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It is well known that numerous operational, material and design variables act upon the performance of a plant-based microbial fuel cell which is an emerging sustainable and versatile energy device like hydrogen fuel cells. However, due to the high complexity of these bioelectrochemical systems, new solutions are required to optimize performance and uncover hidden relationships between dominant fuel cell variables. For this purpose, a database of 229 observations was created for plant-based microbial fuel cells (PMFCs) with 159 descriptor variables and a target variable (maximum power density) based on experimental results from 51 recent publications. Then, some machine learning solutions like principal component analysis (PCA), classification trees and SHapley Additive exPlanations (SHAP) analysis were applied. The PCA indicated mainly two routes involving low and high chemical oxygen demand (COD) towards high maximum power density which consists of the plant family, wastewater type, support media, construction design, separator type, anode and cathode electrodes and light source. SHAP analysis revealed that the most important factors for high performance are operating temperature, natural light, soil support medium, and constructed wetland design. Finally, the classification tree successfully demonstrated nine routes towards high maximum power density which exclude the use of graphite plate cathode electrodes. [Display omitted] • Machine learning was applied to determine high power density routes in PMFC. • Principal component analysis indicated two routes to high maximum power density. • Shapley analysis revealed the most important factors for high-performance. • Most influential variables were found as temperature, natural light, soil support. [ABSTRACT FROM AUTHOR]

With the continuous increase in the resolution of high-resolution remote sensing satellite images, shadows have increasingly impacted feature target recognition and classification in images, urban reconstruction, and image interpretation. Although various shadow detection methods exist for different features, undetected small shadowed areas, misclassified dark areas, and highlighted non-shadowed areas remain. Moreover, it is still difficult to distinguish waters from shadowed regions owing to their similar characteristics. To further solve these problems, we propose a novel mixed shadow detection index (MSDI) for extracting urban feature shadows, using the original information of the first principal component that contains the differences between most of the different objects in the image, as well as the difference features in the shadowed areas and water bodies. We evaluated the effectiveness and robustness of our method by conducting comparison experiments using Gaofen-1, Gaofen-2, and WorldView-3 images from different scenes, times, and acquisition locations. Through visual analysis and data analysis of our method, we found that the method achieved excellent shadow detection results, with an average total accuracy 94% for shadow detection. The proposed shadow detection algorithm could accurately recognize waters, and it could also accurately recognize easily missed small shadowed areas and easily confused non-shadowed areas. [ABSTRACT FROM AUTHOR]

The presence of pesticides in fogwater plays a major role in accumulating relatively substantial levels of trace compounds due to their unique physico-chemical characteristics. The radiation wintertime fog in Alsace has been studied in the past few years (between 2015 and 2021) at four sites (Geispolsheim, Erstein, Strasbourg, and Cronenbourg). Fog samples are extracted using the liquid-liquid extraction (LLE) performed on the XTR Chromabond cartridge coupled with gas/liquid chromatography-tandem mass spectrometry (GC-MS/MS and LC-MS/MS). The samples are found to be contaminated by 25 semi- and non-volatile currently-used and previously-banned pesticides (like procymidone) and 16 organochlorine pesticides (OCPs) at notable levels and high detection frequency (DF). The analysis also reveals that Cronenbourg is the most contaminated site (31.5 ± 3.0 μg. L−1), followed by Erstein (23.1 ± 17.0 μg. L−1), Strasbourg (23.0 ± 3.5 μg. L−1), and Geispolsheim (22.8 ± 7.7 μg. L−1). Pearson and principal component analyses (PCA) prove the simultaneous application of fungicides, insecticides, and herbicides, and their atmospheric transport, mainly through west-southern air currents, from highly impacted sites to near-by urban and less impact sites (Strasbourg and Cronenbourg). The levels of OCPs are found at lower concentrations at all sites than other pesticides, of which dichlorodiphenyltrichloroethane (DDT) and its metabolites have the highest contribution (27%), while hexachlorobenzene (HCB) has the least contribution (3%). Ratio analysis indicates the historical emission of DDTs, whereas a recent and local input of lindane and endosulfan has been observed. [Display omitted] • Fogwater proves its ability to scavenge pesticides including organochlorine. • Fungicides, herbicides, and insecticides are highly correlated with each other. • Pesticide's transport through air currents contaminates non-agricultural areas. • Significant intra-site and year variabilities are detected in many cases. • Sixteen organochlorine pesticides out of twenty-two are still found even their ban. [ABSTRACT FROM AUTHOR]

• A physics-informed geostatistical approach (PIGA) is developed for improving flood prediction in riverine systems during hurricane events. • PIGA integrates reduced spatial correlations derived from hydrodynamic models with a reduced geostatistical approach. • PIGA consistently outperforms physical models in predicting water surface elevations, particularly in areas with limited bathymetry data. • Reduced spatial correlations derived from one hurricane event can effectively predict other events. • PIGA is computationally efficient compared to traditional physical models, rendering its potential to provide real-time prediction during flooding events. This study presents the Physics-Informed Geostatistical Approach (PIGA), a novel methodology aimed at improving flood prediction accuracy in riverine systems during hurricane events. PIGA integrates reduced spatial correlations derived from fluvial hydrodynamic models with a reduced geostatistical approach (RGA) based on monitoring data to provide real-time estimations of water surface elevations (WSE) across river channels and reaches. Focusing on the Savannah River basin, an area prone to riverine flooding during hurricane seasons, we developed a 1D-2D HEC-RAS model as the physical model and compared its performance with PIGA for various hurricane events, including Hurricane Matthew, Irma, Dorian, and Idalia. Our findings reveal that PIGA consistently outperforms the HEC-RAS model in predicting WSE, particularly in areas with limited bathymetry data. Statistical analyses demonstrate significant improvements in prediction accuracy achieved by PIGA, emphasizing its potential as a reliable tool for flood prediction in data-deficient regions. Additionally, we explore the transferability of reduced spatial correlations across different hurricane events. Our results indicate that spatial correlations derived from one hurricane event can effectively predict WSE for other events, with Hurricane Irma's spatial correlations proving to be the most versatile for our study site. However, we also observe variability in spatial correlations among different hurricane events, highlighting the importance of selecting the optimal alternative spatial reference for prediction. Furthermore, we highlight PIGA's computational efficiency, with simulations completed in significantly less time compared to traditional physical models, rendering its potential to provide real-time prediction of WSE during flooding events. [ABSTRACT FROM AUTHOR]

In this study, box and whisker and principal component analysis, as well as classification and regression tree modeling as a part of machine learning were performed on a database constructed on PEM (polymer electrolyte membrane) electrolysis with 789 data points from 30 recent publications. Box whisker plots discovered that pure Pt at the cathode surface, Ti at the anode support, the existence of Pt, Ir, Co, Ru at the anode surface, Ti porous structures at the electrodes, pure water-electrolyte and Nafion and Aquivion type membranes in proton exchange electrolyzer provide the highest performances. Principal component analysis indicated that when cathode surface consists of mostly pure Ni, when anode electrode has no support or vanadium (10–20%) doped TiO 2 support and when anode electrode surface consists of cobalt-iron alloys (0.5:0.5 and 0.333:0.666 mol ratio) or RuO 2 , there is a risk for low-performance. Classification trees revealed that other than current density and potential, cathode surface Ni mole fraction, anode surface Co mole fraction are the most important variables for the performance of an electrolyzer. Finally, the regression tree technique successfully modeled the polarization behavior with a RMSE (root mean square error) value of 0.18. [Display omitted] • Carbon, Pt and Ti based materials have high PEM electrolyzer performance. • Proton conductor electrolyzers are superior to anion exchange electrolyzers. • Ni, unsupported or vanadium doped TiO 2 , CoFe and RuO 2 have performance risks. • Ni and Co mole fractions are the most important input variables. • Regression tree modeling can predict PEM electrolyzer polarization. [ABSTRACT FROM AUTHOR]

• The objective of this research is to examine the possibility of introducing telemedicine-based services in developing countries. • We surveyed developing countries in Asia and Africa. In Asia, the study was conducted in nine developing countries, excluding those whose data were unavailable. In Africa, 13 countries that had a per capita GDP less than USD 1,000, and where data were unavailable, were excluded. • The target countries were classified into five clusters according to economic indicator, medical environment indicator, IT spread index. • The results of this research suggest that there is a good possibility of introducing remote telemedicine services in South Africa and that the possibility for its introduction is also high in Thailand where internet spread is more advanced. In some developing countries, despite advancements in Information Technology (IT), medical resources are scarce; hence, introduction of telemedicine services can solve this problem. In this study, we examined the possibility of introducing telemedicine-based services in developing countries utilizing the available data. In Asia, the study was conducted in nine developing countries, excluding those where data were unavailable. In Africa, thirteen countries whose per capita Gross Domestic Product (GDP) was less than USD 1000, and where data were unavailable, were also excluded. We chose the number of doctors, nurses, and midwives as indicators of the healthcare environment. We used the number of internet contracts and mobile phone contracts as indicators of IT penetration, and set per capita GDP and its growth rate as economic indicators. We combined the two continents' data and performed a principal component analysis (PCA) and cluster analysis. We used cluster analysis to classify the target countries into the following five clusters: Cluster A: Algeria, Egypt, Morocco, Indonesia, Ghana, Tunisia, Madagascar, Nigeria, and Thailand; Cluster B: Bangladesh, Ethiopia, Kenya, Uganda, India, and Pakistan; Cluster C: Sudan, Malaysia, Vietnam, Tanzania, Philippines, and China; Cluster D: South Africa, and Cluster E: Japan and Singapore. As a result of conducting PCA, Cluster A emerged as the region with the highest progressiveness and development possibility. Introduction of telemedicine services has been visualized by using cluster analysis and PCA. However, it is necessary to incorporate future medical needs as indicators to make a more appropriate assessment of its potential. [ABSTRACT FROM AUTHOR]

Modeling time series data is an important issue in many areas. Reservoir computing (RC) is a promising tool to build time series model due to its dynamic characteristic and simple training way. Asynchronous deep reservoir computing (ADRC) is an improvement version of the traditional RC. It solves time-dependent tasks more efficiently than traditional RC because of its rich dynamics and flexible short-term memory (STM). Nevertheless, it has been an open issue to design RC's or ADRC's reservoir topology owing to large amounts of random factors. To promote the solution of this problem, the paper proposes a feature-recombinant ADRC (FR-ADRC) for modeling time series data. In the FR-ADRC scheme, the first sub-reservoir is designed as the feature-adaptive layer, and a trainable matrix C is introduced into this layer. By learning C , the singular value (SV) distribution of the first layer could be adjusted. Further, the principal components of the reservoir topology can be extracted by the principal component analysis (PCA). Then a new temporary reservoir is constructed by recombining these extracted components. The subsequent information processing is carried out based on the recombinant reservoir, which can be adaptive to the input signals. The validity of the FR-ADRC is tested by modeling some numerical and real-life time series data. Experimental results show that the proposed approach is better than the traditional ADRC in modeling precision, generalization ability and stability. ● Introduce the conceptor-like matrix into the topology optimization of the ADRC. ● Propose the FR-ADRC approach and derives the learning algorithm for it. ● Apply the FR-ADRC on modeling time series data. [ABSTRACT FROM AUTHOR]

The ozonation process is an effective method for removing hazardous wastes in water. To better characterize and understand the factors influencing the reaction rate constants of organic compounds during the ozonation process, a quantitative-structure–activity-relationship (QSAR) model was developed using the principal component analysis and multiple linear regression (PCA-MLR) method. In principal component analysis, five of the seven components were found to mechanistically and statistically affect the reaction rate constants. Component 1 was represented by the number of oxygen atoms ( n O ) and minimum value of bond order ( BO n ), component 2 was represented by the energy of the highest occupied molecular orbital ( E HOMO ), and component 3 and 4 were dominated by the largest change in the charge of each atom during nucleophilic attack ( f ( + ) x ) and the energy of the lowest unoccupied molecular orbital ( E LUMO ), respectively. The temperature ( T ) was the most important factor for component 7. The optimal model was l n k O 3 = 4.102 + 0.007 T - 3.419 BO n + 1.765 f ( + ) x + 5.698 E LUMO - 4.016 E HOMO - 0.241 n O , with the following evaluation index values: squared correlation coefficient ( R 2 ) = 0.916, internal validation ( q 2 ) = 0.895 and external validation ( Q ext 2 ) = 0.962. Based on these evaluation indices, Y-randomization validation and the definition of the applicability domain, the optimal model was stable, robust and predictive. We anticipate that our work will provide a credible theoretical foundation for estimating the reaction rate constants for degradation of high-molecular weight organic compounds during ozonation over a temperature range from 25 to 60 °C. [ABSTRACT FROM AUTHOR]

Principal component analysis (PCA) has been used for n/γ discrimination, but the influencing factors were not considered. In this paper, the influence of dataset construction was analyzed with pulses from a liquid scintillator. Four datasets were constructed according to the normalization factor and the start point. After dimensionality reduction, the distributions of the datasets were examined and figure of merits were calculated. Results show that the "tail-peak" dataset has the best performance and outperforms charge comparison method. It means that to optimize the performance and adaptability of PCA-based n/γ discrimination, the dataset should be constructed with the falling edge integration as the normalization factor and the pulse peak as the start point. • n/γ discrimination is achieved with PCA. • Four datasets are constructed according to the normalization factor and the start point. • The "tail-peak" dataset obtains the best discrimination performance and adaptability. [ABSTRACT FROM AUTHOR]

Topography is one of the key factors affecting soil organic carbon (SOC) distribution and soil redistribution (erosion or deposition) because it influences the gravity-driven movement of soil by water flow and tillage operations. In this study, we examined impacts of sixteen topographic metrics derived from Light Detection and Ranging (LiDAR) data on SOC distribution in agricultural fields. We adopted the fallout radionuclide 137 Cesium ( 137 Cs) to estimate soil redistribution rates and patterns of SOC distribution across 560 sampling locations at two field sites and a larger scale for the Walnut Creek watershed in Iowa. Then, using stepwise ordinary least square regression (SOLSR) and stepwise principal component regression (SPCR), topography-based models were developed to simulate spatial patterns of SOC density and soil redistribution rates. Results suggested that erosion and deposition of topsoil were regulated by topography with soil gain in lowland areas and soil loss in sloping areas. Topographic wetness index (TWI) and relief were the most influential variables controlling SOC density and soil redistribution rates, respectively, and were of primary importance in SOLSR models. All topography-based models developed through SPCR and SOLSR demonstrated good simulation performances, explaining > 62% variability in SOC density and soil redistribution rates across two field sites with intensive samplings. However, the SOLSR models showed lower reliability than the SPCR models in predicting soil properties at a watershed scale. Results of this study highlighted the topography-based SPCR model as an effective and a promising tool allowing for scaling of in situ SOC density and soil redistribution rates at crop sites to a large-scale watershed, and provided valuable insight into the spatial patterns of SOC distribution. [ABSTRACT FROM AUTHOR]

The quality of machined surface can be improved by choosing the right values of the associated parameters through an optimization process. Recently, researchers have used nonlinear programming models, soft-computing approaches, and hybrid of them to estimate the surface roughness of abrasive waterjet (AWJ) machining. Some researchers have performed a second order polynomial regression (SOPR) as a basis in developing a nonlinear programming in AWJ machining. Nevertheless, the SOPR was developed without considering the existence of multicollinearity which can lead to inappropriate prediction. Besides, it also needs a specific nonlinear regression model in advance. It will be difficult to specify an appropriate nonlinear model of SOPR since it does not usually involve all combination variables in its model. Instead, kernel principal component regression (KPCR) will be employed to overcome the weaknesses of the SOPR. After development and model selection in the KPCR models, the best KPCR is used as an objective function in a nonlinear programming model of AWJ with a certain set of constraints. Single genetic algorithm (GA) and its variants can be conducted to solve the nonlinear programming problem. However, they can yield different decision values due to randomness of their initial population which implies that the best decision values may not converge in a certain optimum solution. Under this circumstance, multiple adaptive probabilities genetic algorithm (MAPGA) combined with a penalty method to solve the optimization problem is proposed. Hybrid of KPCR and MAPGA gives more stable solution compared to hybrid of KPCR with single genetic algorithm and original adaptive probabilities genetic algorithm. Our proposed technique also provides an optimum solution in reasonable time. [ABSTRACT FROM AUTHOR]

This study aims to determine the seasonal variations and composition of suspended particulate matter in different sizes PM 1.0 , PM 2.5 , PM 10 and the total suspended particles (TSP) emitted at major intra-urban traffic intersections (TIs) of Ilorin metropolis. The concentration levels of PM (PM 1.0 , PM 2.5 , PM 10 ) obtained at the TIs during the rush hours (45.1, 77.9, and 513 μg/m 3 ) are higher than the levels obtained for the non-rush hour periods (42.3, 62.7, and 390 μg/m 3 ). The average on-road respiratory deposition dose (RDD) rates of PM 1.0 , PM 2.5 and PM 10 during the dry period at TIs types was found to be about 24%, 9% and 25% higher than those obtained during the wet period. Based on EF values calculated, Pb and Zn were anthropogenically-derived while Fe, Mn, Cr, Cu and Mg were of crustal source. Principal component analysis (PCA) has been applied to a set of PM data in order to determine the contribution of different sources. It was found that the main principal factors extracted from particulate emission data were related to exhaust and non-exhaust emissions such as tyre wears, oil and fuel combustion sources. [ABSTRACT FROM AUTHOR]

When the number of assets (N) exceeds the number of time periods (T), the sample covariance matrix is singular, and the portfolio optimization problem cannot be solved via traditional mean-variance algebra. In such a case, the Moore–Penrose (MP) generalized inverse becomes handy: In this paper, we critically examine the MP solution of the portfolio optimization problem. Our findings include: i) the MP solution leads to a portfolio of “pseudo-riskfree composite assets”; ii) it is orthogonal to principal components, iii) most importantly, it is poorly diversified. We illustrate our findings using equity market data. [ABSTRACT FROM AUTHOR]

This paper describes the second part of a series of investigation to develop algorithms for simultaneous retrieval of aerosol parameters and surface reflectance from the future hyperspectral and geostationary satellite sensors such as Tropospheric Emissions: Monitoring of POllution (TEMPO). The information content in these hyperspectral measurements is analyzed for 6 principal components (PCs) of surface spectra and a total of 14 aerosol parameters that describe the columnar aerosol volume V total , fine-mode aerosol volume fraction, and the size distribution and wavelength-dependent index of refraction in both coarse and fine mode aerosols. Forward simulations of atmospheric radiative transfer are conducted for 5 surface types (green vegetation, bare soil, rangeland, concrete and mixed surface case) and a wide range of aerosol mixtures. It is shown that the PCs of surface spectra in the atmospheric window channel could be derived from the top-of-the-atmosphere reflectance in the conditions of low aerosol optical depth (AOD ≤ 0.2 at 550 nm), with a relative error of 1%. With degree freedom for signal analysis and the sequential forward selection method, the common bands for different aerosol mixture types and surface types can be selected for aerosol retrieval. The first 20% of our selected bands accounts for more than 90% of information content for aerosols, and only 4 PCs are needed to reconstruct surface reflectance. However, the information content in these common bands from each TEMPO individual observation is insufficient for the simultaneous retrieval of surface’s PC weight coefficients and multiple aerosol parameters (other than V total ). In contrast, with multiple observations for the same location from TEMPO in multiple consecutive days, 1–3 additional aerosol parameters could be retrieved. Consequently, a self-adjustable aerosol retrieval algorithm to account for surface types, AOD conditions, and multiple-consecutive observations is recommended to derive aerosol parameters and surface reflectance simultaneously from TEMPO. [ABSTRACT FROM AUTHOR]

Soils around coal mining are important reservoir of hazardous elements (HEs), nanominerals, and ultrafine compounds. This research reports and discusses the soil concentrations of HEs (As, Cd, Cr, Cu, Ni, Pb, and Zn) in coal residues of abandoned mines. To assess differences regarding environmental impact and risk assessment between coal abandoned mines from the Santa Catarina state, eighteen coal cleaning rejects with different mineralogical and chemical composition, from eight abandoned mines were collected. Nanominerals and ultra-fine minerals from mining-contaminated areas were analyzed by X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), and high-resolution transmission electron microscope (HR-TEM), providing new information on the mineralogy and nano-mineralogy of these coal residues. The total contents of 57 elements (HEs, alkali metals, and rare earth elements) were analyzed by Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The calculation of NWACs (Normalized Average Weighted Concentration), together with the chemometric analysis by Principal component analysis (PCA) confirmed the variability of the samples regarding their city and their mine of origin. Moreover, the results confirmed the existence of hotspots in mines near urban areas. [ABSTRACT FROM AUTHOR]

With traditional driving cycle predictive model, the state point in vehicle-acceleration projection plane couldn’t cover the real driving state completely. And date-missing caused by this lead to interruption of the prediction process. So in this paper, a real-time prediction model with variable horizon is proposed to solve the problem. Real driving data is used to reconstruct the driving cycle and the accuracy of the real time prediction model could be estimated based on historical information. By using principal component analysis and cluster analysis, an online prediction model with variable horizon based on Marco Chain is established. The correctness of this method is verified by experiment of Hardware-in-loop simulation. And the result shows that the accuracy of variable time prediction model is 8.203km/h, which has been improved by 20% comparing with fixed time prediction model. [ABSTRACT FROM AUTHOR]

Natural graphite was oxidized and exfoliated via two different methods, leading to two types of graphene oxide (GO) materials. The obtained materials were reduced by three different reducing agents including: hydrazine hydrate, ascorbic acid and sodium borohydride, giving thus six kinds of reduced graphene oxide (RGO) materials. The obtained materials were characterized using Fourier transform infrared spectroscopy (FT-IR), X-ray photoelectron spectroscopy (XPS) and Raman spectroscopy. The RGOs were then used to fabricate different gas sensors and their electrical resistances were recorded upon exposing to various volatile organic compounds vapors (VOCs). Gas sensing selectivity of each RGO was significantly affected by the synthesis condition. The RGO-based sensor array was fabricated and its capability for discrimination of seven kinds of VOCs was evaluated, utilizing principal component analysis and cluster analysis methods. Loading plot indicated that the presence of five RGO-based sensors could effectively discriminate the aimed vapors. The electronic nose, containing five kinds of RGOs, was used for the classification of seven kinds of VOCs at their different concentrations. [ABSTRACT FROM AUTHOR]

Little is known about the natural phenomena that govern the chemical composition of spring waters in Alvand mountain ecosystem. A total of 50 spring water samples of the Avand mountains, Hamadan, western Iran were collected and analyzed for the main components in an effort to both identify ion chemistry and establish background concentrations of major ions and some trace elements (Cd, Fe, Mn, Ni, Zn).The order of relative abundance of major cations in the spring waters was Ca, Mg, Na, K, while that of anions was HCO 3 , Cl, SO4, NO 3 . Most spring water samples were undersaturated with respect to calcite. The major water types in spring waters were Ca–HCO 3 (group 1), CaHCO 3 –Cl, CaMg–HCO 3 Cl, and CaMg–Cl–HCO 3 (group 2), which were mainly due to the dissolution of carbonate minerals and silicate weathering and partly due to the ion exchange. Activity diagrams showed that spring waters fall into the kaolinite field due to the short interaction with silicate minerals. In general, mineralization, pH, mean concentrations of Ca, Mg, HCO 3 , TDS, saturation indices of calcite, dolomite and PCO 2 were greater in water samples from the group 1 than in water samples from group 2. Spring waters were mainly soft water (84%) in group 2 and hard water (16%) in group 1. The factor analysis performed on spring water samples identified four factors controlling their variability in spring water samples. Four extracted factors explained 84.7% of data set variance. Factor 1 had the highest factor loadings of EC, HCO 3 , SO 4 , Cl, Ca, Mg, Na, Mn, Cd, Ni, and Fe, while factor 2 had the highest factor loadings of Zn. Factors 3 and 4 had the highest factor loading for NO 3 , P and K, respectively. Factors 1, 2 and 4 together may be related to the dissolution of carbonate minerals (group 1) and silicate weathering, while factor 3 may be due to the anthropogenic input. Different statistical methods were used in the evaluation of background values of the spring waters. Trace element concentration in spring waters was low and associated with local mineralogy. [ABSTRACT FROM AUTHOR]

The objective of this paper is to examine whether bank and stock market development contributes to reducing income inequality and poverty in emerging countries. Using dynamic panel data methods with an updated dataset for the period 1987–2011, we assess the finance–inequality–poverty nexus by taking the separate and simultaneous impacts of banks and stock markets into account. Mixed explanatory findings on panel studies suggest that although financial development promotes economic growth, this does not necessarily benefit those on low-incomes in emerging countries. For the finance–poverty link, we find that neither banks nor stock markets play a significant role in poverty reduction. [ABSTRACT FROM AUTHOR]

As a comprehensive capacity of nature, society and humans, integrated carrying capacity (ICC) is the driving force of regional socioeconomic development. Only when an ecosystem is under-loaded can socioeconomic development be sustainable. ICC is an accumulative total value of each indicator's carrying capacity, which reflects a static status. The ICC prediction analysis is one prerequisite to making economic development plans. In this paper, a dynamic prediction model is developed by using the model of set pair analysis (SPA) to predict the growth tendency of ICC. The model is tested in a case comprising eight coastal cities in Yangtze. (1) The average error rate of this prediction model is merely 0.38%, and the lowest error rate is 0.01%. The SPA model is better to predict ICC tendencies. (2) According to the national development plan, the eight cities' ICC is predicted in 2015. (3) The prediction model is a multiple method that can contain all indicators of ICC. This model can estimate the maximal carrying capacity of a natural ecosystem to make the most suitable economic development policy. The socioeconomic development must comply with the under-loaded capacity to maintain sustainable development. [ABSTRACT FROM AUTHOR]

An analysis of enterprise performance is considered. The problem is solved based on the analysis of the base indicators of enterprise performance in terms of improving economic stability. An autoregulation mechanism of the economical stability of an enterprise is developed. An eigenstate method is proposed to analyse the basic indicators of the enterprise as it allows to construct an economomical stability model of such enterprise. The article describe the methodology for analyzing the economic stability of an enterprise on the basis of eigenstate method. It provides formulas for calculating the complex indicator of economic stability. The efficiency of the methodology is demonstrated with evidence from the economic stability analysis of the trading company. [ABSTRACT FROM AUTHOR]

We analyzed reflectance spectra of chromitite samples and its associated rocks using quantitative mineralogical data to identify the diagnostic absorption features of these rocks. Further, we derived few new indices to delineate chromitite and associated ultramafites in satellite image acquired by Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) sensor. The spectral indices were derived based on the ASTER sensor convolved laboratory derived reflectance spectra of these rocks. Analysis of ASTER convolved laboratory spectra of these rocks revealed that chromitite had higher reflectance in band 2 of ASTER data in comparison to band 1 and band 3. Mineralogical analysis of chromitite spectra was used as reference to understand the mineralogical importance of this signature. On the other hand, reflectance of metaultramafite was relatively higher in band 3 in comparison to band 1 of ASTER sensor. Based on above spectral contrast, two new indices termed as ASTER based Chromitite Index ((band 2 * band 2)/(band 1 + band 3)) for chromitite and ASTER based Ultramafic Index ((band 1/band 3)) for meta-ultramafite were proposed. We utilized these indices with other popular ASTER indices for gneiss ((band 5 + band 7)/band 6)) and metabasalt (band 6/band 8) and serpentine rich rock s ((band 7 + band 9)/band 8) to spatially delineate chromitite – ultramafic–mafic rock assemblage of the study area. In this regard, few index derived false color composite (I-FCC) were derived. Results of index based delineation were validated with reference geological map and field data on locations of rock exposures. We also validated two new indices (ACI, AUI) based on spectral parameters in addition to geological validation of these indices and its FCC images. Further, we also made eigenvector based analysis of principal components (PC) derived from aforementioned indices to identify fewer PC’s than the number of indices used to spatially map the rocks of the study area. We found PC-2 was suitable for delineating meta-ultramafite from chromitite; PC-4 was suitable for delineating metabasalt and PC-5 for gneiss. [ABSTRACT FROM AUTHOR]

In beef cattle genetic evaluation, principal component models of the additive genetic effect could be used to incorporate several traits in the same analysis, without an important increase in the number of parameters to be estimated. In this study, multitrait (MT) and reduced-rank models were compared for their ability to estimate parameters and predict breeding values for weaning weight, yearling weight, weaning hip height, yearling hip height, weaning to yearling weight gain, scrotal circumference, and age at the first calving. Data obtained were from 74,388 Nelore animals, born to 1441 sires and 28,502 cows. Six analyses were performed using a MT model that incorporated all the traits simultaneously and five reduced-rank models for the genetic additive direct (co)variance matrix, fitting the first one (PC1), two (PC2), three (PC3), four (PC4), and five (PC5) principal components. The model considering the first three principal components (PC3) provided the best fit. Direct and maternal heritability and the respective standard errors obtained from the MT and PC3 models were similar. In general, the PC3 model provided slightly stronger genetic correlations between the traits when compared with those obtained with the MT model. The rank correlations between the breeding values predicted with the MT and PC3 models for the different traits ranged from 0.93 to 0.99. When 2% and 10% of the best sires were selected on the basis of breeding values predicted by the MT model, the degree of concordance with the PC3 model ranged from 86% to 97%. The first three principal components explained most of the genetic variation among animals, suggesting that major changes should not be expected in the sire's classification on the basis of breeding values predicted for growth and reproductive traits. Models of principal components could be used for beef cattle genetic evaluation, especially when considering several economic traits in the same analysis. [ABSTRACT FROM AUTHOR]

Abstract: This article presents various important tools of chemometrics utilized as data evaluation tools generated by various hyphenated analytical techniques including their application since its advent to today. The work has been divided into various sections, which include various multivariate regression methods and multivariate resolution methods. Finally the last section deals with the applicability of chemometric tools in analytical chemistry. The main objective of this article is to review the chemometric methods used in analytical chemistry (qualitative/quantitative), to determine the elution sequence, classify various data sets, assess peak purity and estimate the number of chemical components. These reviewed methods further can be used for treating n-way data obtained by hyphenation of LC with multi-channel detectors. We prefer to provide a detailed view of various important methods developed with their algorithm in favor of employing and understanding them by researchers not very familiar with chemometrics. [Copyright &y& Elsevier]

Genotypes, phenotypes and pedigrees of 6 breeds of dairy sheep (including subdivisions of Latxa, Manech, and Basco-Béarnaise) from the Spain and France Western Pyrenees were used to estimate genetic relationships across breeds (together with genotypes from the Lacaune dairy sheep) and to verify by forward cross-validation single-breed or multiple-breed genetic evaluations. The number of rams genotyped fluctuated between 100 and 1,300 but generally represented the 10 last cohorts of progeny-tested rams within each breed. Genetic relationships were assessed by principal components analysis of the genomic relationship matrices and also by the conservation of linkage disequilibrium patterns at given physical distances in the genome. Genomic and pedigree-based evaluations used daughter yield performances of all rams, although some of them were not genotyped. A pseudo-single step method was used in this case for genomic predictions. Results showed a clear structure in blond and black breeds for Manech and Latxa, reflecting historical exchanges, and isolation of Basco-Béarnaise and Lacaune. Relatedness between any 2 breeds was, however, lower than expected. Single-breed genomic predictions had accuracies comparable with other breeds of dairy sheep or small breeds of dairy cattle. They were more accurate than pedigree predictions for 5 out of 6 breeds, with absolute increases in accuracy ranging from 0.05 to 0.30 points. They were significantly better, as assessed by bootstrapping of candidates, for 2 of the breeds. Predictions using multiple populations only marginally increased the accuracy for a couple of breeds. Pooling populations does not increase the accuracy of genomic evaluations in dairy sheep; however, single-breed genomic predictions are more accurate, even for small breeds, and make the consideration of genomic schemes in dairy sheep interesting. [ABSTRACT FROM AUTHOR]

Abstract: Recently, we presented a first deterministic discrete time (DDT) analysis of the normalized normalized projection approximation subspace tracking (Normalized PAST) algorithms, for estimating principal and minor components of an input signal. The analysis shows that the DDT systems of the Normalized PAST algorithms converge to the desired eigenvectors under certain sufficient conditions on the forgetting factor . However, it has not yet been clarified whether the sufficient conditions can be relaxed or not for guaranteed convergence. In this paper, by characterizing the maximal ranges of the forgetting factor, we establish the necessary and sufficient conditions for convergence of the DDT systems of the Normalized PAST algorithms. The proposed maximal range of the forgetting factor, for the minor component estimation, is doubled from the range assumed in the first DDT analysis, while the proposed maximal range of the forgetting factor, for principal component estimation, achieves the full range (0, 1]. Numerical examples further confirm the results. [Copyright &y& Elsevier]

This study aims to model data that contain two correlated responses, multicollinearity in predictors, and has a pattern that does not follow a parametric form. We propose the use of principal component analysis of truncated splines in a biresponse model. The use of principal components to overcome correlations between predictors, and biresponse to overcome correlations between responses by involving weighted estimates from the covariance matrix. In the PCA spline contains the optimal knot points which control the accuracy of the regression curve. The knot point chosen is the point which has the smallest GCV value among all knot points. In addition, we also consider the value of MSE in showing the model's ability. We demonstrated the ability of this method through simulation studies and obtained smaller GCV and MSE values compared to parametric regression and PCA. Furthermore, the data for type 2 diabetes mellitus, obtained two main components with different patterns of change. Based on the analysis, it was found that LDL cholesterol, total cholesterol, and triglycerides had a greater effect on changes in the pattern of fasting blood sugar and HbA1C. The small errors of the simulation data indicate the accurate capabilities of the biresponse spline PCA model. The diabetes data analysis, it shows that patients need to pay attention to their cholesterol and triglyceride levels within normal limits. [ABSTRACT FROM AUTHOR]

Abstract: Sensitivity of meteorological variation to air quality has attracted people's attention since climate change became a world issue. The goal of this study is to investigate the sensitivity of ground-level ozone concentrations to temperature variation in Taiwan. Several multivariate regression models were built based on historical data of ozone and meteorological variables at three cities located in northern, mid-western, and southern Taiwan. Results of descriptive statistics indicate that the severe pollution from the highest to the minor conditions following by the order of the southern (Pingtung), mid-western (Fengyuan), and the northern sites (Hsichih). Multiple regression models containing a principal component trigger variable effectively simulated the historical ozone exceedance during 2004–2009. Inclusion of the PC trigger were improved R 2 from the lowest 0.38 to the highest 0.58. High probability of detection and critical success index (mostly between 85% and 90%) and low false alarm rates (0–2.6%) were achieved for predicting the high ozone days (≧100 ppb). The results of sensitivity analysis indicated that (1) the ozone sensitivity was positively correlated with the temperature variation, (2) the sensitivity levels were opposite to that of the ozone problem severity, (3) the sensitivity was mostly apparent in ozone seasons, and (4) the sensitivity strongly depended on the seasonality in the urban cities Hischih and Fengyuan, but weakly depended on seasonality in the rural city Pingtung. [Copyright &y& Elsevier]

Abstract: We present a unified convergence analysis, based on a deterministic discrete time (DDT) approach, of the normalized projection approximation subspace tracking (Normalized PAST) algorithms for estimating principal and minor components of an input signal. The proposed analysis shows that the DDT system of the Normalized PAST algorithm (for PCA/MCA), with any forgetting factor in a certain range, converges to a desired eigenvector. This eigenvector is completely characterized as the normalized version of the orthogonal projection of the initial estimate onto the eigensubspace corresponding to the largest/smallest eigenvalue of the autocorrelation matrix of the input signal. This characterization holds in general case where the eigenvalues are not necessarily distinct. Numerical examples show that the proposed analysis demonstrates very well the convergence behavior of the Normalized PAST algorithms which uses a rank-1 instantaneous approximation of the autocorrelation matrix. [Copyright &y& Elsevier]

Abstract: A varietal description should help to resolve the conflicts of identification that may arise in the registration and protection of cultivars. The objective of this study was to evaluate the discriminatory capacity and to select minimum descriptors for the characterization and evaluation of yellow passion fruit varieties (Passiflora edulis Sims f. flavicarpa Deg.). Twenty quantitative and eight qualitative descriptors related to plant, leaves, flowers and fruits traits were analyzed in 24 genotypes of yellow passion fruit. The quantitative descriptors were subjected to a principal components analysis, and the selection of descriptors was based on direct selection and Singh method, whereas the qualitative descriptors were analyzed by correlation. Eight quantitative descriptors were discarded by direct selection, and seven were discarded using Singh method. However, only four descriptors common to both methods were considered to be redundant. Of the eight qualitative descriptors evaluated, two were discarded because they were correlated with the others. Therefore, the list of minimum descriptors to describe yellow passion fruit varieties can be composed of 22 descriptors, 16 quantitative and six qualitative, with a high contribution to the total variance and low correlation with the others. There was no loss of information with the elimination of those descriptors. In addition to the smaller number of descriptors providing valuable information, it will also allow a reduction in the labor, time and resources spent in the characterization of new varieties of yellow passion fruit. [Copyright &y& Elsevier]

Abstract: Spatiotemporal dimensionality refers to the continuum of spatial and temporal patterns in an image time series. Time-Space characterization refers to a way of representing this continuum of patterns as combinations of spatial and temporal constituents — with a minimum of assumptions about the forms of the patterns. Patterns can be related to processes through modeling. By combining characterization and modeling, two complementary analytical tools can be used together so that each resolves a key limitation of the other. This study describes a straightforward extension of Principal Component Analysis and Spectral Mixture Analysis to multitemporal imagery and illustrates how characterization of the dimensionality and eigenstructure of the data can inform modeling of the processes represented in the data. The relationships among spatiotemporal processes can be represented as combinations of temporal endmembers in a temporal feature space where the dimensions represent different components of the temporal patterns present in the data. The topology of the feature space and the processes being modeled together inform the selection of temporal endmembers and the structure of the model chosen to represent the processes. The dimensionality revealed by the characterization can also provide a partial solution to the problem of endmember variability. The characterization and modeling process is illustrated with the vegetation phenology of the Ganges–Brahmaputra delta using a MODIS vegetation index time series. Additional applications and limitations of Time-Space characterization and mixture modeling are further illustrated by comparing the eigenstructures and temporal feature spaces of Landsat vegetation fraction and DMSP-OLS night light time series. [Copyright &y& Elsevier]

Abstract: The drawbacks of SVD-based image watermarking are false positive, robust and transparency. The former can be overcome by embedding the principal components of the watermark into the host image, the latter is dependent on how much the quantity (i.e., scaling factor) of the principal components is embedded. For the existing methods, the scaling factor is a fixed value; actually, it is image-dependent. Different watermarks need the different scaling factors, although they are embedded in the same host image. In this paper, two methods are proposed to improve the reliability and robustness. To improve the reliability, for the first method, the principal components of the watermark are embedded into the host image in discrete cosine transform (DCT); and for the second method, those are embedded into the host image in discrete wavelets transform (DWT). To improve the robustness, the particle swarm optimization (PSO) is used for finding the suitable scaling factors. The experimental results demonstrate that the performance of the proposed methods outperforms than those of the existing methods. [Copyright &y& Elsevier]

Abstract: The time series of the flight mishap 10000-Hour-Rate usually take on a certain fluctuation law, which can lead to some difficulties in modelling and predicting. A modelling and predicting method based on principal components mean generating function for flight mishap 10000-Hour-Rate is proposed. The method adopts the memory idea before and after system state and the phase space expansion technology to solve the modelling the predicting of the fluctuation data series. The experiment result shows that, the method has a simple modelling process and a good prediction to flight mishap 10000-Hour-Rate, and is a simple and feasible method of flight mishap prediction. [Copyright &y& Elsevier]

Abstract: The purposes of this study were: (1) to apply wavelet and principal component analysis to quantify the spectral properties of the surface EMG and MMG signals from biceps brachii during isometric ramp and step muscle contractions when the motor units are recruited in an orderly manner, and (2) to compare the recruitment patterns of motor unit during isometric ramp and step muscle contractions. Twenty healthy participants (age=34±10.7years) performed step and ramped isometric contractions. Surface EMG and MMG were recorded from biceps brachii. The EMGs and MMGs were decomposed into their intensities in time–frequency space using a wavelet technique. The EMG and MMG spectra were then compared using principal component analysis (PCA) and ANCOVA. Wavelet combined PCA offers a quantitative measure of the contribution of high and low frequency content within the EMG and MMG. The ANCOVA indicated that there was no significant difference in EMG total intensity, EMGMPF, first and second principal component loading scores (PCI and PCII) between ramp and step contractions, whereas the MMGMPF and MMG PCI loading scores were significantly higher during ramp contractions than during step contractions. These findings suggested that EMG and MMG may offer complimentary information regarding the interactions between motor unit recruitment and firing rate that control muscle force production. In addition, our results support the hypothesis that different motor unit recruitment strategy was used by the muscle when contracting under different conditions. [ABSTRACT FROM AUTHOR]

Abstract: In the Seattle Air Toxics Monitoring Pilot Program, we measured 15 hazardous air pollutants (HAPs) at 6 sites for more than a year between 2000 and 2002. Spatial–temporal variations were evaluated with random-effects models and principal component analyses. The potential health risks were further estimated based on the monitored data, with the incorporation of the bootstrapping technique for the uncertainty analysis. It is found that the temporal variability was generally higher than the spatial variability for most air toxics. The highest temporal variability was observed for tetrachloroethylene (70% temporal vs. 34% spatial variability). Nevertheless, most air toxics still exhibited significant spatial variations, even after accounting for the temporal effects. These results suggest that it would require operating multiple air toxics monitoring sites over a significant period of time with proper monitoring frequency to better evaluate population exposure to HAPs. The median values of the estimated inhalation cancer risks ranged between 4.3×10−5 and 6.0×10−5, with the 5th and 95th percentile levels exceeding the 1 in a million level. VOCs as a whole contributed over 80% of the risk among the HAPs measured and arsenic contributed most substantially to the overall risk associated with metals. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose wavelet-based denoising method using principal component analysis, which generalizes the univariate denoising and combines with principal component analysis. Two synthetic data sets, originally designed by Donoho and Johnstone to isolate and mimic various features found in real signals, and their correlated versions corrupted with Gaussian noise are used to test this method and the results show that this method is appropriate to multivariate signal denoising. [Copyright &y& Elsevier]

Multiple-trait genome-wide association study (GWAS) analyses were compared with single-trait GWAS for power to discover and subsequently validate genetic markers (single nucleotide polymorphisms; SNP) associated with dairy traits. The SNP associations were discovered in 1 Holstein population and validated in both a Holstein population consisting of bulls younger than those in the discovery population and a Jersey population. The multivariate methods used were a principal component analysis and a series of bivariate analyses. The statistical power of detecting associations using multiple-trait GWAS was as good as or better than that of the best single-trait GWAS. Additional SNP associations were found with the multivariate methods that had not been discovered in the singletrait analyses; this was achieved without an increase in the false discovery rate. From the multivariate analysis, 4 common pleiotropic patterns were identified among the putative quantitative trait loci (QTL) affecting the Australian selection index. These patterns could be interpreted as a primary effect of the putative QTL on 1 or more milk components and secondary effects on other components. The multivariate analysis did not appear to increase the precision with which putative QTL were mapped. [ABSTRACT FROM AUTHOR]

Abstract: An integrated approach of pollution evaluation indices, principal component analysis (PCA) and cluster analysis (CA) was employed to evaluate the intensity and sources of pollution in irrigation and drinking water systems of northwestern Bangladesh. Temperature, BOD, chemical oxygen demand (COD), Mn, Fe, Co, Ni, Cu and Pb levels in most of the water samples exceed the Bangladesh and international standards. The heavy metal pollution index (HPI) and degree of contamination (C d) yield different results despite significant correlations between them. The heavy metal evaluation index (HEI) shows strong correlations with HPI and C d, and gives a better assessment of pollution levels. Modifications to the existing HPI and C d schemes show comparable results with HEI, and indicate that about 55% of the mine drainage/irrigation waters and 50% of the groundwaters are moderately to highly contaminated. The CA, PCA and pollution indices suggest that the mine drainage water (DW) is contaminated by anthropogenic (mining operation and agrogenic) sources, and the proximal parts are more contaminated than the distal part. The groundwater system in the vicinity of the coal mine site is also heavily polluted by anthropogenic sources. The pollution status of irrigation and drinking water systems in the study area are of great environmental and health concerns. [Copyright &y& Elsevier]

The aim of this study was to estimate the genetic parameters of the mid-infrared (MIR) milk spectrum represented by 1,060 data points per sample. The dimensionality of traits was reduced by principal components analysis. Therefore, 46 principal components describing 99.03% of the phenotypic variability were used to create 46 new traits. Variance components were estimated using canonical transformation. Heritability ranged from 0 to 0.35. Twenty-five out of 46 studied traits showed a permanent environment variance greater than genetic variance. Eight traits showed heritability greater than 0.10. Variances of original spectral traits were obtained by back transformation. Heritabilities for each spectral data points ranged from 0.003 to 0.42. In particular, 3 MIR regions showing moderate to high heritability estimates were of potential genetic interest. Heritabilities for specific wave numbers, linked with common milk traits (e.g., lipids, lactose), were similar to those estimated for these traits. This research confirms the genetic variability of the MIR milk spectrum and, therefore, the genetic variation of milk components. The objective of this study was to better understand the genetics of milk composition and, maybe in the future, to select animals to improve milk quality. [ABSTRACT FROM AUTHOR]

Abstract: Total concentrations of heavy metals in the soils of mine drainage and surrounding agricultural fields in the northern part of Bangladesh were determined to evaluate the level of contamination. The average concentrations of Ti, Mn, Zn, Pb, As, Fe, Rb, Sr, Nb and Zr exceeded the world normal averages and, in some cases, Mn, Zn, As and Pb exceeded the toxic limit of the respective metals. Soil pollution assessment was carried out using enrichment factor (EF), geoaccumulation index (I geo) and pollution load index (PLI). The soils show significant enrichment with Ti, Mn, Zn, Pb, As, Fe, Sr and Nb, indicating inputs from mining activities. The I geo values have revealed that Mn (1.24±0.38), Zn (1.49±0.58) and Pb (1.63±0.38) are significantly accumulated in the study area. The PLIs derived from contamination factors indicate that the distal part of the coal mine-affected area is the most polluted (PLI of 4.02). Multivariate statistical analyses, principal component and cluster analyses, suggest that Mn, Zn, Pb and Ti are derived from anthropogenic sources, particularly coal mining activities, and the extreme proximal and distal parts are heavily contaminated with maximum heavy metals. [Copyright &y& Elsevier]

In the paper entitled “A robust color image watermarking with singular value decomposition method” (Dogan et al., 2011), a robust digital image watermarking algorithm based on singular value decomposition for multi-modal biometric color face and iris images has been proposed. This comment shows that this proposed method has some defects and limits, and consequently, it cannot be used to secure multi-modal biometric systems. [ABSTRACT FROM AUTHOR]

Abstract: This study introduces two-dimensional (2-D) wavelet analysis to the classification of gas chromatogram differential mobility spectrometry (GC/DMS) data which are composed of retention time, compensation voltage, and corresponding intensities. One reported method to process such large data sets is to convert 2-D signals to 1-D signals by summing intensities either across retention time or compensation voltage, but it can lose important signal information in one data dimension. A 2-D wavelet analysis approach keeps the 2-D structure of original signals, while significantly reducing data size. We applied this feature extraction method to 2-D GC/DMS signals measured from control and disordered fruit and then employed two typical classification algorithms to testify the effects of the resultant features on chemical pattern recognition. Yielding a 93.3% accuracy of separating data from control and disordered fruit samples, 2-D wavelet analysis not only proves its feasibility to extract feature from original 2-D signals but also shows its superiority over the conventional feature extraction methods including converting 2-D to 1-D and selecting distinguishable pixels from training set. Furthermore, this process does not require coupling with specific pattern recognition methods, which may help ensure wide applications of this method to 2-D spectrometry data. [Copyright &y& Elsevier]

The visible near infrared hyperspectral imaging systems (HIS) with a xenon light source, Pika XC2 camera having a spectral range of 400–1100 ​nm, and a SpectrononPro software was used for the hypercube data visualization of the fresh and the damaged rice grains. The linear assembly of stage control was set with a scanning speed of 0.79 ​cm/s, homing speed of 0.77 ​cm/s, and a stepping mode of 0.60 ​cm/s. The captured images in the form of RGB data cubes were modified in MATLAB 2017a to gray image, and then further to a binary image. Dimensional reduction using PCA was at first applied to the range of wavelengths of 396.16 ​nm–1003.71 ​nm to obtain the first and second principal component versus wavelength graphs. The images were then cropped and masked in MATLAB to get first versus second principal component plots for both damaged and healthy rice grains. The first and second principal components have a mean value of 699.9 ​nm, and a mode value of 396.2 ​nm in the case of fresh rice grains, and a mean value of 700.1 ​nm, and a mode value of 396.2 ​nm for the damaged rice grains. The cropping of the images was then at significant wavelengths of 904.07, 914.90, 646.32, and 725.38 ​nm for the fresh rice grains, while 910.57, 916.49, 691.80, and 852.63 ​nm for the damaged rice grains respectively. The standard error reported for the fresh rice on the X-axis (XF) and Y-axis (YF) was 1.34 (XF), and 0.17 (YF), while for the damaged rice was 1.15 (XI), and 0.15 (YI) respectively. Therefore, it can be affirmed that the prediction or distinction of rice on the basis of fresh and damaged ones can be done with ease. Further, this approach can be applied to unknown samples to detect insect infestation in rice. • Visible near infrared hyperspectral imaging systems was used for detection of rice. • Dimensional reduction using PCA was done at wavelengths of 396.16–1003.71 ​nm. • The images were cropped and masked in MATLAB to get 1st vs 2nd principal component. • The standard error for fresh rice was 1.34 while for damaged was 1.15 • Distinction of fresh and damaged rice was done with ease. [ABSTRACT FROM AUTHOR]

Summary: We compare commune-level poverty rankings in Cambodia based on three different methods: small-area estimation, principal component analysis using aggregate data, and interviews with local leaders. While they provide reasonably consistent rankings, the choice of the ranking method matters. In order to assess the potential losses from moving away from census-based poverty mapping, we used the concentration curve. Our calculation shows that about three-quarters of the potential gains from geographic targeting may be lost by using aggregate data. The usefulness of aggregate data in general would depend on the cost of data collection. [Copyright &y& Elsevier]

Abstract: Odor databases are compilations of odor descriptions based on semantic or numeric methods. One of the largest databases of numeric odor profiles was obtained by Boelens and Haring [Boelens, H., & Haring, H. G. (1981). Molecular structure and olfactive quality. Bussum, The Netherlands: Internal Report, Naarden International] from a panel of perfumers. Each panelist smelled 309 compounds and rated the smell similarity to 30 standards. Most of these references were perfume raw materials. Hence, this database contains valuable information to understand the underlying psychological dimensions in the perceptual space of perfumery scents. A principal component analysis was applied to this database. The first principal component (PC1) was interpreted as a dimension of freshness, and the second component (PC2) discriminated feminine versus masculine cosmetic odors. The hedonic dimension did not clearly show up, though different studies have revealed that it is the most salient dimension if a wide range of odors is assessed. The loading plot corresponding to PC1–2 explains 32% of the data variance and provides a low-dimensional representation of olfactory perception space that may lead to the development of meaningful sensory maps for perfumery odors. [Copyright &y& Elsevier]

Abstract: Bacterioplankton community diversity was investigated monthly in coastal waters of the Gulf of Trieste (NE Adriatic Sea) throughout 2003. Superficial bacterial assemblages of two differently freshwater influenced stations were studied using PCR-DGGE fingerprinting techniques. Bacterial genetic diversity of the sampled area, as estimates of the number of DGGE bands was high (36–64) compared to that reported in other studies employing this fingerprint technique. The similarity index (Sorensen Index) between assemblages showed a defined operational taxonomic units (OTUs) succession pattern in the more typically marine station with stable winter communities and quickly changing summer ones. On the contrary in the station affected by riverine inputs no clear pattern was detected. In both sites, according to cluster analyses performed on the DGGE banding pattern, three seasonal assemblages were identified: winter-spring, summer and fall. Sequence analysis of fifty-six among the brightest gel bands led to the observation of bacteria affiliated to Gram positive, Cyanobacteria, Cytophaga–Flavobacteria–Bacteroides (CFB) lineages and the alpha-, gamma- and delta- subdivisions of the Proteobacteria. Gamma-Proteobacteria constituted the main fraction (60%) of sequences in the more typically marine station, whereas the river-influenced station was characterised by more heterogeneous assemblages (39% alpha-Proteobacteria, 32% Flavobacteria). [Copyright &y& Elsevier]

Abstract: An analysis of minute-tick data from the Japanese stock index market is reported for a three-year period of 2000/7/4–2003/6/30. Correlation patterns and principal component distributions were determined for 180 constituents of the NIKKEI 225 index, excluding the effects of after-hours trading and constituent revisions. The first principal component describes about 30% of the total variance in constituent log returns (subject to slow decrease with the size of the correlation window), suggesting that a small number of physical parameters may describe the internal dynamics of the index, allowing for an adiabatic representation of index dynamics, and a self-consistent mean-field model of its constituents. Finally, it is shown that the introduction of a time gap into minute-tick data significantly improves the correlations of the price-weighed index with its constituents, even when such gap inserts are strictly penalized. This phenomenon corresponds to a heterogenous response time of index constituents to the adiabatic collective motion and also demonstrates the inhomogeneous nature of equidistant time ticks in financial trading. [Copyright &y& Elsevier]

Abstract: According to linear random wave theory, water particle kinematics at different nodes of an offshore structure form a set of correlated jointly Gaussian random variables. Thus, using principal component analysis, all the foregoing kinematics can be expressed as linear combinations of a few independent Gaussian random variables. This technique can be used to generate statistically independent hydrodynamic load and response values, as opposed to time simulation technique that leads to correlated response values. The sampling variability of the statistics generated from a set of independent data points is considerably smaller than that of a set (of the same size) of correlated values. Therefore, a much smaller number of simulated data points are necessary for accurate prediction of the statistical properties of response. Furthermore, simulating N data points by principal component technique (PCT) has proved to be at least two times faster than simulating the same number of data points by the time simulation technique (TST). As a result, PCT is considerably more efficient (about 25 times) than TST. The forgoing conclusions have been verified by applying both techniques to two test structures under different environmental conditions. [Copyright &y& Elsevier]

Abstract: A method of topological grammars is proposed for multidimensional data approximation. For data with complex topology we define a principal cubic complex of low dimension and given complexity that gives the best approximation for the dataset. This complex is a generalization of linear and non-linear principal manifolds and includes them as particular cases. The problem of optimal principal complex construction is transformed into a series of minimization problems for quadratic functionals. These quadratic functionals have a physically transparent interpretation in terms of elastic energy. For the energy computation, the whole complex is represented as a system of nodes and springs. Topologically, the principal complex is a product of one-dimensional continuums (represented by graphs), and the grammars describe how these continuums transform during the process of optimal complex construction. This factorization of the whole process onto one-dimensional transformations using minimization of quadratic energy functionals allows us to construct efficient algorithms. [Copyright &y& Elsevier]