Statistical Analysis Approach Research Articles

Evaluation of Lifestyle for Women with Breast Cancer

Introduction: Breast cancer is a significant global health concern, affecting millions of women worldwide. While advancements in diagnosis and treatment have improved survival rates, the impact of this disease extends beyond physical health. It also significantly influences a woman's lifestyle and overall well-being. Objectives: The current study intends to analyze the lifestyle of breast cancer patients who are receiving therapy or are being followed up at the Oncology Teaching Hospital in Medical City, Baghdad, Iraq. Method: The present study uses a descriptive design with an application of an evaluation approach. A convenience sample of 100 women with breast cancer was selected from the Teaching Oncology Hospital at the Medical City in Baghdad City, Iraq. The study's objectives are served by a questionnaire. Physical, role, emotional, cognitive, and social functioning are its five functional domains. These domains are evaluated as low, moderate and high. Each study participant received a questionnaire explaining how to complete it, along with a form requesting their approval to remain anonymous. They also had the opportunity to leave the study at any time without being provided an explanation. A panel of (10) experts determines the questionnaire's content validity, and its internal consistency reliability is calculated via the split-half method with a Cronbach’s alpha correlation coefficient of (r=0.92). Data are collected using the study questionnaire, and they are then analyzed via a descriptive statistical data analysis approach on the basis of frequency and percentage. Results: The study revealed that the majority of these women experienced sleep issues, but most of them experienced problems with body weight gain (70%), social and community activity (70%), sexuality (60%), stress (60%), diet (56%), physical activity (50%) and smoking (45%), and some of them took birth control pills and used hormone replacement therapy (43%). Conclusion: This study revealed that women with breast cancer have experienced a variety of lifestyle domain-associated problems. These problems are clearly manifested in the results of this study.

Clinical Trial Design Considerations for Hospitalised Patients With Ulcerative Colitis Flares and Application to Study Hyperbaric Oxygen Therapy in the NIDDK HBOT-UC Consortium.

Patients with ulcerative colitis (UC) who are hospitalised for acute severe flares represent a high-risk orphan population. To provide guidance for clinical trial design methodology in these patients. We created a multi-centre consortium to design and conduct a clinical trial for a novel therapeutic intervention (hyperbaric oxygen therapy) in patients with UC hospitalised for moderate-severe flares. During planning, we identified and addressed specific gaps for inclusion/exclusion criteria; disease activity measures; pragmatic trial design considerations within care pathways for hospitalised patients; standardisation of care delivery; primary and secondary outcomes; and sample size and statistical analysis approaches. The Truelove-Witt criteria should not be used in isolation. Endoscopy is critical for defining eligible populations. Patient-reported outcomes should include rectal bleeding and stool frequency, with secondary measurement of urgency and nocturnal bowel movements. Trial design needs to be tailored to care pathways, with early intervention focused on replacing and/or optimising responsiveness to steroids and later interventions focused on testing novel rescue agents or strategies. The PRECIS-2 framework offers a means of tailoring to local populations. We provide standardisation of baseline testing, venous thromboprophylaxis, steroid dosing, discharge criteria and post-discharge follow-up to avoid confounding by usual care variability. Statistical considerations are provided given the small clinical trial nature of this population. We provide an outline for framework decisions made for the hyperbaric oxygen trial in patients hospitalised for UC flares. Future research should focus on the remaining gaps identified.

Evaluation of an education program upon women's knowledge toward management of Breast Self – Examination(BSE)

To determine the evaluation of education program on women's knowledge regarding managing BSE. The present quasi- experimental study, Non-probability (purposive), sample consisting of (260) women who are employee, and students in both colleges (Nursing college, Medical and Health Techniques College). The sample consist of two groups, study group (130) including those in (Nursing college), and control group (130) in (Medical and Health Techniques College). A questionnaire was constructed which included, Demographic information, Reproductive information, Family history, Previous medical history, and information about women's knowledge toward management of breast self examination (BSE). Instrument validity and reliability was determined. Data were collected through the use of the questionnaire, the application of the educational program a post-test was done for the study only which use the lectures, booklets, training practice of BSE, and video film. Analysis of data was performed through the application of descriptive and inferential statistical data analysis approach. Revealed that the women in the study group had benefited from the implementation of the educational program; their knowledge regarding managing BSE was adequately improved and developed. The study illustrated significant difference between women's knowledge means in pre and post test periods regarding their managing for BSE. Implementation of proposed model of continuous medical education for women for BSE within the scope of their work.

Weight minimization of train floors with acoustic and structural constraints

For high-speed trains, high noise levels are generated in the bogie from wheel-rail contact and aero-acoustic sources. Manufacturers try to minimize the floor transmission to the interior, e.g. by applying damping layers or by other measures. For vehicle noise control measures, the goal is often to achieve the sound reduction needed at lowest possible mass. Here, an optimization scheme is applied to minimize the mass of an extruded floor panel. Constraints are defined by limiting the maximum local floor deflection from passenger loads according to EN 12663-1 and the A-weighted interior sound level. Recognizing the periodic nature of the floor structure, a Finite Element model of a single periodic cell is set up, using periodic boundary conditions and the floor sound reduction is determined using a Statistical Energy Analysis approach. An analytic beam model is applied to determine the floor deflection from the passenger load. Design solutions meeting the constraints with the smallest possible mass are sought. Starting with a floor structure with 23.3 kg/m2, a final design with 18.0 kg/m2 is reached. In addition, the transmitted sound through the floor is reduced by 8 dBA. Convergence from different starting points is verified.

Predicting time to cracking of concrete composites under restrained shrinkage: A review with insights from statistical analysis and ensemble machine learning approaches

Restrained shrinkage cracking significantly undermines the performance of reinforced concrete and shortens the structures' lifespan. Standard methods have been established to examine restrained shrinkage cracking of cementitious composites, i.e., ASTM C1581, AASHTO T 334, AASHTO T 363, and RILEM TC 119-TCE. Deficiencies in testing methods for accommodating and evaluating different cementitious composites have been recognized. Nevertheless, the significance of the restrained shrinkage cracking factors and cracking vulnerability based on the degree of restraint provided by different testing methods remain unexplored. This article provides a critical review of testing methods and studies on restrained shrinkage cracking in cementitious composites, highlighting the limitations, advantages, and further capabilities. Comprehensive experimental test datasets associated with standard restrained shrinkage cracking test methods are compiled. Advanced statistical analysis and machine learning (ML) techniques are utilized to predict time-to-cracking (TTC) of cementitious composites subjected to various testing methods based on the cracking factors. Robust and precise ML's bagging and boosting algorithms such as random forest, gradient boosting machine, extreme gradient boosting, adaptive boosting, categorical gradient boosting, and stacking ensemble models are utilized to merge predictions of the various approaches on TTC. Despite the satisfactory performance of statistical modeling, stacking demonstrates the highest precision in forecasting TTC, achieving an R2 of 0.92 for the ASTM dataset and 0.65 for the AASHTO dataset. The derived statistical and ML models reveal the significance of drying shrinkage, binder content, and moist curing as the most important factors in TTC based on ASTM C1581 and AASHTO T 334 standard testing methods. Equations are derived to predict cracking vulnerability and design concrete composites that offer delayed TTC and prolonged service life.

Nitrate sources and transformation in surface water and groundwater in Huazhou District, Shaanxi, China: Integrated research using hydrochemistry, isotopes and MixSIAR model

Global water resources affected by excessive nitrate (NO3−) have caused a series of human health and ecological problems. Therefore, identification of NO3− sources and transformations is of pivotal significance in the strategic governance of widespread NO3− contaminant. In this investigation, a combination of statistical analysis, chemical indicators, isotopes, and MixSIAR model approaches was adopted to reveal the hydrochemical factors affecting NO3− concentrations and quantify the contribution of each source to NO3− concentrations in surface water and groundwater. The findings revealed that high groundwater NO3− concentration is concentrated in the southwestern region, peaking at 271 mg/L. NO3− concentration in the Wei River and Yuxian River exhibited an increase from upstream to downstream, but in the Shidi River and Luowen River, its concentration was highest in the upstream. Groundwater NO3− has noticeable correlation with Na+, Ca2+, Mg2+, Cl−, HCO3−, TDS, EC, and ORP. In surface water, NO3− level is significantly correlated with NH4+ and ORP. Major sources of NO3− in surface and groundwater comprise manure & sewage and soil nitrogen. Source contribution for surface water was calculated by MixSIAR model to obtain soil nitrogen (57.7%), manure & sewage (23.8%), chemical fertilizer (12%), and atmospheric deposition (6.4%). In groundwater, soil nitrogen and manure & sewage accounted for 19% and 63.8% of nitrate sources, respectively. Both surface water and groundwater exhibited strong oxidation, with nitrification the primary process. It is expected that this study will provide insights into the dynamics of NO3− and contribute to the development of effective strategies for mitigating NO3− contaminant, leading to sustainable management of water resources.

Efficient Nonparametric Estimation of 3D Point Cloud Signals through Distributed Learning

Advancements in technology have elevated the prominence of 3D point cloud data, making its analysis increasingly vital across various applications. This need drives the demand for advanced statistical analytic approaches to handle challenges such as size, sparsity, and irregularity in 3D point clouds while ensuring accurate and efficient information extraction. This paper introduces a novel nonparametric distributed (NPD) learning framework that utilizes trivariate spline smoothing over a triangulation of domain. The proposed NPD algorithm features a straightforward, scalable, and communication-efficient implementation scheme that can achieve near-linear speedup. In addition, we provide rigorous theoretical support for the NPD estimation framework and demonstrate that the NPD spline estimators attain the same convergence rate as the global spline estimators obtained using the entire dataset and achieve the optimal nonparametric convergence rate established by Stone (1982) under some regularity conditions. To evaluate the efficacy of the proposed NPD method, we conduct simulation studies comparing it with several global nonparametric estimation methods used to smooth the 3D data. The results demonstrate the superior performance of the NPD method in accurately and efficiently processing and learning from 3D point clouds, highlighting its potential to advance large and complex data analysis. Supplementary material, which contains related technical details, proofs of the theoretical results, and additional results in simulation studies, is available online.

Pollution source characterization and evaluation of groundwater quality utilizing an integrated approach of Water Quality Index, GIS and multivariate statistical analysis

ABSTRACT The Mid-Gangetic Plain, a vital farmland in India, faces increasing groundwater quality deterioration due to anthropogenic activities. This study aimed to assess groundwater quality and contamination sources in the region utilizing statistical methods. A total of 78 groundwater samples were collected and analyzed using standard methods. The hydrochemistry analysis of samples revealed that several parameters such as Ca2+, Mg2+, HCO3−, NO3−, F− and PO43− surpassed the limits prescribed by the Bureau of Indian Standards (BIS). The principal component analysis yielded three significant factors, explaining 68.96% variation, highlighting geogenic and anthropogenic influences on groundwater chemistry. Hierarchical cluster analysis categorized groundwater into three clusters based on the parameters with similar trends of variation. Furthermore, Discriminant analysis identified four significant variables (Mg2+, F−, Cl− and NO3−) responsible for creating the distinction among the identified clusters. Hydrogeochemical categorization and multivariate statistical analyses indicated that rock–water interaction, weathering, leaching and anthropogenic activities collectively influenced groundwater quality throughout the studied region. The Water Quality Index reveals that 59% of samples have good water quality, while 41% exhibit poor quality predominantly concentrated in the south-western, south-eastern and central regions. This study demonstrates the efficacy of statistical techniques to interpret complex datasets and grasp water quality dynamics, enhancing groundwater management.

PENGARUH RASIO PROFITABILITAS DAN RASIO LIKUIDITAS TERHADAP NILAI PERUSAHAAN PADA PT. LIPPO KARAWACI TBK

The objective of this study is to assess the impact of the profitability ratio and liquidity ratio on the valuation of PT. Lippo Karawaci Tbk. The balance statement comprises Current Assets, Current Liabilities, Total Equity, Inventory, and Profit and Loss represented by Net Profit after Tax for a span of 12 years, from 2011 to 2022. Purposive sampling is a research strategy that involves selecting participants based on specific criteria. Linear regression is a statistical analytic approach used to assess hypotheses, starting with the basic assumption test. The test results indicate that the profitability ratio, as quantified by Return on Equity (ROE), does not exert a substantial influence on the value of the firm. The Quick Ratio (QR), which measures the liquidity ratio, has a significant influence on the value of a company. Based on the F test, it can be inferred that both Return on Equity (ROE) and Quick Ratio have a statistically significant influence on the value of a corporation.

Thalamic-limbic circuit dysfunction and white matter topological alteration in Parkinson's disease are correlated with gait disturbance.

Limbic structures have recently garnered increased attention in Parkinson's disease (PD) research. This study aims to explore changes at the whole-brain level in the structural network, specifically the white matter fibres connecting the thalamus and limbic system, and their correlation with the clinical characteristics of patients with PD. Between December 2020 and November 2021, we prospectively enrolled 42 patients with PD and healthy controls at the movement disorder centre. All participants underwent diffusion tensor imaging (DTI), 3D T1-weighted imaging (3D-T1WI), and routine brain magnetic resonance imaging on a 3.0 T MR scanner. We employed the tract-based spatial statistical (TBSS) analytic approach, examined structural network properties, and conducted probabilistic fibre tractography to identify alterations in white matter pathways and the topological organisation associated with PD. In patients with PD, significant changes were observed in the fibrous tracts of the prefrontal lobe, corpus callosum, and thalamus. Notably, the fibrous tracts in the prefrontal lobe and corpus callosum showed a moderate negative correlation with the Freezing of Gait Questionnaire (FOG-Q) scores (r = -0.423, p = 0.011). The hippocampus and orbitofrontal gyrus exhibited more fibre bundle parameter changes than other limbic structures. The mean streamline length between the thalamus and the orbitofrontal gyrus demonstrated a moderate negative correlation with Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) III (r = -0.435, p = 0.006). Topological parameters, including characteristic path length (L p), global efficiency (E g), normalised shortest path length (λ) and nodal local efficiency (N le), correlated moderately with the MDS-UPDRS, HAMA, MoCA, PDQ-39, and FOG-Q, respectively. DTI is a valuable tool for detecting changes in water molecule dispersion and the topological structure of the brain in patients with PD. The thalamus may play a significant role in the gait abnormalities observed in PD.

Refractory wastewater shapes bacterial assembly and key taxa during long-term acclimatization

Bacterial assembly and key taxa during long-term acclimatization in refractory wastewater treatment systems is of paramount importance for optimizing system performance and improving management strategies. Therefore, this study employed high-throughput sequencing, coupled with machine learning models and statistical analysis approaches, to comprehensively elucidate key features of bacterial communities and assembly processes in pesticide wastewater treatment systems. A nine-month monitoring showed substantial variation in diversity and composition of bacterial community between two interconnected biological treatment units (designated as BA and PA). Dynamics of bacterial communities in both units were similar. Moreover, water quality played crucial roles in regulating the bacterial community structure of activated sludge, which were primarily driven by deterministic patterns. Homogeneous selection contributed to 62.85 % and 64.43 % of the variations in BA and PA samples, respectively. Additionally, network analysis revealed significant modularity in bacterial compositions in both groups. Linear regression analysis identified major bacterial modules associated with metabolism and degradation functions. Notably, Module 2 in PA samples has significant positive correlations with functions related to metabolism of nucleotide, amino acid, and xenobiotics, as well as benzoate degradation. Furthermore, key taxa in ecological modules identified by Random Forest model, such as Pseudomonas, Sphingobium, and PHOS-HE28, were dominant populations with metabolism and degradation functions. Particularly, Sphingobium, appeared to be a potential multifunctional degrading bacterium, related to amino acid and xenobiotics metabolism, as well as fatty acid, valine, leucine, isoleucine, fluorobenzoate, and aminobenzoate degradation. These findings are important for developing operating strategies to maintain stable system performance during refractory wastewater treatment.

The Impact of Return on Asset, Debt to Equity Ratio, Current Ratio and Firm Size on Price to Book Value in Service Companies in the Property and Real Estate Sub-Sector Listed on the Indonesia Stock Exchange from 2015 to 2023

In the Property and Real Estate Sub-Sector Listed on the Indonesia Stock Exchange from 2015 to 2023, this study aims to determine how characteristics such return on asset, debt to equity ratio, current ratio, and company size effect price to book value. The methodology used in this study is quantitative and includes a descriptive and verification approach. The statistical analysis approach of verification makes use of hypothesis testing with partial and simultaneous tests (f and t tests), multiple linear regression, Pearson product moment correlation coefficient, determination coefficient, and traditional assumption testing. The 72 financial reports of companies listed on the Indonesia Stock Exchange that are part of the Property and Real Estate Sub-Sector provide the sample data. In addition to nonprobability sampling, this study used a purposive sample method. For data processing, SPSS software version 26.0 is utilized. The results of an initial investigation indicate that there is a clear correlation between return on asset and price to book value. Price to book value is not significantly impacted by the debt to equity ratio. At most, there is very little effect of institutional ownership current ratio on price to book value. Firm size has no effect on price to book value and does not have a major impact. Concurrently, the study's results show how price to book value is affected by return on asset, debt to equity ratio, current ratio, and firm size.

The effects of debt-to-asset ratio, size of the company, institutional ownership and earnings management on accounting conservatism in manufacturing companies in the food and beverage sub-sector listed on the Indonesia Stock Exchange between 2015 and 2023

This study attempts to ascertain how accounting conservatism is affected in manufacturing businesses in the food and beverage subsector listed on the Indonesia Stock Exchange between 2015 and 2023 by factors such as debt to asset ratio, company size, institutional ownership, and earnings management. This study employs a quantitative methodology that combines a verification and descriptive strategy. Traditional assumption testing, multiple linear regression, Pearson product moment correlation coefficient, determination coefficient, and hypothesis testing with partial and simultaneous tests (f and t tests) are all used in the statistical analysis approach of verification. Sample data comes from 63 financial reports of businesses in the Food and Beverage Manufacturing Subsector. This study employed a purposive sample strategy in conjunction with nonprobability sampling. Version 26.0 of SPSS software is used for data processing. According to preliminary study findings, there is no discernible relationship between accounting conservatism and the debt to asset ratio. Company size has a big impact on accounting conservatism. The impact of institutional ownership on accounting conservatism is negligible at best. Accounting conservatism is neither impacted by or significantly affected by earnings management. The study's findings concurrently demonstrate how the debt to asset ratio, business size, institutional ownership, and earnings management all have an impact on accounting conservatism

Identification of Fatty Acids, Amides and Cinnamic Acid Derivatives in Supercritical-CO2 Extracts of Cinnamomum tamala Leaves Using UPLC-Q-TOF-MSE Combined with Chemometrics.

Cinnamomum tamala leaf (CTL), also known as Indian bay leaf, is used all over the world for seasoning, flavoring, and medicinal purposes. These characteristics could be explained by the presence of several essential bioactive substances and lipid derivatives. In this work, rapid screening and identification of the chemical compounds in supercritical (SC)-CO2 extracts of CTL by use of UPLC-Q-TOF-MSE with a multivariate statistical analysis approach was established in both negative and positive mode. A total of 166 metabolites, including 66 monocarboxylic fatty acids, 52 dicarboxylic fatty acids, 27 fatty acid amides, and 21 cinnamic acid derivatives, were tentatively identified based on accurate mass and the mass spectrometric fragmentation pattern, out of which 142 compounds were common in all SC-CO2 extracts of CTL. Further, PCA and cluster hierarchical analysis clearly discriminated the chemical profile of analyzed extracts and allowed the selection of SC-CO2 extract rich in fatty acids, fatty acid amides, and other bioactive constituents. The result showed that the higher number of compounds was detected in CTL4 (300 bar/55 °C) extract than the other CTL extracts. The mono- and di-carboxylic fatty acids, fatty acid amides, and cinnamic acid derivatives were identified in CTL for the first time. UPLC-Q-TOF-MSE combined with chemometric analysis is a powerful method to rapidly screen the metabolite profiling to justify the quality of CTL as a flavoring agent and in functional foods.

The Influence of Program Effectiveness on the Sustainability of the Farmer Food Corporation (KPP) Business at PT. XYZ

PT. XYZ is one of the Farmer Food Corporations (KPP) in Ciamis Regency, providing services ranging from production facilities to facilitating credit capital applications. This farmer corporatization program benefits farmers by increasing their income, thus necessitating the sustainability of PT. XYZ. Therefore, it is crucial to examine the effectiveness of the PT. XYZ program, its sustainability level, and the impact of its effectiveness on business sustainability. This study employs an integrated (mixed method) approach with a dominant quantitative design. The population comprises rice farmers who are members of the Farmer Food Corporation in Ciamis Regency under PT. XYZ. A sample size of 51 respondents was selected using a simple random sampling method to ensure equal representation. The effectiveness of the PT. XYZ program was analyzed descriptively through a descriptive statistical analysis approach, and the Partial Least Square (PLS) Structural Equation Model (SEM) analysis was performed to understand the influence of program effectiveness on PT. XYZ's business sustainability. The research results indicate that all variables influence the effectiveness of the PT. XYZ programs are constructed by real manifests, with contributions ranging from medium to high. The five manifest variables constructing effectiveness are program objectives, real change, program socialization, precision, and monitoring. The sustainability variable is constructed by market, workplace, environment, and community variables. The program's effectiveness has a significant influence on PT. XYZ's business sustainability. In conclusion, the PT. XYZ program effectively enhances farmers' income and contributes significantly to the sustainability of PT. XYZ's business. Continuous improvement in program implementation and monitoring is essential to maintain and enhance these benefits.

Appendicectomy pathway: Insights from electronic medical records of a local health district in Australia

BackgroundThis study aims to identify the common pathways of appendicectomy, the most common emergency surgery in Australia’s public hospitals and any variations within a regional public health district in New South Wales, Australia. MethodsWe analyzed the electronic medical records of 3,943 patients who underwent appendicectomy between January 2014 and July 2020 at 2 hospitals in the Illawarra Shoalhaven Local Health District, New South Wales, Australia, using the PM2 approach for surgical pathway identification and subsequent statistical analyses. ResultsAmong 3,943 patients, 3,606 (91.5%) followed an 11-step main pathway: (1) emergency department admission, (2) surgery booking, (3) anesthesia start, (4) operating room entry, (5) surgery start, (6) surgery end, (7) anesthesia end, (8) operating room discharge, (9) postanesthesia care unit admission, (10) postanesthesia care unit discharge, and (11) hospital discharge. The median length of stay was 48.13 hours (interquartile range 32.74). The main pathway differed from either variation 1 (n = 246, 6.2%) or variation 2 (n = 30, 0.8%) only in the timing and location of anesthesia administration or conclusion. Variation 3 (n = 26, 0.7%) included patients who underwent appendicectomy twice, whereas variation 4 (n = 25, 0.6%) included patients booked for surgery before emergency department admission through community doctor referrals. Thirteen exceptional cases experienced combinations of the aforementioned pathways. The length of stay and phase durations varied between the main pathway and these variations. ConclusionThe appendicectomy pathway was largely standardized across the studied hospitals, with the location of anesthesia administration or conclusion affecting specific stages but not the overall length of stay. Only a complex 2-surgery pathway increased length of stay.

Ecological Function Zoning Framework for Small Watershed Ecosystem Services Based on Multivariate Analysis from a Scale Perspective

A thorough comprehension of distribution features of ecosystem services (ESs) and the influencing mechanisms can offer scientific guidance for the ecosystem management of small watersheds. We analyzed the spatial distribution patterns, interrelationships, and service hotspots of ESs across pixel and administrative scales using a multi-level statistical analysis approach in the Ashi River Basin. Through the quantitative calculation of the InVEST model, the findings revealed a decrease in soil retention, water yield, total nitrogen and phosphorus export, carbon storage, and habitat quality, while an increase in food production was observed during the period from 1995 to 2015. Through the geographical detector, the spatial heterogeneity of most individual ESs was influenced by land use patterns. Through redundancy analysis, terrain factors had the highest contribution rate to the integral ESs. Socio-economic factors and climate factors also drove the ESs’ amount and spatial distribution. At the pixel scale and administrative scale, there were distinctions in the correlations between all ESs, reflected by the fact that the relationships between ESs at the administrative scale were generally weaker and no more significant than at the pixel scale. Based on the number and distribution of hotspots in ESs and the clustering results of influencing factors, the framework of ecosystem zoning was constructed. This basin was divided into three ecological zones, and the management policies were formulated according to the ecological environment. This study clarifies the internal and mutual connection between ESs and influencing factors across two scales, thus contributing to the advancement of management strategies for ecological conservation and socio-economic development within the context of small watersheds.

Evaluating electrical power yield of photovoltaic solar cells with k-Nearest neighbors: A machine learning statistical analysis approach

The increasing demand for sustainable and renewable energy solutions reflects the critical importance of advancing photovoltaic (PV) technology and its operational efficiency. In response, this study introduces a novel application of the k-Nearest Neighbor (k-NN) algorithm to assess the reliability and applicability of solar panel simulation data which aimed to classify current states of partial shading, open, and short circuit conditions, alongside regression-based analysis for predicting specific operating parameters. In this research, a published dataset that involved various PV module configurations under different environmental conditions was tested and evaluated. The k-NN technique was applied to both classify the operational status and predict performance metrics of the modules. The diagnosis model demonstrated an accuracy of 99.2 % and an F1 score of 99.2 %, indicating a high degree of reliability in identifying the operational states of PV modules. Concurrently, the regression model exhibited a Root Mean Square Error (RMSE) of 0.036 and an R2 value of unity that showcased its effectiveness in predicting the operational parameters based on the simulation data. The concluded results are further enriching the effectiveness of simulation-based data generation to be endorsed and implemented before jumping into real experimental applications, in addition to highlighting the potential applicability of k-NN and machine learning for PV cells productivity statistical analysis.

Exploring factors influencing 21<sup>st</sup> century mathematics in Thai secondary schools: A study in Lopburi Province

The objective of this study was to ascertain the elements that influence the effectiveness of mathematics instruction in the 21<sup>st</sup> century for secondary school students within the jurisdiction of the secondary educational service area office Lopburi (SEAOL). The research utilized survey methodology. The study population consisted of 400 secondary school students from SEAOL during the first semester of 2022. The participants were selected using a stratified random selection technique. The research tool utilized in this study was a questionnaire, which demonstrated a high level of reliability with a coefficient of .934. The research employed principal component analysis as the statistical analysis approach. The findings of the study indicate that there are various factors that influence the effectiveness of mathematics instruction in the 21<sup>st</sup> century for secondary school students in SEAOL. These factors include the level of technology support from schools, use of technology, ability to use basic technology, ability to use specialized technology, knowledge of content, math is crucial, and variety of teaching methods. Additionally, the study reveals that gender, program, and education level have a significant impact on knowledge and opinions regarding mathematics instruction, with statistical significance observed at a significance level of .05.

Exploring Selenoprotein P in Liver Cancer: Advanced Statistical Analysis and Machine Learning Approaches.

Selenoprotein P (SELENOP) acts as a crucial mediator, distributing selenium from the liver to other tissues within the body. Despite its established role in selenium metabolism, the specific functions of SELENOP in the development of liver cancer remain enigmatic. This study aims to unravel SELENOP's associations in hepatocellular carcinoma (HCC) by scrutinizing its expression in correlation with disease characteristics and investigating links to hormonal and lipid/triglyceride metabolism biomarkers as well as its potential as a prognosticator for overall survival and predictor of hypoxia. SELENOP mRNA expression was analyzed in 372 HCC patients sourced from The Cancer Genome Atlas (TCGA), utilizing statistical methodologies in R programming and machine learning techniques in Python. SELENOP expression significantly varied across HCC grades (p < 0.000001) and among racial groups (p = 0.0246), with lower levels in higher grades and Asian individuals, respectively. Gender significantly influenced SELENOP expression (p < 0.000001), with females showing lower altered expression compared to males. Notably, the Spearman correlation revealed strong positive connections of SELENOP with hormonal markers (AR, ESR1, THRB) and key lipid/triglyceride metabolism markers (PPARA, APOC3, APOA5). Regarding prognosis, SELENOP showed a significant association with overall survival (p = 0.0142) but explained only a limited proportion of variability (~10%). Machine learning suggested its potential as a predictive biomarker for hypoxia, explaining approximately 18.89% of the variance in hypoxia scores. Future directions include validating SELENOP's prognostic and diagnostic value in serum for personalized HCC treatment. Large-scale prospective studies correlating serum SELENOP levels with patient outcomes are essential, along with integrating them with clinical parameters for enhanced prognostic accuracy and tailored therapeutic strategies.