Statistical Approach Research Articles

Bidirectional link between periodontitis and systemic inflammation in diabetic retinopathy

Periodontitis is independently associated with numerous lifestyle diseases. Diabetic patients have approximately threefold increased odds of periodontitis, which in turn increases the risk of systemic inflammation. The study by Thazhe Poyil et al is an effort to establish the inflammatory link between diabetic re-tinopathy (DR) and periodontitis based on the periodontal inflamed surface area in diabetic patients with and without DR. To further advance the study, we suggest refining the eligibility criteria to explicitly state the clinical correlates of periodontitis and DR, larger sample size and improved sampling methodology, matching of baseline characteristics of the two groups, as well as improved statistical approach and interpretation of the study findings. Measurement of hemoglobin A1c (HbA1c) in studies comparing type 2 diabetes mellitus patients with DR of matched severity with and without periodontitis could provide a clearer picture of whether HbA1c level is indeed influenced by periodontitis.

Optimizing methane plasma pyrolysis for instant hydrogen and high-quality carbon production

The European Green Deal has set a target for Europe to achieve net-zero greenhouse gas emissions by 2050, necessitating a transition to more sustainable energy sources. Hydrogen gas (H2) has emerged as a promising solution, with methane pyrolysis presenting a viable method for its production. This study explores the optimization of methane plasma pyrolysis for hydrogen and high-quality carbon production. Employing a statistical approach by a design of experiment software, critical process parameters are systematically analyzed to predict their impact within a defined range. Additionally, the paper conducts comprehensive characterization of the solid carbon produced during pyrolysis using imaging, spectroscopic and elemental analysis, and gas sorption analysis methods. The experimental investigation was conducted using a thermal plasma reactor with several settings of influential parameters including methane gas (CH4) content in the plasma gas, electric current, and arc length. The DC-transferred plasma arc is formed using a variable gas mixture of argon gas (Ar) and CH4, with a constant flow rate of 5 Nl/min. Thirteen tests were designed, evaluating responses such as power input, process stability, and H2 yield. The H2 yield indicates the hydrogen produced from CH4, with 100% representing total conversion. While the process exhibited inconstancy, attributed to reactor design constraints, a high H2 yield of 67%–100% was achieved. The results indicate that a higher CH4 content in the plasma gas and extended arc lengths disturb the plasma arc, hence reducing the H2 yield. Increased power input, achieved through higher amperage levels, and a wider reaction zone eased by extending the arc length both led to an improved H2 yield. Scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS) revealed microstructural differences, with carbon samples from the filter exhibiting finer textures and carbon samples from the reactor larger sizes and dendritic particles. Raman spectroscopy confirmed crystalline graphitic-like structures with low defect concentrations, a finding supported by X-ray diffraction (XRD) analysis. Inductively coupled plasma mass spectroscopy (ICP-MS) analysis confirmed high-purity carbon with slight impurities from initial filter contamination. Brunauer-Emmett-Teller (BET) specific surface area calculations based on gas sorption analysis showed significant variations, with filter-collected samples exhibiting 40–170 m2/g and reactor-collected ones showing 7–30 m2/g.

RSM based Statistical approach to Enhance the Compressive Performance of ABS P400 Parts fabricated via FDM Technology

RSM based Statistical approach to Enhance the Compressive Performance of ABS P400 Parts fabricated via FDM Technology

Results of the COMPASS trial analysed using win ratio compared with conventional analytic approaches.

BackgroundWin ratio (WR) is a newer analytic approach for trials with composite endpoints that accounts for the relative importance of individual components. Our objective was to compare the results of the COMPASS trial analysed using WR compared with conventional statistical approaches. MethodsWe used an unmatched WR analysis for first and total (first plus recurrent) events to examine effects of rivaroxaban plus aspirin and rivaroxaban alone versus aspirin alone on primary efficacy (cardiovascular death, stroke, myocardial infarction), safety (modified International Society on Thrombosis and Haemostasis major bleeding), and net clinical benefit (primary efficacy plus fatal or critical organ bleeding) endpoints. We compared the WR results with those obtained using Cox proportional hazards regression model for first events and Anderson-Gill method for total events. We calculated the win difference to estimate absolute treatment effects. ResultsThe WR approach produced results consistent with those obtained using conventional statistical methods for the primary composite endpoint (first event: WR 1.32, 95% CI: 1.14 to 1.52; Cox HR 1.32, 95% CI: 1.16 to 1.52; total [first plus recurrent] events: WR 1.32, 95% CI: 1.14 to 1.52; Anderson-Gill HR 1.32, 95% CI: 1.16 to 1.54) as well as for main safety and NCB endpoints. The absolute benefits of the combination of rivaroxaban and aspirin compared with aspirin alone calculated using the win difference were greatest in those with multiple high-risk features. ConclusionRe-analysis of the COMPASS trial results using WR produced results that were consistent with those obtained by conventional statistical approaches.

Developing Tailored Materials for the Industrial Production of Anion Exchange Membrane Electrolyzers through a Statistical Approach

Developing Tailored Materials for the Industrial Production of Anion Exchange Membrane Electrolyzers through a Statistical Approach

Metabolomics highlights biochemical perturbations occurring in the kidney and liver of mice administered a human dose of colistin

Introduction: Colistin (CMS) is used for the curation of infections caused by multidrug-resistant bacteria. CMS is constrained by toxicity, particularly in kidney and neuronal cells. The recommended human doses are 2.5–5 mg/kg/day, and the toxicity is linked to higher doses. So far, the in vivo toxicity studies have used doses even 10-fold higher than human doses. It is essential to investigate the impact of metabolic response of doses, that are comparable to human doses, to identify biomarkers of latent toxicity. The innovation of the current study is the in vivo stimulation of CMS's impact using a range of CMS doses that have never been investigated before, i.e., 1 and 1.5 mg/kg. The 1 and 1.5 mg/kg, administered in mice, correspond to the therapeutic and toxic human doses, based on previous expertise of our team, regarding the human exposure. The study mainly focused on the biochemical impact of CMS on the metabolome, and on the alterations provoked by 50%-fold of dose increase. The main objectives were i) the comprehension of the biochemical changes resulting after CMS administration and ii) from its dose increase; and iii) the determination of dose-related metabolites that could be considered as toxicity monitoring biomarkers.Methods: The in vivo experiment employed two doses of CMS versus a control group treated with normal saline, and samples of plasma, kidney, and liver were analysed with a UPLC-MS-based metabolomics protocol. Both univariate and multivariate statistical approaches (PCA, OPLS-DA, PLS regression, ROC) and pathway analysis were combined for the data interpretation.Results: The results pointed out six dose-responding metabolites (PAA, DA4S, 2,8-DHA, etc.), dysregulation of renal dopamine, and extended perturbations in renal purine metabolism. Also, the study determined altered levels of liver suberylglycine, a metabolite linked to hepatic steatosis. One of the most intriguing findings was the detection of elevated levels of renal xanthine and uric acid, that act as AChE activators, leading to the rapid degradation of acetylcholine. This evidence provides a naïve hypothesis, for the potential association between the CMS induced nephrotoxicity and CMS induced 39 neurotoxicity, that should be further investigated.

Health Analysis and Recommendation Based on Food Using Data Mining

Suitable nutritional diets have been widely recognized as important measures to prevent and control non-communicable diseases (NCDs). However, there is little research on nutritional ingredients in food now, which are beneficial to the rehabilitation of NCDs. In this project, we profoundly analyzed the relationship between nutritional ingredients and diseases by using data mining methods. First, more than n number of diseases was obtained, and we collected the recommended food and taboo food for each disease. The experiments on reallife data show that our method based on data mining improves the performance compared with the traditional statistical approach. We can assist doctors and disease researchers to find out positive nutritional ingredients that are conducive to the rehabilitation of the diseases as accurately as possible. At present, some data is not available, because they are still in the medical verification. The dataset uploaded will be pre-processed, Feature Extraction, noisy data will be removed, and classification of dataset will take places using random forest algorithm based on this analysis the diseases prediction takes places for the food intake by the individual

Expanding neutrino oscillation parameter measurements in NOvA using a Bayesian approach

NOvA is a long-baseline neutrino oscillation experiment that measures oscillations in charged-current νμ→νμ (disappearance) and νμ→νe (appearance) channels, and their antineutrino counterparts, using neutrinos of energies around 2 GeV over a distance of 810 km. In this work we reanalyze the dataset first examined in our previous paper [] using an alternative statistical approach based on Bayesian Markov chain Monte Carlo. We measure oscillation parameters consistent with the previous results. We also extend our inferences to include the first NOvA measurements of the reactor mixing angle θ13, where we find 0.071≤sin22θ13≤0.107, and the Jarlskog invariant, where we observe no significant preference for the CP-conserving value J=0 over values favoring CP violation. We use these results to examine the effects of constraints from short-baseline measurements of θ13 using antineutrinos from nuclear reactors when making NOvA measurements of θ23. Our long-baseline measurement of θ13 is shown to be consistent with the reactor measurements, supporting the general applicability and robustness of the Pontecorvo-Maki-Nakagawa-Sakata framework for neutrino oscillations. Published by the American Physical Society 2024

Statistical Analysis Applied to the Production of Mirto Liqueur

Preparation of myrtle liqueur through ethanol-based extraction is a widely employed methodology. Nevertheless, optimization of existing processes is possible, especially through a modern statistical multivariate approach. In this context, a Design of Experiments (DoE) approach was used to quantitatively assess for the first time the effect of the time, ethanol concentration, temperature, and the ratio between the Myrtus communis berries’ weight and the extractant volume (v/w) on the amounts of anthocyanins, volatile compounds and dry residues in the liqueur. The kinetic profile relative to the volatile fraction variation during the process was described by gas chromatography (GC), while spectrophotometric analysis allowed quantification of the total anthocyanins and total polyphenols. Multiple response analysis showed that the maximum efficiencies in terms of the considered parameters (desirability function) were reached by setting the temperature to 25 °C and the ethanol percentage to 96% after 20 days of processing. Some hints as to the chemical instability and not negligible sensitivity of anthocyanins in relation to the experimental conditions for longer extraction times were also observed. The statistical model represents a novel tool for industrial production of myrtle liqueur.

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.

A scoping review on word-reading resilience in literacy: Evaluating empirical evidence for protective factors

BackgroundTo date little is known about factors that might contribute to positive literacy outcomes in children with (a risk of) reading difficulties (RD). Research into resilience in literacy is needed to understand why some children with (a risk of) RD can overcome their difficulties in the face of adversity. AimThis scoping review aims to 1) provide a framework and operationalize study designs and statistical approaches for studying academic resilience; and 2) systematically review empirical evidence for promotive, protective, and skill-enhancing factors involved in resilience in atypical literacy development of children with (a risk of) word-level RD. MethodThe systematic literature search included empirical studies with a focus on compensation in literacy development, including samples of 6- to 16-year-old children with a detectable (risk of) word-level RD. Outcome measures had to include at least one relevant literacy measure. ResultsAnalysis of the 22 included studies revealed two main findings: 1) most studies had (very) small sample sizes and thus low statistical power to find relevant effects; 2) study designs and/or statistical analyses used were often insufficient to distinguish between promotive, protective, and skill-enhancing factors. Furthermore, findings point towards underrecognition of evidence for promotive and skill-enhancing factors as well as overinterpretation of the same evidence towards protective effects. ConclusionOverall, empirical evidence for protective factors is sparse and at present based on only a few studies. Based on the current findings, we state implications for the field of educational psychology in planning and conducting research into resilience in literacy.

Measurements of jet quenching using semi-inclusive hadron+jet distributions in pp and central Pb-Pb collisions at sNN=5.02 TeV

The ALICE Collaboration reports measurements of the semi-inclusive distribution of charged-particle jets recoiling from a high transverse momentum (high pT) charged hadron, in pp and central Pb-Pb collisions at center-of-mass energy per nucleon–nucleon collision sNN=5.02 TeV. The large uncorrelated background in central Pb-Pb collisions is corrected using a data-driven statistical approach which enables precise measurement of recoil jet distributions over a broad range in pT,chjet and jet resolution parameter R. Recoil jet yields are reported for R=0.2, 0.4, and 0.5 in the range 7<pT,chjet<140 GeV/c and π/2<Δφ<π, where Δφ is the azimuthal angular separation between hadron trigger and recoil jet. The low-pT,chjet reach of the measurement explores unique phase space for studying jet quenching, the interaction of jets with the quark–gluon plasma generated in high-energy nuclear collisions. Comparison of pT,chjet distributions from pp and central Pb-Pb collisions probes medium-induced jet energy loss and intra-jet broadening, while comparison of their acoplanarity distributions explores in-medium jet scattering and medium response. The measurements are compared to theoretical calculations incorporating jet quenching. ©2024 CERN, for the ALICE Collaboration 2024 CERN

A contextual approach to estimate household activities in instrumented dwellings

Occupants and their activities are important in computer-aided systems such as energy management or energy simulation in residential buildings. Numerous studies have extensively examined detailed household activities at the population level using time-use surveys. Despite the considerable influence of contexts on occupants' activities, a limited number of studies focus on individual households due to the constraints imposed by private policies on collecting occupants' activities. This article proposes an approach to detect detailed occupants' activities (e.g., cooking, entertaining) based on measurements in specific households. Firstly, a camera-free mobile application is proposed to support the data collection process due to the restriction of cameras. Then, relevant features are determined for each activity. Finally, Bayesian networks were built to represent human-understandable relationships between determined features and related activities. An instrumented dwelling with five individuals is tested, and a 4-month dataset is used to evaluate the model quality. Results show that the model can estimate most of the studied activities (e.g., cooking, washing dishes) with a F1-score between 0.77 and 0.92. The approach is better than the statistical approach with time-use surveys in building household activity profiles, which is important in energy simulation and verification. In addition, this approach is replicable for different households, providing them with context-specific and highly useable information to enhance smart energy management systems and occupants’ energy behavior.

Towards robust automated math problem solving: a survey of statistical and deep learning approaches

Towards robust automated math problem solving: a survey of statistical and deep learning approaches

Towards robust automated math problem solving: a survey of statistical and deep learning approaches

Towards robust automated math problem solving: a survey of statistical and deep learning approaches

An Investigation in to Sports Analytics and Related Tools

This study explores the application of sports analytics and the utilization of various tools in the sports industry. Sports analytics, also known as sports data analytics, is the application of data analysis and statistical approaches to extract important insights. Additionally, it aids in making well-informed judgments and enhancing performance in the realm of sports. Sports data analytics include a diverse array of methods and approaches, such as statistical analysis, machine learning, data visualization, predictive modeling, and optimization algorithms. The objective of sports business analytics is to get practical insights from a dataset in order to enhance decision-making and foster innovation in the sports industry

Transcranial thermoacoustic imaging based on the fast back-projection algorithm with nonuniform speed of sound layering

Thermoacoustic imaging (TAI) has the potential for detecting hemorrhagic stroke. However, in transcranial TAI, the speed of sound (SoS) between the skull and brain tissue varies significantly. Therefore, if the image reconstruction assumes a uniform SoS, accurately locating the hemorrhagic lesion becomes challenging. In this Letter, we propose a fast inhomogeneous layer back-projection (BP) method based on the basic boundary line with a statistical approach to reconstruct TA images for noninvasive and non-ionizing hemorrhage detection. To validate our proposed method, we conducted numerical simulations using real human skull data and two phantom transcranial TAI experiments. In the numerical simulation, the proposed method improves the structural similarity index measure from 0.034 879 for BP with uniform SoS to 0.624 44. The phantom experimental results demonstrate that the proposed method renders the targets in the reconstructed image more consistent with the real targets. In the case of considering a three-layer SoS distribution, the time reversal method requires 1 min and 37.391 s to reconstruct a 201 × 201 pixels TA image. Meanwhile, the proposed method accomplishes the same-sized TA image reconstruction in only 2.113 397 s. The simulation and experimental results indicate that the proposed method enhances TAI's ability for accurate and fast identification of cerebral hemorrhage.

Understanding key mineral supply chain dynamics using economics‐informed material flow analysis and Bayesian optimization

AbstractThe low‐carbon energy transition requires significant increases in production for many mineral commodities. Understanding demand, technological requirements, and prices associated with this production increase requires understanding the supply chain dynamics of many minerals simultaneously, and via a consistent framework. A generalized economics‐informed material flow method, global materials modeling using Bayesian optimization, captures the market dynamics of key mineral commodities. The method relies only on a limited set of widely available historical data as input, enabling quantification of economic relationships (elasticities) for supply chain components where data are sparse, and relationships cannot be obtained via traditional statistical approaches. Building upon established material flow analysis (MFA) and economic modeling techniques, Bayesian optimization was applied to fit an economics‐informed MFA model to global historical demand, supply, and price for aluminum, copper, gold, lead, nickel, silver, iron, tin, and zinc. This approach enables estimates for the evolution of ore grades, mine costs, refining charges, sector‐specific demand, and scrap collection for each commodity. Economic relationships were quantified and compared with a database compiled from the literature, including 1333 values from 213 analyses across 65 publications. Discrepancies in methods and limited coverage make use of these parameters in modeling efforts difficult. This work provides a single, homogeneous, probabilistic approach to identifying economic relationships across mineral supply chains, with uncertainty quantification, a literature database for comparison, and a modeling framework in which to use them. This article met the requirements for a Gold‐Gold JIE data openness badge described at http://jie.click/badges.

Acute Ischemic Stroke in Young Adults: Comparative Analysis of Outcomes and Mortality in Metabolically Healthy Obese or Overweight Vs. Non-Obese or Overweight Hospitalizations, 2016-2019 Cohort

IntroductionRising obesity rates and the increasing prevalence of stroke in the metabolically healthy obese and overweight (MHOO) necessitate examining its association in younger (18-44 year) populations and analyzing acute ischemic stroke (AIS) trends and outcomes in MHOO vs. metabolically healthy non-obese or overweight (MHnOO). MethodsData from the United States National Inpatient Sample (2016-2019) was analyzed to identify young MHOO and MHnOO AIS patients using ICD-10-CM codes. Metabolically healthy status was defined by excluding hospitalization records with diagnostic codes for hypertension, diabetes, and dyslipidemia. Demographics, trends, and outcomes were compared using appropriate statistical approaches. ResultsOf 26,949,310 young metabolically healthy hospitalizations between 2016 and 2019, 47,795 had AIS, of which 4,985 were MHOO and 42,810 were MHnOO. The median age of AIS hospitalization was 35 years, and primarily female and white. From 2016 to 2019, AIS incidence rose slightly, which was significant only for the MHnOO cohort. The in-hospital mortality rate was significantly lower in the MHOO cohort (6.0% vs. 8.6%, p < 0.001). Hospitalization length and cost did not differ substantially between groups. Adjusted multivariable analysis revealed no significant difference in AIS hospitalization risk between MHOO and MHnOO (aOR: 1.02, p=0.701), with subgroup analysis in males (aOR: 0.88, p=0.161) or females (aOR: 1.06, p=0.363). However, all-cause in-hospital mortality (ACIHM) in AIS had lower odds in the MHOO vs. MHnOO cohorts (aOR: 0.60, p=0.021). ConclusionOur study finds a rising trend of AIS hospitalizations in young metabolically healthy adults, with obesity or overweight status not being associated with AIS hospitalization. We identify an “obesity paradox” of lower odds for ACIHM for AIS hospitalizations in the MHOO cohort.

Impact of strand deposition and infill strategies on the properties of monolithic copper via material extrusion additive manufacturing

The present research presents a comprehensive study on the process optimisation and characterisation of fully dense and pure Cu parts manufactured by Material Extrusion Additive Manufacturing (MEX). This technology is an emerging approach to additively manufacture high-performance metal components due to its multi-step nature that allows to shape and subsequently sinter complex design parts. A commercially available filament with 60 vol% (93 wt%) copper particles and polymeric binders was used and characterised in this study. Two approaches were combined for the first time to maximise the final density of the copper parts; a statistical approach (using ANOVA) and an optimisation approach based on the strand cross-section. Based on the former, the flow rate multiplier during printing has a significant effect on the density of the as-printed parts. The latter aimed at studying the effect of the ratio between extrusion width, layer height, and nozzle diameter on the deposited strand morphology. An extrusion width equal to and a layer height lower than the nozzle diameter contribute to precise strand dimensions, leading to improved control of the final green density. After solvent and thermal debinding followed by pressureless and supportless sintering in pure H2 at 1050 °C, the copper parts resulted in a relative density > 95 % and an electrical conductivity of ∼93 %IACS as an indication of the purity of the starting material and the quality of the whole process chain. Tensile testing of as-sintered dog-bone samples, built with 0°, 90° and ±45° infill patterns and a single wall contour, revealed the best results for the ±45° strategy with an ultimate tensile strength of 164 MPa and an elongation at fracture of 24 %. Finally, a copper coil for electromagnetic applications was manufactured, for the first time, via filament-based MEX and tested. It reported an electrical conductivity competitive to that reported in the literature (∼70 %IACS). This result was discussed in relation to a simple monolithic Cu geometry to provide insight into the complexity and concomitant scientific relevance of applying MEX to functional components.