Pearson Correlation Coefficient Research Articles

Multiomic meta-analysis suggests a correlation between steroid hormone-related genes and litter size in goats.

Litter size is a key indicator of production performance in livestock. However, its genetic basis in goats remains poorly understood. In this work, a genome-wide selection sweep analysis (GWSA) on 100 published goat genomes with different litter rates was performed for the first time to identify candidate genes related to kidding rate. This analysis was combined with the public RNA-sequencing data of ovary tissues (follicular phase) from high- and low-yielding goats. A total of 2278 genes were identified by GWSA. Most of these genes were enriched in signaling pathways related to ovarian follicle development and hormone secretion. Moreover, 208 differentially expressed genes between groups were obtained from the ovaries of goats with different litter sizes. These genes were substantially enriched in the cholesterol and steroid synthesis signaling pathways. Meanwhile, the weighted gene co-expression network was used to perform modular analysis of differentially expressed genes. The results showed that seven modules were reconstructed, of which one module showed a very strong correlation with litter size (r = -0.51 and p-value <0.001). There were 51 genes in this module, and 39 hub genes were screened by Pearson's correlation coefficient between core genes > 0.4, correlation coefficient between module members > 0.80 and intra-module connectivity ≥5. Finally, based on the results of GWSA and hub gene Venn analysis, seven key genes (ACSS2, HECW2, KDR, LHCGR, NAMPT, PTGFR and TFPI) were found to be associated with steroid synthesis and follicle growth development. This work contributes to understanding of the genetic basis of goat litter size and provides theoretical support for goat molecular breeding.

3D U-Net Neural Network Architecture-Assisted LDCT to Acquire Vertebral Morphology Parameters: A Vertebral Morphology Comprehensive Analysis in a Chinese Population.

To evaluate the feasibility of acquiring vertebral height from chest low-dose computed tomography (LDCT) images using an artificial intelligence (AI) system based on 3D U-Net vertebral segmentation technology and the correlation and features of vertebral morphology with sex and age of the Chinese population. Patients who underwent chest LDCT between September 2020 and April 2023 were enrolled. The Altman and Pearson's correlation analyses were used to compare the correlation and consistency between the AI software and manual measurement of vertebral height. The anterior height (Ha), middle height (Hm), posterior height (Hp), and vertebral height ratios (VHRs) (Ha/Hp and Hm/Hp) were measured from T1 to L2 using an AI system. The VHR is the ratio of Ha to Hp or the ratio of Hm to Hp of the vertebrae, which can reflect the shape of the anterior wedge and biconcave vertebrae. Changes in these parameters, particularly the VHR, were analysed at different vertebral levels in different age and sex groups. The results of the AI methods were highly consistent and correlated with manual measurements. The Pearson's correlation coefficients were 0.855, 0.919, and 0.846, respectively. The trend of VHRs showed troughs at T7 and T11 and a peak at T9; however, Hm/Hp showed slight fluctuations. Regarding the VHR, significant sex differences were found at L1 and L2 in all age bands. This innovative study focuses on vertebral morphology for opportunistic analysis in the mainland Chinese population and the distribution tendency of vertebral morphology with ageing using a chest LDCT aided by an AI system based on 3D U-Net vertebral segmentation technology. The AI system demonstrates the potential to automatically perform opportunistic vertebral morphology analyses using LDCT scans obtained during lung cancer screening. We advocate the use of age-, sex-, and vertebral level-specific criteria for the morphometric evaluation of vertebral osteoporotic fractures for a more accurate diagnosis of vertebral fractures and spinal pathologies.

Cost analysis of radical resection of malignant breast tumors under the China Healthcare Security Diagnosis Related Groups payment system.

Breast cancer is one of the most common malignant tumors in women worldwide and poses a severe threat to their health. Therefore, this study examined patients who underwent breast cancer surgery, analyzed hospitalization costs and structure, and explored the impact of China Healthcare Security Diagnosis Related Groups (CHS-DRG) management on patient costs. It aimed to provide medical institutions with ways to reduce costs, optimize cost structures, reduce patient burden, and improve service efficiency. To study the CHS-DRG payment system's impact on breast cancer surgery costs. Using the CHS-DRG (version 1.1) grouping criteria, 4073 patients, who underwent the radical resection of breast malignant tumors from January to December 2023, were included in the JA29 group; 1028 patients were part of the CHS-DRG payment system, unlike the rest. Through an independent sample t-test, the length of hospital stay as well as total hospitalization, medicine and consumables, medical, nursing, medical technology, and management expenses were compared. Pearson's correlation coefficient was used to test the cost correlation. In terms of hospitalization expenses, patients in the CHS-DRG payment group had lower medical, nursing, and management expenses than those in the diagnosis-related group (DRG) non-payment group. For patients in the DRG payment group, the factors affecting the total hospitalization cost, in descending order of relevance, were medicine and consumable costs, consumable costs, medicine costs, medical costs, medical technology costs, management costs, nursing costs, and length of hospital stay. For patients in the DRG non-payment group, the factors affecting the total hospitalization expenses in descending order of relevance were medicines and consumable expenses, consumable expenses, medical technology expenses, the cost of medicines, medical expenses, nursing expenses, length of hospital stay, and management expenses. The CHS-DRG system can help control and reduce unnecessary medical expenses by controlling medicine costs, medical consumable costs, and the length of hospital stay while ensuring medical safety.

Dry Leaf Biomass Stability of Stevia (Stevia rebaudiana) Clones over Different Environments

In order to identify stable high-yielding stevia (Stevia rebaudiana) genotypes, a multi-environment testing was conducted over four different growing environments. The experiments were conducted in a randomized block design, with three replications during 2019 and 2020. The combined analysis of variance showed significant variation for genotype, environment, and G×E interaction for all studied traits. The highest mean performance for all the traits revealed that Hoshiarpur has favorable conditions for stevia cultivation, and CSIR-IHBT-ST-1801 followed by CSIR-IHBT-ST-G12 were the best performers over all the locations. The Eberhart and Russell model-based stability parameters demonstrated that CSIR-IHBT-ST-1801 was a stable performer for dry leaf biomass, and that was also further confirmed by GGE biplot analysis. Primary shoots were major contributors to the dry leaf biomass, as indicated by the substantial positive leaf biomass contribution shown by Pearson's correlation coefficients. As a result, primary shoots might be utilized as selection criteria to increase the dry leaf biomass. The CSIR-IHBT-ST-1801 could be used as a stable high-yielding variety for the targeted regions and also, can be used for further stevia breeding programs.

The analysis of potentially toxic heavy metal contamination in the Lake Bogoria geothermal springs

Lake Bogoria geothermal system is a natural geological site in Kenya that boasts a habitat for Flamingos which attract tourists, and its water find applications in agriculture and recreation. Accordingly, its geological processes, closed water systems and anthropogenic activities may lead to accumulation of potential toxic metal contamination. The present study focused on the analysis of potentially toxic metals — manganese (Mn), cadmium (Cd), lead (Pb), copper (Cu) and nickel (Ni) using atomic absorption spectrometry (AAS) in geothermal spring water samples after acid digestion method, and evaluated their health risks through dermal and ingestion exposures. Principal component analysis (PCA), enrichment factors (EF) and Pearson correlation coefficient (PCC) were performed to determine the sources and correlation between the metals investigated. Manganese, Cu and Ni recorded mean concentrations (ppm) of 0.19 ± 0.08, 0.02 ± 0.02 and 0.03 ± 0.02, respectively, which were below the permissible World Health Organisation (WHO) limits, whereas Pb (0.06 ± 0.04) and Cd (0.05 ± 0.02) levels were above the threshold standards. PCA revealed that these metals may have originated from geological regimes and surface run-offs. Copper and Cd metals were highly enriched indicating an anthropogenic origin. The Hazard quotients (HQ) of Pb and Cd were >1,\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$>1,$$\\end{document} hence they are potential precursors for non-carcinogenic and carcinogenic risks in both children and adults. The findings of this study showed possible Pb and Cd metal contamination from geothermal waters. Therefore, there is need for remediation, continuous monitoring and assessment of geothermal waters before utilization for recreation, crop production and geothermal energy exploitation to ensure environmental conservation and human safety.

Multimethods study to develop tools for competency-based assessments of implementation research training programmes in low and middle-income countries.

Methods and validated tools for evaluating the performance of competency-based implementation research (IR) training programmes in low-middle-income countries (LMICs) are lacking. In this study, we developed tools for assessing the performance of IR training programmes based on a framework of IR core competencies in LMICs. We developed self-assessment and objective-assessment tools drawing on the IR competency framework. We used exploratory factor analyses and a one-parameter logistic model to establish construct validity and internal consistency of the tools drawing on a survey conducted in 2020 with 166 trainees before and after an index IR course across five universities and LMICs under the Special Program for Research and Training in Tropical Diseases postgraduate IR training scheme. We conducted key informant interviews (KII) with 20 trainees and five trainers to reflect on the usefulness of the tools and framework for guiding IR training in LMICs. Two 16-item tools for self-assessment of IR knowledge and self-efficacy and a 40-item objective assessment tool were developed. The factor loadings of items in the self-assessment tools were 0.65-0.87 with Cronbach's alpha (α) of 0.97, and 0.77-0.91 with α of 0.98 for the IR knowledge and self-efficacy tools, respectively. The distribution of item difficulty in the objective-assessment tool was consistent before and after the index IR course. Pearson correlation coefficient (r) between the self-assessed and objectively assessed IR knowledge before the index IR course was low, r=0.27 (p value: <0.01), with slight improvements after the index IR course, r=0.43 (p value: <0.01). All KII respondents reported the assessment tools and framework were valid for assessing IR competencies. The IR competency framework and tools developed for assessing IR competencies and performance of IR training programmes in LMICs are reliable and valid. Self-assessment methods alone may not suffice to yield a valid assessment of performance in these settings.

TEACHERS INTERPERSONAL BEHAVIOR AND PARENTAL ENGAGEMENT IN RELATION TO THE STUDENT’S ACADEMIC PERFORMANCE

The primary purpose of this study is to determine whether teachers’ interpersonal behavior and parental engagement in relation to the students' academic performance. This study employed a descriptive-correlational research design. Using total population sampling, 315 junior high school students were selected as respondents from one of the junior high school in New Bataan during the school year 2023-2024. Furthermore, this study employed two adapted survey questionnaires and the general average of the students during their third quarter; all validated to collect data and treated using mean, standard deviation, and Pearson-r. The findings showed that teachers’ interpersonal behavior and parental engagement among the students are very high, while their academic performance are very satisfactory. The results also revealed that teachers’ interpersonal behavior positively correlated with students’ academic performance and parental engagement negatively correlated with students’ academic performance. These results encourage teachers and parents to continue support and have a good relationship with the children to maintain a good academic performance of students. Furthermore, to improve the applicability of the results, it is imperative to carry out replication studies in various locales. This will validate the durability of these relationships beyond the present research setting and contribute to a more comprehensive comprehension of these issues. KEYWORDS: Teachers’ interpersonal behavior, parental engagement, student academic performance, junior high school students, descriptive-correlational design, pearson correlation coefficient, Davao de Oro, Philippines.

Investigation of ocular surface parameters in dogs with different cephalic conformations using veterinary ocular surface analyzer (OSA-VET).

To compare ocular surface parameters in dogs with different cephalic conformations and evaluate correlations among tests. Sixty-eight privately owned dogs. The study categorized canine eyes into three groups based on the craniofacial ratio (CFR): brachycephaly (≤0.52), mesocephaly (>0.52 to <0.67), and dolichocephaly (≥0.67). All eyes were examined using an ocular surface analyzer (OSA-VET) to determine lipid layer thickness (LLT) of the tear film, tear meniscus height (TMH), non-invasive tear breakup time (NIBUT), and meibomian gland loss rate of the lower eyelids (MGLRL). Schirmer tear test 1 (STT-1) and tear film breakup time (TBUT) were also performed. Statistical analyses involved one-way ANOVA, Kruskal-Wallis H test, post hoc Holm-Sidak test, and Pearson correlation coefficient. While STT-1 showed no significant difference among dog groups, brachycephalic dogs had significantly lower values in TBUT, NIBUT, and LLT, and a higher TMH, compared to mesocephalic and dolichocephalic dogs. Additionally, brachycephalic dogs exhibited a significantly higher MGLRL than dolichocephalic dogs. Correlations among tests were generally weak to moderate (r < .6) except for a strong correlation between CFR and LLT (r = .641, p < .001), and between TBUT and NIBUT (r = .899, p < .001). Brachycephalic morphology predisposes dogs to a significantly thinner lipid layer and diminished tear film stability, likely due to factors such as impaired meibomian gland function and increased ocular exposure compared to other cephalic conformations, thereby increasing their risk of keratoconjunctivitis sicca (KCS). OSA-VET shows a valuable tool to provide more comprehensive and precise diagnosis for canine ocular surface disorders.

The usefulness of ultrasonography as a dynamic measurement system for visualizing root canal working length: an in vivo study

ObjectiveAlthough apex locators are generally effective tools for determining root canal working length, they may produce inaccurate results in some cases. The present study aimed to evaluate the efficacy of ultrasonography as an alternative method for measuring root canal length.Materials and methodsForty-seven anterior teeth with apical lesions were selected for the study. Initially, an electronic apex locator was used to measure the working length. Subsequently, ultrasonography was employed to visualize the root apex and determine the working length. During ultrasound imaging, a K-file No. 15 was inserted into the root canal until its tip was visible on the ultrasound monitor. Measurements obtained from both methods were compared using an independent sample t-test. Correlations were assessed with the Pearson correlation coefficient, and agreement was determined using the Bland‒Altman plot.ResultsThe mean working canal length was 19.9 mm for the apex locator and 20.6 mm for the ultrasonography-guided method. No significant differences were observed between the data obtained using the apex locator method and the data obtained using the ultrasonography guidance method. Furthermore, a high level of agreement was identified between the two techniques.ConclusionUltrasonography can be used to visualize the apex effectively and determine canal length, especially when canal length determination is uncertain for various reasons.

Cucumber Downy Mildew Disease Prediction Using a CNN-LSTM Approach

It is of great significance to develop early prediction technology for controlling downy mildew and promoting cucumber production. In this study, a cucumber downy mildew prediction method was proposed by fusing quantitative disease information and environmental data. Firstly, the number of cucumber downy mildew spores during the experiment was collected by a portable spore catcher, and the proportion of cucumber downy mildew leaf area to all cucumber leaf area was recorded, which was used as the incidence degree of cucumber plants. The environmental data in the greenhouse were monitored and recorded by the weather station in the greenhouse. Environmental data outside the greenhouse were monitored and recorded by a weather station in front of the greenhouse. Then, the influencing factors of cucumber downy mildew were analyzed based on the Pearson correlation coefficient method. The influencing factors of the cucumber downy mildew early warning model in greenhouse were identified. Finally, the CNN-LSTM (Convolutional Neural Network-Long Short-Term Memory) algorithm was used to establish the cucumber downy mildew incidence prediction model. The results showed that the Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and determination coefficient (R2) of the CNN-LSTM network model were 0.069, 0.0098, 0.0991, and 0.9127, respectively. The maximum error between the predicted value and the true value for all test sets was 16.9398%. The minimum error between the predicted value and the true value for all test sets was 0.3413%. The average error between the predicted and true values for all test sets was 6.6478%. The Bland–Altman method was used to analyze the predicted and true values of the test set, and 95.65% of the test set data numbers were within the 95% consistency interval. This work can serve as a foundation for the creation of early prediction models of greenhouse crop airborne diseases.

Algorithms and Resources for the Monitoring of Very-Low-Frequency Signal Deviations Due to Solar Activity Using a Web-Based Software-Defined Radio-Distributed Network

This work presents the development and testing of an experimental web-based SDR (software-defined radio) monitoring system for indirect solar activity detection, which has the ability to estimate and potentially predict various events in space and on earth, including solar flares, coronal mass ejections, and geomagnetic storms. The proposed system can be used to investigate the effect of solar activity on the propagation of very-low-frequency (VLF) signals. The advantages and benefits of the given approach are as follows: increasing measurement accuracy and eventual solar activity identification by combining measurements from multiple spatially distributed SDRs. The verification process involves carrying out several experiments comparing data from the GOES satellite system and the Dunksin SuperSID system with information received by the SDR monitoring system. Then, utilizing Pearson correlation coefficients, the measured data from the SDRs, along with those from the GOES satellite system and the Dunsing monitoring station, are investigated. At the time of a solar flare, the correlation value is above 90% for most of the stations used. Combining the signal-to-noise ratio via summation also shows an improvement in the results, with a correlation above 98%.

AI-driven generalized polynomial transformation models for unsupervised fundus image registration

We introduce a novel AI-driven approach to unsupervised fundus image registration utilizing our Generalized Polynomial Transformation (GPT) model. Through the GPT, we establish a foundational model capable of simulating diverse polynomial transformations, trained on a large synthetic dataset to encompass a broad range of transformation scenarios. Additionally, our hybrid pre-processing strategy aims to streamline the learning process by offering model-focused input. We evaluated our model's effectiveness on the publicly available AREDS dataset by using standard metrics such as image-level and parameter-level analyzes. Linear regression analysis reveals an average Pearson correlation coefficient (R) of 0.9876 across all quadratic transformation parameters. Image-level evaluation, comprising qualitative and quantitative analyzes, showcases significant improvements in Structural Similarity Index (SSIM) and Normalized Cross Correlation (NCC) scores, indicating its robust performance. Notably, precise matching of the optic disc and vessel locations with minimal global distortion are observed. These findings underscore the potential of GPT-based approaches in image registration methodologies, promising advancements in diagnosis, treatment planning, and disease monitoring in ophthalmology and beyond.

Belief in a just world and fair behavior among clinical nurses: a moderated mediation model of empathy and observer justice sensitivity

BackgroundExploration of the relationship between nursing staffs’ justice in belief world and fair behavior is important to promote equity and access to health services in health organizations, as well as to enhance the quality of care. In order to further dissect the influencing factors of fair behavior among clinical nurses, the current study aims to investigate how belief in a just world influences the fair behavior among nurses. Based on the belief in a just world theory, the empathy-altruism theory and the protective-protective model, the current study aimed to provide a deeper understanding of the effect of belief in a just world on fair behavior by investigating the mediating role of empathy and the moderating role of observer justice sensitivity.MethodThis was a cross-sectional study. 571 registered clinical nurses were included from five hospitals in Fuzhou through a convenience sampling method. Measurements included Chinese translations of belief in a just world scale, empathy scale, observer justice sensitivity scale, fair behavior scale. SPSS 22.0 was used to describe descriptive statistics and the variables’ Pearson correlation coefficient. SPSS PROCESS macro Model 4 and model 14 were used to examine the mediation and the moderation between the relationship of belief in a just world and fairness behavior.ResultThe results shower that fairness behavior was positively correlated with one’s belief in a just world (r = 0.26, p < 0.01); (2)empathy mediated the relationship between belief in a just world and fair behavior. The mediation model explains 20.83%; (3) Observer justice sensitivity moderated the relationship between empathy and fair behavior.ConclusionsBelief in a just world, empathy, and observer justice sensitivity were motivations for nurses’ fair behavior. Nursing administrators should focus on cultivating nurses’ belief in a just world, their empathy abilities, and positive qualities of justice sensitivity to enhance fair behavior in a healthcare setting.

Problem Loans and Working Out the Pinnacle of Bank Failures in Nigeria

This study was designed to evaluate managing problem loans and work out the pinnacle of bank failures in Nigeria. One of the recommendations of the Basel Committee on Banking Supervision was credit risk management which is the optimization of the bank’s risk-adjusted rate of return by maintaining credit risk exposure within an acceptable level. Descriptive survey research was used and data were collected via annual reports of the sampled bank within the period of 2011–2016. The population of the research included the Deposit Money Banks. Pearson Coefficient of Correlation was the statistical tool used to analyse the hypotheses and this was done with the aid of Statistical Package for Social Sciences (SPSS). The researcher concluded that there is no significant relationship between credit risk management and bank failure in Nigeria. However, there were traces of weak negative relationships which keen interest should be given to because of the sensitive nature of the banking sector.

Quality of life and family functioning 12 months after diagnosis of childhood brain tumour: A longitudinal cohort study.

The wellbeing of a child with brain tumour is affected by several factors. We present the first investigation of quality of life and family functioning in a parent and child across the first 12 months after diagnosis, examining potential factors to guide the provision of psychosocial resources to families who most need them. Data were collected from parents/carers in Queensland, Australia, from 2020 to 2023. Child (parent/carer-proxy reported) and carer quality of life was assessed across three timepoints (repeated measures analysis of variance [ANOVA]) and by five potential co-variates (mixed between-within ANOVA). Family functioning was assessed across two timepoints (repeated-measures t-test), and by potential co-variates (repeated measures ANOVA). Univariate relationships were explored with Pearson's correlation coefficient; significant relationships were entered into multiple regression models. Ninety-six diverse families were represented. Quality of life (child, carer) and family functioning did not change across time. Children from households with lower income reported worse cognitive difficulties (p=.023) and pain and hurt (p=.013) than those from a higher income. Caregiver quality of life was poorer for those whose child had received chemotherapy and/or radiation, was aged less than 4 years at diagnosis, and had a lower household income. At 12 months, caregiver quality of life was correlated with family functioning (r=-.45, p<.001), with positive adaptation being a significant key predictor (beta=-.66, p<.005). The following factors indicate a need for increased early psychosocial support: cognitive difficulties, aged<4 years at diagnosis, receiving chemotherapy and/or radiation, and low household income.

Students Attitudes on School Climate and Learning Engagement in Physics of Senior High School Students

This research aims to determine whether there is a substantial correlation between students' attitudes toward school climate and their learning engagement in Physics. It also aims to explore the impact of school climate on Grade 12 students' experiences in attending classes and how it affects learning engagement in Physics. The school climate has an impact on the interactions that exist between educators and students in the classroom. A positive school climate is created where relationships between teachers, students, and the environment are enhanced within the institution. Consequently, the researchers utilized an explanatory mixed-method sequential design. The data collection involved two adapted survey questionnaires, an in-depth interview, and a focus group discussion using simple random sampling with a 335-sample size for quantitative and 10 participants for qualitative. The students' attitudes on school climate acquired an overall mean of 3.89, otherwise described as high, and a 3.98 mean, also described as high, for learning engagement in Physics. The correlation between students' attitudes toward school climate and learning engagement is given by Pearson's correlation coefficient (r) of 0.810, and the associated p-value (P= 0.001), indicating a highly significant correlation. This suggests a comparable rise or fall in school climate as students' attitudes increase or decrease. In the qualitative phase of the study, five main themes emerged from the data which support the quantitative findings and further explain the result of the study.

Influence of Motivation Climate on Service-Learning Benefits among Physical Education Students

&lt;p style="text-align: justify;"&gt;Service-learning (SL) emphasizes the fusion and integration of community service and academic learning, while Physical Education (PE) is oriented towards community health and well-being, aligning with Sustainable Development Goals. The study's primary objective is to discern the benefits perceived by PE students when engaged in SL courses and to test the correlation between benefits and the motivational climate they experience. Additionally, the research explores potential gender-specific variations in students' attitudes. The research was conducted with a survey on a sample of 58 students responding to a Motivational Climate in Physical Education Scale (MPCES) and the SErvice LEarning Benefit Scale (SELEB), which assesses the motivation of students and their perceptions of SL benefits. Descriptive statistics was used to analyse the data. Pearson's correlation coefficient was used to test the relationship between the core constructs - SL benefits and student motivation, and t-tests to evaluate gender differences in SL benefits. Key findings underscore that students primarily perceived the greatest advantages from SL in the domain of knowledge application, with comparatively lower scores in general life skills. Notably, female students, on the whole, exhibited higher ratings in both SL benefits and student motivation compared to their male counterparts. Nonetheless, specific exceptions emerged in certain sub-scales, including social relatedness and ego climate within the MPCES scale, as well as critical thinking within the SELEB scale. According to the results, it is suggested that cultivating a student-centered learning environment can augment engagement and motivation in the realm of physical education (PE). Furthermore, the delineated gender differences offer valuable insights for educators, highlighting the need for tailored approaches to accommodate the diverse preferences and requirements of male and female students.&lt;/p&gt;

Navigating the Dynamics of Brent Crude Oil Prices: Factors, Trends, and Insights

Accurate Brent Crude Oil price prediction is essential for informed decision-making and risk control in the global energy market. This paper examines the dynamics of Brent Crude Oil prices over the previous 35 years, utilizing predictive modelling techniques to project future prices and analyzing historical trends and relationships with the GDPs of major economies. The statistical methods used in this paper include the moving average and Pearson correlation coefficient. While correlation studies show significant positive links between Brent Crude Oil prices and the GDPs of major economies like China, Saudi Arabia, Europe, Russia, and the United States, historical data analysis reveals large price volatility. Highly accurate price forecasting using LSTM (Long Short-Term Memory) modelling approaches provides detailed risk management and decision-making information in the global energy market. The application of the Neural Network and the ARIMA model shows an increase in the price of Brent crude oil in the next year, 2024. Identifying the importance of the Brent crude oil price forecast and its effect on the international market is highly needed. This paper thus might help to increase economic growth. These results highlight the importance of advanced modelling techniques and economic indicators in managing oil price changes and help make informed decisions in the energy industry.

Inter- and Intra-Observer Variability of the AMADEUS Tool for Osteochondral Lesions of the Talus

Background: Managing osteochondral cartilage defects (OCDs) of the talus is a common daily challenge in orthopaedics as they predispose patients to further cartilage damage and progression to osteoarthritis. Therefore, the implementation of a reliable tool to quantify the amount of cartilage damage that is present is of the essence. Methods: We retrospectively identified 15 adult patients diagnosed with uncontained OCDs of the talus measuring &lt;150 mm2, which were treated arthroscopically with bone marrow stimulation. Five independent assessors evaluated the pre-operative MRI scans with the AMADEUS scoring system (i.e., MR-based pre-operative assessment system) and the intra-/inter-observer variability was then calculated by means of the intraclass correlation coefficients (ICC) and Kappa (κ) statistics, respectively. In addition, the correlation between the mean AMADEUS scores and pre-operative self-reported outcomes as measured by the Manchester–Oxford foot questionnaire (MOxFQ) was assessed. Results: The mean ICC and the κ statistic were 0.82 (95% CI [0.71, 0.94]) and 0.42 (95% CI [0.25, 0.59]). The Pearson correlation coefficient was found to be r = −0.618 (p = 0.014). Conclusions: The AMADEUS tool, which was originally designed to quantify knee osteochondral defect severity prior to cartilage repair surgery, demonstrated good reliability and moderate inter-observer variability for small OCDs of the talar shoulder. Given the strong negative correlation between the AMADEUS tool and pre-operative clinical scores, this tool could be implemented in clinical practise to reliably quantify the extent of the osteochondral defects of the talus.

Borehole Breakout Prediction Based on Multi-Output Machine Learning Models Using the Walrus Optimization Algorithm

Borehole breakouts significantly influence drilling operations’ efficiency and economics. Accurate evaluation of breakout size (angle and depth) can enhance drilling strategies and hold potential for in situ stress magnitude inversion. In this study, borehole breakout size is approached as a complex nonlinear problem with multiple inputs and outputs. Three hybrid multi-output models, integrating commonly used machine learning algorithms (artificial neural networks ANN, random forests RF, and Boost) with the Walrus optimization algorithm (WAOA) optimization techniques, are developed. Input features are determined through literature research (friction angle, cohesion, rock modulus, Poisson’s ratio, mud pressure, borehole radius, in situ stress), and 501 related datasets are collected to construct the borehole breakout size dataset. Model performance is assessed using the Pearson Correlation Coefficient (R2), Mean Absolute Error (MAE), Variance Accounted For (VAF), and Root Mean Squared Error (RMSE). Results indicate that WAOA-ANN exhibits excellent and stable prediction performance, particularly on the test set, outperforming the single-output ANN model. Additionally, SHAP sensitivity analysis conducted on the WAOA-ANN model reveals that maximum horizontal principal stress (σH) is the most influential parameter in predicting both the angle and depth of borehole breakout. Combining the results of the studies and analyses conducted, WAOA-ANN is considered to be an effective hybrid multi-output model in the prediction of borehole breakout size.