Pearson Correlation Coefficient Research Articles

Correlation of ESP Learners’ Cognitive, Metacognitive Strategies and Academic Achievement

There have been many studies dealing with the interconnections between the utilization of language learning strategies and the overall academic achievement of EFL learners. Our research aims to investigate whether there is some correlation between the implementation of cognitive strategies, metacognitive strategies, and academic achievement in an ESP context. The study sample incorporated 170 undergraduates studying in business administration programs at the university where we are teaching. We used the Strategy Inventory of Language Learning Strategies (SILL) put together by R. Oxford to examine the implementation of CSs and MCSs by the learners. We generated the results with the help of descriptive and inferential statistics. We compared the data received with the students’ GPAs by using the Pearson correlation coefficient. The findings revealed that students make use of both CSs and MCSs fairly highly. MCSs are used more than CSs. There is a remarkably useful correlation between GPA and most of the CSs and MCSs examined. The findings and recommendations of this research will contribute to the ongoing literature and they will help in updating ESP syllabi and teaching techniques of vocational and practical programs that require ESP.

Promotor Hypomethylation of TFF1 Gene in Ulcerative Colitis is Positively Correlated with Aging

Background: Epigenetic modifications exhibit promising evidence in the etiology and prognosis of important diseases such as inflammatory bowel diseases (IBD). In addition to complex factors involved in IBD, a trend toward better prognosis has been reported in older ages of disease onset, specifically in ulcerative colitis (UC). The gastrointestinal mucous layer is one of the important components that is disturbed in the disease course. The integrity of this layer is maintained with an anti-inflammatory factor called trefoil factors (TFF). We investigated the methylation status of the TFF1 gene in UC patients alongside with correlation of its alteration level with age of disease onset. Method: We analyzed the promoter methylation status of the TFF1 gene, using the real-time quantitative multiplex methylation specific PCR (QM-MSP). DNA was extracted from colorectal biopsies of 15 ulcerative colitis and 14 healthy controls. Correlation analysis was performed between unmethylated DNA level and age through the Pearson correlation coefficient (PPC) test and simple linear regression models. Results: Our data provided a significant positive correlation between age and TFF1 hypomethylation in ulcerative colitis patients. However, no significant difference was observed in overall TFF1 methylation status between ulcerative colitis patients and control subjects. Conclusion: This finding suggests the association between epigenetic upregulation of the TFF1 gene with disease mildness in older patients.

Melanoma overdiagnosis: What do we know and what do we do?

Melanoma overdiagnosis occurs when melanomas, not destined to cause morbidity or death in a patient's lifetime, are identified and treated. This study considers the causes and magnitude of melanoma overdiagnosis in Australia. We also speculate about a possible benefit of overdiagnosis in Australia; namely, a reduction in excess deaths in the geographical areas where melanoma is diagnosed most frequently. Overdiagnosis can arguably be mitigated by factors that reduce the number of lesions treated for each melanoma identified. Data from the Australian Cancer Atlas show that there is a reduction in excess deaths from melanoma in geographical areas where diagnostic rates are higher (Pearson correlation coefficient r=-0.5978, 95% CI: -0.6243 to -0.5699, P<0.0001); this being the strongest inverse correlation observed among the 20 cancer types in the Atlas. Is early diagnosis of actual life-threatening melanomas in these geographical regions impacting survival? Further research is planned.

Correlating Cases of Soil-Transmitted Helminths and Remote Sensing Covariates: A Case Study Utilizing 2015, 2017, and 2019 Metadata of Camarines Sur, Philippines.

Soil-transmitted helminth (STH) is a group of helminths that are considered to be neglected tropical diseases (NTDs) and, globally, affect more than 1.5 to 2.6 million people yearly. Depending on the species, they can be acquired by ingesting embryonated eggs from contaminated matter or by skin penetration. Most species of STH are found in the tropics, such as the Philippines. Despite the Mass Drug Administration (MDA), the cases of STH infection continue to rise in the country. Surveys from the Research Institute of Tropical Medicine (RITM) indicate that a high prevalence of STH (Ascaris lumbricoides, Trichuris trichiura, and Necator americanus) was primarily observed in the provinces of the country, such as in Camarines Sur. To correlate remote sensing covariates such as Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) - to STH-infected cases of the 37 municipalities of Camarines Sur. The available public health record of STH cases from 2015 to 2019 were calculated using the Quantum Geographic Information System (QGIS)and correlated using Pearson Correlation Coefficient. The results showed that infection was higher in children than adults, and A. lumbricoides caused 60% of infection. No correlation of indices with infection cases during 2015 and 2017 was observed; however, 2019 showed a moderate strength (p = 0.037) in correlation. This indicates that infection relied not mainly on vegetation and urbanization but on additional environmental factors and non-environmental variables.

Impairments of neurovascular coupling after stroke lower glymphatic system function and lead to depressive symptom: A longitudinal cohort study

BackgroundRespective changes in neurovascular coupling (NVC) and glymphatic function have been reported in post-stroke depression (PSD). Recent studies have found a link between NVC and waste clearance by the glymphatic system, which has not been illustrated in PSD. MethodWe prospectively recruited ninety-six stroke patients and forty-four healthy controls (HC), with fifty-nine patients undergoing a second MRI scan. NVC metrics were investigated by exploring Pearson correlation coefficients and ratios between cerebral blood flow (CBF) and BOLD-derived quantitative maps (ALFF, fALFF, REHO maps). Diffusion tensor imaging along the perivascular (DTI-ALPS) index was used to reflect glymphatic function. We first analyzed the altered NVC metrics in stroke patients relative to the HC group. Then, we explored the relationship between NVC metrics, ALPS index and depressive symptoms at baseline and during the follow-up period through correlation and mediation analyses. ResultsStroke patients exhibited significantly lower global CBF-fALFF coupling and ALPS index. At the regional level, abnormal NVC alterations in brain regions involved in cognition, emotion, and sensorimotor function in PSD. Baseline analyses showed that ALPS index exhibited positive associations with both global and local NVC and abnormal regional NVC may contribute to generation of PSD by reducing glymphatic function (β = −0.075, p < 0.05, CI = [−0.169 to −0.012]). Longitudinal analyses similarly showed that ALPS index changes were positively associated with changes in NVC and mediated improvements in depressive symptoms. ConclusionOur findings suggest that NVC abnormalities leading to impaired glymphatic system function may be a potential neurobiological mechanism of PSD.

The Impact of Induced Industrial and Urban Toxic Elements on Sediment Quality

Abstract: The increasing population has subjected rivers and streams to high levels of both industrial and domestic pollution. Significant environmental challenges have been brought about by their effects, particularly with regard to biota, ecosystem processes, soil quality, and groundwater pollution. This study examined the effects of human activity by applying pollution index models to evaluate the input of toxic elements in river sediments. Prior to sediment quality analysis, the total amount of arsenic (As), lead (Pb), cadmium (Cd), mercury (Hg), thorium (Th), and uranium (U) was determined in the concentration range of 1.09–10.0 mg/kg, 8.53–475 mg/kg, 0.12–0.16 mg/kg, 4.85–77.5 mg/kg, 3.14–5.9 mg/kg and 0.93–2.86 mg/kg, respectively. The enrichment factor, contamination factor, pollution load index, and geo-accumulation index revealed alarmingly high levels of Pb and Hg contamination at some sampling points, which are related to possible human input, ranging from severe enrichment to considerable contamination. The low ranges of pollution indices of some toxic elements suggest enrichment through the natural weathering process and atmospheric deposition. The Pearson correlation coefficient revealed a significant correlation between Pb-Fe and As-Fe, suggesting the possibility of acid mine contamination. Continual monitoring of river sediment is essential to minimize the impact of toxic elements to sustain sediment health and quality.

General and abdominal adiposity and hypertension in eight world regions: a pooled analysis of 837 population-based studies with 7·5 million participants

Adiposity can be measured using BMI (which is based on weight and height) as well as indices of abdominal adiposity. We examined the association between BMI and waist-to-height ratio (WHtR) within and across populations of different world regions and quantified how well these two metrics discriminate between people with and without hypertension. We used data from studies carried out from 1990 to 2023 on BMI, WHtR and hypertension in people aged 20-64 years in representative samples of the general population in eight world regions. We graphically compared the regional distributions of BMI and WHtR, and calculated Pearson's correlation coefficients between BMI and WHtR within each region. We used mixed-effects linear regression to estimate the extent to which WHtR varies across regions at the same BMI. We graphically examined the prevalence of hypertension and the distribution of people who have hypertension both in relation to BMI and WHtR, and we assessed how closely BMI and WHtR discriminate between participants with and without hypertension using C-statistic and net reclassification improvement (NRI). The correlation between BMI and WHtR ranged from 0·76 to 0·89 within different regions. After adjusting for age and BMI, mean WHtR was highest in south Asia for both sexes, followed by Latin America and the Caribbean and the region of central Asia, Middle East and north Africa. Mean WHtR was lowest in central and eastern Europe for both sexes, in the high-income western region for women, and in Oceania for men. Conversely, to achieve an equivalent WHtR, the BMI of the population of south Asia would need to be, on average, 2·79 kg/m2 (95% CI 2·31-3·28) lower for women and 1·28 kg/m2 (1·02-1·54) lower for men than in the high-income western region. In every region, hypertension prevalence increased with both BMI and WHtR. Models with either of these two adiposity metrics had virtually identical C-statistics and NRIs for every region and sex, with C-statistics ranging from 0·72 to 0·81 and NRIs ranging from 0·34 to 0·57 in different region and sex combinations. When both BMI and WHtR were used, performance improved only slightly compared with using either adiposity measure alone. BMI can distinguish young and middle-aged adults with higher versus lower amounts of abdominal adiposity with moderate-to-high accuracy, and both BMI and WHtR distinguish people with or without hypertension. However, at the same BMI level, people in south Asia, Latin America and the Caribbean, and the region of central Asia, Middle East and north Africa, have higher WHtR than in the other regions. UK Medical Research Council and UK Research and Innovation (Innovate UK).

Translation and Validation of the Persian Version of the Wisconsin Stone Quality of Life Questionnaire.

Patients with kidney stones have a lower quality of life (QOL) than healthy patients; however, treatment guidelines neglect their QOL. Wisconsin stone QOL test (WIS-QOL) is the first specific questionnaire for urolithiasis patients. This study evaluated the reliability and validity of the translated Persian version of the WIS-QOL questionnaire in patients with kidney stones. All patients (> 18 years old) with a history of urolithiasis were included in this cross-sectional. The WIS-QOL questionnaire was translated depending on Hutchinson's guideline. Effects of urolithiasis on the patient's social activity, emotions, disease, and vitality were evaluated. Pearson Correlation and Cronbach's alpha test were used to assess the validity and reliability of the questionnaire. All data were analyzed by SPSS software version 26.0. Among 154 urolithiasis patients, 94 (61%) were males, and 60 (39%) were females. The mean age was 50.4 years (SD: ± 13.6), and the mean QOL score was 84.7 (SD: ±21.8). For each question, the numerical value of the Pearson Correlation Coefficient has been compared with the numerical value, and the validity of the questionnaire was confirmed. The overall Cronbach's alpha was 0.94 for all four areas of the questionnaire, so the reliability of the questionnaire in Persian was confirmed. The present study showed the reliability and validity of the Persian version of the WIS-QOL questionnaire in symptomatic urolithiasis patients. The present study showed the status and impact of urolithiasis on QOL however, longitudinal and prospective studies should be done to specify changes over time.

Invariant correlation under marginal transforms

A useful property of independent samples is that their correlation remains the same after applying marginal transforms. This invariance property plays a fundamental role in statistical inference, but does not hold in general for dependent samples. In this paper, we study this invariance property on the Pearson correlation coefficient and its applications. A multivariate random vector is said to have an invariant correlation if its pairwise correlation coefficients remain unchanged under any common marginal transforms. For a bivariate case, we characterize all models of such a random vector via a certain combination of comonotonicity—the strongest form of positive dependence—and independence. In particular, we show that the class of exchangeable copulas with invariant correlation is precisely described by what we call positive Fréchet copulas. In the general multivariate case, we characterize the set of all invariant correlation matrices via the clique partition polytope. We also propose a positive regression dependent model that admits any prescribed invariant correlation matrix. Finally, we show that all our characterization results of invariant correlation, except one special case, remain the same if the common marginal transforms are confined to the set of increasing ones.

Research on the influence of unsteady cavitation on the dynamics structures of a twisted hydrofoil based on variational mode decomposition method

Cavitation will produce complex dynamic effects on hydrofoil structures. In this paper, the effects of unsteady cavitation conditions on the dynamics of twisted hydrofoil structures are studied by using variational mode decomposition (VMD) and pearson correlation coefficient (PCC), etc. The research results indicate that there is a certain synergy between the flow field pressure pulsation signal and the structural field stress-strain signal in the frequency distribution of the hydrofoil under cavitation conditions. The second-order center frequency of ΔP1 and the first-order center frequency of ΔP7 are close to the second-order center frequency of the stress-strain signal of the hydrofoil (1202.4Hz). Unsteady cavitation can lead to the increase of the high effective force region of the hydrofoil, especially the high effective force region connecting the leading edge and the root, which is the reason for the degradation of the hydrofoil performance. Cavitation increases the natural frequency of the hydrofoil, but has no obvious effect on the natural mode of the hydrofoil. The research results of this study reveal the effect of unsteady cavitation on the dynamics of hydrofoil structure, and provide important theoretical support for the optimization design and performance improvement of hydrofoil structure.

Forest above-ground biomass estimation based on strongly collinear variables derived from airborne laser scanning data

Airborne laser scanning technique (ALS) is the most appealing remote sensing technique for the precise estimation of forest above-ground biomass (AGB). Significantly strong correlations (collinearity) among the independent variables derived from ALS data decrease the accuracy of developed AGB models. To address this issue, we propose a novel variable selection algorithm-an improved sure independence screening (SPV), which integrates the Pearson correlation coefficient, threshold μ, and variance inflation factor. We further compared the performance of SPV-based, stepwise feature selection (SFS)-based, and least absolute shrinkage and selection operator (LASSO)-based AGB models developed with different regression approaches that were sensitive to strong collinearity. Field-measured data and corresponding ALS data, acquired from 1002 sample plots distributed across four distinct forest types within the Guangxi Zhuang Autonomous Region in Southern China, were used to evaluate variable selection techniques and develop AGB models. Results indicated that ALS variables selected by SPV exhibited weaker collinearity compared to those selected by SFS and LASSO. SPV-based AGB models outperformed SFS-based AGB models with higher leave-one-out cross-validation adjusted R2 (LOOCV Radj2) by 0.1% − 27.8%. SPV-based AGB models outperformed LASSO-based AGB models with higher LOOCV Radj2 by 0.4% − 16.3%. Hence, for variable selection in constructing AGB models (linear regression model, log–log regression model, and generalized additive model (GAM)) based on strongly collinear ALS variables, SPV is most preferred, followed by SFS and LASSO. The smooth curves from our GAMs developed using SPV-selected variables revealed that five canopy height variables (whp40, hmean, hp25, hp70, hskew), one canopy density variable (whp85), three density-related variables (nVegPts, bcc, and mcc), and one vertical structural variable (LADmean) were positively correlated with AGB. The canopy height variables (whp40, hmean, hp25, and hp70) were identified as the most important variables in estimating AGB for four forest types. The canopy density variable wdp85 showed a strong effect on AGB of the coniferous forests, whereas it had almost no effect on the AGB of broadleaved forests. Overall, this manuscript proposes a novel variable selection algorithm named SPV, aimed at addressing collinearity of variables derived from ALS data, which has significant implications for the application of ALS in forest inventory and forest modeling.

Cardiovascular disease risk in people of African ancestry with HIV in the United Kingdom.

Our objective was to describe the prevalence of cardiovascular disease (CVD) risk factors in people of African ancestry with HIV in the UK. We conducted a cross-sectional analysis of CVD risk factors in Black people with HIV aged ≥40 years and estimated the 10-year CVD risk using QRISK®3-2018. Correlations between body mass index (BMI) and CVD risk factors were described using Pearson correlation coefficients, and factors associated with 10-year CVD risk ≥5% were described using logistic regression. We included 833 Black people with HIV and a median age of 54 years; 54% were female, 50% were living with obesity (BMI ≥30 kg/m2), 61% had hypertension, and 19% had diabetes mellitus. CVD risk >5% ranged from 2% in female participants aged 40-49 years to 99% in men aged ≥60 years, and use of statins ranged from 7% in those with CVD risk <2.5% to 64% in those with CVD risk ≥20%. BMI was correlated (R2 0.1-0.2) with triglycerides and diastolic blood pressure in women and with glycated haemoglobin, systolic and diastolic blood pressure, and total:high-density lipoprotein (HDL) cholesterol ratio in men. In both female and male participants, older age, blood pressure, diabetes mellitus, and kidney disease were strongly associated with CVD risk ≥5%, whereas obesity, total:HDL cholesterol, triglycerides, and smoking status were variably associated with CVD risk ≥5%. We report a high burden of CVD risk factors, including obesity, hypertension, and diabetes mellitus, in people of African ancestry with HIV in the UK. BMI-focused interventions in these populations may improve CVD risk while also addressing other important health issues.

Adaptation and development of the instruction manual for a videoconference-based Motor Assessment Scale: validity, reliability and measurement error of Tele-MAS

Purpose To develop an instruction manual to administer the Motor Assessment Scale (MAS) via videoconferencing (Tele-MAS), investigate its validity, reliability and measurement error. Materials and methods In-person assessment, conducted at the participant’s home, was compared to remote assessment conducted by rater A. Then, within two days, the rater B, repeat the remote assessment (n = 41). Part of the sample (n = 10) was assessed again within seven days by rater A to determine test-retest reliability. Concurrent validity, agreement of alternate forms (in-person x remote) for total score and reliability of individual items were analyzed using respectively the Pearson correlation coefficient, Bland-Altman plots and weighted Kappa (Kw). Interrater and test-retest reliability were analyzed by Intraclass Correlation Coefficient (ICC). Standard Error of Measurement (SEM) and Minimal detectable changes (MDC) were computed. Results The Tele-MAS instruction manual was developed. In-person MAS and Tele-MAS present a high positive correlation (r = 0.97). Bland-Altman plots showed adequate agreement (MD=-0.0 point). Most Individual items showed excellent reliability (Kw > 0.70). Tele-MAS showed excellent interrater (ICC(2,1)=0.92) and test-retest (ICC(3,1)=0.98) reliability. Tele-MAS presents a SEM = 3% and MDC = 8%. Conclusion The Tele-MAS is a valid and reliable global motor assessment scale to be applied in individuals after stroke.

Self-attention transformer model for pan evaporation prediction: a case study in Australia

ABSTRACT In drought-prone regions like Australia, accurately assessing evaporation rates is essential for effectively managing and maximising the use of precious water resources and reservoirs. Current estimates show that evaporation reduces Australia's open water lake capacity by about 40% annually. With climate change, this water loss is expected to become an even greater concern. This study investigates a transformer-based neural network (TNN) to estimate monthly evaporation in three Australian locations. The models were trained and tested using monthly weather data spanning from 2009 to 2022. Input parameters were chosen based on Pearson's correlation coefficient values to identify the most impactful combinations. The developed TNN model was compared with two widely used empirical methods, namely Thornthwaite and Stephens and Stewart. The TNN model's impressive accuracy in evaporation prediction, attributed to its unique self-attention mechanism, suggests its promising potential for future use in evaporation forecasting. Additionally, the study revealed an intriguing result: Despite using the same input datasets, the TNN model surpassed traditional methods, achieving an average improvement of 18% in prediction accuracy. The TNN prediction model accurately predicts water loss (average R² = 0.970), supports irrigation management and agricultural planning and offers financial benefits to farming and related industries.

AI-smartphone markerless motion capturing of hip, knee, and ankle joint kinematics during countermovement jumps.

Recently, AI-driven skeleton reconstruction tools that use multistage computer vision pipelines were designed to estimate 3D kinematics from 2D video sequences. In the present study, we validated a novel markerless, smartphone video-based artificial intelligence (AI) motion capture system for hip, knee, and ankle angles during countermovement jumps (CMJs). Eleven participants performed six CMJs. We used 2D videos created by a smartphone (Apple iPhone X, 4K, 60 fps) to create 24 different keypoints, which together built a full skeleton including joints and their connections. Body parts and skeletal keypoints were localized by calculating confidence maps using a multilevel convolutional neural network that integrated both spatial and temporal features. We calculated hip, knee, and ankle angles in the sagittal plane and compared it with the angles measured by a VICON system. We calculated the correlation between both method's angular progressions, mean squared error (MSE), mean average error (MAE), and the maximum and minimum angular error and run statistical parametric mapping (SPM) analysis. Pearson correlation coefficients (r) for hip, knee, and ankle angular progressions in the sagittal plane during the entire movement were 0.96, 0.99, and 0.87, respectively. SPM group-analysis revealed some significant differences only for ankle angular progression. MSE was below 5.7°, MAE was below 4.5°, and error for maximum amplitudes was below 3.2°. The smartphone AI motion capture system with the trained multistage computer vision pipeline was able to detect, especially hip and knee angles in the sagittal plane during CMJs with high precision from a frontal view only.

Associations of School-Level Factors and School Sport Facility Parameters with Overweight and Obesity among Children and Adolescents in Pakistan: An Empirical Cross-Sectional Study

Childhood overweight and obesity are increasingly prevalent in Pakistan, posing significant public health challenges. This study explores the associations of school-level factors and school sports facility parameters with overweight and obesity among children and adolescents in Pakistan. A cross-sectional study across seven random districts in Punjab province, Pakistan, was conducted using a representative multistage random cluster sample. Underweight (BMI &lt; 5th percentile), overweight (85th ≤ BMI &lt; 95th percentile), and obese (95th percentile ≤ BMI) were defined using the US Center for Disease Control (CDC) 2000 criteria. Statistical analyses including the Chi-square test, Pearson correlation coefficient, and linear regression were performed to investigate predictive characteristics. Logistic regression analysis assessed the simultaneous impact of several covariates on dichotomous outcomes, with 95% confidence intervals (CIs) computed and a significance level set at p &lt; 0.05. The study included 4108 Pakistani school children aged 9 to 17 years (mean age = 13.92 years, 59.3% boys) from 62 schools. The prevalence of overweight and obesity was 19.4% and 10.7%, respectively. Findings revealed a concerning lack of physical education activities (60% reported 0 sessions per week), morning exercise (60%), and classroom exercises (66%) among school-aged children. Leadership attitudes toward physical education (β = 0.04, p = 0.006) and students’ satisfaction with the playground (β = 0.05, p = 0.015) showed significant associations with body weight status. Conversely, provision of physical education facilities, effective fund utilization for physical education, meeting school sports facility requirements, and weekend opening of school sports grounds did not significantly impact weight status. Satisfaction with the playground was significantly associated with a lower risk of overweight (OR 0.81, 95% CI 0.69–0.95, p &lt; 0.05), indicating reduced overweight likelihood among students satisfied with school playgrounds. The study underscores significant gaps in promoting physical activity within school environments and highlights the urgent need for interventions to enhance physical education resources and sports facilities, aiming to create healthier school environments and reduce overweight and obesity prevalence among students. Further research and interventions are crucial for promoting lifelong physical activity habits and improving student health and well-being.

Both meteorological droughts and human activities modulated groundwater variations in the northern Yellow River Basin

ABSTRACT Groundwater level declines are largely associated with natural processes and human activities. In particular, the drivers of groundwater change can be more complex during meteorological drought owing to human activities. However, disentangling their specific contribution remains poorly understood. By focusing on semiarid ecosystems in the northern Yellow River Basin – the Ordos – here we elucidate the impact of human activities on the propagation of meteorological droughts to groundwater systems. To comprehensively analyze groundwater variations, we employ the K-means, categorizing them into four distinct patterns. Based on the Pearson correlation coefficient analysis between standardized precipitation index (SPI) and groundwater depth (GWD), we found that the majority of lag time for GWD response to SPI is less than 3 months, and the drivers influencing GWD are classified into three categories: SPI, human activities related to SPI, and human activities unrelated to SPI. Our results reveal that both meteorological droughts and human activities jointly influence GWD across the entire region. Notably, human activities unrelated to SPI have the greatest impact in the irrigation district of Ordos, followed by the western part of Ordos and the Mu Us sandy land in central Ordos. Our findings can guide to formulate effective drought management policies and practices in semiarid regions.

State-of-health estimation for lithium-ion batteries based on Kullback–Leibler divergence and a retentive network

Accurate state-of-health (SOH) estimation is crucial for the lithium-ion battery industry, as it underpins the safety, durability, and reliability of lithium-ion batteries. Currently, most researchers use various methods of health indicator (HI) extraction for the SOH estimation of batteries. However, these methods may require certain expertise and prior knowledge to achieve accurate modeling, being affected by measurement noise and other factors. To solve the abovementioned problems, three Kullback–Leibler (KL) divergence features based on partial voltage sequences are proposed as new HIs that are independent of prior knowledge and strongly correlated with SOH. Moreover, a modified retentive network is proposed to enhance SOH estimation accuracy and better utilize HIs than traditional deep learning methods, which have high training costs and insufficient accuracy. To ensure consistent extraction of KL divergence features across various experimental conditions and time intervals, a B-spline algorithm is utilized for interpolation. The effectiveness of the proposed method is validated through analysis of Pearson correlation coefficients and experiments conducted in four dimensions. Additionally, the potential of using the proposed method to compress data on the cloud-side is explored.

Analysis of Hydrological Changes in the Fuhe River Basin in the Context of Climate Change

Against the backdrop of global warming, assessing the effects of climate change on hydrological processes is crucial for local water resource management. Variations in temperature, precipitation, and runoff at four different timescales in the Fuhe River Basin were evaluated based on observational data collected from 1960 to 2020 using the Mann–Kendall test. The findings indicated significant increases in average temperatures for the annual, flood season, and non-flood season periods, rising by 0.0197, 0.0145, and 0.0278 °C every annum, respectively (p &lt; 0.01). Precipitation exhibited non-significant upward trends at all timescales (p &gt; 0.1). The trend in flood season runoff was also non-significantly upward, whereas annual runoff and non-flood season runoff displayed non-significant downward trends (p &gt; 0.1). Flood season temperature decreased with increasing altitude, exhibiting a significant Pearson correlation coefficient of −0.744 at the 0.01 level. Conversely, annual, flood, and non-flood season precipitation significantly increased with increasing altitude, with Pearson correlation coefficients of 0.678 at the 0.01 level, 0.695 at the 0.01 level, and 0.558 at the 0.05 significance level, respectively. Precipitation and runoff exhibited similar trends throughout the year, increasing initially and then decreasing over time, reaching maximum values in June. Climate change is likely responsible for the hydrological alterations in the study basin. The findings of the study could provide references for water resource management decisions in the Fuhe River Basin.

Machine Learning Approaches for Short-Term Photovoltaic Power Forecasting

A photovoltaic (PV) power forecasting prediction is a crucial stage to utilize the stability, quality, and management of a hybrid power grid due to its dependency on weather conditions. In this paper, a short-term PV forecasting prediction model based on actual operational data collected from the PV experimental prototype installed at the engineering college of Misan University in Iraq is designed using various machine learning techniques. The collected data are initially classified into three diverse groups of atmosphere conditions—sunny, cloudy, and rainy meteorological cases—for various seasons. The data are taken for 3 min intervals to monitor the swift variations in PV power generation caused by atmospheric changes such as cloud movement or sudden changes in sunlight intensity. Then, an artificial neural network (ANN) technique is used based on the gray wolf optimization (GWO) and genetic algorithm (GA) as learning methods to enhance the prediction of PV energy by optimizing the number of hidden layers and neurons of the ANN model. The Python approach is used to design the forecasting prediction models based on four fitness functions: R2, MAE, RMSE, and MSE. The results suggest that the ANN model based on the GA algorithm accommodates the most accurate PV generation pattern in three different climatic condition tests, outperforming the conventional ANN and GWO-ANN forecasting models, as evidenced by the highest Pearson correlation coefficient values of 0.9574, 0.9347, and 0.8965 under sunny, cloudy, and rainy conditions, respectively.