2D Principal Component Analysis Research Articles

Electronic nose based on Pd- and Pt-incorporated ZnO nanowires: a case study

Effect of incorporation of sensitizers namely palladium (Pd) and platinum (Pt) on the gas-sensing behaviour of zinc oxide (ZnO) nanowires has been studied. The specificity achieved is further studied and demonstrated for its efficacy towards the simultaneous detection of multiple gases employing the developed sensors in an electronic nose configuration. Incorporation of salt solutions containing the desired sensitizer concentration in the starting reaction mixture of hydrothermal growth has been effectively used to achieve heterostructure ZnO nanowires. Pd and Pt gets incorporated as PdO and metallic Pt, in the host matrix resulting in the formation of random heterojunctions namely p–n junction and Schottky junctions. Consequently, an increase in the work function as studied using Kelvin probe studies is observed. Utilizing statistical implements namely principal component analysis (PCA) and hierarchical cluster analysis (HCA) the discrimination of three gases namely H2, H2S and NO2 has been successfully accomplished. 3D PCA discriminates the three gases successfully with first three components exhibiting a percentage of variance of 42.32, 33.26 and 24.20%, respectively. A reasonable discrimination of H2, H2S and NO2, grouped into three clusters as evident from HCA dendrograms, was achieved using utilizing Ward’s method and Euclidian distance metric approach.

Abstract 3766: Utilizing whole-transcriptome digital spatial profiling for glioblastoma clinical biomarker discovery

Abstract Objective: IDH-wildtype glioblastoma (GBM) is the most aggressive brain tumor in adults. Notable for substantial inter- and intra-tumoral heterogeneity, GBM is highly resistant to standard-of-care treatment. Emerging spatial omics technologies, such as GeoMx Digital Spatial Profiling (DSP), offer novel approaches to interrogate the molecular features of regionally defined cellular ecosystems in cancer. Here, we leverage DSP to study whole-transcriptome gene expression signatures in a spatially defined manner across five IDH-wildtype GBMs. Methods: Formalin-fixed paraffin-embedded tumors were assessed histologically and three spatially distinct areas from each tumor were used to construct a custom “spatial” tissue microarray. From each set of three areas, we identified nine spatially distinct regions of interest (ROIs) for bulk transcriptomic analysis according to two histopathological features: distribution (cellular tumor core or tumor periphery) and vascular proximity (near or far). We evaluated normalized target expression data statistically using linear mixed modeling (LMM) with Benjamini-Hochberg correction. Results: 44 profiled ROIs and 8531 transcriptomic targets were analyzed following quality control trimming. 3D principal component analysis and unsupervised hierarchical clustering analysis showed clustering according to tumor specimen as well as distribution (core versus periphery), thereby substantiating the presence of inter- and intra-tumoral transcriptional heterogeneity. LMM-based differential gene expression analysis identified distinct molecular signatures when comparing core and periphery. Specifically, we observed elevated log2-transformed expression of migration and proliferation markers including LAMC1 (p=0.00297) and FABP7 (p=0.00434) in core ROIs and neurodevelopmental markers including NTRK2 (p=0.00734) and DAAM2 (p=0.00263) in periphery ROIs. Additionally, reactome analysis demonstrated considerable enrichment for the aberrant programmed cell death pathway (R-HSA-9645723, coverage=71.84%) in core ROIs and the MHC class II antigen presentation pathway (R-HSA-2132295, coverage=69.11%) in periphery ROIs. Interestingly, differential gene expression analysis did not reveal any biologically significant targets when subsetting for vascular proximity within cellular tumor core ROIs. However, reactome analysis of this regional subset demonstrated upregulation of various cellular proliferation pathways (G2/M transition, APC/C activity, DNA synthesis, etc.) within vessel-near ROIs when compared with vessel-far ROIs. Conclusions: We preliminarily characterize the molecular heterogeneity of GBM using a spatially guided approach. Ongoing analyses are focused on dataset validation, mixed cell deconvolution, further regional genotyping, and potential biomarkers. Citation Format: Adam Luo, Kelly Casella, Shivang Sharma, Ezra Baraban, Calixto-Hope Lucas, Eugene Shenderov. Utilizing whole-transcriptome digital spatial profiling for glioblastoma clinical biomarker discovery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3766.

CompCorona: A web application for comparative transcriptome analyses of coronaviruses reveals SARS-CoV-2-specific host response.

Understanding the mechanism of host transcriptomic response to infection by the SARS-CoV-2 virus is crucial, especially for patients suffering from long-term effects of COVID-19, such as long COVID or pericarditis inflammation, potentially linked to side effects of the SARS-CoV-2 spike proteins. We conducted comprehensive transcriptome and enrichment analyses on lung and peripheral blood mononuclear cells (PBMCs) infected with SARS-CoV-2, as well as on SARS-CoV and MERS-CoV, to uncover shared pathways and elucidate their common disease progression and viral replication mechanisms. We developed CompCorona, the first interactive online tool for visualizing gene response variance among the family Coronaviridae through 2D and 3D principal component analysis (PCA) and exploring systems biology variance using pathway plots. We also made preprocessed datasets of lungs and PBMCs infected by SARS-CoV-2, SARS-CoV, and MERS-CoV publicly available through CompCorona. One remarkable finding from the lung and PBMC datasets for infections by SARS-CoV-2, but not infections by other coronaviruses (CoVs), was the significant downregulation of the angiogenin (ANG) and vascular endothelial growth factor A (VEGFA) genes, both directly involved in epithelial and vascular endothelial cell dysfunction. Suppression of the TNF signaling pathway was also observed in cells infected by SARS-CoV-2, along with simultaneous activation of complement and coagulation cascades and pertussis pathways. The ribosome pathway was found to be universally suppressed across all three viruses. The CompCorona online tool enabled the comparative analysis of 9 preprocessed host transcriptome datasets of cells infected by CoVs, revealing the specific host response differences in cases of SARS-CoV-2 infection. This included identifying markers of epithelial dysfunction via interactive 2D and 3D PCA, Venn diagrams, and pathway plots. Our findings suggest that infection by SARS-CoV-2 might induce pulmonary epithelial dysfunction, a phenomenon not observed in cells infected by other CoVs. The publicly available CompCorona tool, along with the preprocessed datasets of cells infected by various CoVs, constitutes a valuable resource for further research into CoV-associated syndromes.

Direct observation of the effects of chemical fixation in MNT-1 cells: A SE-ADM and Raman study.

Aldehydes fixation was accidentally discovered in the early 20th century and soon became a widely adopted practice in the histological field, due to an excellent staining enhancement in tissues imaging. However, the fixation process itself entails cell proteins denaturation and crosslinking. The possible presence of artifacts, that depends on the specific system under observation, must therefore be considered to avoid data misinterpretation. This contribution takes advantage of scanning electron assisted-dielectric microscopy (SE-ADM) and Raman 2D imaging to reveal the possible presence and the nature of artifacts in unstained, and paraformldehyde, PFA, fixed MNT-1 cells. The high resolution of the innovative SE-ADM technique allowed the identification of globular protein clusters in the cell cytoplasm, formed after protein denaturation and crosslinking. Concurrently, SE-ADM images showed a preferential melanosome adsorption on the cluster's outer surface. The micron-sized aggregates were discernible in Raman 2D images, as the melanosomes signal, extracted through 2D principal component analysis, unequivocally mapped their location and distribution within the cells, appearing randomly distributed in the cytoplasm. Protein clusters were not observed in living MNT-1 cells. In this case, mature melanosomes accumulate preferentially at the cell periphery and are more closely packed than in fixed cells. Our results show that, although PFA does not affect the melanin structure, it disrupts melanosome distribution within the cells. Proteins secondary structure, conversely, is partially lost, as shown by the Raman signals related to α-helix, β-sheets, and specific amino acids that significantly decrease after the PFA treatment.

FRI103 Developmental Programming: Sex-specific Differences In Lipid Metabolism Of Newborn Lambs Exposed To Prenatal Testosterone Excess

Abstract Disclosure: N. Saadat: None. J.N. Ciarelli: None. B. Pallas: None. V. Padmanabhan: None. A.K. Vyas: None. Gestational hyperandrogenism such as that seen in Polycystic Ovary Syndrome (PCOS), leads to adverse offspring health outcomes in both humans and animals. Prenatal T excess (T) in a sheep model of PCOS phenotype induces peripheral insulin resistance, dyslipidemia, adipocyte defects, liver steatosis, pathological cardiac remodeling and hypertension in the female offspring. Whether these adverse cardiometabolic outcomes evidenced in the female offspring also extends to male offspring needs to be addressed. We hypothesized that prenatal T excess will induce sex-specific disruptions in lipid metabolism of newborn lambs thereby contributing to the aberrant cardiometabolic phenotype. Shotgun Lipidomics was performed on the plasma samples collected at 48 hours after birth from prenatal T treated (100mg T-propionate in corn oil from days 30 to 90 of gestation intramuscularly; Term: 147days) and control (C, vehicle only) lambs (N=26, Female C=4, T=7 and Male C=5, T=10). The data was analyzed by multivariate dimensionality reduction modeling followed by functional enrichment and pathway analyses. Unsupervised 3D principal component analysis and supervised models showed separation between C and T groupings in female and male lambs. Most lipids responsible for this separation were from glycerophospholipids and glycerolipids classes in both sexes. Prenatal T excess led to an increase in cholesterol levels only in female lambs as opposed to a decrease in levels of carnitine species (CAR) CAR 16:1 and CAR 14:1 and phosphatidylcholine species (PC) PC 39:4, PC 39:5 and PC 38:5 in male lambs. To investigate the impact of prenatal T excess on lipid metabolism pathways and active reactions, 738 lipid species were mapped, of which 147 were found to be active in reactions. The active reactions involving conversion of ether linked diglycerides (DG) to ether linked phosphatidylethanolamines (PE, O-DG→O-PE, z-score 3.367, p <0.05) and ether linked PC (O-DG →O-PC, z-score 2.299, p < 0.05) were increased in female lambs. In contrast, the conversion of PE to PC to phosphatidic acids (PA) was suppressed (PE→PC→PA, z-score 2.133, p <0.05) in prenatal T-treated male lambs pointing towards the suppression of Phosphatidylethanolamine N-methyltransferase (PEMT) phosphatidylcholine synthesis pathway. Higher cholesterol levels in T female lambs could contribute toward the adverse cardiometabolic phenotype evidenced earlier in this model, whereas suppressed PEMT pathway could lead to endoplasmic reticulum stress, defects in membrane permeability and lipid transport that may lead to liver steatosis in T male lambs. These novel findings of sex-specific effects of gestational exposure to T excess on lipid metabolism of newborn lamb, a precocial model of translational relevance, may have sex-specific functional outcomes in cardiometabolic systems. Presentation: Friday, June 16, 2023

An Improved Image Compression Algorithm Using 2D DWT and PCA with Canonical Huffman Encoding.

Of late, image compression has become crucial due to the rising need for faster encoding and decoding. To achieve this objective, the present study proposes the use of canonical Huffman coding (CHC) as an entropy coder, which entails a lower decoding time compared to binary Huffman codes. For image compression, discrete wavelet transform (DWT) and CHC with principal component analysis (PCA) were combined. The lossy method was introduced by using PCA, followed by DWT and CHC to enhance compression efficiency. By using DWT and CHC instead of PCA alone, the reconstructed images have a better peak signal-to-noise ratio (PSNR). In this study, we also developed a hybrid compression model combining the advantages of DWT, CHC and PCA. With the increasing use of image data, better image compression techniques are necessary for the efficient use of storage space. The proposed technique achieved up to 60% compression while maintaining high visual quality. This method also outperformed the currently available techniques in terms of both PSNR (in dB) and bit-per-pixel (bpp) scores. This approach was tested on various color images, including Peppers 512 × 512 × 3 and Couple 256 × 256 × 3, showing improvements by 17 dB and 22 dB, respectively, while reducing the bpp by 0.56 and 0.10, respectively. For grayscale images as well, i.e., Lena 512 × 512 and Boat 256 × 256, the proposed method showed improvements by 5 dB and 8 dB, respectively, with a decrease of 0.02 bpp in both cases.

Estimating the vertical direction in a photogrammetric 3D model, with application to visualization

The process of building a Virtual Reality (VR) environment from images involves several steps: choose experimental conditions (scene, camera, trajectory, weather), take the images, reconstruct a textured 3D model thanks to a photogrammetry software, and import the 3D model into a game engine. This paper focuses on a postprocessing technique for the photogrammetry step, mostly for outdoor environments that cannot be reconstructed using an unmanned aerial vehicle. As visualization applications (including VR) need a 3D model with a known vertical direction, a method is introduced to compute it. The method is based on 3D principal component analysis and a 2D Hough transform. In the experiments, we first reconstruct both man-made and natural immersive environments using a helmet-held 360 camera, then we import the 3D models in good coordinate systems (i.e. with a vertical axis and a plausible scale) into Unity, and finally we use VR headsets to explore the scenes like a pedestrian. We also experiment on scanner data and show that our method is competitive with previous work.

Single-cell scattering and auto-fluorescence-based fast antibiotic susceptibility testing for gram-negative and gram-positive bacteria.

In this study, we assess the scattering of light and auto-fluorescence from single bacterial cells to address the challenge of fast (<2 h), label-free phenotypic antimicrobial susceptibility testing (AST). Label-free flow cytometry is used for monitoring both the respiration-related auto-fluorescence in two different fluorescence channels corresponding to FAD and NADH, and the morphological and structural information contained in the light scattered by individual bacteria during incubation with or without antibiotic. Large multi-parameter data are analyzed using dimensionality reduction methods, based either on a combination of 2D binning and Principal Component Analysis, or with a one-class Support Vector Machine approach, with the objective to predict the Susceptible or Resistant phenotype of the strain. For the first time, both Escherichia coli (Gram-negative) and Staphylococcus epidermidis (Gram-positive) isolates were tested with a label-free approach, and, in the presence of two groups of bactericidal antibiotic molecules, aminoglycosides and beta-lactams. Our results support the feasibility of label-free AST in less than 2 h and suggest that single cell auto-fluorescence adds value to the Susceptible/Resistant phenotyping over single-cell scattering alone, in particular for the mecA+ Staphylococcus (i.e., resistant) strains treated with oxacillin.

Anthropometric accommodation percentage comparison for univariate and multivariate representative Mexican hand models.

Adopting awkward postures at work has a great impact on productivity and work-related musculoskeletal disorders. Considering anthropometric data in the design of products and workplaces can diminish this impact. The traditional univariate-percentile-approach is one of the most implemented in the anthropometric analysis, even though it has proved limitations in comparison with multivariate-approaches. To develop univariate and multivariate hand models considering four anthropometric dimensions, and to theoretically compare the univariate and multivariate accommodation percentages. Univariate percentile models corresponding to the database of real subject nearest-neighbors to the 5th and 95th percentiles were obtained for the male and female population. Two multivariate approaches were implemented on the central 90% of both populations: 2D principal component analysis and archetypal analysis. The accommodation percentage for each family of models was obtained based on the population that simultaneously fit all the anthropometric dimensions. The goodness-of-fit and McNemar's tests were performed to statistically analyze the accommodation percentages. Eight human hand models were obtained via Principal Component Analysis while two, three, four, and eight Archetypal Analysis models (male-population) and two, three, six, and eight Archetypal Analysis models (female-population) were selected after a root-sum-of-squares analysis for k = 1, ... ,10 archetypes. The results showed that the Principal Component Analysis models obtained a higher accommodation level, followed by the Archetypal Analysis and percentile models (male population). In the case of the female population, models obtained by multivariate-Archetypal Analysis (k = 8) obtained a higher accommodation percentage.

Seminal infrared-based chemometric analysis of oxygen-bearing structural groups in vitrain samples from ten successive coal seams (Sydney Coalfield, Canada)

Reported are seminal results of a systematic application of infrared spectrometry to a succession of coal seams in a Carboniferous basin from Sydney Coalfield, Nova Scotia, Canada. Fifty-four lithotypic vitrain samples from ten successive coal seams were analyzed by solid-state (potassium-bromide pellet method) Fourier transform infrared spectroscopy and evaluated using principal-component analysis to explore chemical fingerprints of vitrain structural-group contents and distributions. At this level of sampling, the 3D principal-component analysis and ANOVA tests identified chemical structural-group changes related to relatively low contents of aliphatic and oxygen-bearing compounds. These probably confirm, for data represented by “younger” seams, the moderate thermal histories (0.75–0.90 Ro %) from which minimal diagenetic alterations are inferred in the preservation of fossils in the roof rocks of the coal seams. It is concluded that, throughout the lithostratigraphic column, the continual vertical changes in vitrain structural-group composition are related to some changes in the species diversity of the associated macroflora that filled niches in the peat-producing lower flood plains.

Microwaved Vermicast Physicochemical Properties and Active Microbial Groups Impact on Photosynthetic Activity, Growth and Yield of Kale

Microwave technology has wide applications, including extraction of active compounds in biomass and compost for agricultural use. A study was carried out to determine the effects of microwave power level from 0 (control) to 1000 W on the properties and active microbial groups in vermicast, and how it may impact the photosynthesis, plant growth, and yield of kale (Brassica oleracea var. sabellica) ‘Red Russian’. Heat accumulation in the vermicast increased rapidly to a peak of 86 °C at 400 W before declining to 68 °C at 1000 W. Vermicast water loss increased exponentially up to 800 W before declining. The C:N ratio of the vermicast was reduced at ≥600 W while the pH remained the same. In a 2D-principal component analysis biplot, vermicast treated at 600, 800 and 1000 W were associated with Gram-positive (G+), GGram-negative (G−), G + G− bacteria, protozoa, and fungi groups while the 0, 200, and 400 W treated vermicast were associated with eukaryotes. However, the trend for total microbial mass was 200 W = 400 W &gt; 0 W &gt; 600 W = 800 W = 1000 W. Kale leaf anthocyanin, chlorophylls, and carotenoids were significantly (p = 0.001) increased by the 400 W or 600 W treatment compared to the other treatments. Stomatal conductance, transpiration, and photosynthesis rates were increased by the 400 W followed by the 600 W. As a result, yield of kale grown in the 400 W microwaved vermicast was the highest. Future studies will explain the functions of specific microbial populations and elemental composition in microwaved vermicast.

Principal component analysis of comprehensive three-dimensional gas chromatography time-of-flight mass spectrometry data

Comprehensive three-dimensional (3D) gas chromatography (GC3) coupled to time-of-flight-mass spectrometry (GC3-TOFMS) is an intriguing extension of the well-established comprehensive two-dimensional gas chromatography (GC×GC) technique. Although impressive gains have been made in the instrumentation realm, the utility of non-targeted chemometric analysis of GC3-TOFMS data has yet to be explored. Herein, we present the first application of principal component analysis (PCA) to a GC3-TOFMS dataset of jet fuel samples. Five replicates each of four jet fuels (JP8, J1800A, JP4, and JP7) were collected by GC3-TOFMS with commercial thermal modulation from the first-dimension column (1D) to the second-dimension column (2D), and dynamic pressure gradient modulation (DPGM) from the 2D column to the third-dimension column (3D), thus providing full mass transfer (100% duty cycle both modulation stages). A novel re-registration technique is introduced in which a user-selected series of vacant 3D modulations are removed to correct 2D shifting and effectively “center” the data. This shifting has consistently been observed in the 2D versus 1D view of GC3-TOFMS chromatograms, likely due to the slowed flow on 2D in DPGM and/or temperature programming effects. The 3D PCA loadings of the re-registered data revealed subtle chemical differences between the fuels which would not be as easily elucidated using PCA of GC×GC data. Finally, PCA of the two most chemically similar fuels (J1800A and JP7) revealed additional chemical differences which were drowned out in the initial multi-fuel PCA model, highlighting the potential advantage of “pairwise” PCA for multi-class GC3-TOFMS datasets.

ATR-FTIR Spectroscopy Combined with Chemometric Methods for the Classification of Polyethylene Residues Containing Different Contaminants

Low density polyethylene (LDPE) and high density polyethylene (HDPE) are the principal plastics present in solid plastic waste and are found out as the main components of microplastics in marine and terrestrial environments. Currently, efforts have been made to develop new and effective methods to ensure the identification and separation of plastics in waste, ensuring the necessary purity to obtain quality and economically competitive recycled products. In this contribution, we investigated the usage of Fourier-Transform Infrared Spectroscopy in Attenuated Total Reflection mode (ATR-FTIR) combined with Principal Component Analysis (PCA), Linear Partial Least Squares Regression by Intervals (iPLS-R) and Competitive Adaptive Weighted Sampling (CARS/PLS-R) as chemometric methods to classify and determine the compositional fraction of the pristine and recycled mixtures of HDPE and LDPE from plastic waste in São Paulo, Brazil. Here, the 3D PCA plots were not effective in classifying the different polyethylenes and their polymer blends using the three main Principal Components (PC), but 2D PCA diagrams using PC1 and PC3 presented promising performance for this purpose. The iPLS-R presents the best predictive ability than CARS/PLS-R to determine the LDPE content in HDPE/LDPE recycled blends. However, the presence of different contaminants (in 5 wt%), such as silicon dioxide (SiO2), calcium carbonate (CaCO3), recycled polypropylene (PP), and recycled poly(ethylene terephthalate) (PET), reduces the potential usage of the iPLS-R models as identification tools for LDPE and HDPE sorting in industrial recycling processes.

Radio-frequency and optical techniques for evaluating anchovy freshness

The freshness of anchovies ( Engraulis Encrasicolus ) was estimated by radio-frequency and optical techniques in order to propose non-destructive and objective methods for rapid screening. Measurements were conducted until 7 days at 0 °C, storing the fish under ice. Image analysis, dielectric spectroscopy and hyperspectral images were conducted on fish eyes whilst mechanical properties were measured on the fish body. Evaluation of images by RGB scale highlighted differences due to ageing. Maximum force decreased as a function of degradation process time. Cluster analysis on dielectric spectra and 3D-principal component analysis on hyperspectral images of fish eyes revealed a good ability to characterise ageing modification demonstrating suitable methods for the development of a non-destructive, and rapid system for evaluation of fish freshness. • Anchovies freshness was measured by dielectric spectroscopy & hyperspectral imaging. • Dehydration of anchovy eyes was exploited for ageing estimation. • Multivariate approaches allowed to group the samples according to storage days. • Both techniques are rapid and contactless being promising for practical application.

Research on Microvideo Character Perception and Recognition Based on Target Detection Technology

With the rapid development of the Internet short-video platform, microvideo is increasingly attracting users because of its small traffic and time period. However, the content of microvideo on the Internet is complex, with illegal information. This research is an improved algorithm based on target detection technology, aiming at the characteristics of difficult character recognition in microvideo. The algorithm combines Gabor wavelet transform algorithm and 2D principal component analysis (PCA) algorithm and makes full use of video perception technology to realize character recognition in microvideo. Through MATLAB simulation analysis, it can be seen that the accuracy of the optimization algorithm proposed in this paper is 86.34% and the recognition time is 34.28s. Compared with the traditional PCA algorithm and artificial neural network (ANN) algorithm, the optimization algorithm proposed in this paper has better recognition rate and recognition efficiency.

A new quantitative evaluation method for fluid constituents with NMR T1-T2 spectra in shale reservoirs

Saturation estimation of different fluids is crucial for reserve calculation and perforation location selection in shale reservoirs. Due to the low resolution of different fluids on the inverted T1-T2 spectra, different fluid signatures overlap, making it difficult to quantitatively evaluate fluid constituents and accurately calculate the fluid saturations. In this paper, a new quantitative metric that integrates discrete wavelet principal component analysis (PCA) and two-dimensional (2D) PCA is proposed to determine the optimal number of fluid constituents, and a quantitative evaluation method consisting of geometry-based unmixing method and nonnegative matrix factorization (NMF) is proposed to obtain the T1-T2 signatures of different fluids. The geometry-based unmixing method is used to obtain approximate fluid signatures called pseudo-signatures, which are processed by NMF to obtain accurate fluid signatures at a low computational cost. Subsequently, the fluid saturations are obtained from the least-squares method with full constraints. Numerical simulations are used to process the T1-T2 data. The results show that the proposed quantitative metric effectively provides the optimal number of fluid constituents, and the quantitative evaluation method accurately predicts the T1-T2 signatures and saturations of different fluids. In addition, the quantitative evaluation method requires less time than comparable methods to obtain the T1-T2 signatures and saturations of different fluids.

Aircraft system-level diagnosis with emphasis on maintenance decisions

This paper proposes a diagnostic technique that can predict component degradation for a number of complex systems. It improves and clarifies the capabilities of a previously proposed diagnostic approach, by identifying the degradation severity of the examined components, and uses a 3D Principal Component Analysis approach to provide an explanation for the observed diagnostic accuracy. The diagnostic results are then used, in a systematic way, to influence maintenance decisions. Having been developed for the Auxiliary Power Unit (APU), the flexibility and power of the diagnostic methodology is shown by applying it to a completely new system, the Environmental Control System (ECS). A major conclusion of this work is that the proposed diagnostic approach is able to correctly predict the health state of two aircraft systems, and potentially many more, even in cases where different fault combinations result in similar fault patterns. Based on the engineering simulation approach verified here, a diagnostic methodology suitable from aircraft conception to retirement is proposed.

Multiview Machine Vision Research of Fruits Boxes Handling Robot Based on the Improved 2D Kernel Principal Component Analysis Network

In order to better realize the orchard intelligent mechanization and reduce the labour intensity of workers, the study of intelligent fruit boxes handling robot is necessary. The first condition to realize intelligence is the fruit boxes recognition, which is the research content of this paper. The method of multiview two-dimensional (2D) recognition was adopted. A multiview dataset for fruits boxes was built. For the sake of the structure of the original image, the model of binary multiview 2D kernel principal component analysis network (BM2DKPCANet) was established to reduce the data redundancy and increase the correlation between the views. The method of multiview recognition for the fruits boxes was proposed combining BM2DKPCANet with the support vector machine (SVM) classifier. The performance was verified by comparing with principal component analysis network (PCANet), 2D principal component analysis network (2DPCANet), kernel principal component analysis network (KPCANet), and binary multiview kernel principal component analysis network (BMKPCANet) in terms of recognition rate and time consumption. The experimental results show that the recognition rate of the method is 11.84% higher than the mean value of PCANet though it needs more time. Compared with the mean value of KPCANet, the recognition rate exceeded 2.485%, and the time saved was 24.5%. The model can meet the requirements of fruits boxes handling robot.

The Study of pH Effects on Phase Transition of Multi-Stimuli Responsive P(NiPAAm-co-AAc) Hydrogel Using 2D-COS.

The temperature and mechanism of phase transition of poly(N-isopropylacrylamide-co-acrylic acid) [P(NiPAAm-co-AAc)], which is one of the multi-stimuli responsive polymers, were investigated at various pHs using infrared (IR) spectroscopy, two-dimensional (2D) gradient mapping, and two-dimensional correlation spectroscopy (2D-COS). The determined phase transition temperature of P(NiPAAm-co-AAc) at pH 4, 3, and 2 based on 2D gradient mapping and principal component analysis (PCA) showed that it decreases with decreasing pH, because COOH group in AAc changes with variation of pH. The results of 2D-COS analysis indicated that the phase transition mechanism of P(NiPAAm-co-AAc) hydrogel at pH4 is different from that at pH2 due to the effect of COOH group of AAc.

Some Multilinear Variants of Principal Component Analysis: Examples in Grayscale Image Recognition and Reconstruction

Principal component analysis (PCA) has long been used in computer vision applications such as face recognition. Here, we present an overview of some variants of PCA, including 2D PCA (2DPCA), bidirectional 2DPCA (B2DPCA), and coupled subspace analysis (CSA). Unlike conventional PCA, the variants 2DPCA, B2DPCA, and CSA preserve the original image structure, often providing better recognition and reconstruction results than those obtained with PCA. This article considers the background for these techniques and steps involved in applying these methods, including typical preprocessing of sample images, algorithm description, and classification. These variants of PCA have been successfully used in a number of different areas such as identification of wood species, biometrics (not limited to face recognition), medical imaging, and image compression, to name a few examples; we briefly mention some of these to provide an idea of the scope of applications. We address some advantages and disadvantages of these variants in relation to PCA. Utilizing the Modified National Institute of Standards and Technology (MNIST) digits and Fashion-MNIST image sets, we demonstrate application of CSA for image recognition and reconstruction compared to PCA. Finally, we mention how these PCA variants fit into a more general framework using tensors.