P = 0.8; OR, 1.1; 95% CI, 0.3-4.4; N = 8/161) or ≥ 25 (P = 0.08; OR, 1.1; 95% CI, 0.7-1.9; N = 64/179) when sample sizes are small and confidence interval wide. These findings fail to provide robust evidence to support the use of alteplase in the mild or severe stroke patients, though potential for benefit appears likely.In Chapter 8, I present a meta-analysis of trials that investigated mismatch criteria for patients’ selection to examine whether present evidence supports delayed thrombolysis amongst patients selected according to mismatch criteria. I collate outcome data for patients who were enrolled after 3 hours of stroke onset in thrombolysis trials and had mismatch on pre-treatment imaging. I compare favourable outcome, reperfusion and/or recanalisation, mortality, and symptomatic intracerebral haemorrhage between the thrombolysed and non-thrombolysed groups of patients and the probability of a favourable outcome among patients with successful reperfusion and clinical findings for 3 to 6 versus 6 to 9 hours from post stroke onset. I identify articles describing the DIAS, DIAS II, DEDAS, DEFUSE, and EPITHET trials, giving a total of 502 mismatch patients thrombolysed beyond 3 hours. The combined adjusted odds ratios (a-ORs) for favourable outcomes are greater for patients who had successful reperfusion (a-OR=5.2; 95% CI, 3 to 9; I2=0%). Favourable clinical outcomes are not significantly improved by thrombolysis (a-OR=1.3; 95% CI, 0.8 to 2.0; I2=20.9%). Odds for reperfusion/recanalisation are increased amongst patients who received thrombolytic therapy (a-OR=3.0; 95% CI, 1.6 to 5.8; I2=25.7%). The combined data show a significant increase in mortality after thrombolysis (a-OR=2.4; 95% CI, 1.2 to 4.9; I2=0%), but this is not confirmed when I exclude data from desmoteplase doses that are abandoned in clinical development (a-OR=1.6; 95% CI, 0.7 to 3.7; I2=0%). Symptomatic intracerebral haemorrhage is significantly increased after thrombolysis (a-OR=6.5; 95% CI, 1.2 to 35.4; I2=0%) but not significant after exclusion of abandoned doses of desmoteplase (a-OR=5.4; 95% CI, 0.9 to 31.8; I2=0%). Delayed thrombolysis amongst patients selected according to mismatch imaging is associated with increased reperfusion/recanalisation. Recanalisation/reperfusion is associated with improved outcomes. However, delayed thrombolysis in mismatch patients was not confirmed to improve clinical outcome, although a useful clinical benefit remains possible. Thrombolysis carries a significant risk of symptomatic intracerebral haemorrhage and possibly increased mortality. Criteria to diagnose mismatch are still evolving. Validation of the mismatch selection paradigm is required with a phase III trial. Pending these results, delayed treatment, even according to mismatch selection, cannot be recommended as part of routine care.In Chapter 9, I summarise the findings of my research, discuss its impact on the research community, and discuss weaknesses inherent in registry data and limitation of statistical methods. Then, I elaborate the future directions I may take to further research on the theme of this thesis.

Tuning the bulk properties of bidisperse granular mixtures by small amount of fines

We study the bulk properties of isotropic bidisperse granular mixtures using discrete element simulations. The focus is on the influence of the size (radius) ratio of the two constituents and volume fraction on the mixture properties. We show that the effective bulk modulus of a dense granular (base) assembly can be enhanced by up to 20% by substituting as little as 5% of its volume with smaller sized particles. Particles of similar sizes barely affect the macroscopic properties of the mixture. On the other extreme, when a huge number of fine particles are included, most of them lie in the voids of the base material, acting as rattlers, leading to an overall weakening effect. In between the limits, an optimum size ratio that maximizes the bulk modulus of the mixture is found. For loose systems, the bulk modulus decreases monotonically with addition of fines regardless of the size ratio. Finally, we relate the mixture properties to the 'typical' pore size in a disordered structure as induced by the combined effect of operating volume fraction (consolidation) and size ratio