Correlation Component Analysis Research Articles

Metal concentration in bottom sediments for a tropical river, geological or anthropogenic source?

The study analyzes data from a survey of metal concentrations in bottom sediments from both margins (left and right) of the lower main channel of the Orinoco River. This study aims to analyze metal concentrations in the bottom sediments of the lower Orinoco River with different geological settings and anthropological sources. El Almacen (ALM), Las Galderas (G), Los Castillos de Guayana (C), Ciudad Bolivar (CB), and Ciudad Guayana (CG) were the sampling sites. Freshwater physicochemical parameters pH, conductivity, and DO were measured in situ at each sample site. Twenty-four bottom sediment samples were collected with an Eckman grab, dried, and sieved. Trace elements Pb, Cr, Cu, Cd, Ti, and Co were measured in sediments with grain sizes <63μm. Bottom sediment analysis followed EPA method 3050B (digestion with HNO3 + HCl + H2O2). Statistical analyses included the Shapiro-Wilk test, Spearman correlation, Kruskal-Wallis test, and principal component analysis (PCA). Physicochemical parameters highlighted the marked difference between the left and the right river margins, reflecting the cross-channel heterogeneity due to the dissimilar geology and low horizontal mixing. Metal concentrations were generally low, with higher variations associated with urban and industrial sources. Pb, Cr, Cu, Cd, Ba, Ti, and Co concentrations did not generally indicate significant pollution, but potential contamination from sewage and industrial effluents was noted. The differences between the two river margins were common in all the sampling sites, reflecting the sources of water biochemistry related to geochemical origins in the Andes and the Guiana Shield, respectively. The study emphasizes the importance of sampling location within the river channel.

Urban Heat Island and Environmental Degradation Analysis Utilizing a Remote Sensing Technique in Rapidly Urbanizing South Asian Cities

Rapid urbanization in South Asian cities has triggered significant changes in land use and land cover (LULC), degrading natural biophysical components and intensifying urban heat islands (UHIs). This study investigated the impact of LULC changes on land surface temperature (LST) and the role of biophysical indicators in enhancing urban resilience to thermal extremes. We used Landsat satellite imageries from 1993 to 2023, conducted a comprehensive analysis of LULC changes, and estimated LST variations at 6-year intervals in the Dhaka, Gazipur, and Narayanganj districts in Bangladesh. Afterward, we performed statistical analysis upon employing correlation, regression, and principal component analysis (PCA) techniques to summarize information. The results reveal that 339.13 km2 worth of urban expansion has occurred in last 30 years, with an average annual growth rate of 3.5%, accompanied by a substantial reduction in water bodies (−139.17 km2) and vegetation cover. Consequently, summer temperatures exceeded approximately 36.52 °C in dense urban areas. Also, the results highlighted the strong influence of built-up areas (BSI and SAVI) on LST, while vegetation (NDVI) and water indices (NDWI) exhibited a negative association. The findings emphasize the urgency of integrating green infrastructure and deploying sustainable urban planning policies to mitigate the potential adverse impacts of scattered urbanization in the face of climate change.

Moss biomonitoring in areas affected by ashfalls of Shiveluch volcano (Kamchatka).

The moss bag technique was applied on Kamchatka to assess the effect of Shiveluch volcano on the air quality. Moss bags were exposed in settlements located at different distances from the volcano and affected by ashfall to varying degrees. The inductively coupled plasma optical emission spectrometry was used for the determination of the content of Al, Ba, Co, Cd, Cr, Cu, Fe, Mn, P, Pb, Sr, S, V, and Zn in both exposed and unexposed moss samples. Relative accumulation factor revealed enrichment of mosses exposed in the area affected by ashfall with Al, Cr, V, and Zn. Correlation and principal component analysis allowed to identify elements of volcanogenic, geogenic, and anthropogenic origin. Ecological risk values less than 150 indicate low potential ecological risk in the region. Active moss biomonitoring can be considered a cheap and efficient tool for in-depth study of the influence of volcanic activity on air quality on the peninsula.

Osmolytes and CsAQP expression jointly influence water physiology in the peel and pulp of orange (Citrus sinensis (L.) Osbeck) fruit during postharvest water loss.

Water loss is a serious issue affecting the quality of postharvest horticultural products. Aquaporins (AQPs) regulate the transport of water across biological membranes, along the gradient of water potential, and may play a role in water loss. In this study, matured orange fruits (Citrus sinensis) stored at ambinent temperature (RH 85-95%) for 105 d showed that the weight loss persistently increased, and its rate peaked at 45-60 d and 90-105 d. Both water content and potential were higher in the pulp than in the peel. Water content rose before 60 d, and peel water potential fell with an increased gradient after 60 d. Comparing with peel, osmolytes such as soluble sugar, sucrose, glucose, fructose, and organic acids showed higher accumulation, and their levels were the lowest around 60 d. In contrast, soluble protein and inorganic minerals showed low levels of accumulation in the pulp. In total, 31 CsAQP genes were expressed in the fruit, and most of them were down-regulated in the peel but up-regulated in the pulp during storage. These genes were subsequently classified into four clusters based on their expression patterns. Genes in Cluster I - including CsNIP1;1/2;1/2;2/2;3/3;1/4;1/6;1, CsTIP1;3/2;2/2;3/5;1/6;1, CsXIP1;1/1;2, CsSIP1;2, and CsPIP1;2 - were persistently up-regulated in the pulp for the 105 d of storage, especially at day 60, when some genes showed 103-fold higher expression. Pearson's correlation and principal component analysis further revealed a significant positive correlation among weight loss rate, water content, and water potential gradient (R2 = 0.85). Indexes positively correlated with osmolyte content and Cluster I gene expression in pulp samples suggest that increased CsAQP gene expression in pulp is linked to faster water loss in oranges, particularly at 60 days postharvest.

Assessment of heavy metal concentration in pelagic fish species and associated health risks along the Kanyakumari coast, India – a baseline study

ABSTRACT This research examines heavy metal concentration in fish from the Kanyakumari coast of India, emphasising its ecological consequences and possible health hazards for consumers. The study investigates the levels of several heavy metals such as zinc, manganese, iron, cadmium, copper, chromium, cobalt, lead, and nickel in different organs of multiple pelagic fish species obtained from the Kanyakumari fish landing site. Concentrations of heavy metals exhibit considerable variation among different fish organs and species, with distinct patterns identified for each metal. Muscle tissue often demonstrates reduced amounts of heavy metals in comparison to other organs. Except Zn and Fe, other heavy metals in fish muscle were above the acceptable limits set by health organisations. Cadmium was found to be 36-fold higher than the permissible limit, followed by nickel (30.4) and lead (7.6). Copper, nickel, and cadmium provide possible health hazards according to the Target Hazard Quotient (THQ). Lead and cadmium exhibit Excessive Lifetime Cancer Risk (ELCR) markedly above tolerable thresholds. Large predatory fish, such as Thunnus thynnus, exhibit elevated levels of contaminants such as cadmium. Environmental conditions, aquatic habitat, and dietary intake affect metal build-up in fish. The research employs multiple analytical methods and statistical methods, such as Atomic Absorption Spectrophotometry, correlation and Principal Component Analysis, to evaluate heavy metal concentrations and their interrelations. The study underscores the critical necessity for monitoring and mitigation techniques to combat heavy metal pollution in coastal waters and its possible effects on human health through fish intake.

Ecological Implications of Seasonal Variations in Physicochemical Parameters and Phytoplankton Community in an Effluent‐Receiving Wetland in the Gulf of Guinea, Cameroon

ABSTRACTPhysicochemical parameters, chlorophyll a (Chl a) concentration, and phytoplankton community structure were assessed in the Wouri estuarine mangrove sector of the Douala Bassa Industrial Zone of Cameroon over two seasons. Three sets of water samples were collected from 10 sites for these analyses. Some physicochemical parameters were measured in situ using multiparameter equipment (HANNA 8289). The data were subjected to analyses of significant difference by an independent sample t‐test. Correlation and principal component analysis conducted between variables Temperature, salinity, total dissolved solids (TDS), resistivity, and turbidity showed a strong seasonal variation among sites. Mean Chl a concentration (3.7–48.29 µg/L for dry and 0.1–20.1 µg/L for rainy season) revealed that most sites were eutrophic, with the Euglenophycean index less than 1 in all the sites. Phytoplankton species richness exhibited seasonal and spatial variation, with a total of 74 taxa and 51 taxa for the dry and rainy seasons, respectively. The division Bacillariophyta was the most abundant, with 19 families and 24 species during the dry season and 17 families and 16 species during the rainy season. Other phytoplankton divisions present included Chlorophyta &gt; Cyanophyta &gt; Charophyta &gt; &gt; Myzozoa &gt; Euglenophyta in the dry season; the less diverse divisions in the rainy season were Rhodophyta &gt; Cryptophyta &gt; Xanthophyta &gt; Euglenophyta. High concentrations of nutrients and the presence of eutrophic species such as Microcystis aeruginosa suggest pollution and a need to monitor activities carried out along the water course with respect to the quantity and quality of industrial effluents discharged into the stream. The observed eutrophication explains the noxious odor across the city of Douala, especially during the dry season. The human health implications could be severe, with communities dependent on this contaminated stream for fish and other necessities a scenario that repeats itself in communities dependent on urban wetlands in industrial zones globally.

Nitrogen Transport Pathways and Source Contributions in a Typical Agricultural Watershed Using Stable Isotopes and Hydrochemistry

The increasing global nitrogen input poses a significant threat to aquatic environments, particularly in agricultural watersheds, where intensive human activities and insufficient water protection infrastructure exacerbate the risk of nitrogen pollution. Accurate identification of nitrogen pollution sources and the associated transformation processes is essential for protecting watershed ecosystems. In this study, a combination of hydrochemical analysis, correlation and principal component analysis, and stable nitrate isotopes (δ15N-NO3− and δ18O-NO3−) were employed to trace nitrogen transport pathways and source contributions in both surface water and groundwater within a typical agricultural watershed. The results revealed the presence of nitrogen pollution, including total nitrogen (TN), ammonia nitrogen (NH3-N), and nitrate nitrogen (NO3−-N), with significant spatial and seasonal variations in both surface water and groundwater. The spatiotemporal evolution of hydrochemical indicators and nitrate isotope compositions highlighted multiple potential sources of nitrogen, including soil input, agricultural input, and manure and sewage input. The results from stable isotope analysis in an R (SIAR) model indicated that ammonium fertilizers (7.1~78.4%) and manure and sewage (2.6~69.7%) were the primary sources of nitrates in surface water, while manure and sewage were the main sources in groundwater (67.9~73.7%). This research demonstrated that nitrification, seasonal variations, and human activities significantly impact nitrogen migration and transformation in agricultural watersheds. However, the issue of groundwater severely polluted by manure and sewage has received insufficient attention. To effectively control nitrogen pollution in agricultural watersheds, it is necessary to improve septic tanks and sewage networks, as well as implement scientific fertilization practices.

Source distribution, ecological risks, and controlling factors of heavy metals in river sediments: Receptor model-based study in a transboundary river basin

In the context of transboundary rivers, which constitute intricate fluvial ecosystems, the persistent threat of heavy metals (HMs) contamination poses significant risks to ecosystem health. In this study, ecotoxicological hazards, governing factors, and the distribution of nine HMs (uranium (U), lead (Pb), cadmium (Cd), nickel (Ni), chromium (Cr), manganese (Mn), iron (Fe), zinc (Zn), and copper (Cu)), as well as sediment characteristics (sand, silt, clay, organic matter, and pH) are assessed within the sediment. The current investigation encompasses the analysis of twenty-seven sediment samples, utilizing inductively coupled plasma mass spectrometry, in the transboundary river basin of Bangladesh, specifically the Teesta River. Notably, the findings underscore the predominance of Cd as a contaminant, responsible for 51.85%, 81.84%, and 100% of the geo-accumulation index, contamination factor, and enrichment factor, respectively. The Teesta River emerges as moderately to highly polluted, with cumulative probabilities of 7.4%, 85.2%, and 7.4% denoting "medium", "high", and "priority" pollution levels, respectively. Regions in the upstream and downstream middle sections of the study area exhibit relatively higher pollution levels, particularly in proximity to Kaunia Upazila in the Rangpur district. Ecologically, the potential risk index indicates a low likelihood of ecological impacts at 77.8%, alongside a moderate risk observation of 22.2%. The current results attribute the distribution of these HMs to the pH and organic matter content within the sediment, serving as pivotal factors. To unravel the origins of the HMs, the positive matrix factorization (PMF) model successfully identifies four contributing factors, primarily from geogenic sources. Validation of the PMF model through Spearmen correlation and principal component analysis (PCA) reveals a consistent pattern, affirming its efficacy in this analysis. Within the region, HM sources are identified as originating from anthropogenic activities such as irrigation, industrial discharges, and domestic effluent, in addition to substantial inputs from geogenic sources. Recognizing the transboundary nature of metal pollution, the current study underscores the imperative for continuous and vigilant monitoring, coupled with the implementation of robust management practices. The interplay of both anthropogenic and geogenic factors necessitates a comprehensive approach to effectively and sustainably combat HM contamination.

Multivariate Analysis of Geochemical Data to Determine Critical Minerals in Prigi Beach Sand, Trenggalek

Abstract Critical minerals are essential elements mainly for industry. As technological advancements, the demand for critical minerals also increases. Critical minerals include Rare Earth Elements (REE), Lithium (Li), Gallium (Ga), Germanium (Ge), Platinum Group Metals (PGM), Indium (In), Beryllium (Be), Tungsten (W), Cobalt (Co), Molybdenum (Mo), Niobium (Nb), Antimony (Sb), Vanadium (V), Nickel (Ni), Chromium (Cr), Tellurium (Te), Tin (Sn), Thorium (Th), Zirconium (Zr), Hafnium (Hf), Selenium (Se), Rhenium (Re), Phosphate (P), Potassium (K), and others. This study aims is to figure out the distribution of critical minerals in the sand of Prigi Beach, Trenggalek. Chemical elements are determined using X-Ray Fluorescence (XRF). Based on data analysis, critical minerals were detected with the presence of elements such as Al, K, Ca, Si, Ti, P, V, Mn, Cr, Zn, Fe, Sr, Eu, and Cu. Using the Pearson Correlation and Principal Component Analysis (PCA) methods. Significant correlations were found between (Al-Zn), (Si with K-Ti-V-Fe-Cu-Eu), (K with Ti-V-Fe-Cu-Eu), (Ca-Sr), (Ti with V-Mn-Fe-Cu-Eu), (V with Fe-Cu-Eu), (Fe-Eu), and (Cu-Eu). The elements that dominate the critical minerals based on geochemical data analysis of the sand revealed three highest concentrations consisting of Fe (41.44%), Si (35.32%), and Ca (10.96%). Base on several previous studies on critical minerals in beach sands, including a study by Endang (2023) it is know that Fe is a dominant element compared to others such Ca, Al, and K. This is due to the influence of various geological conditions and human activities.

Quantitative elemental analysis and petrographic investigation of ancient potteries from archaeological sites of Tamil Nadu, using total reflection X-ray fluorescence spectroscopy

In order to investigate the elemental composition, a total of 19 potteries and two representative clay samples from Senjikothamangalam (Villupuram district) and 12 potteries and one representative clay sample from the Palayanur (Chengalpattu) archaeological sites in Tamil Nadu were analyzed using total reflection X-ray fluorescence spectroscopy. The presence of elements such as Fe, Ca, K, Ti, Al, Sr, Mn, P, V, Cr, Zn, Rb, Ni, Cu, Pb, Ga, Y, Bi, Br, and As were identified in the pottery and clay samples. Statistical analysis, such as Pearson correlation, cluster and principal component analysis were used to categorize the samples based on their elemental composition. The outcome of the Pearson correlation and cluster analysis corroborated that the Senjikothamangalam pottery samples (P-1 to P-19) were made using the same type of clay, except P-10. Similarly, all Palayanur pottery samples (P-20 to P-31) were manufactured from the same clay. The petrographic analysis of the potteries revealed further information about its provenance and making methods.

Forensic analysis of microtraces using image recognition through machine learning

Traces found at a crime scene can be interpreted as vectors of information that help describe the possible dynamics of the crime. However, some analyses show that pattern recognition, especially in materials, is subjective, as it depends on the analyst’s references. Based on this problem, the work aimed to use different statistical methods to establish pattern recognition in silver tapes. For this, two approaches were used, one based on deep learning (convolutional neural networks) and the other using a different method (Pearson’s correlation, distance metrics, and Principal Component Analysis). The dataset comprised four brands of silver-tape available in the retail market in the crime scene region, and fragments of tape originating after the detonation of a handmade explosive device. These materials were analyzed using a Leica DVM6 microscope. In both approaches, it was possible to recognize patterns. In deep learning, it was possible to establish that the fragments came from a common origin. The best model demonstrated that 92.1 % of the real materials questioned were the same silver-tape, with a confidence level of 0.94. By combining the methods, it was possible to observe a trend among the results. These responses demonstrated that using images in a computer vision context could remove the subjectivity of forensic analysis and correlate the microtraces found at a crime scene. In this way, these techniques can open new perspectives for the forensic area, making the interpretation more objective and transparent in its responses to society.

Inhibitory action of antimicrobial peptides against the formation of carcinogenic and mutagenic heterocyclic amines in meat

Thermal processing of meat leads to the development of Maillard's reaction intermediates, and carcinogenic toxicants. For the first time, the effectiveness of three (HX-12A, HX-12B and HX-12C) antimicrobial peptides (AMPs) against the formation of heterocyclic amines (HAs) in chemical and meat model systems. The results showed that AMPs especially 12A and 12C have strong metal chelation potential (48 and 40% at 1 mg/ml) and antioxidant activity (35 and 25% at 1 mg/ml), respectively, which were endorsed by their secondary structure (FTIR analysis) in terms of high β-sheets (1628 cm−1 and 1672 cm−1) in those AMPs. UPLC-MS analysis revealed that 12A and 12C were the most capable AMPs in MeIQx and PhIP-producing chemical models, respectively, whereas 12B promoted the HAs formation even higher than control. In particular, 12C AMP significantly (P < 0.05) decreased the most abundant carcinogenic HAs (PhIP) up to 90% at 9 mg/100 g of fresh meat, whereas 12A inhibited up to 80% of mutagenic HAs at same level compared to control and 12B. Low Field Nuclear Magnetic Resonance (LF-NMR) test showed that inhibitory effect of 12A and 12C was mediated by means of retaining water (lower T22 and T23 relaxation time) inside the macromolecules. This favorable effect was also evidenced by significantly enhanced tryptophan fluorescent intensity. Finally, based on correlation and principle component analysis, the mechanism of action has been proposed. These outcomes recommend that 12A and 12C are potential AMPs for the attenuation of HAs in thermally processed meat-based products.

Unveiling heavy metal pollution dynamics in sediments of river Ulhas, Maharashtra, India: a comprehensive analysis of anthropogenic influence, pollution indices, and health risk assessment.

Metals and metalloids tainting sediments is an eminent issue, predominantly in megacities like Mumbai and Navi Mumbai, requiring an exhaustive examination to identify metal levels in river bodies that serve various populations. Thus, utilising pollution indices, multivariate analysis, and health risk assessment studies, we propose a novel investigation to examine the metal content in the Ulhas River sediments, a prominent agricultural and drinking water supply (320 million-litre per day) near Mumbai in Maharashtra, India. The eleven metals and metalloids (As, Cd, Cr, Cu, Fe, Hg, Mn, Ni, Pb, Se, and Zn) were examined monthly from 10 stations totaling 120 sediment specimens from October 2022 to September 2023. Investigations revealed that average values of Cr, Cu, Hg, and Ni exceeded Australian and New Zealand Environment and Conservation Council and Agriculture and Resource Management Council values, while all metals exceeded World surface rock average limits except As. Various pollution indices showed that upstream sites had noneto low levelcontamination, whereas downstream locations had moderate to considerablecontamination, suggesting anthropogenic influences. Furthermore, multivariate analysis including correlation, cluster, and principal component analysis identified that sediment pollution was mostly caused by anthropogenic activities. Lastly, health risk assessment indicated Fe was non-carcinogenic to children, whereas Cr and Ni were carcinogenic to children and adults, with children being more susceptible. Thus, from the findings of the study it is clear that, despite low to moderate pollution levels, metals may have significant repercussions, thus requiring long-term planning, frequent monitoring, and metal abatement strategies to mitigate river contamination.

Characteristics of Water-soluble Ion Pollution in PM2.5 and the Causes of High Acidity of PM2.5 in Dalian

To gain a deeper understanding of the pollution status and influencing factors of fine particles （PM2.5） and their water-soluble ions （WSI） in Dalian and to implement precise control of pollution events such as haze and acid rain, PM2.5 samples were collected in Dalian from June 2021 to May 2022. Then, the mass concentrations of PM2.5 and WSI were determined using the weight method and ion chromatography, respectively, and the pollution characteristics and sources were analyzed. Furthermore, the causes of the high acidity of PM2.5 in spring were discussed. The results showed that the annual average mass concentrations of PM2.5 and WSI in Dalian during the sampling period were （33.24 ±28.87） μg·m-3 and （18.66 ±20.52） μg·m-3, respectively, and the secondary ions （SNA, including SO42-, NO3-, and NH4+） accounted for the highest proportion of WSI [（86.2 ±9.3）%]. The order of ion concentration levels from highest to lowest was： NO3-&gt;SO42-&gt;NH4+&gt;Cl-&gt;K+&gt;Ca2+&gt;Na+&gt;Mg2+&gt;F-. Due to the influence of meteorological conditions and coal combustion emissions during the concentrated heating period from late autumn to early spring, the seasonal variation in PM2.5 and WSI was winter&gt;spring&gt;autumn&gt;summer, whereas SNA was the highest in spring and the lowest in summer. The results of correlation and principal component analysis showed that WSI in PM2.5 was mainly from the secondary transformation of atmospheric SO2 and NO2 （contributing to the majority of SNA）, mixed sources of combustion and dust （characterized by K+, Mg2+, Cl-, and F-）, and sources of sand and sea salt （characterized by Na+, Ca2+, and Mg2+）. In summer, the main combustion source was biomass burning, whereas in autumn, winter, and spring, coal combustion emissions were predominant. The change in wind direction from autumn to winter brought by a shift from the source of sea salt to soil dust； additionally, the external pollution transported by northwest winds contributed to the complexity of the sources of WSI in PM2.5 during spring in Dalian. ISORROPIA-II model simulations suggested NH4NO3 as the most present solid aerosol form in PM2.5 in Dalian, followed by CaSO4 and （NH4）2SO4； compared to that in solid aerosols, more SNA existed in liquid aerosols. The annual average pH of PM2.5 in Dalian was 5.65 ±3.00, with pH values close to neutral in summer, autumn, and winter but significantly acidic in spring （2.03 ±3.18）. The high acidity observed in spring was attributed to the combination of low temperature, high humidity, and high SNA concentrations. These conditions resulted in higher aerosol water content and increased gas-to-particle conversion rates, ultimately leading to an ammonia-deficient environment. The backward trajectory and PSCF results indicated that the external transport of high acidity PM2.5 in spring mainly came from the northwest （45.0%） and southwest （40.8%） directions. Mobile source emissions made the most significant contribution to the transportation of pollutants in the former, forming high-pollution source areas in the Beijing-Tianjin-Hebei Region, which may have been mainly related to urban motor vehicle and port vessel emissions； the latter was influenced by relatively strong stationary sources and showed higher SO2 emissions in the southern part of Henan Province and the central part of Jiangsu Province.

Assessment of Genetic Variability for Yield and Yield Related Traits in Safflower Core Subset Germplasm

One hundred and fifty safflower genotypes of Core subset germplasm provided by ICAR-IIOR, were evaluated to study genetic variability, correlation and principal component analysis (PCA). A wide range of phenotypic data with near normal distribution was observed for most of the traits analysed. High variability was observed for all the traits studied indicating diverse nature of the germplasm. Significant positive correlation was observed for seed yield with test weight and oil content. Genotype GMU 6869 has been identified as potential genotype surpassing the best check A1 for seed yield and earliness. Twenty three genotypes significantly superior to the 2nd best check PBNS-12 were identified. High heritability along with GAM was recorded for the traits number of effective capitula/plant, test weight and single plant yield indicating that these characters may be considered as selection criteria for genetic improvement in safflower. Principal component analysis revealed that first four principal components contributed 65.2% of total variance. PCA-variables plot and PCA Bi-plots revealed that two traits viz., LPB and HPB contributed maximum variations in PC1, while two traits viz., DAF &amp; DAM contributed to the maximum variability in PC-2. Safflower accessions displaying highest performance for the traits studied were identified. These genotypes can be used as parental lines for improvement of specific traits in safflower breeding program.

Evaluating Panicle Architecture and Yield Characteristics in Medium Duration Rice Genotypes through Correlation and Principal Component Analysis

Panicle architecture is a crucial factor that significantly improves economic grain yield. The panicle exhibits considerable variation in its structure, including the number of branches, the length and weight of the panicle, and arrangement of the grains. These traits are not only vital for the plants reproductive efficiency but also for optimizing yield potential. This study focuses on assessing variation in panicle architecture among 31 different medium duration rice genotypes by employing principal component analysis and correlation analysis. This study aimed to dissect the complex relationship between different panicle characteristics. The findings revealed that the first three principal components together explained 60.3% of the total variability. In the first principal component (PC1), the traits SF, PBL, YPH, and HI had the highest positive eigenvalues, indicating their significant impact on overall genotype variations. The second principal component (PC2) was mainly influenced by TSP and SBN, while the third principal component (PC3) was driven by PW and SBL. These results suggest that grain yield is significantly affected by the traits SF, PBL, YPH, HI, TSP, SBN, PW, and SBL. The correlation analysis revealed that the number of secondary branches showed a positive association with unfilled spikelet. Spikelet fertility exhibited a strong positive correlation with yield hectare-1 and harvest index, highlighting its significance in overall yield improvement. Therefore, these components are crucial for differentiating rice genotypes with higher grain yield potential. By evaluating correlation and principal component analysis, we aimed to identify traits that can be targeted for crop improvement initiatives. The findings will contribute to the development of rice varieties that are better suited to specific growing conditions and have enhanced yield potential. This approach will enable the identification of key panicle architecture and yield and yield traits that optimize resource allocation and improve grain filling efficiency, ultimately leading to rice varieties with higher productivity and adaptability.

Independent risk factors of left ventricular hypertrophy in non-diabetic individuals in Sierra Leone - a cross-sectional study

BackgroundLeft ventricular hypertrophy (LVH) is a critical factor in heart failure and cardiovascular event-related mortality. While the prevalence of LVH in diabetic patients is well-documented, its occurrence and risk factors in non-diabetic populations remain largely unexplored. This study addresses this issue by investigating the independent risk factors of LVH in non-diabetic individuals.MethodsThis cross-sectional study, conducted meticulously, utilized data from a robust and comprehensive source, DATADRYAD, in the Sierra Leone database, collected between October 2019 and October 2021, including LVH and various variables. All variables were described and screened using univariate analysis, Spearman correlation, and principal component analysis (PCA). The lipid profile, including total cholesterols (TC), triglycerides (TG), high-density lipoprotein (HDL-C), non-high-density lipoprotein (Non-HDL-C), and low-density lipoprotein cholesterol (LDL-C), TC/HDL-C ratio, TG/HDL-C ratio, Non-HDL-C /HDL-C ratio and LDL-C/HDL-C ratio, which quartiles were treated as categorical variables, with the lowest quartile serving as the reference category. Three adjusted models were constructed to mitigate the influence of other variables. To ensure the robustness of the model, receiver operating characteristic (ROC) curves were used to calculate the cutoff values by analyzing the ROC curves. A sensitivity analysis was performed to validate the findings further.ResultsThe dataset encompasses information from 2092 individuals. After adjusting for potential factors that could influence the results, we found that TC (OR = 2.773, 95%CI: 1.805–4.26), Non-HDL-C (OR = 2.74, 95%CI: 1.7723–4.236), TC/HDL-C ratio (OR = 2.237, 95%CI: 1.445–3.463), Non-HDL-C/HDL-C ratio (OR = 2.357, 95%CI: 1.548–3.588), TG/HDL-C ratio (OR = 1.513, 95%CI: 1.02–2.245) acts as independent risk factors of LVH. ROC curve analysis revealed the predictive ability of blood lipids for LVH, with Non-HDL-C exhibiting area under the curve (AUC = 0.6109), followed by TC (AUC = 0.6084).ConclusionsTC, non-HDL-C, TC/HDL-C ratio, Non-HDL-C/HDL-C ratio, and TG/HDL-C ratio were independent risk factors of LVH in non-diabetic people. Non-HDL-C and TC were found to be essential indicators for predicting the prevalence of LVH.

Financial equilibrium: An in-depth look at working capital management and productivity in manufacturing SMEs in Ecuador

The manufacturing industry is one of the main axes of the economy, which has been affected by the global economic crisis, so the competent authorities must address this problem by efficiently using the administration and management of resources. This research aimed to analyze working capital as a function of productivity in manufacturing SMEs in Zone 3 of Ecuador, one of the most important sectors worldwide. It has a quantitative approach, through the statements of financial situation presented in the SUPERCIAS of 112 SMEs in the years 2017 to 2020 that have remained constant over time, to carry out this process, data obtained from secondary sources were recorded by means of an observation sheet. Descriptive statistics, Spearman's Rho correlation, multivariate statistics, and principal component analysis, were used to perform a multiple linear regression model to detect the best year for predictions. The results showed that both small and medium-sized companies presented positive values with respect to the average working capital and productivity. On the other hand, for the year 2020 the two variables were affected due to the pandemic in this sector reducing their values, working capital and productivity have an average positive correlation; it was also determined that the year 2018 is the best year for predictions, being that the variables studied are financial indicators that contribute to the decision making in the companies.

Analysis of the Energy–Carbon Potential of the Pericarp Cover of Selected Hazelnut Varieties

The research presents weight estimation and analysis of the energy and carbon potential of the pericarp cover of four hazelnut varieties. A technical and elementary biofuel analysis was carried out for the biomass studied, as well as a correlation and principal component analysis to demonstrate the influence of individual characteristics on the parameters achieved. In addition, emission factors and the composition and volume of flue gases from the combustion of the material studied were estimated based on stoichiometric equations. The research showed that the highest calorific value (LHV) was characterised by the pericarp cover of the ‘Olga’ variety (14.86 MJ·kg−1) and the lowest by the ‘Kataloński’ variety (14.60 MJ·kg−1). In the case of exhaust volume, the highest volume was obtained from the ‘Olbrzymi z Halle’ variety (250.06 Nm3·kg−1) and the lowest from the ‘Kataloński’ variety (12.43 Nm3·kg−1). The correlation analysis carried out showed that the HHV and LHV parameters in the covers showed a very strong positive correlation with sulphur content and SO2 emissions, and a moderate correlation with nitrogen content and its associated NOx emissions, indicating their direct influence on the higher calorific value of biomass.

Spatial-temporal distribution and eutrophication evaluation of nutrients and trace metals in summer surface seawater of Yantai Sishili Bay, China

Due to coastal development expansion, an increasing influx of pollutants enters the sea through riverine input and land runoff, threatening coastal ecosystems and posing a risk of eutrophication. In this study, trace metals (Fe, Mn, Cu, and Zn), and nutrients (constituents of N, P, and Si) were assessed in the summer surface seawater of Yantai Sishili Bay (YSB), Northern China focusing on the determination of concentration, spatial-temporal distribution and sources identification, while exploring their correlations. It also aimed to clarify the eutrophication status and evaluate the linear relationships between eutrophication, trace metals, and nutrients in YSB. Over three years (2021–2023), the total dissolved concentrations of Fe, Mn, Cu, and Zn ranged from 4.79–26.71, 0.19–6.41, 0.26–1.53, and 0.74–13.12 µg/L, respectively. Concurrently, nutrient concentrations including NO2-, NO3-, NH4+, PO43-, and DSi exhibited a range of 0.37–11.66, 2.04–178.30, 1.69–70.01, 0.02–16.68, and 0.02–0.71 µg/L respectively. These concentrations revealed a gradual decrease from nearshore to offshore and the temporal variation also showed significant patterns from year to year, indicating distinct regional variations. The primary contributors to the trace metals and nutrients in the study region were recognized as external contributions stemming from natural, anthropogenic, and atmospheric deposition through correlation and principal component analysis. More specifically, riverine input and coastal farming contributed large amounts of nutrients to coastal waters, threatening a potential risk of eutrophication. The eutrophication evaluation expressed below the mild eutrophication level and was far lower than the other global and Chinese bays. The linear correlation between eutrophication and trace metals revealed a weak positive correlation but a significant correlation with nutrients. Despite the absence of significant eutrophication in the bay, potential risks were identified due to identifiable sources of nutrient and trace metal inputs. The findings provided insights to guide efforts in preventing and mitigating coastal eutrophication, as well as nutrient and trace metal pollution, in coastal cities.