RMESH Algorithms for Parallel String Matching

String matching problem received much attention over the years due to its importance in various applications such as text/file comparison, DNA sequencing, search engines, and spelling correction. Especially with the introduction of search engines dealing with tremendous amount of textual information presented on the world wide web and the research on DNA sequencing, this problem deserves special attention and any algorithmic or hardware improvements to speed up the process will benefit these important applications. In this paper, we present three algorithms for string matching on reconfigurable mesh architectures. Given a text T of length n and a pattern P of length m, the first algorithm finds the exact matching between T and P in O(1) time on a 2-dimensional RMESH of size (n-m+1) * m. The second algorithm finds the approximate matching between T and P in O(k) time on a 2D RMESH, where k is the maximum edit distance between T and P. The third algorithm allows only the replacement operation in the calculation of the edit distance and finds an approximate matching between T and P in constant-time on a 3D RMESH

Bayesian spatial modelling and the significance of agricultural land use to scrub typhus infection in Taiwan

Scrub typhus is transmitted by the larval stage of trombiculid mites. Environmental factors, including land cover and land use, are known to influence breeding and survival of trombiculid mites and, thus, also the spatial heterogeneity of scrub typhus risk. Here, a spatially autoregressive modelling framework was applied to scrub typhus incidence data from Taiwan, covering the period 2003 to 2011, to provide increased understanding of the spatial pattern of scrub typhus risk and the environmental and socioeconomic factors contributing to this pattern. A clear spatial pattern in scrub typhus incidence was observed within Taiwan, and incidence was found to be significantly correlated with several land cover classes, temperature, elevation, normalized difference vegetation index, rainfall, population density, average income and the proportion of the population that work in agriculture. The final multivariate regression model included statistically significant correlations between scrub typhus incidence and average income (negatively correlated), the proportion of land that contained mosaics of cropland and vegetation (positively correlated) and elevation (positively correlated). These results highlight the importance of land cover on scrub typhus incidence: mosaics of cropland and vegetation represent a transitional land cover type which can provide favourable habitats for rodents and, therefore, trombiculid mites. In Taiwan, these transitional land cover areas tend to occur in less populated and mountainous areas, following the frontier establishment and subsequent partial abandonment of agricultural cultivation, due to demographic and socioeconomic changes. Future land use policy decision-making should ensure that potential public health outcomes, such as modified risk of scrub typhus, are considered

Neural Network Diagnosis of Malignant Melanoma from Color Images

Malignant melanoma is the deadliest form of all skin cancers. Approximately 32,000 new cases of malignant melanoma were diagnosed in 1991 in the United States, with approximately 80% of patients expected to survive 5 years. Fortunately, if detected early, even malignant melanoma may be treated successfully, Thus, in recent years, there has been rising interest in the automated detection and diagnosis of skin cancer, particularly malignant melanoma. Here, the authors present a novel neural network approach for the automated separation of melanoma from 3 benign categories of tumors which exhibit melanoma-like characteristics. The approach uses discriminant features, based on tumor shape and relative tumor color, that are supplied to an artificial neural network for classification of tumor images as malignant or benign. With this approach, for reasonably balanced training/testing sets, the authors are able to obtain above 80% correct classification of the malignant and benign tumors on real skin tumor images

Credit Card Risk Assessment Using Artificial Neural Networks

In recent years, the supply and demand of the plastic currency market has been rapidly increasing, especially, for the growth rate in the credit card market. It has increased about 16 times in the last 10 years. Many banks devoted to make a large investment in credit card marketing for the sake of getting the maximum profits in the worldwide market. However, most banks are trying to reduce the requirements for credit card application in order to increase the motivation of the customers for applying their credit cards. As a result, many banks somehow ignore the risk management of credit card approval which leads to the increases of bad debt in the credit card market. When this scenario happens year by year, those banks will not get profits from the credit card market but a great loss. In this study, a total of 113,048 entries were used which included fundamental customer data, credit card data, and customer history data from Joint Credit Information Center (JCIC) of Taiwan. We used the characteristics of artificial neural networks and grey theory to find out the potential factors of the bad credit and finally used the correlation method to find out the higher (important) relative variables (parameters) of bad credit. 80,000 entries were randomly selected as training data and the remaining 33,084 entries were used as testing data. The experimental results shown that the accuracy of forecasting rate for the proposed early warning system was an overall of 92.7%. These results suggested that the once the collections of the new customer data were available, the proposed approach could be used as an early warning system which can be used to decrease the risk of credit card approval

Construction of Fuzzy Map for Autonomous Mobile Robots Based on Fuzzy Confidence Model

This paper presents the use of fuzzy models to explicitly consider sensor uncertainty and finite resolution in solving the SLAM (simultaneous localization and mapping) problem for autonomous mobile robots. The approach establishes fuzzy confidence models in describing occupied obstacles and available space. The problem is transformed into an optimization task of minimizing the alignment error between newly scanned local fuzzy maps and selected parts of a developing global fuzzy map. In aligning local fuzzy maps into a global fuzzy map, we developed a prediction strategy to crop the most potential part from the sensed local fuzzy maps to be overlapped with the global fuzzy map. A mobile vehicle equipped with a laser range finder, the Hokuyo URG-04LX, is used to demonstrate the procedure of fuzzy map building. Experimental results show that the proposed architecture is effective in generating a comprehensive global fuzzy map, which is suitable for both human comprehension and path design during real-time navigation

Prior Cancer Is Associated with Lower Atherosclerotic Cardiovascular Disease Risk at First Acute Myocardial Infarction

BACKGROUND: Patients with cancer are at increased risk of acute myocardial infarction (AMI). It is unclear if the Atherosclerotic Cardiovascular Disease (ASCVD) risk score at incident AMI is reflective of this higher risk in patients with prior cancer than those without. METHODS: We linked nationwide AMI and cancer registries from 2008 to 2019. A total of 18,200 eligible patients with ASCVD risk score calculated at incident AMI were identified (1086 prior cancer; 17,114 no cancer). RESULTS: At incident AMI, age-standardized mean ASCVD risk was lower in the prior cancer group (18.6%) than no cancer group (20.9%) (p < 0.001). Prior to incident AMI, smoking, hypertension, hyperlipidemia and diabetes mellitus were better controlled in the prior cancer group. However post-AMI, prior cancer was associated with lower guideline-directed medical therapy usage and higher all-cause mortality (adjusted hazard ratio 1.85, 95% confidence interval 1.66-2.07). CONCLUSIONS: AMI occurred despite better control of cardiovascular risk factors and lower age-standardized estimated mean 10-year ASCVD risk among patients with prior cancer than no cancer. Prior cancer was associated with lower guideline-directed medical therapy post-AMI and higher mortality

Second-Hand Smoke–Induced Cardiac Fibrosis Is Related to the Fas Death Receptor Apoptotic Pathway without Mitochondria-Dependent Pathway Involvement in Rats

Exposure to environmental tobacco smoke has been epidemiologically linked to heart disease among nonsmokers. However, the molecular mechanism behind the pathogenesis of cardiac disease is unknown. In this study, we found that Wistar rats, exposed to tobacco cigarette smoke at doses of 5, 10, or 15 cigarettes for 30 min twice a day for 1 month, had a dose-dependently reduced heart weight to body weight ratio and enhanced interstitial fibrosis as identified by histopathologic analysis. The mRNA and activity of matrix metalloprotease-2 (MMP-2), representing the progress of cardiac remodeling, were also elevated in the heart. In addition, we used reverse-transcriptase polymerase chain reaction and Western blotting to demonstrate significantly increased levels of the apoptotic effecter caspase-3 in treated animal hearts. Dose-dependently elevated mRNA and protein levels of Fas, and promoted apoptotic initiator caspase-8 (active form), a molecule of a death-receptor–dependent pathway, coupled with unaltered or decreased levels of cytosolic cytochrome c and the apoptotic initiator caspase-9 (active form), molecules of mitochondria-dependent pathways, may be indicative of cardiac apoptosis, which is Fas death-receptor apoptotic-signaling dependent, but not mitochondria pathway dependent in rats exposed to second-hand smoke (SHS). With regard to the regulation of survival pathway, using dot blotting, we found cardiac insulin-like growth factor-1 (IGF-1) and IGF-1 receptor mRNA levels to be significantly increased, indicating that compensative effects of IGF-1 survival signaling could occur. In conclusion, we found that the effects of SHS on cardiomyocyte are mediated by the Fas death-receptor–dependent apoptotic pathway and might be related to the epidemiologic incidence of cardiac disease of SHS-exposed non-smokers

SimNet: a neural network architecture for pattern recognition and data mining

In this study, a neural network architecture called SimNet is designed and implemented. SimNet is built with the following concepts in mind: simulation, simplicity, and simultaneity. It combines the general neural network structure with the subsethood concept of fuzzy logic to produce a rapid data clustering system that works similar to Adaptive Resonance Theory and Self-Organizing Maps. --Introduction, page 1