Principal Component Analysis Research Articles

A stochastic analysis and bibliometric analysis of COVID-19

COVID-19 a novel Corona Virus Disease which was caused by SARS-CoV-2 (Severe Acute Respiratory Syndrome Corona Virus 2) continues to pose a critical and urgent threat to global health. When an infected person comes in contact with a normal individual or when the infected person sneezes or coughs, the virus that triggers COVID-19 spreads.The spread of novel SARS-CoV-2 was increasing, and the threats caused by it were becoming more severe in 2021. To counter the disease and save countless lives in danger, it is necessary to predict the trend of the number of cases and deaths and then implement the policies accordingly. In this paper, the trends of the growth rate of worldwide cases and deaths were studied, and the future growth for 100 days was predicted using the Neural network model and Polynomial Regression model. For efficient planning, the countries were grouped using Principal Component Analysis and the predictions were made. The cases and deaths in different countries and states were related through the Pearson coefficient, and the heat maps were studied. Additionally, in this paper, a case study to predict the trend of cases, number of deaths and recoveries in India was also performed. The Indian states were grouped into four groups based on the Principal Component Analysis (PCA) results, and relevant remarks and trends were suggested. The growth of cases and deaths was studied, and the peaks were predicted for the next 200 days. In recent months, COVID-19 has generated a significant deal of anxiety as a global pandemic, and an increasing number of studies have been published in this area. Consequently, a bibliometric examination of these papers may offer insight into current research hot subjects and trends. We are the first to join stochastic analysis of COVID-19 effects with bibliometric analysis of COVID-19. This prediction, if taken into consideration strategically during the planning of preventive measures of COVID-19 can help to reduce the cases to a great extent.

Taxonomic review of the genus Johnius (Perciformes: Sciaenidae) from East Malaysian waters, with comments on newly recorded Johnius species

In terms of sheer abundance, taxonomic diversity, and species richness, Malaysia has one of the most diverse Sciaenidae in the Indo-West Pacific. Up to ten valid species of the sciaenid fish genus Johnius (e.g., J. amblycephalus (Bleeker, 1855), J. belangerii (Cuvier, 1830), J. borneensis (Bleeker, 1850), J. carouna (Cuvier, 1830), J. coitor (Hamilton, 1822), J. heterolepis Bleeker, 1873, J. macrorhynus (Lal Mohan, 1976), J. plagiostoma (Bleeker, 1849), J. sasakii Hanafi et al., 2022 and J. weberi Hardenberg, 1936), and with additional newly recorded species as J. trewavasae Sasaki, 1992, have been recorded to occur in East Malaysia (Borneo Island) coastal waters. The newly recorded species is a close congener to J. heterolepis Sasaki, 1992 and can be well distinguished by several characters and molecular evidence. The goal of this study is to review the taxonomic status of Johnius species in East Malaysia, Borneo Island, by using a literature review, morphological inference and molecular characters. Principal Component Analysis (PCA) successfully discriminates based on second anal spine length and gill raker length within sampled specimens. A phylogenetic tree based on cytochrome oxidase subunit I (COI) showed intraspecific levels of genetic differentiation within Johnius of East Malaysia, which forms a monophyletic group. Moreover, the new record of Johnius trewavasae in East Malaysia represented that the distribution of the subtropical genus Johnius extended to the southernmost part of the tropical region.

Adverse childhood experiences and subsequent experiences of intimate partner violence in adulthood: a gender perspective.

Investigate the prevalence of adverse childhood experience (ACE) and intimate partner violence (IPV) using a large representative Chinese sample, explore the association mechanism between ACE and adult exposure to IPV and to examine gender differences. A total of 21,154 participants were included in this study. The ACE scale was used to assess participants' exposure to ACE before the age of 18. Participants were evaluated for IPV experienced after the age of 18 using the IPV Scale. Logistic regression model was used to analyse the association between ACE and the risk of IPV exposure in adulthood. Principal component analysis was used to extract the main patterns of ACEs in the Chinese population. Network analyses were employed to identify the most critical types of ACE and IPV, analyse the association mechanisms between ACEs and IPVs, explore gender differences in this association and compare gender differences in the severity of IPVs experienced in adulthood. Participants with at least one ACE event faced a 215.5% higher risk of IPV compared to those without ACE experiences. In population-wide and gender-specific networks, The ACE and IPV nodes with the highest expected influence are 'ACE1 (Verbal abuse + physical abuse pattern)' and 'IPV5 (Partner compares me to other people and blatantly accuses me, making me feel embarrassed and unsure of myself)'. Positive correlations were found between 'ACE1 (Verbal abuse + physical abuse pattern)'-'IPV3 (Partner does not care about me when I am in bad shape [not feeling well or in a bad mood])', 'ACE4 (Violent treatment of mother or stepmother + criminal acts in the family pattern)'-'IPV1 (Partner has ever directly assaulted or hurt me with the help of an instrument)' and 'ACE2 (Exposure to sexual assault pattern)'-'IPV2 (Partner would have physical or sexual contact with me against my will)', which were the three edges with the highest edge weight values in the ACE pattern and IPV edges. 'ACE1 (Verbal abuse + physical abuse pattern)'-'IPV3 (Partner does not care about me when I am in bad shape [not feeling well or in a bad mood])', 'ACE2 (Exposure to sexual assault pattern)'-'IPV2 (Partner would have physical or sexual contact with me against my will)', 'ACE4 (Violent treatment of mother or stepmother + criminal acts in the family pattern)'-'IPV1 (Partner has ever directly assaulted or hurt me with the help of an instrument)' in the male network and 'ACE1 (Verbal abuse + physical abuse pattern)'-'IPV3 (Partner does not care about me when I am in bad shape [not feeling well or in a bad mood])', 'ACE4 (Violent treatment of mother or stepmother + criminal acts in the family pattern)'-'IPV1 (Partner has ever directly assaulted or hurt me with the help of an instrument)', 'ACE3 (Substance abuse + mental illness + violent treatment of mother or stepmother pattern)'-'IPV1 (Partner has ever directly assaulted or hurt me with the help of an instrument)' in the female network are the three edges with the highest edge weights among the ACE and IPV edges in their networks, respectively, all displaying positive correlations. The strength of 'IPV3 (Partner does not care about me when I am in bad shape [not feeling well or in a bad mood])' was higher in the male network than in the female (male = 0.821, female = 0.755, p = 0.002). The edge weight values of 'ACE3 (Substance abuse + mental illness + violent treatment of mother or stepmother pattern)'-'IPV1 (Partner has ever directly assaulted or hurt me with the help of an instrument)' (P = 0.043) and 'ACE4 (Violent treatment of mother or stepmother + criminal acts in the family pattern)'-'IPV1 (Partner has ever directly assaulted or hurt me with the help of an instrument)' (P = 0.032) are greater for females than males. The most common type of ACE in the Chinese population is verbal violence combined with physical violence, while the predominant type of IPV is verbal violence. Males experience higher levels of emotional neglect from their partners compared to females. The association between witnessing physical violence in childhood and experiencing physical violence from a partner in adulthood is stronger in females than in males. The homotypic continuum between ACE and IPV is a crucial mechanism in understanding intergenerational domestic violence. Enhance economic and educational levels, promote correct parenting concepts, reduce child abuse, establish accurate perceptions of intimate relationships, eliminate shame about violence and further advance gender equality. These efforts are vital for reducing IPV prevalence and breaking the cycle of violence in victims' lives.

A multi-stage feature selection method to improve classification of potential super-agers and cognitive decliners using structural brain MRI data-a UK biobank study.

Cognitive aging is described as the age-related decline in areas such as memory, executive function, reasoning, and processing speed. Super-Agers, adults over 80 years old, have cognitive function performance comparable to middle-aged adults. To improve cognitive reserve and potentially decrease Alzheimer's disease (AD) risk, it is essential to contrast changes in regional brain volumes between "Positive-Agers" who have superior cognitive performance compared to their age peers but are not 80 years old yet and aging adults who show cognitive decline (i.e., "Cognitive Decliners"). Using longitudinal cognitive tests over 7-9 years in UK Biobank, principal component analysis (PCA) was first applied to four cognitive domains to create a general cognition (GC) composite score. The GC score was then used to identify latent cognitive groups. Given cognitive groups as the target variable and structural magnetic resonance imaging (sMRI) data and demographics as predictors, we developed a multi-stage feature selection algorithm to identify the most important features. We then trained a Random Forest (RF) classifier on the final set of 54 selected sMRI and covariate predictors to distinguish between Positive-Agers and Cognitive Decliners. The RF model achieved an AUC of 73%. The top 6 features were age, education, brain total surface area, the area of pars orbitalis, mean intensity of the thalamus, and superior frontal gyrus surface area. Prediction of cognitive trajectory types using sMRI may improve our understanding of successful cognitive aging.

An efficient unsupervised classification model for galaxy morphology: Voting clustering based on coding from ConvNeXt large model

By combining unsupervised and supervised machine learning methods, we have proposed a framework, called USmorph to carry out automatic classifications of galaxy morphologies. In this work, we update the unsupervised machine learning (UML) step by proposing an algorithm based on ConvNeXt large model coding to improve the efficiency of unlabeled galaxy morphology classifications. The method can be summarized into three key aspects as follows: (1) a convolutional autoencoder is used for image denoising and reconstruction and the rotational invariance of the model is improved by polar coordinate extension; (2) utilizing a pre-trained convolutional neural network (CNN) named ConvNeXt for encoding the image data. The features were further compressed via a principal component analysis (PCA) dimensionality reduction; (3) adopting a bagging-based multi-model voting classification algorithm to enhance robustness. We applied this model to I-band images of a galaxy sample with $I_ mag < 25$ in the COSMOS field. Compared to the original unsupervised method, the number of clustering groups required by the new method is reduced from 100 to 20. Finally, we managed to classify about 53<!PCT!> galaxies, significantly improving the classification efficiency. To verify the validity of the morphological classification, we selected massive galaxies with $M_ M_ sun $ for morphological parameter tests. The corresponding rules between the classification results and the physical properties of galaxies on multiple parameter surfaces are consistent with the existing evolution model. Our method has demonstrated the feasibility of using large model encoding to classify galaxy morphology, which not only improves the efficiency of galaxy morphology classification, but also saves time and manpower. Furthermore, in comparison to the original UML model, the enhanced classification performance is more evident in qualitative analysis and has successfully surpassed a greater number of parameter tests. The enhanced UML method will support the Chinese space station telescope in the future.

Machine Learning Reveals Demographic Disparities in Palliative Care Timing Among Patients With Traumatic Brain Injury Receiving Neurosurgical Consultation.

Timely palliative care (PC) consultations offer demonstrable benefits for patients with traumatic brain injury (TBI), yet their implementation remains inconsistent. This study employs machine learning methods to identify distinct patient phenotypes and elucidate the primary drivers of PC consultation timing variability in TBI management, aiming to uncover disparities and inform more equitable care strategies. Data on admission, hospital course, and outcomes were collected for a cohort of 232 patients with TBI who received both PC consultations and neurosurgical consultations during the same hospitalization. Patient phenotypes were uncovered using principal component analysis and K-means clustering; time-to-PC consultation for each phenotype was subsequently compared by Kaplan-Meier analysis. An extreme gradient boosting model with Shapley Additive Explanations identified key factors influencing PC consultation timing. Three distinct patient clusters emerged: cluster A (n = 86), comprising older adult White women (median 87years) with mild TBI, received the earliest PC consultations (median 2.5days); cluster B (n = 108), older adult White men (median 81years) with mild TBI, experienced delayed PC consultations (median 5.0days); and cluster C (n = 38), middle-aged (median: 46.5years), severely injured, non-White patients, had the latest PC consultations (median 9.0days). The clusters did not differ by discharge disposition (p = 0.4) or inpatient mortality (p > 0.9); however, Kaplan-Meier analysis revealed a significant difference in time-to-PC consultation (p < 0.001), despite no differences in time-to-mortality (p = 0.18). Shapley Additive Explanations analysis of the extreme gradient boosting model identified age, sex, and race as the most influential drivers of PC consultation timing. This study unveils crucial disparities in PC consultation timing for patients with TBI, primarily driven by demographic factors rather than clinical presentation or injury characteristics. The identification of distinct patient phenotypes and quantification of factors influencing PC consultation timing provide a foundation for developing for standardized protocols and decision support tools to ensure timely and equitable palliative care access for patients with TBI.

Effects of γ-polyglutamic acid on the rheological, microstructural and sensory properties of low-fat yogurt.

While low-fat yogurt offers numerous health benefits, its texture and sensory qualities are poor. This study aimed to investigate the effects of γ-polyglutamic acid (γ-PGA) on the rheological, microstructural and sensory properties of low-fat yogurt using rheological tests, scanning electron microscopy (SEM) and sensory evaluation. The results showed that the syneresis of low-fat yogurt added with 0.15% γ-PGA was significantly (P < 0.05) reduced to 23.03%, compared to that of low-fat yogurt without γ-PGA (42.87%). An improvement in storage and loss moduli (G', G") and apparent viscosity was also observed. The power law model fitted to rheological data indicated that γ-PGA enhanced the viscoelasticity and strength of the gel network though interaction with casein gel, which might lead to changes in the moduli of yogurts. Microstructural observation via SEM confirmed the enhanced crosslinking between casein micelles and the filling effect of γ-PGA. Sensory liking scores were strongly correlated with rheological properties and apparent viscosity. The results of principal component analysis (PCA) confirmed the positive effects of γ-PGA on quality and rheological attributes of low-fat yogurt. The results of this study provided valuable references for the potential use of γ-PGA as a fat replacer to stabilize the sensory quality of low-fat yogurt. © 2024 Society of Chemical Industry.

Hybrid model approach for hilly sub-watershed prioritization using morphometric parameters: a case study from Bakkhali river watershed in Cox’s Bazar, Bangladesh

ABSTRACT This study investigates the morphometry of the Bakkhali watershed (BW) in the hilly districts of Cox’s Bazar and Bandarban, Bangladesh, to analyze its hydrological characteristics. Morphometric parameters were utilized to prioritize sub-watersheds (SWs) through a hybrid approach combining principal component analysis (PCA) and the weighted-sum approach (WSA). Using ArcGIS Pro 2.7.0, streams, the watershed, and SWs were delineated from a 30-m resolution COP30 DEM. Preliminary priority ranks (PPR) were determined based on direct and inverse relationships of morphometric parameters to soil erodibility. Weighted compound factors (CF) were calculated from PCA results for final prioritization. The BW, a fifth-order watershed with a drainage area of 571.52 km², shows a consistent decrease in the number of streams with increasing stream order, indicating an erosional landform. The mean bifurcation ratio of 4.09 suggests a higher tendency for soil erosion. Shape factors indicate an elongated watershed with less pronounced peak flow characteristics. The analysis identified SW2 and SW3 as high-erosion zones, SW4 and SW9 as medium-erosion zones, and SW1 as a low-erosion zone. The study demonstrates the efficacy of combining geospatial and statistical tools for SW prioritization.

Identifying neural network structures explained by personality traits: combining unsupervised and supervised machine learning techniques in translational validity assessment

AbstractThere have been studies previously the neurobiological underpinnings of personality traits in various paradigms such as psychobiological theory and Eysenck’s model as well as five-factor model. However, there are limited results in terms of co-clustering of the functional connectivity as measured by functional MRI, and personality profiles. In the present study, we have analyzed resting-state connectivity networks and character type with the Lowen bioenergetic test in 66 healthy subjects. There have been identified direct correspondences between network metrics such as eigenvector centrality (EC), clustering coefficient (CC), node strength (NS) and specific personality characteristics. Specifically, N Acc L and OFCmed were associated with oral and masochistic traits in terms of EC and CC, while Insula R is associated with oral traits in terms of NS and EC. It is noteworthy that we observed significant correlations between individual items and node measures in specific regions, suggesting a more targeted relationship. However, the more relevant finding is the correlation between metrics (NS, CC, and EC) and overall traits. A hierarchical clustering algorithm (agglomerative clustering, an unsupervised machine learning technique) and principal component analysis were applied, where we identified three prominent principal components that cumulatively explain 76% of the psychometric data. Furthermore, we managed to cluster the network metrics (by unsupervised clustering) to explore whether neural connectivity patterns could be grouped based on combined average network metrics and psychometric data (global and local efficiencies, node strength, eigenvector centrality, and node strength). We identified three principal components, where the cumulative amount of explained data reaches 99%. The correspondence between network measures (CC and NS) and predictors (responses to Lowen’s items) is 62% predicted with a precision of 90%.

Topology Bench: systematic graph-based benchmarking for core optical networks

Topology Bench is a comprehensive topology dataset designed to accelerate benchmarking studies in optical networks. The dataset, focusing on core optical networks, comprises publicly accessible and ready-to-use topologies, including (a) 105 georeferenced real-world optical networks and (b) 270,900 validated synthetic topologies. Prior research on real-world core optical networks has been characterized by fragmented open data sources and disparate individual studies. Moreover, previous efforts have notably failed to provide synthetic data at a scale comparable to our present study. Topology Bench addresses this limitation, offering a unified resource, and represents a 61.5% increase in spatially referenced real-world optical networks. To benchmark and identify the fundamental nature of optical network topologies through the lens of graph-theoretical analysis, we analyze both real and synthetic networks using structural, spatial, and spectral metrics. Our comparative analysis identifies constraints in real optical network diversity and illustrates how synthetic networks can complement and expand the range of topologies available for use. Currently, topologies are selected based on subjective criteria, such as preference, data availability, or perceived suitability, leading to potential biases and limited representativeness. Our framework enhances the generalizability of optical network research by providing a more objective and systematic approach to topology selection. A statistical and correlation analysis reveals the quantitative range of all of these graph metrics and the relationships between them. Finally, we apply unsupervised machine learning to cluster real-world topologies into distinctive groups based on nine optimal graph metrics using K-means. It employs a two-step optimization process: optimal features are selected by maximizing feature uniqueness through principal component analysis, and the optimal number of clusters is determined by maximizing decision boundary distances via support vector machines. We conclude the analysis by providing guidance on how to use such clusters to select a diverse set of topologies for future studies. Topology Bench, openly available via Dataset 1 (https://zenodo.org/records/13921775) and Code 1 (https://github.com/TopologyBench), promotes accessibility, consistency, and reproducibility.

High-throughput near-infrared spectroscopy for detection of major components and quality grading of peas

Pea (Pisum sativum L.) is a nutrient-dense legume whose nutritional indicators influence its functional qualities. Traditional methods to identify these components and examine the relationships between their contents could be more laborious, hindering the quality assessment of the varieties of peas. This study conducted a statistical analysis of data about the sensory and physicochemical nutritional attributes of peas acquired using traditional techniques. Additionally, 90 sets of spectral data were obtained using a portable near-infrared spectrometer, which were then integrated with chemical values to create a near-infrared model for the basic ingredient content of peas. The correlation analysis revealed significant findings: pea starch displayed a substantial negative correlation with moisture, crude fiber, and crude protein, while showing a highly significant positive correlation with pea seed thickness. Furthermore, pea protein exhibited a significant positive correlation with crude fiber and crude fat. Cluster analysis classified all pea varieties into three distinct groups, successfully distinguishing those with elevated protein content, high starch content, and low-fat content. The combined contribution of PC1 and PC2 in the principal component analysis (PCA) was 51.2%. Partial least squares regression (PLSR) and other spectral preprocessing methods improved the predictive model, which performed well with an external dataset, with calibration coefficients of 0.89–0.99 and prediction coefficients of 0.71–0.88. This method enables growers and processors to efficiently analyze the composition of peas and evaluate crop quality, thereby enhancing food industry development.

Exploring the forensic effectiveness and population genetic differentiation in Guizhou Miao and Bouyei group by the self-constructed panel of X chromosomal multi-insertion/deletions

In this research, a self-developed panel comprising 22 X chromosomal multi-InDels and one X-STR was used to explore the genetic polymorphisms and forensic characteristics of these loci in Guizhou Miao and Guizhou Bouyei populations. Besides, genetic affiliations among Guizhou Miao, Guizhou Bouyei and Guizhou Han populations were investigated using principal component analysis, STRUCTURE and machine learning methods. The findings indicated that these loci in the male and female samples had comprehensive discrimination powers greater than 0.999999999. Meanwhile, the cumulative mean exclusion chance of these 23 loci for trio and duo cases were also greater than 0.9999 in Guizhou Miao and Guizhou Bouyei populations. Population genetic analyses of three Guizhou populations revealed that there were relatively low genetic divergences among these populations based on the self-constructed panel. In conclusion, this system could be utilized as the valuable tool for forensic personal identification and parentage testing in Guizhou Miao and Guizhou Bouyei populations.

Remaining Useful Life Prediction of Pressure Regulating Shutoff Valve based on Feature Fusion and Bidirectional Gated Recurrent Unit

Abstract In the bleed air system of the ARJ21 aircraft, pressure regulating shutoff valve (PRSOV) failures are common, and their failures can lead to disasters and economic losses. Accordingly, prediction of the degradation performance of PRSOV is crucial. This work proposes a life prediction method based on principal component analysis (PCA) and bidirectional gated recurrent units (BiGRU) to achieve accurate prediction. After obtaining pressure data throughout the entire life of PRSOV, considering that the pressure required for PRSOV during the takeoff and climb phases is the most critical, data from this phase are selected for focused monitoring. Classical statistical feature extraction methods are used to extract features from the raw pressure data during the takeoff and climb phases. An empirical feature extraction method with low-pressure weighting is also proposed based on engineering practical experience. Feature fusion is performed using PCA based on these two types of features. Finally, BiGRU is utilized to model the fused degradation feature indicators and estimate the remaining service life of PRSOV. The results of the analysis of the full life data of PRSOV in ARJ21 aircraft indicated that the proposed method can effectively predict its remaining service life. The proposed method demonstrated higher prediction accuracy compared with the related prediction methods.&amp;#xD;

Discrimination of leaf diseases in Maize/Soybean intercropping system based on hyperspectral imaging

In order to achieve precise discrimination of leaf diseases in the Maize/Soybean intercropping system, i.e. leaf spot disease, rust disease, mixed leaf diseases, this study utilized hyperspectral imaging and deep learning algorithms for the classification of diseased leaves of maize and soybean. In the experiments, hyperspectral imaging equipment was used to collect hyperspectral images of leaves, and the regions of interest were extracted within the spectral range of 400 to 1000 nm. These regions included one or more infected areas on the leaves to obtain hyperspectral data. This approach aimed to enhance the accurate discrimination of different types of diseases, providing more effective technical support for the detection and control of crop diseases. The preprocessing of hyperspectral data involved four methods: Savitzky-Golay (SG), Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC) and 1st Derivative (1st Der). The 1st Der was found to be the optimal preprocessing method for hyperspectral data of maize and soybean diseases. Competitive Adaptive Reweighted Sampling (CARS), Successive Projections Algorithm (SPA) and Principal Component Analysis (PCA) were employed for feature extraction on the optimal preprocessed data. The Support Vector Machines (SVM), Bidirectional Long Short-Term Memory Network (BiLSTM) and Dung Beetle Optimization-Bidirectional Long Short-Term Memory Network (DBO-BiLSTM) were established for the discrimination of maize and soybean diseases. Comparative analysis indicated that, in the classification of maize and soybean diseases, the DBO-BiLSTM model based on the CARS extraction method (1st Der-CARS-DBO-BiLSTM) demonstrated the highest classification rate, reaching 98.7% on the test set. The research findings suggest that integrating hyperspectral imaging with both traditional and deep learning methods is a viable and effective approach for classifying diseases in the intercropping model of maize and soybean. These results offer a novel method and a theoretical foundation for the non-invasive, precise, and efficient identification of diseases in the intercropping model of maize and soybean, carrying positive implications for agricultural production.

Enhancing Clustering Stability and Efficiency: A Framework for Optimizing K-means, K-medoids, and K-shape with Intelligent Algorithms

Clustering methods like Kmeans often produce inconsistent results due to the random initialization of cluster centroids, even with optimizations such as Kmeans++, which improve centroid selection but fail to eliminate sensitivity to initialization randomness. Additionally, the choice of the number of clusters and distance metrics significantly impacts clustering performance. This paper proposes an enhanced framework combining intelligent optimization algorithms—Sparrow Search Algorithm (SSA), Dung Beetle Optimizer (DBO), and Sine Cosine Algorithm (SCA)—to optimize clustering outcomes for Kmeans, Kmedoids, and Kshape. The framework also incorporates dimensionality reduction techniques, including Principal Component Analysis (PCA), Non-Negative Matrix Factorization (NNMF), and Singular Value Decomposition (SVD), to address high-dimensional data challenges. Experimental results on benchmark datasets demonstrate the proposed framework’s effectiveness, with SSA achieving the highest silhouette score of 0.68 and reducing runtime by 35% compared to traditional methods. This approach enhances clustering stability and accuracy, offering a robust solution for diverse applications.

Daily Engine Performance Trending Using Common Flight Regime Identification

Abstract Accurate flight regime identification is critical for enhancing aircraft efficiency and safety. Traditionally, predictive models for aircraft operation have relied on complex, black-box machine learning techniques that lack transparency. This study introduces a more interpretable approach by leveraging the New Comprehensive Modular Aero-Propulsion System Simulation (N-CMAPSS) dataset and combining expanding window classification, voting schemes, and spectral clustering to detect distinct flight regimes. The method applies elastic registration to align time-shifted patterns and functional principal component analysis (FPCA) to reduce dimensionality, capturing core dynamics across flight regimes. These transformed features are fed into a genetic algorithm (GA)-assisted orthogonal matching pursuit (OMP) for sparse feature selection. Through evolutionary selection, crossover, and mutation, the most informative features are identified, enabling accurate predictions while maintaining transparency. This method outperforms more complex models in certain test cases, offering a balance between accuracy and interpretability that is essential for predictive maintenance and safety applications.

Short‐term effects of a heavy dairy manure application on soil chemical and biological indicators in an irrigated semiarid cropping system

AbstractIntensive dairy production in southern Idaho is associated with the annual application of manure to croplands. However, a one‐time heavy application of manure could alternatively be used as a means to improve soil fertility and health for years or even decades, circumventing the need for frequent applications. To determine if this practice would negatively affect soil properties in the short term, we analyzed chemical and biological indicators of soil health for 2 years after dairy manure incorporation. Soil indicators measured were pH, electrical conductivity, extractable nitrogen (N) and phosphorus, total carbon (C) and N, enzyme activities, net N mineralization, soil organic C, soil protein, active C, ammonia oxidation potential, and particulate organic matter. Manure (with and without synthetic fertilizer) was found to significantly affect chemical and biological indicators in both topsoil (0–15 cm) and subsoil (15–30 cm), but the responses were greater in the subsoil. This can be attributed to the fact that manure was incorporated to approximately 30 cm via moldboard plow. All indicators responded positively to manure, except pH, which decreased slightly in the subsoil in the first year after application. Principal component analysis of chemical and biological indicators, across all years and depths, showed that the first two components explained 62% and 8.5% of the variance. While soil indicators were not adversely affected by manure, silage corn (Zea mays L.) yields in year 1 were significantly lower in manured plots, though in year 2, barley grain (Hordeum vulgare L.) yields were statistically similar among manure and fertilizer.

Optimizing dental students' use of feedback: Validation of a theoretical model.

A model describing the factors that influence the use of feedback by dental students from three UK dental schools has been proposed. Seven factors that influence feedback utilization were identified. This project aims to test the validity of this model in a larger student population from two UK dental schools in Newcastle upon Tyne and Cardiff. An electronic questionnaire was created and circulated to two dental schools. Data from schools were combined and analyzed using SPSS software. A total of 304 responses were analyzed using principal component analysis resulting in the extraction of nine components which were identified as "most important." The Kruskal-Wallis test was used to analyze the correlations of these components with the demographic characteristics of participants. The nine components were feedback utility, feedback value, credibility, learning approach, the learner's understanding of the feedback's purpose, and its processes, accessibility, future applicability, and institutional processes. According to the Kruskal-Wallis analysis, seven of the components (but not credibility or institutional processes) were found to be influenced by differences in the gender, nationality, and ethnicity of students as well as their previous educational experience. However, no evidence was found that variables such as age, student year group, or religious beliefs affected any of these components. Some demographic characteristics are more engaged with one or more components than others. Understanding that will help optimizing this model and ultimately benefits students and institutions. The main project's goal was met by the research in validating the previously developed model.

Assessing the Impact of Geopolitical Risk on Longevity Bond Pricing: Insights from Bayesian Multivariate Regression

AbstractThis paper investigates the multivariate pricing of coupon longevity bonds (CLBs) using the Fama–French–Lee–Carter (FF–LC) five-vector model in the framework of Bayesian integrated nested Laplace approximation (INLA) in the presence of geopolitical risk (GPR). The variance-covariance and correlation matrices are utilized to capture the interdependence between factors. We prove the generalization of multivariate Bayesian INLA with the basic probability assignment which is utilized as a posterior uncertainty belief associated with the GPR uncertainty category (a rich representation of GPR uncertainty) that is an element of the frame of discernment in the CLB posterior estimation. INLA Bayesian principal component analysis (INLA-BPCA) is applied to the model prediction parameters generating a multivariate normally distributed posterior. The deviance information criterion (DIC) assesses optimal factor selection. The results show that the BPCA posterior gains a feature that allows for a balance between the goodness-of-fit and complexity in hierarchical model selection by incorporating the retained principal components (or the effective number of parameters) from the DIC formula. Furthermore, it is also evident in our results, that the DIC outperforms the Bayesian information criterion (BIC), and Watanabe–Akaike information criterion (WAIC). The DIC is more suitable for Bayesian-based parametric models with high complexity. Lastly, the INLA-BPCA-DIC is applied to select the best longevity factors that yield a low longevity price of risk for insurers and practitioners, to attenuate the risks associated with investing in CLBs in the presence of geopolitical uncertainty shocks.

Impact of Substrates, Volatile Fatty Acids, and Microbial Communities on Biohydrogen Production: A Systematic Review and Meta-Analysis

Hydrogen is becoming recognized as a clean and sustainable energy carrier, with microbial fermentation and electrolysis serving critical roles in its production. This paper provides a thorough meta-analysis of BioH2 production across diverse substrates, microbial populations, and experimental settings. Statistical techniques, including ANOVA, principal component analysis (PCA), and heatmaps, were used to evaluate the influence of various parameters on the hydrogen yield. The mean hydrogen generation from the reviewed studies was 168.57 ± 52.09 mL H2/g substrate, with food waste and glucose demonstrating considerably greater hydrogen production than mixed food waste (p &lt; 0.05). The inhibition of methanogens with inhibitors like 2-bromoethanesulfonate (BES) and chloramphenicol (CES) enhanced hydrogen production by as much as 25%, as demonstrated in microbial electrolysis cell systems. PCA results highlighted Clostridium spp., Thermotoga spp., and Desulfovibrio spp. as the most dominant microbial species, with Clostridium spp. contributing up to 80% of the YH2 in fermentation systems. The study highlights synergistic interactions between dominant and less dominant microbial species under optimized environmental conditions (pH 5.5–6.0, 65 °C), emphasizing their complementary roles in enhancing H2 production. Volatile fatty acid regulation, particularly acetate and butyrate accumulation, correlated positively with hydrogen production (r = 0.75, p &lt; 0.01). These findings provide insights into optimizing biohydrogen systems through microbial consortia management and substrate selection, offering a potential way for scalable and efficient H2 production.