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Processes or products are typically complex systems with numerous interrelated procedures and interdependent components. This results in complex relationships between responses and input factors, as well as complex nonlinear correlations among multiple responses. If the two types of complex correlations in the quality design cannot be properly dealt with, it will affect the prediction accuracy of the response surface model, as well as the accuracy and reliability of the recommended optimal solutions. In this paper, we combine kernel trick-based kernel principal component analysis, spline-based Bayesian semiparametric additive model, and normal boundary intersection-based evolutionary algorithm to address these two types of complex correlations. The effectiveness of the proposed method in modeling and optimisation is validated through a simulation study and a case study. The results show that the proposed Bayesian semiparametric additive model can better describe the process relationships compared to least squares regression, random forest regression, and support vector basis regression, and the proposed multi-objective optimisation method performs well on several indicators mentioned in the paper. [ABSTRACT FROM AUTHOR]

In the semiconductor industry, virtual metrology (VM) is a cost-effective and efficient technique for monitoring the processes from one wafer to another. This technique is implemented by generating a predictive model that uses real-time data from equipment sensors in conjunction with measured wafer quality characteristics. Before establishing a prediction model for the VM system, appropriate selection of relevant input variables should be performed to maintain the efficiency of subsequent analyses considering the large dimensionality of the sensor data inputs. However, wafer production processes usually employ multiple sensors, which leads to cost escalations. Herein, we propose a variant of the sparse principal component analysis (PCA) called true sparse PCA (TSPCA). The proposed method uses a small number of input variables in the first few principal components. The main contribution of the proposed TSPCA is reducing the number of essential sensors. Our experimental results demonstrate that compared to the existing sparse PCA methods, the proposed approach can reduce the number of sensors required while explaining an approximately equivalent amount of variance. [ABSTRACT FROM AUTHOR]

We rely on a total of 23 (cluster size, 8 structural, and 14 connectivity) descriptors to investigate structural patterns and connectivity motifs associated with water cluster aggregation. In addition to the cluster size n (number of molecules), the 8 structural descriptors can be further categorized into (i) one-body (intramolecular): covalent OH bond length (rOH) and HOH bond angle (θHOH), (ii) two-body: OO distance (rOO), OHO angle (θOHO), and HOOX dihedral angle (ϕHOOX), where X lies on the bisector of the HOH angle, (iii) three-body: OOO angle (θOOO), and (iv) many-body: modified tetrahedral order parameter (q) to account for two-, three-, four-, five-coordinated molecules (qm, m = 2, 3, 4, 5) and radius of gyration (Rg). The 14 connectivity descriptors are all many-body in nature and consist of the AD, AAD, ADD, AADD, AAAD, AAADD adjacencies [number of hydrogen bonds accepted (A) and donated (D) by each water molecule], Wiener index, Average Shortest Path Length, hydrogen bond saturation (% HB), and number of non-short-circuited three-membered cycles, four-membered cycles, five-membered cycles, six-membered cycles, and seven-membered cycles. We mined a previously reported database of 4 948 959 water cluster minima for (H2O)n, n = 3–25 to analyze the evolution and correlation of these descriptors for the clusters within 5 kcal/mol of the putative minima. It was found that rOH and % HB correlated strongly with cluster size n, which was identified as the strongest predictor of energetic stability. Marked changes in the adjacencies and cycle count were observed, lending insight into changes in the hydrogen bond network upon aggregation. A Principal Component Analysis (PCA) was employed to identify descriptor dependencies and group clusters into specific structural patterns across different cluster sizes. The results of this study inform our understanding of how water clusters evolve in size and what appropriate descriptors of their structural and connectivity patterns are with respect to system size, stability, and similarity. The approach described in this study is general and can be easily extended to other hydrogen-bonded systems. [ABSTRACT FROM AUTHOR]

Hardness of thin films is a noteworthy property in the electronic and mechanical industry and is generally observed to be dependent on the degree of roughening facilitated from surface heights' surface spatial heterogeneity at the mesoscopic observation scale. Nonetheless, owing to enhanced scale fluctuations and higher-order central moments, conventional parameters provide limitations and errors in capturing the spatial heterogeneity of surfaces. Herein, we have utilized scale-independent fractal parameters to analyze the spatial heterogeneity of surface heights in Ti1−xSixN ternary hard films deposited with varying Si doping concentrations using sputtering technique. The fractal dimension, lacunarity coefficient, Moran index, surface entropy, Otsu's separability, and fractal succolarity were computed to provide an overarching understanding of the surface heights' spatial heterogeneity. Principal component analysis was employed on the data sets to identify the parameter(s) accounting for the maximum variance and accordingly, the structure–property relation between spatial heterogeneity of surface and hardness is analyzed and discussed in the context of the fractal dimension of surface heights. The results indicate the possibility of mesoscopic surface engineering and, consequently, tuning of hardness and modulus of elasticity in Ti1−xSixN hard films by mere changing of surface spatial heterogeneity facilitated by the fractal dimension of surface heights. [ABSTRACT FROM AUTHOR]

We have reanalyzed the rich plethora of ground state configurations of the asymmetric Wigner bilayer system that we had recently published in a related diagram of states [Antlanger et al., Phys. Rev. Lett. 117, 118002 (2016)], comprising roughly 60 000 state points in the phase space spanned by the distance between the plates and the charge asymmetry parameter of the system. In contrast to this preceding contribution where the classification of the emerging structures was carried out "by hand," we have used for the present contribution machine learning concepts, notably based on a principal component analysis and a k-means clustering approach: using a 30-dimensional feature vector for each emerging structure (containing relevant information, such as the composition of the configuration as well as the most relevant order parameters), we were able to reanalyze these ground state configurations in a considerably more systematic and comprehensive manner than we could possibly do in the previously published classification scheme. Indeed, we were now able to identify new structures in previously unclassified regions of the parameter space and could considerably refine the previous classification scheme, thereby identifying a rich wealth of new emerging ground state configurations. Thorough consistency checks confirm the validity of the newly defined diagram of states. [ABSTRACT FROM AUTHOR]

Differences in gait patterns of children with Duchenne muscular dystrophy (DMD) and typically developing (TD) peers are visible to the eye, but quantifications of those differences outside of the gait laboratory have been elusive. In this work, we measured vertical, mediolateral, and anteroposterior acceleration using a waist-worn iPhone accelerometer during ambulation across a typical range of velocities. Fifteen TD and fifteen DMD children from 3 to 16 years of age underwent eight walking/running activities, including five 25 m walk/run speed-calibration tests at a slow walk to running speeds (SC-L1 to SC-L5), a 6-min walk test (6MWT), a 100 m fast walk/jog/run (100MRW), and a free walk (FW). For clinical anchoring purposes, participants completed a Northstar Ambulatory Assessment (NSAA). We extracted temporospatial gait clinical features (CFs) and applied multiple machine learning (ML) approaches to differentiate between DMD and TD children using extracted temporospatial gait CFs and raw data. Extracted temporospatial gait CFs showed reduced step length and a greater mediolateral component of total power (TP) consistent with shorter strides and Trendelenberg-like gait commonly observed in DMD. ML approaches using temporospatial gait CFs and raw data varied in effectiveness at differentiating between DMD and TD controls at different speeds, with an accuracy of up to 100%. We demonstrate that by using ML with accelerometer data from a consumer-grade smartphone, we can capture DMD-associated gait characteristics in toddlers to teens.

Spatial and temporal patterns of water supply and consumptive water use were analyzed from 475 Detailed Analysis Units by County (DAUCOs) spatial units across California during 2002 through 2016 to evaluate spatial and temporal variability and how it might associate with precipitation variability and other factors. Many, but not all, DAUCOs have relatively low total water supply variability compared to that of state-wide precipitation. Such low variability, in DAUCOs having sufficient diversity of water supply sources, is the result of switching between sources as needed to maintain a reliable total water supply. We used multiple approaches to explore these variations which involved four categories of water supply (local, groundwater, imported, and other) and two categories of water use (agricultural and urban). First, a cluster analysis of the volumetric water balance data identified a small set of clusters having similar magnitudes and proportions of water supply sources and water use—some of them composed of only a few DAUCOs but accounting for a disproportionate amount of the state’s water use. Second, a principal components analysis identified leading modes of anomalous water supply and water use among the 475 DAUCOs, capturing most of the time variation during 2002 to 2016. The most prominent mode exhibits a multi-year trend, most strongly involving increasing groundwater supply and agricultural water use, and decreasing urban water use and imported water supply. Over the study period, trends in both supply and use were pronounced, but differed considerably across California DAUCOs. One predominant subset of DAUCOs grew their agricultural water use with increased groundwater supply; in contrast to a widespread group of DAUCOs which reduced their urban water use. An important result for planners is our finding that variation in precipitation—itself important—is amplified by the human response to water supply availability and regulatory policy.

Torsional and bending deformations of DNA molecules often occur in vivo and are important for biological functions. DNA "under stress" is a conformational state, which is by far the most frequent state during DNA–protein and gene regulation. In DNA minicircles of length < 100 base pairs (bp), the combined effect of torsional and bending stresses can cause local unusual conformations, with certain base pair steps often absorbing most of the stress, leaving other steps close to their relaxed conformation. To better understand the superhelical dynamics of DNA under stress, molecular simulations of 94 bp minicircles with different torsional linking numbers were interpreted using Fourier analyses and principal component analyses. Sharp localized bends of nearly 90° in the helical axis were observed, which in turn decreased fluctuations of the rotational register and helped redistribute the torsional stress into writhe, i.e., superhelical turn up to 360°. In these kinked minicircles, only two-thirds of the DNA molecule bends and writhes and the remaining segment stays close to straight and preserves a conformational flexibility typical of canonical B-DNA (bending of 39° ± 17° distributed parsimoniously across 36 bp), which was confirmed and visualized by principal component analysis. These results confirm that stressed DNA molecules are highly heterogeneous along their sequence, with segments designed to locally store and release stress so that nearby segments can stay relaxed. [ABSTRACT FROM AUTHOR]

In order to facilitate the high-quality advancement and digital innovation of the wine industry, a method based on principal component analysis (PCA) and improved quantum particle swarm optimization (IQPSO) to optimize support vector machine (PCA-IQPSO-SVM), was proposed to solve the wine classification problem. First, the feature extraction ability of PCA was used to reduce the input dimension of the model and improve the classification efficiency. At the same time, aiming at the problems that the quantum particle swarm optimization (QPSO) is easy to fall into local optimum and the convergence ability is decreased in the later stage of optimizing SVM, a variety of improvement strategies are used to improve QPSO to find the best parameters of SVM. The experimental results demonstrate that the model of PCA-IQPSO-SVM exhibits superior evaluation indices compared to other models. Moreover, the optimization efficiency of the PCA-IQPSO-SVM model is enhanced by 1.64% to reach an impressive 85.2%, showcasing its remarkable optimization effect. Simultaneously, this study provides a scientific approach for quality classification in the wine industry, thereby facilitating its high-quality development. [ABSTRACT FROM AUTHOR]

To realize space-sea integrated communication between multiple ships under the BeiDou-3 satellite communication topology, this paper proposes a new satellite-ship autonomous communication system based on a deep learning anomaly detection algorithm. A deep learning algorithm combining coarse-grained detection (piecewise oversampling principal component analysis, POsPCA) and fine-grained sorting (VAE and differential ARIMA joint model) was established to realize two-way autonomous communication between BeiDou-3 satellites and ships. Specifically, a segmented oversampling principal component analysis algorithm was proposed to analyze anomalous BeiDou-3 short message data segments in the coarse-grained detection stage. In the fine-grained sorting stage, a joint fusion reconstruction and prediction model was proposed to calculate the fine-grained anomaly score. The autonomous communication system establishes the communication priority of multiple ships based on this score. Using the experimental platform independently built for this study to verify the algorithm performance, the proposed algorithm effectively realizes the priority ranking of anomaly scores by detecting abnormal data segments, effectively saving 64.4% of BeiDou-3 satellite short message communication resources. [ABSTRACT FROM AUTHOR]

Multivariate relationships and variability of initial and post-harvest soil parameters in the North and South Transects of Bengaluru during 2017 and 2018 are assessed. North (South) Transect soils indicated initial pH of 5.9 (7.1), EC of 0.17 (0.16) dS m−1, N of 376.9 (415.3) kg/ha, P2O5 of 83.18 (64.31) kg/ha, K2O of 169.7 (247.3) kg/ha, Fe of 11.78 (10.86) ppm, Zn of 1.59 (1.13) ppm, Cu of 0.71 (0.6) ppm, Mn of 8.24 (7.74) ppm and B of 0.24 (0.21) ppm; compared to post-harvest pH of 6.1 (6.8), EC of 0.26 (0.19) dS m−1, N of 352.5 (383.4) kg/ha, P2O5 of 68.05 (54.14) kg/ha, K2O of 153.5 (222.1) kg/ha, Fe of 9.91 (8.75) ppm, Zn of 1.35 (0.93) ppm, Cu of 0.58 (0.53) ppm, Mn of 6.88 (6.72) ppm and B of 0.20 (0.19) ppm. Four PCs of post-harvest parameters explained a maximum variance of 59.85% in the North compared to 69.94% in the South Transects. Soil pH (−0.623), Fe (0.648), Zn (−0.603) on PC-1; Cu (0.628) on PC-2; EC (0.613) on PC-3; and B (−0.657) on PC-4 were significant in North, while pH (−0.715), Fe (0.891), Zn (−0.624), Mn (0.835) on PC-1; EC (0.771), N (0.618) on PC-2; K2O (0.718), B (0.774) on PC-3; P2O5 (0.673), Cu (0.647) on PC-4 were significant in South Transect. It may be recommended that the package of practices of crops should be modified by considering variability, relationships, loadings, and buildup of soil fertility parameters under semi-arid Alfisols. [ABSTRACT FROM AUTHOR]

Douchi is a highly appreciated product due to its unique flavor, high nutritional value, and health benefits. Douchi from different regions exhibits distinct characteristics. The objective of this study was to investigate the volatile profiles in douchi produced from different geographical origins using gas chromatography-mass spectrometry (GC-MS) and chemometrics. The volatiles were extracted by headspace solid-phase micro-extraction (HS-SPME) and subsequently determined by GC-MS. A comparative analysis was further conducted to identify the variations in volatile compounds among the samples. The correlation between the samples was investigated by employing cluster analysis (CA) and principal component analysis (PCA). An orthogonal partial least squares-discriminant analysis (OPLS-DA) model was established to discriminate samples from different regions and identify key differential compounds. A total of 46 volatile compounds were detected, among which terpenes, alcohols were the main compounds. Furthermore, 14 key differentiating compounds were screened out based on the OPLS-DA modle. The application of GC-MS combined with chemometrics offers a theoretical framework for douchi quality control and classification. [ABSTRACT FROM AUTHOR]

Surface-enhanced Raman spectroscopy (SERS) was employed to detect the biochemical changes in Gordonia-HS126-4N bacterial strain mediated by exposure to 4,6-dimethyldibenzothiophene. SERS has revealed significant changes induced in the bacterial cells after exposure to a compound, confirming its potential for evaluating their desulfurization capability. Principal component analysis (PCA) and partial least squares–discriminant analysis (PLS-DA) were demonstrated to be effective tools for comparison of the exposed and unexposed bacterial strains. PCA was used for differentiating the SERS spectra of unexposed and exposed Gordonia-HS126-4N. PLS-DA was employed on SERS spectra for the discrimination of the unexposed and exposed bacterial strains with 87% specificity, 88% sensitivity, 89% accuracy, and the value of area under the receiver operating characteristics curve (AUROC) of 0.78, which indicates the excellent performance of the chemometric model. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) technology is currently being used in healthcare systems to collect sensor values for heart disease diagnosis and prediction. Although many researchers have focused on the diagnosis of heart disease, the accuracy of the diagnosis results is low. To address this problem, we present a novel method for predicting heart disease. This paper proposes a novel approach for heart disease detection using long short-term memory networks (LSTM) optimized with Self-improved Jellyfish Optimization. Initially, input data are gathered from the smartwatch and heart monitor device that is attached to the patient to monitor the blood pressure and electrocardiogram (ECG). Then the input data are Pre-processed using the Principal Component Analysis (PCA) algorithm. The African vulture's optimization algorithm (AVOA) is employed for feature selection. Selected features are provided to LSTM for classifying the received sensor data into normal and abnormal. The proposed approach aims to optimize LSTM hyperparameters the Self-improved Jellyfish Optimization (SIJO) is utilized. In addition, the proposed algorithm incorporates a self-improvement mechanism. By then, the proposed approach's performance has been tested on the MATLAB platform and its results have been compared to those of other approaches. Thus, the results demonstrate the effectiveness of the Jellyfish Optimization algorithm in enhancing LSTM for heart disease detection. [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]

How can inter-individual variability be quantified? Measuring many features per experiment raises the question of choosing them to recapitulate high-dimensional data. Tackling this challenge on spindle elongation phenotypes, we showed that only three typical elongation patterns describe spindle elongation in C. elegans one-cell embryo. These archetypes, automatically extracted from the experimental data using principal component analysis (PCA), accounted for more than 95% of inter-individual variability of more than 1600 experiments across more than 100 different conditions. The two first archetypes were related to spindle average length and anaphasic elongation rate. The third archetype, accounting for 6% of the variability, was novel and corresponded to a transient spindle shortening in late metaphase, reminiscent of kinetochore function-defect phenotypes. Importantly, these three archetypes were robust to the choice of the dataset and were found even considering only non-treated conditions. Thus, the inter-individual differences between genetically perturbed embryos have the same underlying nature as natural inter-individual differences between wild-type embryos, independently of the temperatures. We thus propose that beyond the apparent complexity of the spindle, only three independent mechanisms account for spindle elongation, weighted differently in the various conditions. Interestingly, the spindle-length archetypes covered both metaphase and anaphase, suggesting that spindle elongation in late metaphase is sufficient to predict the late anaphase length. We validated this idea using a machine-learning approach. Finally, given amounts of these three archetypes could represent a quantitative phenotype. To take advantage of this, we set out to predict interacting genes from a seed based on the PCA coefficients. We exemplified this firstly on the role of tpxl-1 whose homolog tpx2 is involved in spindle microtubule branching, secondly the mechanism regulating metaphase length, and thirdly the central spindle players which set the length at anaphase. We found novel interactors not in public databases but supported by recent experimental publications. Author summary: When quantifying the cell, scientists need to accurately measure cell-to-cell variability as it carries unique information about the underlying mechanisms. In this article, we focused on the spindle length as a proxy for correct mitosis. We used the nematode one-cell embryo, an established model organism, to investigate (stem-like) cell divisions. Through a data-only approach of spindle dynamics over time, we automatically extracted the most informative variability descriptors. We recalled two known ones: spindle length and anaphase elongation rate. We uncovered a new one –late-metaphase shortening –present in all conditions. Such a phenotype was previously confined to cells with defective chromosome attachments. These three descriptors account for 95% of variability, suggesting that the complex spindle choreography relies only on a few core mechanisms. Furthermore, we showed that the final spindle length at anaphase, important to set the daughter cell fates, is already determined in late metaphase, despite a complete spindle reassembly between both phases. Interestingly, the same descriptors explain variability in genetically perturbed and non-treated conditions. This suggests that no novel mechanism appears in defective cells. Only mechanism contributions are changing. Finally, we propose a tool to predict genes co-involved in a mechanism from a known gene to support candidate approaches. [ABSTRACT FROM AUTHOR]

COVID‐19 infection among pregnant women results in more severe symptoms and higher mortality rates. No comprehensive health literacy for protection against COVID‐19 among pregnant women has been available for general use in Thailand. This cross‐sectional study aimed to develop and examine an instrument for measuring health literacy of prevention COVID‐19 infection among pregnant women (HLS‐P). A total of 321 pregnant women aged older than 20 years were participated in this study, Selected through multistage cluster sampling, between September 2021 and January 2022. Data were collected using structured questions that included sociodemographic characteristics and the health literacy scale for protecting against COVID‐19 (HLS‐P) developed by the researchers. The content and construct validity of the health literacy scale were examined. Exploratory factor analysis performed with principal component analysis and Varimax rotation. Confirmatory factor analysis was conducted using IBM SPSS AMOS 26. The model fitting was evaluated using several indices namely root mean square error of approximation, normed fit index, comparative fit index, and goodness‐of‐fit index. The reliability of the scale was evaluated using Cronbach's alpha and item total correlation. As a results of exploratory factor analysis of the scale, 31 items were loaded which indicated a 6‐factors for the scale that collectively explained 62.59% of total variance. Confirmatory factor analysis also indicated a good fit to the six latent structures with root mean square error of approximation 0.03, normed fit index 0.94, comparative fit index 0.97, and goodness‐of‐fit index 0.91. Internal consistency reliability was satisfactory with Cronbach's alpha coefficient of 0.94 and the item‐total correlation between 0.34 and 0.86. The overall scale was sufficiently reliable. As a result, the HLS‐P is a reliable and relevant measure for assessing health literacy in pregnant women. Thus, this scale is profoundly used as an evaluation tool for measuring health literacy among pregnant women, providing critical information for healthcare professionals and policymakers about the health literacy needs and capacity of service receivers. [ABSTRACT FROM AUTHOR]

Summary Finger limes (Citrus australasica) are an Australian native and traditional food that are known for their caviar‐like pearls often referred to as “Citrus Caviar”. This study aimed to spectrally characterise finger lime varieties sourced across South‐East Queensland and Northern New South Wales. The varieties investigated demonstrated a wide diversity in their phenotypic appearance; however, their infrared spectra were similar. The NIR spectra of the samples were collected using a portable instrument and the data analysed using principal component analysis (PCA) and partial least squares (PLS) regression. Differences in the PCA score plot were observed associated with the different varieties analysed. The cross‐validation statistics obtained for the prediction of adulteration of finger with Tahitian lime were R2CV and SECV 0.96% and 7%, respectively (using 8 PLS factors), using PLS regression. This study demonstrated that a portable handheld NIR instrument is able to identify finger lime varieties using both whole fruits and the corresponding peel samples. This study also showed that NIR was able to identify the level of adulteration of finger lime with Tahitian lime. [ABSTRACT FROM AUTHOR]

Key points Physiologists often express the change in the value of a measurement made on two occasions as a ratio of the initial value. This is usually motivated by an assumption that the absolute change fails to capture the true extent of the alteration that has occurred in attaining the final value – if there is initial variation among individual cases. While it may appear reasonable to use ratios to standardize the magnitude of change in this way, the perils of doing so have been widely documented. Ratios frequently have intractable statistical properties, both when taken in isolation and when analysed using techniques such as regression. A new method of computing a standardized metric of change, based on principal components analysis (PCA), is described. It exploits the collinearity within sets of initial, absolute change and final values. When these sets define variables subjected to PCA, the standardized measure of change is obtained as the product of the loading of absolute change onto the first principal component (PC1) and the eigenvalue of PC1. It is demonstrated that a sample drawn from a population of these standardized measures: approximates a normal distribution (unlike the corresponding ratios); lies within the same range; and preserves the rank order of the ratios. It is also shown that this method can be used to express the magnitude of a physiological response in an experimental condition relative to that obtained in a control condition. The intractable statistical properties of ratios and the perils of using ratios to standardize the magnitude of change are well known. A new method of computing a standardized metric, based on principal components analysis (PCA), is described, which exploits the collinearity within sets of initial, absolute change and final values. A sample drawn from a population of these PCA‐derived measures: approximates a normal distribution (unlike the corresponding ratios); lies within the same range as the ratios; and preserves the rank order of the ratios. The method can also be applied to express the magnitude of a physiological response in an experimental condition relative to a control condition. [ABSTRACT FROM AUTHOR]

In order to explore the influencing factors of spatial and temporal evolution of production-living-ecological space in Beijing Tianjin sandstorm source area, the remote sensing images, natural environment and socio-economic data of Duolun County in Inner Mongolia from 2000 to 2020 were selected, and the spatial auto-correlation model and principal component analysis model were used to analyze the spatial pattern evolution and influencing factors of production-living-ecological space. The results show that: (1) the function of production space decreases slightly, and the degree of spatial agglomeration decreases; (2) The function of living space rose slightly, and its spatial agglomeration degree showed an upward trend; (3) The ecological spatial function showed a slow upward trend, and its spatial agglomeration degree increased; (4) The spatial pattern of production-living-ecological space is characterized by "high in the southwest and low in the northeast"; (5) Precipitation has the greatest impact on the spatial evolution of the production-living-ecological space. The distance from the main residential areas, per capita GDP, the distance from the main roads and the distance from the main waters have strong explanatory power on the spatial evolution of the production-living-ecological space. [ABSTRACT FROM AUTHOR]

This study examined the composition, distribution, and origins of rare, Noble, and fissionable elements for the first time in black sand deposits from the Northern Delta coastal region. The findings showed that among the elements under investigation, Fe, Ti, Mn, and Sn had the greatest mean levels, while Hf, Cd, and As had the lowest mean amounts. According to the study's elemental composition, black sand is thought to have economic worth for Ti, Zr, Hf, Sn, Ag, and W. The Zr, Co, Cd, Cu, Hf, V, W, and Zn correlation points to the same source origin. It is clear that the accessory mineral composition in the sediments under study especially the heavy ones controls the geochemical patterns of trace elements. The trace element concentrations of interest show a pattern of element variability related to the mineralogy of the sands, as indicated by the principal component analysis and cluster analysis. To explore and exploit heavy minerals in the research region, the study's findings are important. [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]

Wheat is an important staple crop not only in Pakistan but all over the globe. Although the area dedicated to wheat cultivation expands annually, the quantity of wheat harvested is declining due to various biotic and abiotic factors. Global wheat production and output have suffered as a result of the drought, which is largely driven by a lack of water and environmental factors. Organic fertilizers have been shown to reduce the severity of drought. The current research was conducted in semi-arid climates to mitigate the negative effects of drought on wheat during its critical tillering (DTS), flowering (DFS), and grain filling (DGFS) stages through the application of three different abscisic acid treatments: ABA0 (0 mgL−1) control, ABA1 (100 mgL−1) and ABA2 (200 mgL−1). Wheat growth and yield characteristics were severely harmed by drought stress across all critical development stages, with the DGFS stage being particularly vulnerable and leading to a considerable loss in yield. Plant height was increased by 24.25%, the number of fertile tillers by 25.66%, spike length by 17.24%, the number of spikelets per spike by 16.68%, grain count per spike by 11.98%, thousand-grain weight by 14.34%, grain yield by 26.93% and biological yield by 14.55% when abscisic acid (ABA) was applied instead of the control treatment. Moreover, ABA2 increased the more physiological indices (water use efficiency (36.12%), stomatal conductance (44.23%), chlorophyll a (24.5%), chlorophyll b (29.8%), transpiration rate (23.03%), photosynthetic rate (24.84%), electrolyte leakage (− 38.76%) hydrogen peroxide (− 18.09%) superoxide dismutase (15.3%), catalase (20.8%), peroxidase (− 18.09%), and malondialdehyde (− 13.7%)) of drought-stressed wheat as compared to other treatments. In the case of N, P, and K contents in grain were maximally improved with the application of ABA2. Through the use of principal component analysis, we were able to correlate our results across scales and provide an explanation for the observed effects of ABA on wheat growth and production under arid conditions. Overall, ABA application at a rate of 200 mgL−1 is an effective technique to boost wheat grain output by mitigating the negative effects of drought stress. [ABSTRACT FROM AUTHOR]

Cognitive deficits are prevalent in individuals with psychosis and are associated with neurobiological changes, potentially serving as an endophenotype for psychosis. Using the HCP-Early-Psychosis-dataset (n = 226), we aimed to investigate cognitive subtypes (deficit/intermediate/spared) through data-driven clustering in affective (AP) and non-affective psychosis patients (NAP) and controls (HC). We explored differences between three clusters in symptoms, cognition, medication, and grey matter volume. Applying principal component analysis, we selected features for clustering. Features that explained most variance were scores for intelligence, verbal recognition and comprehension, auditory attention, working memory, reasoning and executive functioning. Fuzzy K-Means clustering on those features revealed that the subgroups significantly varied in cognitive impairment, clinical symptoms, and, importantly, also in medication and grey matter volume in fronto-parietal and subcortical networks. The spared cluster (86%HC, 37%AP, 17%NAP) exhibited unimpaired cognition, lowest symptoms/medication, and grey matter comparable to controls. The deficit cluster (4%HC, 10%AP, 47%NAP) had impairments across all domains, highest symptoms scores/medication dosage, and pronounced grey matter alterations. The intermediate deficit cluster (11%HC, 54%AP, 36%NAP) showed fewer deficits than the second cluster, but similar symptoms/medication/grey matter to the spared cluster. Controlling for medication, cognitive scores correlated with grey matter changes and negative symptoms across all patients. Our findings generally emphasize the interplay between cognition, brain structure, symptoms, and medication in AP and NAP, and specifically suggest a possible mediating role of cognition, highlighting the potential of screening cognitive changes to aid tailoring treatments and interventions. [ABSTRACT FROM AUTHOR]

An abundance of degrees of freedom (DOF) exist when executing a countermovement jump (CMJ). This research aims to simplify the understanding of this complex system by comparing jump performance and independent functional DOF (fDOF) present in CMJs without (CMJNoArms) and with (CMJArms) an arm swing. Principal component analysis was used on 39 muscle forces and 15 3-dimensional joint contact forces obtained from kinematic and kinetic data, analyzed in FreeBody (a segment-based musculoskeletal model). Jump performance was greater in CMJArms with the increased ground contact time resulting in higher external (p = 0.012), hip (p < 0.001) and ankle (p = 0.009) vertical impulses, and slower hip extension enhancing the proximal-to-distal joint extension strategy. This allowed the hip muscles to generate higher forces and greater time-normalized hip vertical impulse (p = 0.006). Three fDOF were found for the muscle forces and 3-dimensional joint contact forces during CMJNoArms, while four fDOF were present for CMJArms. This suggests that the underlying anatomy provides mechanical constraints during a CMJ, reducing the demand on the control system. The additional fDOF present in CMJArms suggests that the arms are not mechanically coupled with the lower extremity, resulting in additional variation within individual motor strategies. [ABSTRACT FROM AUTHOR]

ABSTRACT With the rapid urbanization in China, urban land resources gradually become the core of urban development. This study spatially evaluated the urban land resource carrying capacity (LRCC) with a case study of the built‐up area in Wuhan from 2015 to 2020. Following an evaluation index system, five critical LRCC indicators, including population density, GDP per land area, plot ratio, building density, and road network density, were selected by an analytical hierarchical process. The synthesis of indicators, however, is usually challengeable due to homogeneous assumptions of traditional techniques. In this study, we adopted a local technique, geographically weighted principal component analysis, to calculate a comprehensive carrying pressure (CCP) concerning spatially varying contributions of each indicator on their synthesis across different geographic locations. On mapping these spatial outputs of the built‐up area in Wuhan, the highest CCP was found in the central areas, where population size tends to be influential and the dominant variable in 62.69% of subdistricts. Furthermore, increased construction over the 5 years has led to an increased CCP in some of the peripheries of the built‐up area, and 55.22% of subdistricts show rising changes. With the GWPCA technique, this framework works well in evaluating and analyzing urban LRCC from a new local perspective. [ABSTRACT FROM AUTHOR]

A reasonable land use development around subway stations can balance the utilization rates of the subway system during peak and off-peak hours, thereby enhancing its service levels and operational efficiency. Analyzing the temporal distribution patterns of passenger flow and their influencing factors is crucial for determining the optimum ratio of each land use type surrounding metro stations. Thus, this paper employs principal component analysis (PCA) at first to investigate the temporal distribution of metro ridership, and identify their main patterns and factor loadings. Then, using geographically weighted regression, the study examines the spatial dependencies between the main component proportions and influencing factors, focusing on Xi'an subway stations. The results indicate that the temporal distribution of passenger flow can be decomposed into three principal components: the first representing commuting characteristics, and the second and third representing regulating functions. The overall distribution is a composite of these components in varying proportions. Residential and educational land uses primarily drive morning and evening peak flows, with residential land use in the city center and peripheral areas having a more pronounced effect compared to transitional areas. Conversely, commercial & office, healthcare, and recreational & park land mitigate peak flows and increase off-peak flows. External hub enhances passenger flow throughout the day, while industrial land use has negligible impact. [ABSTRACT FROM AUTHOR]

ABSTRACT Sensor devices, such as accelerometers, are widely used for measuring physical activity (PA). These devices provide outputs at fine granularity (e.g., 10–100 Hz or minute‐level), which while providing rich data on activity patterns, also pose computational challenges with multilevel densely sampled data, resulting in PA records that are measured continuously across multiple days and visits. On the other hand, a scalar health outcome (e.g., BMI) is usually observed only at the individual or visit level. This leads to a discrepancy in numbers of nested levels between the predictors (PA) and outcomes, raising analytic challenges. To address this issue, we proposed a multilevel longitudinal functional principal component analysis (mLFPCA) model to directly model multilevel functional PA inputs in a longitudinal study, and then implemented a longitudinal functional principal component regression (FPCR) to explore the association between PA and obesity‐related health outcomes. Additionally, we conducted a comprehensive simulation study to examine the impact of imbalanced multilevel data on both mLFPCA and FPCR performance and offer guidelines for selecting optimal methods. [ABSTRACT FROM AUTHOR]

Controlling the quality and health of foodstuffs is of great importance. The quality of foods like garlic is strongly influenced by the conditions of processing. Fungal infection is one of the most common hazards of garlic productivity that can affect its processing as well. This research aimed to use the E-Nose to investigate the aroma of garlic as a quality control factor influenced by different treatments such as type of processing, type of fungal infection, and time elapsed since the date of inoculation. The data was investigated and categorized through different methods such as principal component analysis (PCA), linear discriminant analysis (LDA), Support vector machine (SVM), and backpropagation neural network (BPNN). The Index of deterioration toughness increased during the monitoring period. In the analysis of the data related to the unprocessed whole (UW), dried slices (DS), garlic powder (PO), and garlic tablet (TA), the PCA included 55%, 75%, 47%, and 53% of the data, respectively. The LDA was able to classify the aroma of UW, DS, PO, and TA samples based on the TFI treatment with an accuracy of 90%, 93.33%, 88.89%, and 60%, respectively. Also, the BPNN classified the aromas of UW, DS, PO, and TA samples based on the TEI treatment with an accuracy of 90%, 95.6%, 72.2%, and 82.2%, respectively. The results revealed that the aroma alteration can be used as a comprehensive factor in the quality control of processed products. As well, the type of processing had significant effects on the severity of decay caused by fungal infection. [ABSTRACT FROM AUTHOR]

The purpose of this study is to explore the realization of users ‘ information rights (IRs) in the smart service of university libraries. Based on the LibQUAL model, principal component analysis (PCA) is used to measure the quality of smart services in university libraries. The contribution weight of various services to the realization of IRs is calculated by analytic hierarchy process (AHP), then the degree of IR realization is obtained. Users have the highest right to express information, the lowest right to equality, and other rights tend to be moderate. Suggestions to improve the realization of IRs are put forward at the end. [ABSTRACT FROM AUTHOR]

The normalization of X‐ray absorption near‐edge structure (XANES) spectra is required for comparing spectral features and extracting quantitative information in analytical techniques such as linear combination analysis, principal component analysis and multivariate curve resolution. Most published data are normalized to the edge‐jump, but normalization to the spectral area has also been applied. The latter is particularly attractive if only a small energy range around the absorption can be recorded reliably. Here, the two normalization methods are compared at the L3‐edge of Pt, Pd and Rh, and at the Ni K‐edge using experimental and calculated spectra. Normalization to the spectral area is found to be a viable approach if the range for the area normalization is sufficiently large. [ABSTRACT FROM AUTHOR]

Due to the limitations imposed by urban power grid outages for maintenance, on‐line harmonic current detection technology for distribution network cables is expected to become an effective supplement to traditional offline diagnostic methods, enhancing the real‐time diagnosis of distribution network cable insulation conditions. This study established a 10 kV distribution network cable test platform and prepared typical defective cables subjected to moisture and long‐term thermal aging. Using COMSOL finite element electromagnetic simulation, the magnetic flux evolution laws of the cable insulation under typical defects were obtained. Experimental tests provided the harmonic current characteristics and statistical features of cables with typical defects. Based on these data, a method for analysing the degradation degree of distribution network cables was constructed using least absolute shrinkage and selection operator (LASSO) regression analysis. Furthermore, a defect‐type identification method based on cluster analysis was proposed. Results indicate that the odd harmonics and the 4th harmonic of the distribution network cable's harmonic current are closely related to the cable's degradation state. A model integrating principal component analysis (PCA) data dimensionality reduction and expectation‐maximization clustering analysis achieved a recognition accuracy of up to 75.64% in distinguishing between moisture‐affected and normal cable states. The proposed on‐line detection and evaluation methods can effectively identify high‐risk cables with latent defects. [ABSTRACT FROM AUTHOR]

The exponential growth of IoT devices and their interdependency makes the technology more vulnerable to network attacks like Distributed Denial of Service (DDoS) that interrupt network resources. The prevalence of these attacks necessitates the development of robust and effective defense mechanisms. In recent years, many machine learning defense methodologies have been developed to address the ubiquitous growth of DDoS attacks on IoT, and the majority of them suffer from detection time delay issues. Thus, the paper presents an approach focusing on dimensionality reduction and feature selection techniques to minimize long-time detection without compromising accuracy. The proposed approach uses Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Factor Analysis and Recursive Feature Elimination with Cross Validation (RFECV) as the dimensionality reduction and feature selection techniques and Gaussian Naïve Bayes (GNB), Decision Tree (DT), Random Forest (RF), AdaBoost, and Logistic Regression (LR) as machine learning models to classify the malicious traffic. The approach provides a reliable DDoS detection model that effectively enhances the detection time delay with the combination of GNB with LDA and achieves 99.98% accuracy in 0.582 s of detection time. [ABSTRACT FROM AUTHOR]

This study employed headspace solid phase micro‐extraction gas chromatography mass spectrometry (HS‐SPME‐GC–MS) coupled with multivariate statistical analysis to identify volatile markers in various levels of Zhenjiang Aromatic Vinegar (ZAV). A full range of 40 volatile organic compounds (VOCs) were identified and categorized into acids, alcohols, esters, aldehydes, ketones, heterocyclic compounds and others. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) demonstrated a clear clustering effect among different levels of ZAV, indicating distinct volatile characteristics at the relative content level. Furthermore, using Orthogonal partial least squares discrimination analysis (OPLS‐DA), 20 significant volatile organic compounds (VOCs) were identified as markers for distinguishing between the various levels of ZAV. To differentiate among the levels of ZAV, two volatile compounds, acetic acid and n‐hexanol, were identified as characteristic volatile compounds using a combination of Venn diagram, analysis of variance (ANOVA) and boxplot analysis. The results showed notable variations in VOCs among ZAV samples of different grades. Acetic acid and n‐hexanol can serve as reliable markers for grade distinction, providing valuable technical and theoretical support for the production and quality control of ZAV. [ABSTRACT FROM AUTHOR]

Practical Application The study examined the effects of three pretreatments, blanching (5, 10, and 30 min), ultrasound (15, 20, and 30 min), and steam blanching (10, 20, and 30 min) on sugarcane, assessing their impact on sugarcane juice quality parameters and aroma compounds. The control had the highest soluble solid content, while the ultrasound‐15 min (US‐15 min)‐treated sample had the lowest, affecting pH, total acidity, and color values significantly. Color analysis showed lower L* values and less greenish tones in treated samples. The implemented pretreatments effectively reduce the browning index, with the US‐20 min treatment showing the most significant reduction compared to the control sample. All pretreatments deactivated polyphenol oxidase. Carbon isotope analysis yielded significant results. Principal component analysis and hierarchical clustering linked 1,1‐diphenyl‐2‐picrylhydrazyl (DPPH) with 2,2′‐azino‐bis 3‐ethylbenzothiazoline‐6‐sulfonic acid (ABTS) and 5‐hydroxymethyl furfural (HMF) with total phenolic content. The study also highlights significant variations in aroma compound profiles among pretreated sugarcane juice samples, with blanching for 10 min showing notable increases in specific compounds like 2‐heptanone and nonanol. Hierarchical clustering showed similarities between blanching‐30 min and US‐30 min, contrasting with the control. Blanching‐10 min had a positive impact on sugarcane juice quality. In conclusion, the study emphasized how pretreatments affect physicochemical properties and aroma compounds in sugarcane juice.The research findings suggest that blanching and ultrasound pretreatments can be used by the food industry to improve sugarcane juice quality by reducing browning, enhancing color, and altering aroma profiles. These pretreatments could extend the shelf life and appeal of sugarcane juice, making it more attractive to consumers while maintaining its nutritional properties. [ABSTRACT FROM AUTHOR]

Kalamata table olives are a widely exported Greek protected designation of origin (PDO) product with distinct organoleptic profile. This study aimed to evaluate the effect of label color on the flavor perception of pasteurized and unpasteurized Kalamata PDO olives. Consumer perception was assessed in two studies. First, four packaged products with green, blue, red, and purple labeling were assessed by participants (n = 106) for preference using rating scales, and emotional profiles were obtained using a check‐all‐that‐apply list of 33 emotions developed in language native to the participants (Greek). The green label resulted to more positive emotion profiling of olive products (i.e., “healthy,” “pleased,” and “satisfied” with mean scores correspondingly equal to 2.99, 2.95, and 2.87), and red labeled olives resulted to willingness to pay more. In the second study, unpasteurized and pasteurized olives in red and green labeled glass jars were sensorially assessed by participants (n = 124) using rating scales, showing that green labeled products are liked most (mean scores; 5.12 and 5.12), whereas red labeled was scored less (mean scores; 4.96 and 4.67), along emotion profiles with emojis. Principal component analysis of emotional reactions revealed grouping of samples based only on pasteurization/no pasteurization, without been affected by the color of the label, and did not relate to the liking scores. The results showed that sensory perception of olives is significantly affected by the color of packaging (p‐value <0.05) due to emotional reactions and color of packaging can enhance the perception of olives’ quality as improvement of the processing is limited by the PDO status. [ABSTRACT FROM AUTHOR]

The primary objective of this study is to investigate the effects of Zinc toxicity when fishes are exposed in a controlled environment using two-dimensional correlation spectral analysis. This analysis is superior as subtle spectral changes induced by an external perturbation are not readily noticeable in conventional one-dimensional spectra. A higher decrease in protein and carbohydrates and a lesser decrease in lipids (20%–28%) against the control sample occurs due to Zn toxicity. The lowest value of the bioconcentration factor for Zn shows carbohydrate utilization, which was resolved well in our two-dimensional spectral analysis. Synchronous maps show the predominant changes in protein (1668 cm−1), phospholipids (1771 cm−1), and fatty acids (1707 cm−1). The asynchronous two-dimensional spectra show alteration in phosphodiester stretching of glycogen followed by Amide I. In addition, we explored the importance of principal component analysis coupled two-dimensional spectra analysis which shows weak amide III protein changes. The hetero asynchronous shows well-resolved cross peaks of +(1047, 1389), resulting in improved resolution. The study helps in understanding biomolecular changes of muscle tissues of G. affinis under Zn toxicity using two-dimensional correlation infrared spectral analysis. [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]

Inhabitants of rocky intertidal shores, including gastropods, require specific adaptations to cope with numerous challenges that vary across the intertidal levels. We collected Stramonita biserialis snails from upper (wave-protected and intense predation) and lower (wave-exposed and low predation) intertidal sites to compare the following traits: shell skeleton (ventral and abaxial lateral views of shell shape, thickness, and mass), foot size, energy metabolism, and attachment strength to determine whether the trait values of snails from each zone fit the environmental challenges they face. We used a Principal Component Analysis to reduce the dimensionality of the data. Multivariate Analysis of Covariance (MANCOVA) for comparing characteristics between the two intertidal zones, and Partial Least Squares (PLS) analyses for testing the integration of overall snail characteristics. The traits of the snails of the two intertidal sites matched with the adaptations expected to allow them to cope with their contrasting challenges. The snails from the lower intertidal had more streamlined shells (which reduces drag forces) and a larger aperture and foot extension (which increase the strength of their attachment to the substrate) compared to snails from the upper sites. Snails from the lower intertidal also had a high mass-specific metabolism and soft body proportion, indicating that these snails from the wave-exposed sites have an energetically active musculature that matches their strong substrate attachment. The thin shell walls of the snails of the lower intertidal match the relatively low predatory pressure there. [ABSTRACT FROM AUTHOR]

Introduction: Happé's Strange Stories task was developed in 1994 to assess theory of mind, the ability to infer mental states in oneself and others. Since then, it has undergone revisions, translations, and adaptations. A modified version of the task, the Strange Stories-Revised (SS-R), previously showed satisfactory qualities in a study aiming at identifying psychometrically acceptable social cognitive measures. Objective: The current study expands upon the psychometric evaluation study by examining the qualities of a short version of the SS-R in a sample of healthy adult subjects. Methods: One hundred and eighteen healthy adults completed the task along with neurocognitive measures. Mean scores of the long and short versions were compared. Associations between ToM as measured by performance on this abbreviated version of the SS-R, and potential confounders were explored. Internal consistency, dimensionality of the short version, and performance comparisons across three stages of aging (18–34; 35–59; 60–85 years old) were investigated, and standard measurement error was calculated to improve precision and data interpretation. Results: Reliability coefficients were comparable in the short and long versions. Principal component analysis showed that a one-factor structure best fits the data. Significant differences were observed in ToM performance across the three age groups, indicating a decline with time that was also captured by the long version, starting during midlife and increasing in significance with age. Conclusion: The short version of the SS-R is a promising measure that can be profitably used in time-limited settings to assess theory of mind. [ABSTRACT FROM AUTHOR]

Aim: In northern environments, periods of isolation during Pleistocene glaciations and subsequent recolonization and secondary contact have had a significant influence on contemporary diversity of many species. The recent advent of high‐resolution genomic analyses allows unprecedented power to resolve genomic signatures of such events in northern species. Here, we provide the highest resolution genomic characterization of Atlantic salmon in North America to date to infer glacial refugia and the geographic scales of post‐glacial secondary contact. Location: North America. Taxon: Atlantic salmon, Salmo salar. Methods: Samples were collected for 5455 individuals from 148 populations, encompassing the majority of the Atlantic salmon's native range in North America, from Labrador to Maine. Individuals were genotyped using a 220K single nucleotide polymorphism array aligned to the Atlantic salmon (Salmo salar) genome. Spatial genetic structure (principal component analysis, k‐means clustering, admixture) was evaluated in conjunction with genomic comparisons of these identified lineages to infer the refugia during the last glacial maximum and regions of secondary contact following recolonization. Results: Spatial genomic analyses identified three phylogeographic groups, consistent with the northward recolonization from two southern glacial refugia in North America (a western Maritime lineage and an eastern Newfoundland and Labrador lineage), with subsequent differentiation of the eastern lineage into two separate groups. Secondary contact among these North American groups was observed within the northern Gulf of St. Lawrence, and evidence of trans‐Atlantic secondary contact was detected within the eastern Newfoundland and Labrador lineage. Comparison of groups from insular Newfoundland with those from mainland Labrador suggests genomic regions displaying high differentiation were characterized by elevated European admixture, suggesting a possible role of European secondary contact in population divergence. Main Conclusions: These findings present the first evidence suggesting that genomic diversity in extant North American Atlantic salmon populations has resulted from allopatric isolation in two glacial refugia followed by both regional and trans‐Atlantic recolonization and secondary contact and demonstrate the power of genomic tools to resolve historical drivers of diversity in wild populations. [ABSTRACT FROM AUTHOR]

Aim: Given the rapid pace of climate change and its significant impact on species distribution and survival, understanding the dynamics of ecological niches over time becomes imperative. By employing ecological niche models and species distribution models, alongside analyses of historical occurrence records and palaeoclimatic data, we aimed to assess the extent of changes in the niche of Porites astreoides from the Holocene to the present. Specifically, we quantified the amount of: (1) niche stability, (2) niche expansion, (3) niche decline and (4) niche overlap in both the environmental and geographical space. Location: Atlantic Ocean. Taxon: Cnidarian, Scleractinia. Materials and Methods: Through fossil and current records and environmental variables from the same periods, we use two ecological niche modelling approaches (overlap and maximum entropy) to analyse niche dynamics in environmental and geographical space, specifically the degree of expansion, stability and loss of niches over the last ~8000 years. We used a principal component analysis to build the models within two different calibration areas (minimum convex polygon and buffer). Results: Our results were similar, except for the loss of habitat, across the calibration areas used to build the models. The PCA used to build the models accounts for more than 65% of the variation in the data. Most of the environmental niche remained stable with an increase of almost a third and a negligible loss. Models in geographical space showed that most of the suitable area remained stable, but the species expanded its range from the Caribbean to the coast of Brazil. The analysis also showed a high value of niche overlap in environmental and geographical spaces. Main Conclusions: The species studied is one of the corals with the greatest capacity to adapt to new environments and locations, but it also has a high tendency to preserve niche traits. These characteristics helped the species to expand its environmental and geographical niche in a period of relative climatic stability. On the other hand, these same characteristics of niche conservatism can lead the species to population decline and habitat loss, if the speed of adaptation is lower than the rapid climate changes predicted for the middle and end of the 21st century. [ABSTRACT FROM AUTHOR]

Regional economic development objectives are improved economic, political, and social conditions in a certain area. Investment, production, innovation, wealth, and affluence are the four stages of regional economic development that may be identified in each selected area. It becomes more reliant on technological advancements in the industry and less on locally sourced production inputs as the economy region grows. The regional economic growth issue is inequality in the rates of improvement of subnational geographic regions and inequalities in the distribution of wealth. Intellectual algorithms or enhanced and hybrid algorithms based on machine learning, such as Fuzzy C-means clustering (FCM), principal component analysis, and algorithm, can newly achieve more appropriate solutions to practical issues of discrete, non-linear, non-differentiable, and various constraints. A hybrid algorithm combines two or more other algorithms that solve the same problem. Hence, this paper proposes a Principal Component Analysis for the Sustainable Regional Economic Development (PCA-SRED) model to enhance the efficiency in examining regional economic changes and industrial development zones. The data are taken from the Organization for Economic Cooperation and Development (OECD) regional statistics dataset. Using PCA, industries may be categorized based on shared criteria, and the whole spatial distribution law of datasets and common patterns can be uncovered. To create a long-lasting regional economic development plan, it is crucial to categorize, compare, and evaluate the economic growth level of several areas. The research outcomes illustrate that the hybrid algorithms have high accuracy and a fast convergence rate because they can replicate the smart behavior of some clusters in nature while examing the variances in regional economic growth. The experimental outcomes illustrate that the recommended PCA-SRED model enhances the accuracy ratio by 98.2%, industry production ratio by 95.6%, regional economic change prediction ratio by 96.4%, and economic efficiency ratio by 97.8% compared to other popular models. [ABSTRACT FROM AUTHOR]