Residual Analysis Research Articles

Validation of formulae predicting stroke volume from arterial pressure: with particular emphasis on upright individuals in hot ambient conditions.

During heatwaves, it is important to monitor workers' cardiovascular health since 35% of those working in hot environments experience symptoms of heat strain. Wearable technology has been popularized for monitoring heart rate (HR) during recreational activities, but it can also be used to monitor occupational heat strain based on core and skin temperatures and HR. To our knowledge, no devices estimate the cardiovascular strain directly based on stroke volume (SV) or cardiac output (CO). In addition to the hardware, there are limitations regarding the lack of suitable algorithms that would provide such an index based on relevant physiological responses. The validation of the formulae already existing in literature was the principle aim of the present study. We monitored the cardiovascular responses of our participants to a supine and 60° head-up tilt at the same time each day. During the test, we measured blood pressure derived by finger photoplethysmography, which also provided beat-by-beat measures of SV and CO. Afterwards, we compared the SV derived from the photoplethysmography with the one calculated with the different equations that already exist in literature. The evaluation of the formulae was based on comparing the error of prediction. This residual analysis compared the sum of the squared residuals generated by each formula using the same data set. Our findings suggest that estimating SV with existing formulae is feasible, showing a good correlation and a relatively small bias. Thus, simply measuring workers' blood pressure during breaks could estimate their cardiac strain.

Canonical variate residual analysis for industrial processes fault detection

AbstractWith the rapid development of intelligent integration in industrial processes, a challenge emerges. Early failures cannot be detected in a timely manner, potentially leading to significant financial losses. While traditional canonical variate analysis (CVA) methods are effective for dynamic process monitoring, they may lack the flexibility required for early fault detection. To address this challenge, a fault detection method based on canonical variate residual analysis (CVRA) is proposed. CVRA introduces a distinctive residual statistic that preserves critical information about the data. It places heightened focus on the primary components of the data, capturing core features of system changes and enhancing sensitivity to early anomalies. Additionally, by incorporating the geometric properties of the Manhattan distance, it mitigates statistical data errors, thereby improving detection accuracy. Simulation results validate the method's effectiveness in the Tennessee Eastman (TE) process. Furthermore, the successful application of the three‐phase flow facility provides a benchmark for evaluation using real process data.

Residual deformation analysis of laser powder bed fusion-fabricated lattice structures

ABSTRACT Lattice structures are crucial for weight reduction, and laser powder bed fusion (LPBF) is an efficient fabrication method. However, there's a notable research gap in understanding the residual deformation of LPBF-fabricated lattice structures. This study investigates the residual deformation of three basic lattice structures, body centre cell (BCC), face centre cell (FCC), and diamond cell (DC), fabricated via LPBF to reveal their deformation mechanisms. Analysis covers horizontal direction, building direction, and total deformations, unveiling complex structural responses. Lower relative densities exhibit a prevalent structural deformation mode (SDM) causing significant deformations, while higher densities shift to an intrinsic deformation mode (IDM) dominated by contraction during fabrication. The FCC structure shows optimal stability with minimal residual deformation across most densities. This study offers insightful findings and a detailed mechanistic analysis of residual deformation in LPBF-fabricated lattice structures, applicable across various configurations, ensuring design process uniformity and reliability.

Uncertain statistics models for characterizing annual total energy production

In the field of annual total energy production, it is hard to determine whether the experimental data is linear or nonlinear growth characteristics. Therefore, different regression models are used to test the properties of the observed data. This article uses actual data to perform parameter estimation and residual analysis of linear and nonlinear regression models. In addition, uncertain nonlinear time series model is used to fit the experimental data in order to find a more suitable statistics model after obtaining the growth properties of the observed data. A test is introduced to prove that the disturbance terms presented in the regression and time series models are actually uncertain variables, not random variables. Moreover, the uncertain hypothesis tests show that the uncertain time series model is in better agreement with the real data of the annual total energy production.

In situ monitoring of industrial-scale chemical vapor deposition using residual gas analysis

The maintenance of industrial-scale equipment demands more effort and incurs higher costs compared to that of laboratory-scale equipment, necessitating the use of predictive maintenance techniques to mitigate unforeseen outcomes. This paper presents a demonstration of the in situ monitoring of industrial-scale low-pressure chemical vapor deposition equipment by using a residual gas analyzer (RGA). The RGA provides reactions of precursor gases by tracing the concentration and composition of residual gases within vacuum reactors. Furthermore, RGA analysis enables the prediction of the growth rate of the thin-film, providing insights into thin-film growth dynamics during the process. In situ monitoring using an RGA can be applied to diverse semiconductor fabrication processes, including thin-film growth, oxidation, reactive ion etching, and ion implantation, owing to its compatibility with vacuum processes. This study addresses the existing difficulties in the exploration of RGA monitoring techniques for industrial-scale equipment and bridges the disparity between laboratory-scale and industrial-scale processes.

Quantification of tree growth change under climate change using National Forest Inventory of Korea

To achieve carbon neutrality in the face of climate change, accurately estimating forest carbon uptake is crucial. Environmental changes, such as temperature increases or precipitation fluctuations caused by climate change, can alter the growth responses of each species and act as a driver for changes in forest productivity. In particular, using a single-species growth model increases uncertainty in predicting stand growth, given the high species diversity in temperate forests in East Asia. Therefore, in this study, we identified the growth curve of 17 species-specific distributed in Korean temperate forest as well as the growth trend over the last 30 years (1976–2005). For this purpose, the species distribution and growth characteristics of 17 major Korean species of trees were analyzed using 13,808 tree-ring series from the 5th National Forest Inventory, which was conducted between 2006 and 2010. The growth characteristics of each species were analyzed using the average growth curve specific to each species, and the yearly growth trend was examined through residual analysis of the average growth curve. The distribution range of the 17 major tree species varied by species, as did the growth rate, ranging from 7 cm² · year⁻¹ to 17 cm² · year⁻¹ for the 30-year-old average basal area increment (BAI). Over time, fast-growing tree species exhibited initially high BAI, followed by a sharp decline from the 2000s. In contrast, broadleaf trees showed a continuous increase. Furthermore, a comparison of the total volume of individual trees and the volume of the yield table revealed that accurately estimating the actual forest volume was challenging. This study contributes to improving the accuracy of productivity predictions for temperate forests, underscoring the necessity to investigate the interaction between climate and forest succession in the future.

Fatigue damage evolution and residual strength analysis of 3D5D braided composites using X-ray computed tomography, acoustic emission, and digital image correlation

The accumulation of fatigue damage leads to continuous degradation of the residual strength of three-dimensional braided composites, directly determining the fatigue life. However, the inability to continuously collect residual strength and multiple damage modes pose a challenge to study the residual strength degradation mechanisms of three-dimensional braided composites. In this work, digital image correlation and acoustic emission techniques are employed to assess the strain field and damage events of the specimens during the residual strength tests. X-ray computed tomography facilitates the visualization and quantification of the fatigue damage evolution in three-dimensional braided composites. Moreover, the “two-step” damage classification method is applied to isolate the damage mechanisms. The Pearson correlation analysis is preformed between the quantitative results of three non-destructive techniques and residual strength degradation of 3D braided composites. The integration of the outcomes provides a comprehensive depiction of the residual strength degradation mechanisms.

Strain-hardening magnesium-silicate-hydrate composites (SHMSHC) utilizing reactive magnesia cement (MgO) and calcined clay (metakaolin)

This study demonstrated the feasibility of utilizing calcined clay (metakaolin) as a silica source for the development of polyethylene (PE) fiber-reinforced strain-hardening magnesium-silicate-hydrate composite (SHMSHC). The study also discussed the effects of variation in the Mg/Si ratio of the mixture when pure silica source (silica fume with SiO2 content >90 %) is replaced with metakaolin (SiO2 content ⁓ 51 %) on the mechanical performance of SHMSHC. A comprehensive mechanical characterization of SHMSHC at composite scale and fiber-matrix interaction effects was conducted. The damage characterization was carried out using residual crack width and spacing analysis. The chemical characterization of the MSH matrix (SHMSHC without fibers) was performed using XRD, TGA, and FTIR, and the microstructural investigation was done using FE-SEM. The MgO-metakaolin paste based SHMSHC showed compressive strength of 51.9 MPa, tensile strength of 5.0 MPa, and tensile strain capacity of 4.7 %, demonstrating the feasibility of calcined clay utilization in SHMSHC. The tensile strain capacity was reduced after the addition of aggregates in SHMSHC. The study demonstrated that the variation in the Mg/Si ratio significantly affects the toughness of the MSH matrix and fiber-matrix interaction characteristics, influencing the strain-hardening potential of SHMSHC.

Analysis of the multiple drivers of vegetation cover evolution in the Taihangshan-Yanshan region

The Taihangshan-Yanshan region (TYR) is an important ecological barrier area for Beijing-Tianjin-Hebei, and the effectiveness of its ecological restoration and protection is of great significance to the ecological security pattern of North China. Based on the FVC data from 2000 to 2021, residual analysis, parametric optimal geodetector technique (OPGD) and multi-scale geographically weighted regression analysis (MGWR) were used to clarify the the multivariate driving mechanism of the evolution of FVC in the TYR. Results show that: (1) FVC changes in the TYR show a slowly fluctuating upward trend, with an average growth rate of 0.02/10a, and a spatial pattern of "high in the northwest and low in the southeast"; more than half of the FVC increased during the 22-year period. (2) The results of residual analysis showed that the effects of temperature and precipitation on FVC were very limited, and a considerable proportion (80.80% and 76.78%) of the improved and degraded areas were influenced by other factors. (3) The results of OPGD showed that the main influencing factors of the spatial differentiation of FVC included evapotranspiration, surface temperature, land use type, nighttime light intensity, soil type, and vegetation type (q > 0.2); The explanatory rates of the two-factor interactions were greater than those of the single factor, which showed either nonlinear enhancement or bifactorial enhancement, among which, the interaction of evapotranspiration with mean air and surface temperature has the strongest effect on the spatial and temporal evolution of FVC (q = 0.75). Surface temperature between 4.98 and 10.4 °C, evapotranspiration between 638 and 762 mm/a, and nighttime light between 1.96 and 7.78 lm/m2 favoured an increase in vegetation cover, and vegetation developed on lysimetric soils was more inclined to be of high cover. (4) The correlation between each variable and FVC showed different performance, GDP, elevation, slope and FVC showed significant positive correlation in most regions, while population size, urban population proportion, GDP proportion of primary and secondary industries, and nighttime light intensity all showed negative correlation with FVC to different degrees. The results can provide data for formulating regional environmental protection and restoration policies.

Advancements and greenification potential of magnetic molecularly imprinted polymers for chromatographic analysis of veterinary drug residues in milk.

Milk, as a widely consumed nutrient-rich food, is crucial for bone health, growth, and overall nutrition. The persistent application of veterinary drugs for controlling diseases and heightening milk yield has imparted substantial repercussions on human health and environmental ecosystems. Due to the high demand, fresh consumption, complex composition of milk, and the potential adverse impacts of drug residues, advanced greener analytical methods are necessitated. Among them, functional materials-based analytical methods attract wide concerns. The magnetic molecularly imprinted polymers (MMIPs), as a kind of typical functional material, possess excellent greenification characteristics and potencies, and they are easily integrated into various detection technologies, which have offered green approaches toward analytes such as veterinary drugs in milk. Despite their increasing applications and great potential, MMIPs' use in dairy matrices remains underexplored, especially regarding ecological sustainability. This work reviews recent advances in MMIPs' synthesis and application as efficient sorbents for veterinary drug extraction in milk followed by chromatographic analysis. The uniqueness and effectiveness of MMIPs in real milk samples are evaluated, current limitations are addressed, and greenification opportunities are proposed. MMIPs show promise in revolutionizing green analytical procedures for veterinary drug detection, aligning with the environmental goals of modern food production systems.

Exploring KRas Protein Dynamics: An Integrated Molecular Dynamics Analysis of KRas Wild and Mutant Variants.

This study employs a comprehensive approach combining protein retrieval, sequence alignment, and molecular dynamics simulations to investigate the structural dynamics and stability of wild-type KRas and its mutated variants (G12C, G12D, G12V, and G13D). The selected protein structures were retrieved from the Protein Data Bank (PDB) and prepared by using visual molecular dynamics (VMD) software. Sequence alignment using Clustal Omega provided a detailed comparison of the amino acid sequences, focusing on key mutation sites. Molecular dynamics simulations, performed with Gromacs, revealed distinct conformational changes and stability patterns in the wild-type and mutated KRas proteins over 100 ns. Clustering analysis identified higher conformational changes in the second α-helix of the mutated variants. The root-mean-square deviation (RMSD) distribution analysis showed variant-specific conformational dynamics, with G12V and G12D exhibiting slightly higher average RMSD values. Furthermore, clustering and RMSD analyses of specific amino acid residues (12, 13, 51, and 118) highlighted their roles in maintaining overall stability and influencing structural dynamics. The results indicate that mutations at positions 12 and 13 disrupt normal cycling between wild and mutated variants, leading to the persistent activation of KRas. Additionally, principal component analysis (PCA) elucidated unique conformational dynamics in mutated variants. Free energy landscape (FEL) analysis revealed alterations in the thermodynamic stability of mutated variants compared with the wild type. Overall, this study provides a detailed understanding of the structural changes associated with oncogenic mutations in KRas, offering insights crucial for targeted therapeutic strategies in KRas-driven cancers.

Microstructural and mechanical characterization of electron beam welded low-nickel nitrogen-strengthened austenitic stainless steel

In this paper, the Electron Beam Welding (EBW) was used to join thin plates of low-nickel nitrogen-strengthened austenitic stainless steel (LNiASS), a material valued for its superior mechanical properties and cost-effectiveness. Traditional welding techniques often lead to issues such as hot cracking, reduced toughness, and undesirable microstructures. The objective was to address these challenges using EB·W., which offers precise control, minimal heat input, and deeper penetration.Methodology included joining LNiASS plates with E.B.W. and analyzing the resulting microstructures and mechanical properties through optical microscopy, tensile testing, microhardness testing, and scanning electron microscopy (SEM). The findings indicated the presence of various ferrite morphologies without significant precipitation of deleterious phases like carbides and sigma phase. The weldment strength was ∼90 % of the base alloy, with fractures occurring near the weld cord due to nitrogen loss and grain coarsening in the (HAZ). Microhardness increased by ∼12.9 %, attributed to microstructural evolution and a fine-grained structure. Impact testing in Charpy V-Notch (CVN) configuration showed the weld absorbed ∼50 % more impact energy than the base material, due to refined Microstructure and enhanced hardness. Longitudinal residual stress analysis indicated compressive nature below mid-thickness, resulting from thermal expansion and contraction during welding. These results demonstrated E.B·W.'s effectiveness in preserving mechanical properties and enhancing the performance of nitrogen-strengthened stainless steel welds.

Development of accurate contour residual stress measurement technique for sustainable structural integrity assessment in additive manufacturing

Abstract Accurate analysis of residual stress present within an engineering component plays a fundamental role to its sustainable structural integrity assessment. This is particularly important for a safety critical component within the power generation sectors, aerospace, and oil and gas industries. The accurate stress analysis not only allows operators to determine the life remaining in a component but also avoid over-conservative estimates and thereby contribute to life extension of engineering plants and components. By utilizing and unifying multiple residual stress analysis tools of different nature a robust and more accurate method of stress analysis is achieved. The main aim of the current study focusses on the development and optimization of a robust residual stress tool based on the contour technique to accurately analyse inherent stress in an engineering component.

Spatiotemporal Variations of Vegetation and Its Response to Climate Change and Human Activities in Arid Areas—A Case Study of the Shule River Basin, Northwestern China

The Shule River Basin (SRB) is a typical arid area in northwest China with a fragile ecology. Understanding vegetation dynamics and its response to climate change and human activities provides essential ecological and environmental resource management information. This study extracted fractional vegetation coverage (FVC) data from 2000 to 2019 using the Google Earth Engine platform and Landsat satellite images, employing trend analysis and other methods to examine spatiotemporal changes in vegetation in the SRB. Additionally, we used partial correlation and residual analyses to explore the response of FVC to climate change and human activities. The main results were: (1) The regional average FVC in the SRB showed a significant upward trend from 2000 to 2019, increasing by 1.3 × 10−3 a–1. The area within 1 km of roads experienced a higher increase of 3 × 10−3 a–1, while the roadless areas experienced a lower increase of 1.1 × 10−3 a–1. The FVC spatial heterogeneity in the SRB is significant. (2) Partial correlation analysis shows that the FVC correlates positively with precipitation and surface water area, with correlation coefficients of 0.575 and 0.744, respectively. A weak negative correlation exists between the FVC and land surface temperature (LST). FVC changes are more influenced by precipitation than by LST. (3) The contributions of climate change to vegetation recovery are increasing. Human activities, particularly agricultural practices, infrastructure development, and the conversion of farmland to grassland, significantly influence vegetation changes in densely populated areas. (4) The area changes of different land types are closely related to climate factors and human activities. Increased construction, agricultural activity, and converting farmland back to grassland have led to an increase in the area proportions of “impervious surfaces”, “cropland”, and “grassland”. Climate changes, such as increased rainfall, have resulted in larger areas of “wetlands” and “sparse vegetation”. These results provide valuable information for ecosystem restoration and environmental protection in the SRB.

Pressure drop prediction by a polynomial model for two-phase flow in vertical oil wells

In this paper, a polynomial model is developed to predict the pressure drop in oil wells with two-phase flow. The variables used in the model are gas-oil ratio production, oil production, tubing diameter, solution gas-oil ratio, oil formation volume factor, and oil viscosity. A polynomial model is presented to predict the pressure drop with a coefficient of determination of 0.9901. The residual analysis and level surfaces of the pressure drop against the polynomial model's input variables are presented to validate the model. A regression of the experimental and predicted pressure drops values using the polynomial model is presented. This model contributes to a simpler methodology for the calculation of pressure drops and the consequent application in the modeling of production curves in oil wells for NODAL analysis.

Establishment of withdrawal time and analysis of tiamulin residue in edible tissues of broiler chickens administered in drinking water

Establishment of withdrawal time and analysis of tiamulin residue in edible tissues of broiler chickens administered in drinking water

How to Improve Fatigue Life and Residual Stress in Compressor Reeds through Shot Peening

Abstract: Numerous industrial areas involve the utilization of shot peening as a well-known surface enhancement method for increasing the fatigue life and residual stress of cyclically loaded crucial parts. This study examines the effects of shot peening on compressor reeds that can lengthen their life cycle with an accelerated rate of residual stress thereby improving reliability and performance of entire compressor system. The paper explains the principles behind shot peening, its influence on materials, and some specific advantages that it gives to thin reed compressors. Materials selection, parameters for shot peening and testing methods are described as experimental methods. Results from residual stress analysis show how much more efficient shot peening makes compressor reeds tough and reliable. Moreover, this paper provides insight into practical aspects, difficulties faced in practice and directions for further research when applying shot peeing to compressor’s reed components.

Estimating and Forecasting Jakarta Composite Index in Pandemic Era Using ARIMA-GARCH Model

Many industries have suffered financial losses as a result of the COVID-19 epidemic. The stock market's movement has been impacted by this circumstance. Due to the influence of some people, a large number of individuals with limited trading knowledge are attempting to participate in the stock market. Market volatility should be understandable in order to gain profit instead of having losses. Therefore, it's essential to comprehend the market of the future through analysis of the data. The purpose of this study is to use ARIMA-GARCH to predict the Indonesian stock market price during. The period covered by the dataset is January 2020–December 2022. The training data indicates that ARIMA (2,1,2) is the best model for ARIMA. The results showed that data residual fitted by ARIMA (2,1,2)-GARCH (1,2) exhibits heteroscedasticity, according to the residual analysis. The MAPE score is 2%, which is relatively small. It means that ARIMA (2,1,2)-GARCH (1,2) is good enough for forecasting the Jakarta Composite Index.

The Swedish Production of Apartments as a Function of GNP, Building Costs and Population Changes: Generation of Intelligent Media Content via Big Data Analytics

The production of new apartments in Sweden has varied strongly during the period from 1975 to 2021. A new statistical function, which explains these production changes, has been developed. This function is designed, based on a set of hypotheses of how the production level should be affected by different explaining factors, such as the GNP, the size of the population, the growth of the population, and the cost of construction. The following hypotheses could not be rejected: the apartment production is a strictly increasing and strictly convex function of GNP, and a strictly increasing function of the size of the population and the growth of the population, and a strictly decreasing function of the cost of construction. The parameters of the statistical function have been estimated with high precision, via multiple regression analysis. It was not possible to detect heteroscedasticity via residual analysis. Furthermore, no indications that nonlinear transformations would improve the selected model were found. The apartment production model contains a strongly significant negative time trend. The estimated function is used to predict the future apartment production until the year 2050. The predictions are based on assumed growth levels of GNP and the population, and on alternative future time trends of the construction cost index. If the real construction cost index continues to grow with the same average trend as from the year 1993 to 2021, the future apartment construction level will stay almost constant at 40,000 apartments per year until 2050. If the future real construction cost index stays constant at the level in 2022, the production of new apartments will grow to almost 90,000 apartments per year in 2050. If the real construction cost index can be decreased to the level in 1993, the production of new apartments will grow to almost 130,000 apartments per year in the year 2050.

Sustained release and self activation of peroxydisulfate intercalated Ca/Al-layered double hydroxide in the remediation of organic contaminated soil

To keep the sufficient effective concentration of oxidants during the in-situ chemical oxidation (ISCO) for organic polluted soil, peroxydisulfate (S2O82−, PDS) intercalated Ca/Al-layered double hydroxide (LDH) was employed as sustainable released and self-activated material of PDS. In the simulated diesel contaminated soil (1000 mg/kg) column with Ca/Al-PDS LDH dosage of 1 wt% and a water flow rate of 0.2 mL/min, PDS released sustained around 50 d, and the final removal rate of diesel could reach 70%, which mainly attributed to Ca/Al-PDS LDH for the oxidation decomposition of diesel. A coupled mechanism between PDS release from Ca/Al PDS LDH and organics oxidation process was proposed according to the concentration of Ca2+ and Al3+ in solution and the X-ray diffraction (XRD) analysis of the solid residue. The generated organic acids and SO42− after the oxidation promoted the dissolution of LDH layers and ion exchange of PDS with SO42− respectively, thereby enhancing the release of PDS. The result of electron paramagnetic resonance (EPR) showed that the oxidative species (•OH, SO4−• and 1O2) were generated both in the reaction solution and on LDH, resulting from the activation of PDS by the aqueous OH− and the Lewis basicity on Ca/Al-PDS LDH, respectively. Thus the oxidation process occurred both in the homogeneous and heterogeneous pattern. This study provided a novel design strategy of sustained-released and self-activated oxidants for the remediation of organic contaminated soil.