Correlation Matrix Research Articles

Dental biomaterials redefined: molecular docking and dynamics-driven dental resin composite optimization

BackgroundDental resin-based composites are widely recognized for their aesthetic appeal and adhesive properties, which make them integral to modern restorative dentistry. Despite their advantages, adhesion and biomechanical performance challenges persist, necessitating innovative strategies for improvement. This study addressed the challenges associated with adhesion and biomechanical properties in dental resin-based composites by employing molecular docking and dynamics simulation.MethodsMolecular docking assesses the binding energies and provides valuable insights into the interactions between monomers, fillers, and coupling agents. This investigation prioritizes SiO2 and TRIS, considering their consistent influence. Molecular dynamics simulations, executed with the Forcite module and COMPASS II force field, extend the analysis to the mechanical properties of dental composite complexes. The simulations encompassed energy minimization, controlled NVT and NPT ensemble simulations, and equilibration stages. Notably, the molecular dynamics simulations spanned a duration of 50 ns.ResultsSiO2 and TRIS consistently emerged as influential components, showcasing their versatility in promoting solid interactions. A correlation matrix underscores the significant roles of van der Waals and desolvation energies in determining the overall binding energy. Molecular dynamics simulations provide in-depth insights into the mechanical properties of dental composite complexes. HEMA-SiO2-TRIS excelled in stiffness, BisGMA-SiO2-TRIS prevailed in terms of flexural strength, and EBPADMA-SiO2-TRIS offered a balanced combination of mechanical properties.ConclusionThese findings provide valuable insights into optimizing dental composites tailored to diverse clinical requirements. While EBPADMA-SiO2-TRIS demonstrates distinct strengths, this study emphasizes the need for further research. Future investigations should validate the computational findings experimentally and assess the material's response to dynamic environmental factors.

Groundwater recharge estimation using WetSpass-M and MTBS leveraging from HydroOffice and WHAT tools for baseflow in Weyib watershed, Ethiopia.

WetSpass-M model and multi-technique baseflow separation (MTBS) were applied to estimate spatio-temporal groundwater recharge (GWR) to be used to comprehend and enhance sustainable water resource development in the data-scarce region. Identification of unit Hydrographs And Component flows from Rainfall, Evaporation, and Streamflow (IHACRES) techniques outperform the existing 13 MTBS techniques to separate baseflow depending on the correlation matrix; mean baseflow was 5.128 m3/s. The WetSpass-M model performance evaluated by Nash-Sutcliff Efficiency (NSE) was 0.95 and 0.89; R2 was 0.90 and 0.85 in comparison to observed and simulated mean monthly baseflow and runoff (m3/s), respectively. The estimated mean annual water balance was 608.2 mm for actual evapotranspiration, 221.42 mm for the surface runoff, 87.42 mm for interception rate, and 177.66 mm for GWR, with an error of - 3.29 mm/year. The highest annual actual evapotranspiration was depicted in areas covered by vegetation, whereas lower in the settlement. The peak annual interception rates have been noticed in areas covered with forests and shrublands, whereas the lowest in settlement and bare land. The maximum annual runoff was depicted in settlement and bare land, while the lowest was in forest-covered areas. The annual recharge rates were low in bare land due to high runoff and maximum in forest-covered areas due to low surface runoff. The watershed's downstream areas receive scanty annual rainfall, which causes low recharge and drought. The findings point the way ahead in terms of selecting the best approach across multi-technique baseflow separations.

Simulating non-stationary and non-Gaussian cross-correlated fields using multivariate Karhunen–Loève expansion and L-moments-based Hermite polynomial model

In practical engineering, cross-correlated random fields are often used to model structural materials or random loads containing multiple correlations. Effective and accurate simulation of these cross-correlation fields is an important prerequisite for subsequent reliability analysis and uncertainty quantification of complex systems. Therefore, this paper proposes a new simulation method for non-stationary non-Gaussian cross-correlated random fields to satisfy realistic engineering requirements. In this new method, L-moments-based Hermite polynomials model (LHPM) is extended to non-stationary non-Gaussian cross-correlated fields. Then, a transformation model from non-stationary non-Gaussian correlation matrix function (CMF) to the underlying Gaussian CMF is explicitly given. Multivariate K-L expansion is further used to approximate the underlying Gaussian cross-correlated fields, where the eigenvalue and eigenfunctions are estimated via the Nyström method with uniform weights. The approximation permits the application of few random variables to characterize the entire Gaussian cross-correlated fields. Ultimately, the simulated underlying Gaussian cross-correlated fields are mapped back to the target non-stationary non-Gaussian cross-correlated fields based on LHPM. Three typical examples, including exponential kernel, Wiener process kernel and spatially varying non-Gaussian and nonstationary seismic ground motions, are used to validate the effectiveness of the proposed method. The source code is readily available at: https://github.com/zhaozhao23/Simulation-of-non-stationary-and-non-Gaussian-cross-correlated-fields.

FastCCLasso: a fast and efficient algorithm for estimating correlation matrix from compositional data.

The composition and structure of microbial communities on the body surface are closely related to human health. The interaction relationship among microbes can help us understand the formation of the microecological environment and the biological mechanism by which microorganisms influence host health. With the help of high-throughput sequencing technologies, microbial abundances in a natural environment can be directly measured without the isolation of microorganisms in culture. Sequencing experiments in microbiome studies can measure the relative abundance of microbes, which is called compositional data. Although there are already many methods for correlation analysis for compositional data, the computation time or accuracy still needs to be improved for current microbiome studies. We develop a fast and efficient algorithm, called fastCCLasso, based on a penalized weighted least squares for inferring the correlation structure of microbes from compositional data in microbiome studies. We perform a large number of numerical experiments and the simulation results show that fastCCLasso outperforms its competitors in edge detection for inferring the correlation network. We also apply fastCCLasso for estimating microbial networks in microbiome studies and fastCCLasso provides a conservative network with comparable false discovery counts that are derived from shuffled data. FastCCLasso is open source and freely available from https://github.com/ShenZhang-Statistics/fastCCLasso under GNU LGPL v3.

Optimization of investment portfolios of Chinese commodity futures market based on complex networks

Abstract China commodity futures market network is constructed. Commodity is the node of the network, and the network link is defined by the price correlation matrix. We analyze the relationship between the centrality of each commodity in the commodity futures market network and the optimal weight of the commodity portfolio, empirically examine the market system factors and commodity personalized factors that affect the centrality of commodity, and evaluate the effect of network structure on the optimization of commodity portfolio selection under the mean-variance framework. It is found that the commodities with high network centrality are often related to industrial products and have high volatility. Commodities with higher centrality have lower portfolio weights. We put forward a kind of commodity futures investment strategy based on network, according to the network centricity grouping the commodities, the network centricity lower edge of the commodity structure of the portfolio, cumulative yield is better than that of centricity higher core product portfolio, the whole market portfolio yield, but due to large maximum retracement, lead to the stability and ability to resist risk compared with the other two groups of goods combination. The main contribution of this paper is to optimize portfolio selection by establishing the relationship between portfolio weight and commodity centrality by using commodity futures market network as a tool.

Copepod distribution and diversity in the coastal areas of Ban-ao and Lambajon, Davao Oriental, Philippines: Environmental influences and conservation implications

Copepods are essential components of marine ecosystems, facilitating energy transfer within these complex systems. The study investigates copepod diversity and distribution in Ban-ao and Lambajon coastal areas in Baganga, Davao Oriental, highlighting the environmental factors influencing copepod populations. Four copepod families representing different orders were identified, with Harpacticoida, Calanoida, and Cyclopoida prevalent in both sites, while Misophrioida was exclusive to Lambajon. Physico-chemical parameters such as dissolved oxygen (DO), pH, salinity, sediment composition, water depth, and temperature were analyzed to understand their correlation with copepod density. The study reveals variations in copepod density and abundance between the sites, with Lambajon showing a higher total density (49 ind/cm³) compared to Ban-ao (35 ind/cm³). The Pearson correlation matrix illustrates complex relationships between copepod density and environmental parameters in each site. In Ban-ao, strong positive correlations were found between copepod density and DO (r = 0.65) and temperature (r = 0.36). In Lambajon, positive correlations existed between water depth and copepod density (r = 0.20). Both sites exhibit low copepod diversity overall, potentially due to anthropogenic pressures. These findings emphasize the need for further research to understand the interactions between environmental factors and copepod diversity, essential for effective conservation and management strategies in these coastal ecosystems.

Searching for the first radial excitation of the Δ(1232) in lattice QCD

We present a lattice QCD analysis of the Δ-baryon spectrum, with the goal of finding the position of the 2s radial excitation of the Δ(1232) ground state. Using smeared three-quark operators in a correlation matrix analysis, we report masses for the ground, first and second excited states of the J P = 3/2+ spectrum across a broad range of mπ2 . We identify the lowest lying state as being a 1s state, consistent with the well known Δ(1232). The first excitation is identified as a 2s state, but is found to have a mass of approximately 2.15 GeV on our ∼3 fm lattice, which does not appear to be associated with the Δ(1600) resonance in a significant manner. We also report on the spin-1/2 and odd-parity states accessible via our methods. The large excitation energies of the radial excitations provide a potential resolution to the long-standing missing baryon resonances problem.

Machine learning-based model for predicting arrival time of container ships

Forecasting container ship arrival times is challenging, requiring a thorough analysis for accuracy. This study investigates the effectiveness of machine learning (ML) techniques in maritime transportation. Using a dataset of 581 samples with 8 input variables and 1 output variable (arrival time), ML models are constructed. The Pearson correlation matrix reduces input variables to 7 key factors: freight forwarder, dispatch location, loading and discharge ports, post-discharge location, dispatch day of the week, and dispatch week. The ranking of ML performance for predicting the arrival time of container ships can be arranged in descending order as GB-PSO > XGB > RF > RF-PSO > GB > KNN > SVR. The best ML model, GB-PSO, demonstrates high accuracy in predicting the arrival time of container ships, with R2 = 0.7054, RMSE = 7.4081 days, MAE = 5.1891 days, and MAPE = 0.0993% for the testing dataset. This is a promising research outcome as it seems to be the first time that an approach involving the use of minimal and easily collectible input factors (such as freight forwarder, dispatch time and place, port of loading, post port of discharge, port of discharge) and the combination of a machine learning model has been introduced for predicting the arrival time of container ships.

Characterization of olive (Olea europaea L.) cultivars; colour properties, biochemical contents, antioxidant activity and nutrient contents

AbstractThe olive tree, scientifically known as Olea europaea L., is an evergreen xerophytic tree that defines the natural flora, historical background, and cultural essence of the Mediterranean Basin. This study aimed to establish relationships using multivariate analysis methods between the nutrient content of soil conditions in which olive cultivars grown in Türkiye are cultivated and the nutrient element content, phytochemical contents, antioxidant activity, total chlorophyll amount, and leaf colors of these cultivars. All data sets used in the study were analyzed in 2022 and 2023, and average values were used in the research. According to Tukey's comparison result, it was determined that the soil structure of the garden was homogeneous in terms of nutrients it contained. In nutrient analysis conducted on leaves, in the ‘Çelebi’ cultivar, Al (59.25 mg kg−1), B (6.53 mg kg−1), Cu (48.36 mg kg−1), Fe (69.34 mg kg−1), K (1438.11 mg kg−1), Na (197.12 mg kg−1) nutrients are the highest; in the ‘Gemlik-21’ cultivar, Ca (5485.03 mg kg−1) nutrient is the highest; in the ‘Sarı Haşebi’ cultivar, Mg (928.11 mg kg−1), Mn (19.71 mg kg−1), S (632.77 mg kg−1) nutrients are the highest; and in the ‘Tavşan Yüreği’ cultivar, Ni (1.71 mg kg−1), Zn (9.76 mg kg−1) nutrients are the highest. The L* (49.19), b* (29.43) color values are highest in the ‘Sarı Yaprak’ cultivar, while the a* (28.84) value is highest in the ‘Girit Zeytini’ cultivar. The highest leaf chlorophyll content was determined in the ‘Girit Zeytini’ cultivar (95.57). Total phenolics, total flavonoids, antioxidant capacity were determined to be highest in the ‘Manzanilla’ (151.49 mg GAE/100 g), ‘Edincik Su’ (39.01 mg QE/100 g), ‘Nizip Yağlık’ (91.18%) cultivars, respectively. According to the principal component analysis, the first three principal components accounted for 82% of the total variation. The correlation matrix analysis revealed that high levels of certain minerals in the soil led to an increase in the leaves, resulting in positive correlation, while the opposite was true for negative correlation. According to heat map analysis, mineral elements in the leaf were in the same group, while other data sets were in different groups. The data obtained will shed light on future research on similar topics.

Digital ecosystem, entrepreneurial ecosystem and economic development as enablers of digital technology entrepreneurship

AbstractThis paper analyses the digital ecosystem (DES), entrepreneurial ecosystem (EES) and economic development (ED) indicators as key determinants of digital technology entrepreneurship (DTE) across the globe. The correlation matrix depicts an important relationship between DTE, DES, EES and ED. Further, the partial least squares structural equation modelling (PLS‐SEM) results show that DES and EES have a favourable and significant influence on the establishment of digital technology entrepreneurship (DTE) globally. However, the ED, though, has a positive and significant impact on EES and DES, not directly impacting DTE. The study is useful to various stakeholders such as entrepreneurs, funding agencies, policymakers and researchers in their decision‐making process.

Insights from an updated personnel selection meta-analytic matrix: Revisiting general mental ability tests' role in the validity-diversity trade-off.

General mental ability (GMA) tests have long been at the heart of the validity-diversity trade-off, with conventional wisdom being that reducing their weight in personnel selection can improve adverse impact, but that this results in steep costs to criterion-related validity. However, Sackett et al. (2022) revealed that the criterion-related validity of GMA tests has been considerably overestimated due to inappropriate range restriction corrections. Thus, we revisit the role of GMA tests in the validity-diversity trade-off using an updated meta-analytic correlation matrix of the relationships six selection methods (biodata, GMA tests, conscientiousness tests, structured interviews, integrity tests, and situational judgment tests) have with job performance, along with their Black-White mean differences. Our results lead to the conclusion that excluding GMA tests generally has little to no effect on validity, but substantially decreases adverse impact. Contrary to popular belief, GMA tests are not a driving factor in the validity-diversity trade-off. This does not fully resolve the validity-diversity trade-off, though: Our results show there is still some validity reduction required to get to an adverse impact ratio of .80, although the validity reduction is less than previously thought. Instead, it shows that the validity-diversity trade-off conversation should shift from the role of GMA tests to that of other selection methods. The present study also addresses which selection methods now emerge as most valid and whether composites of selection methods can result in validities similar to those expected prior to Sackett et al. (2022). (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Immune profiling reveals umbilical cord blood mononuclear cells from South India display an IL-8 dominant, CXCL-10 deficient polyfunctional monocyte response to pathogen-associated molecular patterns (PAMPs) that is distinct from adult blood cells.

Neonate responses to pathogen-associated molecular patterns (PAMPS) differ from adults; such understanding is poor in Indian neonates, despite recognised significant infectious risk. Immune profiling analysis was undertaken of ten secreted mediators contextualised with cellular source induced by six PAMPs in umbilical cord (CB; n=21) and adult-blood (PBMC, n=14) from a tertiary care hospital in South India. Differential cytokine expression analysis (minimum log2-fold difference; adj p-value<0.05) identified bacterial PAMPs induced higher concentrations of IL-1β, IL-10, TNF-α in adults versus IL-8, GM-CSF, IFN-γ and IL-2 in CB. CB responded to poly I:C and SARS-CoV-2 lysate with a dominant IL-8 response, whereas, in PBMC, CXCL-10 dominated poly I:C, but not SARS-CoV-2, responses, highlighting potential IL-8 importance, in absence of Type I Interferons, in antiviral CB immunity. Candida albicans was the only PAMP to uniformly induce higher secretion of effectors in CB. The predominant source of IL-8/IL-6/TNF-α/IL-1β in both CB and PBMC was polyfunctional monocytes and IFN-γ /IL-2/IL-17 from innate lymphocytes. Correlation matrix analyses revealed IL-8 to be the most differentially regulated, correlating positively in CB versus negatively in PBMC with IL-6, GM-CSF, IFN-γ, IL-2, consistent with more negatively regulated cytokine modules in adults, potentially linked to higher anti-inflammatory IL-10. Cord and adult blood from India respond robustly to PAMPs with unique effector combinations. These data provide a strong foundation to monitor, explore, mechanisms that regulate such immunity during the life course, an area of significant global health importance given infection-related infant mortality incidence.

Variability in Morphological, Biochemical, and Proximate Yield Composition among Predominant Amaranthus hybridus Cultivars in South-West Nigeria

To mitigate food insecurity and the low economic status of amaranth farmers in south-west Nigeria, it is imperative to evaluate the predominant commercial A. hybridus cultivars using key yield and nutrient components to identify promising lines with optimum yield and nutritional quality. The current study evaluated five cultivars of Amaranthus hybridus in south-west Nigeria farms using key morphological, biochemical, and seed-proximate component parameters. The results revealed significant differences in the cultivars’ growth and yield indices. The number of leaves ranged from 16.40 to 29.48 (x- = 22.10) with plant height (x- = 50.28 cm), while other leaf metrics varied at maturity. Days to 50% flowering ranged from 49 to 90 days. Seed proximate composition also varied significantly. Carbohydrate content ranged from 65.01–76.28%, protein content 6.57–12.42%, ash content 2.73–3.59%, total polyphenols 23.36–29.64 mg/100 g GAE, and total antioxidants 31.6–181.59 mg/100 g AAE. The Pearson correlation matrix and path analysis showed significant relationships among the yield components. The phenotypic variance of the yield components mainly stemmed from environmental factors, indicating their crucial role in the traits’ expression. Careful evaluation of the cultivars revealed that AM-1 and AM-3 had the best growth and yield attributes. Additionally, the two cultivars showed a significant degree of relatedness, suggesting a common ancestor. They have the potential to increase productivity, farmers’ profitability, and consumers’ nutrient gain compared to other cultivars. They are recommended for cultivation and can be further crossed in breeding programs to develop superior lines.

Hypo-connectivity of the primary somatosensory cortex in Parkinson’s disease: a resting-state functional MRI study

BackgroundParkinson’s disease (PD) is characterized by a range of motor symptoms as well as documented sensory dysfunction. This sensory dysfunction can present itself either as a “pure” sensory disturbance or as a consequence of sensory-motor integration within the central nervous system. This study aims to investigate changes in the functional connectivity of the primary somatosensory cortex (S1) and its clinical significance in Parkinson’s disease (PD), an area that has received limited attention in previous neuroimaging studies.MethodsThis study included thirty-three patients with PD and thirty-four healthy controls (HCs). Clinical evaluations were conducted to assess the clinical manifestations, severity, and functional capacity of all the patients. Resting-state functional MRI (fMRI) was employed to evaluate the functional connectivity of six paired S1 subregions in the participants. Seed-based correlation (SBC) analysis was utilized to construct the correlation matrix among the subregions and to generate connectivity maps between the subregions and the remaining brain voxels. Finally, the study employed partial least-squares (PLS) correlation analysis to investigate the association between modified functional connectivity and clinical characteristics in PD patients.ResultsIn the correlation matrix, patients with PD demonstrated a notable decrease in functional connectivity across various S1 subregions in comparison to HCs (p &amp;lt; 0.001, corrected using network-based methods). In connectivity maps, hypo-connectivity was primarily observed in the sensorimotor network as common patterns (p &amp;lt; 0.001, corrected for false discovery rate) and in the default mode network (DMN) as distinct patterns. Moreover, this study identified a negative association between the correlation matrix within S1 subregions and the scores for axial symptoms and postural instability/gait difficulty (PIGD) in PD patients. Nevertheless, a direct relationship between the connectivity maps of S1 subregions and clinical assessment scales was not established.ConclusionThis study offers novel insights into the neurobiological mechanisms that contribute to S1 dysfunction in PD, highlighting the significant involvement of S1 hypo-connectivity in the motor disturbances observed in PD patients.

A Comparative Physical Parameters of Sri Kurmanadha Temple Pond and Sri Maninageswara Temple Pond, Srikakulam District, Andhra Pradesh, India

Data collected on certain physical parameters of Sri Kurmanadha, Srikurmam and Sri Maninageswar, Kallepalli, Temple Ponds, Srikakulam District, Andhra Pradesh from May 2014 to April 2016. The seasonal variations in water quality were recorded on monthly basis for a period of two years and correlation studies were also conducted among different physical parameters of both temple ponds. Water samples were analyzed for Physical Parameters like air temperature (28.250 C, 27.40 C), water temperature (240C, 24.350 C), transparency (32.1cm., 20.35cm.), EC (621μs/cm., 513.5μs/cm.), TSS (27mg/l., 35.5mg/l.) TDS (367mg/l., 298mg/l), pH (7.35, 7.5), DO (6.75mg/l, 6.5mg/l), COD (10.63mg/l, 10.36mg/l), BOD (3.31mg/l., 2.86mg/l.) total alkalinity (132.5mg/l., 128.5mg/l.), total hardness (80mg/l., 87.5mg/l.). Inter relationship between physical parameters (Pearson’s Correlation matrix) was also recorded. In this observation temperature showed strong positive correlation with COD (r=0.91, r=0.88), BOD (r=0.96, r=0.89), and negative correlation with DO (r=-0.84, r=-0.97). EC showed significant positive correlation with TDS (r=0.85, 0.89), and negative correlation with DO (r= -0.82, r=-0..84), TDS showed positive correlation with pH (r=0.77, r=0.72), and total hardness (r=0.81, 0.78), pH showed positive correlation with COD (r=0.77, r=0.84), total alkalinity (r=0.94, r=0.86), total hardness (r=0.81, r=0.84), and negative correlation with DO (r= -0.87, r=-0.77), Dissolved Oxygen showed negative correlation with COD (r= -0.88, r=-0.93), BOD (r= -0.95, r=-0.93). Chemical Oxygen Demand showed positive correlation with BOD (r= 0.92, r=0.91) and total alkalinity (r= 0.75, r=0.79). Biological Oxygen Demand showed positive correlation with total alkalinity (r= 0.74, r=0.63). Total Alkalinity showed positive correlation with total hardness (r= 0.90, r=0.87). These observations could useful in the water quality monitoring and regulation in order to enhance the quality of water with better sustainable management of Sri Kurmanadha, Srikurmam and Srimaninageswar, Kallepalli, Temple Ponds.

The Significance of Innovation Orientation in Firm Performance: Technological Capabilities as A Moderating Role

Entrepreneurial marketing is considered to be suitable for small businesses. However, innovation orientation as an Entrepreneurial marketing dimension is a critical instrument that small and medium-sized businesses can use to obtain a competitive advantage. Thus, the general objective of this study is to explore and describe the significant relationship between innovation orientation and firm performance in SMEs in Khartoum-Sudan utilizing technological capabilities as a moderating variable. The resource-based view provides the theoretical foundation for this study regarding the effect of innovation orientation on firm performance through technological capabilities. In this manner, our study is quantitative. Reliable with the purpose of this study. Furthermore, our study relied on the “Positivism philosophy”, a deductive approach to theory development. Accordingly, overall, 255 responses were received in responding to our online questionnaire. To analyze the data firstly Principal Component Analysis, Correlation, and Rotation matrix were utilized to test the appropriateness of the study pre-model and check the validity of the questionnaire measurements. secondly, we used Path analysis to examine the significant relationships between study variables. Consequently, the findings confirm both significant and insignificant relationships between innovation orientation and firm performance. Our analysis revealed significant relationships between innovation orientation, technological capabilities, and firm performance indicators such as profitability, sustainability, and customer satisfaction. Moreover, we found that technological capabilities play a crucial moderating role in enhancing the effects of innovation orientation strategy on firm performance. Anyhow, these results confirmed the partial support of the study’s hypotheses.

Prediction Model of Coal Gas Permeability Based on Improved DBO Optimized BP Neural Network.

Accurate measurement of coal gas permeability helps prevent coal gas safety accidents effectively. To predict permeability more accurately, we propose the IDBO-BPNN coal body gas permeability prediction model. This model combines the Improved Dung Beetle algorithm (IDBO) with the BP neural network (BPNN). First, the Sine chaotic mapping, Osprey optimization algorithm, and adaptive T-distribution dynamic selection strategy are integrated to enhance the DBO algorithm and improve its global search capability. Then, IDBO is utilized to optimize the weights and thresholds in BPNN to enhance its prediction accuracy and mitigate the risk of overfitting to some extent. Secondly, based on the influencing factors of gas permeability, effective stress, gas pressure, temperature, and compressive strength, they are chosen as the coupling indicators. The SPSS 27 software is used to analyze the correlation among the indicators using the Pearson correlation coefficient matrix. Additionally, the Kernel Principal Component Analysis (KPCA) is employed to extract the original data. Then, the original data is divided into principal component data for the model input. The prediction results of the IDBO-BPNN model are compared with those of the PSO-BPNN, PSO-LSSVM, PSO-SVM, MPA-BPNN, WOA-SVM, BES-SVM, and DPO-BPNN models. This comparison assesses the capability of KPCA to enhance the accuracy of model predictions and the performance of the IDBO-BPNN model. Finally, the IDBO-BPNN model is tested using data from a coal mine in Shanxi. The results indicate that the predicted outcome closely aligns with the actual value, confirming the reliability and stability of the model. Therefore, the IDBO-BPNN model is better suited for predicting coal gas permeability in academic research writing.

Genetic diversity analysis and molecular characterization of tropical rose (Rosa spp.) varieties

Characterization of rose varieties becomes essential for understanding, utilizing, and preserving the diversity among them, for their genetic improvement, cultivation, and conservation. With this purpose, 19 tropical rose varieties were evaluated via quantitative morphological and molecular markers. A significant degree of variation was observed across nearly all descriptors, suggesting a notable level of genetic diversity among the released rose varieties. Traits like flower diameter, flower yield/plot, and shelf life, considered to be of vital importance given the aesthetic value of rose, exhibited remarkable differences that would further aid in the selection of desirable varieties. The maximum flower diameter, flower yield/plot, and shelf life were recorded in Yellow Page, Lambada, and Delhi Princess, respectively. The varieties were evaluated by correlation matrix, hierarchical agglomerative clustering method-based dendrogram, network plot on phenotypic correlation, colour intensity graph including Red-Green-Blue (RGB) scattered plot. In the correlation matrix, it was found that more number of primary shoot contributes to higher flower yield, while increase in leaf area degraded the shelf life of the flower. Realizing the importance of molecular characterization, 10 selected polymorphic inter simple sequence repeats (ISSR) and start codon targeted (SCoT) polymorphism primers were tested that amplified total 187 polymorphic loci with 94.4 % polymorphism. Cent percent polymorphism were found with SCoT-2, SCoT-18, SCoT-19, HB-12 and ISSCR3 primers. A principal component analysis based on molecular diversity highlighted that First Red, Local Selection 2, and Himroz exhibited distinct characteristics compared to the other varieties. The 19 varieties were grouped into 8 clusters viaunweighted pair group method with arithmetic mean-based dendrogram at 0.4 similarity co-efficient. The analysis revealed a significant genetic distance between Himroz, First Red, and Grand Gala, suggesting their potential to be utilized in hybridization programs for crop improvement. The distinguishable genetic background and high degree of variation revealed by molecular markers, specifically ISSR and SCoT, offer valuable insights that can contribute to the strategic improvement of roses. These findings provide a foundation for expediting rose breeding programs and comprehensive crop improvement strategies.

ИНДЕКС ФОНДООТДАЧИ: СОВРЕМЕННОЕ СОСТОЯНИЕ И ФАКТОРЫ РОСТА

The scientific article contains an analysis of scientific literature on ensuring the efficient use of fixed assets in enterprises, research on capital productivity and capital intensity, and the development of main directions for the efficient use of fixed capital. The dynamics of the coefficients of renewal and disposal of fixed assets in the economy are presented in graphical form. The information base for the study was data from the official website of the Federal State Statistics Service. The maximum values of the renewal coefficient were observed in 2018, 2019 and 2022, and the minimum in 2015, 2020 and 2021. The maximum values of the retirement rate are presented in 2014, 2015 and 2016, and the minimum in 2019, 2020 and 2021. An econometric study of the capital productivity index showed an inverse and very weak relationship with the value of gross domestic product and the cost of fixed assets (the value of the correlation coefficient (index) (-0.1)): with an increase in the value of gross domestic product and the value of fixed assets in the period under study, no growth was observed capital productivity index. The value of the gross domestic product and the cost of fixed assets have a direct and close relationship (the value of the correlation coefficient (index) is 0.9): the annual growth of the gross domestic product is accompanied by an increase in the cost of fixed assets. A matrix of correlation coefficients (indices) was constructed to calculate the total correlation coefficient (index). The result of calculating the total correlation coefficient (index) (its value is 0.4) showed that there is a direct and weak connection between the capital productivity index, the value of the gross domestic product and the cost of fixed assets: the growth of the capital productivity index is weakly related to the growth of the gross domestic product and the cost of fixed assets funds.

Hydrogeochemical characterization and water quality evaluation for drinking and irrigation purposes of coastal aquifers of Middle Andaman

A comprehensive study to characterize the hydrogeochemistry of the region and the impact of saltwater intrusion on groundwater was conducted along the eastern coast of Middle Andaman of Andaman and Nicobar (A&N) Islands. The escalating population growth and intensified tourism activities have resulted in the over-extraction of groundwater. Seismic activities led to the opening of lineaments to the sea and the dissolution of limestone in the influence of seawater. 24 groundwater samples and 1 reference sample from sea were taken from various locations of middle Andaman. The analysis involved the determination of major cations, anions, and heavy metals using Inductively Coupled Plasma Optical Emission spectroscopy (ICP), spectrophotometry, and flame photometry. Furthermore, X-ray diffraction analysis, binary diagrams, Chloro-alkaline indices (CAI), Gibbs Plot, correlation matrix, Piper plot, Chadha’s plot and Principal Component Analysis (PCA) to the major ions data indicated rock-water interactions, strong correlations among alkali and alkaline earth metals, and interactions between seawater and carbonate minerals respectively. The water quality index indicated “very poor to unsuitable for drinking purposes” in 24% of the samples. Additionally, indices for irrigation suitability; total hardness (TH), residual sodium carbonate (RSC), and Magnesium adsorption ratio (MAR) were found to be detrimental for irrigation in 80%, 08%, and 12% of the water samples, respectively. These results highlight the importance of implementing effective water resource management techniques, such as groundwater extraction rates, adopting appropriate water treatment technologies to mitigate the adverse impacts of heavy metals, saltwater intrusion and maintain water quality for human consumption and agricultural use in the region.