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The present study deals with the medicinal values of Bulbophyllum griffithii (Lindl.) Rchb.f., a rare and unexplored ethnomedicinal orchid, whose therapeutic applications have been included in the traditional Chinese medicine (TCM). Gas chromatography mass spectrometry (GC–MS) and high-resolution liquid chromatography mass spectrometry (HR-LCMS) carried out detected over 100 bioactive compounds belonging to different classes like sesquiterpenoids, phenolics, flavonoids, coumarins, fatty acids, and antibiotics to be present in different tissues of B. griffithii. The analysed compounds are reported to possess anti-cancerous, anti-diabetic, anti-inflammatory, anti-hypertensive, anti-ageing, and antimicrobial properties. Further, the phytochemical contents and antioxidant activity of the tissue extracts were found to be highest in methanol as compared to other solvents. The root tissue extract was found to contain the highest phenolics (25.93±0.32 mg g-1) and tannins (19.53±0.34 mg g-1), while the leaf tissue extract had the highest content of flavonoids (20.72±0.36 mg g-1). The antioxidant activity estimated by ferric reducing antioxidant power (FRAP) and metal chelating activity (MCA) assays, as well as half-maximal inhibitory concentration (IC 50) determined by 2,2-diphenyl-1-picryl-hydrazyl (DPPH) and 2,2-azino-bis-3-ethylbenzothiazoline-6-sulphonic acid (ABTS) assays were found to be maximum for the root tissue extract (DPPH value 44.36±0.42 µg/mL; ABTS value 61.25±0.79 µg/mL). Assessment of the antibacterial activity of B. griffithii using the root and pseudobulb tissue extracts showed a broad-spectrum of antagonistic activity against gram-negative and gram-positive pathogenic bacteria whereas the leaf tissue extract exhibited poor antibacterial activity. The correlation matrices, including the Pearson correlation coefficient (PCC) and Principal component analysis (PCA), showed that the phenolics and flavonoids present in B. griffithii tissue extracts were positively correlated with its radical scavenging power, while antibiotics such as carbenicillin, armillarinin, kolanone, salinomycin, and phytosphingosine provided bacterial inhibition activity. Hence, this study is highly significant as it establishes and validates the medicinal properties of B. griffithii , which has not been explored so far. [Display omitted] • Methanolic tissue extracts of Bulbophyllum griffithii demonstrated potent anti-bacterial properties. • Sesquitepenoids, phenolics, flavonoids, coumarins and fatty acids were the major class of bioactive compounds identified. • Study useful for establishing medicinal attributes of B. griffithii , an unexplored rare orchid species. [ABSTRACT FROM AUTHOR]

Natural compounds are important source of desired biological activity which helps to improve nutritional status and brings many health benefits. Ilex paraguariensis St. Hill. which belongs to the family Aquifoliaceae is a plant rich in bioactive substances (polyphenols, saponins, alkaloids) with therapeutic potential including hepatic and digestive disorders, arthritis, rheumatism, and other inflammatory diseases, obesity, hypertension, hypercholesterolemia. In terms of phytochemical research I. paraguariensis has been the subject of most intensive investigations among Ilex species. Therefore, we concentrated on other available Ilex varieties and focused on the content of fatty acids of these shrubs. The fatty acid compounds present in Ilex sp. samples were analyzed by GC-MS. 27 different fatty acids were identified in the extracts. The results showed that many constituents with significant commercial or medicinal importance were present in high concentrations. The primary component in all samples was α linolenic acid(18:3 Δ9,12,15). Differences of this component concentration were observed between cultivars and extensively analyzed by PCA, one- way ANOVA and Kruskal-Wallis ANOVA. Significant correlations between compound concentrations were reported. [ABSTRACT FROM AUTHOR]

The cross section of 85Rb(n,2n)84mRb and 85Rb(n,p)85mKr reactions were measured at neutron energy range 12–20 MeV using activation analysis followed by off-line γ-ray spectroscopic technique. The quasi mono-energetic neutrons were produced through 7Li(p,n)7Be reaction. The measurements were done relative to 27Al(n,α)24Na reference monitor reaction cross section. The detailed uncertainty propagation from the attributes present in measurements was performed using covariance analysis. The γ-ray self-attenuation and background low neutron energy corrections were performed in the measurement studies. Theoretical calculations were performed by TALYS-1.96 nuclear code. The comparison of measured values with the available data in EXFOR database and evaluated data from JENDL-5 and EAF-2010 shows the measured cross section values of 85Rb(n,p)85mKr reaction were slightly higher than the published values, evaluated data and theoretical data while for 85Rb(n,2n)84mRb, the measured values were consistent with the published data. [ABSTRACT FROM AUTHOR]

The study was carried out to evaluate hydro geochemistry and the risk of groundwater and surface water pollution in the Kombolcha area. To achieve this, hydrogeochemical analysis, water heavy metal, geospatial data analysis, correlation matrix, principal component analysis, Heavy metal Pollution Index (HPI), and Water Quality Index (WQI) methodologies were employed. A total of 36 samples (both water and effluent samples) had been collected and assessed for major physicochemical variables and heavy metals. Hydrogeochemical methods showed groundwater mineralization due to (1) silicate weathering, (2) cation exchange processes, and (3) anthropogenic sources (i.e., contaminated discharge of sulphate, carbonate, and trace metal effluents). The study result revealed that major ions dominating the area are Ca2+ > Na+ > Mg2+ > K+, HCO3− > SO 2− > Cl− > NO3−, and Fe > Mn > Pb > Cr > Cd for cations, anions and trace metals respectively with all heavy netals had mean concentrations above the WHO recommended limits. Calculated Pollution indices revealed 50.7% of the sample belongs to a low level of pollution, while 35% and 14.3% belong to a medium and high level of pollution respectively which consequently translating the area into high groundwater pollution zones. The correlation matrix revealed that no significant correlation exists between the water quality variables (Cl and NO3− with Fe, Pb, Cr, Mn, and Cd). PCA was applied on the data set to identify the spatial sources of pollution in groundwater and in the first principal component analysis, Mn, Fe, Cr, Pb, and Cd (in descending order) were found in amounts greater than 0.5, confirming that these metals were from anthropogenic sources. The combined assessments based on WQI and HPI, the study showed that water samples in the proximity of industrial sites are polluted by factories effluent and uncontrolled waste disposal due to urbanization. [ABSTRACT FROM AUTHOR]

The distribution of the sample correlation matrix when the sample comes from a real multivariate normal population and when the population covariance matrix is diagonal or when the population variables are independently distributed is available in the literature. In the general case, that is, when the covariance matrix in the normal population is a general real positive definite matrix, the distribution of the sample correlation matrix is not available in a compact computable form in the literature. Some scattered results on some aspects of the general case are available, but the representations are not easily tractable. This article deals with a covariance structure coming from a real matrix-variate gamma when the scale parameter matrix is a general real positive definite matrix. General procedures for deriving asymptotic chi-square and asymptotic normal for some general moment structures are also presented in this article. The density in some particular cases are given explicitly. Graphs are also provided in the particular cases. [ABSTRACT FROM AUTHOR]

Identifiability of a mathematical model plays a crucial role in the parameterization of the model. In this study, we established the structural identifiability of a susceptible-exposedinfected-recovered (SEIR) model given different combinations of input data and investigated practical identifiability with respect to different observable data, data frequency, and noise distributions. The practical identifiability was explored by both Monte Carlo simulations and a correlation matrix approach. Our results showed that practical identifiability benefits from higher data frequency and data from the peak of an outbreak. The incidence data gave the best practical identifiability results compared to prevalence and cumulative data. In addition, we compared and distinguished the practical identifiability by Monte Carlo simulations and a correlation matrix approach, providing insights into when to use which method for other applications. [ABSTRACT FROM AUTHOR]

Three stations along the Ase River, Delta State Nigeria provided water and 85 fish samples which were analyzed. The fish were measured and examined for endoparasites according to established protocol. All water quality parameters investigated were within the WHO-acceptable values for surface waters. Station variation of physicochemical parameters was not statistically significant (p > 0.05). Fish body conformation indices positively correlated with the prevalence of parasites in Clarias gariepinus, Heterobranchus longifilis, Parachana africana, Chromidotilapia guntheri guntherii, and Denticeps clupeodes. The overall parasite prevalence of 63.53% was established with the most abundant parasite being Trichodina mutabillis. The parasites had a predilection for the gastrointestinal tract with a high occurrence of 307 individuals. Stations 1, 2, and 3 had 326, 213, and 259 parasites, respectively, out of a total of 798 parasites detected. P. laevis was absent in station 1. All parasites were found in stations 2 and 3. Statistically, there was a significant difference (p < 0.05) in the prevalence in all stations. The correlation index of T. mutabillis and R. congolensis in stations 1 and 3 was positively strong (p < 0.05) with the concentrations of water quality. However, water conditions in stations 1 and 3 had a deleterious impact on P. laevis. T. mutabillis maintained a high positive correlation with physicochemical water quality in all three stations. Shannon-Weiner's index in station 3 (H = 1.337) shows that the parasites were more diverse. PCA and biodiversity indices have enabled us to comprehend how parasite-host-environment systems interact. [ABSTRACT FROM AUTHOR]

This study analyzes the geological and geochemical features of surface sediments in the Hwangdo Tidal Flat, located on Korea’s West Coast. The tidal flat experiences semi-diurnal tides, impacting organic matter decomposition and nutrient cycling. Ninety one sediment samples were collected and analyzed for physical and chemical properties including grain size, density, water content, organic carbon, and nitrogen. Sediments consist mainly of sand and silt, with coarser sediments near the main channel and finer sediments towards the west. Sediment grain size averages 4.12 Φ with a sorting coefficient of 1.96 Φ, indicating diverse energy environments. Total organic carbon and nitrogen correlate positively with grain size and density, reflecting sediment origin and environment. Kriging maps sediment grain size distribution, while correlation and linear regression analyses show relationships between variables. High correlations exist between various parameters, aligning with tidal flat characteristics and aiding understanding of sediment transport and deposition. The study provides baseline data for understanding the tidal flat’s geological, geochemical, and physical aspects, valuable for remote sensing validation and environmental monitoring. The dataset is freely available for research and management purposes.

Abstract Crop productivity relies on the quantity and quality of irrigation water, with groundwater quality playing a major role. Exploitation of groundwater for irrigation is slowly increasing in central districts of Odisha. A study was carried out in these areas to assess the groundwater quality for irrigation for sustainable use of the groundwater resources. This was done by developing a Water Quality Index (WQI) for eight parameters. The groundwater quality was assessed using a framework that utilised the entropy weight approach and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). This framework was employed to determine the relative proximity of each solution to the ideal solution. The TOPSIS range is divided into three categories: low quality (less than 0.1), medium quality (0.1–0.5), and good quality (0.5–0.9). The low-quality category has a percentage of 6.45%, categorized as poor quality and unfit for drinking and irrigation uses; the medium-quality category has 52.07%, and the good quality category has 41.47% of the area falls under the very good category. These parameters are essential when considering water usage for agriculture, especially in regions where farmers use fertilizers and pesticides extensively to enhance crop production.

Identifiability of a mathematical model plays a crucial role in the parameterization of the model. In this study, we established the structural identifiability of a susceptible-exposed-infected-recovered (SEIR) model given different combinations of input data and investigated practical identifiability with respect to different observable data, data frequency, and noise distributions. The practical identifiability was explored by both Monte Carlo simulations and a correlation matrix approach. Our results showed that practical identifiability benefits from higher data frequency and data from the peak of an outbreak. The incidence data gave the best practical identifiability results compared to prevalence and cumulative data. In addition, we compared and distinguished the practical identifiability by Monte Carlo simulations and a correlation matrix approach, providing insights into when to use which method for other applications.

This paper primarily aims to address aspects related to regional development in Romania, which has become a challenge both at the national and global level. The way regional development is approached is different, depending on the particularities of each country, as well as the governing ideologies in each state. However, the goal is common, one aims at shaping a competitive and strong economy, a high standard of living, access to education and medical assistance. The purpose of the paper is to compare socio-economic indicators from two time periods, to highlight similarities and differences and to observe their evolution within a 10-year timespan. The method used is Principal Component Analysis (PCA) to identify groups of statistical indicators (variables) that explain the level of development within a sample, in our case the development regions of Romania. In the context of regional development, PCA can be applied to extract relevant information about economic, social or demographic variability in different regions of the country. Several objectives of regional development analyzed through the lens of this method were pursued: identification of regional disparities, optimization of resource allocation, evaluation of the determining factors of economic development, monitoring of the impact of regional policies. Following the PCA analysis, it was observed that regional development cannot be evaluated by means of variables belonging to a single sector. This method allowed the identification of the variables that have the greatest weight describing economic, social, educational and infrastructure aspects. PCA was applied for two time periods and similar situations were obtained, the first main component called by the authors the economic statistics includes for both periods the indicators: Gross average nominal salary gain, GDP by development regions, Employees from research and development activity at the end of the year, Beds in health facilities (state and private). The second component was called social statistics and includes the indicator Employment rate in working age (15-64 years), and the third component called agricultural statistics includes the indicator Population served by the public water supply system. As a conclusion, we consider that the situations are not overlapping because there are indicators that have undergone changes during the 10 years, which is expected and normal in regional development. For example, the number of graduates decreased due to the continuously decreasing birth rate, the vacancy rate decreased in 2018-2019 compared to 2008-2009 because Romania went through the financial economic crisis.

In this paper, we analyse the computational advantages of the spherical parametrisation for correlation matrices in the context of Maximum Likelihood estimation via numerical optimisation. By using the special structure of correlation matrices, it is possible to define a bijective transformation of an n × n correlation matrix R into a vector of n (n - 1) / 2 angles between 0 and π . After discussing the algebraic aspects of the problem, we provide examples of the use of the technique we propose in popular econometric models: the multivariate DCC-GARCH model, widely used in applied finance for large-scale problems, and the multivariate probit model, for which the computation of the likelihood is typically accomplished by simulated Maximum Likelihood. Our analysis reveals the conditions when the spherical parametrisation is advantageous; numerical optimisation algorithms are often more robust and efficient, especially when R is large and near-singular. [ABSTRACT FROM AUTHOR]

Next Basket Recommendation (NBR) tries to recommend items in a user's coming basket by understanding the user's characteristics from the past baskets of the user. The existing deep learning models for recommendation system (RS) are formulated by combining the long-term and short-term preferences of the user successfully. Recent statistical-based models highlight the importance of repeat purchase behavior, especially in the E-commerce industry, as most customer repeatedly purchases items. Including repeat behaviour dynamics can lead to a certain degree of improvement in the deep learning-based NBR models, as shown in a few recent statistical-based works. In this paper, we introduced a mechanism to extract the user's repetition behaviour along with the user's long-term preferences and short-term preferences. To capture the repetition behavior of the user, we introduced the encoded user's baskets as Repeat Aware Baskets, and to extract the correlation between items, we used a Correlation Sensitive Basket. Further, separate embedding is generated with respect to Repeat Aware and Correlation Sensitive Baskets. These embedding are fed parallel to two layered Long-Short Term Memory architecture for analyzing short-term preference. To evaluate the performance of the proposed model, we experimented on two data sets. Our proposed algorithm outperformed various recently developed models over various performance metrics. [ABSTRACT FROM AUTHOR]

Computer network security represents a major challenge in the digital age, where intrusions threaten data confidentiality, accuracy and accessibility. To safeguard data and online services, Intrusion Detection Systems (IDS) controls the network traffic for any signs of malicious activity. The integration of artificial intelligence into IDSs offers new perspectives, but poses challenges, particularly in terms of feature selection and data imbalance management. Our research focused on identifying DDoS attacks, a major threat to the accessibility of online services. We evaluated the effectiveness of IDS against these attacks by testing the RF, XGB, SGD, LGB and MLP machine learning models on the CICIDS2018 DDOS attacks dataset. To optimize data quality, we adopted a strategic feature selection approach based on correlation matrix, mutual information and feature importance, reducing data dimensionality and improving model performance. Then, by balancing our dataset using oversampling techniques such as SMOTE, BorderlineSMOTE and ADASYN, we achieved better model generalization and reduced false positives. Our results showed that the ADASYN+SMOTE+XGB configuration was the most optimal for DDoS attack detection regarding effectiveness, false positives and execution duration. Our approach, combining judicious feature selection and resampling, has enabled us to create more performing intrusion detection systems, strengthening network security against increasingly sophisticated threats. [ABSTRACT FROM AUTHOR]

We present a method for Bayesian structural equation modeling of sample correlation matrices as correlation structures. The method transforms the sample correlation matrix to an unbounded vector using the matrix logarithm function. Bayesian inference about the unbounded vector is performed assuming a multivariate-normal likelihood, with a mean based on the transformed model-implied correlation matrix, and a covariance assumed to be of known form. Using Monte Carlo simulation, we examine the performance of the method with normal and ordinal indicators, as well as the capacity of the method to estimate models that account for misspecification. The performance of the approach is often adequate suggesting that the proposed method can be used for Bayesian analysis of correlation structures. We conclude with a discussion of potential applications of the approach, as well as future directions needed to further develop the method. [ABSTRACT FROM AUTHOR]

: The association between live body weight and morphometric traits plays a major role in the daily management and improvement of poultry. The objective of the current study was to determine the association between morphometric features and live body weight, as well as to investigate the direct and indirect effects of morphometric traits on White Leghorn laying hens' live body weight. Live body weight (BW) and morphometric traits including shank circumference (SC), body length (BL), wing length (WL), shank length (SL), toe to back length (TBL), beak length (BKL), beak to comb length (BCL), height (CH) and chest girth (CG) were collected from one hundred (n = 100) White Leghorn laying hens aged 40 weeks. The correlation findings showed that BW was positively correlated to SL, WL, SC, BL, and CH (p < 0.05). The Path analysis results reported that BL (0.45) had the highest direct effect on BW while WL (0.14) had the highest indirect effect on BW via BL. Correlation results propose that improvement of BL, SL, SC, CG and CH might increase the BW of White Leghorn hens. Path analysis results on the other hand, imply that BL and SC may be used as selection basis during breeding to improve BW in chickens. This study suggests that BW of White Leghorn is correlated with some morphometric traits that might be used during breeding. The findings also suggest that body length directly influence the live body weight of White Leghorn chicken breed. [ABSTRACT FROM AUTHOR]

In the meteorological department rainfall prediction is one of the complex tasks because it is directly linked to human life and the Indian economy. There is a significant demand for accurate and effective rainfall prediction methods to make better decisions regarding precautionary measures. To predict rainfall amounts effectively, this study proposed a novel rainfall prediction method named the Hybrid Dynamic Arithmetic City Council Optimization (HDACO) algorithm. The proposed HDACO method is a combination of two algorithms namely the Dynamic Arithmetic Optimization (DAO) algorithm and the City Councils Evolution (CCE) algorithm. The study utilizes preprocessing steps namely data cleaning, filling missing values, and data normalization. After preprocessing, the features closely related to rainfall prediction are selected by the computation of the correlation matrix. Finally, based on the features selected the HDACO algorithm predicts the amount of rainfall. The HDACO algorithm is evaluated using an open weather dataset and the effectiveness of the HDACO algorithm is validated using measures such as rainfall rate, Mean Absolute Error (MAE), coefficient of determination (R2), and Root Mean Square Error (RMSE). As a result, the HDACO algorithm achieved RMSE of 0.272, MAE of 0.184, and R2 of 0.97 respectively. The performance of the HDACO algorithm is compared with existing methods and the results demonstrate the better performance of the HDACO algorithm in rainfall prediction. [ABSTRACT FROM AUTHOR]

AbstractThe starting point for much of multivariate analysis (MVA) is an n × p data matrix whose n rows represent observations and whose p columns represent variables. Some multivariate datasets, however, may be best conceptualized not as n discrete p-variate observations, but as p curves or functions defined on a common time interval. Here we introduce a framework for extending techniques of multivariate analysis to such settings. The proposed continuous-time multivariate analysis (CTMVA) framework rests on the assumption that the curves can be represented as linear combinations of basis functions such as B-splines, as in the Ramsay-Silverman representation of functional data; but whereas functional data analysis extends MVA to the case of observations that are curves rather than vectors—heuristically, n × p data with p infinite—we are instead concerned with what happens when n is infinite. We present continuous-time extensions of the classical MVA methods of covariance and correlation estimation, principal component analysis, Fisher’s linear discriminant analysis, and k-means clustering. We show that CTMVA can improve on the performance of classical MVA, in particular for correlation estimation and clustering, and can be applied in some settings where classical MVA cannot, including variables observed at disparate time points. CTMVA is illustrated with a novel perspective on a well-known Canadian weather dataset, and with applications to datasets involving international development, brain signals, and air quality. The proposed methods are implemented in the publicly available R package ctmva. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

This study was undertaken to determine natural radioactivity and potential radiological hazards associated with cement. A NaI (Tl) gamma-ray spectrometer run with gamma vision software was used to analyze the samples. The average value of activity concentrations of 226Ra, 232Th, and 40K were 22.9 ± 7.7, 27.3 ± 8.3, and 336.5 ± 44.9 Bq/kg respectively. These values are well below the global average value for building materials. All the radiological hazard parameters are also lower than the recommended value for safe use. From a radiological point of view, they are therefore suitable for use as building materials. [ABSTRACT FROM AUTHOR]

In recent times, the complex network theory is increasingly applied to characterize, classify, and diagnose a broad spectrum of neuropathological conditions, including attention deficit hyperactivity disorder (ADHD), Alzheimer's disease, bipolar disorder, and many others. Nevertheless, the diagnosis and associated subtype identification majorly rely on the baseline correlation matrix obtained from the functional MRI scan. Thus, the existing protocols are either full of personalized bias or computationally expensive as network complexity-based simple but deterministic protocols are yet to be developed and formalized. This article proposes a deterministic method to identify and differentiate the common ADHD subtypes, which is based on a single complexity measure, namely the eigenvector centrality. The node-wise centrality differences were explored using a classification tree model (p < 0.05) to diagnose the subtypes. Identification of marker nodes from default mode, visual, frontoparietal, limbic, and cerebellar networks strongly vouch for the involvement of multiple brain regions in ADHD neuropathology. [ABSTRACT FROM AUTHOR]

The seasonal water quality of the Surajpur wetland was analysed using 13 physicochemical parameters, including pH, TDS, EC, temperature, total hardness, total alkalinity, DO, BOD, phosphate, fluoride, nitrate, sulphate, and chloride employing standardized procedures. Fifteen samples were collected in triplicate from five distinct locations within the wetland. These samples were then compared to the BIS and WHO standards (2012). The analysis used titrimetric techniques to determine DO, BOD, total hardness, total alkalinity, and chlorides. Moreover, phosphate, sulphate, and nitrate were determined through UV-spectrophotometry. The wetland has an alkaline nature, the hydrogeochemical facies that dominate the surface water are Ca2+, Mg2+, HCO3, and Cl−. The results indicate that TDS, total alkalinity, phosphates, and fluorides exceeded the permissible limits. The evaluation of water quality in the study area was conducted through the implementation of the standard Water Quality Index (WQI), the Canadian Council of Ministers of the Environment Water Quality Index-(CCME-WQI) and the Comprehensive Pollution Index (CPI) and their associated parameters.. The CCME-WQI ranges from 46 to 53, indicating very poor/marginal water quality, the WQI value ranges from 82.80 to 96.18, indicating good water quality, while the CPI ranges from 0.9 to 1.15, indicating very poor/marginal water quality. The pH-EC and pH-TA, TDS-CPI, Cl-TH and the WQI-PO43− are negatively correlated at a significance level p < 0.01. The TA-EC, BOD-DO and TA-PO43− are negatively correlated at a significance level p < 0.05 as per the correlation matrix result. Using hierarchical cluster analysis, 5 sampling sites were divided into two clusters based on the similarity of water quality characteristics: comparatively less polluted and more polluted sites. The four principal components were identified using PCA, principal components contribute to 46.02%, 223.48%, 19.38% and 11.11%, of the total variance of the dataset respectively. The PCA indicate that the variation in water quality is due to anthropogenic activities around the vicinity of the wetland. In order to preserve the wetland's aesthetic value and ecological health, preventive measures such as de-weeding, preventing the infiltration of untreated sewage, and desilting are advised. Incorporating these measures can prevent potential damage to the wetland and ensure the survival of its ecology and aesthetic value. [ABSTRACT FROM AUTHOR]

On the example of data on companies of the mechanical engineering industry, an analysis of the relationship of financial ratios and their impact on the financial risk index (FRI) for self-assessment of the risk of business insolvency is performed using a linear regression model in Python. In the course of construction and quality control of the model, limitations of its practical application are revealed. Conclusions and recommendations are formulated based on analysis results. [ABSTRACT FROM AUTHOR]

Background and Objectives Groundwater quality assessment and related hydrochemical characters are critical for water resources management in the arid and semi-arid regions. Water quality is a function of physico-chemical parameters and strongly influenced by geological formations and human activities. The hydrochemical evolution of groundwater mostly depends on the chemistry of the aquifer materials, water and rock interaction and water residence time. Water quality assessment requires knowledge of mineralogy of the surrounding rocks and identification of relevant chemical reactions. Ions in ground water may be of natural source, human origin or a combination of both factors. The Lanjanat is the largest Plain in Isfahan Province and is located in the Gavkhouni Basin where the Zayandehroud River is passing. According to the geological classification of the country, Lanjanat Region is located in the Sanandaj-Sirjan structural zone. The hard rocks exposed in the heights surrounding area include Permian rocks, Jurassic sediments (including dark gray shales with interlayers of sandstone, quartzite, limestone, and volcanic), shale, marl, and Cretaceous limestone, conglomerates, and Paleogene sandstones. The Quaternary sediments exposed in the plain have formed an alluvial aquifer over the impermeable Jurassic shales. The main recharge source of the Lanjanat Aquifer is meteoric water, which decreases from west to east and from south to north of the basin. Lanjanat plain is one of the most fertile agricultural centers in Isfahan Province, which in recent years has faced a quantitative and qualitative crisis of water resources due to the increasing population, development of industries and agriculture and also excessive extraction of groundwater. In this research, an attempt has been made to update the information related to the quality of surface and groundwater and to determine the origin and hydrochemical evolution of the groundwater in the plain. Methodology In this research, 13 locations of surface and underground water sources, including rivers, springs, Ghanats, agricultural wells, and fields were selected for sampling. The pH, temperature and electrical conductivity of water were measured in fields. Also, total dissolved solids and major ions concentration were determined in the laboratory. After determining the physicochemical characteristics and concentrations of the main ions of the samples, to understand the relationship between the main hydrochemical variables, a Pearson correlation matrix was provided. Besides, the ion ratio diagrams (including Na+/Cl-, HCO3-/Na+, Ca2+/SO42-, Ca2++Mg2+/HCO3-+ SO42-, Ca2+/ HCO3- and HCO3-/Mg2+ ratios) together with the combination charts, and Piper's diagram were used to determine the origin of ions in water sources Findings The results showed that the minimum and maximum concentrations were respectively observed in the river samples and the deep wells located in the central sector of the plain. The range of water electrical conductivity was between 264 5755 µ/cm in the rice field and 5755 µ/cm in the agricultural well. The total dissolved solids were between 271 mg/liter in the river sample and 3889 mg/liter in the agricultural well. The number of dissolved solids was increased by moving from the sides towards the central sector of the plain, as well as by increasing the depth of the wells. The water type of the samples varies from calcium bicarbonate near the carbonate formations to calcium sulfate or sodium chloride in the central sector of the plain. The Pearson correlation matrix showed a very strong positive correlation between the total dissolved solids and electrical conductivity of water and a strong inverse correlation between pH and total dissolved solids and electrical conductivity. Conclusion The concentration of the main anions and cations in the surface and underground waters of the region was mostly affected by natural factors, especially the dissolution of carbonate and evaporative minerals. According to the results of the ion ratio diagrams, the origin of the main ions in the water resources of the region was mostly from the dissolution of geological formations, although human activities were effective in some places. Agricultural activity has caused an increase in concentration of some ions by using fertilizers (such as sodium and sulfate). The present research is only based on the results of a sampling period of 13 surface and underground water sources. In order to better understand the hydrochemistry of the plain, it is suggested to carry out monthly sampling during a water year, and at more points. Also, in addition to the main anions and cations, the concentration of nitrate, phosphate and heavy metals should also be measured and analyzed. [ABSTRACT FROM AUTHOR]

The microbiological quality of municipal water is of prime importance for public health and represents a prominent concern for authorities to maintain and control the bioburden level within safe limits. An appropriately functioning network must ensure the biological stability of the water distribution system throughout the way of water from the processing and purification station until reaching the ultimate consumers. The study examines methodologies used to evaluate different segments from a local distribution system of city water by examining short-term results of the total microbial count obtained from several available points of use through a one-year period. Microbial variations observed in city's water system. Autumn has the highest bioburden. Locations exceeding 10,000 CFU/100 mL threshold warrant investigation. Non-uniform distribution across the system suggests potential influence of distance, pipe type, and biofilms. Early warning system established to monitor bioburden surges. Descriptive analysis showed that six lines from 19 examined sections from the distribution system had lower dispersion in the microbiological data between minimum and maximum results than the remaining parts of the distribution pipes. Moreover, multiple comparison test outputs returned significant differences in microbiological density datasets between high-count and low-count use points. Thus, surveying the layout design of the municipal water distribution infrastructure would be crucial to restate its impact on the sanitary distribution of water. In conclusion, this refined study provides a nuanced examination of microbiological stability in municipal water distribution, offering targeted insights for effective public health management. By implementing the present assessment, public health authorities can spot and identify defective areas in the distribution system and take necessary actions to prevent or mitigate potential risks of waterborne diseases. [ABSTRACT FROM AUTHOR]

Waste pickers pick up reusable and recyclable waste from roadsides, dustbins, and landfills and sell it to local scrap dealers to earn money to fulfil their daily needs. Waste pickers do this in very unhygienic and hazardous conditions. This study examines the socio-economic status of waste pickers working in Ghaziabad using the Kuppuswamy SES (2020) scale. For this, 200 waste pickers, including male and female respondents, participated in this study. IBM SPSS software was used to code and analyse the data. Correlation matrix and regression were used for analysis. The research found that although waste pickers are crucial to society, their socio-economic situation is among the lowest. 51% of waste pickers are from the lower middle class, and 42% are from the upper lower class. These waste pickers of our study are underprivileged, illiterate, and live in poor conditions. For a sustainable and decent living, they need the government to satisfy some of their needs, including free medical services, access to clean, fresh water, social safety, and training for alternative employment.

The rise of technology has heightened work demands, adversely impacting mental health and fitness. The COVID-19 pandemic exacerbates psychological stress, emphasizing the need for non-pharmacological interventions like yoga. Yoga positively influences the autonomic nervous system, benefiting cardio-respiratory health, metabolic efficiency, and conditions like Type-2 diabetes, Chronic Venous disease, and obesity. This study employs a dataset with 100 samples and 43 features related to Chronic Venous Insufficiency (CVI). Logistic and Random Forest classifiers are validated using K-fold cross-validation, with feature selection optimizing prediction accuracy. Hybrid models, enhanced with optimization algorithms, predict Venous Clinical Severity Score (VCSS) before, one, and three months after yoga practices. The Random Forest classifier, particularly RFGT, proves highly accurate in categorizing baseline severity and identifying Mild and Moderate CVI cases. RFGT demonstrated AUC score of 0.9072, 0.8714, 0.7709, and 0.7200 in Absent, Mild, Moderate, and Severe patient groups classification before yoga practices (VCSS-Pre). These values were 0.9158, 0.8644, 0.8142, and 0.6333 for VCSS-1 and reported as 0.9269, 0.8399, 0.7838, and 0.7500 for patients’ classification in VCSS-3. Predicting VCSS scores before yoga intervention assists in categorizing participants for personalized care and efficient resource allocation. The RFC-based models, notably RFGT, show high accuracy in identifying baseline severity, enabling early intervention for high-risk individuals. These models, especially RFGT, perform well in classifying Mild and Moderate CVI cases, informing lifestyle modifications. Predicting VCSS-1 scores evaluates the short-term impact of yoga practices, identifying individuals requiring additional support. RFGT aids in personalized recommendations based on specific factors, crucial for severe conditions. Predicting VCSS-3 scores assesses the sustained impact over three months, identifying intervention responders, particularly in Severe and Moderate groups. RFGT demonstrates optimal predictions, contributing to future interventions tailored to individual responses and improved outcomes.

Analysis and improvement of crop productivity is one of the most important areas in precision agriculture in the world, including Kazakhstan. In the context of Kazakhstan, agriculture plays a pivotal role in the economy and sustenance of its population. Accurate forecasting of agricultural yields, therefore, becomes paramount in ensuring food security, optimizing resource utilization, and planning for adverse climatic conditions. In-depth analysis and high-quality forecasts can be achieved using machine learning tools. This paper embarks on a critical journey to unravel the intricate relationship between weather conditions and agricultural outputs. Utilizing extensive datasets covering a period from 1990 to 2023, the project aims to deploy advanced data analytics and machine learning techniques to enhance the accuracy and predictability of agricultural yield forecasts. At the heart of this endeavor lies the challenge of integrating and analyzing two distinct types of datasets: historical agricultural yield data and detailed daily weather records of North Kazakhstan for 1990-2023. The intricate task involves not only understanding the patterns within each dataset but also deciphering the complex interactions between them. Our primary objective is to develop models that can accurately predict crop yields based on various weather parameters, a crucial aspect for effective agricultural planning and resource allocation. Using the capabilities of statistical and mathematical analysis in machine learning, a Time series analysis of the main weather factors supposedly affecting crop yields was carried out and a correlation matrix between the factors and crops was demonstrated and analyzed. The study evaluated regression metrics such as Root Mean Squared Error (RMSE) and R2 for Random Forest, Decision Tree, Support Vector Machine (SVM) algorithms. The results indicated that Random Forest generally outperformed the Decision Tree and SVM in terms of predictive accuracy for potato yield forecasting in North Kazakhstan Region. Random Forest Regressor showed the best performance with an R2 =0.97865. The RMSE values ranged from 0.25 to 0.46, indicating relatively low error rates, and the R2 values were generally positive, indicating a good fit of the model to the data. This paper seeks to address these needs by providing insights and predictive models that can guide farmers, policymakers, and stakeholders in making informed decisions.

The accelerated growth of cyanobacteria in water bodies is a global critical environmental issue caused by continuous discharges of effluents into the environment that are rich in phosphorus and nitrogen. So, cyanobacteria have found propitious conditions for proliferation, provoking significant ecological imbalances. Cyanobacteria produce cyanotoxins, which are harmful to life, and compounds like 2-methylisoborneol and geosmin that affect water's taste and odor. This study analyzed a long-term database of important environmental parameters from a tropical reservoir in São Paulo State, Brazil. The statistical methods of correlation matrices and principal component analysis were used. Data analysis revealed a significant relationship between cyanobacteria growth and high levels of phosphate and nitrogen. Furthermore, positive correlations were found among concentrations of biocidal elements like antimony, arsenic, and selenium related to cyanobacterial bloomings. These correlations can be attributed to agricultural wastewaters and/or possible algicide used to control these microorganisms. HIGHLIGHTS Maintenance and safe management of public supply reservoirs.; Water quality factors correlating with cyanobacteria blooms.; Consequences of cyanobacteria blooms and contamination by cyanotoxins.; Sustainable management and monitoring of supply reservoirs.; Correlations of pollutants and compounds originating from human activity with cyanobacteria blooms.;

A total of 66,894 landslides were observed in North Dakota. The characteristics of these landslide locations were compared with the properties of areas without landslides to assess the factors that may be contributing to the landslides. Specifically, 68,395 control point locations randomly distributed across the state were selected for these comparisons. All the landslides for this study were found in areas with slopes less than 64°, with the majority of the failures occurring on slopes with inclinations between 9° and 14°. The largest fraction of the landslides occurred in the Sentinel Butte Formation (34,063 or 51% of the total), followed by Bullion Creek (8695 or 13% of the total) and river sediment of the Oahe Formation (6421 or 9.6% of the total). In the t tests, all of the surficial geologic formations had statistically significant differences between the landslides and control points. The t test for the slope inclination indicated statistically significant differences with a p-value less than 0.001 and a huge effect size between the landslide and control points. The sodium adsorption ratio and total dissolved solids were also found to be statistically significant from the t test results. Pearson's correlation matrix showed a negative correlation between the amount of rainfall and various measures of the salt concentrations at the landslide locations, pointing to the reductions in shear strength and slope stability that might result as pore fluid salinity is leached. [ABSTRACT FROM AUTHOR]

The water quality index (WQI) is a practical and comprehensive technique to evaluate water quality. It is often employed to assess water quality in simple terms and determine its suitability for various uses. The current study aimed to assess the temporal and spatial variation of the Al-Dujaila River. Three sites were selected for surveying from January to December 2021. The studied area starts after the AL-Kut dam and goes to the point where the river exits in the city of AL-Kut. Several physico-chemical parameters were addressed to calculate the IQ-WQI based on the Iraqi rivers maintaining limits; namely, dissolved oxygen (DO mg/ l), biological oxygen demand (BOD5 mg/l), hydrogen ion (pH), phosphate (PO4 mg/ l), nitrate (NO3 mg/ l), chloride (Cl mg/ l), sulfate (SO4-2 mg/ l), total dissolved solid (TDS mg/ l), and turbidity (Turb NTU). The concentrations of most parameters were within the standard limits, except for SO4-2, TDS, and Turb, where the highest concentrations reached 390mg/ l, 920mg/ l, and 70 NTU, respectively. The rating of the water quality index in these three sites falls under very poor water quality, where values of the IQ-WQI ranged between 83.41-89.48 in the wet season, and in the dry season, the value was 85.24-91.13. The shift of the index toward the worst conditions is related to the high concentration of TDS and SO4-2, where the standard limits for them are 500 and 200mg/ l according to the Iraqi rivers maintaining limits, respectively, which indicates pollution conditions of the river caused by the increased levels of salts. The principal component analysis (PCA) was used to visualize the data obtained to know how they interrelate in addition to extracting the most affecting parameters on water quality. [ABSTRACT FROM AUTHOR]

This work proposes the use of multi-criteria decision analysis (MCDA) to select a more environmentally friendly analytical procedure. TOPSIS, which stands for Technique for Order of Preference by Similarity to Ideal Solution, is an example of a MCDA method that may be used to rank or select best alternative based on various criteria. Thirteen analytical procedures were used in this study as TOPSIS input choices for mifepristone determination in water samples. The input data, which consisted of these choices, was described using assessment criteria based on 12 principles of green analytical chemistry (GAC). Based on the objective mean weighting (MW), the weights for each criterion were assigned equally. The most preferred analytical method according to the ranking was solid phase extraction with micellar electrokinetic chromatography (SPE-MEKC), while solid phase extraction combined with ultra-high performance liquid chromatography tandem mass spectrometry (SPE-UHPLC-MS/MS) was ranked last. TOPSIS ranking results were also compared to the green metrics NEMI, Eco-Scale, GAPI, AGREE, and AGREEprep that were used to assess the greenness of thirteen analytical methods for mifepristone determination. The results demonstrated that only the AGREE metric tool correlated with TOPSIS; however, there was no correlation with other metric tools. The analysis results suggest that TOPSIS is a very useful tool for ranking or selecting the analytical procedure in terms of its greenness and that it can be easily integrated with other green metrics tools for method greenness assessment. [ABSTRACT FROM AUTHOR]

When analyzing multivariate longitudinal binary data, we estimate the effects on the responses of the covariates while accounting for three types of complex correlations present in the data. These include the correlations within separate responses over time, cross‐correlations between different responses at different times, and correlations between different responses at each time point. The number of parameters thus increases quadratically with the dimension of the correlation matrix, making parameter estimation difficult; the estimated correlation matrix must also meet the positive definiteness constraint. The correlation matrix may additionally be heteroscedastic; however, the matrix structure is commonly considered to be homoscedastic and constrained, such as exchangeable or autoregressive with order one. These assumptions are overly strong, resulting in skewed estimates of the covariate effects on the responses. Hence, we propose probit linear mixed models for multivariate longitudinal binary data, where the correlation matrix is estimated using hypersphere decomposition instead of the strong assumptions noted above. Simulations and real examples are used to demonstrate the proposed methods. An open source R package, BayesMGLM, is made available on GitHub at https://github.com/kuojunglee/BayesMGLM/ with full documentation to produce the results. [ABSTRACT FROM AUTHOR]

Multi-fidelity (MF) surrogate models for incorporating multiple non-hierarchical low-fidelity (LF) datasets, whose rank of fidelity level is unknown, have attracted much attention in engineering problems. However, most of existing approaches either need to build extra surrogate models for LF datasets in the fitting process or ignore the cross-correlations among these LF datasets, resulting in accuracy deterioration of an MF model. To address this, a novel multi-fidelity cokriging model is proposed in this article, termed as MCOK, which can incorporate arbitrary number of non-hierarchical LF datasets without building extra LF surrogate models. A self-contained derivation of MCOK predictor and its mean square error are presented. It puts all the covariances between any two MF datasets into a single matrix and introduces additional parameters "gamma" to account for their cross-correlations. A novel method for tuning these additional parameters in a latent space is developed to deal with the problem associated with non-positive definite correlation matrix. The proposed MCOK method is then validated against a set of numerical test cases and further demonstrated via an engineering example of aerodynamic data fusion for FDL-5A flight vehicle. Results from current test cases show that MCOK outperforms existing non-hierarchical cokriging, linear regression MF surrogate model, and latent-map Gaussian processes model, with more accurate and robust predictions, which makes it more practical for engineering modeling problems. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Central European Agriculture is the property of Journal of Central European Agriculture and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Herein, the time series storage method of multi-valued attribute data, aimed at improving the efficiency of writing and querying data in "energy" big data centers is reported. Through rule construction and rule iteration of feature sequence, the time series features of multi-valued attribute data are extracted, and the "component attribute nearest neighbor propagation method" is used for clustering; the data are divided into cold, warm, and hot data. A storage engine with a solid state disk layer, mechanical hard disk layer, and memory layer has been designed, and the efficiency of data writing and query through migration operation is improved. The experimental results demonstrate that this method can effectively extract the time series features of multi-valued attribute data, and the Pre value of the clustering time series is higher than 0.93, which effectively improves the data writing and query efficiency of the energy big data center. [ABSTRACT FROM AUTHOR]

Analysis and improvement of crop productivity is one of the most important areas in precision agriculture in the world, including Kazakhstan. In the context of Kazakhstan, agriculture plays a pivotal role in the economy and sustenance of its population. Accurate forecasting of agricultural yields, therefore, becomes paramount in ensuring food security, optimizing resource utilization, and planning for adverse climatic conditions. In-depth analysis and high-quality forecasts can be achieved using machine learning tools. This paper embarks on a critical journey to unravel the intricate relationship between weather conditions and agricultural outputs. Utilizing extensive datasets covering a period from 1990 to 2023, the project aims to deploy advanced data analytics and machine learning techniques to enhance the accuracy and predictability of agricultural yield forecasts. At the heart of this endeavor lies the challenge of integrating and analyzing two distinct types of datasets: historical agricultural yield data and detailed daily weather records of North Kazakhstan for 1990-2023. The intricate task involves not only understanding the patterns within each dataset but also deciphering the complex interactions between them. Our primary objective is to develop models that can accurately predict crop yields based on various weather parameters, a crucial aspect for effective agricultural planning and resource allocation. Using the capabilities of statistical and mathematical analysis in machine learning, a Time series analysis of the main weather factors supposedly affecting crop yields was carried out and a correlation matrix between the factors and crops was demonstrated and analyzed. The study evaluated regression metrics such as Root Mean Squared Error (RMSE) and R2 for Random Forest, Decision Tree, Support Vector Machine (SVM) algorithms. The results indicated that Random Forest generally outperformed the Decision Tree and SVM in terms of predictive accuracy for potato yield forecasting in North Kazakhstan Region. Random Forest Regressor showed the best performance with an R2=0.97865. The RMSE values ranged from 0.25 to 0.46, indicating relatively low error rates, and the R2 values were generally positive, indicating a good fit of the model to the data. This paper seeks to address these needs by providing insights and predictive models that can guide farmers, policymakers, and stakeholders in making informed decisions. [ABSTRACT FROM AUTHOR]

This study is carried out to find wastewater quality index in treated wastewater, and Statistical analysis was done in various collected samples in sewage treatment plant, Panjappur, Trichy. This study presents a Statistical analysis of the physicochemical parameters of sewage wastewater. Nearly twenty parameters of two different months were analysed using data from physico -chemical analyses. A Continuous Waste Water Quality Index (CWWQI) was established to gauge the overall state of treated wastewater quality. By taking into account previously set quality limitation norms, the index condenses a substantial number of measured quality characteristics into a single water quality term. To identify treatment process performance, the interdependencies between the variables were determined by using Principal Component Analysis (PCA). This study presents Factor analysis to identify the pollution and its type. The methods and approaches discussed in this study offer a framework for evaluating the monitoring programme for wastewater treatment.

Natural compounds are important source of desired biological activity which helps to improve nutritional status and brings many health benefits. Ilex paraguariensis St. Hill. which belongs to the family Aquifoliaceae is a plant rich in bioactive substances (polyphenols, saponins, alkaloids) with therapeutic potential including hepatic and digestive disorders, arthritis, rheumatism, and other inflammatory diseases, obesity, hypertension, hypercholesterolemia. In terms of phytochemical research I. paraguariensis has been the subject of most intensive investigations among Ilex species. Therefore, we concentrated on other available Ilex varieties and focused on the content of fatty acids of these shrubs. The fatty acid compounds present in Ilex sp. samples were analyzed by GC-MS. 27 different fatty acids were identified in the extracts. The results showed that many constituents with significant commercial or medicinal importance were present in high concentrations. The primary component in all samples was α linolenic acid(18:3 Δ9,12,15). Differences of this component concentration were observed between cultivars and extensively analyzed by PCA, one- way ANOVA and Kruskal-Wallis ANOVA. Significant correlations between compound concentrations were reported.

An Outlier is a data point which appears to be distinct from the majority of the other datapoints within a dataset. Various factors, such as measurement errors, data entry problems, natural variability, and extreme events, can lead to outliers. They may lead to erroneous evaluation measures, biased data analysis, and incorrect model performance resulting in in-suitable outcomes. Hence, identification and elimination of outliers is a crucial pre-processing step. This research paper suggests an algorithm for correctly removing outliers from the Pima Diabetes Dataset. Ittakes into consideration the correlation values of the features with the outcome column, further arranging them in descending order. The normal distribution graphs for each feature in the dataset are plotted for data analysis. For the Diabetes dataset, the features are either Positive (Right) skewed or Symmetrical. For the Symmetrical curve features, it eliminates data points which are lying beyond three standard deviations i.e (µ - 3σ) and (µ + 3σ), whereas for Positively skewed features, it eliminates those data points which are greater than (µ + 2.5σ). Starting from the feature having the highest correlation value, based on the skewness it eliminates the outliers by iterating the features one by one based on the order of correlation values. The proposed algorithmis successful in elimination of 108 rows from Pima Diabetes Dataset. To check the efficacy ofthe algorithm, it is compared with four techniques - Local Outlier Factor, Mahalanobis Distance, Multivariate Normal Distribution (N Dimensional) and DBSCAN. Also, for better evaluation the number of outliers eliminated by each technique lies in the same range. Furthermore, after elimination of outliers, three Machine Learning are implemented - Logistic Regression, Random Forest and Decision Tree. Among all the techniques, logistic regression on the proposed algorithmgives the highest accuracy of 84%, highest precision of 0.88 and highest recall of 0.65. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)