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Although brain structural covariance network (SCN) abnormalities have been associated with suicidal thoughts and behaviors (STBs) in individuals with major depressive disorder (MDD), previous studies have reported inconsistent findings based on small sample sizes, and underlying transcriptional patterns remain poorly understood. Using a multicenter magnetic resonance imaging dataset including 218 MDD patients with STBs, 230 MDD patients without STBs, and 263 healthy control participants, we established individualized SCNs based on regional morphometric measures and assessed network topological metrics using graph theoretical analysis. Machine learning methods were applied to explore and compare the diagnostic value of morphometric and topological features in identifying MDD and STBs at the individual level. Brainwide relationships between STBs-related connectomic alterations and gene expression were examined using partial least squares regression. Group comparisons revealed that SCN topological deficits associated with STBs were identified in the prefrontal, anterior cingulate, and lateral temporal cortices. Combining morphometric and topological features allowed for individual-level characterization of MDD and STBs. Topological features made a greater contribution to distinguishing between patients with and without STBs. STBs-related connectomic alterations were spatially correlated with the expression of genes enriched for cellular metabolism and synaptic signaling. These findings revealed robust brain structural deficits at the network level, highlighting the importance of SCN topological measures in characterizing individual suicidality and demonstrating its linkage to molecular function and cell types, providing novel insights into the neurobiological underpinnings and potential markers for prediction and prevention of suicide. [ABSTRACT FROM AUTHOR]

Acid and peroxide values are important indicators to evaluate the hydrolysis or oxidation level of vegetable oil. In order to achieve rapid determination of these parameters by Raman spectroscopy, rapeseed and soybean oil were analyzed, and a competitive adaptive reweighted sampling-back propagation neural network (CARS-BPNN) method was developed. First, the Raman spectrum was preprocessed to reduce noise using moving window smoothing. Next, competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), and uninformative variable elimination (UVE) was applied to extract the Raman characteristic information that indicated the acid and peroxide values. Based on the extracted characteristic information, back propagation neural network (BPNN), partial least squares regression (PLSR), and support vector machine (SVM) prediction models were established and compared. The results showed that the best accuracy was obtained using CARS-BPNN. For rapeseed oil, the coefficients of determination (R2) of acid value and peroxide value were 0.9207 and 0.9772. The root mean square error (RMSE) values were 0.0909 and 0.0022, respectively. For soybean oil, the coefficients of determination (R2) of acid and peroxide values were 0.9381 and 0.9716. The RMSE values were 0.1250 and 0.0027, respectively. A good correlation coefficient and a small RMSE value were obtained. The results indicate that Raman spectroscopy combined with CARS-BPNN accurately evaluated the acid and peroxide values, which provides a reference for the optimization of the Raman spectrum and creates a new approach for the evaluation of vegetable oil quality. [ABSTRACT FROM AUTHOR]

Real-time Fe content monitoring in iron ore slurry is crucial for evaluating concentrate quality and enhancing mineral processing efficiency. Laser-induced breakdown spectroscopy (LIBS) is a promising technique for the online monitoring of elemental content at industrial sites. However, LIBS measurements are hampered by the matrix effect and the self-absorption effect, limiting the precision of linear analytical processes. To overcome this, we propose to introduce a nonlinear processing unit based on the S-transform to incorporate nonlinearity into the data analysis process. This approach integrates a feature selection unit based on the spectral distance variable selection method (SDVS), a nonlinear processing unit based on the S-transform (ST), and a partial least squares regression model (PLS). To demonstrate the improvement in accuracy achieved through nonlinear processing, a comparative analysis involving five models, Raw-PLS, SDVS-PLS, ST-PLS, SDVS-ANN, and SDVS-ST-PLS, is conducted. The results reveal a significant improvement in the performance of the SDVS-ST-PLS model, effectively facilitating the successful application of the LIBSlurry analyzer to the mineral flotation process. [ABSTRACT FROM AUTHOR]

Cow milk consumption (CMC) and downstream alterations of serum metabolites are commonly considered important factors regulating human health status. Foods may lead to metabolic changes directly or indirectly through remodelling gut microbiota (GM). We sought to identify the metabolic alterations in Chinese Peri-/Postmenopausal women with habitual CMC and explore if the GM mediates the CMC-metabolite associations. 346 Chinese Peri-/Postmenopausal women participants were recruited in this study. Fixed effects regression and partial least squares discriminant analysis (PLS-DA) were applied to reveal alterations of serum metabolic features in different CMC groups. Spearman correlation coefficient was computed to detect metabolome-metagenome association. 36 CMC-associated metabolites including palmitic acid (FA(16:0)), 7alpha-hydroxy-4-cholesterin-3-one (7alphaC4), citrulline were identified by both fixed effects regression (FDR < 0.05) and PLS-DA (VIP score > 2). Some significant metabolite-GM associations were observed, including FA(16:0) with gut species Bacteroides ovatus, Bacteroides sp.D2. These findings would further prompt our understanding of the effect of cow milk on human health. [ABSTRACT FROM AUTHOR]

To address the calibration challenge in 3D printing technology, an improved calibration system has been developed, which facilitates the widespread use of bionic stents. Crucially, this system only requires a reference object with known dimensions for automatic calibration. To acquire the required compensation coefficients, various operations are conducted, including printing cell scaffolds as test cube, capturing images, preprocessing images, detecting contours, and classifying with the K-means algorithm. During the image preprocessing stage, an Embedding Bilateral Filter is employed within a least squares filtering-based method, combined with the Sobel operator, to increase the accuracy in obtaining pixel gradient values. Finally, the capability of the developed printer calibration system is demonstrated by a series of print tests; compared to caliper measurements, this approach considerably reduces the time taken, improving calibration efficiency by 98.51% and enhancing accuracy by 9.62%. This development has the potential to enhance the precision and reliability of 3D printing, which is crucial when it comes to producing medical devices and implants. Overall, this is a promising advancement that could have far-reaching implications for the medical industry. [ABSTRACT FROM AUTHOR]

As the global climate continues to change, plants will increasingly experience abiotic stress(es). Stomata on leaf surfaces are the gatekeepers to plant interiors, regulating gaseous exchanges that are crucial for both photosynthesis and outward water release. To optimise future crop productivity, accurate modelling of how stomata govern plant–environment interactions will be crucial. Here, we synergise optical and thermal imaging data to improve modelled transpiration estimates during water and/or nutrient stress (where leaf N is reduced). By utilising hyperspectral data and partial least squares regression analysis of six plant traits and fluxes in wheat (Triticum aestivum), we develop a new spectral vegetation index; the Combined Nitrogen and Drought Index (CNDI), which can be used to detect both water stress and/or nitrogen deficiency. Upon full stomatal closure during drought, CNDI shows a strong relationship with leaf water content (r2 = 0.70), with confounding changes in leaf biochemistry. By incorporating CNDI transformed with a sigmoid function into thermal‐based transpiration modelling, we have increased the accuracy of modelling water fluxes during abiotic stress. These findings demonstrate the potential of using combined optical and thermal remote sensing‐based modelling approaches to dynamically model water fluxes to improve both agricultural water usage and yields. Summary statement: Using both trait and flux measurements, we have designed a new vegetation index: the combined nitrogen and drought index. This index describes changes in leaf optical properties during nitrogen and or drought stress that can be used to improve transpiration modelling during abiotic stress. [ABSTRACT FROM AUTHOR]

Creativity is an important skill in the 21st century, and design thinking has been proposed as a methodology for fostering creativity. Design creativity involves two non-rational aspects: motivation and empathy. However, little is known about how empathy and motivation work in synergy with creativity. This study explored the underlying mechanisms of empathy and motivation in creativity in design thinking. We built an explanatory model to examine the effect of empathy on motivation and creativity and the mediating effects of four aspects of motivation (i.e., attention, relevance, confidence, and satisfaction). In the study, 220 6th-grade students participated in an integrated STEM curriculum that centered on design thinking activities that lasted one semester, and then completed a questionnaire measuring their perceptions of empathy, motivation, and creativity at the end of the design activities. Partial least squares structural equation modelling was used to analyze the collected data. The results showed that empathy positively predicted creativity. In addition, empathy had a significant impact on creativity through the effect of intrinsic motivation, but not through that of extrinsic motivation. Among the four aspects of motivation, attention, relevance, and confidence each strengthened the association between empathy and creativity. However, satisfaction had a negative effect on creativity and a non-significant mediating effect. These findings increase our understanding of the internal mechanisms of design thinking and creativity. Some practical implications of empathy and motivation in creativity are also discussed. [ABSTRACT FROM AUTHOR]

Spatial heterodyne Raman spectroscopy has been widely applied in various fields due to its non-contact, nondestructive, fast, high stability, and high spectral resolution characteristics. This article integrated spatial heterodyne Raman spectroscopy with chemometric methodologies to assess the feasibility of peak-to-peak ratio regression, partial least squares regression, support vector machine regression, and non-negative matrix factorization for the quantitative analysis of mixtures. Chemometrics methods were used to model and analyze the interference data in the interferogram domain, and variational mode decomposition was used to extract features from the interference data, further improving the interference data's modeling and prediction accuracy. The results demonstrate that modal intensities obtained through variational mode decomposition of interference data improve the prediction accuracy of regression analysis. Support vector regression exhibited the most favorable predictive performance among the tested models. The root mean square error of cross-validation was reduced from 5.55% to 2.64%, and the prediction root mean square error was reduced from 5.08% to 1.5%. The improved model utilizing interference data showed higher fitting accuracy and more precise sample predictions compared to spectral data modeling. [ABSTRACT FROM AUTHOR]

BACKGROUND: The rapid and accurate detection of moisture content is important to ensure maize quality. However, existing technologies for rapidly detecting moisture content often suffer from the use of costly equipment, stringent environmental requirements, or limited accuracy. This study proposes a simple and effective method for detecting the moisture content of single maize kernels based on viscoelastic properties. RESULTS: Two types of viscoelastic experiments were conducted involving three different parameters: relaxation tests (initial loads: 60, 80, 100 N) and frequency‐sweep tests (frequencies: 0.6, 0.8, 1 Hz). These experiments generated corresponding force‐time graphs and viscoelastic parameters were extracted based on the four‐element Maxwell model. Then, viscoelastic parameters and data of force‐time graphs were employed as input variables to explore the relationships with moisture content separately. The impact of different preprocessing methods and feature time variables on model accuracy was explored based on force‐time graphs. The results indicate that models utilizing the force‐time data were more accurate than those utilizing viscoelastic parameters. The best model was established by partial least squares regression based on S‐G smoothing data from relaxation tests conducted with initial force of 100 N. The correlation coefficient and the root mean square error of the calibration set were 0.954 and 0.021, respectively. The corresponding values of the prediction set were 0.905 and 0.029, respectively. CONCLUSIONS: This study confirms the potential for accurate and fast detection of moisture content in single maize kernels using viscoelastic properties, which provides a novel approach for the detection of various components in cereals. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Practical Application The complex composition of braised pork, including lean meat, pigskin, and fat, makes it difficult for sensory evaluation of its texture properties. This study investigated the correlation between sensory texture attributes and physicochemical properties to achieve an objective and comprehensive evaluation of the texture of braised pork. Sensory analysis demonstrated that the overall texture acceptability of braised pork was significantly and negatively influenced by sensory texture attributes (including sensory hardness, chewiness, and toughness), while it was positively impacted by sensory adhesiveness, softness, and juiciness. Shear force and texture profile analysis (TPA) variables, reflecting mastication behavior, were used to characterize the textural properties of braised pork. They were closely related to water distribution, with a higher proportion of immobilized water (P21), indicating a higher water holding capacity and a more tender texture. Correlation analysis between sensory texture attributes and physicochemical properties through partial least squares regression further revealed significant associations between shear force, TPA variables, and sensory texture attributes. Moreover, the proportion of immobilized water (P21) significantly and negatively affected sensory hardness and chewiness, whereas the proportion of free water (P22) significantly influenced sensory toughness. Sensory texture attributes could be well predicted by the physicochemical properties by projecting test samples onto calibration models established by known samples. Therefore, a combination of sensory and instrumental measures can reliably reflect the texture properties of braised pork.The combination of sensory and instrumental methods is an effective strategy to accurately and objectively evaluate the texture properties of braised pork, which overcomes the limitations caused by the complexity of the composition and texture traits of braised pork. The accurate evaluation and standardization of texture properties is an important premise for the repeatable and stable cooking of traditional braised pork. Furthermore, this research method and findings can also be applied to guide the procedural optimization of smart appliances (e.g., induction cookers) for cooking braised pork. [ABSTRACT FROM AUTHOR]

Aesthetic judgements are partly predicted by image statistics, although the extent to which they are calibrated to the statistics of real-world scenes and the 'visual diet' of daily life is unclear. Here, we investigated the extent to which the beauty ratings of Western oil paintings from the JenAesthetics dataset can be accounted for by real-world scene statistics. We computed spatial and chromatic image statistics for the paintings and a set of real-world scenes captured by a head-mounted camera as participants went about daily lives. Partial least squares regression (PLSR) indicated that 6–15% of the variance in beauty ratings of the art can be accounted for by the art's image statistics. The luminance contrast of paintings made an important contribution to the PLSR models: paintings were perceived as more beautiful the greater the variation in luminance. PLSR models which expressed the art's image statistics relative to real-world scene statistics explained a similar amount of variance to models using the art's image statistics. The importance of an image statistic to perceived beauty was not related to how closely art reproduces the value from the real world. The findings suggest that beauty judgements of art are not strongly calibrated to the scene statistics of the real world. [ABSTRACT FROM AUTHOR]

Background: The need for rapid in-field measurement of key traits contributing to yield over many thousands of genotypes is a major roadblock in crop breeding. Recently, leaf hyperspectral reflectance data has been used to train machine learning models using partial least squares regression (PLSR) to rapidly predict genetic variation in photosynthetic and leaf traits across wheat populations, among other species. However, the application of published PLSR spectral models is limited by a fixed spectral wavelength range as input and the requirement of separate custom-built models for each trait and wavelength range. In addition, the use of reflectance spectra from the short-wave infrared region requires expensive multiple detector spectrometers. The ability to train a model that can accommodate input from different spectral ranges would potentially make such models extensible to more affordable sensors. Here we compare the accuracy of prediction of PLSR with various deep learning approaches and an ensemble model, each trained and tested using previously published data sets. Results: We demonstrate that the accuracy of PLSR to predict photosynthetic and related leaf traits in wheat can be improved with deep learning-based and ensemble models without overfitting. Additionally, these models can be flexibly applied across spectral ranges without significantly compromising accuracy. Conclusion: The method reported provides an improved prediction of wheat leaf and photosynthetic traits from leaf hyperspectral reflectance and do not require a full range, high cost leaf spectrometer. We provide a web service for deploying these algorithms to predict physiological traits in wheat from a variety of spectral data sets, with important implications for wheat yield prediction and crop breeding. [ABSTRACT FROM AUTHOR]

Partial least squares regression (PLSR) and support vector regression (SVR) models were optimized for the quantification of U(VI) (10-320 g L-1) and HNO3 (0.6-6 M) by Raman spectroscopy with optimized calibration sets chosen by optimal design of experiments. The designed approach effectively minimized the number of samples in the calibration set for PLSR and SVR by selecting sample concentrations with a quadratic process model, despite complex confounding and covarying spectral features in the spectra. The top PLS2 model resulted in percent root mean square errors of prediction for U(VI), HNO3, and NO3 - of 3.7%, 3.6%, and 2.9%, respectively. PLS1models performed similarly despitemodeling an analyte with a majority linear response (i.e., uranyl symmetric stretch) and another with more covarying vibrational modes (i.e., HNO3). Partial least squares (PLS) model loadings and regression coefficients were evaluated to better understand the relationship between weaker Raman bands and covarying spectral features. Support vector machine models outperformed PLS1 models, resulting in percent root mean square error of prediction values for U(VI) and HNO3 of 1.5% and 3.1%, respectively. The optimal nonlinear SVR model was trained using a similar number of samples (11) compared with the PLSR model, even though PLS is a linear modeling approach. The generic D-optimal design presented in this work provides a robust statistical framework for selecting training set samples in disparate two-factor systems. This approach reinforces Raman spectroscopy for the quantification of species relevant to the nuclear fuel cycle and provides a robust chemometric modeling approach to bolster online monitoring in challenging process environments. [ABSTRACT FROM AUTHOR]

Background: Pancreatic adenocarcinomas (PAADs) often exhibit a "cold" or immunosuppressive tumor milieu, which is associated with resistance to immune checkpoint blockade therapy; however, the underlying mechanisms are incompletely understood. Here, we aimed to improve our understanding of the molecular mechanisms occurring in the tumor microenvironment and to identify biomarkers, therapeutic targets, and potential drugs to improve PAAD treatment. Methods: Patients were categorized according to immunologically hot or cold PAAD subtypes with distinct disease outcomes. Cox regression and weighted correlation network analysis were performed to construct a novel gene signature, referred to as 'Downregulated in hot tumors, Prognostic, and Immune-Related Genes' (DPIRGs), which was used to develop prognostic models for PAAD via machine learning (ML). The role of DPIRGs in PAAD was comprehensively analyzed, and biomarker genes able to distinguish PAAD immune subtypes and predict prognosis were identified by ML. The expression of biomarkers was verified using public single-cell transcriptomic and proteomic resources. Drug candidates for turning cold tumors hot and corresponding target proteins were identified via molecular docking studies. Results: Using the DPIRG signature as input data, a combination of survival random forest and partial least squares regression Cox was selected from 137 ML combinations to construct an optimized PAAD prognostic model. The effects and molecular mechanisms of DPIRGs were investigated by analysis of genetic/epigenetic alterations, immune infiltration, pathway enrichment, and miRNA regulation. Biomarkers and potential therapeutic targets, including PLEC, TRPV1, and ITGB4, among others, were identified, and the cell type-specific expression of the biomarkers was validated. Drug candidates, including thalidomide, SB-431542, and bleomycin A2, were identified based on their ability to modulate DPIRG expression favorably. Conclusions: By combining multiple ML algorithms, we developed a novel prognostic model with excellent performance in PAAD cohorts. ML also proved to be powerful for identifying biomarkers and potential targets for improved PAAD patient stratification and immunotherapy. [ABSTRACT FROM AUTHOR]

Spices and other food products have been permanently susceptible to adulteration, affecting safety and acceptability when commercialized. A relevant alternative to detect contaminants in food products is to couple near-infrared spectroscopy (NIR) with chemometrics. Among the most accurate chemometric techniques employed to analyze food products, partial least squares regression (PLSR) combines features from and generalizes principal component analysis (PCA) to create compact and accurate models. Other techniques inspired in the human brain, such as multilayer perceptron, the long short-term memory (LSTM) models, and other approaches based on deep learning, take advantage of the high complexity of weights and neurons to train models based on large amounts of data. In this paper, a methodology is proposed to evaluate chemometric tools to estimate the percentage of adulterants in paprika powder using NIR spectroscopy, and three approaches are proposed and compared showing different performances. According to the methodology, the paprika samples were dried and separated into pericarp, peduncle, and seed cake. The resulting elements were finely milled, sieved, and mixed into 21 different combinations with a different percentage of each. Spectral profiles were used to train PLSR, multilayer perceptron, and regression models based on LSTM networks. The models were compared following a k-fold cross-validation strategy. Results showed that PLSR presented the highest R 2 = 0.978 for peduncle adulterant estimation, and the lowest R M S E = 6.24 . In particular, when seed cake powder was used as an adulterant, the PLSR approach showed the highest R 2 = 0.981 , and the lowest R M S E = 5.806 . The RPD values were higher than 2.000 for all models that use the peduncle as an adulterant and only for models bound to the PLSR in the adulterated samples with pressed seed cake. In summary, the best predictions were obtained using PLSR models, providing evidence of the feasibility of using NIR spectra to estimate the percentage of adulterants in paprika powder. [ABSTRACT FROM AUTHOR]

Background: Japanese knotweed (Reynoutria japonica var. japonica), a problematic invasive species, has a wide geographical distribution. We have previously shown the potential for attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy and chemometrics to segregate regional differentiation between Japanese knotweed plants. However, the contribution of environment to spectral differences remains unclear. Herein, the response of Japanese knotweed to varied environmental habitats has been studied. Eight unique growth environments were created by manipulation of the red: far-red light ratio (R: FR), water availability, nitrogen, and micronutrients. Their impacts on plant growth, photosynthetic parameters, and ATR-FTIR spectral profiles, were explored using chemometric techniques, including principal component analysis (PCA), linear discriminant analysis, support vector machines (SVM) and partial least squares regression. Key wavenumbers responsible for spectral differences were identified with PCA loadings, and molecular biomarkers were assigned. Partial least squared regression (PLSR) of spectral absorbance and root water potential (RWP) data was used to create a predictive model for RWP. Results: Spectra from plants grown in different environments were differentiated using ATR-FTIR spectroscopy coupled with SVM. Biomarkers highlighted through PCA loadings corresponded to several molecules, most commonly cell wall carbohydrates, suggesting that these wavenumbers could be consistent indicators of plant stress across species. R: FR most affected the ATR-FTIR spectra of intact dried leaf material. PLSR prediction of root water potential achieved an R2 of 0.8, supporting the potential use of ATR-FTIR spectrometers as sensors for prediction of plant physiological parameters. Conclusions: Japanese knotweed exhibits environmentally induced phenotypes, indicated by measurable differences in their ATR-FTIR spectra. This high environmental plasticity reflected by key biomolecular changes may contribute to its success as an invasive species. Light quality (R: FR) appears critical in defining the growth and spectral response to environment. Cross-species conservation of biomarkers suggest that they could function as indicators of plant-environment interactions including abiotic stress responses and plant health. [ABSTRACT FROM AUTHOR]

Slot structure is the equilibrium result of market demand side and slot resource supply side, while slot parameters reflect the operational support capacity of the aviation system. Time parameters reflect the operational support capability of the aviation system. Time structure should not only reflect changes in market demand, but also meet the constraints of operational efficiency. Constructing a reasonable 18–24 h timetable profile for busy airports that meets normal expectations for declared capacity and seasonal scheduling is a challenge in civil aviation slot management. This study utilizes historical data on airport flights and weather conditions to establish a regression prediction model for the time structure using K-means clustering and partial least squares regression. Additionally, ensemble learning is employed to forecast flight delay levels. The findings demonstrate that random forest yields favorable results in regression and prediction tasks, allowing for the integration of upper (good weather) and lower (severse weather) limits of the time profile with delay predictions as time parameter intervals. Consequently, the flights falling within these intervals achieve an average delay level of less than 15 min which meets the expectations of normal flight. [ABSTRACT FROM AUTHOR]

ABSTRACT The quality and the security of preparations in hospital centres are essential to guarantee the patient's safety. The development of fast and efficient analytical methods is required to control the finished product before use. In this context, the study aimed to compare the performance and interchangeability of two spectral analytical methods: the flow injection analysis (FIA) with UV detection and the Raman spectroscopy for the quality control of preparation before use to quantify the remdesivir as a SARS‐CoV‐2 drug. A quantitative study of remdesivir was performed using clinically relevant concentration solutions ranging from 0.25 to 1.625 mg.mL−1 in 0.9% NaCl. Samples were analysed by FIA‐UV at 245 nm and by a handheld Raman spectroscopy at 785 nm. Quantitative models were developed using a calibration set (n = 45 samples) and optimized using a validation set (n = 27). An external validation test set (n = 58) was used to compare the two methods by a Bland–Altman plot. Partial least square regression was used to analyse Raman spectra, while univariate analysis was performed at 245 nm for FIA‐UV. The regression coefficient was higher than 0.990 for both methods, and the root mean square error of prediction was 0.031 mg.mL−1 for Raman spectroscopy. The Bland–Altman plot confirmed the interchangeability of the two methods and the potential of Raman spectroscopy to control remdesivir during clinical preparation in the hospital. [ABSTRACT FROM AUTHOR]

This study aims to investigate the role of digital learning platforms (DLP) as a mediator in the relationship between the social media capability (SMC) and academic performance (AP). It explores the individual impacts of SMC and DLP on academic performance. The study employs Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze the reflective and structural models, aiming to find the correlations among various variables. The adoption of the structural model is supported by evidence of convergent, discriminant, and convergent validity among the constructs. The structural model's findings suggest a positive association between students' use of social media and both Digital Learning Platforms (DLP) and academic performance. Moreover, it identifies DLP as a mediating factor in these relationships, indicating that students are more likely to leverage and harness the benefits of social media when they have control over digital learning platform resources. Consequently, SMC and DLP collectively contribute to improved academic performance. This study highlights the applicability of Partial Least Squares Structural Equation Modeling in understanding the dynamics of social media and digital learning platforms in the context of enhancing academic performance. The results also emphasize the significance of integrating digital learning platforms into educational institutions. Finally, the study discusses implications and provides practical recommendations based on its findings. [ABSTRACT FROM AUTHOR]

Digital technologies can lead to the provision of innovative opportunities for academic entrepreneurship. However, investigations on the rationale for the adoption of digital technologies for academic entrepreneurship, particularly in emerging economies, remain limited. As a result, the determinants of digital technology adoption for academic entrepreneurship are not fully understood. Drawing on technology adoption and entrepreneurship literature, we propose a research model to investigate the adoption of social media – a type of digital technology - for entrepreneurial activities among students in academic settings. Based on survey data collected from 312 participants, and a partial least square structural equation modeling technique for analysis, this study shows that computer self-efficacy and perceived enjoyment significantly influence perceived ease of use. In addition, perceived job relevance and perceived ease of use significantly influence perceived usefulness. Furthermore, perceived ease of use and students' perceived entrepreneurial feasibility significantly affect behavioral intention. Moreover, the study shows that behavioral intention significantly affects actual usage. Through these findings, our study advances knowledge on the antecedents of social media adoption for entrepreneurship in academic settings in emerging economies. [ABSTRACT FROM AUTHOR]

AbstractThis study describes the assembly and testing of an optical fiber Raman probe for use in a portable spectrometer using a 785 nm laser. The Raman probe was tested on edible vegetable oils (extra virgin olive, canola, sunflower, corn, and soybean), with the distal end of the probe immersed in each sample. The spectra of a dipyrone tablet and a mothball were also obtained. The silica peaks were subtracted by taking a spectrum with the probe tip immersed in water. The spectra of the samples exhibited characteristic peaks from vibrations of unsaturated fatty acids between 900 and 1800 cm−1, as well as characteristic peaks of silica from 600 to 1200 cm−1. The spectra of the samples after subtraction of the silica spectrum (probe in water) showed characteristic peaks without alterations in position or relative intensities compared without the probe. Exploratory analysis by principal component analysis was able to identify peaks of the constituent fatty acids in the oils and associate them with the oil type. A classification model based on partial least squares regression allowed for the classification of oil spectra with an accuracy of 77%. This study demonstrated the feasibility of coupling a probe to a portable Raman spectrometer with the goal of monitoring and controlling industrial processes. [ABSTRACT FROM AUTHOR]

Zooplankton are critical indicators of pressures impacting freshwater ecosystems. We analyzed the response of zooplankton communities across different sub-catchment types—headwaters, natural, urban, urban-agricultural, and agricultural—within the Łyna river–lake system in Northern Poland. Using taxonomic groups and functional traits (body size, feeding strategies), we applied Partial Least Squares Regression (PLS-R) to elucidate the relationships between environmental conditions, land use, and zooplankton metacommunity structure. Two-Way Cluster Analysis (TWCA) identified local subsets with characteristic patterns, while Indicator Species Analysis (ISA) determined area-specific taxa. The natural river zone exhibited significant habitat heterogeneity and feeding niches, whereas urban areas created functional homogenization of zooplankton, dominated by small, broad-diet microphages. Agricultural areas promoted diversity among large filter feeders (Crustacea), active suctors (Rotifera), and amoebae (Protozoa). However, intensified agricultural activities, substantially diminished the zooplankton population, biomass, taxonomic richness, and overall ecosystem functionality. The impact of land cover change is more pronounced at small-scale sub-catchments than at the catchment level as a whole. Therefore, assessing these impacts requires detailed spatial and temporal analysis at the sub-catchment level to identify the most affected areas. This study introduces a new sub-catchment-based perspective on ecosystem health assessment and underscores the zooplankton's role as robust indicators of ecological change. [ABSTRACT FROM AUTHOR]

This study delves into the interconnections among corporate social responsibility, green intellectual capital, green ambidextrous innovation, and sustainable performance, particularly in the context of Industry 4.0 and sustainability. A questionnaire-based survey was conducted, and a sample of 317 small and medium enterprises was collected. Using Partial Least Squares Structural Equation Modeling in Smart-PLS v4, the findings reveal a significant relationship between corporate social responsibility and sustainable performance, with green intellectual capital and green ambidextrous innovation serving as mediating factors. Moreover, the study highlights the moderating role of Industry 4.0 among green intellectual capital and green ambidextrous innovation with sustainable performance. These findings may guide the managers in designing and implementing CSR strategies beyond compliance and contributing to competitive advantage through green intellectual capital and green ambidextrous innovation for business success in the era of Industry 4.0. [ABSTRACT FROM AUTHOR]

The Dunhuang murals are a precious treasure of China's cultural heritage, yet they have long been affected by salt damage. Traditional methods for detecting salt content are costly, inefficient, and may cause physical harm to the murals. Among current techniques for measuring salt content in murals, hyperspectral remote sensing technology offers a non-invasive, circumventing issues of high costs, low efficiency. Building on this, the study constructs an inversion model for the Electrical Conductivity (EC) values of mural plaster subjected to phosphate erosion, through the integration of Fractional Order Differentiation (FOD), a novel three-band spectral index, and the Partial Least Squares Regression algorithm. The specific research contents include: (1) Initially, in preparation for the experiments, the materials used to create the samples underwent a rigorous desalting process, and phosphate solutions were prepared using deionized water to ensure uniform experimental conditions and the accuracy of the results. These meticulous preprocessing steps guaranteed that the measured EC values exhibited a clear correlation with the phosphate content. Subsequently, by employing qualitative experimental analysis techniques, this study was able to more accurately simulate the real-world scenarios of mural plaster affected by salt damage, enabling a deeper investigation into the mechanisms by which salts inflict microscopic damage to murals. (2) Explores the absorption mechanisms and characteristic spectral bands of the Electrical Conductivity (EC) values measured after the phosphate erosion of mural plaster. By integrating the optimal spectral indices, a univariate linear regression model is constructed, providing a basis for the rapid quantitative measurement of electrical conductivity in murals. (3) By comparing the accuracy of the Phosphate Simple Ratio (PSR) and Phosphate Normalized Difference Index (PNDI) spectral indices based on the linear regression model, the first six orders of the highest accuracy spectral index were selected as the optimal three-band spectral index combination, used as explanatory variables, with mural plaster electrical conductivity as the response variable, employing the PLSR method to construct the mural phosphate content high-spectral feature inversion model. The study's findings include: (1) Surfaces of samples deteriorated by phosphate erosion formed numerous irregularly shaped crystal clusters, exhibiting uneven characteristics. (2) By comparing the outcomes of different orders of fractional differentiation, it was found that the model performance reached its optimum at a 0.3 order of differentiation for both PSR and PNDI data, with a determination coefficient (Q2) of 0.728. (3) Utilizing PLSR, this study employed the previously determined optimal six-order three-band spectral index combination as explanatory variables, with salt content as the response variable, successfully constructing the high-spectral feature inversion model for mural electrical conductivity with a determination coefficient (Q2) of 0.815. This provides an effective technical means for monitoring the salt damage conditions of precious cultural heritage such as murals. [ABSTRACT FROM AUTHOR]

Axillary malodour is caused by the microbial conversion of human-derived precursors to volatile organic compounds. Thiols strongly contribute to this odour but are hard to detect as they are present at low concentrations. Additionally, thiols are highly volatile and small making sampling and quantification difficult, including by gas chromatography-mass spectrometry. In this study, surface-enhanced Raman scattering (SERS), combined with chemometrics, was utilised to simultaneously quantify four malodourous thiols associated with axillary odour, both in individual and multiplex solutions. Univariate and multivariate methods of partial least squares regression (PLS-R) were used to calculate the limit of detection (LoD) and results compared. Both methods yielded comparable LoD values, with LoDs using PLS-R ranging from 0.0227 ppm to 0.0153 ppm for the thiols studied. These thiols were then examined and quantified simultaneously in 120 mixtures using PLS-R. The resultant models showed high linearity (Q2 values between 0.9712 and 0.9827 for both PLS-1 and PLS-2) and low values of root mean squared error of predictions (0.0359 ppm and 0.0459 ppm for PLS-1 and PLS-2, respectively). To test this approach further, these models were challenged with 15 new blind test samples, collected independently from the initial samples. This test demonstrated that SERS combined with PLS-R could be used to predict the unknown concentrations of these thiols in a mixture. These results display the ability of SERS for the simultaneous multiplex detection and quantification of analytes and its potential for future development for detecting gaseous thiols produced from skin and other body sites. [ABSTRACT FROM AUTHOR]

ABSTRACT The performance of multivariate calibration models ŷ = f(x) for the prediction of a numerical property y from a set of x‐variables depends on the type of scaling of the x‐variables. Common scaling methods are autoscaling (dividing the centered x by its standard deviation s) and Pareto scaling (dividing the centered x by sP with p = 0.5). The adjusted Pareto scaling presented here varies the exponent P between 0 (no scaling) and 1 (autoscaling) with the aim of obtaining an optimum prediction performance for ŷ. Related scaling methods based on the variable spread are range scaling and vast scaling; while level scaling is based on the location (central value) of the variable. These scaling methods and robust versions are compared for models created by partial least‐squares (PLS) regression. The applied strategy repeated double cross validation (rdCV) evaluates the model performance for test set objects and considers its variability. Results with three data sets from chemistry show: (a) the efficacy of the different scaling methods depends on the data structure; (b) optimization of the Pareto exponent P is recommended; (c) range scaling or vast scaling may be better than adjusted Pareto scaling; (d) in general a heuristic search for the best scaling method is advisable. Overall, the consideration of different variants of scaling allow for a flexible adjustment of the variable contributions to the calibration model. [ABSTRACT FROM AUTHOR]

Understanding the complex influencing factors of soil erosion is critical for maintaining regional ecological security. The revised wind erosion equation (RWEQ) and partial least squares regression (PLSR) model were used to reveal the primary factors affecting soil erosion. Based on the results of the RWEQ model, the wind erosion area was 8,439.3 km2 in 2018, accounting for 54.3% of the total planning area, and mild erosion, moderate erosion, intense erosion, very intense erosion, and fierce erosion accounted for 18.22%, 11.43%, 11.78%, 10.16%, and 2.74% of the total planning area, respectively. Based on the results of the PLSR model, the results indicate that the proportion of mining land, meteorological indicators, and mean square deviation of slope difference were positively correlated with the soil erosion area, intensity, and severity, respectively. The proportion of grassland and forest land was negatively correlated with the soil erosion area, intensity, and severity. The mean square deviation of slope difference, the mean square deviation of difference in elevation, and the proportion of urban land use contributed the most to the soil erosion severity index, with variable importance in projection (VIP) scores of 1.55, 1.44 and 1.43, respectively. The mean square deviation of slope difference, the proportion of urban land, and the mean square deviation of difference in elevation contributed the most to the soil erosion area, with VIP scores of 1.53, 1.26 and 1.24, respectively. The results provide new insights into wind erosion and the influences of weather conditions and human influences, which can guide regional soil and water conservation planning in wind erosion regions. [ABSTRACT FROM AUTHOR]

The objective conditions of people's lives have historically played a greater role than subjective perceptions. However, this trend is shifting, accompanied by an ongoing debate about life satisfaction and urbanization. This research has two objectives, the first is to test the hypothesis of the urban paradox. The second is to analyze the differences in the determinants of life satisfaction by urbanization levels. In order to determine the degrees of urbanization, a geographic grid was used to distinguish between rural, intermediate, and urban areas. In addition, a multi-group analysis in structural equation models with partial least squares was conducted to analyze the determinants of life satisfaction. The model has been developed using three general determinants: economic situation, health, and social relations, which has been applied to a case study of the Region of Murcia. The database used for this study includes 2462 samples. The population living in rural areas showed less life satisfaction. Regarding the determinants, it was found that health acts as a partial mediator of social relations. Differences were also observed in the intensity of the determinants according to the degree of urbanization. Thus, the degree of urbanization acts as a moderator of life satisfaction. This research is noteworthy because it provides valuable insights for guiding regional public policies. [ABSTRACT FROM AUTHOR]

Lithium compounds such as lithium hydride (LiH) and lithium hydroxide (LiOH) have a wide range of industrial applications, but are highly reactive in environments with H2O and CO2. These reactions lead to the ingrowth of secondary lithium compounds, which can alter the homogeneity and affect the application of particular lithium chemicals. This study performed an exploratory analysis of different lithium compounds using laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy. Machine learning models are trained on the recorded spectral data to discriminate emission features that differ between LiH, LiOH, and Li2CO3 to perform high-fidelity classification. Support vector machine classifiers yield perfect prediction accuracy between the three compounds with optimal training time. Multivariate methods are then used to produce regression models quantifying the ingrowth of LiOH in LiH. Performing a mid-level data fusion of selected LIBS and Raman features with partial least-squares regression produces the superlative model with a root mean square error of 2.5 wt % and a detection limit of 6.3 wt %. [ABSTRACT FROM AUTHOR]

Biological nitrogen fixation (BNF) by symbiotic bacteria plays a vital role in sustainable agriculture. However, current quantification methods are often expensive and impractical. This study explores the potential of Raman spectroscopy, a non-invasive technique, for rapid assessment of BNF activity in soybeans. Raman spectra were obtained from soybean plants grown with and without rhizobia bacteria to identify spectral signatures associated with BNF. δN15 isotope ratio mass spectrometry (IRMS) was used to determine actual BNF percentages. Partial least squares regression (PLSR) was employed to develop a model for BNF quantification based on Raman spectra. The model explained 80% of the variation in BNF activity. To enhance the model's specificity for BNF detection regardless of nitrogen availability, a subsequent elastic net (Enet) regularisation strategy was implemented. This approach provided insights into key wavenumbers and biochemicals associated with BNF in soybeans. [ABSTRACT FROM AUTHOR]

Soil visible and near–infrared reflectance spectroscopy is an effective tool for the rapid estimation of soil organic carbon (SOC). The development of spectroscopic technology has increased the application of spectral libraries for SOC research. However, the direct application of spectral libraries for SOC prediction remains challenging due to the high variability in soil types and soil–forming factors. This study aims to address this challenge by improving SOC prediction accuracy through spectral classification. We utilized the European Land Use and Cover Area frame Survey (LUCAS) large–scale spectral library and employed a geographically weighted principal component analysis (GWPCA) combined with a fuzzy c–means (FCM) clustering algorithm to classify the spectra. Subsequently, we used partial least squares regression (PLSR) and the Cubist model for SOC prediction. Additionally, we classified the soil data by land cover types and compared the classification prediction results with those obtained from spectral classification. The results showed that (1) the GWPCA–FCM–Cubist model yielded the best predictions, with an average accuracy of R2 = 0.83 and RPIQ = 2.95, representing improvements of 10.33% and 18.00% in R2 and RPIQ, respectively, compared to unclassified full sample modeling. (2) The accuracy of spectral classification modeling based on GWPCA–FCM was significantly superior to that of land cover type classification modeling. Specifically, there was a 7.64% and 14.22% improvement in R2 and RPIQ, respectively, under PLSR, and a 13.36% and 29.10% improvement in R2 and RPIQ, respectively, under Cubist. (3) Overall, the prediction accuracy of Cubist models was better than that of PLSR models. These findings indicate that the application of GWPCA and FCM clustering in conjunction with the Cubist modeling technique can significantly enhance the prediction accuracy of SOC from large–scale spectral libraries. [ABSTRACT FROM AUTHOR]

To non-destructively and rapidly monitor the chlorophyll content of winter wheat leaves under CO2 microleakage stress, and to establish the quantitative relationship between chlorophyll content and sensitive bands in the winter wheat growing season from 2023 to 2024, the leakage rate was set to 1 L/min, 3 L/min, 5 L/min, and 0 L/min through field experiments. The dimensional reduction was realized, fractional differential processing of a wheat canopy spectrum was carried out, a multiple linear regression (MLR) and partial least squares regression (PLSR) estimation model was constructed using a SPA selection band, and the model's accuracy was evaluated. The optimal model for hyperspectral estimation of wheat SPAD under CO2 microleakage stress was screened. The results show that the spectral curves of winter wheat leaves under CO2 microleakage stress showed a "red shift" of the green peak and a "blue shift" of the red edge. Compared with 1 L/min and 3 L/min, wheat leaves were more affected by CO2 at 5 L/min. Evaluation of the accuracy of the MLR and PLSR models shows that the MLR model is better, where the MLR estimation model based on 1.1, 1.8, 0.4, and 1.7 differential SPAD is the best for leakage rates of 1 L/min, 3 L/min, 5 L/min, and 0 L/min, with validation set R2 of 0.832, 0.760, 0.928, and 0.773, which are 11.528, 14.2, 17.048, and 37.3% higher than the raw spectra, respectively. This method can be used to estimate the chlorophyll content of winter wheat leaves under CO2 trace-leakage stress and to dynamically monitor CO2 trace-leakage stress in crops. [ABSTRACT FROM AUTHOR]

In the textile industry, cotton and polyester (PES) are among the most used fibres to produce clothes. The correct identification and accurate composition estimate of fibres are mandatory, and environmentally friendly and precise techniques are welcome. In this context, the use of near-infrared (NIR) and mid-infrared (MIR) spectroscopies to distinguish between cotton and PES samples and further estimate the cotton content of blended samples were evaluated. Infrared spectra were acquired and modelled through diverse chemometric models: principal component analysis; partial least squares discriminant analysis; and partial least squares (PLS) regression. Both techniques (NIR and MIR) presented good potential for cotton and PES sample discrimination, although the results obtained with NIR spectroscopy were slightly better. Regarding cotton content estimates, the calibration errors of the PLS models were 3.3% and 6.5% for NIR and MIR spectroscopy, respectively. The PLS models were validated with two different sets of samples: prediction set 1, containing blended cotton + PES samples (like those used in the calibration step), and prediction set 2, containing cotton + PES + distinct fibre samples. Prediction set 2 was included to address one of the biggest known drawbacks of such chemometric models, which is the prediction of sample types that are not used in the calibration. Despite the poorer results obtained for prediction set 2, all the errors were lower than 8%, proving the suitability of the techniques for cotton content estimation. It should be stressed that the textile samples used in this work came from different geographic origins (cotton) and were of distinct presentations (raw, yarn, knitted/woven fabric), which strengthens our findings. [ABSTRACT FROM AUTHOR]

Turkey is the leading producer of hazelnuts, contributing to 62% of the total global production. Among 18 distinct local hazelnut cultivars, Giresun Tombul is the only cultivar that has received Protected Designation of Origin denomination from the European Comission (EC). However, there is currently no practical objective method to ensure its geographic origin. Therefore, in this study NIR and Raman spectroscopy, along with chemometric methods, such as principal component analysis, PLS‐DA (partial least squares‐discriminant analysis), and SVM‐C (support vector machine‐classification), were used to determine the geographical origin of the Giresun Tombul hazelnut cultivar. For this purpose, samples from unique 118 orchards were collected from eight different regions in Turkey during the 2021 and 2022 growing seasons. NIR and Raman spectra were obtained from both the shell and kernel of each sample. The results indicated that hazelnut samples exhibited distinct grouping tendencies based on growing season regardless of the spectroscopic technique and sample type (shell or kernel). Spectral information obtained from hazelnut shells demonstrated higher discriminative power concerning geographical origin compared to that obtained from hazelnut kernels. The PLS‐DA models utilizing FT‐NIR (Fourier transform near‐infrared) and Raman spectra for hazelnut shells achieved validation accuracies of 81.7% and 88.3%, respectively, while SVM‐C models yielded accuracies of 90.9% and 86.3%. It was concluded that the lignocellulosic composition of hazelnut shells, indicative of their geographic origin, can be accurately assessed using FT‐NIR and Raman spectroscopy, providing a nondestructive, rapid, and user‐friendly method for identifying the geographical origin of Giresun Tombul hazelnuts. Practical Application: The proposed spectroscopic methods offer a rapid and nondestructive means for hazelnut value chain actors to verify the geographic origin of Giresun Tombul hazelnuts. This could definitely enhance consumer trust by ensuring product authenticity and potentially help in preventing fraud within the hazelnut market. In addition, these methods can also be used as a reference for future studies targeting the authentication of other shelled nuts. [ABSTRACT FROM AUTHOR]

The Dunhuang murals, a significant part of Chinese heritage, have suffered deterioration primarily due to environmental and chemical factors, notably salt damage. This study proposes a sophisticated method that synergizes Fractional Order Differentiation (FOD) and Partial Least Squares Regression (PLSR) to accurately invert the phosphate content in the mural plaster of the Dunhuang paintings. The focal points of the research include: (1) To address the issue of information loss and reduced modeling precision caused by integer-order differentiation algorithms, the FOD method is employed for preprocessing spectral data. This approach ensures the fine spectral differences in the phosphate content of the mural plaster are precisely captured, (2) Utilizing PLSR, the study models the spectral bands identified at a significance level of 0.01 with measured conductivity values, thereby enabling the precise prediction of the phosphate content in the murals. The research outcomes reveal: (1) The FOD method can elucidate the nonlinear characteristics and variation patterns of the mural samples in the spectral data. As the order increases from zero to two, the number of spectral bands meeting the 0.01 significance test initially decreases and then increases. The highest absolute value of the positive correlation coefficient is observed at the 1.9 order, corresponding to the 2077 nm band, (2) For predicting the phosphate content in the murals, the model at the 1.9 order is most suitable for inversion. This model, after cross-validation, achieves a maximum R2 value of 0.861. This study created an efficient FOD-based model for estimating phosphate in mural plaster. [ABSTRACT FROM AUTHOR]

Laser induced breakdown spectroscopy (LIBS) is an emerging technique for the analysis of elements in plant tissue. This study reports the validation of a newly developed LIBS instrument and a method for analysis of plant material. The LIBS setup consists of a press, a searing unit, and an analyser with an Nd:YAG laser with a pulse energy of 0.15 mJ operating at a central wavelength of 1064 nm in a nitrogen atmosphere. The LIBS measurements were conducted on 257 plant samples from eight different plant species. The plant samples were also analysed with inductively coupled plasma optical emission spectroscopy (ICP-OES) to obtain reference values for phosphorus (P), potassium (K), magnesium (Mg), sulphur (S), calcium (Ca), iron (Fe), zinc (Zn), manganese (Mn), boron (B), and copper (Cu). Based on the reference values and the LIBS spectra, partial least squares regression was used to build prediction models for each nutrient. Mixed models and specific models for wheat and faba bean were made. Specific models for wheat and faba bean performed better than mixed species models. Prediction models for P, K, Mg, S, Ca, Zn, Fe, B and Cu from wheat were superior and were sufficiently precise and accurate to enable detection of plant nutrient deficiencies. However, for Mn the accuracy needs to be improved. The results document the usefulness of the novel LIBS setup for plant tissue analysis and for detection of plant nutrient deficiencies. [ABSTRACT FROM AUTHOR]

Owing to the complex laser-material interaction, large spectral fluctuation is one of the main causes of the relatively poor qualitative analysis performance of laser-induced breakdown spectroscopy (LIBS). In this study, a data preprocessing method, namely minimum distance and subsequent averaging (MD & A) using plasma image information, is proposed to identify effective spectra and improve the spectral stability of LIBS. The correlations between plasma image features and spectral line intensity were analyzed, which indicated that plasma average area intensity showed high correlation with plasma states. To evaluate the performance of the proposed method, original spectra were preprocessed by other traditional screening methods, and then, different quantitative models for Mn, Cu, Cr and Si elements in steel samples were separately established based on the partial least squares regression (PLSR) algorithm. Compared to the original spectra, the evaluation parameter R2 is improved to nearly 99% from 93% while the values of mean root mean squared error of prediction (RMSEP) and mean average relative error of prediction (AREP) are also reduced by over 50%. In addition, the mean relative standard deviation (RSD) of different spectral line intensities using MD & A can be reduced to the range of 1.07% to 3.12% from the range of 3.76% to 18.48%. The above results indicate that our proposed method can be easy to realize and significantly improve the spectral stability and quantitative analysis performance of LIBS compared to traditional methods. [ABSTRACT FROM AUTHOR]

The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. Minimizing pollution from the dairy sector is paramount; one potential cause of such pollution is excess nitrogen. Nitrogen pollution contributes to a deterioration in water quality as well as an increase in both eutrophication and greenhouse gases. It is therefore essential to minimize the loss of nitrogen from the sector, including excretion from the cow. Breeding programs are one potential strategy to improve the efficiency with which nitrogen is used by dairy cows, but they rely on routine access to individual cow information on how efficiently each cow uses the nitrogen it ingests. A total of 3,497 test-day records for individual-cow nitrogen efficiency metrics along with milk yield and the associated milk spectra were used to investigate the ability of milk infrared spectral data to predict these nitrogen traits; both traditional partial least squares regression and neural networks were used in the prediction process. The data originated from 4 farms across 11 yr. The nitrogen traits investigated were nitrogen intake, nitrogen use efficiency, and nitrogen balance. Both nitrogen use efficiency and nitrogen balance were calculated considering nitrogen intake, nitrogen in milk, nitrogen in the conceptus, nitrogen used for the growth, nitrogen stored in body reserves, and nitrogen mobilized from body reserves. Irrespective of the nitrogen-related trait being investigated, the best predictions from 4-fold cross validation were achieved using neural networks that considered both the morning and evening milk spectra along with milk yield, parity, and DIM in the prediction process. The coefficient of determination in the cross validation was 0.61, 0.74, and 0.58 for nitrogen intake, nitrogen use efficiency, and nitrogen balance, respectively. In a separate series of validation approaches, the calibration and validation was stratified by herd (n = 4) and separately by year. For these scenarios, partial least squares regression generated more accurate predictions compared with neural networks; the coefficient of determination was always lower than 0.29 and 0.60 when validation was stratified by herd and year, respectively. Therefore, if the variability of the data being predicted in the validation datasets is similar to that in the data used to develop the predictions, then nitrogen-related traits can be predicted with reasonable accuracy. In contrast, where the variability of the data that exists in the validation dataset is poorly represented in the calibration dataset, then poor predictions will ensue. [ABSTRACT FROM AUTHOR]

One of the main priorities of current food systems is to develop new and healthy foods to overcome food shortages considering consumer's expectations. Algae are receiving increasing attention as nutritious and sustainable food, though studies are limited mainly to Arthrospira and Chlorella species and cross-national research is scant. This study aims to investigate European consumers' liking and perception of crackers added with powders from Arthrospira platensis (green Spirulina), isolated proteins from Arthrospira platensis (blue Spirulina), Palmaria palmata, Saccharina latissima, Lithothamnium calcareum and a control cracker. Belgian, German, Italian, and Swedish participants (n = 413, 18–69 years, 59.8% females) evaluated cracker liking and perceived sensory attributes using the check-all-that-apply (CATA) task. Food neophobia, familiarity towards, and consumption of algae were also collected. Partial Least Squares Regression (PLSR) analyses showed that across all countries, Lithothamnium calcareum, blue Spirulina and the control samples were perceived as similar and were liked more than the other crackers. "Sweet", "toasted bread", and "bland" attributes were significant contributors to liking, while "off-flavor", "fishy flavor", "umami" and "speckled" contributed negatively. PLSR performed by country provided similar results except for Italians who liked the green Spirulina sample equally as the samples added with Lithothamnium calcareum, blue Spirulina, and the control. These cross-national differences can be ascribed to Italians' lower food neophobia level. The present results suggest that Lithothamnium calcareum is the most promising algal species to be exploited in food formulations and that the use of isolated proteins from Spirulina (blue Spirulina) significantly reduces off-flavors, consequently improving consumers' acceptance. [ABSTRACT FROM AUTHOR]

The content of chromium (Cr) affects the mechanical and wear resistance of steel. Hence, the determination of Cr in steel is of great significance to determine the quality. In this study, a new method based on laser-induced breakdown spectroscopy (LIBS) combined with extreme random tree (ERT) is reported to determine Cr in steel. First, the Cr I 425.43 nm line was selected for univariate quantitative analysis. Next, multivariate analysis of Cr was performed using partial least squares regression (PLSR), random forest (RF), and ERT. The abilities of the models for the determination were validated by the fitting coefficient (R2), root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP), and average relative error (ARE). The R2, RMSEC, RMSEP, and ARE values of ERT were 0.9984, 0.0274 wt.%, 0.0299 wt.%, and 2.43%, respectively. Compared with the univariate model, PLSR, and RF models, the R2 of ERT increased by 0.1317, 0.0306, and 0.013, respectively; RMSEC decreased by 0.0987, 0.0955, and 0.0174 wt.%; RMSEP decreased by 0.1463, 0.0876, and 0.002 wt.%; and ARE decreased by 17.88%, 16.98%, and 5.95%, respectively. The results show that combining LIBS with the ERT model for multivariate analysis improves the elemental analysis of steel. [ABSTRACT FROM AUTHOR]

This research aims to test the dimensions of success in measuring leadership management and to find the factors that shape leadership in an excellent Islamic university (IU). Respondents totaled 422 participants from leaders and lecturers at six Islamic universities in Indonesia. partial least squares structural equation modeling (PLS-SEM) analysis is used to test 8 dimensions, 35 constructs, and 17 hypotheses. Management leadership is determined by the dominant factor, namely job evaluation (IM4, λ=90.70%), and human resources (OR3, λ=74.40%) is the lowest factor. The ability of the structural model to explain the measurement of IU’s leadership management is 79.70%. Furthermore, the 17 hypotheses from 8 dimensions have a positive and significant influence. Measurable dimensions can be used to develop an effective management model to increase the performance and function of leadership. Through the results of this research, higher education management institutions can recommend study substances to comprehensively find out the leadership model to be applied by leaders so they are more skilled and expert in managing institutions in higher education. [ABSTRACT FROM AUTHOR]

Termites are social insects with high species diversity in tropical ecosystems. Multivariate analysis with near-infrared spectroscopy (NIRS) and data interpretation can separate social insects belonging to different colonies of the same species. The objective of this study was to propose the use of discriminant analysis by partial least squares (PLS-DA) combined with NIRS to identify the colonial origin of the Syntermes grandis (Rambur, 1842) (Blattodea: Termitidae) in 2 castes. Six ground S. grandis colonies were identified and mapped; 30 workers and 30 soldier termites in each colony were submitted to spectral measurement with NIRS. PLS-DA applied to the termites' spectral absorbance was used to detect a spectral pattern per S. grandis colony by caste. PLS-DA regression with NIRS proved to be an approach with 99.9% accuracy for identifying the colonial origin of S. grandis workers and 98.3% for soldiers. The methodology showed the importance of qualitatively characterizing the colonial phenotypic response of this species. NIRS is a high-precision approach to identifying the colony origin of S. grandis workers and soldiers. The PLS-DA can be used to design ecological field studies to identify colony territorial competition and foraging behavior of subterranean termite species. [ABSTRACT FROM AUTHOR]

Principal components analysis (PCA) and partial least squares (PLS) have been used for the construction of socioeconomic status (SES) indices to use as a predictor of the well-being status in targeted programs. Generally, these indicators are constructed as a linear combination of the first component. Due to the characteristics of the socioeconomic data, different extensions of PCA and PLS for non-metric variables have been proposed for these applications. In this paper, we compare the predictive performance of SES indices constructed using more than one component. Additionally, for the inclusion of non-metric variables, a variant of the normal mean coding is proposed that takes into account the multivariate nature of the variables, which we call multivariate normal mean coding (MNMC). Using simulations and real data, we found that PLS using MNMC as well as the classical dummy encoding method give the best predictive results with a more parsimonious SES index, both in regression and classification problems. [ABSTRACT FROM AUTHOR]

The quantitative nature of wood production poses a challenge for breeders. The complex interaction of genotypes with environments (G×E) makes cultivars recommendation difficult. Our objective was to model the G×E interaction using environmental covariates and map the adaptability of commercial Eucalyptus clones based on a geographic information system (GIS) across important plantation regions in Brazil. To achieve this, a productivity dataset with 13,483 stands of six commercial clones was utilized. The effects of geography, soil and climate covariates on clone yield were modeled using partial least squares regression, with data from WorldClim and SoilGrids databases. Using the models for each clone, yield maps were generated at a spatial resolution of approximately 5 km². Then, cultivar recommendation was made through a pixel-by-pixel comparison of predicted yield values among the clones. The covariates that most affected the performance of the clones were annual rainfall, rainfall of the driest month, rainfall of the driest quarter, maximum temperature of the hottest month and average temperature of the wettest quarter. Thus, G×E modeling based on environmental covariates combined with GIS enables a large increase in the resolution of cultivar recommendations by mapping the adaptability of genotypes in each site. [ABSTRACT FROM AUTHOR]

ABSTRACT This study presents a case of processing X‐ray computed tomography (CT) data for pork scans using chemometric latent space modeling. The distribution of voxel intensities is shown to exemplify a multivariate, multi‐collinear signal mixture. While this concept is not novel, it is revisited here from a chemometric perspective. To extract meaningful information from such multivariate signals, latent space modeling based on partial least squares (PLS) is an ideal solution. Furthermore, a robust PLS approach is even more effective for latent space modeling, as it can extract latent spaces unaffected by outliers, thereby enhancing predictive modeling. As an example, lean meat percentage is predicted using X‐ray CT data and robust PLS regression. This method is applicable to X‐ray CT quantification analysis, particularly in cases where unclear, erroneous, and outlying observations are suspected in the data. [ABSTRACT FROM AUTHOR]

Soil mineralogy and texture are directly related to soil carbon due to the physical properties of the clay surface. Traditional techniques for quantifying carbon in soil are time-consuming and expensive, making large-scale quantification for mapping unfeasible. The alternative is the use of soil sensors, such as diffuse reflectance spectroscopy (DRS), an economical, fast, and accurate technique for predicting carbon stocks. In this sense, this study aimed to (a) investigate the relationship of C with different soil mineralogical, chemical, and physical attributes for different geological and geomorphological compartments; (b) understand which spectral bands are most important for estimating C content; (c) estimate C content from diffuse reflectance spectroscopy using different mathematical techniques and indicate which one is the best for tropical soil conditions; and (d) map C contents in detail. The study area was the Western Plateau of São Paulo (WPSP), which covers approximately 13 million hectares (~ 48% of the State of São Paulo, Brazil). A total of 265 samples were collected in this area. The attributes clay, silt, sand, crystalline and non-crystalline iron, base saturation, soil density, total pore volume, total C, C stock, kaolinite/(kaolinite + gibbsite) and hematite/(hematite + goethite), hematite and goethite contents, and spectral curves were evaluated. The spectra were recorded at 0.5-nm intervals, with an integration time of 2.43 nm s−1 over the 350 to 2500-nm range (350–800 nm—visible—VIS and 801–2500 nm—near-infrared—NIR). The data were subjected to descriptive statistics, Spearman correlation, stepwise analysis, and cluster grouping for characterization purposes; partial least squares regression (PLSR) and random forest (RF) for estimation purposes; and geostatistics analysis for creation of spatial maps. Our results indicate that the highest C contents are associated with more clayey soils, oxidic mineralogy, higher total pore volume, and lower soil density in highly dissected basalt compartments. The random forest algorithm associated with the Vis–NIR spectral range is more efficient for estimating and mapping C contents. This suggests that integrating diffuse reflectance spectroscopy with machine learning techniques holds promise for shaping public policies related to land use, mitigating CO2 emissions, and facilitating the implementation of carbon credit policies in a rapid and economically efficient manner. [ABSTRACT FROM AUTHOR]

Accurate prediction of nitrogen oxide (NOx) emission is crucial for effectively controlling pollution in municipal solid waste incineration processes. However, it is challenging to construct a NOx emission prediction model with high prediction accuracy and easy engineering application. To address this, this paper proposes a robust and easily applicable NOx emission trend prediction model oriented to engineering applications, utilizing the partial least squares (PLS) method with the time series reconstruction and exponential weighting (TS‐EW‐PLS). The model is verified using operational data from an actual waste incineration process, and comparative analysis with the PLS model showed that the TS‐EW‐PLS model achieved a remarkable improvement of 27–38 % in prediction performance. [ABSTRACT FROM AUTHOR]

It is crucial for winegrowers to make informed decisions about the optimum time to harvest the grapes to ensure the production of premium wines. Global warming contributes to decreasing acidity and increasing sugar levels in grapes, resulting in bland wines with high contents of alcohol. Predicting quality in viticulture is thus pivotal. To assess the average ripeness, typically a sample of one hundred berries representative for the entire vineyard is collected. However, this process, along with the subsequent detailed must analysis, is time consuming and expensive. This study focusses on predicting essential quality parameters like sugar and acid content in Vitis vinifera (L.) varieties 'Chardonnay', 'Riesling', 'Dornfelder', and 'Pinot Noir'. A small near-infrared spectrometer was used measuring non-destructively in the wavelength range from 1 100 nm to 1 350 nm while the reference contents were measured using high-performance liquid chromatography. Chemometric models were developed employing partial least squares regression and using spectra of all four grapevine varieties, spectra gained from berries of the same colour, or from the individual varieties. The models exhibited high accuracy in predicting main quality-determining parameters in independent test sets. On average, the model regression coefficients exceeded 93% for the sugars fructose and glucose, 86% for malic acid, and 73% for tartaric acid. Using these models, prediction accuracies revealed the ability to forecast individual sugar contents within an range of ± 6.97 g/L to ± 10.08 g/L, and malic acid within ± 2.01 g/L to ± 3.69 g/L. This approach indicates the potential to develop robust models by incorporating spectra from diverse grape varieties and berries of different colours. Such insight is crucial for the potential widespread adoption of a handheld near-infrared sensor, possibly integrated into devices used in everyday life, like smartphones. A server-side and cloud-based solution for pre-processing and modelling could thus avoid pitfalls of using near-infrared sensors on unknown varieties and in diverse wine-producing regions. [ABSTRACT FROM AUTHOR]

Grapevine rootstocks are gaining importance in viticulture as a strategy to combat abiotic challenges, as well as enhance scion physiology. Direct leaf-level physiological parameters like net assimilation rate, stomatal conductance to water vapor, quantum yield of PSII, and transpiration can illuminate the rootstock effect on scion physiology. However, these measures are time-consuming and limited to leaf-level analysis. This study used different rootstocks to investigate the potential application of aerial hyperspectral imagery in the estimation of canopy level measurements. A statistical framework was developed as an ensemble stacked regression (REGST) that aggregated five different individual machine learning algorithms: Least absolute shrinkage and selection operator (Lasso), Partial least squares regression (PLSR), Ridge regression (RR), Elastic net (ENET), and Principal component regression (PCR) to optimize high-throughput assessment of vine physiology. In addition, a Convolutional Neural Network (CNN) algorithm was integrated into an existing REGST, forming a hybrid CNN-REGST model with the aim of capturing patterns from the hyperspectral signal. Based on the findings, the performance of individual base models exhibited variable prediction accuracies. In most cases, Ridge Regression (RR) demonstrated the lowest test Root Mean Squared Error (RMSE). The ensemble stacked regression model (REGST) outperformed the individual machine learning algorithms with an increase in R2 by (0.03 to 0.1). The performances of CNN-REGST and REGST were similar in estimating the four different traits. Overall, these models were able to explain approximately 55–67% of the variation in the actual ground-truth data. This study suggests that hyperspectral features integrated with powerful AI approaches show great potential in tracing functional traits in grapevines. [ABSTRACT FROM AUTHOR]

In the manufacturing process of electrical devices, ensuring the cleanliness of technical surfaces, such as direct bonded copper substrates, is crucial. An in-line monitoring system for quality checking must provide sufficiently resolved lateral data in a short time. UV hyperspectral imaging is a promising in-line method for rapid, contactless, and large-scale detection of contamination; thus, UV hyperspectral imaging (225–400 nm) was utilized to characterize the cleanliness of direct bonded copper in a non-destructive way. In total, 11 levels of cleanliness were prepared, and a total of 44 samples were measured to develop multivariate models for characterizing and predicting the cleanliness levels. The setup included a pushbroom imager, a deuterium lamp, and a conveyor belt for laterally resolved measurements of copper surfaces. A principal component analysis (PCA) model effectively differentiated among the sample types based on the first two principal components with approximately 100.0% explained variance. A partial least squares regression (PLS-R) model to determine the optimal sonication time showed reliable performance, with R2cv = 0.928 and RMSECV = 0.849. This model was able to predict the cleanliness of each pixel in a testing sample set, exemplifying a step in the manufacturing process of direct bonded copper substrates. Combined with multivariate data modeling, the in-line UV prototype system demonstrates a significant potential for further advancement towards its application in real-world, large-scale processes. [ABSTRACT FROM AUTHOR]