Multivariate Data Analysis Methods Research Articles

PENGARUH SUPPLY CHAIN MANAGEMENT TERHADAP KINERJA UKM YANG DIMEDIASI OLEH KEUNGGULAN BERSAING

This study aims to determine the effect of supply chain management on SME's performance mediated by competitive advantage. The method in this study uses partial least squares structural equation modeling (PLS-SEM) which is a multivariate data analysis method for complex relationships among constructs and indicators. Researchers used the PLS.3.0 application. The population of this study is SMEs located in Yogyakarta. Determination of the sample using purposive sampling where the SMEs included in the study are SMEs registered in SiBakul and the results obtained as many as 100 research samples. For model feasibility using the Goodness of fit test of 69%. The results obtained are directly supply chain management and competitive advantage have a significant and positive effect on SME's performance. Indirectly supply chain management has a significant and positive effect on SME's performance mediated by competitive advantage. Keywords: supply chain management, SME’s performance, competitive advantage

Application of Multivariate Data Analysis Methods for Rapid Detection and Quantification of Adulterants in Lavender Essential Oil Using Infrared Spectroscopy

ABSTRACTLavender, widely cultivated in the Mediterranean region, produces essential oil known for its significant biological activities and is a key component of the perfume industry due to its high levels of Linalool and Linalyl acetate, along with low Camphor content, which contributes to its high cost. However, the market is plagued by adulterated lavender oil, often mixed with cheaper alternatives such as eucalyptus and rosemary. Current detection methods, primarily gas chromatography, are expensive, time‐consuming and often fail to detect low levels of adulteration. To address these limitations, this study examines the use of mid‐infrared spectroscopy for the detection and prediction of adulteration levels. A set of 105 samples, comprising pure lavender oil and adulterated lavender oil, was prepared in the laboratory. Principal component analysis (PCA), hierarchical clustering ascending (HCA) and K‐means clustering were applied to the FT‐MIR results for qualitative analysis to effectively discriminate between authentic and adulterated essential oils. For quantitative analysis, partial least squares regression (PLSR) was used to develop accurate calibration models for predicting the percentage of adulteration. The results from PCA, HCA and K‐means demonstrated the efficacy of these techniques in detecting adulteration, even at low levels (2%). Calibration models were developed using the PLSR method with different spectral preprocessing techniques to predict the percentage of adulteration, with results indicating that models generated on the raw data and those using MSC (multiplicative signal correction) pre‐processing are optimal. In addition, the use of interval‐partial least squares (IPLS) variable selection techniques (Forward, Backward) improved the predictive accuracy of the models developed by reducing the number of wavelengths used.

Monitoring of the Homogeneity of Primer Layers for Ink Jet Printing on Polyester Fabrics by Hyperspectral Imaging.

Untreated polyester films and fibers can be hardly printed or coated, in particular if aqueous inks or lacquers have to be applied. Therefore, an adequate primer layer has to be applied first. A cationic polymer formulation based on poly(dimethylamine-co-epichlorohydrin-co-ethylenediamine) (PDEHED) was used as primer layer for digital printing on polyester fabrics. Because of the exceedingly high requirements on the homogeneity of such layers, hyperspectral imaging was used for qualitative and quantitative monitoring of the distribution of the primer layer on the textiles. Multivariate data analysis methods based on the PLS algorithm were applied for quantification of the NIR reflection spectra using gravimetry as a reference method. Optimization of the calibration method resulted in various models with prediction errors of about 1.2 g/m2. The prediction performance of the models was proven in external validations using independent samples. Moreover, a special ink jet printing technology was tested for application of the aqueous primer formulation itself. Since possible clogging of jet nozzles in the print head might lead to inhomogeneity in the coatings such as missing tracks, the potential of hyperspectral imaging to detect such defects was investigated. It was demonstrated that simulated missing tracks can be clearly detected. Consequently, hyperspectral imaging has been proven to be a powerful analytical tool for in-line monitoring of the quality of printability improvement layers and similar systems.

Spatio-temporal plant hormonomics: from tissue to subcellular resolution.

Due to technological advances in mass spectrometry, significant progress has been achieved recently in plant hormone research. Nowadays, plant hormonomics is well established as a fully integrated scientific field focused on the analysis of phytohormones, mainly on their isolation, identification, and spatiotemporal quantification in plants. This review represents a comprehensive meta-study of the advances in the phytohormone analysis by mass spectrometry over the past decade. To address current trends and future perspectives, Web of Science data were systematically collected and key features such as mass spectrometry-based analyses were evaluated using multivariate data analysis methods. Our findings showed that plant hormonomics is currently divided into targeted and untargeted approaches. Both aim to miniaturize the sample, allowing high-resolution quantification to be covered in plant organs as well as subcellular compartments. Therefore, we can study plant hormone biosynthesis, metabolism, and signalling at a spatio-temporal resolution. Moreover, this trend has recently been accelerated by technological advances such as fluorescence-activated cell sorting or mass spectrometry imaging.

Chemometrical methods applied to chemical data of raw and cooked spinach (Chenopodium vulvaria) and purslane (Potulacae oleracea)

Hundreds of edible wild plants form a vegetal environment that provides a supply of nutritious and delightful food. It also contains a wealth of nutritious ingredients such as proteins, iron, calcium, and vitamins, as well as fibers, and mineral salts that our bodies require. Knowing these plants also aids in preserving the traditional users' cultural heritage. It is crucial to describe, characterize, and search the nutritional content of these plants. This is a very intriguing topic to explore and advance due to the variety of plants that have been harvested and their usage in various industries (nutritional, medical, cosmetic, pharmaceutical). The leaves and stems of some plants are used in cooking or eaten raw in salads. The limited research that has been done in Algeria has not demonstrated the potential contribution of these items to ration balance. The aim of this work was to gauge the chemical composition of two raw and cooked plants, leaves and petioles of wild spinach (Chenopodium vulvaria) and leaves of purslane (Portulaca oleracea), and to apply chemometric methods to obtain information that permit their characterization. Five samples of ten bunches of each plant were purchased from different retail traders in different markets of Constantine (Algeria) and prepared according to standard AFNOR. The samples were analyzed by determining moisture, total lipids, proteins, ash, minerals, dietary fibers, as well as, total carbohydrates and energy value by calculation. A multivariate data analysis method such as principal components analysis (PCA), was applied on a data matrix consisting of the analytical parameters of raw and cooked spinach leaves and petioles; and of raw and cooked purslane leaves. The results obtained showed that the samples of cooked spinach were characterized by the total carbohydrate and nutritional fiber (ADF, NDF and lignin). As for raw spinach, it was associated with lipid, protein, dry matter, energy value, carbohydrate and ash. The PCA of minerals revealed that the leaves are separated from petioles and are richer in minerals. PCA also enabled demonstration of the distinction between raw and cooked purslane. The dry matter content of the leaves of spinach and purslane decreased after cooking. It would be interesting to continue the work by studying the composition of fatty acids, tocopherols and pigments. In addition, the study could be broadened to include other cooking methods, and harvested plants that grow in Algeria. Key words: Spinach (Chenopodium vulvaria), Purslane (Portulaca oleracea), Nutritional composition, Principal Components Analysis, Cooking

Identification of Geochemical Anomalies Based on RPCA and Multifractal Theory: A Case Study of the Sidaowanzi Area, Chifeng, Inner Mongolia.

The focus of exploration geochemistry is an accurate interpretation of geochemical data and the precise extraction of anomaly information related to mineralization from complex geological information. However, geochemical data are component data and exhibit a closure effect. Thus, traditional statistical methods cannot adequately reveal and identify the distribution of deep-seated anomaly information. This paper focuses on the Sidaowanzi area in Inner Mongolia and uses multivariate component data analysis methods to process 1:50 000 soil geochemical data. Using the Exploratory Data Analysis (EDA) method, the spatial distribution and internal structure characteristics of raw, logarithmic, and isometric logarithmic ratio (ILR) transformed data were compared and, coupled with robust principal component analysis (RPCA) and elemental component biplots, the association between element combinations and mineralization indicated by these three types of data was revealed. The S-A method was used to decompose composite anomalies of the ILR transformed RPCA score data to extract the characteristics of elemental combination anomalies and background distribution, and the Fry analysis method was utilized to analyze the dominant mineralization direction within the area. The results show that (1) data transformed using the ILR eliminated the influence of the closure effect, making the data more uniform on a spatial scale and exhibiting characteristics of an approximately normal distribution. (2) The S-A method was further used to decompose the composite anomaly of the PC1 and PC2 principal component combinations. The screened-out anomaly and background fields can essentially reflect the ore-causing anomalies dominated by Au and Cu-Mo mineralization. Moreover, the extracted anomalies and background information closely align with known mineral deposits (prospects) and can effectively identify weakly retarded geochemical anomaly information. (3) Fry analysis based on geochemical anomalies indicates that the dominant mineralization directions, by an assemblage dominated by Au and Cu-Mo, predominantly occur in the NE, NW, and proximate EW orientations. The combined application of the aforementioned three methods for the quantitative analysis of geochemical data aims to explore a transferable methodological system, providing new insights and approaches for further prediction of mineralization potential.

Investigating the biochemical response of proton minibeam radiation therapy by means of synchrotron-based infrared microspectroscopy

The biology underlying proton minibeam radiation therapy (pMBRT) is not fully understood. Here we aim to elucidate the biological effects of pMBRT using Fourier Transform Infrared Microspectroscopy (FTIRM). In vitro (CTX-TNA2 astrocytes and F98 glioma rat cell lines) and in vivo (healthy and F98-bearing Fischer rats) irradiations were conducted, with conventional proton radiotherapy and pMBRT. FTIRM measurements were performed at ALBA Synchrotron, and multivariate data analysis methods were employed to assess spectral differences between irradiation configurations and doses. For astrocytes, the spectral regions related to proteins and nucleic acids were highly affected by conventional irradiations and the high-dose regions of pMBRT, suggesting important modifications on these biomolecules. For glioma, pMBRT had a great effect on the nucleic acids and carbohydrates. In animals, conventional radiotherapy had a remarkable impact on the proteins and nucleic acids of healthy rats; analysis of tumour regions in glioma-bearing rats suggested major nucleic acid modifications due to pMBRT.

Assessment of carcinogenic and non-carcinogenic risk of exposure to potentially toxic elements in tea infusions: Determination by ICP-OES and multivariate statistical data analysis

BackgroundThe perennial evergreen tea (Camellia sinensis) plant is one of the most popular nonalcoholic drinks in the world. Fertilizers and industrial, agricultural, and municipal activities are the usual drivers of soil contamination, contaminating tea plants with potentially toxic elements (PTEs). These elements might potentially accumulate to larger amounts in the leaves of plants after being taken up from the soil. Thus, frequent monitoring of these elements is critically important. MethodsThe present study intended to determine PTEs (Al, Cr, Mn, Fe, Co, Ni, Cu, Zn, Cd, and Pb) in both tea leaves and infusions using ICP-OES. Various multivariate data analysis methods such as principal component analysis (PCA) and hierarchical cluster analysis (HCA) were employed to elucidate the potential sources of PTEs contamination, whether from anthropogenic activities or natural origins. Additionally, Pearson's correlation coefficient (PCC) was calculated to assess the relationships between the variables under study. ResultsThe mean contents (mg/L) of all studied elements in tea infusions decreased in order Mn (150.59 ± 1.66) > Fe (11.39 ± 0.99) > Zn (6.62 ± 0.89) > Cu (5.86 ± 0.62) > Co (3.25 ± 0.64) > Ni (1.69 ± 0.23) > Pb (1.08 ± 0.16) > Cr (0.57 ± 0.09) > Cd (0.46 ± 0.09) > Al (0.05 ± 0.008), indicating that Mn exhibits the highest abundance. The mean concentration trend in tea leaf samples mirrored that of infusions, albeit with higher concentrations of PTEs in the former. The tolerable dietary intake (TDI) value for Ni and provisional tolerable monthly intake (PTMI) value for Cd surpassed the standards set by the WHO and EFSA. Calculated hazard index (HI < 1) and cumulative cancer risk (CCR) values suggest negligible exposure risk. ConclusionElevated levels of PTEs in commonly consumed tea products concern the public and regulatory agencies.

Multivariate data analysis on multisensor measurement for inline process monitoring of adenovirus production in HEK293 cells.

In the era of Biopharma 4.0, process digitalization fundamentally requires accurate and timely monitoring of critical process parameters (CPPs) and quality attributes. Bioreactor systems are equipped with a variety of sensors to ensure process robustness and product quality. However, during the biphasic production of viral vectors or replication-competent viruses for gene and cell therapies and vaccination, current monitoring techniques relying on a single working sensor can be affected by the physiological state change of the cells due to infection/transduction/transfection step required to initiate production. To address this limitation, a multisensor (MS) monitoring system, which includes dual-wavelength fluorescence spectroscopy, dielectric signals, and a set of CPPs, such as oxygen uptake rate and pH control outputs, was employed to monitor the upstream process of adenovirus production in HEK293 cells in bioreactor. This system successfully identified characteristic responses to infection by comparing variations in these signals, and the correlation between signals and target critical variables was analyzed mechanistically and statistically. The predictive performance of several target CPPs using different multivariate data analysis (MVDA) methods on data from a single sensor/source or fused from multiple sensors were compared. An MS regression model can accurately predict viable cell density with a relative root mean squared error (rRMSE) as low as 8.3% regardless of the changes occurring over the infection phase. This is a significant improvement over the 12% rRMSE achieved with models based on a single source. The MS models also provide the best predictions for glucose, glutamine, lactate, and ammonium. These results demonstrate the potential of using MVDA on MS systems as a real-time monitoring approach for biphasic bioproduction processes. Yet, models based solely on the multiplicity and timing of infection outperformed both single-sensor and MS models, emphasizing the need for a deeper mechanistic understanding in virus production prediction.

DAMPAK GAYA KEPEMIMPINAN BERORIENTASI PRESTASI DAN IKLIM ORGANISASI TERHADAP KOMITMEN AFEKTIF DAAITV MELALUI KEPUASAN KERJA

This study aims to ascertain the impact of independent factors like achievement-oriented leadership style and organizational climate. Affective commitment is a dependent variable, whereas job satisfaction is an intervening variable. The primary source of data for this quantitative study was an instantaneous Likert scale questionnaire delivered to each research participant. Employees of PT. X Jakarta and Medan make up the research population. A sample of 136 participants was selected using non-probability sampling techniques in conjunction with the purposive sampling approach. This study makes use of multivariate data analysis methods with the statistical model SEM-PLS. The results of the analysis show that there is a positive and significant effect of achievement-oriented leadership style on job satisfaction, affective commitment and affective commitment through job satisfaction. There is also a positive and significant effect of organizational climate on job satisfaction, affective commitment and affective commitment through job satisfaction. And there is a positive and significant effect of job satisfaction on affective commitment. These results provide managerial implications regarding how to increase employee affective commitment by paying attention to the achievement-oriented leadership style of the leaders and the organizational climate formed and increasing employee job satisfaction.

A Multi-Scale Convolutional Neural Network Combined with a Portable Near-Infrared Spectrometer for the Rapid, Non-Destructive Identification of Wood Species

The swift and non-destructive classification of wood species holds crucial significance for the utilization and trade of wood resources. Portable near-infrared (NIR) spectrometers have the potential for rapid and non-destructive wood species identification, and while several studies have explored related methodologies, further research on their practical application is needed. To address this research gap, this study proposes a multi-scale convolutional neural network (CNN) combined with a portable NIR spectrometer (wavelengths range: 908 to 1676 nm) for wood species identification. To enhance the capability of directly extracting robust features from NIR spectral data collected by a portable spectrometer, the Gramian angular field (GAF) method is introduced to transform 1-dimensional (1D) NIR spectral data into 2-dimensional (2D) data matrices. Furthermore, a multi-scale CNN model is utilized for direct feature extraction. The representation by 2D matrices, instead of 1D NIR spectral data, aligns with 2D convolutional operations and enables a more robust extraction of discriminative features. In the experimental phase, eight wood species were identified using the proposed method, alongside commonly used multivariate data analysis and machine learning (ML) methods. The StratifiedGroupKFold dataset partitioning approach and five-fold cross-validation were used. Additionally, nine spectral preprocessing methods were compared, and principal component analysis (PCA) was used for feature extraction in the ML method. Evaluation metrics, such as accuracy, precision, and recall, were adopted to assess the performance of the methods. The proposed multi-scale CNN model, in combination with 2D GAF matrices of the 1D spectral data, yielded the most accurate results with a mean accuracy of 97.34% in the five-fold validation. These findings present a new approach for the construction of a rapid, non-destructive, and automatic wood species identification method using a portable NIR spectrometer.

Comparative analysis of the application efficiency of gold nanoparticles-based SERS in four foodborne pathogenic microbes

It has been widely reported that SERS can detect foodborne pathogenic bacteria, but SERS has only been limited to the detection of purified single bacteria. However, there’s few single bacterial infections in the natural state were found at the most time, and rare comparative investigations of different Gram-negative and Gram-positive bacteria by SERS exist. At the present study, we are aiming at the comparative analysis of the detection efficiency toward different foodborne pathogenic microbes by a conventional citrate-reduced gold colloid, including Escherichia coli, Salmonella enteritidis, Staphylococcus aureus and Listeria monocytogenes. The SERS spectra of samples were collected with sterile 0.5% NaCl solution as aggregating compound. First, the stability of each bacterial were detected with gold nanoparticles. Afterward, SERS signals of the four bacteria were compared at different concentrations, and it was found that Gram-positive and Gram-negative bacteria have different minimum sensitivity and detection stability. Next, the mixed bacterial SERS detection was applied to classify and identify the similar Raman spectra. Spectral data were analyzed using multivariate data analysis methods, including principal component analysis (PCA) and hierarchical cluster analysis (HCA). The SERS procedure we proposed, which utilized gold nanoparticles adsorbing on bacteria and multivariate analysis, allowed differentiation of species. The results of this pilot study demonstrate the potential of SERS to rapidly identify Gram-negative and Gram-positive foodborne pathogens.

Brand Trust, Celebrity Endorser, and Product Bundling on Purchasing Interest and Its Impact on Decisions to Purchase Starbucks Drink at Pekanbaru

This research aims to determine and analyze the influence of brand trust, celebrity endorsers and product bundling on purchasing interest and purchasing decisions for Starbucks drinks at Pekanbaru. The population in this study is all residents of the city of Pekanbaru in 2023, the number of which is unknown. Furthermore, with a sampling technique using the Roscoe Formula, a sample size of 100 respondents will be obtained and an accidental sampling technique will be used in sampling. Data analysis in this study used the second-generation multivariate data analysis method or SEM (Structural Equation Model) using PLS (Partial Least Square). Based on the research results, the results show that brand trust has a positive and significant effect on buying interest, celebrity endorsers have a positive and not significant effect on buying interest, product bundling has a positive and not significant effect on buying interest, brand trust has a positive and significant effect on purchasing decisions, celebrity endorsers has a negative and insignificant effect on purchasing decisions, product bundling has a positive and insignificant effect on purchasing decisions and purchase interest has a positive and significant effect on purchasing decisions for Starbucks drinks Pekanbaru.

Lipidomic analysis of facial skin surface lipids in acne in young women.

Alterations in the secretion and composition of skin surface lipids (SSL) are closely associated with the development of acne. Lipidomics is a useful tool to analyse the SSL of different types of acne. Our previous study found that phosphatidylserine and triacylglycerols dominate SSL changes in male acne and infantile acne, respectively. However, skin surface lipids as well as specific lipids in female acne patients remain to be investigated. To analyse and compare the SSL profile of acne women and healthy women and to discuss the involvement of differential lipids in acne development. Systematic lipidomics approach (high-throughput UPLC-QTOF-MS technology in combination with multivariate data analysis methods) was used to analyse the variations of SSL between acne and healthy groups. Analysis revealed significant differences in lipid content and composition between the two groups. Further analysis showed that levels of 13 individual lipids were significantly different and followed the same trend as the main class and subclasses. The largest individual contributor to the subgroup was triglycerides (TG) and phosphatidylinositol (PI). In addition, female acne patients exhibited reduced ceramide chain length (CCL) and increased levels of unsaturated fatty acids (UFAs), The changes of CCL in female acne are identical to male acne. There was a significantly higher level of TG and PI in the SSL of female acne patients. A reduction in CCL and an increase in UFAs content might contribute to the reduced skin barrier function in acne patients. The results suggest that female acne may have different pathogenesis than male acne.

Green Banking Practices and Green Financing of Listed Deposit Money Banks in Nigeria

This study examined the nexus between green banking practices and green financing sources of listed deposit money banks in Nigeria. The study anchored on the institutional theory and cross sectional survey research design was adopted with a population consisting of listed banks in Nigeria. The study used stratified random sampling of seven hundred and fifty (750) bank employees with questionnaire as the primary source of data collection from the respondents while only five hundred (500) were used for data analysis using univairate, bivariate and multivariate methods of data analysis. The results from the regression analysis disclosed that banks’ employee-related practices of green banking positively and significantly influence sources of green financing of deposit money banks in Nigeria; banks’ daily operations-related practices of green banking positively and significantly affect sources of green financing of deposit money banks in Nigeria; banks’ customers-related practices of green banking positively but significantly influence sources of green financing of deposit money banks in Nigeria; banks’ policy-related practices of green banking positively and significantly influence sources of green financing of deposit money banks in Nigeria and banks’ green investment related practices of green banking positively and significantly impact on sources of green financing of deposit money banks in Nigeria. Consequently, on the basis of the findings the study concluded that green banking practices positively influences the sources of green financing of deposit money banks in Nigeria. Therefore, the study recommends amongst others that banks’ in Nigeria should adopt contemporary banking practices that are ecofriendly as a means of enhancing the sources of green financing in Nigeria.

Endosperm structure and Glycemic Index of Japonica Italian rice varieties.

Given that rice serves as a crucial staple food for a significant portion of the global population and with the increasing number of individuals being diagnosed with diabetes, a primary objective in genetic improvement is to identify and cultivate low Glycemic Index (GI) varieties. This must be done while ensuring the preservation of grain quality. 25 Italian rice genotypes were characterized calculating their GI "in vivo" and, together with other 29 Italian and non-Italian genotypes they were studied to evaluate the grain inner structure through Field Emission Scanning Electron Microscopy (FESEM) technique. Using an ad-hoc developed algorithm, morphological features were extracted from the FESEM images, to be then inspected by means of multivariate data analysis methods. Large variability was observed in GI values (49 to 92 with respect to glucose), as well as in endosperm morphological features. According to the percentage of porosity is possible to distinguish approximately among rice varieties having a crystalline grain (< 1.7%), those intended for the preparation of risotto (> 5%), and a third group having intermediate characteristics. Waxy rice varieties were not united by a certain porosity level, but they shared a low starch granules eccentricity. With reference to morphological features, rice varieties with low GI (<55) seem to be characterized by large starch granules and low porosity values. Our data testify the wide variability of Italian rice cultivation giving interesting information for future breeding programs, finding that the structure of the endosperm can be regarded as a specific characteristic of each variety.

The role of cattle manure-driven polysaccharide precursors in humus formation during composting of spent mushroom substrate.

The study examined the impact of adding cattle manure to the composting process of Agaricus bisporus mushroom substrate on compost humification. A control group CK comprised entirely of Agaricus bisporus mushroom substrate, while the experimental group CD (70 percent Agaricus bisporus mushroom substrate and 30 percent cattle manure) comprised the two composting treatments that were established. The study determined that the addition of cow dung has promoted the formation of humus components. Particularly, humic substance (HS-C) and humic acid (HA) increased by 41.3 and 74.7%, respectively, and the ratio of humic acid to fulvic acid (HA/FA) also increased by 2.78. It showed that the addition of cow dung accelerated the synthesis and decomposition of precursors, such as polysaccharides, polyphenols, and reducing sugars. Thereby promoting the formation of humic acid. Network analysis revealed that adding cow dung promoted microbial interactions increased the complexity and stability of the bacterial and fungal symbiotic network, enhanced cooperation and reciprocity among microbes, and assisted in transforming fulvic acid (FA) components. Structural equation modeling (SEM) is a multivariate data analysis method for analyzing complex relationships among constructs and core indicators. SEM illustrated that introducing cattle manure into the composting process resulted in alterations to the correlation between physicochemical parameters and the microbial community, in addition to humus formation. Polysaccharides are the primary precursors for polymerization to form HA, which is an essential prerequisite for the conversion of fulvic acid to humic acid. Additionally, microbes affected the formation of humus, with bacteria substantially more influential than fungi. These findings provide new ideas for regulating the degree of humification in the composting process and have important practical implications for optimizing mushroom cultivation and composting techniques today.

Combining indicators analysis and chemometrics to trace the geographical origin of crude oil

Geographic traceability is crucial to global oil trade security. This study discusses the possibility of using multivariate statistical methods combined with multi-indicator analysis to identify samples of crude oil imports from five major countries to China. The physicochemical properties and trace elements of crude oil were detected by Petroleum product standards and inductively coupled plasma atomic emission spectrometry (ICP-AES). Eight indexes (moisture, density, sulfur content, acid value, organochlorine, carbon residual, V, and Ni) were analyzed. Principal component analysis (PCA), hierarchical clustering analysis (HCA), Orthogonal projections to lateen structures-discriminant analysis (OPLS-DA), and other multivariate data analysis methods were used to determine the geographical origin of crude oil samples. Satisfying results have been obtained using PCA to reduce the dimensions of the indicators of crude oil from different origins. It allows the reduction of 8 variables to 3 principal components and accounts for 80.06% of the total variance. The HCA shows five clusters corresponding to five sources of crude oil. This will help to improve the utilization rate of crude oil with different characteristics, improve the quality of crude oil trade, and ensure the high quality of crude oil trade. For the sample set used for modeling, the model's accuracy was 97.19% after OPLS-DA optimization. These results show that the combination of multi-index analysis and stoichiometry is an effective tool for identifying crude oil origin, which fills the technical gap in the rapid identification of crude oil origin.

Distribution and morphological variation of tree ferns (Cyatheaceae) along an elevation gradient.

Knowing how species and communities respond to environmental change is fundamental in the context of climate change. The search for patterns of abundance and phenotypic variation along altitudinal gradients can provide evidence on adaptive limits. We evaluated the species abundance and the variation in morphometric and stomatal characters in five tree ferns species (Cyathea fulva, C. divergens, C. myosuroides, Alsophila firma and Gymnosphaera salvinii) distributed along an elevation gradient in a well-preserved Mexican cloud forest. Variation at the community and species level was assessed using exploratory and multivariate data analysis methods. We wanted to explore if the species abundance is environmentally determined, to determine the degree of variation along the elevation gradient, to test for differences between zones and associations with elevation, humidity and soil nutrients, and to assess contribution of the intra- and interspecific variation to the community response to elevation and soil nutrients. The studied fern community showed strong species turnover along the elevation gradient, with some influence of soil nutrient concentration, supporting environmental determinism. All measured characters displayed variation along the gradient. Stomatal characters (size and density) had significantly less variation than morphometric characters (trunk diameter, stipe length and blade length), but stomatal density also shows interesting intraspecific patterns. In general, patterns within the fern community suggest a strong influence of species identity, especially of species inhabiting the lower edge of the cloud forest, which showed the clearest morphometric and stomatal patterns, associated to contrasting environments rather than to changes in elevation. The coincidence between morphometric and stomatal patterns in this area suggest hydraulic adjustments in response to contrasting environments. Our results provide evidence that tree ferns species respond to environmental changes through adjustments of morphometric plasticity and stomatal density, which is relevant to predict possible responses to variation in environmental conditions resulting from climate change.

Service Quality Factors Affecting Interest in Repeat Outpatient Patients

Patient satisfaction levels are a good sign of how well a hospital provides services. The likelihood that a patient will return increases if they are satisfied with the hospital's services, which obviously benefits the facility. In this study, Royal Prima Medan outpatients' intentions to return to RSU were examined in relation to several service quality metrics. A cross-sectional design was used for this analytical survey research study. The study's population consisted of 4,011 average outpatients at the Pulmonary and Cardiac Polyclinic from July to August 2022. A sample of 98 respondents using an unintended sampling strategy was drawn from this cohort. Methods for univariate, bivariate, and multivariate data analysis were used in this study. The findings showed that the intention to return to the hospitalized patients road was influenced by the variable quality of infrastructure (sig-p = 0.004 0.05), the variable quality of human resources (sig-p = 0.025 0.05), and the variable quality of security (sig-p = 0.015 0.05), whereas the personal quality variables (sig-p = 0.054 &gt; 0.05, gender = 0.253 &gt; 0.05, education = 0.322 &gt; 0.05, and occupation) were not.. According to the study's conclusions, it is thought that providing health staff training, reducing patient costs for less fortunate patients, and creating an organizational structure focused n duties and education will all enhance the hospital.