Multivariate Analysis Techniques Research Articles

Assessment of Soil Spatial Variability in Agricultural Ecosystems Using Multivariate Analysis, Soil Quality Index (SQI), and Geostatistical Approach: A Case Study of the Mnasra Region, Gharb Plain, Morocco

Accurate assessment of soil quality is crucial for sustainable agriculture and soil conservation. Thus, this study aimed to assess soil quality in the agricultural ecosystem of the Mnasra region within the Gharb Plain of Morocco, employing a comprehensive approach integrating multivariate analysis and geostatistical techniques. Thirty soil samples were collected from the surface layers across thirty selected sites. The results showed significant variations in soil properties across the study area, influenced by factors such as soil texture, parent material, and agricultural practices. Pearson correlation and principal component analysis (PCA) were employed to analyze the relationships among soil properties and compute the Soil Quality Index (SQI). The SQI revealed values ranging from 0.48 to 0.74, with 46.66% of sampled soils classified as “Good” and 53.33% as “Fair”. Geostatistical analysis, particularly ordinary kriging (OK) interpolation and semivariogram modeling, highlighted the spatial variability of soil properties, aiding in mapping soil quality across the landscape. The integrated approach demonstrates the importance of combining field assessments, statistical analyses, and geospatial techniques for comprehensive soil quality evaluation and informed land management decisions. These findings offer valuable insights for decision-makers in monitoring and managing agricultural land to promote sustainable development in the Gharb region of Morocco.

Impact of institutional support and green knowledge transfer on university students' absorptive capacity and green entrepreneurial behavior: The moderating role of environmental responsibility.

Given an escalated interest in fostering environmental protection, scholars have associated green entrepreneurial behavior as a stimulating factor and the cornerstone of green entrepreneurial performance. Nevertheless, the underlying mechanism that nurtures university students' green entrepreneurial behavior is yet to be explored in the extant literature. Our study proposes the antecedent effects of institutional support and green knowledge transfer to enhance university students' green entrepreneurial behavior. Moreover, we also expand the boundary conditions of these relationships and suggest the mediating effect of university students' absorptive capacity and the moderating effect of environmental responsibility. The study samples university graduates in Chinese universities (N = 434) by adopting a lagged research design spanning over three months. We assessed the proposed model through the multivariate analytical technique. The findings indicate that institutional support and green knowledge transfer significantly elevate university students' green entrepreneurial behavior. Further, these relationships are intervened considerably through absorptive capacity's mediating effect and environmental responsibility's moderating effect. By investigating the crucial roles of institutional support and green knowledge transfer in culminating university students' green entrepreneurial behavior, our study extends the boundary conditions of these relationships and investigates the hitherto unexplored moderated mediation model.

Changes in Black Truffle (Tuber melanosporum) Aroma during Storage under Different Conditions.

The enticing aroma of truffles is a key factor for their culinary value. Although all truffle species tend to be pricy, the most intensely aromatic species are the most sought after. Research into the aroma of truffles encompasses various disciplines including chemistry, biology, and sensory science. This study focusses on the chemical composition of the aroma of black truffles (Tuber melanosporum) and the changes occurring under different storage conditions. For this, truffle samples were stored under different treatments, at different temperatures, and measured over a total storage time of 12 days. Measurements of the truffle aroma profiles were taken with SPME/GC-MS at regular intervals. To handle the ample data collected, a systematic approach utilizing multivariate data analysis techniques was taken. This approach led to a vast amount of data which we made publicly available for future exploration. Results reveal the complexity of aroma changes, with 695 compounds identified, highlighting the need for a comprehensive understanding. Principal component analyses offer initial insights into truffle composition, while individual compounds may serve as markers for age (formic acid, 1-methylpropyl ester), freshness (2-Methyl-1-propanal; 1-(methylthio)-propane), freezing (tetrahydrofuran), salt treatment (1-chloropentane), or heat exposure (4-hydroxy-3-methyl-2-butanone). This research suggests that heat treatment or salt contact significantly affects truffle aroma, while freezing and cutting have less pronounced effects in comparison. The enrichment of compounds showing significant changes during storage was investigated with a metabolomic pathway analysis. The involvement of some of the enriched compounds on the pyruvate/glycolysis and sulfur pathways was shown.

Rapid and sensitive approaches for detecting food fraud: A review on prospects and challenges

Precise and reliable analytical techniques are required to guarantee food quality in light of the expanding concerns regarding food safety and quality. Because traditional procedures are expensive and time-consuming, quick food control techniques are required to ensure product quality. Various analytical techniques are used to identify and detect food fraud, including spectroscopy, chromatography, DNA barcoding, and inotrope ratio mass spectrometry (IRMS). Due to its quick findings, simplicity of use, high throughput, affordability, and non-destructive evaluations of numerous food matrices, NI spectroscopy and hyperspectral imaging are financially preferred in the food business. The applicability of this technology has increased with the development of chemometric techniques and near-infrared spectroscopy-based instruments. The current research also discusses the use of several multivariate analytical techniques in identifying food fraud, such as principal component analysis, partial least squares, cluster analysis, multivariate curve resolutions, and artificial intelligence.

Shades of green: Unveiling the impact of municipal green bonds on the environment

Green bonds allocate proceeds towards environmentally beneficial projects and sustainable development goals, distinguishing themselves from traditional bonds primarily in the use of proceeds determination. However, investors often find it challenging to assess the carbon reduction potential of these bonds because of the lack of standardised environmental impact reporting. In response to this, our research constructs a unique set of indicators derived from financial and environmental datasets, using multivariate analysis techniques that can accommodate the detection of both linear and non-linear relationships. A novel method combining kernel Principal Component Analysis (kPCA) and Canonical Correlation Analysis (CCA) is applied to detect spatial–temporal cross-correlation in multivariate datasets. This approach handles variable comparability issues and the differential treatment of categorical and numerical variables. A significant finding of this study emerges when this methodology is applied to financial attributes obtained from green bonds issued by municipal agencies (muni bonds), pollution data and environmental (climate) data from nine California counties.The results of the detailed analysis indicate that there is measurable evidence to indicate relationships between green bond issuance and their use of proceeds for pollution reduction efforts. In particular, the results show a clear and interpretable correlation directly linked to the amount of green bond issuance and the effect this is having on pollution reduction, underscoring the tangible impact these financial instruments have on pollution reduction efforts in California.Conversely, when it comes to detecting spatial–temporal relationships between the use of proceeds from green bond issuance and positive climate change effects, this is inconclusive from the current studies’ analysis. It was found that there were weaker cross-correlation relationships observed between climate and green bond financial data set attributes which is perhaps indicative of the fact that climate change effects take a much longer time frame to occur. As such the findings of the analysis in this regard may not indicate that positive climate change effects are not occurring from green bond initiatives, but rather that the ability to measure detectable improvements to climate with regard to the issuance of green bonds is currently limited and will take longer for such effects to manifest in a statistically detectable fashion from the given data. This is particularly likely to be the case given green bonds are only in their infancy as a financial market, having had the earliest issuance only occurring in the last 15-20 years and only substantial growth in the market over the last 10 years. Therefore, this aspect of the research investigating longer-term climate effects with regard to green bond issuance will take longer to develop.

Distribución espacio-temporal de plaguicidas y clasificación de áreas de contaminación en un embalse al noreste de México: análisis espacial y multivariante

This study focused on the analysis of the spatial and temporal distribution of 13 pesticides in sediment and largemouth bass (Micropterus spp.) tissue, by spatial and multivariate analysis techniques in the “Vicente Guerrero” dam (Tamaulipas, Mexico). The concentration of the compounds was obtained by HPLC-MS/MS using the QuEChERS extraction procedure. The mean pesticide concentration varied in sediment from 0.37 to 8.33 µg/kg, and in largemouth bass from 0.05 to 2.88 µg/kg. Spatial variation was significant at the five sampled sites, both in sediment and fish. The highest concentration and number of pesticides was recorded in the center and east of the reservoir and declined towards the landfill. The concentration of pesticides in sediment was significant (p < 0.05) throughout the evaluated period, while differences in the concentration of largemouth bass were only significant in December and May. The most frequent pesticides in sediment and tissue were amitraz, ethion, parathion, pyriproxyfen and propargite, the latter present only in the center of the dam. The results obtained suggest bioaccumulation of five pesticides from sediment to fish. Parathion is an extremely dangerous pesticide for human health and together with ethion and propargite are highly toxic to aquatic life. The results suggest carrying out frequent monitoring of the dam to detect the presence of high-risk pesticides and preserve the ecosystem health.

A Review on the Recent Advancements and Artificial Intelligence in Tablet Technology.

Tablet formulation could be revolutionized by the integration of modern technology and established pharmaceutical sciences. The pharmaceutical sector can develop tablet formulations that are not only more efficient and stable but also patient-friendly by utilizing artificial intelligence (AI), machine learning (ML), and materials science. The primary objective of this review is to explore the advancements in tablet technology, focusing on the integration of modern technologies like artificial intelligence (AI), machine learning (ML), and materials science to enhance the efficiency, cost-effectiveness, and quality of tablet formulation processes. This review delves into the utilization of AI and ML techniques within pharmaceutical research and development. The review also discusses various ML methodologies employed, including artificial neural networks, an ensemble of regression trees, support vector machines, and multivariate data analysis techniques. Recent studies showcased in this review demonstrate the feasibility and effectiveness of ML approaches in pharmaceutical research. The application of AI and ML in pharmaceutical research has shown promising results, offering a potential avenue for significant improvements in the product development process. The integration of nanotechnology, AI, ML, and materials science with traditional pharmaceutical sciences presents a remarkable opportunity for enhancing tablet formulation processes. This review collectively underscores the transformative role that AI and ML can play in advancing pharmaceutical research and development, ultimately leading to more efficient, reliable and patient-centric tablet formulations.

VAE-Based Interpretable Latent Variable Model for Process Monitoring.

Latent variable-based process monitoring (PM) models have been generously developed by shallow learning approaches, such as multivariate statistical analysis and kernel techniques. Owing to their explicit projection objectives, the extracted latent variables are usually meaningful and easily interpretable in mathematical terms. Recently, deep learning (DL) has been introduced to PM and has exhibited excellent performance because of its powerful presentation capability. However, its complex nonlinearity prevents it from being interpreted as human-friendly. It is a mystery how to design a proper network structure to achieve satisfactory PM performance for DL-based latent variable models (LVMs). In this article, a variational autoencoder-based interpretable LVM (VAE-ILVM) is developed for PM. Based on Taylor expansions, two propositions are proposed to guide the design of appropriate activation functions for VAE-ILVM, allowing nondisappearing fault impact terms contained in the generated monitoring metrics (MMs). During threshold learning, the sequence of counting that test statistics exceed the threshold is considered a martingale, a representative of weakly dependent stochastic processes. A de la Peña inequality is then adopted to learn a suitable threshold. Finally, two chemical examples verify the effectiveness of the proposed method. The use of de la Peña inequality significantly reduces the minimum required sample size for modeling.

Preliminary study on the optical diagnosis of orbital rhabdomyosarcoma by Raman spectroscopy

To investigate the Raman spectral features of orbital rhabdomyosarcoma (ORMS) tissue and normal orbital tissue in vitro, and to explore the feasibility of Raman spectroscopy for the optical diagnosis of ORMS. 23 specimens of ORMS and 27 specimens of normal orbital tissue were obtained from resection surgery and measured in vitro using Raman spectroscopy coupled to a fiber optic probe. The important spectral differences between the tissue categories were exploited for tissue classification with the multivariate statistical techniques of principal component analysis (PCA) and linear discriminant analysis (LDA). Compared to normal tissue, the Raman peak intensities located at 1450 and 1655 cm−1 were significantly lower for ORMS (p < 0.05), while the peak intensities located at 721, 758, 1002, 1088, 1156, 1206, 1340, 1526 cm−1 were significantly higher (p < 0.05). Raman spectra differences between normal tissue and ORMS could be attributed to the changes in the relative amounts of biochemical components, such as nucleic acids, tryptophan, phenylalanine, carotenoid and lipids. The Raman spectroscopy technique together with PCA-LDA modeling provides a diagnostic accuracy of 90.0%, sensitivity of 91.3%, and specificity of 88.9% for ORMS identification. Significant differences in Raman peak intensities exist between normal orbital tissue and ORMS. This work demonstrated for the first time that the Raman spectroscopy associated with PCA-LDA diagnostic algorithms has promising potential for accurate, rapid and noninvasive optical diagnosis of ORMS at the molecular level.

Machine Learning and Deep Learning Strategies for Chinese Hamster Ovary Cell Bioprocess Optimization

The use of machine learning and deep learning has become prominent within various fields of bioprocessing for countless modeling and prediction tasks. Previous reviews have emphasized machine learning applications in various fields of bioprocessing, including biomanufacturing. This comprehensive review highlights many of the different machine learning and multivariate analysis techniques that have been utilized within Chinese hamster ovary cell biomanufacturing, specifically due to their rising significance in the industry. Applications of machine and deep learning within other bioprocessing industries are also briefly discussed.

Health and ecological risk assessment of heavy metals in water and sediments within a data scarce urban catchment in Tanzania – A case of Ngerengere River, Morogoro Municipality

Health and ecological risk assessment of heavy metals in water and sediments within a data scarce urban catchment in Tanzania – A case of Ngerengere River, Morogoro Municipality

A Study on the Spill Over Effects on Nifty 50 Returns: Evidence from Stock Market Performances of Major Asian Counterparts of India

This study investigates the spillover effects of major Asian stock market performances on the returns of the Nifty 50, India's benchmark index. Employing advanced econometric techniques, we analyze the dynamic relationships between the Nifty 50 and key Asian counterparts, considering both short-term and long-term horizons. Our research delves into return spillovers aiming to comprehensively assess the interconnectedness and transmission of market movements across the region. This study utilizes an extensive dataset covering Nifty 50 returns from April 1, 2013, to March 31, 2023, along with meticulously documented returns from stock market indexes linked to major strategic alliances in India. By applying advanced multivariate time series analysis techniques, encompassing Vector Autoregression (VAR), Granger causality tests, and impulse response functions, we model the intricate dynamics interweaving these indexes with the Nifty 50. The research endeavors to answer fundamental questions, including assessing short- term and long-term effects resulting from major Asian counterparts of India on Nifty returns, as well as exploring noticeable patterns that indicate the direction of causality between these counterparts and Nifty returns. The outcomes of this study carry substantial implications for decision-makers. Investors can make more informed decisions, businesses planning strategic partnerships can refine their strategies, and policymakers can formulate economic and financial regulations that accurately reflect the complexities of these dynamics. By unveiling the ripple effects of these counterparts on the Nifty 50, this research offers valuable insights into the evolving dynamics of India's financial markets and their integration into the global economy.

Profiling the socioeconomic characteristics, dietary intake, and health status of Korean older adults for nutrition plan customization: a comparison of principal component, factor, and cluster analyses.

This study was conducted to establish profiles of socioeconomic characteristics, dietary intake, and health status among Korean older adults by employing 3 multivariate analysis techniques. Data were obtained from 1,352 adults aged 65 years and older who participated in the 2019 Korea National Health and Nutrition Examination Survey. Principal component analysis (PCA), factor analysis (FA), and cluster analysis (CA) were utilized for profiling, with data preprocessing undertaken to facilitate these approaches. PCA, FA, and CA yielded similar results, reflecting the high common variance among the variables. PCA identified 4 components, accounting for 71.6% of the accumulated variance. FA revealed 5 factors, displaying a Kaiser-Meyer-Olkin value of 0.51 and explaining 74.3% of the total variance. Finally, CA grouped the participants into 4 clusters (R2=0.465). Both PCA and FA identified dietary intake (energy, protein, carbohydrate, etc.), social support from family (incorporating family structure, number of family numbers, and engagement in social eating), and health status (encompassing oral, physical, and subjective health) as key factors. CA classified Korean older adults into 4 distinct typologies, with significant differences observed in dietary intake, health status, and household income (p<0.01). The study utilized PCA, FA, and CA to analyze profiling domains and derive characteristics of older adults in Korea, followed by a comparison of the results. The variables defining the clusters in CA were consistent with those identified by PCA and FA.

Developing soft skills in the business classrooms of industrial engineering students in Brazil

PurposeIn today’s highly competitive global business environment, there is a growing demand for professionals who possess well-developed soft skills. Such abilities include flexibility, effective communication and other skills. Soft skills are personal attributes and qualities that are more closely related to the emotional side of human beings. Individuals must cultivate and hone soft skills during their undergraduate studies. These skills, also known as interpersonal or non-technical skills, are essential to complement hard skills and pave the way for a thriving career trajectory. Soft skills are developed over the course of one’s career and are indispensable in establishing a strong, professional presence in the corporate or academic realm. The field of engineering is no exception in this regard, and the business approach during the engineering course is of significant relevance. By acquiring soft skills, engineering graduates will become competitive and adaptable professionals capable of handling the current and future challenges of the job market. The purpose of this study is to investigate the soft skills that students perceive as being better developed during their business classrooms in the engineering course and identify areas for improvement in the business education process.Design/methodology/approachThe research method consisted in four different phases from variables identification to statistical analyses. Then, as part of this approach a structured questionnaire was administered at the end of the engineering course, where students rated their perception of the degree of development of each of the soft skills covered in the course on a scale of zero to ten. The collected data were analysed using multivariate analysis techniques, including factorial analysis.FindingsThe results of the study demonstrate that the set of skills acquired by individuals in business classrooms pursuing a degree in industrial engineering is in high demand by potential employers. Such skills are deemed essential for the successful operation of businesses in modern-day industries. The findings of this research validate the significant role that industrial engineering students play in fulfilling the requirements of the job market and pave the way to meaningful insights on how to approach this topic during the business education process in engineering courses.Practical implicationsThe findings bring about significant insights for national educational councils and ministries, universities and educational stakeholders in the process of updating, rethinking and implementing new curricula criteria in higher education, particularly in the Latin American context.Originality/valueThis paper enriches the literature by assessing the development of soft skills of engineering students in the Latin American context. The research reinforces the importance of developing soft skills aligned with those required for the context of current and future labour markets.

Mandarin Variety Significantly Affects the Metabolites Present in the Leaves

Late-season varieties of mandarin (Citrus reticulata Blanco) have a high economic value, so their study, characterization, and comparison among different commercial varieties is of great interest for agriculture. Detailed metabolomic analysis of mandarin leaves can provide valuable information on agronomic characteristics, vegetative development, and tree response to abiotic and biotic stresses. In this study, an analysis of the main metabolites presents in the leaves of three late-season mandarin orange varieties (‘Afourer’, ‘Orri’ and ‘Tango’), cultivated under homogeneous conditions, was carried out using nuclear magnetic resonance (1H NMR) and multivariate statistical analysis techniques. The results show that organic acids and sugars are the metabolites with the highest presence in mandarin leaves, especially malate and sucrose. Ten amino acids and other metabolites such as choline and trigonelline were also detected. Metabolites such as asparagine and isoleucine were widely implicated in the metabolic pathways of the detected compounds. The ‘Orri’ variety showed significantly more differences in metabolite concentrations compared to the other two varieties studied. Malate and sucrose were shown to be the metabolites with the greatest significant differences between the varieties compared. From an agronomic point of view, the ‘Orri’ variety differs from the other two varieties because it has concentrations of metabolites that provide good resistance to abiotic and biotic stresses and fruits of higher quality and sweetness.

Study of Correlated Motions to Detect the Conformational Transitions of the Intrinsically Disordered Sheep Prion Peptide.

Intrinsically disordered proteins (IDPs) are known for their random structural changes throughout their sequence based on the environment. The mechanism underlying these structural changes is difficult to explain. All biological processes are known to follow the direction through which they act. A study of the correlated motion can help to understand the direction of the change. Herein, we introduced the multivariate statistical analysis (MSA) technique to study the correlated motion of the peptide. The correlated motion of the sheep prion peptide was studied with the change in the temperature and solvent. These techniques helped to identify the contributing residual motions that helped to form the different secondary structures of the protein and also the triggering factors that drive these sorts of residual motions. The structural details match the experimentally reported data. It was found that the direction of the change of the secondary structure for this peptide shifted from the C-terminal to the N-terminal with an increase in the temperature. It was found that the involvement of the hydrophobic residues present at the C-terminal and the middle residues (residues 12-17) is responsible for forming a β-sheet at the normal temperature. Hydration water was found to play an important role in this change. Insights gained from this study can be used to design strategies for desirable structural changes in the IDPs.

Selection of chemical marker for Cnidii Rhizoma by Gyeongbuk production area and harvest time using UPLC-QTOF/MS and multivariate statistical analysis techniques

Selection of chemical marker for <i>Cnidii Rhizoma</i> by Gyeongbuk production area and harvest time using UPLC-QTOF/MS and multivariate statistical analysis techniques

Label-Free SERS of Urine Components: A Powerful Tool for Discriminating Renal Cell Carcinoma through Multivariate Analysis and Machine Learning Techniques.

The advent of Surface-Enhanced Raman Scattering (SERS) has enabled the exploration and detection of small molecules, particularly in biological fluids such as serum, blood plasma, urine, saliva, and tears. SERS has been proposed as a simple diagnostic technique for various diseases, including cancer. Renal cell carcinoma (RCC) ranks as the sixth most commonly diagnosed cancer in men and is often asymptomatic, with detection occurring incidentally. The onset of symptoms typically aligns with advanced disease, aggressive histology, and unfavorable prognosis, and therefore new methods for an early diagnosis are needed. In this study, we investigated the utility of label-free SERS in urine, coupled with two multivariate analysis approaches: Principal Component Analysis combined with Linear Discriminant Analysis (PCA-LDA) and Support Vector Machine (SVM), to discriminate between 50 RCC patients and 44 healthy donors. Employing LDA-PCA, we achieved a discrimination accuracy of 100% using 13 principal components, and an 88% accuracy in discriminating between different RCC stages. The SVM approach yielded a training accuracy of 100%, a validation accuracy of 99% for discriminating between RCC and controls, and an 80% accuracy for discriminating between stages. The comparative analysis of raw and normalized SERS spectral data shows that while raw data disclose relative concentration variations in urine metabolites between the two classes, the normalization of spectral data significantly improves the accuracy of discrimination. Moreover, the selection of principal components with markedly distinct scores between the two classes serves to alleviate overfitting risks and reduces the number of components employed for discrimination. We obtained the accuracy of the discrimination between the RCC patients cases and healthy donors of 90% for three PCs and a linear discrimination function, and a 88% accuracy of discrimination between stages using six PCs, mitigating practically the risk of overfitting and increasing the robustness of our analysis. Our findings underscore the potential of label-free SERS of urine in conjunction with chemometrics for non-invasive and early RCC detection.

Delineation of groundwater prospective zones using multivariate and spatial analysis techniques in Henan Province North China Plain

Groundwater is an essential resource that meets all of humanity’s daily water demands, supports industrial development, influences agricultural output, and maintains ecological equilibrium. The objective behind this research is to find groundwater prospective zones and their sustainable development in Henan Province, located in the North China Plain, with the help of analytical hierarchical process (AHP) and frequency ratio (FR) models. Consequently, a groundwater inventory map was created, utilizing 70% of the data for training and 30% for testing purposes. The resulting maps were overlaid with groundwater levels and the locations of wells within the study area. The performance and validation of the models were assessed using the Area Under Curve (AUC) curve, which indicated an accuracy of 70% for the AHP model and 69% for the FR model. The study indicates that areas with suitable gradients and rainfall patterns generally have high groundwater prospective zonation (GWPZ), i.e., Zhumadian and Nanyang (ZN). However, AHP demonstrated a higher predictive capability in comparison to FR the broader context. Whereas GIS-RS methodology aligns with existing wells and field data that validate it in range of good and excellent. In conclusion, this study provides valuable insights for effective groundwater exploration and sustainable water supply planning in Henan Province.

SOCIAL HEALTH INSURANCE ADMINISTRATION BODY PARTICIPANTS’ SATISFACTION ON DECISION TO USE MOBILE APPLICATION

Mobile JKN is an application developed by Social Health Insurance Administration Body to provide easy access to information about the National Healthcare Insurance Program (JKN Program). The number of participants in the Social Security Agency on Health (BPJS Health) has almost reached 250 million people. The government must manage public services so Social Security Agency on Health participants can access health facilities conveniently. The study aims to analyze the Social Security Agency on Health participants’ satisfaction with the decision to use the Mobile JKN application using the Expectancy Disconfirmation Model (EDM). EDM described how perceived expectations and performance influence public satisfaction. The study method is quantitative, with the study unit being Social Security Agency on Health participants and locus in Bogor. Structural Equation Modeling (SEM), a multivariate data analysis technique, was used in this study to examine the data and assess the intricate relationships between the constructs and indicators. The data processing technique uses SmartPLS. The finding shows that using the mobile JKN application has positively impacted user satisfaction. The Social Security Agency on Health participants were satisfied after utilizing the Mobile JKN application to access healthcare services.