Absolute Method Research Articles

Predicting thyroid involvement in primary Sjögren’s syndrome: development and validation of a predictive nomogram

IntroductionPatients with Primary Sjögren’s syndrome (pSS) are at a higher risk of thyroid disorders than the general population. This retrospective analysis of 202 patients with pSS was conducted to uncover risk factors associated with thyroid involvement and to create a predictive model for this condition.MethodsWe analyzed 202 patients with pSS from Guang’anmen Hospital, China Academy of Chinese Medical Sciences, with 105 cases of thyroid involvement and 97 without. The Least Absolute Shrinkage and Selection Operator method was used to identify key variables for our risk model. These variables were then subjected to multivariate logistic regression to develop the model. The accuracy of the model was assessed through the C-index, receiver operating characteristic curves, calibration plots, and decision curve analysis, with internal validation via bootstrapping.ResultsHigh-sensitivity C-reactive protein (HCRP), pulmonary disease, pharyngeal dryness, forgetfulness, night sweats, hyperuricemia, nasal dryness, anxiety, Ro52, and aspartate aminotransferase (AST) were incorporated into the nomogram. The model showed robust discrimination and calibration abilities. Decision curve analysis indicated the clinical utility of our nomogram in intervening on the probability thresholds of thyroid disease.ConclusionBy integrating HCRP, pulmonary disease, pharyngeal dryness, forgetfulness, night sweats, hyperuricemia, nasal dryness, anxiety, Ro52, and AST, our thyroid risk nomogram can predict the risk of thyroid involvement in patients with pSS, aiding in more informed treatment strategies.

ANCF-based modelling of rigid-flexible coupling of helicopter flexible rotor blades

Abstract A high-precision rotor structure dynamic model applicable to a variety of helicopter blade hub structures is established considering rotor blade flexible deformation. The absolute nodal coordinate method is applied to the traditional rotor blade rigid-flexible coupling dynamics model, making the new model applicable to both fully articulated rotor blades and other advanced blade hub structures. No small-volume assumptions were made in the modelling process. Both Green’s strain expression under finite deformation and the geometrical relation of elastic deformation motion based on the absolute nodal coordinate method are used to derive the blade strain energy, kinetic energy, and the external load virtual work. The rotation angle at the articulation constraint at the root of the blade coupled with the elastic deformation degrees of freedom of the blade in three independent rigid body degrees of freedom, respectively. The rotor blade rigid-flexible coupling dynamics equations were established according to the Hamilton action principle. The time-domain simulation is carried out under a free single pendulum and fixed rotational speed/fixed torque for the established flexible blade model, which verifies the validity and high accuracy of the rigid-flexible coupling articulation model of the flexible rotor blade and the blade hub.

Multi-type feature fusion visual navigation for asteroid landing

In order to address the challenges encountered in visual navigation for asteroid landing using traditional point features, such as significant recognition and extraction errors, low computational efficiency, and limited navigation accuracy, a novel approach for multi-type fusion visual navigation is proposed. This method aims to overcome the limitations of single-type features and enhance navigation accuracy. Analytical criteria for selecting multi-type features are introduced, which simultaneously improve computational efficiency and system navigation accuracy. Concerning pose estimation, both absolute and relative pose estimation methods based on multi-type feature fusion are proposed, and multi-type feature normalization is established, which significantly improves system navigation accuracy and lays the groundwork for flexible application of joint absolute-relative estimation. The feasibility and effectiveness of the proposed method are validated through simulation experiments through 4769 Castalia.

Macro-Driven Stock Market Volatility Prediction: Insights from a New Hybrid Machine Learning Approach

This study comprehensively investigates stock market volatility based on over one hundred monthly macroeconomic variables, applying machine learning models. Methodological contribution integrating the random forest (RF) with the least absolute shrinkage and selection operator methods (LASSO). Importantly, the RF-LASSO model can robustly achieve the best forecasting performance under different circumstances. In addition, we focus on model explanation from different perspectives based on permutation importance and shapley additive explanation (SHAP) methods. This study illuminates novel insights into the realm of stock market volatility, harnessing the transformative potential of machine learning methodologies.

Highly accurate absolute optical transfer delay measurement over a long distance assisted by the pulse time signal

In this paper, an absolute optical transfer delay measurement method based on pulse time signal, pseudo-random code phase, and microwave phase is proposed. By employing two-stage integer ambiguity resolution, not only can a measurement range of several hundred kilometers be achieved, but sub-picosecond level measurement accuracy can also be attained. A test system was built in the laboratory and experimentally verified on a fiber optic link. The experimental results verify an accuracy of ±0.1 ps in measuring an ultrahigh-accuracy optical delay line. In addition, long fiber is also tested, which proves that a measurement range of at least 100 km can be achieved. The refresh rate of the measurement results can reach 100 ms each time.

Reaction of F Atoms with Carbonyl Sulfide: Experimental and Theoretical Study.

The gas phase reaction of fluorine atoms with carbonyl sulfide, F + OCS → SF + CO (1), has been investigated over a wide temperature range, T = 220-960 K, in a discharge-flow system combined with mass spectrometry for the analysis of the reactive mixture. The reaction rate coefficient was determined as a function of temperature using both absolute (under pseudo-first-order conditions using an excess of F atoms) and relative rate methods: k1 = (8.95 ± 0.52) × 10-11 exp((102 ± 20)/T) cm3 molecule-1 s-1 (with an estimated total uncertainty of 15% independent of temperature). The SF radical was observed as the main primary reaction product. The experimental results are supported by theoretical calculations, which evidence that the reaction is exothermic with a weak negative temperature dependence.

How Connected Cars Could Capture Cloud Dynamics for Solar Forecasting—Evidence from Two Simulation Scenarios

The rapidly increasing share of fluctuating electricity from photovoltaics calls for accurate approaches to estimate cloud motion, the primary source for the varying power supply. While local sensor networks are prominent in targeting forecast horizons too short for image‐based methods, they have minimal spatial coverage. This work presents the first step towards expanding those approaches to spatially scalable sensor networks: With the motivation of using automotive light sensors as a sensor network, two excerpts from a microscopic traffic simulation serve as simulative sensor networks. A fractal‐based cloud shadow pattern passes the sensor network areas with defined velocities and directions, which shall be estimated using the cumulative mean absolute error method. The evaluation results indicate that the more extensive observation areas compensate for the dynamics in the sensor network when compared to a reference work with a static sensor grid. Furthermore, this work shows how the estimates deteriorate with lower vehicle penetration rates (PR) and longer building shadows due to a lower solar elevation angle. At a penetration rate of 40%, the root mean square errors for both sensor networks are still below 5 ms−1. In conclusion, the spatiotemporal characteristics of a vehicle network offer a potential for estimating cloud movements.

On Absolute Tribonacci Series Spaces and Some Matrix Operators

Recently, a number of studies related to the spaces of all sequences of summable by absolute summabilty methods and their matrix transformations have been presented by several authors. In this article, the absolute Tribonacci space $\left|T_\theta\right|_{q}$ is introduced as the domain of the Tribonacci matrix on $\ell_q$. First, certain algebraic and topological structure such as $BK-$space, isomorphism, duals and Schauder base are studied. Then, some characterizations of compact and matrix operators on this space are given, their norms and Hausdorff meausures of noncompactness are determined.

Factor analysis: application of the absolute difference method in additive factor models

The peculiarity of economic analysis in modern conditions is a significant change in its tasks and functions, the emergence and development of new relevant directions. Under these conditions, attention to traditional issues of economic analysis, including its classical theoretical and methodological foundations, has somewhat weakened. Meanwhile, in the field of theory of economic analysis, one can find points that require separate explanations, justifications, and adjustments to the methodology. In particular, in our opinion, this applies to such an issue as factor analysis, and the application of its methods to calculate the influence of factors on the effective indicator in additive factor models. This question is considered in this article.

Absolute mass spectrometry: A theoretical analytical technique for quantification

Mass spectrometry (MS) is an analytical chemistry technique that requires calibration with standards in order to quantify an analyte in a sample. In many cases the reference standards may be difficult to obtain or unavailable. In fact, when MS is applied to study reactions that yield multiple products, their quantification requires several standards and involves time-consuming and tedious calibration procedures. In other applications like Mass Spectrometry Imaging (MSI), quantification becomes even more tedious when on-tissue standards or mimetic tissue models are required. In this work, we introduce a new measurement technique that we called the Absolute Mass Spectrometry (AbMS), which allows the absolute quantification of an analyte without the need for calibration standards. This technique is based on the “Influence Method” developed in 2015 for nuclear measurements which, when applied to MS, produces this improvement for analyte quantification. Two possible applications of this technique are presented. One is the conceptual development of a mass spectrometer with electronic ionization that does not require calibration to determine the amount of analyte. The other one describes how to apply this technique in a mass spectrometer with other ionization sources like MALDI, DESI, and SIMS, which are used in MSI. AbMS technique uses the conceptual basis of Influence Method and two consecutive measurements to estimate the amount of analyte. Specifically, AbMS can be directly employed as an absolute method for quantification in MSI. Therefore, it constitutes an attractive complement for research applications in pharmaceutical and health sciences.

Analisis Penilaian Atas Harga Wajar Saham Menggunakan Metode Discounted Cash Flow (DCF) dan Dividend Discount Model (DDM) Untuk Pengambilan Keputusan Investasi (Studi pada Indeks LQ-45 Periode 2018-2022)

This study aims to analyze a stock fundamentally in the LQ-45 Index for the 2018-2022 period using absolute methods, namely the Dividend Discount Model (DDM) and Discounted Cash Flow (DCF) methods so that it can be seen whether the stock condition is undervalued, overvalued, or fairvalued and this will also provide an investment decision recommendation for investors. This type of research is quantitative with a descriptive approach. The main object of research is companies included in the LQ-45 index for the period 2018-2022. This research sampling technique uses purposive sampling so as to produce as many as eight companies in accordance with certain criteria. The results of research using the Dividend Discount Model (DDM) method show that some stocks in undervalued conditions are in ASII, BBNI, BMRI, ICBP, INDF, and TLKM stocks. Then for stocks in overvalued conditions there are ANTM and HMSP shares. In using the Discounted Cash Flow (DCF) method, stocks in undervalued conditions are ANTM, ASII, BMRI, and INDF shares. Then for stocks in overvalued conditions are BBNI, HMSP, ICBP, and TLKM. The results of determining the most accurate method between Discounted Cash Flow (DCF) and Dividend Discount Model (DDM) where the results of measurements using Root Mean Square Error (RMSE) show that the Dividend Discount Model (DDM) method is more accurate in this study.

Dual-energy computed tomography-based radiomics for differentiating patients with and without gout flares.

There is a current lack of data pertaining to the potential link between gout flares and dual-energy computed tomography radiomic features. This study aimed to construct and validate a comprehensive dual-energy computed tomography-based radiomics model for differentiating patients with and without gout flares. The analysis included 200 patients, of whom 150 were confirmed to have experienced at least one flare in the past 12months; the remaining 50 patients did not experience flares. The radiomic features of the tophi at the bilateral first metatarsophalangeal joints were extracted and analyzed. Optimal radiomic features were selected using the least absolute shrinkage and selection operator method, and logistic regression analysis was used to screen clinical characteristics and establish a clinical model. The optimal radiomic features were then combined with the identified independent clinical variables to develop a comprehensive model. The performances of the radiomic, clinical, and comprehensive models were evaluated using receiver operating characteristic curve analysis, calibration curves, and decision curve analysis. Four radiomic features distinguished patients with at least one flare from those without flares and were used to establish the radiomic model. Disease duration and hypertension were independent factors that differentiated flare occurrences. The radiomic, clinical, and comprehensive models showed favorable discrimination, with areas under the receiver operating characteristic curves of 0.76 (95% CI, 0.69-0.83), 0.72(95% CI, 0.63-0.80), and 0.79(95% CI, 0.73-0.86), respectively. The calibration curves (P > 0.05) showed that the differentiated values of the comprehensive model agreed well with the actual values. Decision curve analysis demonstrated that the comprehensive model achieved higher net clinical benefits than the use of either the radiomic or clinical model alone. The results of this study suggest that a radiomics model can distinguish patients with and without gout flares. Our proposed clinical radiomics nomogram can increase the efficacy of differentiating flare occurrence, which may facilitate the clinical decision-making process.

Radiological and biomechanical evaluation of the ulnar nerve after the percutaneous application of the medial K-wire in the extended position on supracondylar humerus fractures: Does the postoperative long arm splint position matter?

Our study aimed to evaluate and compare the changes in ulnar nerve tension and strain at different elbow positions radiologically and mechanically before and after applying the medial K-wire on the supracondylar humerus fracture cadaver model. We used ten fresh frozen cadaver upper extremity specimens to measure strain and tension on the ulnar nerve in 3 different elbow positions: elbow full extension, elbow flexion-forearm supination, and elbow flexion-forearm pronation. We employed Shear wave elastography (Siemens Acuson S3000 USG, 9L4 linear probe) and a microstrain gauge (Microstrain, Inc., Burlington) to obtain our measurements. Minimum, maximum and mean stress and strain values on the nerve and its surroundings were measured and compared statistically. The mean values of elbows with full extension are statistically lower than those in elbows with 90° flexion-forearm supination and those with 90° flexion-forearm pronation positions. Statistical evaluations were performed between all of the groups. Elbow 90° flexion-forearm pronation, both minimum and maximum and mean values were statistically higher in the group, including the specimens with Kirschner applied. The mean values in the elbow full extension and elbow 90° flexion-forearm supination positions were statistically similar in the specimens with and without the K-wire applied. Despite the numerous techniques described in the literature, there is no absolute technical method to prevent ulnar nerve damage. K-wire application to the medial epicondyle with the elbow in a slightly extended position is a technique that can be applied to reduce the risk of ulnar nerve paralysis. However, it has been reported that ulnar nerve damage can be observed in cases where a splint is placed in the 90° flexion position. We hypothesize that the position of the elbow joint in the postoperative period may contribute to ulnar nerve paralysis due to soft tissue tension and strain and as a result of changing the balance of the surrounding tissues. Our findings suggest that the long arm splint applied in elbow 90° flexion and forearm pronation position should not be preferred in the postoperative period. The maximum strain values obtained in the elbow full extension were lower, suggesting that it would be appropriate to stabilize the elbow in the extension position as much as possible postoperatively. Level of evidence: Level V.

Measurement of absolute abundance of crystallins in human and αA N101D transgenic mouse lenses using 15N-labeled crystallin standards

Stable isotope labeled standards of all major human lens crystallins were created to measure the abundance of lens endogenous crystallins from birth to adulthood. All major human crystallins (αA, αB, βA2, βA3/A1, βA4, βB1, βB2, βB3, γA, γB, γC, γD, γS) were cloned with N-terminal 6 x His tagged SUMO for ease of purification and the ability to generate natural N-termini by SUMO protease cleavage when producing crystallins for structure/function studies. They were then expressed in 15N-enriched media, quantified by mass spectrometry, and mixed in proportions found in young human lens to act as an artificial lens standard. The absolute quantification method was tested using soluble protein from 5-day, 23-day, 18-month, and 18-year-old human lenses spiked with the 15N artificial lens standard. Proteins were trypsinized, relative ratios of light and heavy labeled peptides determined using high-resolution precursor and data independent MS2 scans, and data analysis performed using Skyline software. Crystallin abundances were measured in both human donor lenses and in transgenic mouse αA N101D cataract lenses. Technical replicates of human crystallin abundance measurements were performed with average coefficients of variation of approximately 2% across all 13 crystallins. αA crystallin comprised 27% of the soluble protein of 5-day-old lens and decreased to 16% by 18-years of age. Over this time period αB increased from 6% to 9% and the αA/αB ratio decreased from 4.5/1 to 2/1. γS-crystallin also increased nearly 2-fold from 7% to 12%, becoming the 3rd most abundant protein in adult lens, while βB1 increased from 14% to 20%, becoming the most abundant crystallin of adult lens. Minor crystallins βA2, βB3, and γA comprised only about 1% each of the newborn lens soluble protein, and their abundance dropped precipitously by adulthood. While 9 of the SUMO tagged crystallins were useful for purification of crystallins for structural studies, γA, γB, γC, and γD were resistant to cleavage by SUMO protease. The abundance of WT and N101D human αA in transgenic mouse lenses was approximately 40-fold lower than endogenous mouse αA, but the deamidation mimic human αA N101D was less soluble than human WT αA. The high content of αA and the transient abundance of βA2, βB3, and γA in young lens suggest these crystallins play a role in early lens development and growth. βB1 becoming the most abundant crystallin may result from its role in promoting higher order β-crystallin oligomerization in mature lens. The full set of human crystallin expression vectors in the Addgene repository should be a useful resource for future crystallin studies. 15N labeling of these crystallins will be useful to accurately quantify crystallins in lens anatomic regions, as well as measure the composition of insoluble light scattering crystallin aggregates. The standards will also be useful to measure the abundance of crystallins expressed in transgenic animal models.

Experimental study on mechanical and acoustic emission characteristics of red sandstone containing combined flaws of hole-fissure under biaxial compression

To investigate the mechanical behaviour and failure mechanism of flawed rock masses under biaxial stress conditions, a series of biaxial compression experiments are conducted on red sandstone specimens containing combined flaws of the circular hole and perforated symmetric fissure combined with acoustic emission (AE) technology in this work. The results show a close association between the stress-strain curve morphology and confining stress, both the biaxial compression strength and elastic modulus exhibit an upward trend with increasing fissure angle and confining stress. The maximum AE energy and cumulative AE energy both increase with higher confining stress. The crack types in the specimens are identified by analyzing the AF/RA value distribution, revealing a gradual decrease in the percentage of tensile cracks with increasing fissure angle. An AE localization algorithm based on the least absolute value method is applied to pinpoint AE events during biaxial compression, and AE events distribution is analyzed through the Kernel density estimation (KDE). The crack extension behaviour is described based on the movement of the maximum kernel density points, which initially exhibits a progression from both ends of the specimen towards the central region and subsequently from the central fissure tip towards the two ends.

Predictive models for cholesterol crystals and plaque vulnerability in acute myocardial infarction: Insights from an optical coherence tomography study

BackgroundCholesterol crystals (CCs) are recognized as a risk factor for vulnerable atherosclerotic plaque rupture (PR) and major adverse cardiovascular events. However, their predictive factors and association with plaque vulnerability in patients with acute myocardial infarction (AMI) remain insufficiently explored. Therefore, This study aims to investigate the association between CCs and plaque vulnerability in culprit lesions of AMI patients, identify the factors influencing CCs formation, and develop a predictive model for CCs. MethodsA total of 431 culprit lesions from AMI patients who underwent pre-intervention optical coherence tomography (OCT) imaging were analyzed. Patients were divided into groups based on the presence or absence of CCs and PR. The relationship between CCs and plaque vulnerability was evaluated. A risk nomogram for predicting CCs was developed using the least absolute shrinkage and selection operator and logistic regression analysis. ResultsCCs were identified in 64.5 % of patients with AMI. The presence of CCs was associated with a higher prevalence of vulnerable plaque features, such as thin-cap fibroatheroma (TCFA), PR, macrophage infiltration, neovascularization, calcification, and thrombus, compared to patients without CCs. The CCs model demonstrated an area under the curve (AUC) of 0.676 for predicting PR. Incorporating CCs into the TCFA model (AUC = 0.656) significantly enhanced predictive accuracy, with a net reclassification improvement index of 0.462 (95 % confidence interval [CI]: 0.263–0.661, p < 0.001) and an integrated discrimination improvement index of 0.031 (95 % CI: 0.013–0.048, p = 0.001). Multivariate regression analysis identified the atherogenic index of plasma (odds ratio [OR] = 2.417), TCFA (OR = 1.759), macrophage infiltration (OR = 3.863), neovascularization (OR = 2.697), calcification (OR = 1.860), and thrombus (OR = 2.430) as independent risk factors for CCs formation. The comprehensive model incorporating these factors exhibited reasonable discriminatory ability, with an AUC of 0.766 (95 % CI: 0.717–0.815) in the training set and 0.753 (95 % CI: 0.704–0.802) in the internal validation set, reflecting good calibration. Decision curve analysis suggested that the model has potential clinical utility within a threshold probability range of approximately 18 % to 85 %. ConclusionsCCs were associated with plaque vulnerability in the culprit lesions of AMI patients. Additionally, this study identified key factors influencing CCs formation and developed a predictive model with potential clinical applicability.

Research on capacitance-coupled electrical impedance imaging fusion algorithm based on wavelet transform.

Capacitively Coupled Electrical Impedance Tomography (CCEIT), as a new electrical tomography method, effectively overcomes the drawbacks of traditional electrical resistance tomography that requires direct contact with liquids, so as to achieve non-contact measurement. In order to make full use of the complete electrical impedance information (real and imaginary) obtained by CCEIT and further improve the quality of image reconstruction, an image fusion algorithm based on wavelet transform was proposed. First, the Landweber algorithm is used to image the real part information and the imaginary part information, and the wavelet transform algorithm is used to decompose the two images at multiple scales, in which the low-frequency coefficient is fused by the improved logic filter algorithm based on the adaptive threshold, the high-frequency coefficient is selected based on the absolute value maximum method for fusion, and finally, the fused image is obtained by wavelet reconstruction. Numerical simulations and experiments verify the feasibility of the proposed method, and the results show that the relative error is reduced by up to 17% and the correlation coefficient is increased by up to 10%.

Assessment of structural concrete made by sustainable sand at high temperatures: An experimental research

This study assesses the mechanical properties of sustainable sand concrete mixes of DM1*, DM2, DM3, and DM4 by absolute volume method when exposed to elevated temperatures. The concrete specimens were placed in a muffle furnace for two hours at (150–600 °C). Experimental investigations show that the mix strength increases (up to 300 °C), a sudden fall in strength at 450 °C, and a sharp decline at 600 °C was seen. At 600 °C, the highest reduction in compressive and flexural strength of the mixes was reported (21.2, 11.2, 16.6, and 20.4 MPa) and (2.87, 1.41, 2.42, 1.95 MPa), respectively. The concrete mix DM2 with (100 % recycled crushed sand) shows a more significant loss than other mixes. The concrete mix DM4 (50 % recycled crushed sand + 50 % desert sand + 12.5 % silica fume) performs significantly better than other mixes at elevated temperatures. Hence, 100 % recycled crushed sand in structural concrete is not recommended for engineering work.

The origin of the Protogeometric style in northern Greece and its relevance for the absolute chronology of the Early Iron Age

Classical archaeological chronologies are steeped in relative dating, but the application of absolute methods does not always support such clear-cut seriation. Here, the authors consider the significance of a Macedonian vase in reconciling the conventional and absolute chronologies of Early Iron Age Greece. Decorated with compass-drawn concentric circles and found in a Late Bronze Age context at ancient Eleon, Boeotia, the authors argue that this vessel establishes a chronological anchor and supports a twelfth-century BC emergence of the Protogeometric style in central Macedonia. A model for the indigenous development and dispersal of the Macedonian Protogeometric style is presented for future elaboration.

Computed tomography-based multi-organ radiomics nomogram model for predicting the risk of esophagogastric variceal bleeding in cirrhosis.

Radiomics has been used in the diagnosis of cirrhosis and prediction of its associated complications. However, most current studies predict the risk of esophageal variceal bleeding (EVB) based on image features at a single level, which results in incomplete data. Few studies have explored the use of global multi-organ radiomics for non-invasive prediction of EVB secondary to cirrhosis. To develop a model based on clinical and multi-organ radiomic features to predict the risk of first-instance secondary EVB in patients with cirrhosis. In this study, 208 patients with cirrhosis were retrospectively evaluated and randomly split into training (n = 145) and validation (n = 63) cohorts. Three areas were chosen as regions of interest for extraction of multi-organ radiomic features: The whole liver, whole spleen, and lower esophagus-gastric fundus region. In the training cohort, radiomic score (Rad-score) was created by screening radiomic features using the inter-observer and intra-observer correlation coefficients and the least absolute shrinkage and selection operator method. Independent clinical risk factors were selected using multivariate logistic regression analyses. The radiomic features and clinical risk variables were combined to create a new radiomics-clinical model (RC model). The established models were validated using the validation cohort. The RC model yielded the best predictive performance and accurately predicted the EVB risk of patients with cirrhosis. Ascites, portal vein thrombosis, and plasma prothrombin time were identified as independent clinical risk factors. The area under the receiver operating characteristic curve (AUC) values for the RC model, Rad-score (liver + spleen + esophagus), Rad-score (liver), Rad-score (spleen), Rad-score (esophagus), and clinical model in the training cohort were 0.951, 0.930, 0.801, 0.831, 0.864, and 0.727, respectively. The corresponding AUC values in the validation cohort were 0.930, 0.886, 0.763, 0.792, 0.857, and 0.692. In patients with cirrhosis, combined multi-organ radiomics and clinical model can be used to non-invasively predict the probability of the first secondary EVB.