Multivariate Statistical Methods Research Articles

Predicting PM2.5 concentrations in Ba Ria - Vung Tau province using an artificial neural network model

Abstract Air pollution has increased rapidly and significantly recently, particularly in big cities. Various methods for predicting air pollution are available, including traditional air quality models, statistical techniques, and artificial intelligence. In this study, the author developed a model using a Feed Forward Neural Network with multivariate statistical methods to predict air pollution. Data from three automated air monitoring locations in Ba Ria-Vung Tau province were gathered between 2020 and 2022 to forecast the concentration of PM2.5. The results demonstrated that the FFNN model with an I(6)-HL(5)-O(1) structure outperformed other models in predicting PM2.5 concentration. The training, validation, and testing phases yielded mean squared error values of 9.2×10−6, 8.2×10−6, and 8.6×10−6, respectively. The regression coefficient obtained consistently high values across a range of experiments (above 0.99). The MSE value of the FFNN model of the prediction set was lower than that of the NSE value, which was higher than those obtained from the multiple linear regression.

Analysis of heavy metal and arsenic sources in mangrove surface sediments at Wulishan Port on Leizhou Peninsula, China, using the APCS-MLR model

Mangrove forests are sources and sinks for various pollutants. This study analyzed the current status of heavy metal and arsenic (As) pollution in mangrove surface sediments in rapidly industrializing and urbanizing port cities. Surface sediments of mangroves at Wulishan Port on the Leizhou Peninsula, China, were analyzed using inductively coupled plasma emission spectrometry (ICP-AES) and inductively coupled plasma mass spectrometry (ICP-MS) for the presence of Cr, Pb, Ni, Zn, Cd, Cu, As, and Hg. The Pollution load index, Nemerow pollution index, and Potential ecological risk index were employed to evaluate the pollutant. Multivariate statistical methods were applied for the qualitative analysis of pollutant sources, and the APCS-MLR receptor model was used for quantification. This study indicated the following results: (1) The average content of eight pollutants surpassed the local background level but did not exceed the Marine Sediment Quality standard. The pollution levels across the four sampling areas were ranked as Ⅲ > Ⅳ > Ⅰ > Ⅱ. The area Ⅱ exhibited relatively lower pollution levels with the grain size of the sediments dominated by sand, which was not conducive to pollutant adsorption and enrichment. (2) The factor analysis and cluster analyses identified three primary sources of contamination. As, Cr, Ni, and Pb originated from nearby industrial activities and their associated wastewater, suggesting that the primary source was the industrial source. Cd, Cu, and Zn stem from the cement columns utilized in oyster farming, alongside discharges from mariculture and pig farming, establishing a secondary agricultural source. Hg originated from ship exhaust burning oil and vehicle emissions in the vicinity, representing the third traffic source. (3) The APCS-MLR receptor model results demonstrated industrial, agricultural, and traffic sources contributing 47.19 %, 33.13 %, and 13.03 %, respectively, with 6.65 % attributed to unidentified sources.

Spatio-Temporal Variability of Water Quality in the Middle Danube—The Influence of Air Temperature and Discharge

Large watercourses are characterised by significant spatial and temporal changes in water quality due to both natural and anthropogenic impacts. The paper analyses changes in the Danube’s spatial and seasonal water quality in its middle part at five monitoring stations for the period 2018–2022. Examined water quality parameters include oxygen saturation (Os), ammonium (N-NH4), pH, 5-day biochemical oxygen demand (BOD), nitrate nitrogen (N-NO3), orthophosphates (P-PO4), suspended solids (SS), electrical conductivity (EC), and water temperature (WT). Furthermore, the analysis took into account the influence of two additional factors: air temperature (AT) and discharge (D). Throughout the entire period under study, all parameters were characterised by low concentration values, which met the environmental objective of good ecological status. The use of multivariate statistical methods allowed for the identification of EC, N-NH4, WT, Os, SS, and N-NO3 as determining the greatest spatio-seasonal variability of water quality in a selected section of the Danube. Regression models determined WT, EC and nitrogen nitrate changes as depending on AT, flow, and exposure time. Knowing models not only gives a better understanding of the dynamics of changes in water quality in the stretch of the Danube under study but potentially allows the prediction of these parameters based on easily measurable environmental variables.

Comparative evaluation of sensory and instrumental flavor profiles of four edible microalgae: Spirulina platensis, Chlorella pyrenoidosa, Chlamydomonas reinhardtii, and Haematococcus pluvialis

Microalgae sensory features are an important parameter to consider in food applications. The current study comparatively evaluated the flavor profiles of four commercial edible microalgae species, i.e., Spirulina platensis, Chlorella proteinosa, Chlamydomonas reinhardtii, and Haematococcus pluvialis through sensory evaluation by a trained expert panel (n = 15) and chemical profiling of flavor compounds, with multivariate statistical methods used to correlate descriptive taste and odor analysis with non-volatile and volatile compounds. S. platensis displayed characteristic salty and umami tastes arising from inorganic ions and 5′-nucleotides, respectively, as well as “muddy” and “floral” odor attributes mainly associated with sulfur compounds and ketones. C. proteinosa and C. reinhardtii shared the taste attributes of sweetness and bitterness mainly contributed by free amino acids, as well as the odor attributes of “seaweed” and “mushroom” in association with aldehydes and alcohols. H. pluvialis had a typical sour taste owning to the presence of abundant organic acids, and smelled “fatty”, “phenolic”, “licorice”, and “fruity” due to aromatic ester and phenolic compounds. Overall, our results provide fundamental insights into the sensory features of common edible microalgae to guide future food applications.

Grouping and evaluation of relationships between traits in triticale (X TriticosecaleWittmack) genotypes using statistical multivariate methods

Grouping and evaluation of relationships between traits in triticale (X TriticosecaleWittmack) genotypes using statistical multivariate methods

Identification of reversible OATP1B1 and time-dependent CYP3A4 inhibition as the major risk factors for drug-induced cholestasis (DIC).

Hepatic bile acid regulation is a multifaceted process modulated by several hepatic transporters and enzymes. Drug-induced cholestasis (DIC), a main type of drug-induced liver injury (DILI), denotes any drug-mediated condition in which hepatic bile flow is impaired. Our ability in translating preclinical toxicological findings to human DIC risk is currently very limited, mainly due to important interspecies differences. Accordingly, the anticipation of clinical DIC with available in vitro or in silico models is also challenging, due to the complexity of the bile acid homeostasis. Herein, we assessed the in vitro inhibition potential of 47 marketed drugs with various degrees of reported DILI severity towards all metabolic and transport mechanisms currently known to be involved in the hepatic regulation of bile acids. The reported DILI concern and/or cholestatic annotation correlated with the number of investigated processes being inhibited. Furthermore, we employed univariate and multivariate statistical methods to determine the important processes for DILI discrimination. We identified time-dependent inhibition (TDI) of cytochrome P450 (CYP) 3A4 and reversible inhibition of the organic anion transporting polypeptide (OATP) 1B1 as the major risk factors for DIC among the tested mechanisms related to bile acid transport and metabolism. These results were consistent across multiple statistical methods and DILI classification systems applied in our dataset. We anticipate that our assessment of the two most important processes in the development of cholestasis will enable a risk assessment for DIC to be efficiently integrated into the preclinical development process.

Latitudinal patterns of shifts in cladoceran communities throughout the Holocene: A paleoecological case study of northwestern Russia

Paleolimnology is a rapidly expanding field within the natural sciences. To date, many scientific papers have been published focusing on studies of remains from single sediment cores in specific lakes, or a few cores from the same water body. However, few previous publications have provided a comprehensive comparative analysis of taphocenosis (and maternal community) changes in different water bodies from a large geographical region during whole Holocene using multivariate statistical methods. We conducted a joined statistical analysis of data obtained as a result of the cladoceran analysis of the sediment samples from six cores taken along the North-South transect from the Kola Peninsula to the Karelian Region (North-West Russia). Our aim was to identify, based on quantitative statistical methods, general whole-Holocene patterns in cladoceran taphocenosis changes. All taphocenoses in all cores from six lakes can be classified into six clusters based on their species composition. Our unexpected conclusion is that different taphocoenoses are formed by the same set of species, but their proportions vary significantly. This suggests that the same species may play different functional roles in different maternal communities. During whole Holocene, we observed a single taphocenosis cluster in the Holocene core from Lake Antyukh-Lambina, and four taphocenosis clusters in the Lake Medvedevskoe, with an intermediate number of clusters in the lakes between them. Furthermore, it can be asserted that the potential for cluster changes in the core is greater for the more southern studied water bodies (0–3 clusters for northern lakes vs. 4 clusters for southern lakes). Based on our data, we hypothesize that the diversity of cladoceran associations during the Holocene was lower in Arctic/Subarctic lakes, possibly due to their formation by ubiquitous species. Our study suggests that at least some Arctic lake ecosystems have remained unchanged since deglaciation times. Further studies are necessary to determine if this is a general rule.

Research on the measurement of general risk preference based on multivariate statistical analysis methods

Research on the measurement of general risk preference based on multivariate statistical analysis methods

Morphological and Anatomical Differentiation of Potamogeton gramineus in Relation to the Presence of Invasive Species Elodea nuttallii: A Case Study from Vlasina Lake, Serbia.

Elodea nuttallii represents non-native and highly invasive species in Europe that significantly influence freshwater plant communities by decreasing the diversity of native species. This study aimed to determine whether the morphological and anatomical features of Potamogeton gramineus, a native species in Vlasina Lake, differ between sites where it coexists with E. nuttallii and those where E. nuttallii is not present. Environmental variables such as water depth, temperature, pH, conductivity, saturation, and O2 concentration were included in the analysis. Analyses were conducted on 32 morphological and anatomical features of P. gramineus collected from six sites within Vlasina Lake, comprising three sites where E. nuttallii was present and three sites where it was absent. The datasets containing morphometric and environmental variables underwent analysis using standard univariate techniques (Descriptive, ANOVA), Tukey's Honest Significant Difference (HSD) test, Student's t-test, and the Mann-Whitney U test, as well as multivariate statistical methods such as Canonical Discriminant Analysis (CDA). The results show the presence of morphological differentiation among P. gramineus individuals across the analyzed sites. These findings suggest that morphological and anatomical features, such as epidermis, mesophyll, palisade, and aerenchyma tissue thickness in floating leaves, number, length, width, and the surface area of stomata, as well as the width of submersed leaves and stem aerenchyma tissue thickness, effectively differentiate individuals that coexist with E. nuttallii and individuals that growth without its presence. Moreover, they indicate that P. gramineus exhibits a notable ability to modify its morphological traits in response to invasion.

NMR-Based Metabolomics of Blood Serum in Predicting Response to Induction Chemotherapy in Head and Neck Cancer-A Preliminary Approach.

The role of induction chemotherapy (iCHT) in locally advanced head and neck squamous cell carcinoma (LA-HNSCC) is still to be established due to high toxicity and variable response rates. The aim of this retrospective study is to use NMR-based serum metabolomics to predict the response rates to iCHT from the pretreatment samples. The studied group consisted of 46 LA-HNSCC patients treated with iCHT. The response to the treatment was evaluated by the clinical, fiberoptic, and radiological examinations made before and after iCHT. The proton nuclear magnetic resonance (1H NMR) serum spectra of the samples collected before iCHT were acquired with a 400 MHz spectrometer and were analyzed using multivariate and univariate statistical methods. A significant multivariate model was obtained only for the male patients. The treatment-responsive men with >75% primary tumor regression after iCHT showed pretreatment elevated levels of isoleucine, alanine, glycine, tyrosine, N-acetylcysteine, and the lipid compounds, as well as decreased levels of acetate, glutamate, formate, and ketone bodies compared to those who did not respond (regression of the primary tumor <75%). The results indicate that the nutritional status, capacity of the immune system, and the efficiency of metabolism related to protein synthesis may be prognostic factors for the response to induction chemotherapy in male HNSCC patients. However, larger studies are required that would validate the findings and could contribute to the development of more personalized treatment protocols for HNSCC patients.

Optimization of supercritical CO2 fluid extraction process, component analysis, and antioxidant spectral activity correlation of Huoluo Xiaoling Dan

ABSTRACT This study elucidated the optimization conditions, GC-MS analysis conditions, total antioxidant activity, and components closely related to antioxidant activity for supercritical extraction of active ingredients in Huoluo Xiaoling Dan (HLXLD). The yield, octyl acetate content, and total antioxidant activity of HLXLD under different conditions were obtained through orthogonal experiments, and the optimal supercritical extraction conditions were determined to be 45°C, 25 MPa, and 4 h. The supercritical extract was subjected to GC-MS analysis and matched with the WILEY and NIST databases, resulting in the qualitative identification of 37 chemical components with a matching degree above 60. The similarity evaluation system of traditional Chinese medicine fingerprint was used to evaluate all orthogonal experimental samples, with a similarity ranging from 0.522 to 0.989. Thirteen common characteristic peaks were selected, and the quantification of these peaks was performed using peak area normalization method. The combination of the content of common characteristic peaks in different samples and their total antioxidant activity was analyzed using multivariate statistical methods such as principal component analysis and multiple regression equation. It was found that five components, F3, F2, F5, F7 and F12, among the common characteristic peaks were closely related to antioxidant activity.

Analysis of Codon Usage Bias in Chloroplast Genomes of Dryas octopetala var. asiatica (Rosaceae).

Dryas octopetala var. asiatica, a dwarf shrub belonging to the Rosaceae family and native to Asia, exhibits notable plasticity in photosynthesis in response to temperature variations. However, the codon usage patterns and factors influencing them in the chloroplast genome of this species have not yet been documented. This study sequenced and assembled the complete genome of D. octopetala var. asiatica. The annotated genes in the chloroplast genome were analyzed for codon composition through multivariate statistical methods including a neutrality plot, a parity rule 2 (PR2) bias plot, and an effective number of codons (ENC) plot using CodonW 1.4.2 software. The results indicated that the mean GC content of 53 CDSs was 38.08%, with the average GC content at the third codon base position being 27.80%, suggesting a preference for A/U(T) at the third codon position in chloroplast genes. Additionally, the chloroplast genes exhibited a weak overall codon usage bias (CUB) based on ENC values and other indicators. Correlation analysis showed a significant negative correlation between ENC value and GC2, an extremely positive correlation with GC3, but no correlation with GC1 content. These findings highlight the importance of the codon composition at the third position in influencing codon usage bias. Furthermore, our analysis indicated that the CUB of the chloroplast genome of D. octopetala var. asiatica was primarily influenced by natural selection and other factors. Finally, this study identified UCA, CCU, GCU, AAU, GAU, and GGU as the optimal codons. These results offer a foundational understanding for genetic modification and evolutionary dynamics of the chloroplast genome of D. octopetala var. asiatica.

Anchorage and lap capacity of plain surface and square twisted bars in existing R.C. Structures: A comprehensive approach

AbstractAlthough plain round or square section reinforcement is no longer used in new construction, there are many older structures still in service built with such bars. In recognition of the need for guidance on the assessment of load‐carrying capacity of structures reinforced with such bars, design codes are now introducing or re‐introducing provisions to assess the capacity of laps and anchorages of such reinforcement. The authors have contributed to the introduction of design rules in the fib Model Code 2020. This paper describes the derivation of these provisions. Starting from the form of expression proposed earlier by the authors, the data from which these proposals are derived is specified and modifications in interpretation preparatory to a statistical analysis are outlined. A rigorous multivariate statistical procedure is then employed firstly to determine a mean strength expression for the capacity of anchorages and laps which is then validated against test data. Further statistical analysis is subsequently used to determine a design expression using the approach outlined in EN 1990, taking appropriate account scatter in test data. Finally, some comparisons are presented between the assessment expressions proposed and design provisions that were in place when these types of bars were in common use. Areas where earlier design provisions may be non‐conservative are identified.

Two-Part Mixed Effects Mixture Model for Zero-Inflated Longitudinal Compositional Data

Compositional data (CD) is mostly analyzed using ratios of components and log-ratio transformations to apply known multivariable statistical methods. Therefore, CD where some components equal zero represents a problem. Furthermore, when the data is measured longitudinally, and appear to come from different sub-populations, the analysis becomes highly complex. Our objective is to build a statistical model addressing structural zeros in longitudinal CD and apply it to the analysis of radiation-induced lung damage (RILD) over time. We propose a two-part mixed-effects model extended to the case where the non-zero components of the vector might come from a two-component mixture population. Maximum likelihood estimates for fixed effects and variance components were calculated by an approximate Fisher scoring procedure base on sixth-order Laplace approximation. The expectation-maximization (EM) algorithm estimates the mixture model’s probability. This model was used to analyze the radiation therapy effect on tissue change in one patient with non-small cell lung cancer (NSCLC), utilizing five CT scans over 24 months. Instead of using voxel-level data, voxels were grouped into larger subvolumes called patches. Each patch’s data is a CD vector showing proportions of dense, hazy, or normal tissue. Proposed method performed reasonably for estimation of the fixed effects, and their variability. However, the model produced biased estimates of the nuisance parameters in the model.

The Influence of Wildfire Climate on Wildfire Incidence: The Case of Portugal

Although the influence of climate on the fire regime is unanimously recognized, most publications and studies on this influence are on a global scale. Therefore, this study aims to demonstrate the role of climate in wildfire incidence at the country and regional scale using multivariate statistical analysis and machine learning methods (clustering and classification algorithms). Mainland Portugal was chosen as a case study due to its climate and because it is the European region most affected by wildfires. The results demonstrate the climate signature in the spatial and temporal distribution of the wildfire incidence. The conclusions of the study include (i) the existence of two pyro-regions, with different types of climate (Csb and Csa) composed of NUTS II regions: the northern region composed of the Norte and Centro regions and the southern region composed of Alentejo and Algarve; (ii) the intra-annual variability in the wildfire incidence, characterized by two peaks, one in the spring and the other in the summer, are a consequence of the country’s type of climate; and (iii) how the annual cycle of wildfire incidence varies over the years depends on the weather conditions throughout each year. These results are of fundamental importance for wildfire managers, especially in the context of climate change.

Regional fertility predictors based on socioeconomic determinants in Slovakia

The study's primary purpose was to recognise the effects of determinants on the level of fertility and thereby explain the differences in trends in the regions of Slovakia. At the turn of the century, the differences in fertility in regions increased, but the total fertility rate decreased. Multivariate statistical methods clarified the regional effects of the level and nature of fertility. Initial regression surveys indicated weak effects between regions, which led to applying factor and cluster analysis to establish regional types. Comprehensive regression analysis was then applied. The strength and nature of regional relationships differed at the inter- and intra-regional levels. Research has demonstrated significant differences in fertility rates dependent on the socioeconomic environment, as regional types uniquely link to determinants. Moreover, each determinant has specific spatial patterns with unequal regression coefficients at different regional levels, which cannot be evaluated constantly. Knowing how spatial variation in fecundity occurs will enable future studies to elucidate the processes involved. Finally, fertility is vital for social assessment and policy formulation, the study’s findings could inform local decision-makers and planners in identifying the socioeconomic conditions underlying fertility at the regional level and planning appropriate intervention strategies.

Effectiveness of Compound Dilong Capsules in Patients with Asymptomatic Cerebral Carotid Artery Stenosis: A Retrospective Case-Control Study.

Asymptomatic cerebral carotid artery stenosis (ACCAS) benefits from secondary prevention via statins and antiplatelets; nonetheless, the impact of medication alone is often limited. Evidence has suggested enhanced therapeutic outcomes when Chinese patent medicine-specifically, compound dilong capsules (CDC)-is integrated with conventional secondary prevention measures. We retrospectively analyzed 319 ACCAS patients from January 2018 to December 2022 at Xuanwu Hospital, Capital Medical University. Depending on the clinical outcomes-improvement or stabilization versus progression-patients were classified into effective or ineffective treatment groups. Patient medical records and questionnaire responses were the primary data sources. The study accounted for demographic variables, clinical history, and medication details, with the primary focus on CDC use and its duration. Treatment outcomes were gauged alongside Transcranial color-coded sonography and Carotid Doppler ultrasonography findings. We employed both univariate and multivariate statistical methods to assess the data. CDC administration (aOR=2.51, 95% CI 1.39-4.54, P=0.002) and extended usage beyond six months (aOR=3.54, 95% CI 1.71-7.32, P=0.001) demonstrate a statistically significant correlation with treatment efficacy. Gender (aOR=2.54, 95% CI 1.30-5.00, P=0.007), hypertension management (aOR=0.56, 95% CI 0.33-0.95, P=0.031), and antiplatelet therapy with aspirin (aOR=9.53, 95% CI 1.15-78.89, P=0.037) or clopidogrel (aOR=9.97, 95% CI 1.10-90.12, P=0.041) also influenced the therapeutic outcome significantly. Incorporating CDC as part of a secondary prevention strategy for over six months can beneficially modulate and limit the progression of vascular stenosis in ACCAS. These findings underscore the value of combining traditional Chinese medicine with modern pharmacological interventions in ACCAS management.

Retrospective Examination of Risk Factors Affecting Iron Deficiency Anemia Using Machine Learning Methods

Iron deficiency anemia is one of the most common types of anemia worldwide. In recent years, new developments in the field of medicine have offered early diagnosis and treatment opportunities for anemia patients. In the field of data science, in parallel with the developments in medicine, significant developments are taking place in subjects such as data collection, storage, processing, and reporting. Interdisciplinary joint studies positively contribute to patients’ quality of life and lifespan. In this study, the accuracy of the statistical results was tested with Machine Learning Method (MLM) while investigating the factors that affect the correct prediction of Iron Deficiency Anemia (IDA) diagnosis. In the first stage, the relationships between all variables in the data set and their effects on the differentiation of disease groups were investigated using univariate and multivariate statistical methods. In the second step, the data set was analyzed in detail using four different methods with Artificial Neural Network (ANN) classifier. Weka 3.8 application was preferred for these operations. In the last stage, the results obtained in both stages were compared. Accordingly, it has been observed that hemoglobin (Hb), mean cell volume (MCV), iron (Fe), and ferritin (FERR) have more effects on IDA. ANN (98.06%) is a better discriminator with a correct classification rate

Methodological Approach to Assessing International Logistics Management in Commercial Activities in The Recreational Field: Innovations in Modeling

The main purpose of the study is to optimize the logistics systems of our chosen recreational area in the context of attracting new international tourist flows in accordance with the principles of sustainable development. The object of the study is the logistics system of our chosen recreational area - the Prague Zoo. The scientific task of the study is to determine the logical potential of the recreational zone, including indicators that influence the sustainable development of the recreational zone. The research methodology includes the use of a multivariate mathematical and statistical method. As a result of the study, a strategy for optimizing logistics systems to attract international tourists was proposed, while adhering to the principles of sustainable development. Despite this, the work has its limitations. Most of them are due to the fact that the Prague Zoo was chosen as the object of study, its commerce, logistics system and the impact of the latter on sustainable development. All these characteristics may be good for other recreational areas. The prospect of further research will be to expand the analysis of other recreational areas, and their commercial and logistics systems. In addition, further research will be aimed at more detailed development of measures to attract international tourists to recreational areas, while maintaining the implementation of the principles of sustainable development.

Exploratory Factor Analysis (EFA) in Quantitative Researches and Practical Considerations

Explanatory factor analysis (EFA) is a multivariate statistical method frequently used in quantitative research and has begun to be used in many fields such as social sciences, health sciences and economics. With EFA, researchers focus on fewer items that explain the structure, instead of considering too many items that may be unimportant and carry out their studies by placing these items into meaningful categories (factors). However, for over sixty years, many researchers have made different recommendations about when and how to use EFA. Differences in these recommendations confuse the use of EFA. The main topics of discussion are sample size, number of items, item extraction methods, factor retention criteria, rotation methods and general applicability of the applied procedures. The abundance of these discussions and opinions in the literature makes it difficult for researchers to decide which procedures to follow in EFA. For this reason, it would be beneficial for researchers to gather different information about the general procedures (sample number, rotation methods, etc.) in the use of EFA. This paper aims to provide readers with an overview of what procedures to follow when implementing EFA and share practical information about the latest developments in methodological decisions in the EFA process. It is considered that the study will be an important guide for the researchers in the development of clear decision paths in the use of EFA, with the aspect of presenting the most up-to-date information collectively.