Accurate Index Research Articles

P0458 The burden, risk factors and laboratory evaluation of anemia in patients with Inflammatory Bowel Disease

Abstract Background Anemia is the most common extraintestinal manifestation in inflammatory bowel disease (IBD) (1). The laboratory evaluation and determination of anemia in IBD remains challenging (2). The aim of this study was to evaluate the burden, risk factors and types of anemia in IBD patients in Crete. Methods This is a cross sectional study where consecutive IBD patients were included. Demographic, clinical and laboratory data [Hemoglobin (Hgb), Ferritin (Fer)] were recorded (based on an updated IBD registry) while serums of consecutive IBD patients were collected and Soluble transferrin receptor (sTFR), sTFR Index and Hepcidin (Hepc) were measured. Results A total of 151 [69 (45.7%) females, 90 (59.6%) with Crohn’s Disease (CD), 34 (22.5%) anemic and 117 (77.5%) non-anemic] were included. Anemic patients were found to be less frequently smokers (P=0.0182), to have shorter IBD duration (P=0.0112) and more often active disease (P=0.0270) compared to non-anemic IBD patients. No other significant differences in clinical and demographic characteristics were found. In addition, they had significantly lower Hgb, MCV [11.6 (10.7-12.4) vs 14.1 (13.3-15), 80.3 (78.4-85.8) vs 90.6 (86.5-94.5) respectively, both P< 0.0001], Ferritin [35.5 (8.2-69) vs 60 (33-112), P=0.0164], and higher RDW, ESR, sTFR [16.2 (14.9- 17.8) vs 14.2 (13.5-15), 27 (12.5-45) vs 9.5 (8.5-15), 1.85 (1.33-2.67) vs 1.22 (1.02-1.52) respectively, all P<0.0001], CRP [0.6 (0.2-2.5) vs 0.3 (0.1-2), P=0.0028] and sTFR Index [1.14 (0.75-3.10) vs 0.68 (0.55-1.03), P=0.0002)]. In terms of risk factors for anemia development younger age (OR 1.04, CI95% 1.02-1.07), smoking (OR 0.22, CI95% 0.06-0.80) and higher ESR (OR 1.04, CI95% 1.02-1.07) were found to be significant in the multivariate analysis. From the anemic patients (N=34), 17 had iron deficiency (IDA) and the rest non-IDA (anemia of chronic disease and mixed). Patients with IDA compared to non-IDA had significantly higher sTFR and sTFR Index while no difference was found in Hepc (Table 1). sTFR >1.68 mg/l had sensitivity of 76.5% and specificity of 73.3% while sTFR Index >1.1365 has sensitivity 82.4% and specificity 86.7% for IDA diagnosis suggesting that sTFR Index is the most accurate index for IDA diagnosis in IBD patients (Figure 1). Conclusion One out of 5 IBD patients have anemia in our cohort. Risk factors for anemia are the age, smoking status and ESR. sTFR and especially sTFR Index could be useful for true IDA diagnosis in IBD patients in the presence of chronic inflammation.

Research on the Prediction Model of Coal Spontaneous Combustion Hazard Level Based on IGWO-GRNN

ABSTRACT To accurately predict the risk level of spontaneous coal combustion, we introduce an improved grey wolf optimization strategy (IGWO) to optimize the generalized regression neural network (GRNN), thereby establishing the IGWO-GRNN coal spontaneous combustion risk level prediction model. The superiority of the IGWO algorithm over the grey wolf optimization algorithm (GWO), whale optimization algorithm (WOA) and particle swarm optimization algorithm (PSO) was verified. Fifty sets of sample data of spontaneous coal combustion in the mining area were selected as the research object, and the data were randomly divided into the training set and the test set according to the ratio of 8:2. The prediction results of the IGWO-GRNN prediction model were obtained and compared with those of the GWO-GRNN, GRNN, BP (Backpropagation), and GRU (Gated Recurrent Unit) models, and the absolute value of the relative error was introduced as an evaluation index of the model prediction accuracy. The results demonstrate that IGWO outperforms GWO, WOA, and PSO in terms of optimization efficiency, with the IGWO-GRNN prediction model yielding the most accurate results. Specifically, the maximum absolute value of the relative error does not exceed 4.27%, and the average relative error is only 3.29%, indicating a substantial improvement over the other models. Therefore, this study achieves high-precision prediction under small-sample data, which provides an important reference value for the small-sample prediction problem in the mining field.

Establishment and Evaluation of Atmospheric Water Vapor Inversion Model Without Meteorological Parameters Based on Machine Learning.

Precipitable water vapor (PWV) is an important indicator to characterize the spatial and temporal variability of water vapor. A high spatial and temporal resolution of atmospheric precipitable water can be obtained using ground-based GNSS, but its inversion accuracy is usually limited by the weighted mean temperature, Tm. For this reason, based on the data of 17 ground-based GNSS stations and water vapor reanalysis products over 2 years in the Hong Kong region, a new model for water vapor inversion without the Tm parameter is established by deep learning in this paper, the research results showed that, compared with the PWV information calculated by the traditional model using Tm parameter, the accuracy of the PWV retrieved by the new model proposed in this paper is higher, and its accuracy index parameters BIAS, MAE, and RMSE are improved by 38% on average. At the same time, the PWV was inverted by radiosonde data in the study area as a reference to verify the water vapor inversion results of the new model, and it was found that the BIAS of the new model is only 0.8 mm, which has high accuracy. Further, compared with the LSTM model, the new model is more universal when the accuracy is comparable. In addition, in order to evaluate the spatial and temporal variation characteristics of the atmospheric water vapor retrieved by the new model, based on the rainstorm event caused by typhoon in Hong Kong of September 2023, the ERA5 GSMaP rainfall products and inverted PWV information were comprehensively used for analysis. The results show that the PWV increased sharply with the arrival of the typhoon and the occurrence of a rainstorm event. After the rain stopped, the PWV gradually decreased and tended to be stable. The spatial and temporal variation in the PWV have a strong correlation with the occurrence of extreme rainstorm events. This shows that the PWV inverted by the new model can respond well to extreme rainstorm events, which proves the feasibility and reliability of the new model and provides a reference method for meteorological monitoring and weather forecasting.

Performance of A-DROP, NEWS2, and REMS in predicting in-hospital mortality and mechanical ventilation in pneumonia patients in the emergency department: a retrospective cohort study

BackgroundPneumonia is a potentially life-threatening respiratory tract infection. Many Early Warning Scores (EWS) were developed to detect patients with high risk for adverse clinical outcomes, but few have explored the utility of these EWS for pneumonia patients in the Emergency Department (ED) setting. We aimed to compare the prognostic utility of A-DROP, NEWS2, and REMS in predicting in-hospital mortality and the requirement for mechanical ventilation among ED patients with pneumonia.MethodsA retrospective study was conducted at the ED of Siriraj Hospital, Thailand. Adult patients diagnosed with non-COVID-19 pneumonia between June 1, 2021, and May 31, 2022, were included. We calculated and analyzed their EWS at ED arrival. The primary outcome was all-cause in-hospital mortality. The secondary outcome was mechanical ventilation.ResultsWe enrolled 735 patients; 272 (37%) died at hospital discharge, and 75 (10.2%) required mechanical ventilation. A-DROP had the highest discrimination capacity for in-hospital mortality (AUROC: 0.698, 95% CI 0.659–0.737) compared to NEWS2 (AUROC 0.657; 95%CI 0.617, 0.698) and REMS (AUROC 0.637; 95%CI 0.596, 0.678). A-DROP also had superior performances than NEWS2 and REMS in terms of calibration, overall model performance, and balanced diagnostic accuracy indices at its optimal cut point (A-DROP ≥ 2). No EWS could perform well in predicting mechanical ventilation.ConclusionA-DROP had the highest prognostic utility for predicting in-hospital mortality in non-COVID-19 pneumonia patients in the ED compared to NEWS2 and REMS.

Color-multiplexed 3D differential phase contrast microscopy with optimal annular illumination.

Quantitative phase imaging (QPI) has become a valuable tool in the field of biomedical research due to its ability to quantify refractive index variations of live cells and tissues. For example, three-dimensional differential phase contrast (3D DPC) imaging uses through-focus images captured under different illumination patterns deconvoluted with a computed 3D phase transfer function (PTF) to reconstruct the 3D refractive index. In conventional 3D DPC with semi-circular illumination, partially spatially coherent illumination often diminishes phase contrast, exacerbating inherent noise, and can lead to a large number of zero values in the 3D PTF, resulting in strong low-frequency artifacts and deteriorating imaging resolution. To overcome the above drawbacks, we obtain the conditions for acquiring the optimal 3D PTF based on the analysis of the 3D imaging model and the derivation of the 3D PTF calculation process and propose a 3D DPC microscopy based on optimal annular illumination. The proposed optimal annular illumination pattern minimizes the missing frequency components in the 3D Fourier space, resulting in the best noise-robustness and significantly increased phase contrast. To expedite imaging speed, we utilize a 1/2 annular multiplexed illumination, reducing data acquisition volume by 75%. The 3D refractive index tomography of a simulated 3D phase object, unstained tongue sections, and oral epithelial cells demonstrates that our proposed method achieves the above advantages. In conclusion, we demonstrate a novel 3D DPC microscope that only requires replacing the illumination of a commercial microscope with a programmable LED array. The accurate 3D refractive index tomography and the compactness of the system setup allow the method to play a significant role in the biomedical field.

Shedding Light on the Mid‐Infrared Complex Refractive Index of Anodic Aluminum Oxide

AbstractIn the current scientific landscape, the understanding of optical properties in the mid‐infrared (mid‐IR) range (3–30 µm) is crucial in simulations and models to explore the potential of materials for various applications. However, due to the challenges associated with mid‐IR characterization, accurate refractive index (n) and extinction coefficient (κ) data are often lacking in the literature. This study addresses this gap by investigating the mid‐IR n and κ spectra of anodic aluminum oxide (AAO) nanostructures anodized under different conditions, using two distinct approaches: IR ellipsometry and a theoretical model based on multilayer reflection and effective medium. The results demonstrate a strong agreement: the anodizing conditions have a significant influence on the optical properties of the AAO nanostructures. These differences enable accurate simulations of the emissivity spectra of AAO nanostructures on Al foils, which align closely with experimental measurements. This theoretical approximation is versatile and extensible to a broad range of materials. Different materials are tested, namely, a sapphire, a polycarbonate film, and a polyethylene terephthalate (PET) film achieving a useful qualitative description. This study paves the way for a novel approach in the engineering of new micro and nano‐optical materials, facilitating their evaluation for suitability in mid‐IR applications.

Geospatial Modeling of Aeolian Dynamics in the Algerian Steppe from Zahrez Chergui to Hodna

Assessing the hazards associated with aeolian geomorphological processes requires a fundamental understanding of their spatial distribution. These phenomena often have detrimental impacts on the environment, economy, and society. This problem is prevalent in the Algerian steppe, encompassing the Zahrez, Chergui, and Hodna regions. This study proposes a research method for developing more accurate and simpler indices to evaluate the extent and directionality of sand migration. Specifically, it examines surface characteristics, such as altitude, slope, and slope exposure. However, some tools used for spatial modeling of wind dynamics necessitate corrections to account for the effects of topography and surface features on wind, which for this study are implemented using spatial techniques. The results are incorporated into the model developed by Fryberger, which requires wind data and a Digital Surface Model (DSM) to estimate the factors included in this model. The findings indicate that the average potential quantity of sand movement is 64 t m-1 yr-1 over the entire study area, with 37.3% of the region experiencing severe deflation of 140 t m-1 yr-1. This result can be utilized to enhance the understanding of the direction and magnitude of sand movement in any region.

Modeling feed efficiency over productive lifetime and integrating a submodel for body reserve management in Nordic dairy cattle.

Genetic enhancement of feed efficiency can improve the economic sustainability and environmental responsibility of dairy farming. Although genetic selection holds promise for improving feed efficiency across the lifespan of dairy cows, comprehensive data spanning whole lactations or even a productive lifetime are currently limited. To address this, we used production data and data from a camera-based feed intake and BW recording system, along with records of production, feed intake, and weight on Holstein cows from a research herd. We aimed to estimate variance components for a multivariate, multiparity model of production, feed intake, and BW data to calculate genetic residual feed intake (gRFI) for each of the Nordic breeds (Holstein, Jersey, and Red Dairy Cattle). Our approach included investigating a new definition of energy balance (EBbody) calculated from changes in body reserves, serving as an energy sink in gRFI. The data in our analysis consisted of 4,751 Holstein cows (7,851 lactations), 2,068 Jersey cows (3,486 lactations), and 3,235 Red Dairy Cattle cows (5,419 lactations). We used Gibbs sampling to estimate posterior means and SD for all model parameters. Our findings revealed moderate lactation-wise heritability of gRFI (0.15-0.38) across all breeds and parities. Moreover, gRFI genetic correlations varied (-0.2 to 0.4) between early- and mid- to late-lactation stages across all breeds, and for lactation-wise gRFI, there were moderately high genetic correlations (0.39-0.59) between primi- and multiparous lactations across the 3 breeds. Those results suggest the importance of recording phenotypes in most time periods within and across lactations. Our analysis indicated that improving gRFI with one genetic SD unit corresponded to a 2% to 3% gain in net return profit per cow-year, with no or minimal impact on production and body reserve management. We demonstrated the feasibility of incorporating EBbody into gRFI. Comparing gRFI calculated with EBbody or changes in BW as an energy sink trait for body reserve management were highly genetically correlated (>0.95). This result shows that the choice of the energy sink trait for body reserve management in gRFI will yield limited reranking among cows and sires when based on BW records only. However, EBbody offers an opportunity to incorporate BCS information without increasing the number of genetic parameters to be estimated, but it relies on parameters estimated in experimental settings. In conclusion, our study demonstrates the feasibility of developing a model for gRFI over most of the productive lifetime of dairy cattle, offering significant economic benefits without compromising productivity or body reserve management. Moving forward, comprehensive recording schemes covering whole lactations and productive lifetimes are advantageous for accurate selection indices of gRFI.

An Adaptation-Aware Interactive Learning Approach for Multiple Operational Condition-Based Degradation Modeling.

Although degradation modeling has been widely applied to use multiple sensor signals to monitor the degradation process and predict the remaining useful lifetime (RUL) of operating machinery units, three challenging issues remain. One challenge is that units in engineering cases usually work under multiple operational conditions, causing the distribution of sensor signals to vary over conditions. It remains unexplored to characterize time-varying conditions as a distribution shift problem. The second challenge is that sensor signal fusion and degradation status modeling are separated into two independent steps in most of the existing methods, which ignores the intrinsic correlation between the two parts. The last challenge is how to find an accurate health index (HI) of units using previous knowledge of degradation. To tackle these issues, this article proposes an adaptation-aware interactive learning (AAIL) approach for degradation modeling. First, a condition-invariant HI is developed to handle time-varying operation conditions. Second, an interactive framework based on the fusion and degradation model is constructed, which naturally integrates a supervised learner and an unsupervised learner. To estimate the model parameters of AAIL, we propose an interactive training algorithm that shares learned degradation and fusion information during the model training process. A case study that uses the degradation data set of aircraft engines demonstrates that the proposed AAIL outperforms related benchmark methods.

Braking failure anti-rollover control and hardware-in-the-loop verification of wire-controlled heavy vehicles

Considering the fault tolerance of EMB (Electro-Mechanical-Brake) braking failure and anti-rollover control at the same time is one of the urgent problems to be solved in the driving safety of X-by-wire vehicles. Accurate rollover index is a key part of anti-rollover control. Aiming at the problem that the traditional rollover index reflects that the unsprung mass of the vehicle is insufficiently affected by road excitation, a tripped vehicle rollover dynamic model is established based on single-wheel braking failure, and a rollover evaluation index NLTR (New Load-Transfer-Rate) suitable for braking failure is proposed. In order to improve the lateral safety of the vehicle when the line control fails, a hierarchical anti-rollover controller based on road adhesion coefficient identification, SM-ABS (Sliding-Mode-ABS) control and DBR (Differential-Braking-Redistribution) control is designed. Taking the rollover index threshold as the controller trigger condition, the controller effects under NLTR, traditional RI2 and standard LTR indicators are compared respectively. Simulation and HIL test show that the traditional index controller has failure risk under extreme conditions. The designed NLTR index controller can accurately evaluate the rollover risk of the vehicle, control the vehicle in time, and improve the vehicle stability by 68.18% under Fish-Hook condition.

Intelligent hybrid approaches utilizing time series forecasting error for enhanced structural health monitoring

Over the past decade, the growing importance of machine learning-based structural health monitoring (SHM) for early-stage damage detection has become evident. Time series forecasting, using deep learning, has emerged as a key focus, significantly contributing to improving damage detection, localization, and quantification processes. Researchers in SHM have conducted numerous studies utilizing neural networks based on time series forecasting, grounded in traditional methods. This study diverges from existing research by directly incorporating neural network prediction errors in time series for detecting, localizing, and quantifying damage. The proposed methods are well-suited for online structural monitoring. They eliminate the need for data classification methods and damage-sensitive feature extraction techniques by relying solely on training the neural network with data from structurally sound conditions. However, the testing process does require data from damaged conditions. To address the non-linear and non-stationary characteristics of the signals, the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) method is applied for signal processing. This method processes response signals (i.e., time series) from three well-known benchmark structures: the University of Central Florida structure, the Qatar University Grandstand Simulator, and the Z24 Bridge. Subsequently, the first intrinsic mode function (IMF) obtained from signal decomposition is independently input into Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks for time series prediction. Optimal parameter values for the LSTM and GRU neural networks are chosen using the Bayesian Optimization (BO) algorithm before the prediction process. By introducing three indices—Statistical Distance Function (SDF), error index, and accuracy index—the evaluation not only emphasizes the accuracy of the methods but also explores the localization and quantification of damage. The results demonstrate that both ICEEMDAN-BO-LSTM-SDF and ICEEMDAN-BO-GRU-SDF methods have successfully achieved accurate detection, localization, and quantification without the need for data classification and damage-sensitive feature extraction methods, and merely by utilizing data from healthy states for neural network training.

Utility of PSA free-to-total ratio for clinically significant prostate cancer in men with a PSA level of

To investigate the relationship between the prostate-specific antigen (PSA) free-to-total ratio (FTR) and International Society of Urological Pathology Grade Group ≥2, clinically significant prostate cancer (csPCa) in men with a low PSA level (≤4 ng/mL). Patients and Methods Data were obtained from the Prostate Cancer Prevention Trial. Patients with a PSA level of ≤4 ng/mL and who received a biopsy within a year of this PSA measurement were included. Associations between FTR and csPCa were investigated with logistic regression, adjusting for age and PSA, a re-scaled Brier score (index of predictive accuracy), and decision curve analysis. A total of 406 patients were analysed with 139 (34%) having csPCa and 204 (50%) having any grade PCa. For those with an FTR ≤0.15, 46% had csPCa, vs 22% for those with a ratio ≥0.20. In a regression model, the predicted probability of csPCa for a 60-year-old with a PSA of 3 ng/mL was 61% if the FTR was 0.05, falling to 18% if the FTR was 0.30. A clear negative relationship between increasing FTR and probability of csPCa was observed. A model containing FTR additional to PSA and age provides greater net benefit as per decision curve analysis and likely superior discrimination and calibration measured by a higher index of predictive accuracy. In middle-aged men with a PSA level between 1.5 and 4 ng/mL but otherwise indicated for biopsy, a low FTR is associated with higher rates of csPCa. It should be utilised as an additional, readily available and inexpensive test to improve prediction of csPCa and aid in patient counselling.

Achieving quality assurance in accepting automated pavement condition data: threshold development and error pattern analysis

ABSTRACT Automated pavement condition survey systems are widely used for network-wide pavement distress detection, yet concerns about data quality remain. To address this, it is crucial to develop effective data quality assurance (QA) procedures. This study proposes two indexes for QA procedures: the accuracy index based on the differences between automated measurements and manual audit measurements, and the precision index based on the differences between automated measurements of two consecutive years. Using automated distress data (2017–2021) and manual audit data from Texas, the Difference of Two-Sigma (D2S) method was applied to determine the thresholds of various distress types for the accuracy and precision indexes. Then, a validation of the proposed thresholds was conducted by visually checking the 2022 image data of a selected pavement network. Through this process, the reliability of the thresholds was evaluated, and the error patterns present in the automated data were analysed. The study results demonstrated that the developed thresholds were able to effectively identify data quality issues. The primary source of errors in the automated condition survey system was false-positive detections of pavement distresses, frequently caused by features in the pavement surface background, primarily due to the textures of the pavements.

Croplands Quality Evaluation of Whole Tillage Layer Based on the Minimum Data Set in Jilin Province, China

The aim of this study is to accurately evaluate the quality characteristics of whole tillage cropland and deepen the knowledge of sub-tillage soil quality evaluation in Jilin Province, China. In this study, top-tillage and sub-tillage soil samples were collected from 185 maize continuous cropping areas in Jilin Province, and 12 physicochemical indexes (pH, cation exchange capacity (CEC), soil organic matter (SOM), total nitrogen (TN), total phosphorus (TP), total potassium (TK), available nitrogen (AN), available phosphorus (AP), available potassium (AK), sand, silt, and clay) were used to evaluate the whole tillage layer soil quality index (SQI). The results showed that the whole tillage soil physicochemical indexes in Jilin Province were generally above the moderate level, and nutrient contents increased from West to East among the regions. The minimum data set SQI (SQI-MDS) of the top-tillage and sub-tillage layers were 0.22–0.98 (0.46) and 0.23–0.93 (0.55), respectively. The suitable ranges of MDS parameters for reasonable tillage layers were as follows: top-tillage layer SOM ≥ 34.5 g kg−1, 31.5% ≤ sand ≤ 53.5%, AP ≥ 32.1 mg kg−1, and TK ≥ 15.18 g kg−1; sub-tillage layer 31.3% ≤ sand ≤ 51.2%, TN ≥ 1.48 g kg−1, 6.4 ≤ pH ≤ 7.1, and AK ≥ 157.6 mg kg−1. In summary, the SQI and evaluation indexes of the top-tillage and sub-tillage layers in different ecological zones are varied. It is necessary to adjust the evaluation index thresholds in combination with the actual conditions to establish a more accurate evaluation index system of the whole tillage soil quality.

Validity of Mehrviru® and Sacace® in molecular detection of HPV: a latent class analysis

BackgroundValid diagnostic tests for human papillomavirus (HPV) detection are crucial to identify individuals at high risk of cervical cancer. We assessed and compared the validity of Mehrviru HPV genotyping and Sacace (HPV Genotypes 14 Real-TM Quant) for molecular detection of 14 high-risk human papillomaviruses.MethodsWe used three HPV test results to identify HPV-positive individuals (14 high-risk genotypes) in a specialist gynecology clinic. The HPV test results were collected using Mehrviru®, Sacace®, and a third kit from the clinical diagnostic laboratory. We used Latent class analysis to determine the actual status of HPV infection in study participants. The sensitivity, specificity, positive and negative predictive values, positive and negative likelihood ratios, Youden, and area under the ROC curve indices of each diagnostic kit and their 95% confidence intervals were calculated. The agreement between the Mehrviru and Sacace kits was determined using the Kappa statistic.ResultsWe examined 117 women at high risk of HPV infection. The mean age (SD) was 37.2 (9.1). According to LCA, 28.6% of participants had an HPV infection. The sensitivity and specificity (95% CI) of the Mehrviru were 90.8% (73.7-97.2%) and 90.9% (82.1-95.6%), and corresponding figures for Sacace were 92.0% (72.3-98.1%) and 97.4% (90.2-99.3%). The kappa index between the Mehrviru and Sacace kits was 69.7% (55.6-83.9%). The area under the ROC curve for Mehrviru and Sacace test were 91.4% (85.7-97.1%) and 94.4% (89.3-99.5%).ConclusionsThere is an excellent agreement between Mehrviru and Sacace test results, and the diagnostic accuracy indices were similar.

Evaluating the Impact of Retinal Vessel Segmentation Metrics on Retest Reliability in a Clinical Setting: A Comparative Analysis Using AutoMorph.

Current research on artificial intelligence-based fundus photography biomarkers has demonstrated inconsistent results. Consequently, we aimed to evaluate and predict the test-retest reliability of retinal parameters extracted from fundus photography. Two groups of patients were recruited for the study: an intervisit group (n = 28) to assess retest reliability over a period of 1 to 14days and an intravisit group (n = 44) to evaluate retest reliability within a single session. Using AutoMorph, we generated test and retest vessel segmentation maps; measured segmentation map agreement via accuracy, sensitivity, F1 score and Jaccard index; and calculated 76 metrics from each fundus image. The retest reliability of each metric was analyzed in terms of the Spearman correlation coefficient, intraclass correlation coefficient (ICC), and relative percentage change. A linear model with the input variables contrast-to-noise-ratio and fractal dimension, chosen by a P-value-based backward selection process, was developed to predict the median percentage difference on retest per image based on image-quality metrics. This model was trained on the intravisit dataset and validated using the intervisit dataset. In the intervisit group, retest reliability varied between Spearman correlation coefficients of 0.34 and 0.99, ICC values of 0.31 to 0.99, and mean absolute percentage differences of 0.96% to 223.67%. Similarly, in the intravisit group, the retest reliability ranged from Spearman correlation coefficients of 0.55 and 0.96, ICC values of 0.40 to 0.97, and mean percentage differences of 0.49% to 371.23%. Segmentation map accuracy between test and retest never dropped below 97%; the mean F1 scores were 0.85 for the intravisit dataset and 0.82 for the intervisit dataset. The best retest was achieved with disc-width regarding the Spearman correlation coefficient in both datasets. In terms of the Spearman correlation coefficient, the worst retests of the intervisit and intravisit groups were tortuosity density and artery tortuosity density, respectively. The intravisit group exhibited better retest reliability than the intervisit group (P < 0.001). Our linear model, with the two independent variables contrast-to-noise ratio and fractal dimension predicted the median retest reliability per image on its validation dataset, the intervisit group, with an R2 of 0.53 (P < 0.001). Our findings highlight a considerable volatility in the reliability of some retinal biomarkers. Improving retest could allow disease progression modeling in smaller datasets or an individualized treatment approach. Image quality is moderately predictive of retest reliability, and further work is warranted to understand the reasons behind our observations better and thus ensure consistent retest results.

Leakage prediction approach and influencing factor analysis from seal test

PurposeLeakage serves as a core indicator of sealing performance degradation, particularly under high-speed and heavy-duty operational where increased leakage is common. Within heavy-duty vehicle transmissions, the leakage can lead to excessive pressure loss and eventual transmission failure. This study aims to introduce a predictive method for assessing sealing ring leakage in vehicle transmissions based on operating conditions.Design/methodology/approachSeal test was carried out using a specialized seal test rig. Various data points were collected during this test, including leakage, friction torque, oil temperature, oil pressure and rotating speed. The collected data underwent noise separation and reconstruction using the complete ensemble empirical mode decomposition with adaptive noise method. Subsequently, a leakage prediction model is developed using the random forest regression with parameter optimization. A quantitative evaluation for influencing factors in leakage prediction process is investigated.FindingsThe results achieve a mean accuracy index exceeding 95%, demonstrating close alignment between predicted and actual leakage values. Feature contribution results highlight that the trends of the oil temperature, friction torque and oil pressure significantly affect the leakage prediction, with the oil temperature trend exerting the most substantial influence.Originality/valueThis work sheds light on the interplay between operating conditions and sealing performance degradation, offering valuable insights for understanding and addressing sealing issues effectively.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2024-0271/

Evaluation of V-type titanium cable internal fixation for the treatment of young adult fifth lumbar spondylolysis: technical notes and a retrospective clinical study

BackgroundVarious strategies, each with its own set of limitations, are available for managing lumbar spondylolysis. In response, our department has developed an innovative solution: a V-shaped titanium cable integrated with a pedicle screw internal fixation system specifically designed for lumbar spondylolysis in young adults.AimThe objective of this study was to thoroughly investigate the long-term efficacy of V-type titanium cable internal fixation for the management of spondylolysis, especially in young adults.MethodsTwenty-one patients with fifth lumbar spondylolysis were treated using V-shaped titanium cables and pedicle screw internal fixation at the General Hospital Xinjiang Military Command. The duration of low back pain before surgery was 6 ~ 48 (15.85 ± 11.57) months. The Medtronic (S7) navigation system was used during surgery to guide the placement of pedicle screws, aiming to avoid damaging the L4-5 facet joint by positioning the screws lower and further to the side. Intraoperative indices (operative time and intraoperative blood loss) were recorded. The visual analog scale (VAS), Oswestry Disability Index (ODI), pelvic incidence (PI), and intraoperative imaging measurements of screw accuracy were meticulously recorded and assessed in a comprehensive manner. This thorough evaluation was conducted both intraoperatively and throughout the follow-up period, which lasted for at least one year.The Macnab efficacy criteria were used to assess postoperative outcomes during the final follow-up period.ResultsAll patients successfully completed the 1-year follow-up on time. Two patients experienced postoperative wound liquefaction and successfully recovered after undergoing dressing changes. The average duration of the surgical procedure was 113.09 ± 6.97 min, and an intraoperative blood loss of 50.47 ± 21.32 millilitres was observed. Significant differences were noted in visual analog scale (VAS) scores before and after surgery at various time intervals, indicating improvement with the progression of rehabilitation exercises. No significant changes were found in the pelvic incidence (PI), and there were no notable differences between the preoperative and postoperative periods. No loosening, breakage, or failure of the internal fixation was found during the long-term follow-up. Furthermore, there were no serious complications, such as infection or vascular or nerve injuries. occurred during the procedure. A patient who presented with a considerable cryptic fissure of sacrum 1 experienced dural injury during the surgical procedure. Intraoperatively, dural sutures were skillfully applied, and the adjacent muscles were fortified. Remarkably, the patient achieved successful healing in a single stage. On the first day postsurgery, a gradual improvement in mobility was noted.ConclusionsThe use of a V-shaped titanium cable in conjunction with a pedicle screw internal fixation system for the management of fifth lumbar spondylolysis in young individuals is characterized by its ease of execution and ability to yield favorable outcomes. This approach relies on the prerequisite that patients with minimal intervertebral disc injury or mild lumbar spondylolisthesis demonstrate high overall success rates but experience low failure rates associated with internal fixation. Most significantly, this technique involves segmental internal fixation, which safeguards the functional spinal unit (FSU).

Investigations of Acute Ischemic Stroke Detection Techniques from CT Images Using Machine Learning Approaches

Stroke is a kind of cerebrovascular accident (CVA) that primarily affects individuals over the age of 50, while it can afflict people of all ages. Stroke patients suffer from a chronic condition that leaves them physically unable till their death. To identify the onset of this illness, numerous studies have been carried out. Common signs of a stroke include stiffness, a shift in posture, and tremors in the arms and legs. Functional and emotional mediation are linked to a significant area of the brain. The symptoms of many brain disorders are similar in the early stages because of disruptions to dopamine and other regulating mechanisms. This is a crucial step in how stroke lesions develop. The CAD system is the most efficient method for identifying a stroke. By speeding up the analysis of the required aberration, the CAD system improves the process of illness diagnosis. The goal of the proposed study is to create a compact system that can lessen processing errors, false positive rates, and complexity—three major problems with the current system. The proposed study uses a machine learning model to interpret and classify a CT brain image that shows an Acute Ischaemic Stroke (AIS) lesion. To distinguish between normal and AIS stroke lesions, the pre-processed image is segmented and classed using a Random Forest classifier. Performance criteria like Peak signal ratio, average gradient, accuracy, specificity, sensitivity, and dice index are used to evaluate the experimental output of the proposed work. The suggested model has a straightforward processing structure and the highest efficiency.

Forest Fire Burn Scar Mapping Based on Modified Image Super-Resolution Reconstruction via Sparse Representation

It is of great significance to map forest fire burn scars for post-disaster management and assessment of forest fires. Satellites can be utilized to acquire imagery even in primitive forests with steep mountainous terrain. However, forest fire burn scar mapping extracted by the Burned Area Index (BAI), differenced Normalized Burn Ratio (dNBR), and Feature Extraction Rule-Based (FERB) approaches directly at pixel level is limited by the satellite imagery spatial resolution. To further improve the spatial resolution of forest fire burn scar mapping, we improved the image super-resolution reconstruction via sparse representation (SCSR) and named it modified image super-resolution reconstruction via sparse representation (MSCSR). It was compared with the Burned Area Subpixel Mapping–Feature Extraction Rule-Based (BASM-FERB) method to screen a better approach. Based on the Sentinel-2 satellite imagery, the MSCSR and BASM-FERB approaches were used to map forest fire burn scars at the subpixel level, and the extraction result was validated using actual forest fire data. The results show that forest fire burn scar mapping at the subpixel level obtained by the MSCSR and BASM-FERB approaches has a higher spatial resolution; in particular, the MSCSR approach can more effectively reduce the noise effect on forest fire burn scar mapping at the subpixel level. Five accuracy indexes, the Overall Accuracy (OA), User’s Accuracy (UA), Producer’s Accuracy (PA), Intersection over Union (IoU), and Kappa Coefficient (Kappa), are used to assess the accuracy of forest fire burn scar mapping at the pixel/subpixel level based on the BAI, dNBR, FERB, MSCSR and BASM-FERB approaches. The average accuracy values of the OA, UA, PA, IoU, and Kappa of the forest fire burn scar mapping results at the subpixel level extracted by the MSCSR and BASM-FERB approaches are superior compared to the forest fire burn scar mapping results at the pixel level extracted by the BAI, dNBR and FERB approaches. In particular, the average accuracy values of the OA, UA, PA, IoU, and Kappa of the forest fire burn scar mapping at the subpixel level detected by the MSCSR approach are 98.49%, 99.13%, 92.31%, 95.83%, and 92.81%, respectively, which are 1.48%, 10.93%, 2.47%, 15.55%, and 5.90%, respectively, higher than the accuracy of that extracted by the BASM-FERB approach. It is concluded that the MSCSR approach extracts forest fire burn scar mapping at the subpixel level with higher accuracy and spatial resolution for post-disaster management and assessment of forest fires.