Robust Correlations Research Articles

Relationship Between Depression, Anxiety, Stress, and Weight Self-Stigma Among Youths in Saudi Arabia: A Nationwide Study.

Background and objectives Many research studies show that self-stigma related to weight can exacerbate mental health issues. There is also evidence suggesting that depression, anxiety, and stress could be predictors of weight stigma. However, these connections have not been thoroughly investigated among young people in Saudi Arabia, where there is a high prevalence of obesity. This study aimed to explore the relationships between depression, anxiety, stress, and weight self-stigma in Saudi Arabian adolescents and young adults. Materials and methods This cross-sectional study was conducted between January and March 2022 and utilized online surveys, including the Weight Self-Stigma Questionnaire (WSSQ) and Depression, Anxiety, and Stress Scale 21 (DASS-21). We conducted descriptive analysis, independent samples t-tests, analysis of variance (ANOVA), and linear regression for the statistical analysis using SPSS version 25 (IBM Corp., Armonk, NY). Results A total of 1624 participants were enrolled in this survey. Most participants (889, 54.7%) were females. The mean age was 20.73 years (SD: 2.63). Males were more likely to report self-stigma compared to females (p = 0.018). Weight self-stigma demonstrated significant positive associations with body mass index (BMI) (p= 0.0001) and depression, anxiety, and stress (p = 0.0001) scores. Further analysis revealed weight self-stigma was positively correlated with psychological distress levels in the study population (p = 0.0001). The results demonstrated a statistically significant difference between different regions of Saudi Arabia with weight self-stigma being more prevalent in the Al-Baha region and least prevalent in the Al-Madinah region. Conclusions The results of this study indicate robust positive correlations between weight self-stigma scores and depression, anxiety, and stress scale scores among adolescents and young adults in Saudi Arabia.Further epidemiological and clinical studies on the national level are warranted.

Examination of the Effects of Climate Change on Türkiye through the Google Earth Engine Platform

The objective of this research is to assess the effects of climate change on Türkiye by utilizing data catalogues provided by the Google Earth Engine (GEE) cloud-computing platform. The utilized data catalogues encompassed precipitation, Land Surface Temperature (LST), EvapoTranspiration (ET), Potential EvapoTranspiration (PET), Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Vegetation Condition Index (VCI), and Forest Area Loss (FAL). Data spanning the years 2001-2022 were collected, and analyses were conducted separately for seven geographical regions of Türkiye on both an annual and seasonal basis. Initially, trend analysis techniques were applied to the individual data sets, followed by an examination of correlations among them. Notably, significant decreasing and increasing trends were observed in annual precipitation and LST data in the Eastern Anatolia region, respectively. Furthermore, a significant increasing trend was identified in annual ET data across all regions except Eastern Anatolia. Conversely, significant increasing trends were noted in annual PET data in Eastern Anatolia and the Aegean regions. Additionally, significant increasing trends were discerned in annual NDVI, EVI, and VCI data across all regions. Experiments revealed that the ET exhibited robust correlations with the NDVI (0.77), EVI (0.79) and VCI (0.81). Furthermore, the NDVI demonstrated strong correlations with EVI (0.99) and VCI (0.96).

Influence of variation in grain boundary parameters on the evolution of atomic structure and properties of [111] tilt boundaries in aluminum

Grain boundaries (GBs) are fundamental planar defects in metallic materials, exerting a profound influence on material behaviour and properties. Understanding the influence of GB structure on material performance presents challenges due to the intricate atomic arrangements involved. To overcome inconsistencies observed in previous simulations, experimental validation is necessary. However, research on [111] tilt GBs in metals other than copper, such as aluminium, remains limited. This study aims to address these gaps by investigating the characterization of [111] tilt GBs in Al, employing advanced experimental techniques and validating the findings through simulations. By exploring a range of misorientation angles and plane inclinations, two distinct misorientation groups are identified, each exhibiting unique structural configurations. However, the structural differences resulting from plane inclination are more pronounced than those stemming from misorientation angle. Additionally, it is found that deviations from precise misorientation angles and different plane inclinations significantly influence the excess properties of GBs, such as GB energy and excess volume, etc. Furthermore, comparisons between Al and Cu structures enhance our understanding of GB behaviour in different metallic materials. This study lays the foundation for robust structure-property correlations in GBs and emphasizes the importance of considering intricate GB structural details to better understand and predict their properties accurately.

Unraveling sub-seasonal precipitation variability in the Middle East via Indian Ocean sea surface temperature

This study examines sub-seasonal precipitation anomalies, challenging to predict yet vital for society and the environment. Focusing on October, we investigate correlations between the Indian Ocean Dipole Mode Index (DMI), West Tropical Indian Ocean Index (WTIO), and Middle Eastern precipitation. We find robust correlations (~ 0.7), up to a two-month lag, demonstrating strong links between these climate indices and rainfall patterns, potentially suggesting sub-seasonal precipitation predictability. Over the past four decades, DMI and WTIO have shown a significant upward trend of ~ 0.4 °C, intensifying their impact on precipitation dynamics. This trend signifies evolving Indian Ocean climate patterns with potential regional consequences and is projected to continue in the twenty-first century. Significant correlations also emerge between DMI, WTIO, and maximum daily precipitation, highlighting their role in extreme rainfall events. Finally, our study attributes most of October’s precipitation variability to Indian Ocean sea surface temperature variations. These temperature anomalies influence the Indian Ocean’s Walker circulation, affecting water vapor flux to the Middle East and shaping regional precipitation. Our findings underscore the importance of these indices in understanding and predicting Middle East climate variability, revealing intricate ocean–atmosphere interactions.

Genetic parameter changes and age−age correlations in Pinus koraiensis growth over 40-year progeny testing

BackgroundEarly selection in tree breeding could be achieved by addressing the longevity of tree improvement activities. Genetic parameter changes and age–age correlations are essential for determining the optimal timing of early selection. Practical tracking of genetic parameters of Pinus koraiensis, a major timber species with economic and ecological value, has become feasible as its progeny testing has entered the mid-term age in Korea. However, research on the age−age correlation of P. koraiensis as progeny trials approach rotation age is limited. This study aimed to investigate genetic parameter trends and age−age correlations in P. koraiensis progeny. P. koraiensis progeny were assessed at two sites using a linear mixed-effects model with two-dimensional spatial autoregressive structure. Height, diameter, and volume growth were measured in 11 assessments over 40 years.ResultsGenetic parameters, such as height and diameter, showed different patterns of change. The heritability ranged for the three growth traits in 0.083–0.710, 0.288–0.781, and 0.299–0.755 across the sites and age. Height heritability and its coefficient of variance decreased, whereas the diameter and volume estimates remained relatively constant. Correlations with Age 40 for phenotypic, genetic, and rank of breeding values ranged between 0.16 and 0.92, 0.594 and 0.988, and 0.412 and 0.965, respectively. These correlations generally increased as the age approached Age 40, with particularly high levels observed at Age 26 and Age 30.ConclusionThe observed genetic trends in P. koraiensis progeny testing offer valuable insights for early and precise selection. Notably, selecting superior genotypes at Ages 26–30 is supported by discernible genetic gains and robust correlations. Future research should integrate unbalanced data for selecting mother trees or families and conduct a comprehensive economic analysis of early selection to validate its practical benefits.

The Role of Verbal Fluency as a Variable in Reading and Comprehension Skills in Bengali.

The study explored the role of verbal fluency in determining reading and comprehension skills in Bengali among 10-year old typically developing Bengali children. Robust correlations were found between semantic fluency and word reading (0.63) as well as semantic fluency and comprehension (0.70). Good correlation was found between letter fluency and comprehension (0.49), and, word reading and comprehension (0.62). The findings suggest that good word storing capacity and executive functions led to enhanced automaticity of retrieval and verbal fluency, which together with improved orthographic and phonological processing led to good word reading scores, contributing to lesser cognitive load thereby easing out the complex text comprehension task. Transcending narrow empirical base of reading theories derived from Western writing typologies delimited to orthographic depth and psycholinguistic grain size, the study contends that verbal fluency is another interesting variable determining the reading and comprehension skills in Bengali along with visuo-spatial complexity and complex phonology-orthography inconsistencies.

Stomatal dimensions of Laguncularia racemosa leaves in restored and degraded mangrove ecosystems

ABSTRACT Mangrove leaves have adapted to effectively manage water conservation and regulate toxic saline conditions through the morphological characteristics of stomatal dimensions. The present study examines the stomatal dimensions of 120 Laguncularia racemosa leaves from both a degraded mangrove ecosystem and a restored mangrove ecosystem located along the Guyana coastline. Datasets were obtained during the wet and dry seasons using the nearest individual sampling method. The nail polish blotting technique, microscopy, and digital photography were used to obtain measurements of various stomatal dimensions, including guard cell length and width, pore length and width, circumference, surface area, stomatal frequency, density, index, and epidermal cell frequency. Our hypothesis is that there is significant variation in the stomatal dimensions of Laguncularia racemosa leaves as a function of mangrove ecosystem type and seasonal change. The results of this study confirmed this hypothesis as significant variations were observed in stomatal dimensions as a function of seasonal changes and mangrove ecosystem type. Statistically significant differences (p < 0.05, R > 0.70) were observed between the degraded ecosystem during the dry season and the restored ecosystem during the wet season, specifically regarding guard cell length and width, pore length and width, area, and circumference. Multiple regression analysis and PCA provide compelling evidence to support the existence of robust correlations between ecosystem types and stomatal dimensions. The results of our study indicate that the leaves of Laguncularia racemosa display certain small-scale adjustments that enable them to manage the stress factors present in different ecosystems and seasons.

An antibacterial lead identification of novel 1,3,4-oxadiazole derivatives based on molecular computer aided design approaches

Merging Density Functional Theory (DFT) with the Quantitative Structure-Activity Relationship (2D/3D-QSAR) modeling represents a promising avenue for exploring antibacterial activity and discovering potential drugs effective against both gram-positive and gram-negative microorganisms. In this study, we employed this integrated approach to investigate a newly synthesized and promising class of 1,3,4-oxadiazole derivatives renowned for their high performance as antibacterial agents. To ensure an accurate characterization and thorough description of the targeted biological activity, we systematically evaluated various DFT functionals to precisely predict the geometrical and electronic descriptors of the investigated compounds. These descriptors were crucial for developing and validating the proposed 2D/3D-QSAR models. Our results reveal that the introduction of a donor group enhances the antibacterial activity of the derivatives. The analysis of molecular descriptors underscores the positive impact of this modification on the compounds' efficacy against bacteria. Additionally, our experimental compounds exhibit favorable characteristics concerning oral bioavailability, a crucial aspect in drug development. The identification of robust correlations between antibacterial activity and specific descriptors was achieved by conducting an extensive analysis that encompassed multiple linear regression (MLR), Random Forest (RF), and Artificial Neural Networks (ANN). Subsequently, a partial least squares (PLS) model was employed to construct 3D-QSAR models based on the Comparative Molecular Field Analysis (CoMFA) and Comparative Similarity Indices Analysis (CoMSIA) descriptors. Validation of the output models was performed using leave-one-out and bootstrapping strategies. The resultant 2D/3D-QSAR models demonstrated a high correlation between experimental and predicted activity values. Leveraging these models, the developed MLR model, expressed as pMIC = -12.704 + 0.260 logP – 6.104 10-03 SAG – 51.385 qN33, serves as a valuable tool for predicting antibacterial activity. we indicate that the machine learning methods, Artificial Neural Networks (ANN) and Random Forest (RF), outperform traditional models in accurately predicting antibacterial activity. We designed ten novel molecules, subjecting them to molecular docking and molecular dynamics simulations to predict their optimal postures when docked in the target and gain insights into the formed interactions. Additionally, Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) analysis was applied to discern the potential behavior of these novel compounds in the human body. As a result, the newly suggested chemical, X4, X10, exhibited robust inhibitory potential against gram-negative microbes.

Understanding teacher emotional exhaustion: exploring the role of teaching motivation, perceived autonomy, and teacher-student relationships.

This research investigates the complex interplay of factors influencing teacher emotional exhaustion within the educational environment. It draws upon a diverse sample of 680 teachers from 15 primary educational institutions in various districts of Shanghai, China. The study examines the interrelationships among teacher-student relationships, perceived autonomy, teaching motivation, and emotional exhaustion. It employs validated scales to measure these constructs and utilizes Structural Equation Modeling (SEM) for analysis. The analyses using SEM reveal significant associations among the variables under study. The findings indicate robust correlations between positive teacher-student relationships, perceived autonomy, and higher teaching motivation. Concurrently, these factors exhibit a negative relationship with emotional exhaustion. Mediation analysis further unveils that teaching motivation plays a significant mediating role in the associations between teacher-student relationships, perceived autonomy, and emotional exhaustion. This emphasizes the intricate indirect effects of these constructs. The study underscores the multifaceted nature of these factors and their collective impact on teacher well-being within educational settings. The implications derived highlight the importance for educational institutions to prioritize interventions fostering positive teacher-student relationships, autonomy-supportive environments, and intrinsic motivation among educators. These interventions aim to alleviate emotional exhaustion and enhance teacher well-being.

Optimizing Solar Synergy: Unveiling Robust Nusselt Number Correlations for Helical Coil Energy Storage in Solar Water Heating Systems

Optimizing Solar Synergy: Unveiling Robust Nusselt Number Correlations for Helical Coil Energy Storage in Solar Water Heating Systems

Decoding the DSCC1 gene as a pan-cancer biomarker in human cancers via comprehensive multi-omics analyses.

While dysregulation of DSCC1 (DNA Replication And Sister Chromatid Cohesion 1) has been established in breast cancer and colorectal cancer, its associations with other tumors remain unclear. Therefore, this study was launched to explore the role of DSCC1 in pan-cancer. In this study, we investigate the biological functions of DSCC1 across 33 solid tumors, elucidating its role in promoting oncogenesis and progression in various cancers through comprehensive analysis of multi-omics data. We conducted a comprehensive analysis of DSCC1 expression using RNA-seq data from TCGA and GTEx databases across 30 cancer types. Striking variations were observed, with significant overexpression of DSCC1 identified in numerous cancers. Elevated DSCC1 level was strongly associated with poorer prognosis, shorter survival, and advanced tumor stages in kidney renal papillary cell carcinoma (KIRP), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), as indicated by Kaplan-Meier curves and GEPIA2 analysis. Further investigation into the molecular mechanisms revealed reduced DNA methylation in the DSCC1 promoter region in KIRP, LIHC, and LUAD, supporting enhanced RNA transcription. Protein expression analysis via the Human Protein Atlas (HPA) corroborated mRNA expression findings, showcasing elevated DSCC1 protein in KIRP, LIHC, and LUAD tissues. Mutational analysis using cBioPortal revealed alterations in 0.4% of KIRP, 17% of LIHC, and 5% of LUAD samples, predominantly characterized by amplification. Immune cell infiltration analysis demonstrated robust positive correlations between DSCC1 expression and CD8+ T cells, CD4+ T cells, and B cells, influencing the tumor microenvironment. STRING and gene enrichment analyses unveiled DSCC1's involvement in critical pathways, emphasizing its multifaceted impact. Notably, drug sensitivity analysis highlighted a significant correlation between DSCC1 mRNA expression and responses to 78 anticancer treatments, suggesting its potential as a predictive biomarker and therapeutic target for KIRP, LIHC, and LUAD. Finally, immunohistochemistry staining of clinical samples validated computational results, confirming elevated DSCC1 protein expression. Overall, this study provides comprehensive insights into the pivotal role of DSCC1 in KIRP, LIHC, and LUAD initiation, progression, and therapeutic responsiveness, laying the foundation for further investigations and personalized treatment strategies.

Jaak Panksepp’s primary emotions are associated with Diener’s global life satisfaction: How low arousal of the SADNESS/separation distress system could form the core of life satisfaction?

Abstract We compared Ed Diener’s Satisfaction With Life Scale (SWLS), which was designed as a purely cognitive measure of global life satisfaction, with the Affective Neuroscience Personality Scales 3.1, which provides self-report measures of Panksepp’s six primary emotions (excluding LUST), in two English-speaking samples: a main sample and a hold-out validation sample. Our data showed robust negative correlations between higher satisfaction with life and lower FEAR, lower SADNESS/Separation Distress, and positive associations (albeit less strong) between higher satisfaction with life and higher PLAY and SEEKING in both samples. The relationships between the SWLS and at least four of Panksepp’s primary emotions suggest Diener’s SWLS is not purely cognitive and includes a strong affective component. In addition, detailed analysis of the negative correlation between the SWLS and the ANPS 3.1 SADNESS scale provides insight into the importance of the low arousal end of the SADNESS/Separation Distress brain system and supports the idea of a continuum of psychological states from high SADNESS including loneliness and depression to low SADNESS psychological states characterized by social comfort, self-confidence, and social strength.

Altered trace elements, antioxidant vitamin C, and malondialdehyde levels are associated with the pathophysiology and development of pre-hepatic jaundice: A case-control study.

Pre-hepatic jaundice results from an imbalance between bilirubin production and clearance, often linked to hemoglobinopathies. Antioxidant vitamin C, malondialdehyde, and trace elements play roles in jaundice, yet their specific associations remain unclear. The objective is to assess and compare these biomarkers in pre-hepatic jaundice patients and healthy controls, aiming to identify potential diagnostic markers and understand distinctive characteristics related to the disease's pathogenesis. This case-control study enrolled 50 pre-hepatic jaundice patients and 50 healthy controls, utilizing advanced techniques for biomarker quantification. We completed blood sample collection from study participants between 1 September 2023 and 31 December 2023. This study investigates the correlation between various biomarkers and pre-hepatic jaundice using serum samples with a focus on antioxidant vitamin C, malondialdehyde, and trace elements. This study demonstrates elevated concentrations of malondialdehyde in patients with pre-hepatic jaundice, suggesting alterations in bilirubin metabolism and increased oxidative stress. We found that the serum levels of malondialdehyde were significantly higher in pre-hepatic jaundice patients compared to healthy controls. Our observations revealed a notable decrease in the average serum vitamin C levels in patients with pre-hepatic jaundice compared to healthy controls. The patients had lower serum Zn levels and higher serum Cu and Mn levels compared to the healthy controls. The correlation study demonstrates robust positive correlations among these biomarkers in pre-hepatic jaundice. As the levels of vitamin C rise, the levels of the other criteria often fall, and vice versa. There is an inverse relationship between higher levels of vitamin C and lower levels of malondialdehyde. The current investigation identifies possible changes in antioxidant vitamins, malondialdehyde levels, and trace elements, which provide significant insights for targeted interventions. The present research highlights the integrated significance of vitamin C, malondialdehyde, and trace elements in the progression of the disease.

HRV and EEG correlates of well-being using ultra-short, portable, and low-cost measurements.

Wearable electroencephalography (EEG) and electrocardiography (ECG) devices may offer a non-invasive, user-friendly, and cost-effective approach for assessing well-being (WB) in real-world settings. However, challenges remain in dealing with signal artifacts (such as environmental noise and movements) and identifying robust biomarkers. We evaluated the feasibility of using portable hardware to identify potential EEG and heart-rate variability (HRV) correlates of WB. We collected simultaneous ultrashort (2-min) EEG and ECG data from 60 individuals in real-world settings using a wrist ECG electrode connected to a 4-channel wearable EEG headset. These data were processed, assessed for signal quality, and analyzed using the open-source EEGLAB BrainBeats plugin to extract several theory-driven metrics as potential correlates of WB. Namely, the individual alpha frequency (IAF), frontal and posterior alpha asymmetry, and signal entropy for EEG. SDNN, the low/high frequency (LF/HF) ratio, the Poincaré SD1/SD2 ratio, and signal entropy for HRV. We assessed potential associations between these features and the main WB dimensions (hedonic, eudaimonic, global, physical, and social) implementing a pairwise correlation approach, robust Spearman's correlations, and corrections for multiple comparisons. Only eight files showed poor signal quality and were excluded from the analysis. Eudaimonic (psychological) WB was positively correlated with SDNN and the LF/HF ratio. EEG posterior alpha asymmetry was positively correlated with Physical WB (i.e., sleep and pain levels). No relationships were found with the other metrics, or between EEG and HRV metrics. These physiological metrics enable a quick, objective assessment of well-being in real-world settings using scalable, user-friendly tools.

Advancing Rice Yield Forecasting and Crop Assessment in Kazakhstan’s Kyzylorda Region: A Multisource Satellite Data Approach

The monitoring and prediction of rice yields are essential across various domains, including agriculture, environmental conservation, ecology, agricultural insurance, and land resource management. The central districts of Kazakhstan’s Kyzylorda region, encompassing 70% of the total rice cultivation area, are of particular importance for ensuring national food security. This study focuses on the central Kyzylorda region, specifically the Syrdarya, Zhalagash, and Karmakshi districts, which serve as the primary hub for rice cultivation in Kazakhstan. The primary objective is to advance remote sensing techniques tailored to evaluate crop conditions and forecast rice yields, accounting for the region’s unique soil and climatic attributes. The research methodology involves comprehensive analysis of Sentinel-2 and Landsat-8 satellite data, harmonized with ground-truth information. This analysis encompasses the latest rice crop data for 2020 and precise sowing dates. Extensive field surveys were conducted to gather crucial data on growth, development, crop health, and productivity under specific agrometeorological conditions. The study provides a detailed flowchart outlining the sequential processing of remote sensing data and ground-based information, with a primary focus on forecasting rice yields in the central Kyzylorda districts for 2020. Rigorous satellite data analysis established robust correlations between the Normalized Difference Vegetation Index (NDVI) and various crop conditions, directly linked to rice yield, with high precision (R2 values ranging from 0.82 to 0.91). Validation exercises, cross-referencing satellite data with real-time field data, further enhanced accuracy. The research outcomes have extensive applications, benefiting agriculture, environmental preservation, ecology, insurance, statistics, and land management. This study underscores the significance of addressing critical challenges across various sectors through multifaceted findings.

Lymphocyte activation gene 3 served as a potential prognostic and immunological biomarker across various cancer types: a clinical and pan-cancer analysis.

Lymphocyte activation gene 3 (LAG3), an inhibitory receptor in T-cell activation, is a negative prognostic factor. However, its impact on tumours has yet to be comprehensively elucidated on a pan-cancer scale. Thus, we aim to reveal its role at the pan-cancer level. We performed IHC staining on a retrospective cohort of 370 patients. Then we assessed the prognostic effect of LAG3 using Kaplan-Meier survival analysis and multivariate Cox regression analysis. In pan-cancer analysis, we constructed competing endogenous RNA and protein-protein interaction networks, conducted gene set enrichment analysis and identified correlations between LAG3 gene expression and various factors, including clinical characteristics, tumour purity, mutations, tumour immunity and drug sensitivity across 33 cancer types. LAG3 was expressed higher in normal kidney tissues than in tumours. A high level of LAG3 gene expression was an independent prognostic factor for OS (HR = 6.60, 95% CI = 2.43-17.90, P < 0.001) and PFS (HR = 3.44, 95% CI = 1.68-7.10, P < 0.001). In pan-cancer analysis, LAG3 exhibited robust correlations with survival and tumour stages in various cancers. Moreover, LAG3 was strongly associated with immune-related genes, proteins and signalling pathways. LAG3 gene expression was positively associated with increased infiltration of activated immune cells and decreased infiltration of several resting cells. LAG3 gene expression was associated with tumour mutation burden and microsatellite instability in multiple cancers. High LAG3 gene expression was an independent risk factor in kidney neoplasms. It also functioned as a biomarker for prognosis, TIME and immunotherapy efficacy in the pan-cancer dimension.

Relationship between Physicochemical and Cooking Quality Parameters with Estimated Glycaemic Index of Rice Varieties.

Rice is a significant staple food in the basic diet of the global population that is considered to have a high glycaemic index. The study of the physical and chemical parameters in rice that are related to the starch digestion process, which allows us to quickly predict the glycaemic index of varieties, is a major challenge, particularly in the classification and selection process. In this context, and with the goal of establishing a relationship between physicochemical properties and starch digestibility rates, thus shedding light on the connections between quality indicators and their glycaemic impact, we evaluated various commercial rice types based on their basic chemical composition, physicochemical properties, cooking parameters, and the correlations with digestibility rates. The resistant starch, the gelatinization temperature and the retrogradation (setback) emerge as potent predictors of rice starch digestibility and estimated glycaemic index, exhibiting robust correlations of r = -0.90, r = -0.90, and r = -0.70 (p ≤ 0.05), respectively. Among the rice types, Long B and Basmati stand out with the lowest estimated glycaemic index values (68.44 and 68.10), elevated levels of resistant starch, gelatinization temperature, and setback values. Furthermore, the Long B showcases the highest amylose, while the Basmati with intermediate, revealing intriguingly strong grain integrity during cooking, setting it apart from other rice varieties.

Tuning Microstructural Attributes of Spinel Cobalt Ferrite (CoFe2O4) Nanoparticles through Annealing Temperature

Background: The study systematically investigates the influence of annealing temperatures, ranging from 500°C to 900°C with 100°C increments, on the microstructural characteristics of cobalt ferrite (CoFe2O4) nanoparticles. Methods: The nanoparticles, with sizes between 7-18 nm, were synthesized using the co-precipitation method. X-ray diffraction (XRD) analysis reveals that higher annealing temperatures correspond to noticeable increases in crystallite size, lattice parameter, unit cell volume, and interatomic distances within both octahedral and tetrahedral sites. Concurrently, a substantial decrease is observed in the average theoretical X-ray density, dislocation density, and microstructural strain. This investigation elucidates the underlying physical and chemical processes driving these transformations. To explore and quantify the intricate relationships between annealing temperature and various microstructural attributes of CoFe2O4 nanoparticles, Pearson’s correlation coefficient (r) serves as a robust statistical tool. The study establishes significant associations and elucidates the strength and direction of these correlations. Results: Regression analysis yields highly robust correlations (Adjusted R-Squared &gt; 0.99) between microstructural features and annealing temperature. These correlations provide valuable predictive insights into microstructural characteristics, offering substantial support for optimizing CoFe2O4 nanoparticle applications across a temperature range spanning from 500°C to 900°C. Conclusion: This research contributes to the scientific understanding of materials engineering and offers practical guidance for applications requiring precise control over nanoparticle properties.

Self-Terminated Surface Reconstruction of Lanthanum Nickelates Promotes Alkaline Oxygen Evolution

Oxygen evolution reaction (OER) itself is expected to have a strong impact on the state of the surface of the electrocatalyst, yet the actual structure of the surface under operating conditions is rarely considered while formulating structure-activity correlations in electrocatalysis. In this study, changes in the surface structure of Ruddlesden-Popper-type lanthanum nickelate, La2NiO4+δ, and the perovskite LaNiO3 are probed and contrasted using a series of complementary surface sensitive techniques, including XPS, HRTEM and O K-edge XAS. We found noticeable surface changes to occur on La2NiO4+δ after exposure its exposure to the alkaline electrolyte that is accompanied by the emergence of a strong Nin+/n+1 redox feature and the coincides with a 45-fold enhancement of the OER activity (at η = 370 mV) in 0.1 M KOH, as compared to the pristine La2NiO4+δ phase. We observe that the in situ formation of an amorphous nickel (oxy)hydroxide layer on the surface increases the electrochemically-active surface area of La2NiO4+δ and imparts a high stability of the catalyst under OER conditions (the formation of the amorphous layer self-terminates at ca. 3 nm). In addition, the transition from octahedral Ni sites (O h ) to square-planar Ni sites (D 4h ) in the reconstructed surface was observed using O K-edge X-ray absorption spectroscopy. Further, we found no evidence for any surface transformations in the perovskite-based catalyst LaNiO3 that also did not demonstrate any improvement in its activity over cycling. This work highlights the importance of a detailed surface analysis of the catalysts under operation to build robust structure-activity correlations for the metal oxides under electrochemical reaction conditions. Figure 1

Assessing quality management systems in physical therapy: a cross-sectional analysis of service performance and patient satisfaction

Background: This study examined the quality of physical therapy services across di-verse settings through a cross-sectional correlational analysis. Its fundamental objective lies in exploring the nuanced factors that contextualize the quality of physical therapy practices. (2) Methods: We introduced the Physical Therapy Services Evaluation Ques-tionnaire (PSEQ) and gathered patient satisfaction data through a Visual Analog Scale (VAS). The study involved 30 physical therapists (average age 39.57 ± 9.37 years, 40% males and 60% females), assessed via PSEQ, with their work experience (WE) specified. Each physical therapist received feedback from 10 patients using VAS. (3) Results: Follow-ing content validation, PSEQ demonstrated strong internal consistency (Cronbach's alpha coefficient = 0.89). Correlational analyses showed significant positive associations be-tween age and WE, age and PSEQ global score, WE and PSEQ global score, and between VAS score and PSEQ global score (p ≤ 0.001). Linear regression analyses confirmed these relationships (p ≤ 0.001) and offered a quantifiable basis for predictions, essential for in-formed decision-making in physical therapy service management and research. (4) Con-clusions: The study established robust correlations between physical therapists' age, WE, and service evaluations. Patient satisfaction, measured through VAS, significantly influ-ences service evaluations, underscoring the importance of continuous professional devel-opment and patient-centered care in physical therapy services.