Cardiovascular imaging analysis is a useful tool for the diagnosis, treatment and monitoring of cardiovascular diseases. Imaging techniques allow non-invasive quantitative assessment of cardiac function, providing morphological, functional and dynamic information. Recent technological advances in ultrasound have made it possible to improve the quality of patient treatment, thanks to the use of modern image processing and analysis techniques. However, the acquisition of these dynamic three-dimensional (3D) images leads to the production of large volumes of data to process, from which cardiac structures must be extracted and analyzed during the cardiac cycle. Extraction, three-dimensional visualization, and qualification tools are currently used within the clinical routine, but unfortunately require significant interaction with the physician. These elements justify the development of new efficient and robust algorithms for structure extraction and cardiac motion estimation from three-dimensional images. As a result, making available to clinicians new means to accurately assess cardiac anatomy and function from three-dimensional images represents a definite advance in the investigation of a complete description of the heart from a single examination. The aim of this article is to show what advances have been made in 3D cardiac imaging by ultrasound and additionally to observe which areas have been studied under this imaging modality.

Fire, a phenomenon occurs in most parts of the world and causes severe financial losses, even, irreparable damages. Many parameters are involved in the occurrence of a fire; some of which are constant over time (at least in a fire cycle), but the others are dynamic and vary over time. Unlike the earthquake, the disturbance of fire depends on a set of physical, chemical, and biological relations. Monitoring the changes to predict the occurrence of fire is efficient in forest management. Method: In this research, the Persian and English databases were structurally searched using the keywords of fire risk modeling, fire risk, fire risk prediction, remote sensing and the reviewed papers that predicted the fire risk in the field of remote sensing and geographic information system were retrieved. Then, the modeling and zoning data of fire risk prediction were extracted and analyzed in a descriptive manner. Accordingly, the study was conducted in 1995-2017. Findings: Fuzzy analytic hierarchy process (AHP) zoning method was more practical among the applied methods and the plant moisture stress measurement was the most efficient among the remote sensing indices. Discussion and Conclusion: The findings indicate that RS and GIS are effective tools in the study of fire risk prediction.

Money laundering has become a vital issue all over the world especially in the emerging economy over the last two decades. Till now, the developing and emerging countries face challenges about the remedies and inceptions of anti-money laundering issues. The objective of the study is to provide a thorough picture of the diversified movements of academic research on money laundering and anti-money laundering activities all over the world. This study aims at exploring the contemporary issues in Anti-money laundering based on the academic points of view. Further, the study is explored to render a portrayal of anti-money laundering activities from an emergency country context. A review of publicly available reports, published documents, daily newspapers, case studies, and previous academic research comprised the main sources of data for the study. It is found that the contemporary money laundering and anti-money laundering academic research might be classified into four broad categories. An emerging country like Bangladesh has taken little initiative to inductee anti-money laundering initiatives. It implies that for the successful implementation of anti-money laundering activities, good governance along with a congenial regulatory framework is a prerequisite in an emerging country context. In addition, the machine learning may enhance the quality of money laundering detections in Bangladesh.

The importance of improving industrial transformation processes for more efficient ones is part of the current challenges. Specifically, the development of more efficient processes in the production of biofuels, where the reaction and separation processes can be intensified, is of great interest to reduce the energy consumption associated with the process. In the case of Biodiesel, the process is defined by a chemical reaction and by the components associated to the process, where the thermochemical study seeks to develop calculations for the subsequent understanding of the reaction and purification process. Thus, the analysis of the mixture of the components using the process simulator Aspen Plus V9^® unravels the thermochemical study. The UNIFAC-DMD thermodynamic method was used to estimate the binary equilibrium parameters of the reagents using the simulator. The analyzed aspects present the behavior of the components in different temperature conditions, the azeotropic behavior and the determined thermochemical conditions.

Objective: To investigate the value of differential diagnosis of hepatocellular carcinoma (HCC) and cirrhotic nodules via radiomics models based on magnetic resonance images. Background: This study is to distinguish hepatocellular carcinoma and cirrhotic nodules using MR-radiomics features extracted from four different phases of MRI images, concluded T1WI, T2WI, T2 SPIR and delay phase of contrast MRI. Methods: In this study, the four kind of magnetic resonance images of 23 patients with hepatocellular carcinoma (HCC) were collected. Among them, 12 patients with liver cirrhosis were used to obtain cirrhotic nodules (CN). The dataset was used to extract MR-radiomics features from regions of interest (ROI). The statistical methods of MRradiomics features could distinguish HCC and CN. And the ability of radiomics features between HCC and CN was estimated by receiver operating characteristic curve (ROC). Results: A total of 424 radiomics features were extracted from four kind of magnetic resonance images. 86 features in delay phase of contrast MRI，86 features in spir phase of T2WI，86 features in T1WI and 88 features in T2WI showed statistical difference (p < 0.05). Among them, the area under the curves (AUC) of these features larger than 0.85 were 58 features in delay phase of contrast MRI, 54 features in spir phase of T2WI, 62 features in T1WI and 57 features in T2WI. Conclusions: Radiomics features extracted from MRI images have the potential to distinguish HCC and CN.