An approach for cross-modality guided quality enhancement of liver image

A novel approach for multimodal liver image contrast enhancement is put forward in this paper. The proposed approach utilizes magnetic resonance imaging (MRI) scan of liver as a guide to enhance the structures of computed tomography (CT) liver. The enhancement process consists of two phases: The first phase is the transformation of MRI and CT modalities to be in the same range. Then the histogram of CT liver is adjusted to match the histogram of MRI. In the second phase, an adaptive histogram equalization technique is presented by splitting the CT histogram into two sub-histograms and replacing their cumulative distribution functions with two smooths sigmoid. The subjective and objective assessments of experimental results indicated that the proposed approach yields better results. In addition, the image contrast is effectively enhanced as well as the mean brightness and details are well preserved

Utilising stored wind energy by hydro-pumped storage to provide frequency support at high levels of wind energy penetration

Wind farms (WFs) contribution in frequency deviations curtailment is a grey area, especially when WFs replace large conventional generation capacities. This study offers an algorithm to integrate hydro-pumped storage station (HPSS) to provide inertial and primary support, during frequency drops by utilising stored wind energy. However, wind turbines follow maximum power tracking, and do not apply frequency support methods, thus the wasted wind energy is mitigated. First, HPSS rated power and energy capacity are determined based on several givens, including wind speed and load characteristics. Thus, HPSS major aspects are estimated [e.g. pump(s), reservoir layout and generator(s)]. Second, offered algorithm coordinates energy storage, and releasing through several dynamic and static factors. HPSS output is continuously controlled through a timed approach to provide frequency support. A hypothetical system is inspired from Egyptian grid and real wind speed records at recommended locations to host WFs. Case studies examine the algorithm impact on frequency recovery, at 40% wind power penetration. The responses of thermal generation and HPSS are analysed to highlight the influence of tuning the parameters of the proposed algorithm. The assessment of several frequency metrics insures the positive role of HPSS in frequency drops curtailment. Simulation environments are MATLAB and Simulink

Using Feature Selection with Machine Learning for Generation of Insurance Insights

Insurance is a data-rich sector, hosting large volumes of customer data that is analysed to evaluate risk. Machine learning techniques are increasingly used in the effective management of insurance risk. Insurance datasets by their nature, however, are often of poor quality with noisy subsets of data (or features). Choosing the right features of data is a significant pre-processing step in the creation of machine learning models. The inclusion of irrelevant and redundant features has been demonstrated to affect the performance of learning models. In this article, we propose a framework for improving predictive machine learning techniques in the insurance sector via the selection of relevant features. The experimental results, based on five publicly available real insurance datasets, show the importance of applying feature selection for the removal of noisy features before performing machine learning techniques, to allow the algorithm to focus on influential features. An additional business benefit is the revelation of the most and least important features in the datasets. These insights can prove useful for decision making and strategy development in areas/business problems that are not limited to the direct target of the downstream algorithms. In our experiments, machine learning techniques based on a set of selected features suggested by feature selection algorithms outperformed the full feature set for a set of real insurance datasets. Specifically, 20% and 50% of features in our five datasets had improved downstream clustering and classification performance when compared to whole datasets. This indicates the potential for feature selection in the insurance sector to both improve model performance and to highlight influential features for business insights

Phase Diagram and Membrane Desalination

Desalination technologies have made a significant impact in seawater and brackish water desalination. Recently, the evolution of membrane development has improved performance to lower operating costs and membranes have become the preferred technology for water desalination. Fortunately, different raw materials can be used for preparing membrane sheets which include either organic or inorganic materials, such as cellulose acetate, polyamide, polyimide, ceramic, natural, or artificial polymers. On the one hand, as a result of the variety of the raw materials which already exist in the entire world, different membrane separation processes might be applied dependent on the nature of the membrane sheet and the requirements of treatment process. On the other hand, there are different types of membranes can be used for membrane desalination by using different technologies such as reverse osmosis (RO), membrane distillation (MD), and forward osmosis (FO). The ternary phase diagram for membrane casting solution has an important role to get the required membranes

Using Satellite Images Datasets for Road Intersection Detection in Route Planning

Understanding road networks plays an important role in navigation applications such as self-driving vehicles and route planning for individual journeys. Intersections of roads are essential components of road networks. Understanding the features of an intersection, from a simple T-junction to larger multi-road junctions is critical to decisions such as crossing roads or selecting safest routes. The identification and profiling of intersections from satellite images is a challenging task. While deep learning approaches offer state-of-the-art in image classification and detection, the availability of training datasets is a bottleneck in this approach. In this paper, a labelled satellite image dataset for the intersection recognition problem is presented. It consists of 14,692 satellite images of Washington DC, USA. To support other users of the dataset, an automated download and labelling script is provided for dataset replication. The challenges of construction and fine-grained feature labelling of a satellite image dataset are examined, including the issue of how to address features that are spread across multiple images. Finally, the accuracy of detection of intersections in satellite images is evaluate

A Systematic Review of Urban Navigation Systems for Visually Impaired People

Blind and Visually impaired people (BVIP) face a range of practical difficulties when undertaking outdoor journeys as pedestrians. Over the past decade, a variety of assistive devices have been researched and developed to help BVIP navigate more safely and independently. In~addition, research in overlapping domains are addressing the problem of automatic environment interpretation using computer vision and machine learning, particularly deep learning, approaches. Our aim in this article is to present a comprehensive review of research directly in, or relevant to, assistive outdoor navigation for BVIP. We breakdown the navigation area into a series of navigation phases and tasks. We then use this structure for our systematic review of research, analysing articles, methods, datasets and current limitations by task. We also provide an overview of commercial and non-commercial navigation applications targeted at BVIP. Our review contributes to the body of knowledge by providing a comprehensive, structured analysis of work in the domain, including the state of the art, and guidance on future directions. It will support both researchers and other stakeholders in the domain to establish an informed view of research progress

Clinico-pathological features of breast carcinoma in elderly Egyptian patients: A comparison with the non-elderly using population-based data

AbstractBackgroundBreast cancer (BC) is a major worldwide health care problem that mostly afflicts the elderly population in the more developed countries. It is not known how common is breast cancer among elderly Egyptian patients and whether this differs from the disease in younger patients.AimsTo study the clinico-pathological features of BC in elderly Egyptian patients (⩾65years of age) among the population of an Egyptian Governorate, Gharbiah, and to compare these features with those of younger patients (<65years).MethodsThis is a cross sectional study that compares elderly BC (EBC) and the non-elderly BC (NEBC) using the information from the Gharbiah Population-based Cancer registry (GPCR) during the years 1999–2007.ResultsOut of 6078 BCs, 12% were EBCs and 88% were NEBCs. Between 1999 and 2007, the crude incidence rate (CIR, per 100,000 populations) of EBC increased from 47 to 71 and that of NEBC increased from 16 to 17. Compared to NEBC patients, EBC patients were more likely to have a positive family history and present with a distant disease and less likely to present with a localized disease. EBCs were more likely to have lung metastases and less likely to have liver metastases. Histology, grade, hormone and HER-2 receptor statuses were comparable in both groups. Apart from hormonal therapies, the elderly were less likely to receive surgery, radiotherapy or chemotherapy.ConclusionEBC patients in Egypt present with advanced disease and are less likely to receive surgery, radiotherapy or chemotherapy compared to NEBC patients

Insurance Reserve Prediction: Opportunities and Challenges

Predicting claims’ reserve is a critical challenge for insurers and has dramatic consequences on their managerial, financial and underwriting decisions. The insurers’ capital and their underwriting capacity of further business are impacted by inaccurate reserve estimates. Increasing premium rates and adjusting the underwriting policy decisions may balance the impact of unexpected claims, but will have a negative impact on their business opportunities. To address this, several papers focusing on the prediction of insurance reserve have been published in the literature. In this paper, we provide a comprehensive review of the research on the insurance reserve prediction techniques in economics and actuarial science literature as well as machine learning and computer science literature. Moreover, we classify these techniques into different approaches based on the prediction mechanism they use in estimation. For each approach, we survey reserve prediction methods, and then show the similarities and differences among them. In addition, the review is armed with a discussion on the challenges and the future opportunities

The effect of biventricular pacing on cardiac function after open heart surgery

Background: Temporary postoperative pacing could enhance recovery of the cardiac function. The right ventricular pacing (RV) is commonly used, but it can cause dyssynchronous contraction of both ventricles. Biventricular pacing (BV) could improve the systolic function by synchronizing the ventricular contraction. The aim of this study is to evaluate the effectiveness of biventricular pacing in improving the hemodynamics in the early postoperative period compared to other pacing modes. Methods: This is a clinical crossover trial including 50 patients who underwent open cardiac surgery in the period from September 2017 to September 2018. Mean age was 46.78± 12.09 years, and 50% were males. Temporary pacing leads were attached to the anterior wall of the right ventricle 1-2 cm paraseptally and the lateral wall of left ventricle 1-2 cm paraseptally. Each patient was paced for 3 minutes in the first 1-4 postoperative hours with 20 minutes washout period between different pacing modes. Study endpoints included cardiac output, ejection fraction (EF) and wall motion abnormality. Results: Biventricular and right ventricular pacing increased postoperative cardiac output (6.31± 1.28 and 5.2±0.72 L/min; respectively), but BV pacing was superior to RV pacing (P-value &lt;0.001). The effect of BV pacing was more evident in patients with EF &lt; 50% (7.27± 0.895 vs. 5.26 ± 0.634 L/min; p&lt; 0.001). The postoperative EF improved during BV pacing (53.16± 4.71%) compared to RV pacing (49.4± 4.07%; P-value &lt;0.001). Both BV and RV pacing were associated with less paradoxical septal wall motion abnormality (P-value &lt;0.001). Conclusions: Temporary postoperative biventricular pacing improves hemodynamics compared to right ventricular and no pacing. Routine BV pacing is recommended especially in patients with low ejection fraction