Meaningful Correlation Research Articles

The Relationship between Sleep Quality and Smartphone Addiction among Adolescents

Introduction: Technological developments have increased the usage of mobile equipment. Using a smartphone became a part of life and accentuated its usage day aftr day especially in adolescent groups. Aim: The study was aimed to examine the relationship between sleep quality and smartphone addiction of adolescents. Method: This cross-sectional study was carried out with 1192 high school students between March and June 2019. Data were collected with the Personal Information Form, Pittsburg Sleep Quality Index, Smartphone Addiction Scale. The data were evaluated through percentile distributions, means, independent samples t-test, Kruskal-Wallis test, ANOVA, and Pearson correlation analyses. Results: The mean age of adolescents was determined to be 15.79 ± 0.96, with the mean total score of the Pittsburg Sleep Quality Index was 6.05 ± 3.04 and the Smartphone Addiction Scale was 36.01 ± 10.82. Statistically signifiant diffrences were observed among adolescents concerning gender, grade, daily smartphone usage duration, internet accessibility, and the use of smartphones before bedtime, impacting both sleep quality and smartphone addiction (p < 0.05). A positive and meaningful correlation was found between sleep quality and smartphone addiction in adolescents (p < 0.05). Conclusion: As the smartphone addiction increases, sleep quality decreases. Adequate sleep is crucial during the growth spurt period. It is evaluated that early diagnosis and prevention of smartphone addiction in adolescents could make a signifiant contribution to the protection and promotion of adolescent health.

The superiority of innovative spiral-interdigital microelectrode pattern in increasing the sensitivity of tracing synchronization via serum starvation in cellular metabolism

In order to investigate the changes in the properties of the cell culture solution in the effect of cell synchronization via cell starvation (for 12, 24, and 36 h), a new spiral-interdigital pattern of microelectrode as a biosensor has been proposed. Then, to test its superiority, the results of this spiral-interdigital pattern with the results of the commercial pattern have been compared. The cells were selected from breast cancer standard lines (MDA-MB-231). Changes in CV peaks of the secretions were recorded by the spiral-interdigital pattern, in which increasing the interactive surface with homogenous electric paths had been considered by simulation before fabrication. The results of the simulation and experimental procedures showed a meaningful correlation. The occurrence of CV oxidative peaks at about 0.1–0.4 V and reductive peaks at approximately 0 V in the spiral-interdigital biosensor in the starved MDA-MB-231 cell line has been observed. The starvation situation resembles one that does not cause meaningful cell apoptosis or necrosis, and this method is only used to make the cells synchronized. Also, no peak is observed in normal cell growth conditions. In addition, by using the commercial design of the electrodes, no peak is observed in any of the conditions of normal and synchronized growth of the cells. Therefore, it seems that the observed peaks are caused by the agents that are secreted in the cell culture solution in a synchronized situation. Moreover, the design of the new spiral-interdigital electrode can significantly increase the sensitivity of the sensor to receive these peaks due to more space and a uniform electric field.

Selected Social Media as a Digital Platform and its Perceived Value Among Small Businesses

Small businesses play a crucial role in the global economy, and they often operate with limited resources. Social media can be a game changer, providing a cost-effective way to reach a wider audience. The objective of this study is to establish the most widely used social media in terms of its perceived value. This study employed quantitative analysis. A self-designed survey questionnaire was used with a Cronbach’s alpha of 0.80. A total of 21 statements were included in the self-made questionnaire on social media factors (cost consumption, brand awareness, attractiveness and accessibility) and perceived value (revenue, customer satisfaction and customer retention). A total of 380 respondents who are small business owners in Metro Manila, Philippines participated in the study and were selected using simple random sampling. Descriptive statistics and Pearson’s chi-square tests were used to establish the results and hypotheses of the study. Among the three social media platforms used, Facebook was the most preferred for its perceived value and effectiveness. Social media use as a digital marketing tool was significantly correlated with perceived value in terms of revenue, customer satisfaction, and customer retention; however, the only meaningful correlation found was between attractiveness and business ownership. The results of this study will help small business operators create marketing strategies to attract more consumers, which will lead to an increase in sales and revenue in the future. In addition, the results can be used as a basis for small businesses to attract more customers by utilizing different social media platforms.

Examining the Criterion and Incremental Validity of the MMPI-3 Impulsivity Scale.

The Minnesota Multiphasic Personality Inventory-3 (MMPI-3) includes a new Impulsivity (IMP) scale designed to assess for poor impulse-control and non-planful behavior, which was added to broaden the utility of the instrument. The current study aimed to examine the criterion and incremental validity of the IMP scale. A university student sample (n = 1,440) and a community sample oversampled for externalizing tendencies (n = 231) were used for this purpose, and IMP scores were compared to scores on various well-validated criterion measures of impulsivity and externalizing psychopathology. To examine the scale's incremental validity, hierarchical multiple regression analyses were conducted to determine whether IMP adds to other MMPI-3 Specific Problem (SP) scales in the prediction of relevant criteria. The IMP scale primarily showed meaningful correlations with the Negative Urgency and Positive Urgency on the UPPS-P. Significant correlations were also observed with the cognitive, behavioral, disinhibition, and lifestyle domains of various psychopathy measures, as well as measures of antisocial personality disorder and substance use. The IMP scale scores accounted for incremental variance in most of the directly relevant criterion measures above and beyond scores of other MMPI-3 SP scales. Several important caveats, limitations, and future directions are discussed.

Leading role of Saharan dust on tropical cyclone rainfall in the Atlantic Basin.

Tropical cyclone rainfall (TCR) extensively affects coastal communities, primarily through inland flooding. The impact of global climate changes on TCR is complex and debatable. This study uses an XGBoost machine learning model with 19-year meteorological data and hourly satellite precipitation observations to predict TCR for individual storms. The model identifies dust optical depth (DOD) as a key predictor that enhances performance evidently. The model also uncovers a nonlinear and boomerang-shape relationship between Saharan dust and TCR, with a TCR peak at 0.06 DOD and a sharp decrease thereafter. This indicates a shift from microphysical enhancement to radiative suppression at high dust concentrations. The model also highlights meaningful correlations between TCR and meteorological factors like sea surface temperature and equivalent potential temperature near storm cores. These findings illustrate the effectiveness of machine learning in predicting TCR and understanding its driving factors and physical mechanisms.

Reliability and Validity of Ecological Momentary Assessment Response Time-Based Measures of Emotional Clarity: Secondary Data Analysis.

Emotional clarity has often been assessed with self-report measures, but efforts have also been made to measure it passively, which has advantages such as avoiding potential inaccuracy in responses stemming from social desirability bias or poor insight into emotional clarity. Response times (RTs) to emotion items administered in ecological momentary assessments (EMAs) may be an indirect indicator of emotional clarity. Another proposed indicator is the drift rate parameter, which assumes that, aside from how fast a person responds to emotion items, the measurement of emotional clarity also requires the consideration of how careful participants were in providing responses. This paper aims to examine the reliability and validity of RTs and drift rate parameters from EMA emotion items as indicators of individual differences in emotional clarity. Secondary data analysis was conducted on data from 196 adults with type 1 diabetes who completed a 2-week EMA study involving the completion of 5 to 6 surveys daily. If lower RTs and higher drift rates (from EMA emotion items) were indicators of emotional clarity, we hypothesized that greater levels (ie, higher clarity) should be associated with greater life satisfaction; lower levels of neuroticism, depression, anxiety, and diabetes distress; and fewer difficulties with emotion regulation. Because prior literature suggested emotional clarity could be valence specific, EMA items for negative affect (NA) and positive affect were examined separately. Reliability of the proposed indicators of emotional clarity was acceptable with a small number of EMA prompts (ie, 4 to 7 prompts in total or 1 to 2 days of EMA surveys). Consistent with expectations, the average drift rate of NA items across multiple EMAs had expected associations with other measures, such as correlations of r=-0.27 (P<.001) with depression symptoms, r=-0.27 (P=.001) with anxiety symptoms, r=-0.15 (P=.03) with emotion regulation difficulties, and r=0.63 (P<.001) with RTs to NA items. People with a higher NA drift rate responded faster to NA emotion items, had greater subjective well-being (eg, fewer depression symptoms), and had fewer difficulties with overall emotion regulation, which are all aligned with the expectation for an emotional clarity measure. Contrary to expectations, the validities of average RTs to NA items, the drift rate of positive affect items, and RTs to positive affect items were not strongly supported by our results. Study findings provided initial support for the validity of NA drift rate as an indicator of emotional clarity but not for that of other RT-based clarity measures. Evidence was preliminary because the sample size was not sufficient to detect small but potentially meaningful correlations, as the sample size of the diabetes EMA study was chosen for other more primary research questions. Further research on passive emotional clarity measures is needed.

Polarized social media networks: a novel approach to quantify the polarization level of individual users

ABSTRACT This paper presents a novel methodology to quantify users’ polarization within social media networks, focusing on network structure instead of content. We model polarized networks as directed graphs, collecting users’ follower/following connections data, and propose a simple algorithm to compute a Connectivity Score for each node in sub-graphs associated with identified contrary groups of the polarized network. Our method has several advantages: it relies solely on users’ follower/following connections data, which is objective, often public and easy to obtain. Also, this input data is usually memory-efficient, unlike the input data of content-based methods which may require a whole corpus. The algorithm's performance is assessed through application to Twitter's 116th Congressmembers network, comparing against a content analysis method: Sentiment score (using VADER), and two political behavior-based methods: Ideology score (based on co-sponsorship frequency) and Roll Call score (based on bill-voting similarity). The results demonstrate that: (1) users’ choice of connections on the social media can represent polarization behavior; (2) a meaningful correlation between Connectivity Scores and Ideology or Roll Call Scores shows that the political behavior of users is reflected in their social media connections; (3) Democrats’ Twitter following behavior and their bill-voting and bill co-sponsorship behavior (represented by Roll Call and Ideology) are all significantly more correlated than that of Republicans. We believe applying our algorithm in conjunction with other methods is a valuable contribution resulting in more comprehensive analysis of the social media polarization space.

EXPLORING THE NEW GREEN GOVERNANCE FRAMEWORK ON ENERGY COMPANIES' FINANCIAL PERFORMANCE: A PILOT STUDY

The COP26 framework focuses on energy transformation and green governance (GG) as its central priorities. This paper critically evaluated the examination of green governance in the context of Nigerian oil and gas companies, exploring its implications on financial performance (FP) measures. Recent years have presented significant hurdles for companies in the energy sector because of the high expense of ecological remediation following spills and the use of conventional systems of operation. The study collected primary data using a well-structured questionnaire. The researchers employed a systematic approach to select fifteen (15) oil and gas companies for inquiry, utilizing a rigorous random sampling method. Subsequently, three executives from the management, accounting, and auditing departments participated based on predetermined criteria. Utilizing direct linear regression analysis, the study explored the relationship between GG and FP. "The regression analysis showed a coefficient for green governance (GG) of 0.391 with p = 0.004."), signifying a statistically significant correlation. The correlation coefficient (R = 0.413) suggests a substantive association between GG and FP, with 17% of FP variance explained by GG (R2 = 0.170). Hypothesis testing via the F-test demonstrates a positive, significant relationship (F = 9.236, p &lt; 0.001) between GG and FP, this suggests that there is a strong and meaningful correlation between GG and FP. The findings of this research can provide significant direction for policymakers in developing relevant legislation, at the same time, industry practitioners can leverage these findings to optimize their operational strategies and augment their organizations' financial performance.

Role of proinflammatory biomarkers in Parkinson's disease with non-motor symptoms: A systematic review and meta-analysis

Background and objectives. Neuroinflammation, characterized by microglial activation and elevated proinflammatory cytokines, plays a crucial role in Parkinson's disease (PD) pathogenesis and is linked to neurodegeneration and non-motor symptoms. This review aims to identify specific biomarkers associated with these non-motor symptoms, highlighting their roles and implications in PD management and therapy. Materials and methods. This systematic review followed PRISMA guidelines to assess proinflammatory biomarkers in PD patients with non-motor symptoms. We included 10 studies identified through comprehensive searches in PubMed/Medline, Wiley Online Library, Science Direct, Google Scholar, and Neurona. Bias was assessed using the ROBINS-I tool, and study quality was evaluated with GradePro. Meta-analysis was conducted using the Ordmeta test. Results. Proinflammatory biomarkers such as IL-6, TNF-α, CRP, and hs-CRP are notably associated with non-motor symptoms in PD. TNF-α shows a moderate correlation with depressive disorders in 194 patients (p=0.007±0.011), while IL-17A correlates similarly with anxiety, and IL-2 with mood disorders. Furthermore, TNF-α has a low yet meaningful correlation with sleep disorders. Ordmeta results reveal significant elevations of TNF-α in 52 PD patients with cognitive impairment (p=0.0003) and of IL-6 in 361 PD patients (p=0.023±0.008), underlining their roles in PD pathology. Conclusions. This study confirms that proinflammatory biomarkers such as CRP, hs-CRP, IL-6, IL-2, IL-17A, and TNF-α are significantly linked to non-motor symptoms in PD. TNF-α is notably elevated in patients with cognitive impairments and depressive disorders, while IL-6 is also significantly increased in those with cognitive issues.

Determinants of Socially Responsible Investment (SRI) Intention

There has been a recent surge of interest in socially responsible investment, attracting attention from all industries, including investors. Nevertheless, the factors that impact one's attitude and attention towards SRI remain relatively unexplored. Hence, this study aims to identify important factors that shape the attitude and attention of consumers when making investment decisions in the stock market. The data is obtained from investors who are making investments in the Pakistani stock exchange. The data analysis employs the technique of partial least squares structural equation modelling (PLS-SEM). The study findings indicate a substantial relationship between collectivism, knowledge, and SRI bias, and the attitude towards SRI. However, there is no significant association between perceived danger and attitude towards SRI. Attitude exhibits a robust and meaningful correlation with SRI.

대학생 씨름선수의 마인드셋과 승부근성이 자아탄력성에 미치는 영향

Objectives The purpose of examining the impact of mindset and winning desire of Ssireum players to self-elasticity. Methods For this, questionnaire sheets collected from 199 Ssireum club students in 1st-4th grades of 16 colleges registered in Korea Ssireum Association were processed with WINDOW SPSS Ver. 25.0 and, Amos 26.0 through descriptive statistical analysis, frequency analysis, correlation analysis, and multiple regression analysis of the conclusion was obtained as shown below. Results First, partially meaningful correlation was found among mindset characteristics, winning desire, self- elasticity recognized by Ssireum players. Second, mindset characteristics recognized by Ssireum players were found to give positive influences to self-elasticity characteristics. Third, winning desire characteristics recognized by Ssireum players were found to give positive influences to self-elasticity characteristics. Conclusions The mindset and winning desire of Ssireum players have an effect on the to self-elasticity.

Klasifikasi Sel Tumor Payudara Menggunakan Algoritma Support Vector Machine (SVM)

Cancer is a large group of diseases that can begin in almost any organ or tissue of the body when abnormal cells grow uncontrollably, beyond their normal limits to invade adjacent parts of the body and/or spread to other organs. There is a lot of information about breast cancer that can be accessed easily. Information about breast cancer can be processed with machine learning. Machine learning can discover new meaningful correlations, patterns and trends by sifting through large amounts of data stored in repositories, using pattern recognition technology and statistical and mathematical techniques. The purpose of this research is to determine the value of the accuracy of the SVM model on training data and testing data; and to determine the precision value of the SVM model on training data and testing data. Wisconsin Breast Cancer (WBC) data available in the UCI Machine Learning Repository. The data have been processed using the Python programming language with a support vector machine (SVM) modeling algorithm. The results of this research indicate that the value of accuracy in training data was equal to , the value of accuracy in testing data was equal to , and the value of precision in the SVM model algorithm was obtained as large as for training data and as large as for data testing.

Serum Vitamin D, Mania and Depression-Related Scores: A Comparison among Mixed Bipolar, Mania, and Healthy Subjects.

Objective: Manic and mixed episodes of bipolar disorder are important episodes of this disorder. The aim of the current study was to assess serum vitamin D (SVD) levels in patients with mania and mixed bipolar disorder, compared to healthy subjects. Method : The current cross-sectional study was conducted on 75 subjects, including healthy subjects (n = 25), patients with acute-phase mania (n = 25), and patients with mixed bipolar disorder (n = 25). The SVD levels were measured in all of the enrolled subjects. The Hamilton Depression Rating Scale (HDRS), Young Mania Rating Scale (YMRS), and Clinical Global Impression- Severity (CGI-S) were used to assess disease activity in patient groups. Data analysis was performed using SPSS version 18. For statistical analysis, analysis of variance (ANOVA), independent-sample t test, Pearson correlation, and Chi-square tests were utilized. P-values < 0.05 were considered statistically significant. Results: The results showed that the mean of SVD was significantly lower in mania and mixed bipolar patients compared to healthy subjects (P < 0.05). In addition, the number of subjects with SVD ≥ 20 ng/ml was higher in the healthy group compared to the patient groups (P < 0.05). Also, SVD was negatively correlated with the CGI-S (r = -0.311; P = 0.028), YMRS (r = -0.464; P = 0.001), and HDRS (r = -0.393; P = 0.005) in the total patient subjects. Conclusion: Prevalence of low SVD was considerably high in mania and mixed bipolar patients compared to healthy subjects. Additionally, meaningful negative correlations were found between SVD and disease activity-related variables including the HDRS, YMRS, and CGI-S.

Analysis of pile end bearing capacity in Holocene interlayered silty deposits based on CPT soil behavior type index

The existing correlations between pile and cone penetration test (CPT) results often lack versatility for silty geomaterials, as they were primarily developed for clays or sands. To establish meaningful correlations, it is essential to understand the geology of the environment under examination. The CPT and its derived parameters, including the Soil Behavior Type (SBT) index, have proven to provide reliable soil information, particularly in the field of pile foundation engineering. This paper presents the findings and interpretations from several pile load tests conducted alongside field cone penetration tests on a Holocene saline interlayered silty formation, a distinct condition encountered in practice. Various CPT-based pile design methods were employed to evaluate pile capacity based on CPT results. As observed, predictions for pile shaft resistance in this geological formation remained consistent across all methods, while notable variations emerged in estimating pile end bearing. This was inferred to be a result of limited research on pile end bearing capacity in the literature, leading to proposals of broad ranges for pile influence zone and toe coefficient values, as well as various CPT tip resistance averaging techniques across different methods. Consequently, to improve accuracy, modifications based on site-specific test data may be necessary. Drawing from the assessments, effective modifications involved considering the SBT index near the pile tip. Subsequently, the current study introduces a new region-specific adjustment to the widely recognized Enhanced Unicone method based on the SBT index, resulting in a more accurate estimate of pile end bearing in such interlayered silty contexts, with less scatter compared to other available approaches.

Aspects of Identity Questionnaire-IV: An Examination of Structural Validity, Gender Invariance, and Relationships with Mental Health and Basic Psychological Needs Among Adolescents

The present research evaluated evidence for structural and convergent validity and measurement invariance across gender of the Aspects of Identity Questionnaire-IV (AIQ-IV), which is designed to measure four identity orientations: Personal, Relational, Public, and Collective. We recruited two independent samples of Serbian adolescents aged 15–19 years (n 1 = 436, n 2 = 452). Both exploratory structural equation modeling (ESEM) and confirmatory factor analyses (CFA) were applied to examine the factor structure of the AIQ-IV. The ESEM model with four factors demonstrated a superior fit to the data in both samples and resulted in well-defined factors and deflated latent factor correlations compared to the CFA model. Multiple-group analysis supported the scalar invariance of the four-factor ESEM model across gender. The evidence for the convergent validity of the ESEM AIQ-IV factors was supported by meaningful correlations with various mental health indicators (life satisfaction, positive affect, negative affect, internalizing behaviors, and externalizing behaviors) and basic psychological needs (autonomy, relatedness, and competence) satisfaction and frustration. Our findings support the use of AIQ-IV as a multidimensional measure of identity orientations among adolescents and confirm the usefulness of applying the ESEM framework to evaluate the validity aspects of this scale.

Diagnostically distinct resting state fMRI energy distributions: A subject-specific maximum entropy modeling study.

Objective Existing neuroimaging studies of psychotic and mood disorders have reported brain activation differences (first-order properties) and altered pairwise correlation based functional connectivity (second-order properties). However, both approaches have certain limitations that can be overcome by integrating them in a pairwise maximum entropy model (MEM) that better represents a comprehensive picture of fMRI signal patterns and provides a system-wide summary measure called energy. This study examines the applicability of individual-level MEM for psychiatry and identifies image-derived model coefficients related to model parameters. Method MEMs are fit to resting state fMRI data from each individual with schizophrenia/schizoaffective disorder, bipolar disorder, and major depression (n=132) and demographically matched healthy controls (n=132) from the UK Biobank to different subsets of the default mode network (DMN) regions. Results The model satisfactorily explained observed brain energy state occurrence probabilities across all participants, and model parameters were significantly correlated with image-derived coefficients for all groups. Within clinical groups, averaged energy level distributions were higher in schizophrenia/schizoaffective disorder but lower in bipolar disorder compared to controls for both bilateral and unilateral DMN. Major depression energy distributions were higher compared to controls only in the right hemisphere DMN. Conclusions Diagnostically distinct energy states suggest that probability distributions of temporal changes in synchronously active nodes may underlie each diagnostic entity. Subject-specific MEMs allow for factoring in the individual variations compared to traditional group-level inferences, offering an improved measure of biologically meaningful correlates of brain activity that may have potential clinical utility.

Unveiling Systemic Immune Inflammation Index Correlations in Women with Polycystic Ovary Syndrome: A Cross-sectional Study

Background: Polycystic ovary syndrome (PCOS) is a common endocrinopathy of reproductive-age women that imposes metabolic and cardiovascular risks. Systemic immune inflammation index (SII), an inflammatory biomarker calculated from a complete blood count, was linked to many metabolic and cardiovascular illnesses. Objective: To evaluate the SII correlation with clinical, biochemical, and hormonal parameters characteristic of PCOS cases for screening and diagnostic values. Methods: For fifty PCOS patients who were eligible, we did a cross-sectional study and collected three types of information on each person: demographic information like age, menstrual cycle length, and body mass index (BMI); hormonal information like LH/FSH ratio, testosterone, and fasting insulin; and biochemical information like the homeostasis model assessment of insulin resistance (HOMA-IR), serum cholesterol, serum HDL, and serum LDL. The systemic inflammatory index (SII) was calculated from a complete blood count. Results: We found a strong positive correlation between SII and BMI, LH/FSH ratio, testosterone, fasting serum insulin, HOMA-IR, cholesterol, and LDL. However, the correlation between SII, HDL and menstrual cycle days was negative. Regarding the other parameters (HOMA-IR, testosterone, cholesterol, BMI, and LH/FSH ratio), the best subset regression model figures out how strongly SII is linked to each one. Conclusions: SII was strongly and significantly correlated to PCOS clinical, biochemical, and hormonal parameters, which makes it recommendable for screening. Its feasibility, affordability, and meaningful correlation inspired therapeutic and prognostic applications in practice.

Factors Influencing Tourist Visit to Jumog Waterfall Ngargoyoso Sub-District Karanganyar District

Abstract The influence of accessibility, facilities, and tourist location on visitor satisfaction to Jumog Waterfall. There are three objectives in this study, namely: Analyzing the effect of accessibility on visitor satisfaction to Jumog Waterfall; Knowing the effect of facilities on visitor satisfaction with Jumog Waterfall; and Analyzing the effect of location on visitor satisfaction with Jumog Waterfall. The tool used in multiple linear regression analysis is SPSS 16.0. Accessibility, facilities, and location as independent variables. Visitor satisfaction is dependent. The test model for multiple linear regression analysis is the F-Test and T-Test, where the Coefficient Regression Full Model and Anova Model are selected as the best models. The first analysis indicates a significant positive impact of enhanced accessibility on visitor satisfaction at Jumog Waterfall, supported by a notable F-Test value of 2.70. The findings highlight a meaningful correlation between improved facilities and heightened visitor satisfaction at Jumog Waterfall, as evidenced by the obtained t-test distribution value of 1.984. The study identifies a substantial positive effect of favorable location characteristics on visitor satisfaction at Jumog Waterfall, further supported by the overall significance level of 0.000 &lt; 0.05, leading to the rejection of the null hypothesis (H0).

An Enhanced Study on Gold Price Prognosis using Machine Learning

Machine learning has emerged as a prominent research area for predicting gold prices, utilizing historical data and algorithms. The field aims to uncover patterns, trends, and connections among various factors that influence gold prices, including economic indicators, geopolitical events, and supply and demand dynamics. By employing machine learning algorithms, predictive models can be constructed to provide valuable insights into potential patterns in gold price movements. This enables traders, investors, and other stakeholders to make informed decisions when it comes to gold investments. In our study, we delve into the realm of data science and machine learning techniques to forecast gold prices. We meticulously analyze historical gold price data, develop sophisticated forecasting models, and rigorously evaluate their performance. Through this process, we are able to identify meaningful patterns and correlations that significantly contribute to the prediction of future gold prices. One of the key aspects of our study is the assessment of the reliability and accuracy of various machine learning models specifically designed for gold price prediction. We examine different algorithms and approaches, comparing their effectiveness in capturing the underlying patterns in gold price movements. This evaluation provides us with important findings and insights, enabling us to determine the most suitable models for accurate gold price forecasting. However, it is crucial to acknowledge the limitations inherent in our study. The forecasting of gold prices is a complex task influenced by a multitude of factors, some of which may be unpredictable or subject to sudden changes. Therefore, our models may not capture all the nuances and intricacies of gold price dynamics. To address these limitations, we propose recommendations for future research, such as exploring novel data sources, incorporating additional variables, or improving the models' adaptability to changing market conditions. Machine learning plays a pivotal role in the field of gold price prediction. By leveraging historical data and employing sophisticated algorithms, we can uncover valuable insights and patterns that assist in forecasting future gold prices. Our study aims to contribute to this growing body of research by developing reliable models and providing important insights for traders, investors, and other stakeholders in the gold market

The Response of the Amputee Athlete Heart to Chronic Core Exercise: A Study on Hematological and Biochemical Parameters, and N-Terminal Pro Brain Natriuretic Peptide.

Background and Objectives: mortality and morbidity due to cardiovascular causes are frequently experienced in amputees. Research on the effects of chronic exercise on biomarkers and cardiac damage indicators in these individuals is limited. The aim of this study was to investigate the effects of a core training program on brain natriuretic-related peptide, as well as hematological and biochemical parameters in amputee soccer players. Materials and Methods: The participants were randomly allocated to the following two groups: a core exercise group (CEG) and a control group (CG). While the CG continued routine soccer training, the CEG group was included in a core exercise program different from this group. During the study, routine hemogram parameters of the participants, various biochemical markers, and the concentration of brain natriuretic-related peptide (NT-pro-BNP) were analyzed. Results: after the training period, notable improvements in various hematological parameters were observed in both groups. In the CEG, there were significant enhancements in red blood cell count (RBC), hematocrit (HCT), mean corpuscular hemoglobin concentration (MCHC), and mean corpuscular hemoglobin (MCH) values. Similarly, the CG also showed substantial improvements in RBC, HCT, mean corpuscular volume (MCV), MCHC, MCH, red cell distribution width-standard deviation (RDW-SD), platelet-to-lymphocyte ratio (PLCR), mean platelet volume (MPV), and platelet distribution width (PDW). Moreover, in the CEG, serum triglycerides (TG) and maximal oxygen uptake (MaxVO2) exhibited significant increases. Conversely, TG levels decreased in the CG, while high-density lipoprotein (HDL), low-density lipoprotein (LDL), and MaxVO2 levels demonstrated substantial elevations. Notably, the N-terminal pro-brain natriuretic peptide (BNP) levels did not undergo significant changes in either the CEG or the CG following the core exercise program (p > 0.05). However, in the CEG, a meaningful positive correlation was observed between NT-pro-BNP and creatine kinase (CK) levels before and after the core exercise program. Conclusions: the findings emphasized the potential benefits of core training in enhancing specific physiological aspects, such as erythrocyte-related parameters and lipid metabolism, as well as aerobic capacity. Furthermore, the observed correlation between NT-pro-BNP and CK levels in the CEG provides intriguing insights into the unique physiological adaptations of amputee athletes.