Decision making in surgery and cancer care

Decision making in surgery and cancer care is an interesting, challenging, and yet little explored area of surgical sciences research. This research addresses that paucity. In performing this research, health outcomes research (HOR) literature was comprehensively reviewed. Health outcome measures including quality of life and health-related quality of Life were described, in addition to their measurements. Subsequently health outcome measures in relation to oncoplastic and aesthetic breast surgery were described, and health outcome measures in a number of benign breast and colorectal pathologies were studied.Decision making in surgery and cancer care was explored using a mixed methodology of quantitative and qualitative studies. To derive a more comprehensive view, different specialties were explored: breast, colorectal, and head and neck surgery. To address socio-cultural factors the qualitative focus group discussions were undertaken in England, Wales, and India. Quantitative studies included literature reviews, prospective studies, retrospective studies, and questionnaire surveys. Qualitative studies were based on focus group discussions.The results showed that raw quantitative data is only one of the factors influencing the decision making process. A number of other factors play an important role in the decision making process. These include: health outcome measures (quality of life, health-related quality of life), clinician factors (knowledge, skill, expertise, judgment), patient factors (socio-economic, education, cultural), nursing factors, translational research, and resource infrastructure.Important themes and outcomes emerged from the qualitative studies. The focus group discussions showed that decision making in surgery and cancer care varies not only between the developing and the developed world, but also within different regions in the western world. In England, a small minority of patients was driving the decision making process, compared with Wales, where joint decision making is the norm. However, in India decision making is predominantly led by the clinicians and the patient’s family members. As modern health care moves towards a patient centered care approach, evidence based patient choice and patient decision making clearly has a greater role to play, and the cultural and practical issues demonstrated in this thesis must be considered

A Methodology for Vertically Partitioning in a Multi-Relation Database Environment

Vertical partitioning, in which attributes of a relation are assigned to partitions, is aimed at improving database performance. We extend previous research that is based on a single relation to multi-relation database environment, by including referential integrity constraints, access time based heuristic, and a comprehensive cost model that considers most transaction types including updates and joins. The algorithm was applied to a real-world insurance CLAIMS database. Simulation experiments were conducted and the results show a performance improvement of 36% to 65% over unpartitioned case. Application of our method for small databases resulted in partitioning schemes that are comparable to optimal.Facultad de Informátic

A Methodology for Vertically Partitioning in a Multi-Relation Database Environment

Vertical partitioning, in which attributes of a relation are assigned to partitions, is aimed at improving database performance. We extend previous research that is based on a single relation to multi-relation database environment, by including referential integrity constraints, access time based heuristic, and a comprehensive cost model that considers most transaction types including updates and joins. The algorithm was applied to a real-world insurance CLAIMS database. Simulation experiments were conducted and the results show a performance improvement of 36% to 65% over unpartitioned case. Application of our method for small databases resulted in partitioning schemes that are comparable to optimal.Facultad de Informátic

Machine Learning Technique for Prediction of Breast Cancer

Breast cancer is one of the most prominent disease and the second foremost source of death among middle-aged women in the world. Removing of breast tumour by using a surgical treatment and chemotherapy could work excellently if it can be identified as a primary tumour or at an early stage of transmutation, however it is a costly process. The quick development of machine learning techniques continues to burn the medical tomography enthusiasm in implementing to improve the accurateness of tumour findings. To identify breast cancer in the area of machine learning lots of attempts were made, but these techniques are not too accurate. In the proposed Machine Learning Technique for Prediction of Breast Cancer (MLTPBC) is an automated system used to remove a label, pectoral muscles, noise, and identification of cancer. The experimental results of the proposed MLTPBC shows the preferable accuracy over the existing methods

Modeling CO2 electrolysis in solid oxide electrolysis cell

A modified Butler-Volmer equation for the reduction of CO2 by considering multi-step single-electron transfer reactions is presented. Exchange current density formulations free from arbitrary order dependency on the partial pressures of reactants and products are proposed for Ni and Pt surfaces. Button cell simulations are performed for Ni-YSZ/YSZ/LSM, Pt-YSZ/YSZ/Pt, and Pt/YSZ/Pt systems using two different electrochemical models, and simulation results are compared against experimental observations. The first electrochemical model considers charge transfer reactions occurring at the interface between the electrode and dense electrolyte, and the second model considers the charge transfer reactions occurring throughout the thickness of the cermet electrode. Single-channel simulations are further performed to asses the O2 production capacity of CO2 electrolysis syste

On the Maturity of the MIS Research Field

Researchers benefit considerably from understanding the developments in their field, especially in a relatively young field like MIS. The difficulties of identity and maturity in MIS field are exacerbated by rapidly changing technology and by the eclectic, inter-disciplinary nature of the field. To help clarify the nature of the MIS field and its developments, a stream of studies has examined the emerging pattern of research activities in the field (Culnan (1986), Culnan and Swanson (1986), Farhoomand (1987), Hamilton and Ives (1982), Vogel and Wetherbe (1984), Gorla (1989)). Most of these studies concur that MIS field of research has not made very significant progress as a scientific discipline, and is devoid of unique body of knowledge. At the present stage of the field’s development these previous studies provide the opportunity for self-examination which should propel research more directly. Most of the studies assessing the maturity of MIS field considered articles published in 80’s. As there is little research with recent MIS articles, we intend to evaluate MIS research field using MIS articles published during 1986 – 1995. In this research, we use three desirable characteristics a mature field should demonstrate: number of references per MIS article, immediacy factor of citations, and proportion of references to other disciplines

Factors Affecting MIS Project Success

Measuring Information System success is an important issue for both researchers and practitioners. Evaluating success helps managers in assessing MIS performance and in improving MIS function. Several measures of MIS success were proposed in the past [DeLone and McLean, 1992]. What is even more important for MIS managers and researchers is the set of independent variables that affect the success of MIS. The factors that affect MIS success can be related to organization, individual, and technology. There are numerous studies in the past on MIS success factors, but they consider either technical issues or behavioral/managerial issues and not both. In this research, we take a comprehensive look at the factors in determining critical variables. We considered both these factors in our model to predict MIS success. Our research will highlight critical success variables (whether internal or external, whether technical or managerial/behavioral) that influence the success of MIS. Thus, the objectives of our research are: i) to identify the independent variables from past models, which could affect the success of MIS. ii) to find the dimensions of these variables which represent the success of MIS, by using factor analysis. iii) to build a model for success of MIS project, which identifies most critical variables. iv) to predict the success of MIS projects based on these critical factors

Reappearance of the rare clam, Anatinella nicobarica (Gmelin, 1791) (Family: Anatinellidae) after 72 years in India

1524-1527Anatinella nicobarica is a very rarely reported species of bivalve mollusc in the world. It was discovered in the Nicobar Sea by Gmelin, (1791) for the first time. After so many years, its rave occurrence was observed in India. Reappearance of this rare clam was reported after 72 years from Chennai beach (mainland coast of India)

Recognition of compound characters in Kannada language

Recognition of degraded printed compound Kannada characters is a challenging research problem. It has been verified experimentally that noise removal is an essential preprocessing step. Proposed are two methods for degraded Kannada character recognition problem. Method 1 is conventionally used histogram of oriented gradients (HOG) feature extraction for character recognition problem. Extracted features are transformed and reduced using principal component analysis (PCA) and classification performed. Various classifiers are experimented with. Simple compound character classification is satisfactory (more than 98% accuracy) with this method. However, the method does not perform well on other two compound types. Method 2 is deep convolutional neural networks (CNN) model for classification. This outperforms HOG features and classification. The highest classification accuracy is found as 98.8% for simple compound character classification. The performance of deep CNN is far better for other two compound types. Deep CNN turns out to better for pooled character classes