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Copyright of Food Research & Development is the property of Food Research & Development Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Information on the genetic and agronomic relationship among the crop characters is important for the breeding programs. This study aimed at determining the relationship among grain yield, popping expansion and other agronomic characters in 19 popcorn lines evaluated in replicated trials at two locations. Correlation analysis was carried out to determine the relationship between agronomic traits while multiple stepwise regression analyses was used to determine the contribution of other agronomic traits to grain yield. Results showed that plant and ear heights as well as cob length exhibited positive and significant association with grain yield. Popping volume showed negative and significant association (-0.45**) with grain yield while 100-grain mass had a negative and significant correlation (-0.37**) with popping volume. Stepwise multiple regression analysis showed that ear height, cob length, plant aspect and 100-grain mass contributed a total of 53.66 % to variation in grain yield, with ear height contributing the highest portion (22.51 %). Cluster analysis grouped popcorn lines into four different clusters, where ‘Small Pearl Shaped’ and ‘Popcorn 33-1-Y’ belonged to cluster II and IV, respectively, showing how divergent they are and possible utilization for hybrid formation. Improvement for popcorn should focus on identifying lines with acceptable level of popping volume and improved on their grain yield and yield attributes.

Global climate change has become one of the core issues of world governance. Many countries have put forward the goal of carbon neutrality one after another, leading to the intensification of international low-carbon economy competition. To assess the current low-carbon competitiveness among countries, this article constructs an evaluation index system of international low-carbon economy development, and obtains the scores and rankings of countries in energy, society, economy and environment, as well as overall. Taking 20 countries with the highest carbon emissions in the world in 2019 as samples, starting from the concept of low-carbon economy and five evaluation principles, this article selects 40 low-carbon evaluation indicators from five aspects, including economy, society, science and technology, environment, and energy structure. By using the principal component factor analysis method to calculate and test, the four factors, energy factor, society factor, economy factor, and environment factor, are finally extracted to construct the evaluation index system. Results show that South Korea, France, China, Canada, and Germany are among the world’s top five low-carbon economies. The overall competitiveness of China’s low-carbon economy is in a relatively favorable position (3rd overall), with the most outstanding performance in terms of economic strength (1st), but poor performance in terms of social development (9th) and environmental carrying capacity (9th), and the biggest disadvantage in terms of energy structure (13th).

The study of functional connectivity (FC) of the brain using resting-state functional magnetic resonance imaging (rs-fMRI) has gained traction for uncovering FC patterns related to autism spectrum disorder (ASD). It is believed that the neurodynamic components of neuroimaging data enhance the measurement of the FC of brain nodes. Hence, methods based on linear correlations of rs-fMRI may not accurately represent the FC patterns of brain nodes in ASD patients. In this study, we proposed a new biomarker for ASD detection based on wavelet coherence and singular value decomposition. In essence, the proposed method provides a novel feature-vector based on extraction of the principal component of the neuronal dynamic FC patterns of rs-fMRI BOLD signals. The method, known as principal wavelet coherence (PWC), is implemented by applying singular value decomposition (SVD) on wavelet coherence (WC) and extracting the first principal component. ASD biomarkers are selected by analyzing the relationship between ASD severity scores and the amplitude of wavelet coherence fluctuation (WCF). The experimental rs-fMRI dataset is obtained from the publicly available Autism Brain Image Data Exchange (ABIDE), and includes 505 ASD patients and 530 normal control subjects. The data are randomly divided into 90% for training and cross-validation and the remaining 10% unseen data used for testing the performance of the trained network. With 95.2% accuracy on the ABIDE database, our ASD classification technique has better performance than previous methods. The results of this study illustrate the potential of PWC in representing FC dynamics between brain nodes and opens up possibilities for its clinical application in diagnosis of other neuropsychiatric disorders.

Wheat crop contributes to a major portion of the agriculture economy of Nepal. It is ranked as the third major cereal crop of the country even though, it faces terminal heat stress which speeds up the grain filling rate and shortens the filling period, causing reduction in grain weight, size, number and quality losses. We can minimize this loss through a genotypic selection of high-yielding lines by understanding the genotype-environment interaction. The objective of this research is to obtain a high yielding line with a stable performance across the environments. In order to do so, we conducted an experiment using eighteen elite wheat lines and two check varieties in alpha-lattice design with two replications in different environments viz. irrigated and terminal heat stress environment from November 2019 to April 2020. The analysis of variance revealed that genotype, environment and their interaction had a highly significant effect on the yield. Furthermore, the which-won–where model indicated specific adaptation of elite lines NL 1179, NL 1420, BL 4407, NL 1368 to the irrigated environment and Bhirkuti to the terminal heat-stressed environment. Similarly, the mean-versus-stability study indicated that elite lines BL 4407, NL 1368, BL 4919, NL 1350, and NL 1420 had above-average yield and higher stability whereas elite lines Gautam, NL 1412, NL 1376, NL 1387, NL 1404, and NL 1381 had below-average yield and lower stability. The ranking of elite lines biplot, PC1 explaining 73.6% and PC2 explaining 26.4% of the interaction effect, showed the rank of elite line, NL 1420 > NL 1368> NL 1350 > other lines, close to the ideal line. On the basis of the obtained results, we recommend NL 1420 with both the high yield and stability is suited across both the environments, while NL 1179 and Bhirkuti is adapted specifically for irrigated and terminal heat stress environment, respectively.

The increase in the use of diesel engines in the transport sector and in agriculture has led researchers to study new alternative sources of fuels. The objective was to identify the agglomerates existing in a set of biodiesels through multivariate exploratory analysis and determine the variables that most influenced the division of agglomerates to assist in the understanding of the functioning of the engines with these biodiesels. The present work was carried out in two tests: test I - tractor performance test and test II - exhaust smoke opacity. The tractors used in the experiment were the so-called traction tractor (tractor 1), the Valtra brand, model BM100, 4X2 with auxiliary front-wheel drive, 74 kW (100 hp) at 2,300 rpm on the engine and braking tractor (Tractor 2), the Valtra brand, model BH140, 4X2 with auxiliary front-wheel drive, 103 kW (140 hp) at 2,400 rpm on the engine. The hierarchical, non-hierarchical analysis and the main component analysis grouping allowed the ordering of the biodiesels into three groups. The variables that most influenced the division of the groups were volumetric hourly consumption, hourly weight consumption, and specific consumption. The biodiesels that are part of group one have greater calorific power resulting in less consumption of biodiesel in agricultural operations. [ABSTRACT FROM AUTHOR]

Salinity stress can reduce rice (Oryza sativa L.) productivity and cause crop failure. This problem needs a solution by development of tolerant varieties, and this development closely relates to the effectiveness of its screening. Screening at the phase of germination and seedling are the most common ones. However, the interactions between screenings based on the growth character selection index have not been widely reported, particularly with the principal component analysis (PCA). Therefore, the aim of this study was to determine the interaction between phases of rice salinity screening through a selection index based on PCA. This study consisted of two phases of salinity screening, namely growth phase of germination and seedlings. Both screenings were designed with a nested randomized complete block design, where replicates were nested in a selection environment. The selection environment consisted of two levels, normal (0 mM NaCl) and saline (120 mM NaCl). The genotypes consisted of eight varieties and was repeated three times. Observations were based on morphological and physiological characters, especially in seedling phase screening. Results showed that morphological character approach of the seedlings had a large distribution of salinity tolerances. The use of stress tolerance index and PCA were considered effective in the formation of the selection index on salinity screening. As for, the morphology index was formulated as 0.32 shoot height + 0.33 root fresh weight + 0.33 shoot fresh weight + 0.26 root length + 0.01 number of tillers, length + 0.34 total biomass fresh weight. Therefore, the use of this analytical concept is recommended in screening the tolerance of rice lines to salinity stress. [ABSTRACT FROM AUTHOR]

For ranking and grouping of full-sibs and hybrids of sugar beet obtained from breeding programs, two separate experiments were carried out at the farm of the Agricultural and Natural Resources Research Station of Miandoab in 2017 under saline and normal conditions. For this purpose, 17 full-sibs and 28 hybrids differing in resistance and susceptibility to environmental stresses were investigated in for different traits. The ranking results in both conditions indicated that genotypes 18 and 22 in full-sibs test and, genotypes 6, 23, 25, 27 and 29 in the hybrid test were superior to all other genotypes. Cluster analysis showed that in both experiments (full-sibs and hybrids), there was a slight matching between the normal and saline environments and the environment affects the grouping of genotypes. Principle Components Analysis in the full-sibs test indicated that in both conditions, leaf area, root yield, sugar yield, root dry weight, relative water loss, sugar content, purity of syrup and extractable sugar content had the highest contribution in the first and second components. In contrast, in the hybrid test, leaf area, root yield and leaf water content had the highest role in the first and second components. Most of the traits studied in the experiments contained diversity and can be used for grouping sugar beet genotypes. Also, groupings will vary depending on environmental conditions. [ABSTRACT FROM AUTHOR]

Copyright of Roa Iktissadia Review is the property of Roa Iktissadia Review and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Plant Production is the property of Egyptian National Agricultural Library (ENAL) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Acta Agriculturae Slovenica is the property of Biotechnical Faculty of the University of Ljubljana and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The volatile components of wood, bark, and root from Ulmus pumila at room temperature were detected by Gas Chromatography-Ion Mobility Spectrometer (GC-IMS). The volatile components released from the three parts of Ulmus pumila were analyzed and compared using methods including two-dimensional top view, fingerprint, principal component analysis, and molecular weight comparison. The results showed that 37 organic volatiles were identified from 3 parts of Ulmus pumila at room temperature, mainly including alcohols, ketones, aldehydes, esters, acids and other volatile compounds. These 37 volatile substances could be detected in all 3 parts of Ulmus pumila, but there were significant differences in the component content of each part. The molecular weights of the main organic compounds in the 3 parts were between 40 and 110, the molecular weight was small, and they can be released into the environment through the cell wall. Most of them were beneficial bioactive ingredients, which was beneficial to purify the air and improve the environmental quality. [ABSTRACT FROM AUTHOR]

Background: Information on the genetic variability of plants on the basis population is important for conservation and utilization of genetic resources. However, information on such genetic diversity is not still yet available at individual level in Yayu coffee germplasm, southwestern Ethiopia. Objective: The study was conducted to estimate the genetic variability among coffee collections with respect to morpho-agronomic traits. Materials and Methods: Sixty-two Coffee (Coffea arabica L.) collections with two standard check varieties (74110 and 74112) were evaluated using 8 x 8 simple lattice design at Metu Agricultural Research Sub Center. The experiment was conducted on six-year old coffee trees during the 2018 main cropping seasons. The coffee trees were managed as per the recommendation for coffee production practices. Results: Cluster analysis was employed using 19 quantitative traits and 64 coffee collections grouped into seven clusters. Significant inter cluster-distance was found between most of the paired clusters. The results revealed the chance of developing hybrids by crossing coffee collections from cluster-V and VI followed by cluster-IV and VI. Principal component analysis revealed that, the first seven principal components with Eigen values exceeding one were responsible for about 74.94 % of the observed variation among the coffee collections. Out of the entire variations, the first and the second principal components accounted for more than one-third of the total variation (35.32 %). Conclusion: The information and genetic variability obtained in the present study could be used to plan conservation, effective pure line selection, and crossing of coffee germplasm in future coffee improvement programs. [ABSTRACT FROM AUTHOR]

Copyright of Yuzuncu Yil Universitesi Journal of Agricultural Sciences (YYU J Agr Sci) is the property of Yuzuncu Yil University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Solar-induced chlorophyll fluorescence (SIF), one of the three main releasing pathways of vegetation-absorbed photosynthetic active radiation, has been proven as an effective monitoring implementation of leaf photosynthesis, canopy growth, and ecological diversity. There exist three categories of SIF retrieval methods, and the principal component analysis (PCA) retrieval method is obtrusively eye-catching due to its brief, data-driven characteristics. However, we still lack a lucid understanding of PCA's parameter settings. In this study, we examined if principal component numbers and retrieval band regions could have effects on the accuracy of SIF inversion under two controlled experiments. The results revealed that the near-infrared region could remarkably boost SIF's retrieval accuracy, whereas red and near-infrared bands caused anomalous values, which subverted a traditional view that more retrieval regions might provide more photosynthetic information. Furthermore, the results demonstrated that three principal components would benefit more in PCA-based SIF retrieval. These arguments further help elucidate the more in-depth influence of the parameters on the PCA retrieval method, which unveil the potential effects of different parameters and give a parameter-setting foundation for the PCA retrieval method, in addition to assisting retrieval achievements. [ABSTRACT FROM AUTHOR]

ABSTRACT The inadequate agricultural management in the semiarid region of northeastern Brazil, associated with the fragility of most soils, has caused degradation of the quality of this resource. This study aimed to evaluate the quality of Quartzipsamments under different uses through the multivariate analysis of chemical and physical attributes. Five areas were selected: four of them with agricultural use (onion, banana, cassava, and corn) and the other with Caatinga. Soil samples were collected at depths of 0.00–0.10, 0.10–0.20, and 0.20–0.40 m for chemical and physical analyses. The data were submitted to descriptive analysis and then to the multivariate analysis using the principal component analysis (PCA) technique and Ward’s clustering method. PCA and clustering techniques allowed the identification of different production systems in subsurface layers, suggesting the effect of fertility on the differentiation of areas with onion and banana, as well as the clustering of cassava and corn cultivations. Physical attributes and sodium content of the surface layer of the area with onion showed a tendency of compaction and salinization process of this environment. In general, fertility was the main responsible for the improvement of soil quality with agricultural use when compared to the Caatinga.

Purpose – The purpose of this paper is to examine the relationship between financial development, openness in financial services trade and economic growth in BRICS countries for the period 1990–2012. Design/methodology/approach – An index for financial development has been constructed using principal component analysis technique by including banking sector development, stock market development, bond market development and insurance sector development. For the robustness of the result, the long-run cointegrating relationship amongst the variables has been analyzed. Findings – Overall financial development has a positive and significant impact on economic growth. To take the full advantage of openness in financial services trade, countries need to put more emphasis on the development of their stock markets, bond markets and the insurance sector. The result shows that openness in financial services trade has a positive impact on economic growth when the stock market, bond market and insurance sector are included in the system. Research limitations/implications – The policy implication of the findings is that policymakers should focus more on developing all four areas of finance to get the full benefit of the financial system on the process of economic growth. Originality/value – The authors have constructed the better indicators of financial development in the case of BRICS economies. Most of the studies in BRICS economies have measured the development of the financial sector as either banking sector development or stock market development. However, the present study includes all four areas of finance (banking sector development, stock market development, insurance sector development and bond market development) into account.

Abstract Subsurface hydrocarbon traps are not correctly sealed, and hydrocarbons move vertically from the reservoir as invisible traces in the form of microseepages. Long-term hydrocarbon microseepages cause surface or near-surface alterations such as bleaching of red beds, enrichment of ferrous iron minerals and higher concentrations of clay and carbonate minerals in soils/rocks. Multi- and hyperspectral remote sensing data have successfully been used to detect such alterations in many parts of the world. In India, such studies have not been carried out till now. In this study, Landsat 7 ETM+ images have been used to find out hydrocarbon microseepage-bearing areas in part of Assam–Arakan Fold Belt in the northeastern part of India. Based on the spectral characteristics of the hydrocarbon microseepage-induced altered minerals, two spectral enhancement techniques, viz. principal component analysis (PCA) and band ratio analysis, have been carried out on the Landsat 7 ETM+ images. PCA reveals that three principal component images—1457PC3, 1345PC2 and 3457PC4—show relatively better enhancement for the hydrocarbon-bearing alteration areas. Again, band ratio analysis of the images indicates that ratio images—3/1, (2 + 5)/(3 + 4) and 7/5—show excellent spectral enhancement for the hydrocarbon-induced mineral alterations. The three PC images have been combined with the three band ratio images to find out probable hydrocarbon microseepage areas. The remote sensing-derived prospect areas have been validated with surface geochemical, seismic/geologic and gravity data available in the area.

Cultivar development of cowpea with increased grain yield depends on evaluation and selection of genetically unique and complementary breeding lines. The objective of this study was to evaluate cowpea collections using agronomic traits to select promising parents to develop breeding population. Twenty-two cowpea genotypes were field evaluated using a randomised complete block design with three replications in two locations in South Africa. Analysis of variance revealed significant genotypic (G) and locations (L) differences for all agronomic traits evaluated. Significant differences were also observed for most of the traits due to G X L, G X Year (Y), L X Y and G X L X Y interactions effects. The genotypes tested at two locations showed considerable genetic variation for the following agronomic traits that varied for number of leaves (NL) (20.08–179.78), leaf length (LL) (12.36–67.72 cm), leaf width (LW) (4.78–22.02 cm), number of branches (NB) (6.81–13.85), number of pods per plant (NPPP) (14.33–54.04), number of seeds per pod (NSPP) (11.7286.28), pod diameter (PD) (5.75–22.73 mm), plant height (PH) (6.57–233.71 cm), pod length (PL) (3.94–100.92 mm), hundred seed weight (HSWt) (8.11–49.52 g) and grain yield (GY) (0.10-1.48 t ha−1). Grain yield was significant and positively correlated with all of the traits except leaf length. The principal component analysis (PCA) identified four principal components (PCs) contributing to 73.62% of the total explained variation amongst the tested genotypes. The selected cowpea genotypes for most of agronomic characteristics are useful and candidate genetic resources for the development of breeding population in cowpea breeding and enhanced production and productivity for food and nutrition in the country.

Grain legumes, including cowpea, are the cheapest sources of minerals and protein to enhance human nutrition. Cultivar development and deployment of cowpea with increased grain mineral content and protein composition rely on selection of genetically unique and complementary breeding lines. The objective of this study was to assess the grain minerals and protein composition of diverse cowpea collections of eastern, southern and western Africa to select promising parents to develop a breeding population. Twenty-two genetically diverse cowpea genotypes were field evaluated using a randomised complete block design with three replications in two locations in South Africa. The dried grain mineral contents were determined using an Atomic Absorption Spectrophotometre, while the protein content was determined by the combustion method. Analyses of variance showed significant (P

The special causes of variations, which is also known as a shift, can occur in a single or more than one related process characteristics. Statistical process control tools such as control charts are useful to monitor shifts in the process parameters (location and/or dispersion). In real-life situation, the shift is emerging in different sizes, and it is hard to identify it with classical control charts. Moreover, more than one process of characteristics required special attention because they must monitor jointly due to the association among them. This study offers two adaptive control charts to monitor the different sizes of a shift in the process mean vector. The novelty behind this study is to use dimensionally reduction techniques such as principal component analysis (PCA) and an adaptive method such as Huber and Bi-square functions. In brief, the multivariate cumulative sum control chart based on PCA is designed, and its plotting statistic is utilized as an input in the classical exponentially weighted moving average (EWMA) control chart. The run length (RL) properties of the proposed and other control charts are obtained by designing algorithms in MATLAB through a Monte Carlo simulation. For a single shift, the performance of the control charts is assessed through an average of RL, standard deviation of RL, and standard error of RL. Likewise, overall performance measures such as extra quadratic loss, relative ARL, and the performance comparison index are also used. The comparison reveals the superiority over other control charts. Furthermore, to emphasize the application process and benefits of the proposed control charts, a real-life example of the wind turbine process is included.

The water (W; W50, W75, and W100)–zeolite (Z; Z0, Z3, Z6 and Z9) coupling (W-Z) regulation strategy of high-quality and high-yield tomato was explored with alternate drip irrigation under mulch. Greenhouse planting experiments were used in monitoring and analyzing tomato growth, physiology, yield, quality, and water use efficiency (WUE). Suitable amounts of W and Z for tomato growth were determined through the principal component analysis (PCA) method. Results showed that tomato plant height (Ph), stem thickness (St), root indexes, leaf area index (LAI), photosynthetic rate (Pn), transpiration rate (Tr), stomatal conductance (Gs), organic acid (OA), and yield showed a positive response to W, whereas nitrate (NC), vitamin C (VC), soluble solid (SS), intercellular CO2 concentration (Ci), fruit firmness (Ff), and WUE showed the opposite trend. The responses of Ci and Ff to Z were first negative and then positive, whereas the responses of other indexes to Z showed an opposite trend (except yield under W50). The effects of W, Z, and W-Z on tomato growth, physiological, and quality indexes and yield were as follows: W > Z > W-Z; the effects on WUE were as follows: Z > W > W-Z. The two principal components of growth factor and water usage factor were extracted, and the cumulative variance contribution rate reached 93.831%. Under different treatments for tomato growth, the comprehensive evaluation score F was between −1.529 and 1.295, the highest treated with Z6W100, the lowest treated with Z0W50. The PCA method showed that under the condition of alternate drip irrigation under mulch, the most suitable W for tomato planting was 100% E (E is the water surface evaporation), and the amount of Z was 6 t·ha−1.

Maize is one of the most widely used plants in the agricultural industry, and the fields of application of this plant are broad. The experiment was conducted at the Látókép Crop Production Experimental Station of the University of Debrecen, Hungary. Three mid-ripening maize hybrids with different FAO numbers were used in the present study. The effects of different nitrogen supplies were examined as a variable rate of abiotic stress and the interrelationship among the essential nutrients through the nutrient acquisition and partitioning of the different vegetative and generative plant parts. The results showed that NPK application compared to the control treatment (no fertilizer application) increased DM in all tissues of maize, while increasing nitrogen application from 120 to 300 kg ha−1 had no significant effect on this trait. The highest protein content was obtained with the nitrogen application of 120 kg ha−1, and the higher nitrogen fertilizer application had no significant effect on this trait. Seeds and leaves had a maximum zinc and manganese value in terms of nitrogen content (protein). Dry matter was positively correlated with nitrogen, potassium, and manganese content, while the dry matter had a negative correlation with nickel content. In general, to achieve a maximum quantitative and qualitative yield, it is recommended to use NPK fertilizer with a rate of 120 kg ha−1 N for maize cultivation.

While the movement for open research has gained momentum in recent years, there remain concerns about the broader commitment to openness in knowledge production and dissemination. Increasingly, universities are under pressure to transform themselves to engage with the wider community and to be more inclusive. Open knowledge institutions (OKIs) provide a framework that encourages universities to act with the principles of openness at their centre; not only should universities embrace digital open access (OA), but also lead actions in cultivating diversity, equity, transparency and positive changes in society. This leads to questions of whether we can evaluate the progress of OKIs and what are potential indicators for OKIs. As an exploratory study, this article reports on the collection and analysis of a list of potential OKI indicators. Data for these indicators are gathered for 43 Australian universities. The indicators provide high-dimensional and complex signals about university performances. They show evidence of large disparities in characteristics such as Indigenous employment and gender equity, and a preference for repository-mediated OA across Australian universities. We demonstrate use of the OKI evaluation framework to categorise these indicators into three platforms of diversity, communication and coordination. The analysis provides new insights into the Australian open knowledge landscape and ways of mapping different paths of OKIs.

The following methods are used to analyze correlations among stock returns. 1) The meaningful part of the correlation is obtained by applying random matrix theory to the equal-time cross-correlation matrix of assets returns. 2) Null-model randomness is implemented via rotational random shuffling. 3) Principal component analysis and Helmholtz-Hodge decomposition are used to extract leading and lagging relationships among assets from the complex correlation matrix constructed from the Hilbert-transformed data set of asset returns. These methods are applied to price data for 445 assets from the S&P 500 from 2010 to 2019 (2,510 business days). Additional analysis and discussion clarify key aspects of leading and lagging relationships among business sectors in the market. Numerical investigation of these dataset reveals the possibility that leading and lagging relationships among business sectors may depend on gross market conditions.

Copyright of Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban) is the property of Zhongguo Xue shu qi Kan (Guang Pan Ban) Dian zi Za zhi She and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Henan Agricultural Sciences is the property of Editorial Board of Journal of Henan Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Nowadays, the serious situation that affects the entire world goes beyond the social, cultural, economic problems and other conflicts that occur day by day. These were left aside to move to a global alert; we refer to the pandemic crisis that all the nations of the world are facing. Confinement forced people all over the world to stay at home; therefore, communications through electronic devices became very necessary. This study does not seek to analyze the pandemic crisis; its purpose is to analyze the use that students give to their mobile phone, to determine if this has generated addiction, in addition to identifying if use differs in men and in women. Participants were 184 high school students enrolled in a public sector institution in the Port of Veracruz, Mexico. To obtain the data, the SAS-CV test was used. This contains questions related to the profile of the respondent and 10 items in Likert format. It was distributed via electronic devices for their response. The data were statistically analyzed using polychoric correlation matrices and factor analysis with component extraction. The main findings demonstrate the obtaining of three components: physiological, dependence and distraction, which account for 68% of the total variance, and it was also shown that there are no differences by gender. [ABSTRACT FROM AUTHOR]

One of the most primitive problems in face recognition is illumination variation, which is of significance for image processing and pattern recognition. Existing studies concentrate on the wavelet transform (WT) without elaborately considering high-frequency information and most do not efficiently tackle a computation explosion of facial dimensions in classification. Therefore, a novel framework based on wavelet transform and principal component is proposed to improve the accuracy under illumination variation in this paper. In the proposed framework, low-frequency sub-band (LFSB) and high-frequency sub-band (HFSB) images from wavelet transform are simultaneously enhanced and denoised, unlike previous studies that usually cause loss of details due to less consideration of HFSB. For LFSB image, a multiple scale Retinex-based steering kernel is designed to enhance more details, and then an adaptive strategy of gamma correction is developed to automatically expand gray-dynamic range. For HFSB image, a non-local mean filtering is established to suppress the noise and subsequently, more image details are preserved by local of mean of local variance. Moreover, the principal component technique based Fisherface and virtual auxiliary sample strategy is developed in order to overcome the computation explosion of facial dimensions, in which a sample strategy with interpolation mechanism is employed to avoid the complicated singularity and Fisherface analysis is further applied to extract features and dimensionality reduction. In addition, the particle swarm optimization-neural network (PSO-NN) is employed to perform classification in the framework. Experimental results prove that the proposed framework can effectively obtain the robust visual effect under varying illumination and significantly improve the recognition performance in comparison to existing methods. [ABSTRACT FROM AUTHOR]

This study examines the influence of spatial effect and demographic characteristics on postdisaster households' relocations at the block group level. Data for 479 block groups were collected in the city of New Orleans following Hurricane Katrina. The explanatory variables were transformed into five uncorrelated principal components using principal component analysis and spatial autocorrelation of the relocation decision of households tested using Moran's I index. The results indicate households' decisions exhibit significant spatial clustering. A better statistical fit was found for spatial lag regression than standard linear regression and other spatial models. Four statistically significant components influencing the block group level percentage of relocated households were identified. Specifically, places characterized by a greater proportion of the population with higher income population and out-of-county employment are more likely to experience high rates of relocation. In addition, family ties and place attachment had significant negative effects on the rate of block group-level relocation. [ABSTRACT FROM AUTHOR]

Wheat landraces are valuable genetic resource for diversity and specific adaptation to local environmental conditions as well as increasing agronomic traits in breeding programs. Admittedly, this type of genotypes has become more crucial to face the challenges of recent climate change. The aim of this study was to investigate the genetic diversity among Egyptian bread wheat landraces and commercial cultivars based on morpho-phenological and agronomic characters. Thirty-two wheat landraces were collected from eleven Egyptian provinces through collecting trips during 1987 to 1999 and, in addition, three commercial cultivars were evaluated all for fifteen qualitative and quantitative characters. Wide genetic diversity was detected based on evaluated morphological characters was observed among the landraces compared to commercial cultivars. Various landraces exhibited strong glaucosity on flag leaf, culm and ear, which is crucial character especially under dry and hot cultivation conditions as Mediterranean region. Furthermore, considerable variation among tested genotypes was demonstrated on the basis of yield-related traits and they were classified into four groups (A–D) from very high-yielding to low-yielding genotypes. Wheat landraces number 3, 8, 14, 27, 29, 33 and 35 exhibited superiority for earliness, grain yield/plant and its components compared to commercial cultivars (number 18, 19 and 20). The association among morpho-phenological and agronomic characters was studied according to principal component analysis. The evaluated characters were divided into four groups. Certain morphological characters (coleoptiles anthocyanin coloration and awns or scurs at tip of ear length) alongside the phenological characters (days to heading and maturity) and certain agronomic characters (plant height and 1000-grain weight) displayed strong association with grain yield/plant. Furthermore, estimated broad sense heritability was high for plant height, number of grains/spike, 1000-grain weight, days to heading, moderate for days to maturity and grain yield/plant presented and low for number of spikes/plant. [ABSTRACT FROM AUTHOR]

We model the conditional risk premium by combining principal component analysis and a statistical learning technique, known as boosted regression trees. The method is validated through various out‐of‐sample tests. We apply the estimates to test the positivity restriction on the risk premium and find evidence that the risk premium is negative in periods of low corporate and government bond returns, high inflation and downward‐sloping term structure. These periods are linked with changes in business cycles; the states when theories predict the existence of negative risk premium. Based on the evidence, we reject the conditional capital asset pricing model and raise a question over the practice of imposing the positive risk premium constraint in predictive models. [ABSTRACT FROM AUTHOR]

In this Letter, the authors propose a variational mode decomposition method for quantifying diagnostic information of myocardial infarction (MI) from the electrocardiogram (ECG) signal. The multiscale mode energy and principal component (PC) of multiscale covariance matrices are used as features. The mode energies determine the strength of the mode, and the PCs provide the representation of the ECG signal with less redundancy. K-nearest neighbour and support vector machine classifier are utilised to assess the performance of the extracted features for the detection and classification of MI and normal (healthy control). The proposed method achieved a specificity of 99.88%, sensitivity of 99.90%, and accuracy of 99.88%. Experimental results demonstrate that the proposed method with the multiscale mode energy and PC features achieved better output compared to the previously published work.

This study introduces Pakistan’s multidimensional energy security index (PMESI) and indices across dimensions from 1991 to 2020 through indicator optimization. Based on criteria, expert participation, and reliability testing, 27 indicators were identified and weighted based on dimension reduction utilizing the Varimax Rotation technique. As a result of robust evaluation framework, there has been a considerable change in Pakistan’s energy security when compared to other studies such as the energy security indicator of Pakistan (ESIP) and the energy security index of Pakistan (ESIOP). According to the findings, energy security decreased by 25% between 1991 and 2012, followed by a modest increase through 2020. During the study period, the “Affordability” dimension improved; however, the other four dimensions, namely “Availability,” “Technology,” “Governance,” and “Environment,” regressed. Few goals under the petroleum policy (1991), petroleum policy (2012), and power policy (2013) were partially met, while conservation programs, such as the renewable policy (2006) and national climate change policy (2012), fell short. Indicators such as price, reserves, governance, corruption, and consumption contributed to PMESI across five dimensions. Thus, PMESI and indices guiding policymakers to focus on improving governance and exploiting local energy resources in order to provide affordable and sufficient energy in the long run.

This study presents a spatial assessment of eight potentially toxic elements (PTE: Cd, Cr, Cu, Fe, Pb, Ni, Mn, and Zn) in white button (Agaricus bisporus J.E. Lange) mushroom samples collected from the local vegetable markets of Uttarakhand State, India. Fresh A. bisporus samples were collected from thirteen districts and fifteen sampling locations (M1-M15) and analyzed for the concentration of these PTE using atomic absorption spectroscopy (AAS). The results revealed that A. bisporus contained all eight selected PTE in all sampling locations. Based on the inverse distance weighted (IDW) interpolation, principal component (PC), and hierarchical cluster (HC) analyses, the areas with a plane geographical distribution showed the highest PTE concentrations in the A. bisporus samples as compared to those in hilly areas. Overall, the decreasing order of PTE concentration in A. bisporus was recognized as Fe > Zn > Mn > Cr > Cu > Ni > Cd > Pb. The Kruskal–Wallis ANOVA tests displayed a highly significant (p < 0.05) difference among the sampling locations. However, the concentration of PTE was below permissible limits, indicating no potential hazard in consuming the A. bisporus. Similarly, the health risk assessment studies using the target hazard quotient (THQ) also showed no significant health risk associated with the consumption of A. bisporus being sold in the local mushroom markets of Uttarakhand, India. This study is the first report on state-level monitoring of PTE in A. bisporus mushrooms, which provides crucial information regarding the monitoring and occurrence of potentially toxic metallic elements.

It has become common in evolutionary biology to characterize phenotypes multivariately. However, visualizing macroevolutionary trends in multivariate datasets requires appropriate ordination methods.In this paper we describe phylogenetically aligned component analysis (PACA): a new ordination approach that aligns phenotypic data with phylogenetic signal. Unlike phylogenetic principal component analysis (Phy‐PCA), which finds an alignment of a principal eigenvector that is independent of phylogenetic signal, PACA maximizes variation in directions that describe phylogenetic signal, while simultaneously preserving the Euclidean distances among observations in the data space.We demonstrate with simulated and empirical examples that with PACA, it is possible to visualize the trend in phylogenetic signal in multivariate data spaces, irrespective of other signals in the data. In conjunction with Phy‐PCA, one can visualize both phylogenetic signal and trends in data independent of phylogenetic signal.Phylogenetically aligned component analysis can distinguish between weak phylogenetic signals and strong signals concentrated in only a portion of all data dimensions. We provide empirical examples that emphasize the difference. Use of PACA in studies focused on phylogenetic signal should enable much more precise description of the phylogenetic signal, as a result.Overall, PACA will return a projection that shows the most phylogenetic signal in the first few components, irrespective of other signals in the data. By comparing Phy‐PCA and PACA results, one may glean the relative importance of phylogenetic and other (ecological) signals in the data. [ABSTRACT FROM AUTHOR]

Rainfall variability associated with the South Asian Summer Monsoon has increased in recent decades, particularly at the northwestern monsoon margins over Pakistan, leading to more frequent and intense hydro-meteorological extremes that have adversely affected the agrarian economy, water and food security in the country. Devising effective strategies to ensure sustainable development in Pakistan thus requires that the monsoonal rainfall be predicted on an inter-annual scale. Here, we predicted the inter- and intra-annual variability of the monsoonal rainfall over Pakistan and its possible drivers using a linear statistical forecast model of the principal component (PC) regression analysis. For this purpose, highly correlated PCs of the National Centre for Environmental Prediction (NCEP) based sea level pressure, horizontal and meridional winds to the observed rainfall for the period 2001–2013 were ingested in a stepwise multiple regression model, which was further validated for the duration of 2014–2015. Our results suggest that featuring correlation coefficient, mean absolute error, mean bias, and root mean square error of 0.75, 42.23, −14.92 and 60.65, respectively, the model exhibits robust skill in predicting the inter-annual monsoonal rainfall variability at its extreme northwestern margins over Pakistan. [ABSTRACT FROM AUTHOR]

It is important to understand the growth process under way in China. However, analyses of Chinese growth became increasingly more difficult after the real GDP doubling target was announced in 2012 and the official real GDP statistics lost their fluctuations. With a dataset covering 31 Chinese provinces from two decades, we have substantially more variation to work with. We find robust evidence that the richness of the provincial data provides information relevant to understand and project Chinese aggregates. Using this provincial data, we build an alternative indicator for Chinese growth that is able to reveal fluctuations not present in the official statistical series. Additionally, we concentrate on the determinants of Chinese growth and show how the drivers have gone through a substantial change over time both across economic variables and provinces. We introduce a method to understand the changing nature of Chinese growth that can be updated regularly using principal components derived from the provincial data. [ABSTRACT FROM AUTHOR]

The potato, as an affordable and nutritious food, could be a major aid to development. In many present-day developing countries, however, its profitable cultivation and wider use are hindered by supply constraints. For production, a chief problem is in input use conditions, which we here approach via output price level differentials. To explain the cross-border differentials in the producer price of the potato, averaged over a decade, we use principal component (PC) regression. We perform PC analysis on 33 variables of 40 developing countries. We retain the seven most important PC's expecting them to represent factors or trends in the data. Loaded by agricultural and social conditions, the PC's are put to explain potato producer price in linear regression. The basic factors of production, land, labor, capital, and technology correspond to the four PC's that statistically significantly explain potato price. They explain half of the cross-border variation in potato producer price. Studying macroeconomic inputs, we find also likely paths of their accumulation. Land input depends on potato suitability. Labor input correlates with certain conditions, namely those of agricultural poverty, that bring poor terms of trade for small farmers. Capital input, to agricultural land development, is inversely dependent on urban business opportunities. Technology input correlates with human capital or knowledge. Greater input use, through its effects on production, decreases potato price. The three other PC's are macroeconomic trends; economic growth, growing potato importance, and improving market economy infrastructures. Economic growth and greater potato importance may lead to higher potato prices in developing countries, whereas improving infrastructures may lower average prices. Concerning potato related agricultural policies, our results support the view that potato supply constraints, surveyed in literature, prevent effective and coordinated input use in developing countries generally. Efficiency in input use would likely be increased by infrastructures that are targeted specifically for the potato. The results also lend support to the practice of sequentially addressing the constraints, but since they exist due to a high degree of complementarity between inputs, we should prudently consider the order in efforts, and the sustainability of benefits. [ABSTRACT FROM AUTHOR]

The objective of this study was to evaluate the agronomic characteristics of the plant and silage based on the principal component analysis of the clusters, dispersion plot and genetic correlation between Sorghum (L.) bicolor hybrids. Twenty sorghum hybrids were used in a randomized blocks design with three replications. The number of tillers, total plant height, leaf/stem and live matter/dead mass ratios, and the dry matters of leaf, stem, panicle and grains were evaluated. The means were compared using the Student Newman Keuls (SNK) test at a significance level of P<0.05. Three principal component analysis were responsible an accumulation of about 67% of all phenotypic diversity (number of tillers, lodging and total green forage mass). There was a positive and strong correlation between the productivity variables (green forage mass with height, leaf/stem ratio, leaf dry mass). There was genetic variability within the groups, indicating that some hybrids can be highlighted by agronomic characteristics (number of tillers number, lodging, total green forage mass, total dry forage mass, height, leaf/stem ratio, dead material, leaf dry mass), and thus group III (12F042224, FEPAGRO 11.9929012, 9929026, SF 25, 947216, 12F042150, 9929012, PROG 134 IPA) presented the hybrids more productive and more suitable for the production of silage in semi-arid regions, among the four groups. [ABSTRACT FROM AUTHOR]

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This paper presents an uncertainty-set-shrinkage (USS) algorithm that aims to reconstruct a precise interference-plus-noise covariance matrix (INCM) and improve the performance of adaptive beamformers when steering vector (SV) mismatch exists. Both of the interference covariance matrix (ICM) and the desired signal covariance matrix (DSCM) can be divided into two parts, namely the nominal matrix reconstructed using the nominal SVs and the error matrix consisting of the residual component of the covariance matrix. By using a two-step uncertainty set shrinkage method, the proposed beamformer constructs the error matrices by integrating the estimated spatial spectrum over the shrinked uncertainty set at a cost of low computational complexity. After extracting the principal component of the reconstructed ICM and DSCM, the INCM and the SV of the source of interest (SOI) can be estimated without solving any optimization problem. Both of numerical simulations and experimental results demonstrate that the performance of the proposed algorithm is robust with several categories of SV mismatches. [ABSTRACT FROM AUTHOR]