Correlation Coefficient Values Research Articles

THE RELATIONSHIP OF BALANCED NUTRITIONAL BEHAVIOR WITH THE NUTRITIONAL STATUS OF ADOLESCENTS

Background: Adolescents with poor nutritional status will have an impact on low intelligence, productivity and body immunity, while higher nutritional status can cause an increased risk of non-communicable diseases. Research Method: This research is an analytical research with a cross sectional research design. The research uses primary data, namely data collected by the researcher himself in the form of data on general characteristics, balanced nutritional behavior and nutritional status of adolescents. The research also used secondary data from IKesT Muhammadiyah Palembang for the number of undergraduate students in Nutrition at IkesT Muhammadiyah Palembang. Results: Based on the results of statistical tests, it shows that there is a relationship between balanced nutritional behavior and adolescent nutritional status (p value = 0.000). The level of strength of the relationship is strong with a correlation coefficient value of 0.597. The variable relationship is unidirectional, which means that the better the balanced nutritional behavior, the better the nutritional status. Conclusion: The results of the study show that there is a relationship between balanced nutritional behavior and adolescent nutritional status, p-value 0.000 < 0.05. Balanced nutritional behavior can help teenagers maintain normal nutritional status and prevent nutritional problems

#2836 Prevalence and risk factors associated with chronic kidney disease among type-2 diabetes mellitus patients – a single centre experience

Abstract Background and Aims Globally, diabetes mellitus is one of the major causes of increased morbidity. One of the key complications of T2D is chronic kidney disease (CKD). Current evidence demonstrates that people with type 2 diabetes (T2D) are at a higher risk of developing chronic kidney disease (CKD) with greater morbidity and mortality. So, we aimed to find out the prevalence of CKD among T2DM patients and to evaluate the correlation between age, duration of diabetes, HbA1c, and UACR with eGFR among T2DM patients. Method An observational cross-sectional study was conducted at department of medicine, tertiary care hospital for 2 months. Along with demographic and anthropometric data The following clinical data were collected: estimated glomerular filtration rate (eGFR), serum creatinine, urinary albumin creatinine ratio (UACR), and HbA1c levels. Spearman rank correlation was used to determine the correlation between eGFR and various parameters. Results A total of 96 patients were included in the study. 65% of the study participants were sedentary and 35% were physically active. 41 (42.7%) were obese and 21 (21.9%) were overweight. The mean Duration of diabetes is 5.62 ± 4.68 years. The mean estimated glomerular filtration rate (eGFR) is 74.33 ± 31.01 mL/min/1.73 m2. The Prevalence of CKD is 35.4%. 21.8% belongs to G3a category and 8.3% belongs to G3b category. Spearman's correlation coefficient test revealed negative correlation between eGFR and duration of diabetes, HbA1c and UACR (Spearman's correlation coefficient values are −0.577, −0.595 and −0.709 respectively). Conclusion In conclusion, nearly 1/3rd of study participants reported CKD. Our study provides consistent and recent clinical data showing an inverse association of eGFR with age and duration of diabetes. This study highlights that age and duration of diabetes are key parameters associated with declining renal functions in people with T2DM.

The WOUND-Q Function and Symptoms Scales for Chronic Lower Extremity Wounds: A Validation study.

Determine the validity and reliability of the LIMB-Q scales, Function and Symptoms, in patients with chronic lower extremity wounds. Cognitive debriefing interviews with people with current or previous wounds were conducted to examine content validity. Scales were field-tested in an international sample of people with chronic lower extremity wounds sourced from an online platform (i.e., Prolific). Psychometric properties were examined using Rasch Measurement Theory analysis. A test-retest reproducibility study was performed, and construct validity examined. Content validity was established after 10 cognitive interviews. A total of 233 people with lower extremity wounds (age 19-80 years, mean 39.3) participated in the field-test. All 25 items tested demonstrated good fit to the Rasch model with ordered thresholds. One item had a fit residual outside ± 2.5, but no items had significant χ2 values after Bonferroni adjustment. Reliability was high with Person Separation Index, Cronbach alpha, and intraclass correlation coefficients values >0.8. Strong correlations were found between the Function and Symptoms scales and EQ-5D dimensions measuring similar constructs as well as the EQ-5D global score. All hypothesis for construct validity were confirmed. PROMs are an important component of patient-centered care, as they capture the patient's perspective in a rigorous and reproducible way. Adding these two scales to the WOUND-Q provides a means to measure function and symptoms associated with lower extremity wounds. These new WOUND-Q scales can be used to measure outcomes important to patients with lower extremity wounds in clinical settings and research studies.

Biodegradation of endocrine disrupting compounds from the wastewater by the immobilized indigenous bacteria

Abstract4‐tert‐octyl phenol is one of the important endrocrine‐disrupting compounds and is considered a major health hazard. A total of six isolates degraded 4‐tert‐octylphenol, and the strains Bacillus velezensis LG16 and Pseudomonas aeruginosa AC20 exhibited maximum 4‐tert‐octylphenol degradation. Co‐culture of these two strains improved 4‐tert‐octylphenol degradation from the wastewater. One variable at a time approach showed that 40°C incubation temperature, pH 8.0, and an initial 4‐tert‐octylphenol (60 mg/L) influenced biodegradation. The bacterial strains were immobilized in sodium alginate beads, and improved biodegradation was achieved. The biocatalytic process mediated by the immobilized cells was optimized by a statistical approach (two‐level full factorial design and response surface methodology). In a two‐level factorial model, 4‐tert‐octylphenol degradation varied from 1.1% to 55.2%. The 4‐tert‐octylphenol degradation was maximum at pH 6, 0.01 mg/L 4‐tert‐octylphenol, and 10 mg/L glucose with 20 g beads/L. ANOVA revealed that the designed model was statistically significant (p = 0.0310). A central composite design was used to analyze the interactive effect of significant variables and to explore the optimum conditions for 4‐tert‐octylphenol degradation by immobilized bacteria. The maximum phenol degradation was observed (97.4%) at pH 7.0, 0.06 4‐tert‐octylphenol, and 13.75 g sodium alginate beads/L. ANOVA showed that the designed model was statistically significant (p = 0.0091). The designed CCD model, the correlation coefficient value, and the lack of fit value showed that the designed CCD model was significant. The immobilized bacterial cells could more effectively degrade 4‐tert‐octylphenol than free bacterial cells. The high degradation potential indicated its application in degrading 4‐tert‐octylphenol from wastewater under optimized conditions.

A vulnerability evaluation method of earthen sites based on entropy weight-TOPSIS and K-means clustering

The degradation of earthen sites due to natural and human factors has become a pressing issue, necessitating urgent protection measures. In this context, accurate assessment of the vulnerability of earthen sites is essential for the development of effective conservation strategies. In this study, a comprehensive evaluation framework that incorporates multiple indicators is proposed. In particular, the entropy weight- (Technique for Order Preference by Similarity to an Ideal Solution) TOPSIS method is employed for quantitative vulnerability assessment and combined with K-means clustering to define vulnerability levels for earthen sites. To validate the proposed approach, the vulnerability of 29 sections of the Ming Great Wall is evaluated. Eventually, the 29 earthen sites are categorized into three levels: high, medium, and low, according to their degree of vulnerability. The results of gray correlation analysis and entropy weight-TOPSIS method are compared using the ontology missing amount in the original data as the validation standard. The results show that the Pearson correlation coefficient value of the entropy weight-TOPSIS method with the ontology missing amount was 0.859, while the Pearson correlation coefficient value of the gray correlation analysis method with the ontology missing amount was 0.691, so that the results of the entropy weight-TOPSIS method can more accurately reflect the actual vulnerability of earthen sites.

Dual-time-point dynamic 68Ga-PSMA-11 PET/CT for parametric imaging generation in prostate cancer.

To investigate the optimal dual-time-point (DTP) approaches using dynamic 68Ga-PSMA-11 PET/CT imaging to generate parametric images for prostate cancer patients. Fifteen patients with prostate cancer were intravenously administered 68Ga-PSMA-11 of 181.9 ± 47.2MBq, followed by an immediate 60min dynamic PET/CT scan. List-mode data were reconstructed into 25 timeframes (6 × 10s, 8 × 30s, and 11 × 300s) and corrected for motion and partial volume effect. DTP parametric images were generated using different interval time points of 5min and 10min, with a minimum of 30min time interval. Net influx rates (Ki) were calculated through the fitting of a single irreversible two-tissue compartmental model. Intraclass correlation coefficient (ICC) values between DTP protocols and 60min Ki were obtained. Lesion-to-background ratios (LBRs) of Ki and standardized uptake value (SUV) images in each DTP protocol were determined. The DTP protocol of 5-10min with a 40-45min interval showed the highest ICC of 0.988 compared with the 60min Ki, whereas the ICC values for the intervals of 0-5min with 55-60min and 0-10min with 50-60min were 0.941. The LBRs of the 60min Ki, 5-10min with 40-45min Ki, 0-5min with 55-60min Ki, 0-10min with 50-60min Ki, SUVmean, and SUVmax images were 29.53 ± 27.33, 13.05 ± 15.28, 45.15 ± 53.11, 45.52 ± 70.31, 19.77 ± 23.43, and 25.06 ± 30.07, respectively. The 0-5min with 55-60min DTP parametric imaging exhibits a comparable Ki to 60min parametric imaging and remarkable image quality and contrast than SUV imaging, enhancing prostate cancer diagnosis while maintaining time efficiency.

The role of catchment population size, data normalization, and chronology of public health interventions on wastewater-based COVID-19 viral trends

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) pandemic presented the most challenging global crisis in recent times. A pandemic caused by a novel pathogen such as SARS-CoV-2 necessitated the development of innovative techniques for the monitoring and surveillance of COVID-19 infections within communities. Wastewater surveillance (WWS) is recognized as a non-invasive, cost-effective, and valuable epidemiological tool to monitor the prevalence of COVID-19 infections in communities. Seven municipal wastewater sampling sites representing distinct sewershed communities were selected for the surveillance of the SARS-CoV-2 virus in Durham Region, Ontario, Canada over 8 months from March 2021 to October 2021. Viral RNA fragments of SARS-CoV-2 and the normalization target pepper mild mottle virus (PMMoV) were concentrated from wastewater influent using the PEG/NaCl superspeed centrifugation method and quantified using RT-qPCR. Strong significant correlations (Spearman's rs = 0.749 to 0.862, P < 0.001) were observed between SARS-CoV-2 gene copies/mL of wastewater and clinical cases reported in each delineated sewershed by onset date. Although raw wastewater offered higher correlation coefficients with clinical cases by onset date compared to PMMoV normalized data, only one site had a statistically significantly higher Spearman's correlation coefficient value for raw data than normalized data. Implementation of community stay-at-home orders and vaccinations over the course of the study period in 2021 were found to strongly correspond to decreasing SARS-CoV-2 wastewater trends in the wastewater treatment plants and upstream pumping stations.

Impact of Thunderstorm Activities on Tropospheric NOx Over Koshi Province of Nepal and Their Possible Role in Climate Change

Lightning, a natural atmospheric phenomenon, plays a significant role in altering atmospheric chemistry and air quality by generating nitrogen oxides (NOX). This research aims to explore the correlation between lightning stroke density and atmospheric NOX over Koshi Province of Nepal by using observational data. Over the course of three year (2018, 2019 and 2020), sessional (lightning pre-dominant; pre-monsoon and post-monsoon) measurements of lightning activity (lightning stroke density) were obtained from Global Lightning Dataset (GLD 360) and NOX data acquired from NASA’s Ozone Monitoring Instrument (OMI). The computation was conducted by using programming language PYTHON. Correlation analysis was performed to examine the relationship between lightning stroke density and NOX concentrations by considering lightning stroke density as independent variable and average NOX as dependent variable. Our analysis shows a notable positive correlation between lightning stroke density and average NOX over Koshi Province of Nepal. The values of correlation coefficient (R) was found 0.625, 0.630 and 0.511 in pre-monsoon of years 2018, 2019 and 20120 respectively and 0.654, 0.605 and 0.370 respectively in post-monsoon. Increased lightning activity corresponded to heightened NOX concentrations, especially during pre-monsoon and post-monsoon. These results indicate that lightning significantly contributes to NOX emissions in Koshi Province. Understanding the relationship between lightning and NOX is essential for study atmospheric chemistry and assessing the impact of lightning on atmosphere.

Modelling and prediction of aeration efficiency of the venturi aeration system using ANN-PSO and ANN-GA

The significance of this study involves the optimisation of the aeration efficiency (AE) of the venturi aerator using an artificial neural network (ANN) technique integrated with an optimisation algorithm, i.e., particle swarm optimisation (PSO) and genetic algorithm (GA). To optimise the effects of operational factors on aeration efficiency by utilising a venturi aeration system, aeration experiments were conducted in an experimental tank with dimensions of 90cm×55cm×45cm. The operating parameters of the venturi aerator include throat length (TL), effective outlet pipe (EOP), and flow rate (Q) to estimate the efficacy of the venturi aerator in terms of AE. A 3–6-1 ANN model was developed and integrated with the PSO and GA techniques to find out the best possible optimal operating variables of the venturi aerator. The coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) determined from the experimental and estimated data were used to assess and compare the performance of the ANN-PSO and ANN-GA modelling. It is shown that ANN-PSO provides a better result as compared to ANN-GA. The operational parameters, TL, EOP, and Q, were determined to have the most optimum values at 50 mm, 6 m, and 0.6 L/s, respectively. The optimised aeration efficiency of the venturi was found to be 0.105 kg O2/kWh at optimum operational circumstances. In fact, the neural network having an ideal design of (3-6-1) and a correlation coefficient value that is extremely close to unity has validated the results indicated above.

Artificial intelligence enhances whole-slide interpretation ofPD-L1 CPS in triple-negative breast cancer: Amulti-institutional ring study.

Evaluation of the programmed cell death ligand-1 (PD-L1) combined positive score (CPS) is vital to predict the efficacy of the immunotherapy in triple-negative breast cancer (TNBC), but pathologists show substantial variability in the consistency and accuracy of the interpretation. It is of great importance to establish an objective and effective method which is highly repeatable. We proposed a model in a deep learning-based framework, which at the patch level incorporated cell analysis and tissue region analysis, followed by the whole-slide level fusion of patch results. Three rounds of ring studies (RSs) were conducted. Twenty-one pathologists of different levels from four institutions evaluated the PD-L1 CPS in TNBC specimens as continuous scores by visual assessment and our artificial intelligence (AI)-assisted method. In the visual assessment, the interpretation results of PD-L1 (Dako 22C3) CPS by different levels of pathologists have significant differences and showed weak consistency. Using AI-assisted interpretation, there were no significant differences between all pathologists (P = 0.43), and the intraclass correlation coefficient (ICC) value was increased from 0.618 [95% confidence interval (CI) = 0.524-0.719] to 0.931 (95% CI = 0.902-0.955). The accuracy of interpretation result is further improved to 0.919 (95% CI = 0.886-0.947). Acceptance of AI results by junior pathologists was the highest among all levels, and 80% of the AI results were accepted overall. With the help of the AI-assisted diagnostic method, different levels of pathologists achieved excellent consistency and repeatability in the interpretation of PD-L1 (Dako 22C3) CPS. Our AI-assisted diagnostic approach was proved to strengthen the consistency and repeatability in clinical practice.

Catalytic Activity of 2-Imino-1,10-phenthrolyl Fe/Co Complexes via Linear Machine Learning.

In anticipation of the correlations between catalyst structures and their properties, the catalytic activities of 2-imino-1,10-phenanthrolyl iron and cobalt metal complexes are quantitatively investigated via linear machine learning (ML) algorithms. Comparatively, the Ridge Regression (RR) model has captured more robust predictive performance compared with other linear algorithms, with a correlation coefficient value of R2= 0.952 and a cross-validation value of Q2= 0.871. It shows that different algorithms select distinct types of descriptors, depending on the importance of descriptors. Through the interpretation of the RR model, the catalytic activity is potentially related to the steric effect of substituents and negative charged groups. This study refines descriptor selection for accurate modeling, providing insights into the variation principle of catalytic activity.

A novel proposed classification system for rock slope stability assessment

The present study introduces “rock slope instability score (RSIS)” a novel classification system for assessing rock slope stability. It takes into account geological and geotechnical parameters, as well as the impact of human activities and triggering parameters, which have become more frequent due to climate change and few of them have been ignored in existing classifications. The study focuses on rock slopes of various lithologies from the Indian Himalayas. The development of this new classification system is based on the examination of 81 different rock slopes from various states of the Indian Himalayas. Extensive field surveys, rock sampling, geotechnical laboratory tests, and ground measurements have been conducted at the various slope sites to establish a comprehensive scoring system for the stability assessment. The distributions of weightage to each parameter have been considered, corresponding to its degree of impact in causing slope instability. Sensitivity analysis of all defined parameters of RSIS system has revealed that the majority of the parameters exhibit a strong positive correlation, with Pearson correlation coefficients ranging from 0.74 to 0.61. However, two parameters, namely discontinuity dip and the relationship between slope & discontinuity direction, gives moderate relationship with correlation coefficient values of 0.48 and 0.41, respectively. To avoid any designer biasness in the system, several individuals gathered data set at different times. The proposed classification system has demonstrated a strong correlation with the actual slope condition, and it is quite promising. The outcome of RSIS classification for studied 81 slopes classified 2 slopes under stable condition, 21 slopes as partially stable, 44 as unstable, and 14 as completely unstable.

A high-robustness radial intensity-orientated mode decomposition with reliable noise elimination

Mode decomposition (MD) provides profound evidence to reveal the internal modal transmission mechanism. However, the indelible noise has always been the main stubborn hindrance in practical MD. In the complex superposition case with a large number of modes, the traditional MD is not capable enough to distinguish the real modal intensity and the annoying noise, sustaining an unacceptable accuracy and fluctuation. This paper proposes a radial intensity-orientated MD (RIO-MD) method with reliable noise elimination. Our approach focuses on the inherent modal radial features in Polar coordinates, getting rid of the traditional two-dimensional image processing in Cartesian ones. The RIO-MD introduces the inherent radial intensity relationship into MD for better extracting mode coefficients. Based on the expectable real radial modal intensity, the RIO-MD enables to recognize and extraction of the three kinds of stubborn noise, including interference pattern noise, device noise, and random noise. The RIO-MD works well in mode decomposition case. The values of correlation coefficients (C) between the experimental and reconstructed image are higher than 93%. The mean square error (MSE) is lower than 3 × 10−3. Both the C and MSE keep stable, with the standard deviation 30 times lower than the other widely used methods, demonstrating the high-robustness of the RIO-MD. Due to the reliable noise recognition, the RIO-MD shows great possibility in mode number expansion.

Construction of a Scale Towards E-Crop Booking Services of Rythu Bharosa Kendras (RBKs)

In this modern era, farmers encounter difficulties in receiving timely advices and services from extension agents. Due to technological advancements in the recent past quality of services, speed in deliverables and precision actions are possible by creating proper interface between farmers, scientists, extension officers, service providers and by bringing innovation/sustainable interventions in agriculture and allied sectors. With this context as a new extension reform, state government of Andhra Pradesh, India has launched Rythu Bharosa Kendras for bringing the extension system nearer to the farming community and more transparency to ensure quality of services. Hence, this study represents the construction of a perception scale to understand the perception of farmers towards Rythu Bharosa Kendras. Keeping this in view, an attempt has been made to develop a scale for measuring the perception of farmers towards e-crop booking services of Rythu Bharosa Kendras. Likert’s method of summated rating technique was adopted to construct the scale. Based on the available literature and expert’s opinion, 50 statements were developed. After editing as per informal criteria, 40 statements were subjected to item analysis among the farmers of non-sample area. Out of which, 32 statements were retained in the developed perception scale based on the highest ‘t’ value. The ‘r’ (correlation coefficient) value was found to be 0.852 which represents significant at 1 per cent level and the reliability and validity of the constructed scale were found appropriate to measure the perception of farmers towards e-crop booking services of Rythu Bharosa Kendras.

Unveiling COVID-19’s Influence on Diet-Related Lifestyle: Insights from Google Trends Query Volumes

Aim: This study aimed to showcase the particular lifestyle and behavioural pattern changes during the pandemic using Google Trends (GT). Background: The pandemic hit by the Coronavirus Disease 2019 (COVID-19) globally has brought basic living into the context of surprise. Since the lockdown and social restrictions, the population has faced many changes in their daily routine. objective: To demonstrate how COVID-19 affects eating habits and lifestyle in India. Objective: The purpose of the study was to demonstrate how COVID-19 has affected eating habits and lifestyle in India. Method: A retrospective analysis was carried out with the aid of Google Trend Relative Search Volume (RSV) data obtained for interest areas, such as COVID-19 cases, symptoms, preference of medicines, choice of nutrients, reduced physical activity, and watching varied Over-the-top media service (OTT) platforms as the source of entertainment; these were the variables that were taken into consideration to derive correlation coefficient values. Results: There was a strong positive correlation found between zinc and vitamin C, zinc and vitamin D, and vitamin C and D, which was 0.83, 0.78, and 0.72, respectively. Symmetrical observation has been observed in Netflix and Amazon Prime as 0.81; a notable negative correlation was found between COVID-19 and gym as -0.32. Conclusion: This study can be used as a checkpoint list for future predictions and past timelines and how they have a cohabitant role in the spread of the epidemic and its line of mitigation strategies. other: NIL

A Deep Learning Approach for Chromium Detection and Characterization from Soil Hyperspectral Data.

High levels of chromium (Cr) in soil pose a significant threat to both humans and the environment. Laboratory-based chemical analysis methods for Cr are time consuming and expensive; thus, there is an urgent need for a more efficient method for detecting Cr in soil. In this study, a deep neural network (DNN) approach was applied to the Land Use and Cover Area frame Survey (LUCAS) dataset to develop a hyperspectral soil Cr content prediction model with good generalizability and accuracy. The optimal DNN model was constructed by optimizing the spectral preprocessing methods and DNN hyperparameters, which achieved good predictive performance for Cr detection, with a correlation coefficient value of 0.79 on the testing set. Four important hyperspectral bands with strong Cr sensitivity (400-439, 1364-1422, 1862-1934, and 2158-2499 nm) were identified by permutation importance and local interpretable model-agnostic explanations. Soil iron oxide and clay mineral content were found to be important factors influencing soil Cr content. The findings of this study provide a feasible method for rapidly determining soil Cr content from hyperspectral data, which can be further refined and applied to large-scale Cr detection in the future.

Deep Learning Method for Precise Landmark Identification and Structural Assessment of Whole-Spine Radiographs.

This study measured parameters automatically by marking the point for measuring each parameter on whole-spine radiographs. Between January 2020 and December 2021, 1017 sequential lateral whole-spine radiographs were retrospectively obtained. Of these, 819 and 198 were used for training and testing the performance of the landmark detection model, respectively. To objectively evaluate the program's performance, 690 whole-spine radiographs from four other institutions were used for external validation. The combined dataset comprised radiographs from 857 female and 850 male patients (average age 42.2 ± 27.3 years; range 20-85 years). The landmark localizer showed the highest accuracy in identifying cervical landmarks (median error 1.5-2.4 mm), followed by lumbosacral landmarks (median error 2.1-3.0 mm). However, thoracic landmarks displayed larger localization errors (median 2.4-4.3 mm), indicating slightly reduced precision compared with the cervical and lumbosacral regions. The agreement between the deep learning model and two experts was good to excellent, with intraclass correlation coefficient values >0.88. The deep learning model also performed well on the external validation set. There were no statistical differences between datasets in all parameters, suggesting that the performance of the artificial intelligence model created was excellent. The proposed automatic alignment analysis system identified anatomical landmarks and positions of the spine with high precision and generated various radiograph imaging parameters that had a good correlation with manual measurements.

Psychometric properties of the experiences of maternity care scale among Iranian women

BackgroundAssessing women’s perceptions of the care they receive is crucial for evaluating the quality of maternity care. Women’s perceptions are influenced by the care received during pregnancy, labour and birth, and the postpartum period, each of which with unique conditions, expectations, and requirements. In England, three Experience of Maternity Care (EMC) scales – Pregnancy, Labour and Birth, and Postnatal – have been developed to assess women’s experiences from pregnancy through the postpartum period. This study aimed to validate these scales within the Iranian context.MethodsA methodological cross-sectional study was conducted from December 2022 to August 2023 at selected health centers in Tabriz, Iran. A panel of 16 experts assessed the qualitative and quantitative content validity of the scales and 10 women assessed the face validity. A total of 540 eligible women, 1–6 months postpartum, participated in the study, with data from 216 women being used for exploratory factor analysis (EFA) and 324 women for confirmatory factor analysis (CFA) and other analyses. The Childbirth Experience Questionnaire-2 was employed to assess the convergent validity of the Labour and Birth Scale, whereas women’s age was used to assess the divergent validity of the scales. Test-retest reliability and internal consistency were also examined.ResultsAll items obtained an impact score above 1.5, with Content Validity Ratio and Content Validity Index exceeding 0.8. EFA demonstrated an excellent fit with the data (all Kaiser-Meyer-Olkin measures > 0.80, and all Bartlett’s p < 0.001). The Pregnancy Scale exhibited a five-factor structure, the Labour and Birth Scale a two-factor structure, and the Postnatal Scale a three-factor structure, explaining 66%, 57%, and 62% of the cumulative variance, respectively, for each scale. CFA indicated an acceptable fit with RMSEA ≤ 0.08, CFI ≥ 0.92, and NNFI ≥ 0.90. A significant correlation was observed between the Labour and Birth scale and the Childbirth Experience Questionnaire-2 (r = 0.82, P < 0.001). No significant correlation was found between the scales and women’s age. All three scales demonstrated good internal consistency (all Cronbach’s alpha values > 0.9) and test-retest reliability (all interclass correlation coefficient values > 0.8).ConclusionsThe Persian versions of all three EMC scales exhibit robust psychometric properties for evaluating maternity care experiences among urban Iranian women. These scales can be utilized to assess the quality of current care, investigate the impact of different care models in various studies, and contribute to maternal health promotion programs and policies.

Monitoring of Chlorophyll Content of Potato in Northern Shaanxi Based on Different Spectral Parameters.

Leaf chlorophyll content (LCC) is an important physiological index to evaluate the photosynthetic capacity and growth health of crops. In this investigation, the focus was placed on the chlorophyll content per unit of leaf area (LCCA) and the chlorophyll content per unit of fresh weight (LCCW) during the tuber formation phase of potatoes in Northern Shaanxi. Ground-based hyperspectral data were acquired for this purpose to formulate the vegetation index. The correlation coefficient method was used to obtain the "trilateral" parameters with the best correlation between potato LCCA and LCCW, empirical vegetation index, any two-band vegetation index constructed after 0-2 fractional differential transformation (step size 0.5), and the parameters with the highest correlation among the three spectral parameters, which were divided into four combinations as model inputs. The prediction models of potato LCCA and LCCW were constructed using the support vector machine (SVM), random forest (RF) and back propagation neural network (BPNN) algorithms. The results showed that, compared with the "trilateral" parameter and the empirical vegetation index, the spectral index constructed by the hyperspectral reflectance after differential transformation had a stronger correlation with potato LCCA and LCCW. Compared with no treatment, the correlation between spectral index and potato LCC and the prediction accuracy of the model showed a trend of decreasing after initial growth with the increase in differential order. The highest correlation index after 0-2 order differential treatment is DI, and the maximum correlation coefficients are 0.787, 0.798, 0.792, 0.788 and 0.756, respectively. The maximum value of the spectral index correlation coefficient after each order differential treatment corresponds to the red edge or near-infrared band. A comprehensive comparison shows that in the LCCA and LCCW estimation models, the RF model has the highest accuracy when combination 3 is used as the input variable. Therefore, it is more recommended to use the LCCA to estimate the chlorophyll content of crop leaves in the agricultural practices of the potato industry. The results of this study can enhance the scientific understanding and accurate simulation of potato canopy spectral information, provide a theoretical basis for the remote sensing inversion of crop growth, and promote the development of modern precision agriculture.

Comparative Study of Groundwater Quality for Drinking and Irrigation Purposes in Selected Villages of Manipur, India

A comparative study was done to monitor the groundwater qualities, both for drinking as well as irrigation purposes in some villages of Imphal East district, Manipur, in the period of pre-monsoon and monsoon of 2021. Totally, fifteen (15) groundwater samples were collected and examined for some important parameters like temperature, pH, TDS, TH, TA, Mg2+, K+, HCO3-, Ca2+, CO32-, Na+, Cl-, NO3- and SO42-. The results revealed that all the groundwaters were slightly alkaline in nature and fall under freshwater category. In both the seasons, majority of the parameters for various groundwaters have found their values/ concentrations below its acceptable limits, as given by BIS and WHO. In pre-monsoon, 73.33% of groundwaters were found as ‘moderately hard water’ and 26.67% (hard water) whereas in monsoon, 60% (moderately hard water) and 40% (hard water). Dominant ions in both seasons were Na+ (sodium) and HCO3- (bicarbonate) for cations and anions respectively. In most of the samples of monsoon, values and concentrations of the studied parameters were observed to be greater/higher as compared to that of pre-monsoon. It was mainly due to the leaching out of the soluble salts in the earth’s crust by rainwaters and finally mingled with the groundwater bodies in the aquifers in monsoon season. WQI values showed that in pre-monsoon, 26.67% of groundwaters were found as excellent water, 66.67% (good) and 6.66% (poor) while in monsoon, 60% (good) and 40% (poor). For irrigation water qualities, the indices (RSC, SAR, %Na, PI and KR) were examined. In the overall, all the samples can be utilized for irrigation (agriculture) purposes in both seasons. Correlation co-efficient (r) values showed that in both the seasons, the TH of various groundwaters was mainly because of dissolved sulphates of Ca2+ along with Mg2+.