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

Interconnection, Interface And Instrumentation For Micromachined Chemical Sensors

In realizing a portable chemical analysis system, adequate partitioning of a reusable component and a disposable is required. For successful implementation of micromachined sensors in an instrument, reliable methods for interconnection and interface are in great demand between these two major parts. This thesis work investigates interconnection methods of micromachined chip devices, a hybrid fluidic interface system, and measurement circuitry for completing instrumentation. The interconnection method based on micromachining and injection molding techniques was developed and an interconnecting microfluidic package was designed, fabricated and tested. Alternatively, a plug-in type design for a large amount of sample flow was designed and demonstrated. For the hybrid interface, sequencing of the chemical analysis was examined and accordingly, syringe containers, a peristaltic pump and pinch valves were assembled to compose a reliable meso-scale fluidic control unit. A potentiostat circuit was modeled using a simulation tool. The simulated output showed its usability toward three-electrode electrochemical microsensors. Using separately fabricated microsensors, the final instrument with two different designs--flow-through and plug-in type was tested for chlorine detection in water samples. The chemical concentration of chlorine ions could be determined from linearly dependent current signals from the instrument

Location Estimation of Beacon in MEOSAR System

This paper describes the satellite aided search and rescue system that provides services to save the lives at the distress or emergency regions. COSPAS - SARSAT is a satellite-based system designed to provide distress alert and location data to facilitate SAR operations. The ground receiver named as Local User Terminals (LUTs) is in charge of processing the incoming signal and determining the beacon position via Frequency Difference of Arrival (FDOA) and Time Difference of Arrival (TDOA) technique. A common method of calculating TDOA and FDOA is the Cross Ambiguity Function (CAF). This paper proposes the implementation of CAF map method, which is a simple method that reduces the processing complexity over CAF method and also as the ability to locate several beacons that eliminates the false location of beacons

Opportunistic routing using Wireless Sensor Networks

Wireless Sensor Networks (WSN) are used in our daily life for monitoring and controlling application because of it’s unique features such as low power consumption, reduced cost, and implementation with ease. To improve the lifetime of wireless sensor networks energy efficient routing protocol is very necessary to choose in the network layer of WSN. A comparative analysis based on performance and energy consumption referring to opportunistic routing algorithm is done. It is being evaluated in terms of energy consumed, packets lost, flow rate and throughput. We see that Opportunistic routing algorithm performs way better than the entire traditional routing algorithm. The results provided show that Opportunistic routing give significant improvement in power consumption

Globetrotters of the Asian Elephant Society

It was an early morning in March that I found myself watching birds swooping in to catch fish in the large expanse of water in front of me. It was a water body, a thousand acres in size and filled to the brim with water in summer! It was nothing short of a paradise for the myriad insects, amphibians, reptiles, fish, birds and also a few elephants. I could barely see the elephants though, playing and jostling in the mist-engulfed lake as they usually would. The only way I was sure of their presence in the water was by the fresh tracks left behind when they moved into the lake, and the ‘swish-swash’ sounds they made while wading through knee-deep water. My binoculars and camera were of no use and I knew I had to wait till the sun was up and the mist cleared, for me to recognise who these elephants were

Analysis and prediction of seed quality using machine learning

The mainstay of the economy has always been agriculture, and the majority of tasks are still carried out without the use of modern technology. Currently, the ability of human intelligence to forecast seed quality is used. Because it lacks a validation method, the existing seed prediction analysis is ineffective. Here, we have tried to create a prediction model that uses machine learning algorithms to forecast seed quality, leading to high crop yield and high-quality harvests. For precise seed categorization, this model was created using convolutional neural networks and trained using the seed dataset. Using data that can be used to forecast the future, this model is used to learn about whether the seeds are of premium quality, standard quality, or regular quality. While testing data are employed in the algorithm’s predictive analytics, training data and validation data are used for categorization reasons. Thus, by examining the training accuracy of the convolution neural network (CNN) model and the prediction accuracy of the algorithm, the project’s primary goal is to develop the best method for the more accurate prediction of seed quality

Customized mask region based convolutional neural networks for un-uniformed shape text detection and text recognition

In image scene, text contains high-level of important information that helps to analyze and consider the particular environment. In this paper, we adapt image mask and original identification of the mask region based convolutional neural networks (R-CNN) to allow recognition at 3 levels such as sequence, holistic and pixel-level semantics. Particularly, pixel and holistic level semantics can be utilized to recognize the texts and define the text shapes, respectively. Precisely, in mask and detection, we segment and recognize both character and word instances. Furthermore, we implement text detection through the outcome of instance segmentation on 2-D feature-space. Also, to tackle and identify the text issues of smaller and blurry texts, we consider text recognition by attention-based of optical character recognition (OCR) model with the mask R-CNN at sequential level. The OCR module is used to estimate character sequence through feature maps of the word instances in sequence to sequence. Finally, we proposed a fine-grained learning technique that trains a more accurate and robust model by learning models from the annotated datasets at the word level. Our proposed approach is evaluated on popular benchmark dataset ICDAR 2013 and ICDAR 2015

The outliers: An interplay of space, knowledge, and capabilities in defining human–elephant relations in rurban southern India

Hiristor and Mak, two rurban male Asian elephants, culturally more synurbised than their forest counterparts, traverse a production landscape – once a forest but now teeming with people and infrastructure – in southern India, trying to come to terms with their newly lived spaces and the novel experiences driving them. A complex interplay of space, knowledge, and capabilities has created a new generation of ‘outlier’ elephants, who have no option but to develop novel, potentially adaptive, behavioural strategies to live alongside humans. This rapid synanthropisation of the elephant populations of peninsular India, we suggest, requires an urgent realisation of the latent capabilities of both people and elephants to enable their peaceful, but vital, coexistence in the troubled times of the Anthropocene

Establishing a circular economy framework in the agro-waste to ethanol-based supply chain in Karnataka, India

Waste handling is one of the biggest problems across the globe. An additional challenge of mitigating climate change also arises during the burning of fossil fuel. The fossil fuel-based transportation system in India that gives rise to air pollution has a worse impact on people. To overcome these problems, a sustainability assessment framework is introduced in this study. Ethanol, a member of the biofuel family, is one of the cleanest and most sustainable forms of energy sources. The purpose of this research is to develop a sustainability assessment framework to investigate the environmental feasibility of a supply chain system that provides an end-to-end solution and how it would benefit the transportation sector. A second-generation (2G) ethanol-based supply chain is envisaged, and agro-waste such as corncobs and paddy straw are considered in this study. The assessment is carried out using a heuristic-based method, known as particle swarm optimization (PSO) technique, within the framework of circular economy. The results show that the biomass collection in the proposed area produces 2.5 tons of CO2 per hectare area on average, which is much lower in comparison to 996.6 tons of CO2 per hectare in the Middle East, the main source of fossil fuel. In the future, this research would benefit the enterprises and government organizations that establish 2G ethanol plants