Statistically Research Articles

Seasonal variations in chlorophyll–a and sea surface temperature in the exclusive economic zone of Sri Lanka

Oceanic phytoplankton plays a crucial role in regulating the biogeochemical cycle in marine ecosystems. Light harvesting pigments such as chlorophyll–a (chl–a) can be used to understand marine primary productivity, especially phytoplankton abundance. Sea surface temperature is closely linked with phytoplankton abundance and climatic changes. The objective of the current study is to determine spatial and temporal variations of chl–a and sea surface temperature and their co-variation, over 12 years using statistical methodologies and Remote Sensing, Geographic Information System (GIS) and Cloud Computing Approach. In this study, the available sea surface temperature and chl–a data acquired from a Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor were proceeded through Google Earth Engine and Earth Science Data System. Data visualization was carried out using ArcMap™ software. Results show a weak negative linear relationship (−0.40, P < 0.05) between sea surface temperature and chl–a. Therefore, additional favorable environmental factors such as coastal upwelling, and nutrient discharge through river run-off can control the primary productivity of phytoplankton. We also illustrated spatial and temporal maps for chl–a and sea surface temperature distribution in the exclusive economic zone of Sri Lanka. Chlorophyll–a concentration is enhanced during the southwest monsoon, and the lowest concentration is observed during the first inter-monsoon period. Next, the highest sea surface temperature is recorded during the first inter-monsoon period, and the lowest sea surface temperature is observed during the northeast monsoon. Seasonal behavior of chl–a concentration and sea surface temperature are changed as chl–a (SWM) > chl–a (NEM) > chl–a (SIM) > chl–a (FIM) and SST (FIM) > SST (SIM) > SST (SWM) > SST (NEM), respectively. In addition, both trend lines for each variable show a positive gradient, which may be attributed to global warming. Finally, we observed the alteration of chl–a and sea surface temperature that linked to changes in ocean chemistry during the global COVID–19 pandemic.

Identifiability and estimation of possibly non-invertible SVARMA Models: The normalised canonical WHF parametrisation

This article focuses on the parametrisation, identifiability, and (quasi-) maximum likelihood (QML) estimation of possibly non-invertible structural vector autoregressive moving average (SVARMA) models. SVAR models are routinely adopted due to their well-known implementation strategy. However, for various economic and statistical reasons, multivariate SVARMA settings are often more suitable. These settings introduce complexity in the analysis, primarily due to the presence of the moving average (MA) polynomial. We propose a novel representation of the MA polynomial matrix using the Wiener–Hopf factorization (WHF). A significant advantage of the WHF is its ability to handle possible non-invertibility and thus models with informational asymmetry between economic agents and outside observers. Since solutions of Dynamic Stochastic General Equilibrium (DSGE) models often involve this informational asymmetry, SVARMA models in WHF parametrisation can be considered data-driven alternatives to DSGE models and used for their evaluation. Furthermore, we provide low-level conditions for the asymptotic normality of the (Q)ML estimator and analytic expressions for the score and information matrix. As application, we estimate the Blanchard and Quah model, and compare our results and implied impulse response function with the ones in the SVAR model by Blanchard and Quah and a non-invertible SVARMA model by Gouriéroux and co-authors. Importantly, we have implemented this novel method in a well-documented R-package, making it readily accessible for researchers and practitioners.

Smoothness and monotonicity constraints for neural networks using ICEnet

Abstract Deep neural networks have become an important tool for use in actuarial tasks, due to the significant gains in accuracy provided by these techniques compared to traditional methods, but also due to the close connection of these models to the generalized linear models (GLMs) currently used in industry. Although constraining GLM parameters relating to insurance risk factors to be smooth or exhibit monotonicity is trivial, methods to incorporate such constraints into deep neural networks have not yet been developed. This is a barrier for the adoption of neural networks in insurance practice since actuaries often impose these constraints for commercial or statistical reasons. In this work, we present a novel method for enforcing constraints within deep neural network models, and we show how these models can be trained. Moreover, we provide example applications using real-world datasets. We call our proposed method ICEnet to emphasize the close link of our proposal to the individual conditional expectation model interpretability technique.

Reducing intravenous antibiotics in neonates born ≥35 weeks' gestation: A quality improvement study.

To assess the impact of the Early Onset Sepsis (EOS) calculator, implemented as a quality improvement study, to reduce the rate of unnecessary antibiotics in neonates born ≥35 weeks' gestation. An audit of routinely collected hospital data from January 2008 to March 2014 (retrospective) and from January 2018 to September 2019 (prospective) determined baseline incidence of EOS intravenous antibiotic use in neonates born ≥35 weeks' gestation in a tertiary level perinatal centre. Plan-do-study-act (PDSA) cycles were applied to implement the EOS calculator. Statistical process control methodology and time series analysis assessments were used to assess the potential impact of the PDSA cycles on the rate of intravenous antibiotics, blood culture collection, EOS, length of stay and health care costs (not adjusted for potential confounders). In the study population, from January 2008 to March 2014, the baseline incidence of intravenous antibiotic use was 10.49% (2970/28290), whilst only 0.067% (19/28290) neonates had culture proven EOS. From January 2018 to October 2019, prior to implementation of the EOS calculator, 13.3% (1119/8411) neonates were treated with intravenous antibiotic and the use decreased to 8.3% (61/734) post-implementation. The rate of blood culture collection decreased from 14.4% (1211/8411) to 11.9% (87/734). There were no cases of missed EOS. Length of stay decreased from 2.68 to 2.39 days, with an estimated cost saving of $366 per patient per admission. Implementing the EOS calculator in a tertiary hospital setting reduced invasive investigations for EOS and intravenous antibiotic use among neonates ≥35 weeks' gestation. This can result in reduced length of neonatal hospital stays, and associated health care cost savings and may reduce separation of mother and baby.

Seeing beyond the blot: A critical look at assumptions and raw data interpretation in Western blotting.

Rapid advancements in technology refine our understanding of intricate biological processes, but a crucial emphasis remains on understanding the assumptions and sources of uncertainty underlying biological measurements. This is particularly critical in cell signaling research, where a quantitative understanding of the fundamental mechanisms governing these transient events is essential for drug development, given their importance in both homeostatic and pathogenic processes. Western blotting, a technique developed decades ago, remains an indispensable tool for investigating cell signaling, protein expression, and protein-protein interactions. While improvements in statistical analysis and methodology reporting have undoubtedly enhanced data quality, understanding the underlying assumptions and limitations of visual inspection in Western blotting can provide valuable additional information for evaluating experimental conclusions. Using the example of agonist-induced receptor post-translational modification, we highlight the theoretical and experimental assumptions associated with Western blotting and demonstrate how raw blot data can offer clues to experimental variability that may not be fully captured by statistical analyses and reported methodologies. This article is not intended as a comprehensive technical review of Western blotting. Instead, we leverage an illustrative example to demonstrate how assumptions about experimental design and data normalization can be revealed within raw data and subsequently influence data interpretation.

Simplified detection of genetic background admixture using artificial intelligence.

Admixture refers to the mixing of genetic ancestry from different populations. Admixture is important for genomic medicine because it can affect how an individual responds to certain medications, how they metabolize drugs, and susceptibility to certain diseases. For example, some genetic variants associated with drug metabolism and response may be more common in certain populations, and individuals with admixed ancestry may have a different frequency of these variants than individuals from the ancestral populations. Understanding the patterns of admixture in a population can also help researchers identify new genetic variants associated with diseases or traits and develop more personalized and targeted treatments. In this study, we compared and classified the known and self-reported genetic backgrounds from 1000 Genomes Project and admixed samples from GTEx projects using supervised, unsupervised and statistical classification methodologies. We developed a novel tool called Admix-AI that uses a one-dimensional convolutional neural network to understand and classify admixed genetic backgrounds using 213 DNA-marker based genetic background labels. Admix-AI can be used to discover admixed proportions in samples and ultimately aid personalized genomic medicine by identifying specific biomarker systems. We compared Admix-AI to the existing admixture categorization software and found our tool to be computationally faster with 2× speedup and streamlined usage. Admix-AI is available as open-source code under GPL version 3.0 license at https://github.com/rpauly/Admix-AI.

7 Flexible probabilistic methods to unlock the clinical potential of liquid biopsy sampling

OBJECTIVES/GOALS: Decoding the origins of cell-free DNA (cfDNA) released from dying cells in a liquid biopsy sample (e.g. blood) offers the potential to provide insight into the dynamic, organism-wide changes reflective of health and disease. Thus, making cfDNA an ideal target for serial, minimally invasive monitoring of disease-related changes. METHODS/STUDY POPULATION: We develop a probabilistic method that leverages the co-regulation of neighboring CpG sites on individual methylome-wide sequencing (WGBS) reads to more flexibly model cell-specific methylation compared to prior methods that focus on the methylation rate of a single CpG site. We then extend our cross-sectional model to account for sequential sampling within the same subject. The increased sampling frequency is critical to identifying the evolutionary dynamics of disease progression influencing treatment response and resistance, and disease recurrence. We utilize Bayesian inference techniques to model patient-specific longitudinal profiles of cell-type turnover in simulated serial samples. RESULTS/ANTICIPATED RESULTS: We found our model more effective at capturing a range of methylation patterns on cfDNA fragments with lower Root Mean Square Error across simulations compared to a single CpG model. We apply our model to detect significant (p &lt; 0.05, Friedman’s test) increases in cellular contributions from lung and cardiac tissue in breast cancer patients (n=15) undergoing radiation therapy compared to baseline. We also identify signals of radiation induced toxicity to the liver in right-sided breast cancer patients (n=8) receiving radiation treatment compared to left-sided breast cancer patients (n=7). Finally, we show our extended model results in more efficient estimates of simulated cell-type turnover profiles compared to analyzing serial samples cross-sectionally, ignoring the longitudinal nature of the data. DISCUSSION/SIGNIFICANCE: Here we address an unmet need in developing novel statistical methodologies to decode the origins of methylated cfDNA obtained from liquid biopsy samples. We demonstrate the far-ranging clinical utility of serial liquid biopsy sampling to complement and advance the standards of clinical care in oncology and other pathologies.

Network Analysis of the Relationships Between Symptoms, Functioning, and Sleep/Fatigue in Colorectal Cancer Patients

IntroductionAmong the various patient experiences, cancer-related fatigue and sleep disturbances emerge as pivotal aspects that can substantially impact individuals’ quality of life. There exists a relative scarcity of research focusing on the intricate relationship between symptoms, functioning, fatigue, and sleep disturbances in colorectal cancer (CRC) patients.ObjectivesIn this context, the current research endeavors to apply advanced statistical methodologies to elucidate the complex relationships between symptoms, functioning, fatigue, and sleep disturbances. By exploring the intricate web of patient characteristics, clinical factors, psychosocial elements, this study aims to construct a holistic model that not only captures the nuances of colorectal cancer patients’ experiences but also uncovers potential avenues for intervention and support.MethodsIn our cross sectional study, we administered the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ-C30), the Quality of Life Questionnaire Colorectal Cancer Module (QLQ-CR29) to 987patients who were surgically-treated for CRC from the tertiary hospital from 2013 through 2018. To confirm the relationship between symptoms of CRC patients, univariable logistic regression was used to examine the potential relationship between independent variables and the occurrence of fatigue and sleep disorders. Least Absolute Shrinkage and Selection Operator (Lasso) was used for variable selection. The selected variables were then applied to a multivariate logistic regression analysis to examine the most influential predictors of fatigue and sleep disturbance. Finally, gaussian graphical models (GGM) were used to identify potential interactions between characteristics, symptoms, functioning, with fatigue, and sleep disturbances in CRC. In this study, Directed Acyclic Graph (DAG) was used to identify causal dependancy and path of variables.Results About 10.4% of study participants reported experiencing fatigue. Sleep problems were reported by 15.8% of the study participants. Multivariable logistic regression analysis using Lasso showed that sleep problem (odds ratio [OR]=2.34; 95% CI, 1.03-5.31), physical, role, and emotional functioning, pain, dyspnoea, and appetite loss were significant predictors of fatigue, while emotional functioning, dyspnoea, and appetite loss were significant predictors of sleep problem. The variables that were directly linked to fatigue were role functioning, emotional functioning, dyspnoea, appetite loss, body image and trouble with taste. The variables that were directly linked to sleep problem were emotional functioning and appetite loss.ConclusionsIn conclusion, there were complex relationships between symptoms, functioning, fatigue, and sleep disturbances. The symptom network of CRC patients showed different patterns toward fatigue and sleep.Disclosure of InterestNone Declared

Permeability Prediction and Facies Distribution for Yamama Reservoir in Faihaa Oil Field: Role of Machine Learning and Cluster Analysis Approach

Empirical and statistical methodologies have been established to acquire accurate permeability identification and reservoir characterization, based on the rock type and reservoir performance. The identification of rock facies is usually done by either using core analysis to visually interpret lithofacies or indirectly based on well-log data. The use of well-log data for traditional facies prediction is characterized by uncertainties and can be time-consuming, particularly when working with large datasets. Thus, Machine Learning can be used to predict patterns more efficiently when applied to large data. Taking into account the electrofacies distribution, this work was conducted to predict permeability for the four wells, FH1, FH2, FH3, and FH19 from the Yamama reservoir in the Faihaa Oil Field, southern Iraq. The framework includes: calculating permeability for uncored wells using the classical method and FZI method. Topological mapping of input space into clusters is achieved using the self-organizing map (SOM), as an unsupervised machine-learning technique. By leveraging data obtained from the four wells, the SOM is effectively employed to forecast the count of electrofacies present within the reservoir. According to the findings, the permeability calculated using the classical method that relies exclusively on porosity is not close enough to the actual values because of the heterogeneity of carbonate reservoirs. Using the FZI method, in contrast, displays more real values and offers the best correlation coefficient. Then, the SOM model and cluster analysis reveal the existence of five distinct groups.

Developing University Students’ Statistical Reasoning via a Research-informed Course

Statistical reasoning (SR) is an essential skill for students in studying statistics. This study aims to develop SR for university students through an appropriately designed course in statistics. Employing a quasi-experimental design, this study evaluated students' SR changes as they participated in a research-informed statistics course, designed to integrate statistical reasoning. The results showed that the course helps students improve their SR, with their scores increased by 23% from the pre-test to the post-test. Specifically, the SR scores on measures of central tendency of the experimental class increased by 8%; while the SR scores on the measures of variability of the experimental class increased by 34%. Suggestions about curriculum and teaching are included, such as offering opportunities for statistical investigations with real data; going deeper into building and understanding formulas; and training lecturers to teach statistics using approaches that are different from traditional teaching methods, as well as evaluating students' statistical learning with reliable tools.

LITERATURE REVIEW: BASIC STATISTICS IN EDUCATIONAL RESEARCH

This paper provides a comprehensive exploration of the role and significance of basic statistics in educational research. Drawing upon historical foundations and theoretical frameworks, the study elucidates the evolution of statistical methodologies and their application in understanding complex educational phenomena. Key pioneers such as Francis Galton and Karl Pearson are highlighted for their foundational contributions, laying the groundwork for statistical analysis in education. The paper examines various statistical techniques, including descriptive and inferential statistics, correlation analysis, regression analysis, and analysis of variance (ANOVA), showcasing their relevance in analyzing diverse aspects of teaching, learning, and educational outcomes. Moreover, the integration of statistical methodologies into educational research is examined across domains such as educational management, policy-making, and teaching practice, emphasizing their role in informed decision-making, resource allocation, and policy formulation. Despite the benefits, challenges such as sample bias and misapplication of statistical tests are acknowledged, underscoring the importance of methodological rigor and statistical literacy among researchers and practitioners. Looking ahead, the paper discusses future directions in educational research, including the integration of advanced statistical techniques and efforts to promote statistical literacy. Overall, this paper offers a comprehensive framework for understanding the complexities of basic statistics in educational research, highlighting its transformative potential in driving positive change and innovation within the education sector.

Assessment of the Global Experience of Strategies for Gender Asymmetry Reduction

Gender asymmetry often denotes significant gender inequality, encompassing disparities in societal norms, cultural convictions, and historical legacies. This asymmetry results in an unequal distribution of opportunities, resources, and power between individuals of different genders, with one gender often enjoying certain privileges over the other. This article analyzes the effectiveness of implemented and tested strategies in reducing gender asymmetry, with a special focus on four countries such as the United States, Great Britain, Germany and Kazakhstan. Special attention is paid to comparing approaches and results in different countries, which makes it possible to identify the most successful practices and general trends. Despite the implementation of various government policies and standards aimed at achieving equal pay for employees of all genders, women still tend to receive lower salaries compared to their male counterparts, even when they hold similar positions and perform similar job responsibilities. Through meticulous investigation and the application of statistical methodologies, it is attempted to gauge whether these strategies have contributed to a reduction in gender asymmetry, comparing the statistics from 2019 to 2023 for the outcomes’ evaluation. In conclusion, the article offers recommendations for the development and implementation of effective strategies that can be used by governments and organizations to further promote gender equality.

THE EFFECT OF FINANCIAL PERFORMANCE, CRS AND CORPORATE GOVERNANCE ON FIRM VALUE

This research investigates the intricate relationship between financial performance, Corporate Social Responsibility (CSR), Corporate Governance (CG), and firm value within the context of the Vietnamese business landscape. Drawing upon the Efficient Market Semi-Strong Hypothesis, the study delves into the dynamic interplay of these factors to discern their collective impact on firm value. Utilizing a comprehensive dataset and employing robust statistical methodologies, our findings reveal a noteworthy and significant influence of financial performance on firm value. This phenomenon is interpreted through the lens of the Efficient Market Semi-Strong Hypothesis, highlighting the responsiveness of the market to information related to a firm's financial health. Moreover, the study extends beyond financial metrics to explore the broader dimensions of corporate behavior, incorporating CSR and CG as integral components of the analysis. The results underscore the interconnectedness of financial performance, CSR initiatives, and CG practices in influencing firm value. The simultaneous examination of these factors allows for a more holistic understanding of the mechanisms that underpin firm valuation.

Some Questions Related to Rao-Blackwellization and Association Rule Mining

Prof. CR Rao has been awarded the prestigious 2023 International Prize in Statistics. The citation reads: “In his remarkable 1945 paper published in the Bulletin of the Calcutta Mathematical Society, Calyampudi Radhakrishna (C.R.) Rao demonstrated three fundamental results that paved the way for the modern field of Statistics and provided statistical tools heavily used in science today……”. These three results are ‘Cramer-Rao Lower Bound’ (CRLB), ‘Rao- Blackwellization’ (RB) and the third one now flourished as ‘Information Geometry’. In this paper, we shall discuss two offshoots from his work over the eight decades. Several articles have appeared on his life and work (see for example, T. J. Rao (2019 and 2023a, 2023b) and Kumar (2023)). The first offshoot is based on one of the three breakthrough results, namely, Rao–Blackwell Theorem, first proved by C.R. Rao in 1945, when he was just 25 years old and also by Blackwell later in 1947. The second one is on Association Rule Mining (ARM), which he developed when he was 96 years old. These two papers reveal the transition of statistical methodologies from Fisherian concepts to recent applications of AI and ML. In this paper we shall pose some questions which need further study. International Journal of Statistical Sciences, Vol.24(1), March, 2024, pp 85-89

Computational statistical modelling for parameters optimization of LDL-cholesterol levels in patients with type 2 diabetes

Introduction LDL is the acronym used to denote low-density lipoproteins. When the level of LDL is high, it leads to the accumulation of cholesterol in the arteries, and as a result, it is commonly known as “bad” cholesterol. Recognizing the significance, a study is conducted on computational and statistical modelling for parameter optimization of LDL-cholesterol levels. In recent years, there has been an increase in the application of precise statistical analysis methodologies. Consequently, scientists are more focused on producing reliable results and are more determined to provide credible findings.&#x0D; Objective This study aims to develop and validate a proposed hybrid method that combines Multilayer Feedforward Neural Network (MLFFNN), and Multiple Linear Model (MLR) and to present the R-syntax applications of the proposed hybrid method with clinical study data.&#x0D; Material and Methods The proposed hybrid model will be built using the generalized mixture model, which is formulated using an MLFFNN framework and Linear Model (LM). The performance of the hybrid model will be assessed utilizing the predicted mean square error neural network (MSE.net) and the predicted mean square error (P.MSE), which will serve as a benchmark for precision and efficacy.&#x0D; Results: The result indicates that hybrid method modelling is superior, with the highest R-squared value and the lowest MSE.net value. The hybrid model technique was found to produce a more precise forecast of the outcome when the data is separated into training and testing datasets. The R-square score in this report demonstrates that the LM model is a good fit (84.97%), and the MSE.net value of 0.00529 indicates that the model is both accurate and predictive.&#x0D; Conclusion: The research concludes that the hybrid model method proposed is preferable. This critical conclusion helps us comprehend the hybrid method’s proportional contribution to this illustration’s result.&#x0D; Bangladesh Journal of Medical Science Vol. 23 No. 02 April’24 Page : 498-506

Scrutinizing the impact of energy transition on GHG emissions in G7 countries via a novel green quality of energy mix index

This study delves into the critical nexus between energy transition and greenhouse gas (GHG) emissions across the G7 nations from 1970 to 2021. Its primary aim is to elucidate the influence of energy consumption composition on GHG emissions within these economies. A notable feature of this investigation is the incorporation of a novel metric known as the green quality of energy mix, which offers a significant perspective on the energy landscape. To unravel the complexities of this interrelationship, the study employs sophisticated statistical methodologies, leveraging quantile-based approaches such as quantile regression and quantile-on-quantile regression (QQR). By harnessing these advanced techniques, the research endeavors to capture the nonlinear dynamics inherent in the dataset, thereby enriching our understanding of the intricate interplay between energy transition and GHG emissions within the G7 nations. To bolster the reliability of our findings, the KRLS estimator is introduced as a supplementary validation tool for both the QQR and QR estimators. The results show that energy transition promote ecological quality by reducing GHG emissions in the G7 nations. This study's findings hold significant promise in augmenting ecological quality and bolstering sustainability efforts within these economies.

Approaches for identifying and managing publication bias in meta-analysis

The consequences of publication bias in meta-analysis pose significant risks, potentially leading to erroneous conclusions within the meta-analytic framework. The objective of this article was to explore the methodologies for identifying publication bias and approaches for mitigating its effects. The techniques employed to detect publication bias can generally be distinguished into two major categories: graphical and statistical methodologies. Graphical approaches utilize techniques such as funnel plots and meta-plots, which visually depict the distribution of effect sizes and standard errors across studies. Statistical methods encompass various computations, including Fail-Safe N, rank correlation, Egger regression, tests for excess significance (TES), and selection models tailored for evaluating publication bias through quantitative analyses. The combination of these methods is recommended for a more comprehensive assessment, rather than relying on individual approaches. Methods for addressing publication bias include the trim and fill (T&amp;F) method, Publication Error and True Effect Size Estimation (PET-PEESE) method, and the Weight-Function Model, each offering unique strategies for adjusting effect size estimates. The selection of these methods should consider the specific characteristics of the meta-analysis under consideration, ensuring the most appropriate approach is employed. Publication bias poses a significant risk in the field of meta-analysis, and selecting methods for its identification and mitigation requires comprehensive consideration

The Effect of Cash Conversion Cycle on Company Earnings and Profitability

"Finance serves as the lifeblood of every business, and effectively managing finances stands as a crucial element for any company’s sustenance. This study delves into the examination of how a Cash Conversion Cycle (CCC) influences a company’s earnings. For this purpose, five companies from the Fortune 500 list are selected and ten years of data are extracted from their annual reports for analysis. Employing statistical methodologies, using a sample of firms from diverse industries, we employ regression analysis to examine how variations in the CCC affect profitability yardsticks like Return on Assets (ROA) and Return on Equity (ROE). Implications of this probe extend to financial managers, policymakers, and investors, emphasizing the paramountcy of efficient working capital management in enhancing firm profitability, earnings, and sustainability in competitive markets."

Solving Satisfiability Modulo Counting for Symbolic and Statistical AI Integration with Provable Guarantees

Satisfiability Modulo Counting (SMC) encompasses problems that require both symbolic decision-making and statistical reasoning. Its general formulation captures many real-world problems at the intersection of symbolic and statistical AI. SMC searches for policy interventions to control probabilistic outcomes. Solving SMC is challenging because of its highly intractable nature (NP^PP-complete), incorporating statistical inference and symbolic reasoning. Previous research on SMC solving lacks provable guarantees and/or suffers from suboptimal empirical performance, especially when combinatorial constraints are present. We propose XOR-SMC, a polynomial algorithm with access to NP-oracles, to solve highly intractable SMC problems with constant approximation guarantees. XOR-SMC transforms the highly intractable SMC into satisfiability problems by replacing the model counting in SMC with SAT formulae subject to randomized XOR constraints. Experiments on solving important SMC problems in AI for social good demonstrate that XOR-SMC outperforms several baselines both in solution quality and running time.

STATISTICAL TECHNIQUES IN PRECISION METROLOGY, APPLICATIONS AND BEST PRACTICES

Statistical techniques play a pivotal role in precision metrology, ensuring accurate measurements and reliable data analysis in various industries. This review delves into the applications and best practices of statistical techniques in precision metrology, highlighting their significance in enhancing measurement accuracy, reducing uncertainties, and optimizing manufacturing processes. In precision metrology, statistical methods are employed to analyze measurement data, assess measurement uncertainty, and make informed decisions regarding process control and quality assurance. One of the fundamental applications of statistical techniques is in experimental design, where methodologies such as Design of Experiments (DOE) aid in optimizing measurement processes by identifying influential factors and their interactions. Moreover, statistical process control (SPC) techniques like control charts are widely utilized in monitoring and maintaining measurement processes within specified limits, thereby ensuring consistency and reliability in metrology operations. These techniques enable early detection of deviations or anomalies, facilitating timely corrective actions to prevent the production of non-conforming products. Furthermore, the application of statistical analysis in uncertainty evaluation is crucial for quantifying the reliability of measurement results. Techniques such as Monte Carlo simulation and GUM (Guide to the Expression of Uncertainty in Measurement) provide systematic approaches for assessing and expressing measurement uncertainty, considering various sources of error and their contributions to the overall uncertainty budget. In addition to applications, adopting best practices in statistical techniques is essential for ensuring the effectiveness and efficiency of precision metrology processes. This includes proper data collection, adherence to statistical assumptions, selection of appropriate statistical models, and interpretation of results with caution. Overall, this review underscores the importance of statistical techniques in precision metrology, emphasizing their role in improving measurement accuracy, reducing variability, and enhancing overall quality in manufacturing processes. By implementing robust statistical methodologies and adhering to best practices, organizations can achieve higher levels of precision, consistency, and reliability in their metrology operations, ultimately leading to improved product quality and customer satisfaction.&#x0D; Keywords: Statistics, Precision Metrology, Best Practices, Application, Review.