Sensitive Analytical Method Research Articles

Comprehensive and Sensitive Analysis of Total PAEs Using a Label-Free Zero-Voltage Photoelectrochemical Biosensor.

The sensing of phthalate esters (PAEs) is vital for people's health and environmental protection. This study aimed to develop a highly sensitive and selective photoelectrochemical (PEC) biosensor for PAEs analysis in complex samples. The biosensor is based on a CdS nanoparticle/TiO2 nanotube (CdS NP/TiO2 NT) electrode substrate and a truncated PAEs aptamer (PAEs-apt). By exploiting spatial variations in the potential resistance of the sensing interface, the biosensor achieved superior sensitivity in determining the concentration of PAEs compared to the SELEX aptamer. It exhibited a linear correlation in the range of 0.005 to 1 ng/mL with a detection limit of 1.67 ng/L. Furthermore, the biosensor displayed excellent selectivity for PAEs, with an analysis error factor below 0.277 when the concentration of interfering species was 100 times that of the target. The high performance of the biosensor was attributed to the excellent photoelectronic properties of CdS NPs/TiO2 NTs, high density of PAEs-apt for PAEs, high affinity of PAEs-apt for PAEs, and specific recognition of PAEs. Notably, this PEC biosensor could be used for the PAEs assay in urine and water samples, providing a sensitive and simple analytical method for detecting the same class of compounds with similar chemical structures in complex samples.

Small volume solid phase extraction method for comprehensive analysis of neonicotinoids, their metabolites, and related pesticides in water

Neonicotinoid insecticides (NEOs) such as clothianidin, imidacloprid, and thiamethoxam are used worldwide. The occurrence of their degradates, for instance, clothianidin-n-desmethyl (CLO-N-DES), clothianidin-urea (CLOU), imidacloprid urea (IMIU) and olefin (IMIO), as well as thiamethoxam urea (THXU), have seldom been documented in water due to the lack of a sensitive analytical method. In this study, a method only requiring 12 mL of water sample was developed and validated to quantify 8 NEOs, 13 metabolites, and 3 related insecticides using solid phase extraction (SPE) coupled with HPLC-MS/MS. The method demonstrated good linearity (r2 > 0.99), with limits of detection (LOD) ranging from 0.16 to 1.21 ng/L and limits of quantification (LOQ) from 0.54 to 4.03 ng/L in water samples. Validation showed accuracy between 70 and 130 % and precision below 15 % for most analytes. The method's performance was comparable to, or better than, existing methods, with the advantage of requiring much smaller sample volumes. Using this method, we monitored the occurrence and seasonal variability of NEOs and their metabolites in various surface water and groundwaters matrices from across Iowa. For example, analysis of water samples from private wells across three Iowa counties detected several NEOs, with notable findings including the first detection of flupyradifurone (FLU) in Iowa well water. Surface water analysis from five locations revealed frequent detection of NEOs and their metabolites, with some concentrations exceeding U.S. EPA chronic toxicity benchmarks for freshwater invertebrates. In addition, this is the first study to demonstrate the occurrence of CLO-N-DES, CLOU, and THX-U in US surface water. The study helps advance analytical methods for NEOs and their metabolites while also highlighting their widespread occurrence in Iowa waters and associated ecological risks, emphasizing the need for more comprehensive monitoring of these compounds.

Impacts of Climate Change on Energy-Saving Sensitivity of Residential Building Envelope Design Parameters in Three Hot-Dry Cities

Warming climatic conditions are projected to intensify extreme heat events, especially in hot climates, affecting the energy-saving effectiveness of various building envelope design parameters. However, limited studies exist on the energy-saving sensitivity of building envelope design parameters for residential buildings in hot and dry climates under future climate change. This study aims to fill this gap by examining typical residential buildings in three cities in Iraq, classified as hot desert, hot semiarid, and Mediterranean. First, the most common type of residential building was selected as a case study, and its energy modelling was validated using measured energy bills. Second, future climatic weather files were constructed using the latest general circulation models to assess their impacts on building energy consumption. Third, the energy-saving sensitivity of applicable building envelope design parameters under different future climatic conditions was obtained through local and global sensitivity analysis methods. The results of global sensitivity analysis indicate that the solar heat gain coefficient of windows, the specific heat of exterior walls, and the projection of side fins are the most important factors, with standardized regression coefficients (SRC) ranging from -0.92 to 0.62. Notably, the SRC of windows' solar heat gain coefficient is expected to increase under future climate scenarios. In contrast, the significance of the specific heat of exterior walls depends on local climates. Although this study only included envelope design parameters and excluded economic analysis, it provides insights for policymakers and architects to prioritize building envelope design strategies that enhance the climate resilience of residential buildings.

Sensitivity Analysis for Effects of Multiple Exposures in the Presence of Unmeasured Confounding: Non‐Gaussian and Time‐to‐Event Outcomes

ABSTRACTIn epidemiological studies, evaluating the health impacts stemming from multiple exposures is one of the important goals. To analyze the effects of multiple exposures on discrete or time‐to‐event health outcomes, researchers often employ generalized linear models, Cox proportional hazards models, and machine learning methods. However, observational studies are prone to unmeasured confounding factors, which can introduce the potential for substantial bias in the multiple exposure effects. To address this issue, we propose a novel outcome model‐based sensitivity analysis method for non‐Gaussian and time‐to‐event outcomes with multiple exposures. All the proposed sensitivity analysis problems are formulated as linear programming problems with quadratic and linear constraints, which can be solved efficiently. Analytic solutions are provided for some optimization problems, and a numerical study is performed to examine how the proposed sensitivity analysis behaves in finite samples. We illustrate the proposed method using two real data examples.

Sensitivity analysis and parameter selection of the modified Mohr–Coulomb model in foundation pit excavation in granite residual soil areas

In foundation pit excavation, granite residual soil is frequently used as a foundation-bearing layer, necessitating the consideration of its unique engineering characteristics in stability studies. Numerical analysis is a crucial method for evaluating the stability of excavation pits, with the Modified Mohr–Coulomb (MMC) model showing good applicability. However, research on the MMC model in granite residual soil regions remains limited. This study investigates the impact of key stiffness parameters (Eurref\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${E}_{ur}^{ref}$$\\end{document}, Eoedref\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${E}_{oed}^{ref}$$\\end{document}, and E50ref\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${E}_{50}^{ref}$$\\end{document}) of the MMC model on the stability of excavation pit retaining structures, using case studies from the Zhuhai area. The research addresses the selection of stiffness parameters for different soil layers and proposes a proportional relationship between model stiffness parameters and field test mechanical parameters. Using the horizontal displacement of the underground diaphragm wall as an indicator, single-factor sensitivity analysis and orthogonal experimental methods were employed to determine the sensitivity and proportional relationships of stiffness parameters in the Zhuhai region. The findings indicate that Eurref\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${E}_{ur}^{ref}$$\\end{document} has the most significant impact on model uncertainty, while E50ref\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${E}_{50}^{ref}$$\\end{document} has the least effect on horizontal displacement. The ratios of Eurref\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${E}_{ur}^{ref}$$\\end{document}/E50ref\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${E}_{50}^{ref}$$\\end{document}, Eoedref\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${E}_{oed}^{ref}$$\\end{document}/E50ref\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${E}_{50}^{ref}$$\\end{document}, and E50ref\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${E}_{50}^{ref}$$\\end{document} to the deformation modulus (E0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${E}_{0}$$\\end{document}) provide essential references for numerical simulation of excavation pits in the Zhuhai area.

Analysis of vericiguat via ion-pairing with eosin Y as a spectrofluorimetric and spectrophotometric probe: Application to content uniformity test

Using spectroscopy, quick and sensitive analytical methods based on Eosin Y ion pairing were developed and assessed in order to determine vericiguat with high selectivity and sensitivity. The drug is used for treating symptomatic chronic heart failure (HF), The quenching impact of vericiguat on the Eosin Y’s fluorescence at a pH 3.3, in 0.1 M acetate buffer solution was observed using spectrofluorometric technique. This technique is regarded as the original spectrofluorometric technique for the assay of vericiguat. The quenching effect on fluorescence was ranged from 100 to 1000 ng mL−1. The absorbance of the generated ion-pair was measured using spectrophotometric method at 550 nm in aqueous buffered solutions with pH 3.3. In the concentration range of 1.0–10.0 µg mL−1, Beer’s law was followed. The two approaches were applied to the analysis of dosage forms with a high percent recovery successfully, and they were assessed in compliance with ICH guidelines. Moreover, the method’s sustainability was evaluated and compared to the published method using two greenness assessment tools termed analytical eco-scale and Analytical GREENness (AGREE). That findings suggest that the method is more sustainable than the published method.

Sensitivity analysis of aero-engine performance optimization control system and its hardware test verification

Abstract The performance seeking control (PSC) method is aimed at improving the performance of aircraft engine without changing hardware devices. However, the complexity of the system makes it difficult to achieve its application in engineering. Thus, the key issues in PSC of turbofan engine are studied based on global sensitivity analysis, and the improvements are made to the optimization system according to the analysis. Firstly, the principle of PSC is analyzed based on traditional frameworks. And an improved linear programming method based on task scheduling is proposed to reduce the time consumption within a single closed-loop simulation. The impact of the estimation model accuracy on optimization results is studied adopting a sensitivity analysis method based on Monte Carlo sampling to address the optimization lose efficacy. Finally, the hardware-in-the-loop simulation is carried out utilizing model-based design method in a certain type of turbofan engine electronic controller.

Temperature Gradient Characteristics of Rubber-Modified Asphalt Pavement Under Dramatic Cooling–Heating Cycles

The periodic changes in climatic factors cause the pavement temperature field to change significantly, resulting in fatigue damage to the pavement caused by temperature stress, and the influence depth has a critical value. To reveal the influence range and variation pattern of the rubber-modified pavement temperature field under frequent rainfall and high temperatures, based on indoor tests and the finite element model, the evolution law of different influencing factors and pavement temperature fields was determined by a single factor sensitivity analysis method. The degree of influence of each influencing factor on the pavement temperature field was analyzed using the Pearson correlation. The results showed that with different asphalt mixture initial temperatures, the road surface temperature decreased from 20 °C to 40 °C under sudden rainfall. Repeated rainfall following high temperatures induces cyclic temperature changes 30 mm below the road surface. The pavement temperature difference increased linearly with the dramatic temperature difference, and the changes in the pavement temperature field were small when the number of cycles exceeded 30. The number of cycles and cycle temperature difference were the main factors affecting the changes in the pavement temperature field under dramatic cooling–heating cycles.

The associations between risk factors and pituitary neuroendocrine tumors: A bidirectional Mendelian randomization study

The etiological basis of pituitary neuroendocrine tumors is uncertain. We used Mendelian randomization technique to investigate the potential influence of several risk factors on the likelihood of developing pituitary neuroendocrine tumors. We admitted 8 risk factors, divided into 3 lifestyle factors and 5 chronic diseases as exposure factors. We used weighted median, simple model, weighted model, inverse-variance weighted, and the MR-Egger regression method for causal effect estimations and sensitivity analyses. We observed that genetically forecasting increased moderate to vigorous physical activity levels (OR = 5.21 [1.38–19.72], P = .015) was linked with a higher incidence of pituitary neuroendocrine tumors. Allergic disease (asthma, hay fever, or eczema) (OR = 0.81 [0.66–0.99], P = .039), chronic kidney disease (OR = 0.67 [0.50–0.90], P = .008), increased sleep duration (OR = 0.07 [0.01–0.37], P = .001), and types of physical activity (e.g., swimming, cycling, keeping fit, and bowling) (OR = 0.02 [0.01–0.66], P = .029) were connected with lower incidence of pituitary neuroendocrine tumors. There was no evidence that the other 3 risk factors notably correlated with pituitary neuroendocrine tumors. This study provides evidence that allergic diseases, chronic kidney disease, sleep duration, and physical activity are associated with the development of pituitary neuroendocrine tumors. The findings highlight the importance of reconsidering causality in epidemiological studies to better understand risk factors and prevention strategies for pituitary neuroendocrine tumors.

Implementation of Sensitive Method for Determination of Benzophenone and Camphor UV Filters in Human Urine

The level of the human body’s burden of benzophenone and camphor ultraviolet (UV) filters can be estimated from their urinary levels. The present study describes the implementations and validation of the sensitive analytical method for the analysis of seven benzophenone and two camphor UV filters in urine. Sample preparation includes overnight enzymatic hydrolysis and ethyl acetate extraction followed by purification by dispersive solid-phase extraction using a sorbent Z-Sep. For the analysis, ultra-high performance liquid chromatography coupled with tandem mass spectrometry was used. Validation was performed using a Standard Reference Material® 3673 and an artificially contaminated urine sample. Target analyte recoveries ranged from 79–113% with repeatability expressed as a relative standard deviation of 2–15%. The limits of quantification were between 0.001 and 0.100 ng/mL in urine. This method was subsequently applied to examine the urine samples collected from Czech women. The analytes benzophenone-1 and 4-hydroxy-benzophenone were the most common analytes present in 100% of the samples, whereas benzophenone-3 was quantified in only 90% of the urine samples. The other four determined benzophenone derivatives were quantified in ≤33% of the samples. The derivatives of camphor were not detected in any samples. This method could be applied in biomonitoring studies.

Targeted Chiral Metabolomics of D-Amino Acids: Their Emerging Role as Potential Biomarkers in Neurological Diseases with a Focus on Their Liquid Chromatography-Mass Spectrometry Analysis upon Chiral Derivatization.

In neuroscience research, chiral metabolomics is an emerging field, in which D-amino acids play an important role as potential biomarkers for neurological diseases. The targeted chiral analysis of the brain metabolome, employing liquid chromatography (LC) coupled to mass spectrometry (MS), is a pivotal approach for the identification of biomarkers for neurological diseases. This review provides an overview of D-amino acids in neurological diseases and of the state-of-the-art strategies for the enantioselective analysis of chiral amino acids (AAs) in biological samples to investigate their putative role as biomarkers for neurological diseases. Fluctuations in D-amino acids (D-AAs) levels can be related to the pathology of neurological diseases, for example, through their role in the modulation of N-methyl-D-aspartate receptors and neurotransmission. Because of the trace presence of these biomolecules in mammals and the complex nature of biological matrices, highly sensitive and selective analytical methods are essential. Derivatization strategies with chiral reagents are highlighted as critical tools for enhancing detection capabilities. The latest advances in chiral derivatization reactions, coupled to LC-MS/MS analysis, have improved the enantioselective quantification of these AAs and allow the separation of several chiral metabolites in a single analytical run. The enhanced performances of these methods can provide an accurate correlation between specific D-AA profiles and disease states, allowing for a better understanding of neurological diseases and drug effects on the brain.

Uncertainty analysis of geomechanical responses: China’s first offshore carbon capture and storage project

Enping 15–1 project is China’s first Carbon Capture and Storage (CCS) project to store CO2 in the offshore deep saline aquifer formations. Due to the lack of detailed observed data, geomechanical responses are fraught with uncertainty, which can result in unanticipated risks during the implementation of CO2 injection. Thus, the uncertainty analysis of geomechanical responses shows great importance and necessity for CO2 storage projects. To bridge the gap, in this work, a Thermal-Hydraulic-Mechanical-Chemical (THMC) coupling model was built to study the uncertainty of geomechanical responses including surface uplift and rock damage. First, Monte Carlo sampling technology was used to create 616 cases. Then the sensitivity of 13 parameters (including reservoir, cap, and operating parameters) on geological responses was analysed using the Sobol sensitivity analysis method. The results show that 90.10 % of cases in the uncertainty analysis have a maximum surface uplift of less than 8 mm, and 80.84 % of cases have a safety factor higher than 0.5. However, it is the most likely to cause surface uplift over 100 mm and rock failure when the reservoir permeability is 1 mD. Therefore, the local low permeable zone is at more risk of rock damage and geological hazards. In addition, five parameters (i.e., reservoir permeability, reservoir Young’s modulus, initial horizontal/vertical stress ratio, cap cohesion, and well length) have large contributions to the surface uplift. Three parameters (i.e., Reservoir permeability, reservoir cohesion, and initial horizontal/vertical stress ratio) have considerable effects on the reservoir safety factor. Eventually, proxy models using Regression Trees (RTs) were proposed to predict the surface uplift and reservoir safety factor, which is efficient and saves computational resources. This work provides insights and suggestions into the geomechanical responses of CCS project.

A novel condensation heat transfer correction based on non-equilibrium film theory and degradation mechanism for zeotropic mixture

Energy conversion based on thermodynamic cycle using zeotropic mixtures is an effective strategy for advancing global low-carbon development in the context of carbon neutrality. However, the deficiencies of existing heat transfer mixtures models and the complex condensation mechanisms of zeotropic refrigerants pose significant challenges to the size design and optimization of heat exchangers, which can directly impact the economic efficiency and operational safety of thermodynamic cycle systems. Thus, there is an urgent need for a simplified and universal correction method that incorporates mixing effects to improve prediction accuracy of conventional models. In the present study, a non-equilibrium condensation heat transfer model based on film theory was developed and validated to analyze the influence of various parameters on heat transfer degradation and heat transfer coefficients of zeotropic mixtures inside horizontal circular tubes. Through multi-factor sensitivity analysis and dimensionless method, a degradation factor characterizing the gradient contributions to heat transfer degradation was proposed. Based on the proposed factor, a simplified non-equilibrium model considering mixing effect without complex computations was developed. Using the new developed non-equilibrium model, prediction of a database containing 1813 experimental points was conducted. A deviation of 15.7 % between the predicted values and the experimental values was achieved, demonstrating remarkable predictive accuracy and significant generalizability compared to existing mixture models. Extending this corrective method to various pure substance models resulted in a significant improvement in accuracy.

Health technology assessment to support health benefits package design: a systematic review of economic evaluation evidence in Zambia.

Health technology assessment uses explicit economic evaluation evidence to support health benefits package design. However, the limited availability of technical expertise, data, and methods has restricted the production of economic evaluation evidence in low- and middle-income countries. Zambia has initiated a roadmap to support its policy of reviewing and implementing its national benefits package. This study characterises economic evaluation evidence to support this process's evidence mapping, synthesis, and appraisal stages. This systematic review applies deductive analysis and the preferred reporting items for systematic review and meta-analyses. Four databases were searched to identify studies from 1993 that coincided with Zambia's health benefits package reform. A total of 61 studies met the inclusion criteria. Most of the studies were first authored by nonlocal authors, and the number of local-based authors in each study was low. Almost all funding for economic evaluation research was not local, and only a few studies sought local ethical clearance to conduct research. Infectious diseases were the highest disease control priority for the studies, with HIV research having the highest output. Most of the studies were cost-effectiveness studies that utilised trial-based data and a combination of program, published, and unpublished data for analysis. The studies generally utilised direct cost and applied the ingredient-based costing approach. Natural units were predominantly used for outcomes alongside DALYs. Most studies reported using a 3% discount rate for both costs and outcomes, with only a few reporting methods for sensitivity analysis. Economic evaluation evidence in Zambia has increased, revealing limited local research leadership, methodological inconsistencies, and a focus on infectious diseases. These findings are crucial for revising Zambia's benefits package and may guide researchers and decision-makers in improving the transparency and quality of future research.

On-Site Sensitive Colorimetry for Free Cyanide by Using Ion-Pair Solid-Phase Extraction with Sedimentable Dispersed Particulates and Mobile Photography Box.

We propose a sensitive and simple colorimetric method using dispersed particle extraction for the highly sensitive detection of free cyanide in water samples. The method involves the direct capture of the color-producing compound by dispersed microparticles in a sample vessel containing colorimetric reagents and an adsorbent. The color of the microparticles that have naturally settled to the bottom of the microtube can be directly measured by visual observation or image analysis. A mobile photography box (MPB) suitable for use with a smartphone was developed to ensure reproducibility in the lighting environment during image acquisition. The MPB was then used to develop a highly sensitive analytical method for cyanide. This newly developed method allows direct measurement of the color tone of the target component collected on particles by irradiating light from below and photographing it perpendicularly. The method offers excellent portability, sensitivity, and reproducibility and is less affected by interfering color components. When applied to cyanide analysis, the detection limit reached 0.005 mg/L and measurements could be completed within 10 min, significantly shorter than the conventional absorbance spectrophotometric method, which requires 30 min. Thus, this method achieves highly sensitive cyanide analysis.

Aerodynamic robustness optimization of aeroengine fan performance based on an interpretable dynamic machine learning method

Aeroengines and gas turbines are susceptible to uncertainties during manufacturing and operation, leading to reduced efficiency and dispersed performance. Current engine design system often produces deterministic performance databases that cannot be effectively used to guide the uncertainty analysis and robust design process of turbomachinery. This paper proposes an interpretable dynamic machine learning method for sensitivity analysis and robust optimization of turbomachinery blades. A dynamic extreme gradient boosting (XGBoost) is trained to predict fan aerodynamic performance, and the SHapley additional explanation (SHAP) method is introduced to explain regression model behavior and identify the impact of uncertain variables. On this basis, the Lipschitz-based trust region (MAXLIPO-TR) optimization algorithm is used to obtain the optimal configuration with the best robustness performance. Finally, the method is applied to data mining for design guidelines of robustness performance enhancement of an aeroengine fan. The results show that maximum camber and tangential stacking have major effects on fan performance dispersion. The standard deviation of the isentropic efficiency, pressure ratio and mass flow rate of the optimized configuration are reduced by 42.4%, 35.6% and 22.7% respectively at design conditions. The proposed data mining method has scientific significance and industrial application value in the robust design of advanced turbomachinery.

Global reliability sensitivity analysis of cradle-type double rotary table

The rotary table is a crucial component of the machine tool, and its accuracy and reliability have an important impact on the machining accuracy and machining efficiency of the machine tool. In this paper, a global reliability sensitivity analysis method of the rotary table is proposed considering the random uncertainty for material parameters of each component. Firstly, the mechanical model of the rotary table is established based on the finite element method. Subsequently, the functional relationship between the parameters of each component and the deformation of the rotary table is reconstructed using the adaptive Kriging surrogate model. Finally, a global reliability sensitivity analysis of the rotary table is performed based on the Bayesian formulation. The results show that the proposed global reliability sensitivity analysis method of the rotary table demonstrates satisfactory efficiency and can provide theoretical guidance for the design and optimization of the rotary table.

Genetically predicted circulating linoleic acid levels and risk of osteoarthritis: a two-sample mendelian randomization study.

This study aimed to provide insight into the effect of genetically predicted linoleic acid (LA) levels on osteoarthritis (OA). The LA dataset was obtained from the UK Biobank (UKBB) consortium and contained 114,999 samples. The OA discovery dataset was derived from MRC-IEU consortium and included 38,472 cases and 424,461 controls. The OA validation set was derived from a summary-level genome-wide association study (GWAS) and included 39,427 cases and 378,169 controls. Genetic variants strongly associated with LA (p < 5 × 10- 8) were extracted as instrumental variables (IVs). The inverse variance weighted (IVW) approach was adopted as the primary analysis method in this study. In addition, multiple sensitivity analysis methods were used to assess the reliability of our results. The IVW approach showed that circulating LA levels were negatively associated with OA risk in the discovery set (odds ratio (OR) = 0.993, 95% confidence interval (95% CI): 0.988-0.998, p = 0.011). A consistent result was obtained in the validation set (OR = 0.904, 95%CI: 0.845-0.967, p = 0.003). These results were validated by sensitivity analysis. This study provides new evidence for the causal relationship between LA and OA, which provides new insights for the treatment of OA.

Building performance optimization through sensitivity Analysis, and economic insights using AI

Optimizing building designs for energy efficiency and occupant comfort presents significant challenges due to the complex and often conflicting requirements of various stakeholders. Consequently, this study conducts a multifaceted sensitivity and economic impact analysis that aims to improve building performance in terms of energy efficiency and occupant comfort by implementing machine learning techniques. Using a broad dataset comprising of 12,000 energy simulation runs for Tvedestrand Upper Secondary School in Norway, several machine learning models were employed with Multi-Layer Perceptron outperforming the others. In addition, several sensitivity analysis methods were used to explore the influence of individual parameters on building performance. The analysis reveals that ventilation rate, room depth, U-value of the facade, and heat gains significantly affect energy consumption. Economic impact analysis was also carried out to compare the cost-effectiveness of traditional HVAC systems with Building Management System (BMS) HVAC solutions. The BMS HVAC system shows significantly lower operational costs over time, with investment costs averaging around 1200 Norwegian kroner (NOK)/m2 and operational costs of approximately 150 NOK/m2 per year. Sensitivity analysis under different economic scenarios highlights the economic viability of the BMS HVAC system. This study identifies optimal building parameters that balance energy efficiency and thermal comfort, achieving total energy consumption between 11.05 and 22.51 kWh/m2 and zero discomfort hours (h > 26 °C). In sum, the findings offer valuable insights for stakeholders, enabling informed decisions about sustainable building design and energy efficiency improvements, ensuring both technical soundness and financial viability under a wide range of conditions, while using the tested tools.

Forced stability studies and estimation of encapsulated ellagic acid in nano-formulations using UV-spectroscopy

Abstract There is a growing interest in dietary materials to explore their therapeutic activities. Ellagic acid is considered as a dietary supplement and is naturally present in fruits and other foods. It has anticancer, antimalarial, antiviral, antioxidant, and anti-inflammatory activities. These activities can be enhanced with nanoformulations, which can increase its oral bioavailability. However, there is a need for an economical, simple, sensitive, and robust analytical method for the estimation of entrapped ellagic acid in the nanoformulations. Therefore, the present study presents the development and validation of a UV–visible spectroscopy method for the estimation of EA in nanoformulations. The phosphate buffer (pH 7.2) and the detection wavelength of 253.5 nm were used for the method development, and its validation was performed according to the ICH Q2A (R1) guidelines. The coefficient of determination value of the developed method was found to be 0.9988 in the concentration range of 1 μg mL−1 to 6 μg mL−1. The method was found to be linear, precise, sensitive, and robust. This method can be used for the estimation of EA in nanoformulations, bulk dosage forms, and other pharmaceutical formulations.