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An ideal fusion method preserves the spectral information in fused image without spatial distortion. The PCA is believed to be a well-known pan-sharpening approach and being widely used for its efficiency and high spatial resolution. However, it can distort the spectral characteristics of multispectral images. The current paper tries to present a new fusion method based on the same concept. In the conventional standard PCA method, PCA transform is applied to spectral bands of multispectral images, but we applied the PCA transform to pixel blocks instead. Since PCA coefficients are extracted from statistical properties of the image, it is more consistent with type and texture of remotely sensed image compared to other kernels such as wavelets. After that, a new hybrid algorithm is proposed which uses both the spatial PCA and the spectral PCA method to improve the quality of the merged images. Visual and statistical analyses show that the proposed algorithm clearly improves the merging quality in terms of RASE, ERGAS, SAM, correlation coefficient and UIQI; compared to fusion methods such as IHS, Brovey, PCA, HPF, and HPM.

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Patients with medial compartment knee osteoarthritis (OA) may exhibit different kinematics during walking according to the disease stage, also most of differences are in the frontal plane. The objective of this study was to compare lower extremity kinematics in frontal plane between medial knee OA patients and control subjects. Three dimensional gait analysis was performed on 25 women (35 to 53 years old): 10 control subjects, 10 mild medial knee OA and 5 moderate medial knee OA patients. Kinematics waveforms were reduced dimensionally by using Principal Component Analysis (PCA). PCA scores were compared between three groups (control, mild OA and moderate OA) with ANOVA and Post-Hoc TukeyHSD statistical analysis. Ankle of mild OA patients had a leaning towards inversion and moderate OA patients had a leaning towards eversion. Patients with mild OA, had smaller range of ankle motion than two other groups (p>0.05). Knee adduction angle increased with progression of OA severity (p>0.05). Range of hip motion in frontal plane decreased with progression of OA severity and this difference was significant between mild and moderate OA groups (p=0.05).

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Detection of tool wear and breakage during machining operations is one of the major problems in control and optimization of the automatic machining process. In this study, the relationship between tool wear with vibration in the two directions, one in the machining direction and the other perpendicular to machining direction was investigated during face milling. For this purpose, a series of experiment were conducted in a vertical milling machine. An indexable sandvik insert and ck45 work piece were used in the experiments. Tool wear was measured by a microscope. It was observed that there was an increase in vibration amplitude with increasing tool wear. In this study adaptive neuro - fuzzy inference systems (ANFIS) and multi-layer perceptron neural network (MLPNN) were implemented for classification of tool wear. In this study for the first time, five different states of tool wear was used for accurate tool wear classification. Also to accuracy and speed of the network Principle Component Analysis (PCA) was implemented. Using PCA, the input matrix size was reduced to an acceptable order causing more efficient networks. ANFIS and MLP were trained using feature vectors extracted from the spectrum frequency and time signals. The results showed that for 86 final measurements, the ANFIS and MLP networks were successful in classifying different tool wear state correctly for 91 and 82 percent, respectively. ANFIS due to its high efficiency in diagnosing tool wear and breakage can be proposed as proper technique for intelligent fault classification.

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Besides economic factors affecting economic growth, some cultural, political and social factors influence economic growth and development too; inter alia, social components play important roles. Social instability originating from social threats is one of the most important social components, which affects economic growth. This study aims to investigate the consequences of social instability on economic growth in Iran during 1981-2011. For this purpose, the Auto- Regressive Distributed LagModel (ARDL) and Error Correction Model (ECM) are estimated by Eviews.5 and Microfit 4.1. Using Principal Component Analysis (PCA), an index for social instability (absence of social capital) is made. The results show that physical capital, labor and social instability have the highest effectiveness on economic growth, respectively. Paying attention of policymakers to improving social conditions and reducing social instabilities may lead to higher economic growth.  

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Spatial patterns are useful descriptors of the horizontal structure in a plant population and may change over time as the individual components of the population grow or die out. But, whether this is the case for desert woody annuals is largely unknown. In the present investigation, the variations in spatial patterns of Tribulus terrestris during different pulse events in semi-arid area of the Thar Desert, India, was quantified. Further ordination technique and path analysis were utilized to link the pattern and process of spatial distribution of T. terrestris. Dispersal indices like index of dispersal (I_D), index of clumping (I_C), Green’s Index, Lloyd’s mean crowding and Morisita’s index of dispersion (I_δ) revealed uniform distribution pattern during non-pulse events, showing intense competition among plants for limited resources. Kaiser-Meyer-Olkin (KMO) and Bartlett’s test of sphericity indicated the appropriate use of factor analysis and the significant relationships between variables. Principal Component Analysis (PCA) exhibited the significant correlation of the index of dispersion with the index of clumping and with the Lloyd index, while the Lloyd index correlated with the index of clumping and with the Morisita index. Path analysis suggested the association of soil organic carbon, nitrogen, and C/N ratio with the transition from clumped to uniform pattern. Further, lower soil phosphorus also supported the uniform distribution of this plant. Diversity indices like evenness and Simpson index are associated with uniform and clumped distribution patterns. Higher and intermediate level of percent cover and seed out-put of T. terrestris were also related to uniform and clumped patterns. Path analysis also indicated that salinity tolerance capacity of the species could be utilized for reclamation programme.

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In this study some physical and geometrical properties of 11 superior apricot genotypes were determined. These properties such as respiration rate, fruit dimensions, perimeter, surface area, volume, compact factor, geometric mean diameter, projected area, shape factor, circularity, length to width ratio, length to thickness ratio, and length to mass ratio were measured at harvesting moisture content ranging from 75.19 to 87.67 %. Then the correlation among average values of the attributes was performed and the genotypes were classified using principal component analysis (PCA). The results showed that all the genotypes had significant differences in terms of all the studied attributes (P<0.01). There was a significant and positive correlation between fruit length and weight and fruit length and moisture content. Fruit shape factor and compression factor showed a positive and significant correlation, while these attributes had negative and significant correlations with circularity. This study showed that Iranian apricot genotypes could be discriminated by differences in their geometrical characteristics using principal component analysis. Based on the PCA results, the first two components account for the most of the variation in the data (91%) and five distinct groups were observed. Overall, the results of this study can be beneficial for the design of equipment for harvesting, transportation, separating, packaging, and processing of apricot fruit.
 

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Regarding the bilateral relationship between financial decentralization and regional development at the national level, this study examines the relationship between regional inequality and vertical balance financial decentralization across the provinces of Iran. For this purpose, the calculations are carried out in two stages: in the first stage, the Composite Index of Regional Development (CIRD) is calculated in five dimensions (macroeconomics, science and innovation, environmental sustainability, human capital and public services) by using the two-stage principal components analysis (PCA) during 2006-2016. In the second stage, the interactions between vertical balance financial decentralization and regional development are estimated using the simultaneous equations and error component two-stage least squares (EC2SLS). The results of the first stage analysis show that Tehran province is at the highest level of development, and Sistan and Baluchestan province is at the lowest level of development, and these two provinces practically reflect the wide inequality in the provinces of Iran. In addition, the highest regional inequality is related to the dimensions of science, innovation and human capital. The results of the second stage indicate that the effect of vertical balance financial decentralization on negative regional development is significant and negative, meaning that if provinces spend based on their income, a slight decrease in provincial development has it Because provinces play lesser role in determining tax rates and bases, less developed provinces are not able to generate sufficient revenue to cover their provincial expenditures. Financial decentralization also increases with an increase in regional development, meaning that provinces with different levels of development are likely to have different tendencies toward the quality and quantity of public goods.