It has been found that, if a (negative) bias is applied to a substrate during the sputtering ther... more It has been found that, if a (negative) bias is applied to a substrate during the sputtering thereto of Alfesil, selective re-sputtering from the substrate film of aluminum and silicon will leave that film rich in iron and, attendantly, of higher saturation magnetization (17,000 gauss) than the starting material Alfesil (10,000 gauss). Such being the case, the invention provides that the sputtering of Alfesil-type material during the manufacture of a magnetic head be performed in two phases, first, while applying a bias of a first sense to a substrate to be sputtered upon, and, second, while applying a bias of different sense (e.g. a zero bias) to the substrate, thereby to cause a composite thin film to be formed on the substrate. The composition of the thin film in question is: 1. a (generally thin) region of material of high saturation magnetization layered with 2. a (generally thicker) region of lesser saturation magnetization.
World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering, Jan 24, 2008
ABSTRACT Selecting optimal locations for new facilities is a critical decision in organizations t... more ABSTRACT Selecting optimal locations for new facilities is a critical decision in organizations that provide field-based services such as delivery, maintenance and emergency services. The total logistics cost and facility establishment cost are the main objectives of the location selection procedure. With the increasing size of this problem in today's applications, the aspects of efficiency and scalability have developed into major challenges. In this paper, we study the use of spatial clustering methods to solve this problem and propose two new algorithms. The new algorithms determine the optimal locations of the new facilities plus their optimal total count during the search process. We have conducted many experiments for empirical comparative study on the application of several spatial clustering algorithms for optimal facility establishment. The benchmarks are conducted with both real world and synthetic data sets. The results reveal advantages of the proposed algorithms and confirm that these algorithms have better performance in terms of efficiency and objectives in the field-based services. Hence, the higher scalability and effectiveness of the proposed algorithms make them suitable solutions for the problem of optimal facility establishment with large databases.
Sar and Qsar in Environmental Research, Jul 25, 2016
Abstract Prediction of drug–disease associations is one of the current fields in drug repositioni... more Abstract Prediction of drug–disease associations is one of the current fields in drug repositioning that has turned into a challenging topic in pharmaceutical science. Several available computational methods use network-based and machine learning approaches to reposition old drugs for new indications. However, they often ignore features of drugs and diseases as well as the priority and importance of each feature, relation, or interactions between features and the degree of uncertainty. When predicting unknown drug–disease interactions there are diverse data sources and multiple features available that can provide more accurate and reliable results. This information can be collectively mined using data fusion methods and aggregation operators. Therefore, we can use the feature fusion method to make high-level features. We have proposed a computational method named scored mean kernel fusion (SMKF), which uses a new method to score the average aggregation operator called scored mean. To predict novel drug indications, this method systematically combines multiple features related to drugs or diseases at two levels: the drug–drug level and the drug–disease level. The purpose of this study was to investigate the effect of drug and disease features as well as data fusion to predict drug–disease interactions. The method was validated against a well-established drug–disease gold-standard dataset. When compared with the available methods, our proposed method outperformed them and competed well in performance with area under cover (AUC) of 0.91, F-measure of 84.9% and Matthews correlation coefficient of 70.31%.
World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering, Sep 26, 2008
Protein data patterns which are discriminative can be used in many beneficial applications if the... more Protein data patterns which are discriminative can be used in many beneficial applications if they are defined correctly such as molecular medicine, agriculture, and microbial genome applications. Prediction of protein folding patterns by which the function of a protein ...
World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering, Jul 23, 2008
It has been found that, if a (negative) bias is applied to a substrate during the sputtering ther... more It has been found that, if a (negative) bias is applied to a substrate during the sputtering thereto of Alfesil, selective re-sputtering from the substrate film of aluminum and silicon will leave that film rich in iron and, attendantly, of higher saturation magnetization (17,000 gauss) than the starting material Alfesil (10,000 gauss). Such being the case, the invention provides that the sputtering of Alfesil-type material during the manufacture of a magnetic head be performed in two phases, first, while applying a bias of a first sense to a substrate to be sputtered upon, and, second, while applying a bias of different sense (e.g. a zero bias) to the substrate, thereby to cause a composite thin film to be formed on the substrate. The composition of the thin film in question is: 1. a (generally thin) region of material of high saturation magnetization layered with 2. a (generally thicker) region of lesser saturation magnetization.
World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering, Jan 24, 2008
ABSTRACT Selecting optimal locations for new facilities is a critical decision in organizations t... more ABSTRACT Selecting optimal locations for new facilities is a critical decision in organizations that provide field-based services such as delivery, maintenance and emergency services. The total logistics cost and facility establishment cost are the main objectives of the location selection procedure. With the increasing size of this problem in today's applications, the aspects of efficiency and scalability have developed into major challenges. In this paper, we study the use of spatial clustering methods to solve this problem and propose two new algorithms. The new algorithms determine the optimal locations of the new facilities plus their optimal total count during the search process. We have conducted many experiments for empirical comparative study on the application of several spatial clustering algorithms for optimal facility establishment. The benchmarks are conducted with both real world and synthetic data sets. The results reveal advantages of the proposed algorithms and confirm that these algorithms have better performance in terms of efficiency and objectives in the field-based services. Hence, the higher scalability and effectiveness of the proposed algorithms make them suitable solutions for the problem of optimal facility establishment with large databases.
Sar and Qsar in Environmental Research, Jul 25, 2016
Abstract Prediction of drug–disease associations is one of the current fields in drug repositioni... more Abstract Prediction of drug–disease associations is one of the current fields in drug repositioning that has turned into a challenging topic in pharmaceutical science. Several available computational methods use network-based and machine learning approaches to reposition old drugs for new indications. However, they often ignore features of drugs and diseases as well as the priority and importance of each feature, relation, or interactions between features and the degree of uncertainty. When predicting unknown drug–disease interactions there are diverse data sources and multiple features available that can provide more accurate and reliable results. This information can be collectively mined using data fusion methods and aggregation operators. Therefore, we can use the feature fusion method to make high-level features. We have proposed a computational method named scored mean kernel fusion (SMKF), which uses a new method to score the average aggregation operator called scored mean. To predict novel drug indications, this method systematically combines multiple features related to drugs or diseases at two levels: the drug–drug level and the drug–disease level. The purpose of this study was to investigate the effect of drug and disease features as well as data fusion to predict drug–disease interactions. The method was validated against a well-established drug–disease gold-standard dataset. When compared with the available methods, our proposed method outperformed them and competed well in performance with area under cover (AUC) of 0.91, F-measure of 84.9% and Matthews correlation coefficient of 70.31%.
World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering, Sep 26, 2008
Protein data patterns which are discriminative can be used in many beneficial applications if the... more Protein data patterns which are discriminative can be used in many beneficial applications if they are defined correctly such as molecular medicine, agriculture, and microbial genome applications. Prediction of protein folding patterns by which the function of a protein ...
World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering, Jul 23, 2008
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