Formation of nanocrystalline calcia from calcite has been studied in situ via transmission electron microscopy. The crystallographic transformation occurred via two mechanisms: the first is by distortion of the cleaved rhombohedron of calcite, formed by {104} planes in hexagonal coordinates, into a cube. This produced a microstructure of oriented, elongated nanocrystals of calcia with planar boundaries. In the second mechanism, the micrometer-sized parent calcite particles broke up into nano-sized grains as the decomposition began, leading to irregularly shaped, randomly oriented nanocrystals of calcia. 相似文献
Chlorendic anhydride based polyester ( I ,) tetrachlorophthalic anhydride based polyester ( II ), dibromoneopentyl glycol based polyester ( III ,) general purpose polyester ( IV ,) blend of dibromoneopentyl glycol based polyester with general purpose polyester ( V ,) chlorendic anhydride and dibromoneopentyl glycol based polyester ( VI ), and a blend of chlorendic anhydride based polyester and dibromoneopentyl glycol based polyester ( VII ) were prepared and their chemical resistance and moisture absorption studied in various reagents, acid, alkali, and water, at 25 and 65°C. It is found that the polyester ( III ) is the least affected in the presence of the acids. In 20% NaOH, there was a decrease in weight for all polyesters at both the temperatures compared to the control except the polyester ( VII ). Increase in weight of all the polyesters was observed during the absorption of moisture both at 25 and 65°C. The increase was higher at higher temperature. Polyester ( III ) thus shows the least absorption of moisture. 相似文献
Diploid genetic algorithms (DGAs) promise robustness as against simple genetic algorithms which only work towards optimization. Moreover, these algorithms outperform others in dynamic environments. The work examines the theoretical aspect of the concept by examining the existing literature. The present work takes the example of dynamic TSP to compare greedy approach, genetic algorithms and DGAs. The work also implements a greedy genetic approach for the problem. In the experiments carried out, the three variants of dominance were implemented and 115 runs proved the point that none of them outperforms the other. 相似文献
In classification tasks, the error rate is proportional to the commonality among classes. In conventional GMM-based modeling technique, since the model parameters of a class are estimated without considering other classes in the system, features that are common across various classes may also be captured, along with unique features. This paper proposes to use unique characteristics of a class at the feature-level and at the phoneme-level, separately, to improve the classification accuracy. At the feature-level, the performance of a classifier has been analyzed by capturing the unique features while modeling, and removing common feature vectors during classification. Experiments were conducted on speaker identification task, using speech data of 40 female speakers from NTIMIT corpus, and on a language identification task, using speech data of two languages (English and French) from OGI_MLTS corpus. At the phoneme-level, performance of a classifier has been analyzed by identifying a subset of phonemes, which are unique to a speaker with respect to his/her closely resembling speaker, in the acoustic sense, on a speaker identification task. In both the cases (feature-level and phoneme-level) considerable improvement in classification accuracy is observed over conventional GMM-based classifiers in the above mentioned tasks. Among the three experimental setup, speaker identification task using unique phonemes shows as high as 9.56 % performance improvement over conventional GMM-based classifier. 相似文献
Barrages are hydraulic structures constructed across rivers to divert flow into irrigation canals or power generation channels.
The most of these structures are founded on permeable foundation. The optimum cost of these structures is nonlinear function
of factors that cause the seepage forces under the structure. There is, however, no procedure to ascertain the basic barrage
parameters such as depth of sheet piles or cutoffs and the length and thickness of floor in a cost–effective manner. In this
paper, a nonlinear optimization formulation (NLOF), which consists of an objective function of minimizing total cost, is solved
using genetic algorithm (GA). The mathematical model that represents the subsurface flow is embedded in the NLOF. The applicability
of the approach has been illustrated with a typical example of barrage profile. The results obtained in this study shows drastic
cost savings when the proposed NLOF is solved using GA than that of using classical optimization technique and conventional
method. A parametric analysis has also been performed to study the effect of varying soil and hydrological conditions on design
parameters and on over all cost. 相似文献
This special issue showcases articles written from five of the best presentations at the Hot Chips 19 conference, held in August 2007. The guest editors give highlights of the conference and introduce the articles, which cover the mobile-optimized northbridge of AMD's Griffin microprocessor family; the IBM z10 next-generation mainframe microprocessor; fault tolerance in IBM's Power6 microprocessor; NVIDIA's Tesla unified graphics and computing architecture; and SiBEAM's 4-Gbps 1080p-capable uncompressed HD A/V wireless 60-GHz transceiver chipset. 相似文献
For a long time, legal entities have developed and used crime prediction methodologies. The techniques are frequently updated based on crime evaluations and responses from scientific communities. There is a need to develop type-based crime prediction methodologies that can be used to address issues at the subgroup level. Child maltreatment is not adequately addressed because children are voiceless. As a result, the possibility of developing a model for predicting child abuse was investigated in this study. Various exploratory analysis methods were used to examine the city of Chicago’s child abuse events. The data set was balanced using the Borderline-SMOTE technique, and then a stacking classifier was employed to ensemble multiple algorithms to predict various types of child abuse. The proposed approach successfully predicted crime types with 93% of accuracy, precision, recall, and F1-Score. The AUC value of the same was 0.989. However, when compared to the Extra Trees model (17.55), which is the second best, the proposed model’s execution time was significantly longer (476.63). We discovered that Machine Learning methods effectively evaluate the demographic and spatial-temporal characteristics of the crimes and predict the occurrences of various subtypes of child abuse. The results indicated that the proposed Borderline-SMOTE enabled Stacking Classifier model (BS-SC Model) would be effective in the real-time child abuse prediction and prevention process. 相似文献
The world has been challenged since late 2019 by COVID-19. Higher education institutions have faced various challenges in adapting online education to control the pandemic spread of COVID-19. The present study aims to conduct a survey study through the interview and scrutinizing the literature to find the key challenges. Subsequently, an integrated MCDM framework, including Stepwise Weight Assessment Ratio Analysis (SWARA) and Multiple Objective Optimization based on Ratio Analysis plus Full Multiplicative Form (MULTIMOORA), is developed. The SWARA procedure is applied to the analysis and assesses the challenges to adapt the online education during the COVID-19 outbreak, and the MULTIMOORA approach is utilized to rank the higher education institutions on hesitant fuzzy sets. Further, an illustrative case study is considered to express the proposed idea's feasibility and efficacy in real-world decision-making. Finally, the obtained result is compared with other existing approaches, confirming the proposed framework's strength and steadiness. The identified challenges were systemic, pedagogical, and psychological challenges, while the analysis results found that the pedagogical challenges, including the lack of experience and student engagement, were the main essential challenges to adapting online education in higher education institutions during the COVID-19 outbreak.