Principal Component Analysis Model Research Articles

Statistical shape analysis and computational modeling reveal novel relationships between tibiofemoral bony geometry and knee mechanics in young, female athletes

Young female athletes participating in sports requiring rapid changes of direction are at heightened risk of suffering traumatic knee injury, especially noncontact rupture of the anterior cruciate ligament (ACL). Clinical studies have revealed that geometric features of the tibiofemoral joint are associated with increased risk of suffering noncontact ACL injury. However, the relationship between three-dimensional (3D) tibiofemoral geometry and knee mechanics in young female athletes is not well understood. We developed a statistically augmented computational modeling workflow to determine relationships between 3D geometry of the knee and tibiofemoral kinematics and ACL force in response to an applied loading sequence of compression, valgus, and anterior force, which is known to load the ACL. This workflow included 3D characterization of tibiofemoral bony geometry via principal component analysis and multibody dynamics models incorporating subject-specific knee geometries. A combination of geometric features of both the tibia and the femur that spanned all three anatomical planes was related to increased ACL force and to increased kinematic coupling (i.e., anterior, medial, and distal tibial translations and internal tibial rotation) in response to the applied loads. In contrast, a uniplanar measure of tibiofemoral geometry that is associated with ACL injury risk, sagittal plane slope of the lateral tibial plateau subchondral bone, was not related to ACL force. Thus, our workflow may aid in developing mechanics-based ACL injury screening tools for young, active females based on a unique combination of bony geometric features that are related to increased ACL loading.

Data fusion of Raman spectra in MSPC for fault detection and diagnosis in pharmaceutical manufacturing

This study investigates the use of Raman spectroscopy fused with other types of data (e.g., pH, temperature and turbidity) for multivariate statistical process control of two pharmaceutical case studies: one simulated industrial-scale fed-batch process for the production of penicillin and one real lab-scale crystallization process. The monitoring schemes are built on local principal component analysis models and hyper-parameters are tuned with regards to highest accuracy in fault detection. Accuracies above 90% are obtained for all types of data and level of DF. Furthermore, for the first case study the model built solely on spectra achieves higher fault detection rates, when only considering faults that also result in off-specification quality. This is supported by the fact that the fault is not necessarily detected when it occurs, but rather when it starts to affect quality variables as measured by the spectra.

The impact of behavioral attributes on rural youth’s propensity to participate in non-primary agribusinesses: Evidence from KwaZulu-Natal, South Africa

ABSTRACT Youth participation in agriculture in general and agribusinesses in particular remains limited in Africa and empirical insight on the enablers and inhibitors is limited. This paper aims to investigate the impact of behavioral attributes (such as entrepreneurial spirit and business skills endowment) on the potential participation of rural youth in non-primary agribusinesses. Principal Component Analysis and Fractional Logit Model were employed on a data set of 152 rural youth. The results show that most rural youth are endowed with entrepreneurial spirit and relatively well capacitated with business skills. The results further show that rural youth endowed with entrepreneurial spirit are less likely to engage in non-primary agribusinesses. Other factors that influence rural youth potential participation included psychological capital, agricultural perceptions, and demographics. The results suggest that interventional programs should consider behavioral attributes when aiming to attract rural youth into the sector.

A statistical approach to system suitability testing for mass spectrometry imaging.

Mass spectrometry imaging (MSI) elevates the power of conventional mass spectrometry (MS) to multidimensional space, elucidating both chemical composition and localization. However, the field lacks any robust quality control (QC) and/or system suitability testing (SST) protocols to monitor inconsistencies during data acquisition, both of which are integral to ensure the validity of experimental results. To satisfy this demand in the community, we propose an adaptable QC/SST approach with five analyte options amendable to various ionization MSI platforms (e.g., desorption electrospray ionization, matrix-assisted laser desorption/ionization [MALDI], MALDI-2, and infrared matrix-assisted laser desorption electrospray ionization [IR-MALDESI]). A novel QC mix was sprayed across glass slides to collect QC/SST regions-of-interest (ROIs). Data were collected under optimal conditions and on a compromised instrument to construct and refine the principal component analysis (PCA) model in R. Metrics, including mass measurement accuracy and spectral accuracy, were evaluated, yielding an individual suitability score for each compound. The average of these scores is utilized to inform if troubleshooting is necessary. The PCA-based SST model was applied to data collected when the instrument was compromised. The resultant SST scores were used to determine a statistically significant threshold, which was defined as 0.93 for IR-MALDESI-MSI analyses. This minimizes the type-I error rate, where the QC/SST would report the platform to be in working condition when cleaning is actually necessary. Further, data scored after a partial cleaning demonstrate the importance of QC and frequent full instrument cleaning. This study is the starting point for addressing an important issue and will undergo future development to improve the efficiency of the protocol. Ultimately, this work is the first of its kind and proposes this approach as a proof of concept to develop and implement universal QC/SST protocols for a variety of MSI platforms.

THE NEW APPROACHES TO IDENTIFICATION OF TINCTURES AND MEDICINAL PLANTS

Objective: The objective of this study is to develop methods for identifying herbal medicines and tinctures by processing spectral results across a wide range of wavelengths using principal component analysis (PCA). Methods: Medicinal plants and tinctures of valerian, motherwort, and hawthorn have been analyzed using UV spectrophotometry, spectrofluorimetry, ATR FTIR spectrometry, and X-ray fluorescence spectrometry. PCA was used to process the results of spectral analysis. Statistical processing of spectral results was carried out using the OriginPro program (OriginLab Corporation, USA, 2021). Results: For herbal medicines with sedative, hypotensive, and cardiotonic effects, spectral data libraries have been created in the following dimensions: UV spectrophotometry with 1800 absorption units (Ai), spectrofluorimetry with 4010 fluorescence intensity units (Ii), IR spectroscopy with a light transmittance of 50250 units (Ti), and X-ray fluorescence spectrometry with an intensity of 1568 (Ii). These libraries were used as the primary matrices for PCA. Visualization of the PCA results was done using a scores plot and a loadings plot, which illustrate the contribution of each principal component (PC) to the PCA model. After performing chemometric processing on the original spectral results, it was discovered that samples belonging to the same botanical genus occupy distinct and compact regions in two-dimensional or three-dimensional space. Unknown plant samples (blind samples) and samples of other botanical species were successfully tested using new method. Conclusion: For the first time, tinctures and medicinal plants were identified based on their botanical genus using spectral techniques coupled with principal component analysis, eliminating the need for a chemical reference substance.

Confounding effects of seasonality and anthropogenic river regulation on suspended particulate matter-driven mercury transport to coastal seas

Riverine mercury (Hg) is mainly transported to coastal areas in suspended particulate matter (SPM)-bound form, posing a potential threat to human health. Water discharge and SPM characteristics in rivers vary naturally with seasonality and can also be arbitrarily disrupted by anthropogenic regulation events, but their effects on Hg transport remain unresolved. Aiming to understand the confounding effects of seasonality and anthropogenic river regulation on Hg and SPM transport, this study selected the highly sediment-laden Yellow River as a representative conduit. Significant variations in SPM concentrations (108 − 7097 mg/L) resulted in fluctuations in total mercury (THg, 3.79 − 111 ng/L) in river water corresponding to seasonality and anthropogenic water/sediment regulation. Principal component analysis and structural equation model revealed that SPM was the essential factor controlling THg and particulate Hg (PHg) in river water. While SPM exhibited equilibrium state in the dry season, a net resuspension during the anthropogenic regulation and net deposition in the wet season demonstrated the impact of SPM dynamics on Hg distribution and transport to coastal regions. Combining water discharge, SPM, and Hg concentrations, a modified model was developed to quantify Hg flux (2256 kg), over 98% of which was in particulate phase.

Antibiotic prescribing practices of medical doctors in a resource-limited setting and the influence of individual perceptions and stewardship support: a survey in three tertiary hospitals in Vietnam.

To understand antibiotic prescribing and influencing factors to inform antimicrobial stewardship (AMS) interventions to reduce unwanted consequences of antibiotic use in hospitals in Vietnam, a lower-middle-income country in Asia. We conducted a cross-sectional study of doctors at three tertiary hospitals using non-probability convenience sampling, through a paper-based (Hospitals 1 and 2) or electronic (Hospital 3) survey. Questions included items on perceptions regarding antibiotic resistance and AMS, prescribing practices, knowledge, demographics and training. We used principal components analysis and mixed-effects models to examine practices and identify influencing factors. Among 314 surveyed participants, 61%, 57% and 59% in Hospitals 1, 2 and 3, respectively, felt certain about the appropriateness of their antibiotic prescriptions. In total, 9% reported sometimes prescribing antibiotics when not needed to meet patients' expectations, and 13% reported doing so to avoid perceived complications. Higher prescribing confidence was found among those with positive perceptions about AMS (P < 0.0001), whereas negative perceptions about colleagues' practices reduced this confidence (P < 0.0001). Individual preference for branded antibiotics was associated with more unnecessary prescribing whereas having higher prescribing confidence decreased the habits of prescribing when not needed. This study provides important implications for design of hospital interventions to address influencing factors on antibiotic prescribing in Vietnam and similar resource-limited settings. Specific interventions should target improving knowledge through education and training for doctors, enhancing the support from the AMS team, and promoting guidelines and policies for appropriate antibiotic use in hospital.

Mobility-as-a-Service and unsustainable travel behaviour: Exploring the car ownership and public transport trip replacement side-effects of the MaaS paradigm

Car-centred development adds significantly to the climate change threat our planet faces. Mobility-as-a-Service (MaaS) is a concept looking to generate modal shift via the digital and physical integration of alternative on-demand transport services. Recent research raises concerns, however, that contrary to the hype, MaaS may result in unsustainable travel behaviour. On the one hand, future MaaS users that are now primarily drivers may not be ready to shift away from their cars, while, on the other hand, people regularly using public transport may substitute some of these journeys with MaaS-enabled car-sharing, ride-sharing and ride-hailing services. Herein, we study the effects of MaaS on intentions to own a car and to substitute public transport trips with car-centric services, via a UK-based quantitative survey. We use Principal Component Analysis and Ordinal Regression Modelling to see how MaaS-related perceptions referring to Added Value, Car Use Convenience and Enjoyment, Human Element Externalities, Trust in Functionality, Cost Incentives, Car Use Morality, Trust in Enabling Technology and Car Ownership Necessity affect usage intentions. Our results highlight some, typically less discussed, grey areas of MaaS and inform the design of policies, including car service disincentivitisation, responsible user benefits, MaaS rebranding and public transport investment prioritisation.

Robust and Sparse Principal Component Analysis With Adaptive Loss Minimization for Feature Selection.

Principal component analysis (PCA) is one of the most successful unsupervised subspace learning methods and has been used in many practical applications. To deal with the outliers in real-world data, robust principal analysis models based on various measure are proposed. However, conventional PCA models can only transform features to unknown subspace for dimensionality reduction and cannot perform features' selection task. In this article, we propose a novel robust PCA (RPCA) model to mitigate the impact of outliers and conduct feature selection, simultaneously. First, we adopt σ -norm as reconstruction error (RE), which plays an important role in robust reconstruction. Second, to conduct feature selection task, we apply l2,0 -norm constraint to subspace projection. Furthermore, an efficient iterative optimization algorithm is proposed to solve the objective function with nonconvex and nonsmooth constraint. Extensive experiments conducted on several real-world datasets demonstrate the effectiveness and superiority of the proposed feature selection model.

Qingxiangxing Baijiu sensory quality grade classification by 1H NMR and GC combined with multivariate statistical analysis

The traditional uniform artificial sensory evaluation makes it difficult to standardize the classification of different sensory quality grades of Baijiu. In this study, a total of 92 authentic Qingxiangxing Baijiu samples with 3 sensory quality grades were carefully collected. Gas chromatography (GC) was used to determine 46 main flavor components and proton nuclear magnetic resonance (1H NMR) spectroscopy was employed to obtain hydrogen atom characteristic information of organic compounds. The principal component analysis (PCA), k-nearest neighbor (KNN) and linear discriminant analysis (LDA) models were conducted and fully validated by internal leave-one-out cross validation (LOOCV) and external repeated double random cross validation (RDRCV). The sensory quality grades of Qingxiangxing Baijiu were effectively classified by using GC and 1H NMR techniques coupled with PCA/KNN analysis with the averaged accuracy higher than 80%. In addition, synthetic minority oversampling technique (SMOTE) algorithm was successfully used to address the model overfitting problem caused by an unbalanced sample composition. This study demonstrated that 1H NMR and GC combined with multivariate statistical analysis were effective for sensory quality classification of Qingxiangxing Baijiu.

Empowering housewives through green marketing: Fostering eco-friendly household products for sustainable consumption

The escalating concern over the green gap phenomenon, which underscores a discord between consumers' environmental concerns and purchasing behaviours, has accentuated the importance of exploring eco-friendly consumption patterns. This study delves into the multifaceted factors influencing the purchasing behaviour of eco-friendly household products, focusing on the dynamic interplay among consumer knowledge, values, subjective norms, and perceived behavioural control. Specifically, it aims to elucidate how these variables collectively inform housewives' attitudes towards eco-friendly products and purchasing behaviours. The research gathered data from 300 respondents across Jambi City and Sungai Penuh City within Jambi Province, employing principal component analysis and structural equation modelling to scrutinize the hypothesized relationships between the constructs. The findings underscore consumer values, subjective norms, and perceived behavioural control significantly and positively influence consumer attitudes towards eco-friendly household products. Moreover, a pronounced positive correlation between consumer attitudes and eco-friendly purchasing behaviour was identified, whereas the impact of consumer knowledge on consumer attitudes emerged as negligible. This research enriches the theoretical discourse on eco-friendly purchasing behaviour, particularly concerning household products. It offers critical insights for marketers, policymakers, and environmental advocates aiming to foster sustainable consumption practices by pinpointing the pivotal factors that shape consumer attitudes and behaviours. Additionally, the study lays a robust groundwork for subsequent research endeavours to bridge the green gap and advance environmental sustainability through enlightened consumer decisions.

Acoustic Echo Cancellation using Adaptive Filter for Quranic Accent Signals

Quranic recordings andechoed portions of the emphasis are susceptible to signal reverberation, particularly when being listened to in a conference room. Tajweed and Quranic verse rule identification are susceptible to additive noise, which could lower classification accuracy. In order to reflect the most correct rate following pattern categorization, this study suggested the appropriate use of three adaptive algorithms: Affine Projection (AP), Least Mean Square (LMS), and Recursive Least Squares (RLS). For feature extraction, Mel Frequency Cepstral Coefficient is used together with Probabilities Principal Component Analysis (PPCA), K-Neural Network (KNN) and Gaussian Mixture Model (GMM). AP indicates 93.9% for all of the classification algorithm in used, while for LMS and RLS the results are differed varies on different pattern classification algorithm stated whereby with LMS and PPCA classification, 96.9 % for accuracy and 84.8% accuracy for LMS and KNN. While for RLS and GMM, 96.9% was achieved and the results were reduced for both KNN and PPCA. The analysis has resulted for both on accuracies within different filtering algirithm and classification for accuracy and ERLE(dB).Towards this research it is hope will embark more understanding towards echo cancellation and quality of sound recordings that may affected even to the Quranic recordings.KeywordsAdaptive filtering, acoustic echo cancellation, recursive least squares, least mean square, affine projection, accuracy rate1.IntroductionUnusually big peaks have been produced by frequency response, masking, and emerging peaks from sound systems. Noise can be introduced into music recordings from the surrounding environment, during the recording system when audio signals change, or during the indexing process. These noise signals can disrupt and lower the quality and efficiency of the recording. Quranic recordings may also be affected by echoes, which can interfere with the recording process. The speaker, microphone, and transmission path are among the variables that affect.

Differentiation of Cannabis seeds employing digital morphological screening and infrared spectroscopy coupled with multivariate modeling

Cultivation of Cannabis for medicinal purposes primarily relies on seed propagation with expected variations in yield, cannabinoid content, growth rate and seed material non-uniformity. This study aims to employ digital methods for morphological analysis and infrared spectroscopy, combining them with multivariate analysis to characterize and differentiate Cannabis seeds. Morphological traits of 100 seeds from both commercial Cannabis specimens and wild-growing local varieties were analyzed using the high-throughput phenotyping software in addition to their collected infrared spectra in the mid-IR region. Subsequently, a chemometrics approach by means of Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), and Partial Least Square-Discriminatory Analysis (PLS-DA) was applied. The statistical indicators of the PLS-DA model (R2X=0.99, R2Y=0.63, Q2=0.64) demonstrate strong predictive capabilities for the differentiation of Cannabis seed specimens based on morphological attributes. The score scatter plot clearly shows a distinct grouping pattern, primarily driven by seed size. Wild-type seeds predominantly cluster into group 1, characterized by smaller diameters, while commercial seeds cluster into group 2. By analysing spectral data, in contrast to the expected differentiation based on secondary metabolites (cannabinoids) in the seeds, differentiation was based on the macronutrient profile with characteristic bands at 3275 cm−1, 2921 cm−1, 2852 cm−1, 1743 cm−1, 1630 cm−1, 1532 cm−1, 1459 cm−1, 1239 cm−1, 1157 cm−1, 1094 cm−1, 1018 cm−1, identified as the most distinctive spectral features. The PCA model (R2X=0.88 and Q2=0.85) was composed of 5 principal components explaining 88% of the spectral variations. The loading plot of the PLS-DA model reveals the distinctive spectral features for both groups (lipid and carbohydrate bands - group 2 samples, protein and water content - group 1 samples). The developed models have the potential for application for rapid screening of quality parameters of Cannabis seeds and their differentiation.

Risk governance and regulatory adjustments in the public commercial banks of OECD

PurposeThis study aims to explore the relationship between risk governance characteristics (chief risk officer [CRO], chief financial officer [CFO] and senior directors [SENIOR]) and regulatory adjustments (RAs) in Organization for Economic Cooperation and Development public commercial banks.Design/methodology/approachUsing principal component analysis (PCA) and regression models, the research analyzes a representative data set of these banks.FindingsA significant negative correlation between risk governance characteristics and RAs is found. Sensitivity analysis on the regulatory Tier 1 capital ratio and the total capital ratio indicates mixed outcomes, suggesting a complex relationship that warrants further exploration.Research limitations/implicationsThe study’s limited sample size calls for further research to confirm findings and explore risk governance’s impact on banks’ capital structures.Practical implicationsEnhanced risk governance could reduce RAs, influencing banking policy.Social implicationsThe study advocates for improved banking regulatory practices, potentially increasing sector stability and public trust.Originality/valueThis study contributes to understanding risk governance’s role in regulatory compliance, offering insights for policymaking in banking.

Executive team risk appetite, financial development and re-innovation strategy after enterprise innovation failure: evidence from China

PurposeThis article empirically tests the impact of risk appetite of the executive team on the re-innovation strategy after technological innovation failure using a panel regression model from the perspective of regional financial development level of enterprises.Design/methodology/approachBy means of time series global principal component analysis and panel regression model method, the study validated and analyzed the impact of risk appetite of the executive team on the re-innovation strategy after enterprise technological innovation failure.FindingsThe research found that the higher the risk appetite of executive team, the more inclined the enterprise is to choose the “focusing on quantity, ignoring quality” re-innovation strategy after technological innovation failure. The better the financial development level of the region where the enterprise is located, the better it can effectively reduce the re-innovation strategy of “focusing on quantity, ignoring quality” of the enterprise due to the high risk appetite of the executive team.Originality/valueThe findings of this study are helpful in improving the financial development level of the region where the enterprise is located. It can help the executive team of the enterprise to more objectively choose the innovation strategy after technological innovation failure, and reduce the phenomenon that the executive team of the enterprise only pays attention to the quantity of re-innovation and underestimates the quality of re-innovation after technological innovation failure due to its high risk appetite.

Comparative Analysis of Characteristic Volatile Compounds in Five Types of Infant Nutrition Powders by HS-GC-IMS and GC-MS.

This study employed the headspace-gas chromatography-ion migration spectrum (HS-GC-IMS) in conjunction with the gas chromatography-mass spectrometer (GC-MS) technique for the assessment of the flavor quality of complementary food powder intended for infants and young children. A total of 62 volatile compounds were identified, including aldehydes, esters, alcohols, ketones, pyrazines, and furans, among which aldehydes were the most abundant compounds. Based on the principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) models, infant nutritional powder (YYB) from different manufacturers could be clearly distinguished. Among them, 2-hydroxybenzaldehyde, 1, 2-dimethoxyethane, 2-isobutyl-3-methoxypyrazine, and methyl butyrate were the four most critical differential volatiles. In addition, these differences were also manifested in changes in fatty acids. The reason for this phenomenon can be attributed to the difference in the proportion of raw materials used in nutrition powder, micronutrient content, and the packaging process. In conclusion, this study provides comprehensive information on the flavor quality of YYB, which can be used as a basis for quality control of YYB.

Application of stable isotopes, mixing models, and K-means cluster analysis to detect recharge and salinity origins in Siwa Oasis, Egypt

Waterlogging, soil salinization, and groundwater depletion in non-rechargeable aquifers are among the causes endangering the sustainability of Siwa Oasis. The water resources include the tertiary carbonate aquifer (TCA), Nubian sandstone aquifer (NSSA), springs, drains, and hypersaline lakes. Geophysical logs were used to identify various aquifer systems. Stable isotopes, major ions, and heavy metals were examined. K-means cluster analysis based on principal component analysis (PCA), hydrochemistry, stable isotopes, and mixing models using NETPATH were applied in order to understand the complex hydrogeological conditions in the different aquifer systems. The results showed that all water samples were under-saturated (SI < 1) with respect to anhydrite, halite, and gypsum minerals, indicating the water's ability to dissolve more from these minerals and increase water salinity.In contrast, calcite, dolomite, Ca-montmorillonite, and illite minerals were supersaturated in most water samples (SI > 1), showing their ability to precipitate these minerals. There is no recharge for the aquifer systems, and all the groundwater resources have paleo meteoric origin, where the maximum values of δ18O and δ2H are −8.45 and −65.63, respectively. All of the springs originated from the TCA, as indicated from the K-mean cluster analysis. There is a rapid increase in the water salinity of the TCA from 1998 to 2022 due to evaporation, rock weathering, mixing with old trapped sea water, and seepage of saline water from salt lakes. The significant variations in the water salinity of the TCA aquifer are due to the mixing between fresh water of NSSA (dilution) and salt water from Zeituna and Siwa lakes through downward flow in some locations, where over-extraction of water for irrigation purposes has decreased the water pressure in the TCA. The mixing model confirmed salt lakes' contribution in increasing the TCA salinity by 2%–4%. Mixing water from TCA and NSSA could be the best management of the water resources in Siwa Oasis, as well as the application of subsurface drip irrigation.

Association between Heavy Metals and Trace Elements in Cancerous and Non-cancerous Tissues with the Risk of Colorectal Cancer Progression in Northwest China.

Alterations in heavy metals and trace element levels may be associated with various cancers. However, the role of this interaction in colorectal cancer (CRC) progression is unclear. In recent years, Principal Component Analysis (PCA) and Bayesian Kernel Machine Regression (BKMR) models have provided new ideas for analyzing the effects of metal mixtures on CRC progression. Herein, we assessed the differences in the levels of arsenic (As), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), nickel (Ni), selenium (Se), and zinc (Zn) in tumors and adjacent healthy tissues, to investigate the relationship between heavy metals/trace elements and CRC progression. Surgical samples of CRC and noncancerous tissues were collected, and trace metal levels were analyzed using inductively coupled plasma mass spectrometry (ICP-MS). Logistic regression, PCA, and BKMR models were used to investigate the relationship between heavy metals and trace elements and the degree of tumor differentiation and lymph node metastasis in CRC. Cancer tissues showed lower As, Cd, Co, and Cr concentrations, and higher Se concentrations than healthy tissues (P < 0.05). In addition, CRC patients with poorly differentiated tumors and/or positive lymph node metastases had lower levels of Cd, Zn, Cu, and Se (P < 0.05). Logistic regression showed that single metal concentration was negatively correlated with CRC progression. PCA and BKMR models also showed that the metal mixture concentration was negatively correlated with CRC progression, with Cd contributing the most. Overall, changes in heavy metal and trace element levels may be related to the development of CRC; however, further mechanistic studies are required.

The Positive Association between Grit and Mental Toughness, Enhanced by a Minimum of 75 Minutes of Moderate-to-Vigorous Physical Activity, among US Students

Drawing from the 2015 Gucciardi et al.’s mental toughness (MT) framework, this study examines the association between grit and MT in US college students, while considering the moderating role of at least 75 min of moderate-to-vigorous physical activity (MVPA) based on recommendations from the American College of Sports Medicine. We administered the Grit-S Scale and the Mental Toughness Index in two samples of a total of 340 US undergraduate student-athletes and graduate students. The Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies was employed to ensure internal validity, while statistical procedures including principal component analysis and regression models were utilized to analyze the collected data. A weighted component combining grit and the interaction between MVPA and grit significantly predicted MT, explaining 23% of its variability. Drawing from a specific conceptual framework, this study provides novel insights into the relationship between grit, engagement in at least 75 min of MVPA per week, and MT among US collegiate students. The findings support a positive association between grit, MVPA, and both MT and a specific component of MT, highlighting the significance of these factors in enhancing performance and suggesting potential implications for future research and practical applications in the field.

DIY google trends indicators in social sciences: A methodological note

Various branches of the social sciences often require high-frequency measures of collective behavior. Examining peoples' Google search patterns enables uncovering otherwise hard-to-measure social trends or constructing different leading indicators. Google Trends (GT) data presents a unique and rich dataset that enables a deeper look at the popularity of peoples’ search interests. This paper delves into the main empirical approaches of constructing GT indicators, conducts a thorough methodological review, and elucidates the nuances and potentials of GT data. Specifically, the paper furnishes a step-by-step methodological guide for constructing GT indicators: from data pre-treatment to composite estimation using an array of machine learning techniques (principal component analysis, XGBoost, and dynamic factor models). We scrutinize the prevalent construction strategies of GT indicators, compare the use of keyword-based vs. category-based estimations, single indicators vs. composite indicators, and evaluate the significance of various data transformations. Illustrating the utility of our methodological framework through a case study, we create GT indicators for US retail trade and examine their forecasting accuracy in comparison to a benchmark autoregressive model. Our findings reveal that XGBoost yields the optimal test sample fit among composite indicators, while category-specific indicators like Shopping and Retail Trade exhibit worse predictive accuracy. As an additional contribution, we provide a detailed and user-friendly R code for replicating our analysis.