Basis Of Principal Component Analysis Research Articles

Features of phytoplankton changes in the Sevastopol and Karadag coastal areas (the Black sea) in spring

Based on field-monitoring and satellite dataset obtained during spring season, we studied the features of phytoplankton changes under effect of hydrometeorological factors in the coastal areas of Sevastopol in 2000–2021 and Karadag in 1989–2021. For these areas, significant positive correlations were observed between phytoplankton biomass and winds causing coastal upwellings which lift nutrients from deep layers to the surface. On the basis of Principal Component Analysis (PCA) and ANOVA, a positive effect of the north wind speed on phytoplankton biomass in the Sevastopol and Karadag coastal areas was established. Different mechanisms of the north wind effect on the nutrient-rich waters rise for two investigated areas were revealed and it was due to peculiarities of these coastal areas. The north wind also contributes to nutrient supply from the river runoff of the northwestern shelf to the Sevastopol coastal area. High values of phytoplankton biomass and chlorophyll a concentration were mainly observed in cold years.

An Improved On-Line Recursive Subspace Identification Method Based on Principal Component Analysis and Sliding Window for Polymerization

Polymerization products are indispensable for our daily life, and the relevant modeling process plays a vital role in improving product quality. However, the model identification of the related process is a difficult point in industry due multivariate, nonlinear and time-varying characteristics. As for the conventional offline subspace identification methods, the identification accuracy may be not satisfying. To handle such a problem, an enhanced on-line recursive subspace identification method is presented on the basis of principal component analysis and sliding window (RSIMPCA-SW) in this paper to obtain the state space model for polymerization. In the proposed on-line subspace identification approach, the initial L-factor is acquired by the LQ decomposition of the sampled historical data, firstly, and then it is updated recursively through the bona fide method after the new data have been handled by the sliding window rule. Subsequently, principal component analysis (PCA) is introduced to calculate the extended observation matrix, and finally the on-line model parameters are extracted. Compared with the traditional subspace schemes, smaller computation complexity and higher identification precision are anticipated in the proposed method. A case study on the modeling of the ethylene polymerization verifies the effectiveness of the developed approach, in which the related statistical indexes of the obtained identification model are better.

Voltage measurement-based recursive adaptive method for internal short circuit fault diagnosis in lithium-ion battery packs

Internal short circuit (ISC) has been identified as a major cause of thermal runaway in lithium-ion (Li-ion) battery systems, making the investigation of ISC fault diagnosis a focal research topic in electric vehicles and battery energy storage systems. Recently, several studies have applied multivariate analysis techniques to analyze abnormal behavior in cell voltage measurements and achieved an accurate diagnosis of ISC faults. However, model parameter variations under different operating conditions and state of charge (SOC), which results in unacceptable false alarm rates, is yet to be fully considered. To effectively address this problem, this study introduces a recursive approach on the basis of principal component analysis (PCA) to update the monitoring model and adapt to parameter changes. Moreover, an adaptive threshold with fusion factors is devised to reduce fault false alarm rates. In particular, this study employs sliding window techniques to amplify weak fault features to tackle the challenge of early-stage detection difficulty for ISC faults and provides an optimal window width determination algorithm based on fault-free data. Experimental results on a real Li-ion battery pack test platform under various operating conditions and SOC levels demonstrate that the proposed method maintains extremely high fault detection rates for ISC faults of different intensities. Furthermore, it is established that the adaptive updating of the model in the absence of faults greatly reduces false alarm rates.

A New Basis for Sparse Principal Component Analysis

Previous versions of sparse principal component analysis (PCA) have presumed that the eigen-basis (a p × k matrix) is approximately sparse. We propose a method that presumes the p × k matrix becomes approximately sparse after a k × k rotation. The simplest version of the algorithm initializes with the leading k principal components. Then, the principal components are rotated with an k × k orthogonal rotation to make them approximately sparse. Finally, soft-thresholding is applied to the rotated principal components. This approach differs from prior approaches because it uses an orthogonal rotation to approximate a sparse basis. One consequence is that a sparse component need not to be a leading eigenvector, but rather a mixture of them. In this way, we propose a new (rotated) basis for sparse PCA. In addition, our approach avoids “deflation” and multiple tuning parameters required for that. Our sparse PCA framework is versatile; for example, it extends naturally to a two-way analysis of a data matrix for simultaneous dimensionality reduction of rows and columns. We provide evidence showing that for the same level of sparsity, the proposed sparse PCA method is more stable and can explain more variance compared to alternative methods. Through three applications—sparse coding of images, analysis of transcriptome sequencing data, and large-scale clustering of social networks, we demonstrate the modern usefulness of sparse PCA in exploring multivariate data. An R package, epca, and the supplementary materials for this article are available online.

Occurrence, seasonal variations, distribution patterns, and risk assessment of volatile monoaromatic hydrocarbons in soils of industrial parks in Yangtze River Delta, China

Monoaromatic hydrocarbons (MACHs) are a ubiquitous category of volatile compounds found in various environmental media. Despite their prevalence, systematic studies of MACHs on a large regional scale are still lacking. Herein, a comprehensive investigation of the occurrence, seasonal variations, distribution characteristics, and health risks of MACHs was carried out by analyzing soil samples (372 surface soils and 96 soil columns) from 33 typical industrial parks in the Yangtze River Delta (YRD) region. MACHs were detected in all surface soil samples. BTEXS (benzene, toluene, ethylbenzene, xylene, and styrene) were the five predominant congeners with the highest detection frequencies (90.9 %–100 %), collectively accounting for >78.2 % of the total MACHs content. Higher residual levels of MACHs were observed in winter compared to summer (P < 0.01), with total concentrations of 24 MACHs ranging from 30.9 ng/g to 1536 ng/g (median: 135 ng/g) in winter and 16.3 ng/g to 931 ng/g (median: 87.9 ng/g) in summer. Soils collected from the northeast of Jiangsu Province and the southwest of Anhui Province exhibited relatively higher levels of MACHs. On the basis of principal component analysis, we proposed that industrial emissions and vehicle exhaust may be the main sources of MACHs contamination in the soils of YRD industrial parks. Vertically, the concentrations of total MACHs decreased with the soil depth. Soil organic matter (OM) content and the concentration of MACHs in the surface soil layer (0–15 cm) were significant factors influencing the vertical migration and distribution of MACHs (P < 0.05). It was verified that residual MACHs in the soils posed lower lifetime non-carcinogenic and carcinogenic risks to the inhabitants of the study area. The field study provides valuable evidence for the formulation of MACHs pollution control policies in the YRD region.

Does Drone Data Allow the Assessment of Phosphorus and Potassium in Soil Based on Field Experiments with Winter Rye?

The evaluation of the nutrient content in the soil, such as potassium and phosphorus, is very important, especially in precision agriculture, where the fertilizer rate should be adjusted to field variability in terms of nutrient content. Soil chemical analysis is time-consuming and expensive, and dense soil sampling is not always possible. In recent years, remote sensing methods have been used to assess the within-field variability of soil and crop nutritional status. The main aim of this study was to evaluate the relationship between UAV-derived spectral reflectance for winter rye in a long-term experiment for different fertilization with phosphorus and potassium. The study was conducted in 2022 in two field experiments in which winter rye was cultivated in monoculture and with crop rotation. The experiments were located in central Poland in Skierniewice. Statistical analyses were performed using univariate and multivariate methods, e.g., analysis of correlation, regression, and principal component analysis (PCA). The effect of phosphorus and potassium fertilization on the UAV-derived spectral reflectance of winter rye was weak, weaker in comparison to the effect of nitrogen fertilization. The effect of phosphorus and potassium fertilization on spectral reflectance was stronger in the experiment with monoculture than in the experiment with crop rotation. On the basis of correlation coefficients and PCA, negative relationships were proven between available soil potassium and spectral reflectance in the range of blue, green, and red bands and positive with red edge and near-infrared bands. The first principal component (PC1) was very strongly correlated with almost all spectral bands, either positively or negatively. The correlation of potassium and phosphorus content was very weak with PC1 in the experiment with crop rotation, while in the experiment with rye monoculture, the correlation was slightly stronger, indicating a stronger effect of nutrient deficiency in monoculture.

Targeted and non-targeted metabolomics uncovering the effects of Er-Miao-Wan formula on rats with hyperuricemia

Er-Miao-Wan formula (EMW), composed of Phellodendri Chinensis Cortex and Atractylodis Rhizoma, is widely used in the treatment of hyperuricemia (HUA), gout, and related complications as a classic compound formula. However, its mechanisms for the treatment of HUA still need to be further systematically investigated. The study aimed to perform modern analytical techniques to elucidate the mechanisms of EMW in improving the symptoms of HUA from the perspective of metabolomics. We used a high-fructose diet to establish a rat model of HUA to evaluate the effects of EMW on improving HUA. Next, we established a targeted metabolomics analysis method to quantitatively analyze purine metabolites in plasma by using ultra-high-performance liquid chromatography with ultraviolet and triple quadrupole mass spectrometry (UHPLC-UV-QQQ MS), and combined with plasma non-targeted metabolomics analysis by using ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-Q/TOF MS) to clarify the potential mechanisms of EMW to improve HUA. Oral administration of EMW could significantly increase the urinary uric acid and decrease the serum uric acid, and exhibited a remarkable effect on improving HUA. Plasma targeted metabolomics analysis showed that six purine metabolites related to HUA, including uric acid, hypoxanthine, xanthine, deoxyadenosine, deoxyguanosine, and deoxyinosine, were changed in the EMW-treated group. Further, principal component analysis (PCA) and partial least squares discrimination analysis (PLS-DA) showed that the mechanism of EMW interfering with purine metabolic pathway in the rats with HUA could be different from that of allopurinol. On the basis of plasma non-targeted metabolomics, PCA and orthogonal partial least squares discriminant analysis (OPLA-DA) screened and identified 23 potential biomarkers in the rats with HUA, and 11 biomarkers showed a trend of reversion after the intervention of EMW. The pathway analysis suggested that EMW might have therapeutic effects on the rats with HUA via the metabolic pathways including phenylalanine metabolism, glycerophospholipid metabolism, and tryptophan metabolism. In this study, a plasma targeted metabolomics method that can simultaneously quantify nine purine metabolites in rats with HUA was established for the first time, which can be used to study diseases closely related to HUA. In addition, we further explored the overall effect of EMW on HUA in combination with the metabonomic method established by non-targeted metabolomics, which was helpful to solve the defect that the pharmacological mechanism caused by multi-components and multi-targets of traditional Chinese medicine was difficult to explain scientifically and comprehensively. In summary, EMW could effectively alleviate the symptoms of high-fructose-induced HUA, and the study provided a reference for the potential therapeutic mechanism of EMW.

Ensemble Machine Learning for Predicting the Power Output from Different Solar Photovoltaic Systems

In this paper, ensemble-based machine learning models with gradient boosting machine and random forest are proposed for predicting the power production from six different solar PV systems. The models are based on three year’s performance of a 1.2 MW grid-integrated solar photo-voltaic (PV) power plant. After cleaning the data for errors and outliers, the model features were chosen on the basis of principal component analysis. Accuracies of the developed models were tested and compared with the performance of models based on other supervised learning algorithms, such as k-nearest neighbour and support vector machines. Though the accuracies of the models varied with the type of PV systems, in general, the machine learned models developed under the study could perform well in predicting the power output from different solar PV technologies under varying working environments. For example, the average root mean square error of the models based on the gradient boosting machines, random forest, k-nearest neighbour, and support vector machines are 17.59 kW, 17.14 kW, 18.74 kW, and 16.91 kW, respectively. Corresponding averages of mean absolute errors are 8.28 kW, 7.88 kW, 14.45 kW, and 6.89 kW. Comparing the different modelling methods, the decision-tree-based ensembled algorithms and support vector machine models outperformed the approach based on the k-nearest neighbour method. With these high accuracies and lower computational costs compared with the deep learning approaches, the proposed ensembled models could be good options for PV performance predictions used in real and near-real-time applications.

Evaluation of underground space perception: A user-perspective investigation

The underground environment has different characteristics from the above-ground space. An in-depth understanding of the user perception towards the underground space is essential for constructing a human-friendly underground space. However, research on the requirements of underground space from the user’s perspective is lacking. To fill this knowledge gap, the evaluation indexes of the user perception of underground space (UPUS) were constructed in this study on the basis of principal component analysis and the information entropy method, and the fuzzy comprehensive evaluation method was used to assess the UPUS of the Xinjiekou underground complex in China. Then, the advantages and disadvantages of the Xinjiekou underground complex were analysed using importance-performance analysis. Results indicate that the UPUS is multidimensional and can be evaluated from eight aspects: space connectivity and positioning, physical environment, convenience, safety, facility, landscape, application of smart technology and environmental diversity. The findings shed light on the theoretical investigation and practical construction of underground spaces in urban areas.

Dimensionality reduction and unsupervised classification for high-fidelity reacting flow simulations

The development of reduced-order combustion models able to accurately reproduce the physics of reactive systems has been a highly challenging aspect of numerical combustion research in the recent past. The complexity of the problem can be reduced by identifying and using low-dimensional manifolds able to account for turbulence-chemistry interactions. Recently, Principal Components Analysis (PCA) has shown its potential in reducing the dimensionality of a chemically reactive system while minimizing the reconstruction error. The present work demonstrates the application of the Manifold Generated by Local PCA (MG-L-PCA) approach in direct numerical simulation (DNS) of turbulent flames. The approach is enhanced with an unsupervised clustering based on Vector Quantization PCA (VQPCA) and an on-the-flyPCA-based classification technique. The reduced model is then applied on a three-dimensional (3D) turbulent premixed NH3/air flame by transporting only a subset of the original state-space variables on the computational grid and using the PCA basis to reconstruct the non-transported variables. Results are compared with both a detailed reaction mechanism and a Computational Singular Perturbation (CSP) reduced skeletal mechanism. A comparison between training the reduced model using one-dimensional (1D) and 3D data sets is also included. Overall, the MG-L-PCA allows not only for a reduction in the number of transport equations, but also a significant reduction in the stiffness of the system, while providing highly accurate results.

A Hybrid Approach Based on Principal Component Analysis for Power Quality Event Classification Using Support Vector Machines

Power quality has emerged as a sincere denominator in the planning and operation of a power system. Various events affect the quality of power at the distribution end of the system. Detection of these events has been a major thrust area in the last decade. This paper presents the application of Support Vector Machine (SVM) in classifying the power quality events. Well-known signal processing techniques, namely Hilbert transform and Wavelet transform, are employed to extract the potential features from the observation sets of voltages. Supervised architecture consisting of SVM has been constructed by tuning the parameters of SVM by various algorithms. It has been observed that Augmented Crow Search Algorithm (ACSA) yields the best accuracy compared to other contemporary optimizers. Further, Principal Component Analysis (PCA) is employed to choose the most significant features from the available features. On the basis of PCA, three different models of tuned SVMs are constructed. Comparative analysis of these three models, along with recently published approaches, is exhibited. Results are validated by the statistical one-way analysis of variance (ANOVA) method. It is observed that SVM, which contains attributes from both signal-processing techniques, gives satisfactory results.

Geochemical Fractal Characteristics of Deep-Sea REE-Rich Sediments in the Western Pacific

At present, the challenge for geochemical prospecting of deep-sea, rare, earth-rich sediments is the selection of exploration sites. Because of the unpredictability of offshore operations, the distribution and selection of survey line stations face great challenges. In this paper, we study the fact that the concentration distribution of geochemistry in space may be a part of a special complex process, which is called the multifractal. It requires a large number of indexes to characterize its scale characteristics. Based on the multifractal spectrum, 38 geochemical indices in the Pigafetta Basin region of the western Pacific Ocean are divided into three categories. The three indexes are distinguished by the multifractal parameters. The results of multifractals are in good agreement with those of the cluster analysis and principal component analysis. In addition, on the basis of principal component analysis, we further used the multifractal filtering method to extract element anomalies and delineate element enrichment regions. The first principal component clearly represents the potential element enrichment area. The spatial analysis technique and multifractal method proposed in the paper provide a new idea for geochemical prospecting of deep-sea, rare, earth-rich sediments.

Introduction to statistical, algorithmic and theoretical basis of principal components analysis

The difference between NIPALS and svd is discussed; the criteria for principal components based on maximum sum of squares and minimum residuals is described and illustrated via two small numerical examples.

Dominant Processes of HONO Derived from Multiple Field Observations in Contrasting Environments

The sources of atmospheric nitrous acid (HONO) have been frequently investigated in recent decades. However, the dominant mechanisms of HONO formation and its significant daytime source in contrasting environments remain largely unknown. In this study, 46 observational data sets were collected worldwide, and the underlying processes driving diurnal HONO patterns were extracted on the basis of principal component analysis (PCA). We identified three dominant processes: a NOx process (PC1) that was dominant in polluted inland areas, a photodecomposition process (PC2) that was dominant in marine or mountain environments, and a soil-related process (PC3). The three processes together explained >88% of the total variance. HONO variations in coastal regions were governed by either PC1 or PC2, determined by the effects of the sea–land breeze. These PCs were then applied to the identification of unknown HONO sources. The results showed that the unknown source was mainly related to the NOx process in most urban areas; however, it was associated with the photodecomposition-dominated process in coastal regions. The application of PCA to isolate the common behaviors of HONO provides valuable insights into the dominant HONO formation processes in different environments.

A Red-noise Eigenbasis for the Reconstruction of Blobby Images

Abstract We demonstrate the use of an eigenbasis that is derived from principal component analysis (PCA) applied on an ensemble of random-noise images that have a “red” power spectrum; i.e., a spectrum that decreases smoothly from large to small spatial scales. The pattern of the resulting eigenbasis allows for the reconstruction of images with a broad range of image morphologies. In particular, we show that this general eigenbasis can be used to efficiently reconstruct images that resemble possible astronomical sources for interferometric observations, even though the images in the original ensemble used to generate the PCA basis are significantly different from the astronomical images. We further show that the efficiency and fidelity of the image reconstructions depends only weakly on the particular parameters of the red-noise power spectrum used to generate the ensemble of images.

Ground-based VLF wave intensity variations investigated by the principal component analysis

Very low frequency wave intensity variations measured by the Kannuslehto station, Finland in the frequency range 0–12 kHz between 2016 and 2020 are analyzed by the principal component analysis (PCA). As the analyzed ground-based measurements are basically continuous, the length of individual basis vectors entering into PCA is fundamentally arbitrary. To better characterize both long- and short-period variations, two PCAs with different lengths of the basis vectors are eventually performed. Specifically, either daily frequency–time spectrograms or individual frequency spectra are chosen as the PCA basis vectors. Analysis of the first three principal components shows substantial variations of the wave intensity due to seasonal and local time effects. Intensity variations related to the geomagnetic activity characterized by Kp and AE indices and standard deviation of the magnetic field magnitude are less significant. Moreover, PCA allows one to distinguish between nighttime and daytime Kannuslehto variations and study them independently. Solar and geomagnetic activity effects on the daytime and nighttime measurements are discussed. Wave intensity variations related to substorm occurrence are also analyzed.Graphic

Geospatial assessment of water quality using principal components analysis (PCA) and water quality index (WQI) in Basho Valley, Gilgit Baltistan (Northern Areas of Pakistan).

Public health quality in Gilgit Baltistan (GB) is at threat due to multiple water-borne diseases. Anthropogenic activities are accelerating the burden of pollution load on the glacio-fluvial streams and surface water resources of Basho Valley in Skardu district of GB. The present research has investigated the drinking water quality of the Basho Valley that is being used for domestic purposes. The study also comprehends public health status by addressing the basis drinking water quality parameters. A total of 23 water samples were collected and then analyzed to elucidate the current status of physico-chemical, metals, and microbial parameters. Principal component analysis (PCA) was applied and three principal components were obtained accounting 53.04% of the total variance, altogether. PCA identified that metallic and microbial parameters are the major factor to influence the water quality of the valley. Meanwhile, water quality index (WQI) was also computed and it was observed that WQI of the valley is characterized as excellent in terms of physico-chemical characteristics; however, metals and microbial WQI shows most of the samples are unfit for drinking purpose. Spatial distribution is also interpolated using the Inverse distance weight (IDW) to anticipate the results of mean values of parameters and WQI scores. The study concludes that water quality is satisfactory in terms of physico-chemical characteristics; however, analysis of metals shows that the concentrations of copper (Cu) (0.40 ± 0.16mg/L), lead (Pb) (0.24 ± 0.10mg/L), zinc (Zn) (6.77 ± 27.1mg/L), manganese (Mn) (0.19 ± 0.05), and molybdenum (Mo) (0.07 ± 0.02mg/L) are exceeding the maximum permissible limit as set in the WHO guidelines for drinking water. Similarly, the results of the microbial analysis indicate that the water samples are heavily contaminated with fecal pollution (TCC, TFC, and TFS > 3 MPN/100mL). On the basis of PCA, WQI, and IDW, the main sources of pollution are most likely to be concluded as the anthropogenic activities including incoming pollution load from upstream channels. A few underlying sources by natural process of weathering and erosion may also cause release of metals in surface and groundwater. This study recommends ensuring public health with regular monitoring and assessment of water resources in the valley.

Biochemical Evaluation of Moong Bean (Vigna radiata L.) Characterstics for Nutraceutical Applications

Background: Moong bean (Vigna radiate L.) plays an important role in antioxidant, antimicrobial, antidiabetic, anti inflammatory, antihypertensive and antitumor effects. Mostly beans and legumes cause gas and abdominal bloating in some people but the moong beans are the easy to digest. Soaking the beans in water before cooking and also sprouting them reduces this issue. The high levels of amino acids, oligosaccharides and polyphenols provide high antioxidant levels not only fight tumors and cancer but also protect against DNA damage and cell mutation. The seed of moong bean acts as a nutraceutical because it provides beneficial effects on human health and also helps in the prevention of cardiovascular disease, diabetes, digestive tract diseases, overweight, obesity and cancer etc. The flavonoids vitexin and isovitexin have high free radical scavenging activities which help lower oxidative stress, the cancer causative factor. It is not only lower blood glucose levels, but also reduce total cholesterol levels, triglycerides, plasma C peptides and glucagon levels. Samples of moong bean were collected from Legume Section of the University. Methods: Through NIRS-2500 evaluated samples of moong bean. It is an instrument through which Near Infrared (NIR) analysis a spectroscopic technique that makes use of the naturally occurring electromagnetic spectrum. This works in the region of the spectrum defined by wavelengths between 700 nm and 2500 nm. All the cultivars were found to cluster in major four groups on the basis of principal component analysis. The result showed the diversity between nutritional and antinutritional factors in the cultivars that could be further used by plant breeders to develop superior genotypes. The moong bean has lots of advantages which cure the several diseases such as cardiovascular disease, type 2 diabetes, digestive diseases and some cancers. Result: Histidine amino acid was varied from 6.40% to 8.81%, arginine amino acid from 2.21% to 3.87%, valine amino acid from 2.92% to 4.44%, phenylalanine from 9.73% to 13.86%, isoleucine from 9.28% to 10.02%, methionine from 0.09% to 1.26%, leucine from 6.02% to 7.65%, tryptophan from 0.30% to 0.68%, lysine from 2.03% to 2.22% and threonine from 4.32% to 5.56%. The grain color have the direct related to carotenoids and polyphenols. The hardness of the grain is due to fiber amount of the grain. The moisture is related to the properties such as sphericity, porosity, bulk density. Mostly anti-nutrients factors such as phytic acid, tannins, hemagglutinins and polyphenols associated with the grain. The effects of storage and processing on nutrients and food processing properties are needed to enable optimization of processing steps, for better mung bean food quality and process efficiency.

Effect of Al hydrates on minimization of disinfection-by-products precursors by coagulation with intensified pre-oxidation towards cyanobacteria-laden water

Pre-oxidation is warranted to improve cyanobacteria removal and minimize disinfection by-products (DBPs) precursors for subsequent coagulation with polyaluminum chloride (PACl) in drinking water treatment. However, the reduction in DBP precursors strongly depends on the Al hydrates for PACl coagulation. This study aimed to investigate the effects of intensified NaOCl and ClO2 pre-oxidation on the removal of Microcystis aeruginosa (MA) and the corresponding halogenated DBP precursors by PACl coagulation with different Al hydrates. Two PACl coagulants, namely PACl-W with 51% monomeric Al and PACl-H with 71% polymeric Al, were used for FlocCAM jar test. The results have shown that the reductions in MA cell and algogenic organic matter (AOM) are more pronounced by sweep flocculation in PACl-W coagulation coupled with NaOCl pre-oxidation. In contrast, ClO2 pre-oxidation with PACl-H coagulation outperforms the floc formation and the reduction in each fluorescent DOM substance, especially for humic acid-like (HAL) substances reduction in response to charge neutralization. Regardless of pre-oxidation approach, PACl-H coagulation exhibits a superior reduction in carbonaceous DBP formation potential (C-DBPFP) comparative PACl-W coagulation, especially for intensified pre-oxidation (Cl2:DOC = 3:1). Intensified NaOCl pre-oxidation is effective to enhance DBPFP reduction in a similar way to ClO2 oxidation by coagulation with both PACl coagulants. In addition, it clearly demonstrates that the halogenated DBP precursors are well-correlated with UV254 absorbance on the basis of principal component analysis (PCA) inference. It is concluded that intensified NaOCl pre-oxidation is an alternative approach to ClO2 pre-oxidation for the minimization of DBP precursors in oxidation-coagulation processes for cyanobacteria-laden water treatment.

Impaired Recognition of Emotional Facial Expressions in Dementia

Emotional Facial Expressions (EFE) recognition is a major marker of emotional abilities. EFE recognition ability decreases with aging and is impaired in patients with mild cognitive impairment (MCI), Alzheimer's disease (AD) and Parkinson’s disease (PD). The present study aims to explore brain mechanisms related to such impairment through the analysis of evoked potentials (ERPs). Nineteen patients with amnestic mild cognitive impairment (aMCI), 20 AD patients, 13 PD patients with mild cognitive impairment (PD-MCI), 14 PD patients with dementia, 20 healthy elderly (HE) and 18 healthy young (HY) were presented with 5 different EFE (angry, happy, neutral, sad, fearful) from the Ekman's Pictures of Facial Affect. An electroencephalogram (EEG) was recorded and, in order to distinguish evoked components underlying the apparent emotional modulation of ERP differences, a principal component analysis (PCA) over both time and space was applied to the ERPs during an EFE recognition task. Several components were identified, based on their latency and scalp topography, corresponding to the N1, P1, N170, VPP, P2, P3 and LPP. Regardless of EFE, patients with PD-MCI showed reduced N1 compared to HE. Patients with PD showed a reduced N170 to angry faces compared to HE. HE and aMCI patients showed greater VPP than HY. Finally, in response to EFE, aMCI patients showed an enhanced P2 and a decreased P3 compared to HY. To our knowledge, this is the first time that such a comparison of patients with different kinds of dementia is made on the basis of a temporo-spatial PCA of the ERPs. Results suggests a deficit in the processing of unpleasant information in patients with PD and difficulties to process faces, particularly EFE, in patients with aMCI. Results also support a deficit in the allocation of attentional resources toward the stimuli in PD-MCI patients. No significant difference was observed between patients with AD and healthy participants, old or young.