Determination of Thermal and Blue Light Photochemical Hazard Protection needed by Automatic Welding filters

Recent developments in biological research, has shown that the initial maximum permissible exposure (MPE) limits for protection of workers from risks associated with artificial optical radiations were more stringent than needed. Using the most recent MPE limits for artificial optical radiation this piece of work was focused on the investigation of the level of visible light attenuation needed by automatic welding filters in case of switching failure. Results from the comparison of different exposure standards were employed in investigating the need of Vis/IR and blue light transmittance requirement for automatic welding filters. Real and arbitrary spectra were taken into consideration for the worst and best case scenarios of artificial optical radiations. An excel worksheet developed during the execution of this project took into consideration the exposure from different light sources and the precision of the spectrometer used in measuring the transmittances of a welding filter. The worksheet was developed and tested with known product properties to investigate the validity of its formulation. The conclusion drawn from this project was that attenuation in the light state will be needed for products with the darkest state shade 11 or higher. Also shown is that current welding filter protects the eye well enough even in the case of switching failure.

Student Thesis Level: Master’s Thesis in Business Administration Corporate Social Responsibility (CSR) networking : A case study on why members join CSR network and why are they sharing their knowledge

Purpose – This research focuses on finding the reasons, why members from different sectors join a cross-sector/multi-stakeholder CSR network and what motivates them to share (or not to share) their knowledge of CSR and their best practices. Design/methodology/approach – Semi-structured interviews were conducted with members of the largest cross-sector CSR network in Sweden. The sample base of 15 people was chosen to be able to represent a wider variety of members from each participating sectors. As well as the CEO of the intermediary organization was interviewed. The interviews were conducted via email and telephone. Findings – The findings include several reasons linked to the business case of CSR such as stakeholder pressure, competitive advantage, legitimacy and reputation as well as new reasons like the importance of CSR, and the access of further knowledge in the field. Further reasons are in line with members wanting to join a network, such as access to contact or having personal contacts. As to why members are sharing their CSR knowledge, the findings indicate to inspire others, to show CSR commitment, to be visible, it leads to business opportunity and the access of others knowledge, and because it was requested. Reasons for not sharing their knowledge would be the lack of opportunity, lack of time and the lack of experience to do so. Originality/value – The research contributes to existing studies, which focused on Corporate Social Responsibility and cross-sector networking as well as to inter-organizational knowledge sharing in the field of CSR.

Predictive models for chronic renal disease using decision trees, naïve bayes and case-based methods

Data mining can be used in healthcare industry to “mine” clinical data to discover hidden information for intelligent and affective decision making. Discovery of hidden patterns and relationships often goes intact, yet advanced data mining techniques can be helpful as remedy to this scenario. This thesis mainly deals with Intelligent Prediction of Chronic Renal Disease (IPCRD). Data covers blood, urine test, and external symptoms applied to predict chronic renal disease. Data from the database is initially transformed to Weka (3.6) and Chi-Square method is used for features section. After normalizing data, three classifiers were applied and efficiency of output is evaluated. Mainly, three classifiers are analyzed: Decision Tree, Naïve Bayes, K-Nearest Neighbour algorithm. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals. Efficiency of Decision Tree and KNN was almost same but Naïve Bayes proved a comparative edge over others. Further sensitivity and specificity tests are used as statistical measures to examine the performance of a binary classification. Sensitivity (also called recall rate in some fields) measures the proportion of actual positives which are correctly identified while Specificity measures the proportion of negatives which are correctly identified. CRISP-DM methodology is applied to build the mining models. It consists of six major phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.