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The objective of this study was to identify the manufacturer of Hanji, Korean handmade paper widely used in conservation science. To achieve this, machine learning models utilizing attenuated total reflectance–infrared spectroscopy (ATR–IR) were developed to assess the robustness and effectiveness of the computed models. Principal component analysis (PCA), partial least squares–discriminant analysis (PLS–DA), decision tree (DT), and k-NN models were constructed using IR spectral data, with the spectral region between 1800 and 1500 cm⁻1 identified as the critical input variable through Variable Importance in Projection (VIP) scores. The transformation of the obtained spectra into second derivative spectra proved beneficial in this key spectral region, leading to significant improvements in model performance. Additionally, the application of DBSCAN for outlier detection was effective in refining the dataset, further enhancing the performance of the models. Specifically, the k-NN model, when applied to the selected variables and preprocessed with the second derivative transformation, achieved an F1 score of 0.92. These findings underscore the importance of focusing on the 1800–1500 cm⁻1 spectral range and applying outlier detection techniques, such as DBSCAN, to enhance the robustness and accuracy of the Hanji classification models by eliminating the influence of atypical data points. [ABSTRACT FROM AUTHOR]

Objective. To develop a 2D MR acceleration method utilizing principal component analysis (PCA) in a hybrid fashion for rapid real-time applications. Approach. Retrospective testing was performed on 10 lung, 10 liver and 10 prostate 3T MRI data sets for image quality and target contourability. Sampling of k-space is performed by acquiring central (low-frequency) data in every frame while the high-frequency data is incoherently undersampled such that all of k-space is acquired in a pre-determined number of frames. Firstly, principal components (PCs) representative of intra-frame correlations between central and outer k-space data are used to estimate unsampled data in the frame of interest. Then to add further stability, PCs representative of time-domain fluctuations within a reconstruction window of the most recent frames are fit to outer k-space data (including above estimations) to obtain final estimates in the frame of interest. Accelerated reconstructions between 3x and 8x were tested for image quality and contourability along with the optimal number of PCs for fitting. Main results. It was found that at higher acceleration rates, image quality did not deteriorate significantly. Similarly, it was found that the images were of sufficient quality to contour a target using auto-contouring software at all tested acceleration rates and sites. SSIM values were found to be ⩾0.91 at all accelerations tested. Similarly dice coefficients at the different sites were found to be ⩾0.89 even at 8x accelerations which is on par with or better than intra-observer variation. Significance. This method appears to produce improved image quality and contourability compared to previous PCA methods while also allowing a greater number of PCs to be used in reconstruction. The method can be run using a simple single-channel coil and does not require significant computing power to meet real-time interventional standards (reconstruction times ∼60 ms/frame on Intel i5 CPU). [ABSTRACT FROM AUTHOR]

The 35 wooden coffin paintings from the Tubo period of the Tang Dynasty, housed in the collection of Qinghai Tibetan Medicine Cultural Museum, are valuable materials for studying Tubo culture. Research has shown that the wood used for coffin paintings was cypress. The adhesives contain bovine collagen and chicken ovalbumin. The pigments used include carbon black, azurite, cinnabar, orpiment, minium, and lead white. This article provides a statistical analysis of the types and areas of diseases that appear in wooden coffin paintings. Through the application of descriptive statistics, correlation coefficient analysis, principal component analysis, and cluster analysis, it was determined that deterioration phenomena such as rotten, crack, pulverization, and discoloration disease were particularly pronounced in coffin paintings. Key variables influencing the disease of wooden coffin paintings include crystal salt, drop, and pulverization. The dataset was categorized into three distinct clusters, each exhibiting significant differences. This study offers valuable insights and foundational support for the future conservation and restoration of coffin paintings. [ABSTRACT FROM AUTHOR]

Aiming at the Terrestrial Water Storage(TWS) changes in the Amazon River basin, this article uses the coordinate time series data of the Global Navigation Satellite System (GNSS), adopts the Variational Mode Decomposition and Bidirectional Long and Short Term Memory(VMD-BiLSTM) method to extract the vertical crustal deformation series, and then adopts the Principal Component Analysis(PCA) method to invert the changes of terrestrial water storage in the Amazon Basin from July 15, 2012 to July 25, 2018. Then, the GNSS inversion results were compared with the equivalent water height retrieved from Gravity Recovery and Climate Experiment (GRACE) data. The results show that (1) the extraction method proposed in this article has better denoising effect than the traditional method; (2) the surface hydrological load deformation can be well calculated using GNSS coordinate vertical time series, and then the regional TWS changes can be inverted, which has a good consistency with the result of GRACE inversion of water storage, and has almost the same seasonal variation characteristics; (3) There is a strong correlation between TWS changes retrieved by GNSS based on surface deformation characteristics and water mass changes calculated by GRACE based on gravitational field changes, but GNSS satellite's all-weather measurement results in a finer time scale compared with GRACE inversion results. In summary, GNSS can be used as a supplementary technology for monitoring terrestrial water storage changes, and can complement the advantages of GRACE technology. [ABSTRACT FROM AUTHOR]

Antibiotics can be effectively removed from wastewater using constructed wetlands (C.W.s). However, little is known about using attractive garden plants in C.W.s to eliminate antibiotics. Thus, the current study aims to treat amoxicillin (AMX)-contaminated wastewater through a Rhapis excelsa-based bioretention system (BS). The investigation was done at 15 days hydraulic retention time (HRT) under two conditions: set-1, varied AMX 5 to 25 ppm with constant NPK (nitrogen, phosphorus, potassium) source; and set-2, varied NPK sources with constant AMX (25 ppm). During the study, it was observed that in the set-1 condition with increasing AMX concentration, the removal of AMX through BS decreased; however, in the set-2 experiment, with enhancing NPK source, the performance of the BS treating 25-ppm AMX-contaminated wastewater increased. AMX removal of 2.3%, 66.3%, 60.6%, 52.2%, 46.7%, and 44.9% was achieved for control, BS-1, BS-2, BS-3, BS-4, and BS-5, respectively, during set-1 experiment. However, in the set-2 experiment, 23.4% (control), 43.3% (BS-1), 60.3% (BS-2), 75.9% (BS-3), 88.8% (BS-4), and 99% (BS-5) AMX removal were achieved. Removing pollutants like AMX, COD, PO43−-P, NO3−-N, and NH4+-N followed first-order kinetics. A positive correlation of COD with AMX was observed through principal component analysis and correlation matrix. The microbial community study was also covered to prioritize the role of microbes in treating AMX through BSs. The AMX treatment through Rhapis excelsa-based BS supported plant growth and development with increasing chlorophyll content, fresh weight, and C, H, N value. [ABSTRACT FROM AUTHOR]

In the context of hyperspectral image (HSI) analysis, a widely used feature extraction method, Principal Components Analysis (PCA) suffers from limitations such as wavelength bias and a lack of consideration for local spectral information. While various segmentation based PCA methods attempt to address these issues by incorporating local relationships, they still overlook band similarity beyond immediate neighbours. To address these challenges, this paper introduces a novel approach called dependency based segmented PCA (dPCA). This method employs hierarchical clustering-driven mutual information-based segmentation, facilitating more comprehensive feature extraction from HSI data. By utilizing this dependency-based segmentation, both global and local structures are effectively captured, providing enhanced details for classification tasks. The proposed dPCA is evaluated on four prominent HSI datasets in remote sensing for land use classification, and the experimental results underscore its superiority over conventional PCA, and other segmentation based PCA methods in terms of classification performance. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has transformed our world by connecting smart devices and enabling seamless interactions. This reliance, however, has led to new security issues and types of attacks. It is of the utmost importance to safeguard the security of IoT networks, with network intrusion detection systems (NIDS) having a significant impact. This paper proposes a novel approach integrating Principal Component Analysis (PCA), Pearson Correlation Coefficient (PCC), and Convolutional Neural Network (CNN) to overcome these security issues. Our innovative method reduces data dimensionality and selects highly correlated features using PCC and PCA, addressing overfitting and improving model performance while maintaining high computational speed and low costs. Our approach uniquely distinguishes between benign and threat packets by employing 1D-CNN, 2D-CNN, and 3D-CNN algorithms trained on Edge-IIoTset and NSL-KDD benchmark datasets. The findings from our experiments indicate that the proposed framework significantly enhances accuracy, precision, recall, and F1-score compared to existing models for both binary and multiclass classifications. Our binary classification models achieved exceptional performance, with an average accuracy of 99.76%, 99.79% precision, 99.89% recall, and 99.85% F1-score on the Edge-IIoTset dataset. On the NSL-KDD dataset, the models attained 99.20% accuracy, 98.07% precision, 97.95% recall, and 97.71% F1-score. For multiclass classification, the proposed model demonstrated an average accuracy of 99.41%, precision of 98.61%, recall of 98.49%, and an F1-score of 98.56% on the Edge-IIoTset dataset. On the NSL-KDD dataset, the model achieved 92.43% accuracy, 93.21% precision, 93.60% recall, and a 93.7% F1-score. Our research introduces a significant advancement that substantially improves NIDS capabilities, making IoT networks safer and more connected. [ABSTRACT FROM AUTHOR]

The purpose of this study was to assess volatile component profiles and the antioxidant activity of propolis samples from eight different locations in Türkiye. α‐Pinene, β‐pinene, 3‐carene, limonene, 2‐acetylfuran, benzaldehyde, acetic acid, benzoic acid, longifolene, isopentyl acetate, m‐cymene, styrene, and δ‐cadinene were the most common volatile components found in the most of propolis samples. Principal component analysis (PCA) and hierarchical cluster analysis were performed on the gas chromatography–mass spectrometry (GC–MS) data to identify trends and clusters in the propolis samples. As a result of the PCA, the common components in all propolis were m‐cymene, decanal, α‐pinene, limonene, pinocarvone, benzaldehyde, and butanoic acid. The samples had 2,2‐diphenyl‐1‐picrylhydrazyl radical (DPPH) inhibition activity ranging from 19.2% to 92.5% and 2,2′‐azinobis(3‐ethylbenzothiazoline‐6‐sulfonic acid) (ABTS) values ranging from 480 to 1370 μM trolox/g extract. The total phenolic content (TPC) ranged from 10,900 to 34,033 mg GAE/100 g. Compared with butylated hydroxytoluene (BHT) (control), all but one of the propolis samples exhibited higher DPPH activity. In addition, ABTS levels of propolis extracts were higher than those of BHT. These results unequivocally show that Turkish propolis has remarkable antioxidant qualities, which makes it a viable option for addition to food and medicine products. [ABSTRACT FROM AUTHOR]

BACKGROUND: Mycotoxin surveys play an essential role in our food safety system. The obtained occurrence data form the basis for the assessment of the exposure of humans and animals to these toxic fungal secondary metabolites. Liquid chromatography coupled with tandem mass spectrometry (LC‐MS/MS) has become the gold standard for mycotoxin determination because it enables selective and sensitive multi‐toxin analysis. Simultaneous determination of several hundreds of secondary fungal metabolites is feasible using this technique. In this study, we combined a targeted dilute‐and‐shoot LC‐MS/MS‐based multi‐analyte approach with multivariate statistics for the analysis of Austrian wheat from two different years and different geographical origins. RESULTS: We quantified 47 secondary fungal metabolites, including regulated emerging and masked mycotoxins. The resulting multi‐mycotoxin occurrence data were further analyzed using both multivariate and univariate statistics. Principal component analysis (PCA) and analysis of variance (ANOVA) simultaneous component analysis (ASCA) were employed to identify regional and yearly trends within the dataset and to quantify the variance in metabolite occurrence attributed to the different effects. In addition, secondary fungal metabolites significantly impacted by these factors were selected via ANOVA. Of the 47 secondary metabolites identified, 39 were affected by the year, region or a combined effect. Moreover, our findings show that 43 of the secondary fungal metabolites were significantly influenced by the weather conditions. CONCLUSION: The results presented in this study underline the added value of combining targeted LC‐MS/MS with multivariate statistics for monitoring a broad spectrum of secondary fungal metabolites in food crops. Through multivariate statistics, trends associated with the year or region can be readily studied. The approach presented could pave the way for a better understanding of the impact of climate change on plant pathogenic fungi and its implications for food safety. © 2024 The Author(s). Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Summary: Wireless Multimedia Sensor Networks (WMSNs) are composed of a large number of sensor nodes that are distributed in a region to collect and transmit data. Video transmission is one of the most important applications of WMSNs because it can provide critical information about monitored areas. WMSNs face challenges related to energy consumption, bandwidth usage, and network congestion related to huge amounts of data collected by sensors. To tackle this problem, this paper proposes the Distributed Video Transmission Reduction Approach for Energy Saving in WMSN (DiViTRA). The method involves two phases: sensing and transmission phases. DiViTRA achieves frame rate adaptation to reduce the number of captured video frames and save energy during the sensing phase. In the transmission phase, three effective techniques, ORB (Oriented FAST and Rotated BRIEF), Brute‐Force (BF) Matcher, and Grid‐based Motion Statistics (GMS) are applied to decide whether to transmit the current captured frame or remove it and adjust the frame capturing rate of the video sensor accordingly. In the case of frame transmission, the DiViTRA approach compresses the frame using two data reduction approaches: PCA (Principal Component Analysis) and Huffman encoding. Through simulations, DiViTRA demonstrates a 12% reduction in energy consumption, and 71% is a ratio of reduction in sent frames while preserving stream quality. The approach has been validated in scenarios involving critical events, showcasing its efficacy in maintaining data integrity during transmission. [ABSTRACT FROM AUTHOR]

An automatic focusing system is a crucial component of automated microscopes, adjusting the lens-to-object distance to find the optimal focus by maximizing the focus measure (FM) value. This study develops reliable autofocus methods for hyperspectral imaging microscope systems, essential for extracting accurate chemical and spatial information from hyperspectral datacubes. Since FMs are domain- and application-specific, commonly, their performance is evaluated using verified focus positions. For example, in optical microscopy, the sharpness/contrast of visual peculiarities of a sample under testing typically guides as an anchor to determine the best focus position, but this approach is challenging in hyperspectral imaging systems (HSISs), where instant two-dimensional hyperspectral images do not always possess human-comprehensible visual information. To address this, a principal component analysis (PCA) was used to define the optimal ("ideal") optical focus position in HSIS, providing a benchmark for assessing 22 FMs commonly used in other imaging fields. Evaluations utilized hyperspectral images from visible (400–1100 nm) and near-infrared (900–1700 nm) bands across four different HSIS setups with varying magnifications. Results indicate that gradient-based FMs are the fastest and most reliable operators in this context. [ABSTRACT FROM AUTHOR]

Site‐specific weed management (SSWM) is increasingly employed to reduce herbicide inputs. Incorporating functional traits of weed species allows for the selection of SSWM methods that effectively reduce the abundance of weeds with a high competitive potential (disservice) while preserving weeds that provide beneficial ecosystem services (service). In this study, we aim to assess relevant weed functional traits and translate this information into a spatial trait distribution map for weed (dis‐)service provision. The distribution of weed abundance in a field was recorded using a spatial grid. Data on functional traits for the recorded weed species were extracted from published datasets and combined into the two variables, service and disservice. Individual traits (service/disservice) were weighted for each pixel of the weed distribution map based on the number of individual plants per species. Principal component analysis was employed to generate independent variables to describe the potential for service and disservice provision. As a result, two (dis‐)service trait‐based distribution maps were generated: one highlights field areas that provide enhanced ecological services, while the other displays areas with a high disservice potential. The results show that around 61% of the area in the field had a high service potential. The area with a high disservice was slightly higher than the half of the area with a high service, while about 32% of the field has both high service and disservice potential in the same area. This study presents a spatially explicit approach to incorporate information on weed functional traits into SSWM approaches targeted at reducing weed competition while at the same time enhancing weed functional diversity. [ABSTRACT FROM AUTHOR]

Predicting suspended sediment loads in areas without detailed measurements, or with only short-term records, is crucial for the sustainable management of water resources. This study aimed to establish the relationships between specific suspended sediment loads and the characteristics of 23 sub-basins within the Haraz-Neka River basin, in Iran, to create regional models to estimate the sediment loads. To achieve this, several analytical methods were used, including cluster analysis, principal component analysis, principal component and classification analysis, and general linear modelling. Among these, the principal component analysis regression model was the most effective for estimating suspended sediment loads in the clusters. The principal component and classification analysis revealed that the best predictor was the first principal component, which strongly correlated with the minimum and mean elevation of the sub-basins. The general linear model regression showed the best overall performance for estimating regional suspended sediment loads in the study area. [ABSTRACT FROM AUTHOR]

There is a lack of dietary fiber and some essential amino acids in instant noodles. Enriching this popular food with protein‐ and fiber‐rich sources is important to improve the nutritional quality of noodles. The current study is aimed at enriching instant noodles by substituting wheat flour with lentil flour (7%–35%: L7%, L21%, and L35%), soy‐protein isolate (1%–5%: S1%, S3% and S5%), egg‐white protein (1–5: E1%, E3%, and E5%) and Spirulina platensis (1%–5%: S.p1%, S.p3% and S.p5%). The physicochemical, microbial, textural, and sensorial properties of noodle samples were investigated. The spatial relationship between parameters was also evaluated based on principal component analysis (PCA) to select the suitable noodle formulation. The highest protein (17.06%) and lowest carbohydrate contents (76.44%) were for L35%. The lowest (1.69%) and highest fat contents (2.04%) were for S5% and S.p5%m, respectively (p > 0.05). Energy values varied from 394.83 (E5%) to 396.37 kcal/100 g (S.p5%). There was no significant difference between the microbial quality of noodle samples (p > 0.05). The hardness of noodles with 1%–5% soy protein, 21% or more lentil flour, and 3% or more egg‐white/Spirulina was higher than the control/unenriched group (p < 0.05). The color difference of E1 and E3% with the control sample was not obvious (ΔE∗ < 3). Although all the ingredients improved the nutritional value of the noodles, the overall acceptance of samples with 3% or more of Spirulina was lower than the acceptable limit (a score of 3). According to PCA, when the nutritional value and sensory acceptance are important, the L35% may be a better choice. E1%, E3%, S1%, S5%, and L7% noodles received almost the same sensory score as the control sample, while they had more nutritional values. A combination of animal, plant, and microalgae protein sources may provide a noodle with high nutritional value, containing a wide range of essential amino acids and bioactive compounds. More research is needed to optimize such a formulation. [ABSTRACT FROM AUTHOR]

A comparative application of major dynamic light scattering (DLS)‐based image methodologies applied to transcranial cerebral blood flow imaging is presented. In particular, the study delves into assessing capability of Laser Doppler Flowmetry (LDF), Laser Speckle Contrast Imaging (LSCI), and Diffuse Correlation Spectroscopy (DCS) in enhancing the spatial and temporal resolution of transcranial blood flow imaging. An integral part of the study is focused on the modulation of blood flow through the administration of the vasodilator drug, Sodium Nitroprusside (SNP). This pharmacological intervention facilitated a direct observation of cerebral vasculature's responsiveness to external stimuli, illuminating the physiological adaptations within the brain's microvascular architecture. Advanced LSCI processing techniques are incorporated, notably entropy and principal component analysis (PCA). Entropy is providing a quantifiable measure of the randomness and complexity within the speckle patterns of transcranial blood flow images, revealing remarkably similar outcomes with DSC approach in terms of blood flow dynamics and its quantitative evaluation. The application of PCA approach is provided a more nuanced understanding of blood flow dynamics, facilitating the identification of subtle changes induced by drug administration. This method proved instrumental in enhancing the visualization and detection of nuanced blood flow dynamics, thereby allowing for a more detailed examination of cerebral circulation alterations induced by SNP administration. The study seeks to offer a wider‐ranging insight into comprehending the translating further the concept of DLS into transcrainial blood flow vizualization and explore its practical applications, considering hardware, advanced quantitative image processing, and data acquisition. [ABSTRACT FROM AUTHOR]

The gardenia flower not only has extremely high ornamental value but also is an important source of natural food and spices, with a wide range of uses. To support the development of gardenia flower products, this study used headspace gas chromatography–ion mobility spectrometry (HS-GC–IMS) technology to compare and analyze the volatile organic compounds (VOCs) of fresh gardenia flower and those after using four different drying methods (vacuum freeze-drying (VFD), microwave drying (MD), hot-air drying (HAD), and vacuum drying (VD)). The results show that, in terms of shape, the VFD sample is almost identical to fresh gardenia flower, while the HAD, MD, and VD samples show significant changes in appearance with clear wrinkling; a total of 59 volatile organic compounds were detected in the gardenia flower, including 13 terpenes, 18 aldehydes, 4 esters, 8 ketones, 15 alcohols, and 1 sulfide. Principal component analysis (PCA), cluster analysis (CA), and partial least-squares regression analysis (PLS-DA) were performed on the obtained data, and the research found that different drying methods impact the VOCs of the gardenia flower. VFD or MD may be the most effective alternative to traditional sun-drying methods. Considering its drying efficiency and production cost, MD has the widest market prospects. [ABSTRACT FROM AUTHOR]

Objective. Respiratory motion correction is beneficial in positron emission tomography (PET), as it can reduce artefacts caused by motion and improve quantitative accuracy. Methods of motion correction are commonly based on a respiratory trace obtained through an external device (like the real time position management system) or a data driven method, such as those based on dimensionality reduction techniques (for instance principal component analysis (PCA)). PCA itself being a linear transformation to the axis of greatest variation. Data driven methods have the advantage of being non-invasive, and can be performed post-acquisition. However, their main downside being that they are adversely affected by the tracer kinetics of the dynamic PET acquisition. Therefore, they are mostly limited to static PET acquisitions. This work seeks to extend on existing PCA-based data-driven motion correction methods, to allow for their applicability to dynamic PET imaging. Approach. The methods explored in this work include; a moving window approach (similar to the Kinetic Respiratory Gating method from Schleyer et al (2014)), extrapolation of the principal component from later time points to earlier time points, and a method to score, select, and combine multiple respiratory components. The resulting respiratory traces were evaluated on 22 data sets from a dynamic [18F]-FDG study on patients with idiopathic pulmonary fibrosis. This was achieved by calculating their correlation with a surrogate signal acquired using a real time position management system. Main results. The results indicate that all methods produce better surrogate signals than when applying conventional PCA to dynamic data (for instance, a higher correlation with a gold standard respiratory trace). Extrapolating a late time point principal component produced more promising results than using a moving window. Scoring, selecting, and combining components held benefits over all other methods. Significance. This work allows for the extraction of a surrogate signal from dynamic PET data earlier in the acquisition and with a greater accuracy than previous work. This potentially allows for numerous other methods (for instance, respiratory motion correction) to be applied to this data (when they otherwise could not be previously used). [ABSTRACT FROM AUTHOR]

Vitrinite reflectance (VR) is a critical measure of source rock maturity in geochemistry. Although VR is a widely accepted measure of maturity, its accurate measurement often proves challenging and costly. Rock–Eval pyrolysis offers the advantages of being cost-effective, fast, and providing accurate data. Previous studies have employed empirical equations and traditional machine learning methods using T-max data for VR prediction, but these approaches often yielded subpar results. Therefore, the quest to develop a precise method for predicting vitrinite reflectance based on Rock–Eval data becomes particularly valuable. This study presents a novel approach to predicting VR using advanced machine learning models, namely ExtraTree and XGBoost, along with new ways to prepare the data, such as winsorization for outlier treatment and principal component analysis (PCA) for dimensionality reduction. The depth and three Rock–Eval parameters (T-max, S1/TOC, and HI) were used as input variables. Three model sets were examined: Set 1, which involved both Winsorization and PCA; Set 2, which only included Winsorization; and Set 3, which did not include either. The results indicate that the ExtraTree model in Set 1 demonstrated the highest level of predictive accuracy, whereas Set 3 exhibited the lowest level of accuracy, confirming the methodology's effectiveness. The ExtraTree model obtained an overall R2 score of 0.997, surpassing traditional methods by a significant margin. This approach improves the accuracy and dependability of virtual reality predictions, showing significant advancements compared to conventional empirical equations and traditional machine learning methods. [ABSTRACT FROM AUTHOR]

Yield, a multifaceted trait influenced by genetic and environmental factors, requires a comprehensive assessment beyond emphasizing only on yield alone. Thus, understanding the intricate relationship between yield and its associated traits across diverse chickpea genotypes is essential. The current investigation was undertaken at the Breeder seed production unit, JNKVV, Jabalpur, Madhya Pradesh, during the 2021-22 cropping season. Significant variations were found by analysis of variance (ANOVA) among the 40 genotypes under study, indicating substantial variability. Post hoc DMRT analysis further confirmed notable genetic diversity across all traits. Regression and Principal component analysis highlighted the significance of optimizing biological yield per plant, effective pods, total number of pods, and secondary branches to enhance grain yield. Strategic selection prioritizing these traits can facilitate the development of high-yielding chickpea varieties with enhanced agronomic characteristics. This analysis identified five principal components explaining 80.74% of genotypic variability. Ultimately, the genotypes ICCV 211210, RVG-204, and ICCV 211206 emerged as promising based on PC scores. [ABSTRACT FROM AUTHOR]

Near-infrared (NIR) dyes have a unique ability to interact favorably with light in the NIR region, which is particularly interesting where stealth and camouflage are paramount, such as in military uniforms. Characterization of cotton fabric dyed with NIR-absorbing dyes using visible–NIR (Vis-NIR) and short-wave infrared (SWIR) hyperspectral imaging was done. The aim of the study was to discern spectral changes caused by variations in dye concentration and dyeing temperature as these parameters directly influence color- and crocking-fastness of fabrics impacting the camouflage effect. The fabric was dyed at three concentrations (2.5, 5, and 10%) and two dyeing temperatures (55 °C and 85 °C) and principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were performed on the spectra to discriminate the fabrics based on dye concentrations. The PCA models successfully segregated the fabrics based on the dye concentration and dyeing temperature, while PLS-DA models demonstrated classification accuracies between 75 and 100% in the Vis-NIR range. Spectra in the SWIR region could not be used to detect the differences in the concentrations of the NIR dyes. This finding is promising, as it aligns with the objective of creating NIR-dyed camouflage fabrics that remain indistinguishable under varying dye concentrations. These results open possibilities for further exploration in enhancing the stealth capabilities of textiles in military applications. [ABSTRACT FROM AUTHOR]

Fluorescence spectroscopy is an attractive candidate for analyzing samples of nylon. Impurities within the polymers formed during the synthesis and processing of nylons give rise to the observed fluorescence, allowing for nylons to be analyzed based on their impurities. Nylons from the same source are expected to display similar fluorescence profiles, and nylons with different fluorescence are expected to be from different sources. This paper investigates an important case where different nylons displayed similar fluorescence, preventing easy discrimination. Samples of Nylon 6 and Nylon 6/12 had visually indistinguishable excitation–emission matrices (EEM), excitation spectra, fluorescence spectra, and synchronous fluorescence spectra at larger Δλ. By collecting synchronous fluorescence spectra at smaller Δλ, additional features in the fluorescence profiles were identified that allowed for some discrimination between the two nylons. Combining the EEM and synchronous fluorescence data with chemometric algorithms provided a clearer differentiation between the two nylons. parallel factor analysis, principal component analysis, and common dimension partial least squares (ComDim-PLS) showed two distinct clusters in the data, with ComDim-PLS providing the greatest distinction between the clusters. The loadings revealed the variables of interest to the ComDim-PLS were the 400 nm and 335 nm bands for all synchronous fluorescence spectra, the 460 nm and 310 nm bands for the Δλ = 20 nm and Δλ = 30 nm synchronous fluorescence spectra, and the 440 nm band for the Δλ = 20 nm synchronous fluorescence spectra. The linear discriminant analysis performed with the PLS data yielded a classification accuracy of 95% with the EEM data and 100% with the synchronous fluorescence data, displaying the power of this technique to differentiate two different nylons with visually indistinguishable fluorescence spectra. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is an important component of the smart environment, which produces a large volume of data that is considered challenging to handle. In addition, the IoT architecture is vulnerable to many cyberattacks that can target operational devices. Therefore, there is a need for monitoring IoT traffic to analyze, detect malicious activity, and classify cyberattack types. This research proposes a security framework to monitor asymmetrical network traffic in an IoT environment. The framework offers a network intrusion detection system (NIDS) to detect and classify cyberattacks, implemented using a machine learning (ML) model residing in the middleware layer of the IoT architecture. A dimensionality reduction technique known as principal component analysis (PCA) is utilized to facilitate data transmission, which is intended to be sent from the middleware layer to the cloud layer with reduced complexity and fewer unnecessary inputs without compromising the information content. Therefore, the reduced IoT traffic data are sent to the cloud and the PCA data are retransformed to approximate the original data for visualizing the IoT traffic. The NIDS is responsible for reporting the attack type to the cloud in the event of an attack. Our findings indicate that the proposed framework has promising results in classifying the attack type, which achieved a classification accuracy of 98%. In addition, the dimension of the IoT traffic data is reduced by around 50% and it has a similarity of around 90% compared to the original data. [ABSTRACT FROM AUTHOR]

Speech emotion recognition is key to many fields, including human–computer interaction, healthcare, and intelligent assistance. While acoustic features extracted from human speech are essential for this task, not all of them contribute to emotion recognition effectively. Thus, reduced numbers of features are required within successful emotion recognition models. This work aimed to investigate whether splitting the features into two subsets based on their distribution and then applying commonly used feature reduction methods would impact accuracy. Filter reduction was employed using the Kruskal–Wallis test, followed by principal component analysis (PCA) and independent component analysis (ICA). A set of features was investigated to determine whether the indiscriminate use of parametric feature reduction techniques affects the accuracy of emotion recognition. For this investigation, data from three databases—Berlin EmoDB, SAVEE, and RAVDES—were organized into subsets according to their distribution in applying both PCA and ICA. The results showed a reduction from 6373 features to 170 for the Berlin EmoDB database with an accuracy of 84.3%; a final size of 130 features for SAVEE, with a corresponding accuracy of 75.4%; and 150 features for RAVDESS, with an accuracy of 59.9%. [ABSTRACT FROM AUTHOR]

Built heritage materials assessment is an important task for planning and managing future conservation works. The uniqueness of each historical building makes reconnaissance operations more complex and specific for every single building. In the past, visual inspection and invasive techniques were widely used to investigate surface materials. Non-destructive techniques (NDTs) such as multi-band photogrammetry and remote sensing can help to assess the buildings without any contact with the investigated objects, restricting the disruptive tests on limited areas and reducing the testing time and costs of the surveys. This paper presents the results obtained using multi-band images acquired with a low-cost imaging solution after interchanging several filters, and the application of the principal components analysis (PCA) to recognize different materials of a significant historical monument. The Serranos Towers, built between 1392 and 1398, suffered several interventions in the past that affected their state of conservation with the replacement of different materials. The results of the study show the usefulness of applying PCA to distinguish different surface materials, often similar to the original ones, in a fast and efficient way to investigate and analyze our heritage legacy. [ABSTRACT FROM AUTHOR]

Sea ice is a crucial component of the global climate system. The China–French Ocean Satellite Scatterometer (CFOSAT/SCAT, CSCAT) employs an innovative rotating fan beam system. This study applied principal component analysis (PCA) to extract classification features and developed an ensemble machine learning approach for sea ice detection. PCA identified key features from CSCAT's backscatter information, representing outer and sweet swath observations. The ensemble model's performances (OA and Kappa) for the Northern and Southern Hemispheres were 0.930, 0.899, and 0.844, 0.747, respectively. CSCAT achieved an accuracy of over 0.9 for close ice and open water but less than 0.3 for open ice, with misclassification of open ice as closed ice. The sea ice extent discrepancy between CSCAT and the National Snow and Ice Data Center (NSIDC) was −0.06 ± 0.36 million km2 in the Northern Hemisphere and −0.03 ± 0.48 million km2 in the Southern Hemisphere. CSCAT's sea ice closely matched synthetic aperture radar (SAR) imagery, indicating effective sea ice and open water differentiation. CSCAT accurately distinguished sea ice from open water but struggled with open ice classification, with misclassifications in the Arctic's Greenland Sea and Hudson Bay, and the Antarctic's sea ice–water boundary. [ABSTRACT FROM AUTHOR]

Repackaging of expired medicines has become common in developed countries and can cause serious health problems. Therefore, the identification of these products is of considerable importance. This study investigated the applicability of an analytical method based on Fourier Transform Infrared spectroscopy combined with chemometrics for separating two types of expired and non-expired tablets. The study focuses on vildagliptin, collagen, and vitamin tablets and represents a preliminary study for different types of tablets. Instrumental conditions were determined by central composite design. As a result of the the variance analysis, it was found that the pressure parameter was significant, while the number of scans and resolution were insignificant. The optimum instrumental conditions were determined as attenuated total reflection module pressure 57, scan number 20, and spectral resolution 4 cm−1. Derivatizing spectra were used to find major peaks that make a significant difference. As a result of principal component analysis, PC1 in the 92-96.9% and PC2 in the range of 6.6-3% range explained the total variance. In the results obtained, significant discrimination and clustering between the species were seen, and for all three species, expired and non-expired drug samples with different storage conditions could be separated. Fourier Transform Infrared spectroscopy can be helpful in screening for counterfeit drugs as it is easy to use and requires little sample preparation. It is also thought that this approach may be helpful for the initial evaluation of repackaged tablets. [ABSTRACT FROM AUTHOR]

In this study, the volatile flavour compounds of baked potatoes from different cultivars were identified and analysed via headspace–gas chromatography–ion mobility spectrometry (HS-GC-IMS) together with principal component analysis (PCA). Totally, 89 signal peaks were detected, and 64 compounds were identified and classified into 25 aldehydes, 14 alcohols, 11 ketones, 5 esters, 2 furans, 1 pyrazine and others. Based on the signal intensity of the identified compounds, topographic plots and fingerprints were established to investigate the differences caused by cultivars. The main compounds including aldehydes, alcohols, ketones, esters, furan and pyrazine varied with cultivars; they were more abundant in B5 and B6 cultivars. Moreover, PCA and fingerprint similarity analysis clearly showed that these samples are independent and distinguishable. Results also showed that the GC-IMS technique might be helpful and useful in the rapid detection of volatile compounds in cooked potatoes and provide support for the domestic and industrial products. The obtained information could serve for breeding projects of new varieties with specific flavour profiles which comply with consumer demand. [ABSTRACT FROM AUTHOR]

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The motor is essential for manufacturing industries, but wear can cause unexpected failure. Predictive and health management (PHM) for motors is critical in manufacturing sites. In particular, data-driven PHM using deep learning methods has gained popularity because it reduces the need for domain expertise. However, the massive amount of data poses challenges to traditional cloud-based PHM, making edge computing a promising solution. This study proposes a novel approach to motor PHM in edge devices. Our approach integrates principal component analysis (PCA) and an autoencoder (AE) encoder achieving effective data compression while preserving fault detection and severity estimation integrity. The compressed data is visualized using t-SNE, and its ability to retain information is assessed through clustering performance metrics. The proposed method is tested on a custom-made experimental platform dataset, demonstrating robustness across various fault scenarios and providing valuable insights for practical applications in manufacturing. [ABSTRACT FROM AUTHOR]

The chemical composition of the essential oils (EOs) from leaves and bark of two morphotypes, white (WM) and black (BM) of Salmea scandens obtained by microwave-assisted extraction (MAE) were analyzed by GC-MS. The major components of the bark EOs were aliphatic hydrocarbons (38.0% in WM, 48.6% in BM) and oxygenated sesquiterpenes (27.6% in WM, 11.3% in BM); in leaves EOs major components were oxygenated sesquiterpenes (43.9% in WM, 45.7% in BM) and oxygenated aliphatics (13.7% in WM, 1.1% in BM) stood out. Nine components have been reported to have antimicrobial and anti-inflammatory potential. The agglomerative hierarchical clustering and principal component analysis confirmed the variability of the EOs. These results suggest that WM may have a better therapeutic effect for treating infectious and inflammatory processes in traditional medicine. [ABSTRACT FROM AUTHOR]

This study assessed the presence of potentially toxic elements (PTEs) in China's northeastern black soil belt, an area with limited prior research. We collected 304 soil samples (0–20 cm) from Gonghe Town, Hailun City, and analyzed the PTE contamination degree using the single-factor pollution index and Nemerow pollution index. The results demonstrated that the mean concentrations of arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), mercury (Hg), nickel (Ni), lead (Pb), and zinc (Zn) were 11.16, 0.11, 65.29, 22.56, 0.03, 27.07, 26.09, and 66.01 mg/kg, respectively. Source apportionment was conducted via correlation analysis, principal component analysis, and positive matrix factorization, identifying four main sources: natural (33.2%), irrigation (29.5%), fuel (23.4%), and fertilizer (13.2%). The ecological risk index indicated a slight ecological risk, while the human health risk showed that non-carcinogenic risks were negligible and carcinogenic risks were acceptable. Our findings emphasize the need to prioritize controlling PTEs from fertilizer, particularly cadmium, and to a lesser extent, irrigation and fuel sources, focusing on As, Pband Hg. This research provides critical insights for policymakers aiming to manage PTE contamination in black soils. [ABSTRACT FROM AUTHOR]

Soybean (Glycine max (L.) Merr.) is one of the important oilseed crops that is widely cultivated around the world. It has a high amount of protein content as well as other essential vitamins that are important in our daily lives. For continuous improvement, the development of novel plant species that are resistant to biotic and abiotic stress using effective genetic diversity strategies is important. In the current study, a total of 25 diverse genotypes of soybean using 21 important agro morphological traits were studied. For qualitative traits significant level of variation was found for most of the traits. For quantitative traits, the highest coefficient of variance 32.74% was found for days to flower initiation, followed by days to flower completion 29.62%, while the lowest was found for a number of pods/plants 2.96%. Based on cluster analysis, all the genotypes were separated into two groups at 25% distance and further subdivided at 75% distance, where the genotypes NARC-2 and SWAT-84 were found the most diverse. The cluster three genotypes were found to have early mature genotypes (89 ± 2.44). Based on a number of pods/plant and yield per plant, the cluster 4 genotypes were found to have a maximum number of pods/plant (98.26 ± 32.10), and (38.66 ± 6.85). Among the studied genotypes the highest grain yield (49 g) was produced by genotype C/B 28, whereas the lowest (14.56 g) was observed for C/B 7. Principal component analysis with an eigenvalue of 1.48 accounted for the total variation of 67.73%. The total seed storage proteins analysis resulted in 13 bands, and the molecular weight ranged from 15 to 170 kDa. Two-way cluster analysis was performed and all the populations were divided into two main lineages at 25% distance and further sub divided into six subgroups at distance of a 75%, where the genotypes NARC-2 and SWAT-84 were found the most diverse genotypes. These findings provide a basis for developing elite, locally adapted soybean genotypes as well as implications for understanding the diversity and relationships among these diverse Glycine max genotypes. [ABSTRACT FROM AUTHOR]

Purpose: Local muskmelon germplasm is being lost because of genetic erosion caused by the replacement of hybrid varieties, lack of conservation climate change, etc. Particularly, the foremost requisites of the industry for muskmelon is a genetic diversity assessment of landraces for identification and trait improvement. Therefore, this study was conducted to estimate the genetic diversity of muskmelon germplasm Research Method: Forty-two muskmelon (Cucumis melo L.) germplasm was collected from different geographical territories of Bangladesh and was studied to identify the morphological diversity among the germplasm using an augmented design. Thirty-four (34) agro-morphological characters were recorded, of which twenty-eight (28) were scored as qualitative characters and six (6) were measured as quantitative characters. Findings: In qualitative characters, variations in morphometrics were found such as the fruit’s shape, and different color patterns of fruit skin and flesh, etc. A maximum coefficient of variation of 73.5 % was estimated in fruit weight. Principal Component Analysis (PCA) of different quantitative traits was used to distinguish muskmelon germplasm. The cumulative percentage of variance reached 75.7% by the first two PCA components. Germplasm RC-186, IAH-194, IAH-207, AHM-15, IAH-197, IAH-345, AC-422, and RAI-239, were divergent from others concerning traits such as early maturity, fruit length, width, and weight. Clustering of accessions by D²statistic resulted in five clusters and cluster III was found to be the largest containing twenty-two (22) accessions. Value: This research work will be helpful for the further development of the muskmelon variety and the improvement of specific characters. [ABSTRACT FROM AUTHOR]

Developing and identifying high-yielding genotypes with shoot fly resistance in sorghum is a complex task that requires a detailed examination of genetic variability and relationships between numerous component traits related to grain yield and pest resistance. The purpose of this study was to use 15 morphological traits to assess genetic variability in 64 sorghum genotypes, which were analysed using principal component analysis (PCA) and the box plot technique. PCA identified seven components that account for almost 74% of the total variability in grain yield and shoot fly resistance. Two of the most reliable components (PC1 and PC2) were strongly correlated with a number of traits, including deadhearts per cent (21 and 28 DAE), seedling vigour, number of eggs per plant and trichome density on upper and lower surfaces, fodder yield per plot, fodder yield per plant, days to maturity and days to 50% flowering. PCA biplots identify groups of genotypes that can be suitable for specific breeding strategies. These include genotype clusters that combine grain yielding ability with resistance to shoot flies. According to the results of the box plot analysis, most of the traits showed greater variation towards grain yield and shoot fly resistance. This research has provided useful information on sorghum genotype genetic variability and its potential use in sorghum development programmes. [ABSTRACT FROM AUTHOR]

Contemporary descriptions of motor control suggest that variability in movement can be indicative of skilled or unskilled performance. Here we used principal component analysis to study the kicking performance of elite and sub-elite soldiers who were highly familiar with the skill in order to compare the variability in the first and second principal components. The subjects kicked a force plate under a range of loaded conditions. and their movement was recorded using optical motion capture. The first principal component explained >92% of the variability across all kinematic variables when analyzed separately for each condition, and both groups and explained more of the variation in the movement of the elite group. There was more variation in the loading coefficient of the first principal component for the sub-elite group. In contrast. for the second principal component, there was more variation in the loading coefficient for the elite group, and the relative magnitude of the variation was greater than for the first principal component for both groups. These results suggest that the first principal component represented the most fundamental movement pattern, and there was less variation in this mode for the elite group. In addition. more of the variability was explained by the hip than the knee angle entered when both variables were entered into the same PCA. which suggests that the movement is driven by the hip. [ABSTRACT FROM AUTHOR]

Purpose: To enhance the accuracy of real-time four-dimensional cone beam CT (4D-CBCT) imaging by incorporating spatiotemporal correlation from the sequential projection image into the single projection-based 4D-CBCT estimation process. Methods: We first derived 4D deformation vector fields (DVFs) from patient 4DCT. Principal component analysis (PCA) was then employed to extract distinctive feature labels for each DVF, focusing on the first three PCA coefficients. To simulate a wide range of respiratory motion, we expanded the motion amplitude and used random sampling to generate approximately 900 sets of PCA labels. These labels were used to produce 900 simulated 4D-DVFs, which in turn deformed the 0% phase 4D-CT to obtain 900 CBCT volumes with continuous motion amplitudes. Following this, the forward projection was performed at one angle to get all of the digital reconstructed radiographs (DRRs). These DRRs and the PCA labels were used as the training data set. To capture the spatiotemporal correlation in the projections, we propose to use the convolutional LSTM (ConvLSTM) network for PCA coefficient estimation. For network testing, when several online CBCT projections (with different motion amplitudes that cover the full respiration range) are acquired and sent into the network, the corresponding 4D-PCA coefficients will be obtained and finally lead to a full online 4D-CBCT prediction. A phantom experiment is first performed with the XCAT phantom; then, a pilot clinical evaluation is further conducted. Results: Results on the XCAT phantom and the patient data show that the proposed approach outperformed other networks in terms of visual inspection and quantitative metrics. For the XCAT phantom experiment, ConvLSTM achieves the highest quantification accuracy with MAPE(Mean Absolute Percentage Error), PSNR (Peak Signal-to-Noise Ratio), and RMSE(Root Mean Squared Error) of 0.0459, 64.6742, and 0.0011, respectively. For the patient pilot clinical experiment, ConvLSTM also achieves the best quantification accuracy with that of 0.0934, 63.7294, and 0.0019, respectively. The quantification evaluation labels that we used are 1) the Mean Absolute Error (MAE), 2) the Normalized Cross Correlation (NCC), 3)the Structural Similarity Index Measurement(SSIM), 4)the Peak Signal-to-Noise Ratio (PSNR), 5)the Root Mean Squared Error(RMSE), and 6) the Absolute Percentage Error (MAPE). Conclusion: The spatiotemporal correlation-based respiration motion modeling supplied a potential solution for accurate real-time 4D-CBCT reconstruction. [ABSTRACT FROM AUTHOR]

In the face of escalating credit card fraud due to the surge in e-commerce activities, effectively distinguishing between legitimate and fraudulent transactions has become increasingly challenging. To address this, various machine learning (ML) techniques have been employed to safeguard cardholders and financial institutions. This article explores the use of the Ensemble Hidden Markov Model (EHMM) combined with two distinct feature extraction methods: principal component analysis (PCA) and a proposed statistical feature set termed MRE, comprising Mean, Relative Amplitude, and Entropy. Both the PCA-EHMM and MRE-EHMM approaches were evaluated using a dataset of European cardholders and demonstrated comparable performance in terms of recall (sensitivity), specificity, precision, and F1-score. Notably, the MRE-EHMM method exhibited significantly reduced computational complexity, making it more suitable for real-time credit card fraud detection. Results also demonstrated that the PCA and MRE approaches perform significantly better when integrated with the EHMM in contrast to the conventional HMM approach. In addition, the proposed MRE-EHMM and PCA-EHMM techniques outperform other classic ML models, including random forest (RF), linear regression (LR), decision trees (DT) and K-nearest neighbour (KNN). [ABSTRACT FROM AUTHOR]

Dietary fiber incorporation in bread offers potential health benefits but poses challenges due to its impact on dough rheology and bread quality. This study evaluated the effects of pea, cocoa, and apple fiber on wheat-based dough and bread properties using rheological methods (farinograph, alveograph, pasting, and proofing) and baking trials. Substituting flour with fiber at 1%, 5%, or 10% increased water absorption and affected dough development, stability, and extensibility, particularly at high fiber concentrations. Pasting properties showed varying gelatinization behaviors influenced by fiber type and concentration. Principal component analysis (PCA) highlighted the clustering of dough and bread characteristics based on fiber concentration and type. At low fiber concentrations (up to 5% of flour replacement), negative effects were minimal, suggesting no need for comprehensive compositional analysis. However, high fiber concentrations (10%) introduced significant variability and complexity in dough properties. New farinographic parameters (FU4, FU6, FU8, FU10, and FU12) improved the explanatory power of PCA, enhancing the understanding of fiber-rich dough dynamics. The significant alterations in moisture content and texture underscore the intricate relationship between type of fiber, concentration, and dough functionality. Optimizing rheological parameters for fiber-enriched flour is crucial for adapting the bread-making process to produce high-quality bread with desired characteristics and enhanced nutritional benefits. [ABSTRACT FROM AUTHOR]

Soundscape analysis has become integral to environmental monitoring, particularly in marine and terrestrial settings. Fish choruses within marine ecosystems provide essential descriptors for environmental characterization. This study employed a month-long sequence of continuous underwater recordings to generate 24-h spectrograms, utilizing Principal Component Analysis (PCA) specifically adapted to analyze fish choruses. The spectrograms were constructed using a frequency range from 0 to 5 kHz, represented by 1,025 spectral points (frequency binwidth 5 Hz) on a linear scale. A preliminary spectral subsampling reduced the frequency components to 205 spectral points. PCA was then applied to this subsampled data, selecting 7 principal components (PCs) that explained 95% of the variance. To enhance visualization and interpretation, we introduced "acoustic maps" portrayed as heatmaps. This methodology proved valuable in characterizing the structure of the observed environment and capturing pertinent diel patterns of fish choruses. Additionally, these PCA components can be analyzed using acoustic maps to reveal hidden dynamics within the marine acoustic environment. The dimensionality reduction achieved by PCA not only streamlined data handling but also enabled the extraction of spectral information pertinent to fish choruses and the temporal dynamics of the soundscape. In conclusion, our methodology presents a versatile framework extendable to diverse biological choruses and ecoacoustic studies. The straightforward, easily interpretable analysis leverages computations derived from 24-h spectrograms, offering novel insights into the daily dynamics of biological. Choruses and contributing to future advancements in ecoacoustic research. [ABSTRACT FROM AUTHOR]

Cotton (Gossypium hirsutum L.) is a vital cash crop in Pakistan, but climate change scenarios threatened its production by biotic and abiotic stress, especially drought. Nutrient management, specifically potassium (K) fertilization, typically alleviates the effects of drought. To this end, a greenhouse experiment evaluated the genotypes for drought stress tolerance and its management by K fertilization. The experiment consisted of 70 cotton genotypes factorially combined with two water levels (standard irrigation and drought stress) and two potassium levels (control and 102 mg/kg of potassium). Data collection occurred for shoot and root lengths, fresh shoot and root weights, dry shoot and root weights, root shoot ratio, total dry matter production, and K uptake after 45 days of germination. Results depicted that mean squares for genotypes, drought, potassium, and their interaction were significant for shoot and root lengths, fresh shoot and root weights, dry shoot and root weights, total dry weight, and potassium uptake, while some traits showed nonsignificant differences. Based on the principal component analysis, membership function value, and genotypic diversity, five genotypes emerged as tolerant: CIM-496, IR-3701, Cp-15/2, FH-113, and CIM-1100, and three, i.e., 4-F, MNH-129, and FH-1000, as susceptible. Tolerant and susceptible genotypes can further benefit breeding programs to develop cotton genotypes adaptable to drought stress and with better K uptake. [ABSTRACT FROM AUTHOR]

The morphological analysis of buckwheat (Fagopyrum esculentum) is crucial in developing germplasm resources. The study used 19 local common buckwheat landraces to characterize their morphophysiological and yield-related traits for germplasm development. The analysis of variance revealed no statistically significant variations in most morphophysiological and agronomic features, except for plant height and chlorophyll content (SPAD Value). Landraces L7 and L14 stood out for their superior morphological and yield-related traits, while L2 had the highest grain yield per raceme. Employing principal component analysis helped identify key morphological and yield-related traits. The first six principal components accounted for 99% of the total variance, with the first component having the highest Eigenvalues. Cluster analysis assessed the similarities among landraces based on their morphophysiological and agronomic traits. Notably, the distribution of landraces from similar locations often appeared across different clusters, indicating diverse genetic variability. These findings are crucial for selecting suitable landraces for germplasm development in common buckwheat. Additionally, the insights gained into morphophysiological and yield-related variations can help enhance buckwheat breeding programs in Bangladesh. [ABSTRACT FROM AUTHOR]

Background: Broad bean (Vicia faba L.) is an important protein rich underutilized vegetable crop cultivated for its tender green pods and dry seeds. The present study was carried out to assess the genetic divergence among the broad bean genotypes collected for developing high-yielding cultivars. Methods: The experimental material consisted of 20 broad bean genotypes, raised in randomized block design with three replications at Horticultural Research Station, Nilgiris, Tamil Nadu, India during Kharif 2023. Data concerning 12 quantitative traits and 7 qualitative traits underwent examination using Mahalanobis D² statistic, principal component analysis and stepwise multiple regression analysis. Result: The cluster analysis categorized the genotypes into nine clusters, with the highest number of genotypes in Cluster II (4) and the lowest in Clusters VII, VIII and IX (1 each). Cluster IV displayed the highest intra-cluster distance (D²=3358.2), signifying substantial variation among the genotypes within this cluster. The largest inter-cluster distance (D²=316.9) was observed between Clusters I and VII, indicating significant divergence from other clusters, suggesting that genotypes within these clusters could be valuable as diverse parents for hybridization and selection. The PCA combined with stepwise multiple regression analysis highlighted the primary variations and traits influencing pod yield. [ABSTRACT FROM AUTHOR]

White feces syndrome (WFS) in Penaeus vannamei has a dynamic etiology in shrimp culture. The etiological factors associated with WFS, namely Enterocytozoon hepatopenaei (EHP), aggregated transformed microvilli (ATM), midgut parasites, and environmental factors (temperature, pH, salinity, ammonia, nitrate, and nitrite) were investigated within shrimp farms in the coastal districts of Tamil Nadu. The investigation encompassed 62 WFS-affected shrimp farms, along with a subset of three WFS-negative farms to ensure a comprehensive analysis. Severity scores (0, 1, and 2) were assigned for each factor and their interrelationship was investigated using principal component analysis (PCA). The PCA analysis highlights the endemic presence of EHP (52 farms), along with elevated nitrate, ammonia, and water salinity, as key driving factors of WFS in shrimp farms among the coastal districts of Tamil Nadu. Histological evidence unveils the high prevalence of ATM structures (54 farms) in the hepatopancreas of WFS-affected shrimps. Micro eukaryotes, including gregarines, cestodes, and digeneans, were commonly observed parasites in the white midgut of WFS-affected shrimps. The occurrence of WFS appears to be more frequent during rainy days and is particularly prevalent between 40 and 50 days of culture (DOC). The insights on the interrelationship between the etiological factors associated with WFS can contribute to a better understanding of management strategies and aid in the development of preventive measures to mitigate the occurrence of WFS in shrimp farms across Tamil Nadu, India. [ABSTRACT FROM AUTHOR]

We live in the era of large data analysis, where processing vast datasets has become essential for uncovering valuable insights across various domains of our lives. Machine learning (ML) algorithms offer powerful tools for processing and analyzing this abundance of information. However, the considerable time and computational resources needed for training ML models pose significant challenges, especially within cascade schemes, due to the iterative nature of training algorithms, the complexity of feature extraction and transformation processes, and the large sizes of the datasets involved. This paper proposes a modification to the existing ML-based cascade scheme for analyzing large biomedical datasets by incorporating principal component analysis (PCA) at each level of the cascade. We selected the number of principal components to replace the initial inputs so that it ensured 95% variance retention. Furthermore, we enhanced the training and application algorithms and demonstrated the effectiveness of the modified cascade scheme through comparative analysis, which showcased a significant reduction in training time while improving the generalization properties of the method and the accuracy of the large data analysis. The improved enhanced generalization properties of the scheme stemmed from the reduction in nonsignificant independent attributes in the dataset, which further enhanced its performance in intelligent large data analysis. [ABSTRACT FROM AUTHOR]

East Asian inks are a major component of calligraphy, paintings, and prints in China, Japan, and Korea and are historically made from either pine soot or oil‐lamp soot mixed with a proteinaceous binder. Although the inks from the two different soot sources have different properties in East Asian works of art, no non‐destructive methods to differentiate them scientifically currently exist. Raman spectroscopy (RS) of carbonaceous materials is commonly used to extract information about their properties and has been applied here to East Asian inks. Soots used in making modern inks were collected from 10 sources in China and Japan and analyzed using RS. RS using 405‐, 633‐, and 785‐nm excitation has been able to differentiate pine soot from oil‐lamp soot, also called lampblack. In addition, principal component analysis (PCA) of only 785‐nm Raman spectra has been able to discriminate between two different soots used in a 19th‐century Japanese woodblock printing of Kaishien Gaden. In addition to allowing discrimination between inks on East Asian works of art, these results may be of use to other fields using carbonaceous materials. [ABSTRACT FROM AUTHOR]

Medical image registration is vital for precise healthcare diagnosis, treatment planning, and disease progression tracking, but traditional methods fail to capture complex spatial transformations and anatomical variations. A Kernel Principal Component Analysis (KPCA) driven Teaching Learning based optimization (TLBO) approach is proposed to overcome these limitations. The proposed approach is categorized into three phases, i.e., pre-processed phase, contour extraction phase with feature extraction using KPCA, and evaluating robust affine transformation parameters leveraging TLBO for accurate alignment. In the pre-processing phase, gaussian filter is applied to remove noise from source and target images, followed by normalization process. Afterwards, contour extraction is carried out to create a feature image that represents the boundaries of an image. Centroid localization is then utilized to compute translation parameters, which determine the spatial alignment between the images. By utilizing KPCA, this method captures non-linear relationships in the data to enhance the representation of image features. TLBO is employed to optimize the rigid transformation parameters to improve the accurate alignment of source and target images. Extensive experiments are carried out on monomodal and multimodal medical images such as CT and MRI taken from the Harvard Brain ATLAS, Kaggle as well as in-house clinical dataset to demonstrate the effectiveness of the proposed approach. The proposed approach significantly outperforms state-of-the-art methods, with improvements of 44.37% in Root Mean Square Error (RMSE), 19.93% in Structural Similarity Index Measure (SSIM), 20.01% in Peak Signal-to-Noise Ratio (PSNR), and 16.21% in Cross Correlation (CC) quality assessment metrics. [ABSTRACT FROM AUTHOR]

Analysing migration patterns can be used to predict the likelihood of invasion by exotic species, and disease prevalence and spread through contact networks in animals. This paper presents an analytical method for surveying the characteristics of wildlife migration, which can be challenging due to the varying distances and directions that animals travel and the diverse characteristics of different species. To gain a comprehensive understanding of migratory behaviour, it is necessary to integrate migration interrelationships within the populations and correlated migratory modes, e.g., aggregation or dispersed patterns. To achieve this, we propose the SSS-PCA method, which combines the small-shuffle surrogate (SSS) method and principal component analysis (PCA). The SSS-PCA method involves three major steps: (1) applying the SSS method to break down the time-series correlational structure of the movement data with different time frames, (2) obtaining some mobility indicators, e.g., the range and variance of movement, from the original and SSS data with different time frames, and (3) performing PCA on the mobility indicators, and clustering the results. The proposed method can reveal the migration patterns of individuals and the influence of individuals on the whole group. The SSS-PCA method can be used to examine the migration patterns of marine and terrestrial animal species. We suggest that the method can be applied to further studies of various species' migration data. [ABSTRACT FROM AUTHOR]

This study assessed eleven elements in 24 edible-podded peas, including sugar snap pea and snow pea genotypes aiming to identify promising parents for nutraceutical breeding. Elemental concentrations of pods (dry weight basis) were estimated through inductively coupled plasma-optical emission spectroscopy (ICP-OES). The ranges for these elements varied significantly, highlighting the diverse elemental profiles within the edible-podded pea genotypes. All the elements exhibited a high genotypic and phenotypic coefficient of variation along with considerable heritability and hereditary progress. Positive and significant correlations were recorded among all elements, suggesting the potential for simultaneous selection for these traits. Principal component analysis (PCA) revealed that the first two components accounted for 80.56% of the variation. Further, cluster analysis, based on Euclidean distance, grouped the 24 cultivars into two major clusters. Cluster I exhibited higher means for all estimated concentrations compared to Cluster II. Notably, Dwarf Grey Sugar and Arka Sampoorna from the snap pea group and PED-21-5 and Sugar Snappy from the sugar snap pea in Cluster II demonstrated superior elemental concentration in whole pods. The selected edible-podded pea genotypes serve as valuable genetic resources for new cultivar development, particularly in biofortification efforts targeting whole pod nutrient composition. [ABSTRACT FROM AUTHOR]

In arid and semiarid zones, groundwater is a vital and indispensable natural resource. Indeed, these water resources have become extremely limited due to several factors, including climate change, salinization, and overexploitation. This study covers the hydrochemical characterization of groundwater in the Nekor-Ghiss plain which is located in northern Morocco. To achieve this objective, a set of 79 water samples was analyzed, for various physical and chemical parameters of the samples, including E.C, pH, TDS, HCO3−, SO42−, Ca2+, Mg2+, Na+, K+, PO43−, NO3−, NO2− and NH4+. Several methods were used to interpret the hydrochemical data, namely graphical methods, principal component analysis (PCA), hierarchical ascending classification, and ion exchange indices. A detailed geochemical study of groundwater is described to identify the origin of the chemical composition of groundwater. The results show that the samples studied are characterized by very high mineralization (> 1500 mg/l), with the predominance of Na+, Cl− and SO42− ions. The interpretation of geochemical signatures of groundwater in the Nekor-Ghiss plain shows sulfate-sodium (Na–SO4) and chloride-sodium (Na–Cl) facies. Natural geochemical processes are responsible for water regulation, while human activities exert a limited influence on this control. Marine intrusion, evaporation, and the ion exchange process largely control the chemistry of the aquifer. Using PCA to confirm controlled water chemistry processes revealed three homogeneous groups. The study contributes to a better understanding of the quality and mineralization of groundwater in the Nekor-Ghiss plain, and it will serve as a reference for other plains with similar characteristics. [ABSTRACT FROM AUTHOR]

Flunixin (usually formulated as flunixin meglumine (FLN)), a common non-steroidal anti-inflammatory drug (NSAID) given to cattle, raises human health concerns when it is present in milk. Maximum residue limit (MRL) of 10 µg/kg has been set by Food Safety and Standards Authority of India (FSSAI) for FLN residues in milk. Attenuated Total Reflectance- Fourier-Transform Mid-Infrared (FT-MIR) spectroscopy in conjunction with multivariate techniques was applied to detect the presence of FLN residues in milk. Samples of FLN, pure milk as well as milk samples spiked with FLN at concentrations (1, 5, 10, 20 and 50 µg/kg) below and above the MRL were analysed. ATR-FTIR measurements were conducted in the 4000 –400 cm− 1 wavenumber range, and the wavenumber regions (2942 − 2838, 1748 − 1734 and 1149 –1012 cm− 1) were selected based on the maximum variability in the intensity of peaks and chemometrics techniques such as Principal Component Regression (PCR), Partial Least Square Regression (PLSR), Principal Component Analysis (PCA) and Soft Independent Modeling of Class Analogy (SIMCA) were applied. The study concluded that the presence of parent drug- FLN residues in milk even at 1 µg/kg level (below MRL prescribed by regulatory bodies) can be detected using ATR-FT-MIR coupled with chemometrics. [ABSTRACT FROM AUTHOR]