Accurate Evaluation Research Articles

Simulations of Dedicated LEO-PNT Systems for Precise Point Positioning: Methodology, Parameter Analysis, and Accuracy Evaluation

Simulations of Dedicated LEO-PNT Systems for Precise Point Positioning: Methodology, Parameter Analysis, and Accuracy Evaluation

199 Evaluation of the Diagnostic Accuracy of Exhaled Nitric Oxide as a Marker of Infection and Sepsis in Emergency Department Patients

199 Evaluation of the Diagnostic Accuracy of Exhaled Nitric Oxide as a Marker of Infection and Sepsis in Emergency Department Patients

A Novel Multi-criteria Decision Making Approach Based on Bipolar Neutrosophic Set for Evaluating Financial Markets in Egypt

In the evolving landscape of financial markets, assessing company performance with precision and reliability is crucial for investors and analysts. This paper introduces an innovative framework for evaluating the financial performance of companies listed on the Egyptian Exchange (EGX) by integrating Bipolar Neutrosophic Sets (BNS) with Multi-Criteria Decision-Making (MCDM) methods. The proposed framework utilizes BNS to incorporate the inherent uncertainty and imprecision of financial data, and integrates MCDM methods to provide a comprehensive assessment of multiple performance indicators. Specifically, the evaluation focuses on seven critical criteria: Stock Returns, Volatility, Liquidity, Market Capitalization, Earnings Growth, Revenue Growth, and Analyst Ratings. By utilizing Bipolar Neutrosophic Weighted Average (BNWA) operators and similarity measures, the study effectively ranks companies through a nuanced analysis of these criteria. The results highlight the effectiveness of the proposed approach in providing a robust and accurate financial performance evaluation framework tailored to the unique dynamics of the Egyptian market. The study demonstrates that the hybrid model not only accommodates variations in criteria weights but also offers valuable insights for investment decisions in emerging markets. The approach exhibits resilience and adaptability, providing a more informed and reliable basis for evaluating financial performance. This research contributes to the advancement of financial performance assessment methodologies by showcasing the potential of combining BNS with MCDM techniques. The research also emphasizes the framework's practical applications and suggests future research directions for refining the model and exploring its use in diverse financial contexts.

A dynamic risk assessment model for evacuation in healthcare facilities during fire scenarios

Evacuation is a critical procedure for safeguarding individuals during a fire incident within a healthcare facility. Assessing the risk associated with the evacuation process is instrumental in enhancing emergency decision-making and developing contingency strategies. Evacuation, a dynamic process, poses challenges in accurate risk evaluation using conventional static quantitative risk assessment (QRA) techniques. This study introduces a dynamic QRA approach to precisely evaluate the evacuation risk to individuals in healthcare settings during a fire. The dynamic event tree methodology is initially utilized to determine the evolving probabilities of various fire scenarios, considering the equipment failure and repair rates. Subsequently, data on fire products and the evacuation of individuals under different fire scenarios are gathered through simulations conducted with Fire Dynamics Simulator (FDS) and Pathfinder software. In addition, this research proposes a dynamic incapacitation model to calculate the consequences of fire, incorporating changes in individuals' locations and the fire products surrounding them during evacuation to accurately estimate the casualty risk. Lastly, the dynamic scenario probability was integrated with the average incapacitation probability for all individuals, introducing the concept of a fire evacuation safety time window to comprehensively assess hospital fire evacuation risk. The findings revealed that the dynamic quantitative risk analysis (DQRA) approach more effectively captures the influence of fire products on the evacuation risk compared to static methods. Specifically, the peak incapacitation probability in a hospital fire reached 0.85, with firefighting equipment, especially the sprinkler system, reducing this risk by over 97% and decreasing evacuation time by 45%. In addition, the risk of fire evacuation in hospitals reaches a critical threshold in the fourth year, enhancing the repair rate of the firefighting system postpones the risk's escalation to a critical threshold until the fourteenth year.

BrainQCNet: A Deep Learning attention-based model for the automated detection of artifacts in brain structural MRI scans

Abstract Analyses of structural MRI (sMRI) data depend on robust upstream data quality control (QC). It is also crucial that researchers seek to retain maximal amounts of data to ensure reproducible, generalizable models and to avoid wasted effort, including that of participants. The time-consuming and difficult task of manual QC evaluation has prompted the development of tools for the automatic assessment of brain sMRI scans. Existing tools have proved particularly valuable in this age of Big Data; as datasets continue to grow, reducing execution time for QC evaluation will be of considerable benefit. The development of Deep Learning (DL) models for artifact detection in structural MRI scans offers a promising avenue toward fast, accurate QC evaluation. In this study, we trained an interpretable Deep Learning model, ProtoPNet, to classify minimally preprocessed 2D slices of scans that had been manually annotated with a refined quality assessment (ABIDE 1; n = 980 scans). To evaluate the best model, we applied it to 2141 ABCD T1-weighted MRI scans for which gold-standard manual QC annotations were available. We obtained excellent accuracy: 82.4% for good quality scans (Pass), 91.4% for medium to low quality scans (Fail). Further validation using 799 T1w MRI scans from ABIDE 2 and 750 T1w MRI scans from ADHD-200 confirmed the reliability of our model. Accuracy was comparable to or exceeded that of existing ML models, with fast processing and prediction time (1 minute per scan, GPU machine, CUDA-compatible). Our attention model also performs better than traditional DL (i.e., convolutional neural network models) in detecting poor quality scans. To facilitate faster and more accurate QC prediction for the neuroimaging community, we have shared the model that returned the most reliable global quality scores as a BIDS-app (https://github.com/garciaml/BrainQCNet).

Performance of the New CKD-EPI Creatinine-and Cystatin C-based Glomerular Filtration Rate Estimation Equation in Living Kidney Donor Candidate.

Accurate preoperative evaluation of renal function in living kidney donor candidates (LKDCs) is crucial to prevent kidney failure after nephrectomy. We examined the performance of various estimated glomerular filtration rate (eGFR) equations, including the new chronic kidney disease epidemiology collaboration (CKD-EPI) equation in LKDCs. We analyzed 752 LKDCs who were assessed for measured GFR by inulin clearance as part of routine pretransplant examination from 2006 to 2020. CKD-EPI2012 from cystatin C (CKD-EPI12cys), CKD-EPI2021 from creatinine (CKD-EPI21cr), CKD-EPI21cr-cys, Japanese modified (JPN) eGFRcr, and JPN eGFRcys were compared in determining the suitability for LKDCs. CKD-EPI12cys had the lowest absolute and relative biases, with higher P30 and P10, followed by JPN eGFRcys, CKD-EPI21cr, and CKD-EPI21cr-cys. The root mean square error was least for CKD-EPI12cys, then JPN eGFRcys, CKD-EPI21cr-cys, CKD-EPI21cr, and JPN eGFRcr. CKD-EPI21cr, CKD-EPI12cys, and CKD-EPI21cr-cys estimated GFR higher, whereas JPN eGFRcr estimated GFR lower. At the threshold of 90 mL/min/1.73 m2, CKD-EPI21cr had the highest percentage of misclassification at 37.37%, whereas JPN eGFRcr had the lowest percentage of misclassification at 6.91%. Using the age-adapted approach, JPN eGFRcr had the lowest percentage of misclassification into overestimation at 7.31%. All eGFR had >5.0%, and CKD-EPI21cr had the highest percentage of misclassification at 21.94%. Conversely, CKD-EPI21cr-cys had the lowest percentage of misclassification into underestimation at 3.19%, both at the threshold of 90 mL/min/1.73 m2 and the age-adapted approach. JPN eGFRcr had the highest percentage at 33.38% and 40.69%, respectively. In evaluating the renal function of Japanese LKDCs, the new CKD-EPI equation had a lower rate of underestimation but a relatively high rate of overestimation. New GFR estimation formulas are needed to be tailored to each ethnic group to enhance the accuracy and reliability of donor selection processes.

Intelligent Handwritten Identification Using Novel Hybrid Convolutional Neural Networks – Long-short-term Memory Architecture

Handwritten character identification finds broad applications in document analysis, digital forensics, and human-computer interaction. Conventional methods encounter challenges in accurately deciphering a range of handwriting styles and variations. Consequently, investigating intelligent system for handwriting identification becomes crucial to enhance accuracy and efficiency. This paper introduces an innovative hybrid deep learning architecture, seamlessly integrating the strengths of convolutional neural networks (CNN) and long-short term memory (LSTM) within a one single framework. The combination of these structures enables the model to effectively capture both spatial features and temporal dependencies inherent in handwritten strokes, resulting in improved recognition performance. The proposed 2DCNN-LSTM algorithm has been tested on MNIST dataset. The proposed hybrid CNN-LSTM structure has been compared to conventional intelligent machine learning methods, and the results demonstrate the superior performance of the hybrid CNN-LSTM, showcasing heightened accuracy, sensitivity, specificity, and other evaluation metrics.

Findings from transoesophageal echocardiographic follow-up after mitral transcatheter edge-to-edge repair.

Transoesophageal echocardiography (TOE) provides accurate evaluation of mitral valve (MV) function following mitral transcatheter edge-to-edge repair (M-TEER) and may better detect complications in case of suboptimal result. We aimed to evaluate midterm anatomical changes and structural complications after M-TEER using TOE and investigate their association with clinical outcomes at 2 years. A follow-up TOE at 6 months was systematically recommended to all patients included in our institutional prospective M-TEER registry until December 2021. We assessed changes in the incidence of mitral regurgitation (MR), MV stenosis (≥5 mmHg), and partial or complete single leaflet device attachment (SLDA) between the index procedure and follow-up and evaluated MV area and annular dimensions in a subset of patients with available three-dimensional (3D) datasets. The clinical endpoint was a composite of mortality and heart failure (HF) rehospitalisation at 2 years. Among the 373 patients included in the registry between February 2012 and December 2021, 128 patients (34%) underwent elective TOE at 6 months. Using TOE, severe MR was observed in 13.3% (n=17) of the patients. The number of patients with an elevated MV gradient increased from 17 (13.3%) after the procedure to 23 (18%) at 6 months, and a new partial or complete SLDA was detected in 7.8% (n=10). Based on 3D TOE measurements, significant increases in MV area, annular area, annular perimeter, and intercommissural (but not anteroposterior) diameter were observed compared to intraprocedural images. A mean MV gradient ≥5 mmHg (hazard ratio [HR] 2.30, 95% confidence interval [CI]: 1.10-4.81; p=0.023) and the presence of severe MR at 6 months (HR 3.26, 95% CI: 1.18-8.99; p=0.023) were associated with the primary endpoint, which was met in 34 (26.6%) patients at 2 years. TOE follow-up allowed the detection of complications that would not be diagnosed using transthoracic echocardiography only and should therefore be used liberally in the patients presenting with a suboptimal result. A mean MV gradient ≥5 mmHg and severe MR, diagnosed at the 6-month TOE follow-up, were associated with adverse clinical outcomes.

Development of polarizable and hydration-focused water models for the Martini 3 force field

Coarse-grained simulations have been increasingly employed in many fields of molecular chemistry. These models enable the evaluation of complex molecular structures by simplifying their description compared to all-atoms models. Specifically, the Martini 3 force field has been developed to describe biomolecular structures such as proteins, lipids, and carbohydrates. Their “building block” approach has been successfully used in modeling complex systems ranging from carbohydrate solutions, biological membranes, and even deep eutectic solvents. However, one main issue arises when considering interfacial systems: water is represented as a single particle with no effect of electrostatic interactions. In this work, we developed a polarizable water model for the Martini 3 force field, introducing charged particles to model the electrostatic interactions present in water systems accurately. Gaussian Processes were trained with simulation data for both bulk water and cross-interaction parameterization to consistently explore the parameter space and fine-tune our model to the target properties. The developed model showed good performance in reproducing the density and dielectric constant of bulk water. Regarding cross-interaction with other beads, two different sets of parameters were developed. The first, called the polarizable model, is parameterized to reproduce the hydration free energies of the original Martini 3 organic beads. This allows for a good description of free energies of transfer between water and organic phases, the main target property for the Martini 3 force field. The second, called the polarizable-hydration model, is parameterized to match the experimental hydration free energy of selected molecules. This allows for more accurate evaluation of hydration properties, which can be useful for water solutions. In particular, we show that the partitioning and interfacial behavior of nonionic surfactants is improved by the polarizable-hydration model when compared to the standard Martini 3 water model.

Estimation of human airway deposition of size-segregated particulate and their health impact in monsoon and post-monsoon season in semi-urban area of northern India

Numerous studies have shown the negative association between Particulate Matter (PM) exposure and health impact, understanding this risks associated with PM exposure for generating appropriate risk-control strategies, an accurate evaluation of PM concentration and respiratory deposition is necessary. So, the present study aims to describe the temporal and diurnal variation of Particulate Matter and Black Carbon concentrations, as well as their health risks. Real-time monitoring of PM and BC was conducted near a national highway (NH-19) in the semi-urban area. Our findings indicated that the washout effect played its role in reducing the PM level in monsoon season while meteorological parameters, local anthropogenic activities, and stubble-burning influenced the high PM concentration in the post-monsoon season. The current study also estimates the particle deposition in the human respiratory tract using Multiple Path Dosimetry Model (MPPD) for understanding the health effects of particle inhalation. The results revealed that different seasons exhibit different deposition patterns. For example, during the monsoon season, the trend was PM10>PM2.5>PM1.0 while in post-monsoon season PM2.5 >PM10 > PM1.0 respectively. The findings indicate that small particles easily deposited deeper into the lungs have a significant impact on various age groups, particularly children, who are the most affected. Furthermore, the health risks of PM and BC exceeded the safer limit (HQ = 1) and ELCR≥10−6, suggesting prolonged inhalation exposure to particulate could lead to cancer. The present study will give an insight into designing interventions and policies to control PM pollution in smaller cities, where a significant portion of the population resides.

The Antecedents, Consequences and Key Determinants of Corporate Accelerators’ Success: A Systematic Literature Review

Over the past decade, accelerators have demonstrated their pivotal contribution to the economic and entrepreneurial ecosystem growth. In recent years, a large number of businesses and organizations use corporate accelerators to internalize opportunities provided by startups and idea generators outside the organization. Although this phenomenon has received much attention, especially in Iran, there are still many unclear points regarding the structure, processes, and acceleration programs. In this article, we did a systematic review of 49 articles out of 486 articles published in different journals in the last 5 years. Then, through the PRISMA method, the reviewed data was converted into a structured format in Excel software. At the end, we examined the findings of this study by presenting a conceptual framework detailing the antecedents and consequences of corporate accelerators. Also, in this research, we found that the five main success factors of corporate accelerators are the acceleration program, the creation of a large and committed external communication network, mentoring, the determination of accurate evaluation criteria and the competitive process in selecting startups.

Upper Limb Rehabilitation Robots Training Analysis based on Multi-sensor Trajectory Data and Human-computer Interaction

Upper limb rehabilitation robots have important practical significance in helping patients recover their motor function, but traditional training methods often lack real-time and accurate evaluation of patient movement status and intention and have low interactive participation. Therefore, the study proposes a training system for upper limb rehabilitation robots based on multi-sensor trajectory data and human-computer interaction. The system is designed from three aspects: inertial sensor trajectory tracking, Kinect sensor image restoration, and interaction system design. A polynomial joint zero value constraint algorithm is introduced to correct errors, and variable parameter pixel filtering combined with a weighted average moving algorithm is used to improve image quality. The training results showed that the robot trajectory tracking accuracy significantly improved, and the improvement in motion trajectory error was greater than 20%. Compared with traditional training methods, interactive training had a recognition accuracy of over 85% in rehabilitation actions, and the system had better stability. This rehabilitation robot can effectively meet the feasibility and effectiveness of improving the interaction system, providing new technological means for intelligent medical services.

Design of optical performance for self-luminous pavement materials

The lower visibility in the nighttime environment makes higher accident risks during driving. Using self-luminous materials in pavement construction can effectively improve the luminance of load surface, thus significantly improve nighttime driving safety. This study has designed three types of self-luminous pavement materials (SLPM) with different optical properties by incorporating long-persistent luminescent aggregates into the mixture and using polymer binders with varying degrees of light transmittance, such as transparent polyurethane, transparent asphalt, and traditional asphalt. On this basis, utilizing digital image processing techniques to compare the optical performance differences among these three SLPM and established corresponding computational models based on the obtained data to enable efficient and accurate evaluation of the optical properties of pavement materials. The experimental results indicate that the durability performance of SLPM gradually diminishes over time, and this degree of attenuation is negatively correlated with the light transmittance of the binder. Specifically, a higher light transmittance of the binder results in a smaller decline in SLPM durability, whereas a lower transmittance leads to a more significant reduction. The optical performance calculation model established by this research can predict the reflective performance of SLPM through the optical parameters of aggregate, binders, and the doping amount of long persistent luminescent materials. In addition, the model can accurately calculate the minimum content of self-luminous materials according to the optical requirements required by the project, which can provide a reference for the performance evaluation of self-luminous pavement materials and the selection of optimal proportioning scheme.

Forest structure explains spatial heterogeneity of decadal carbon dynamics in a cool-temperate forest

Abstract Accurate evaluation of forest biomass distribution and its long-term change over wide areas is required for effective forest carbon management and prediction of landscape-scale forest dynamics. We evaluated a landscape-scale (225 km2) decadal forest carbon budget at a 1 ha spatial resolution in a cool-temperate forest, by repeating airborne laser observations 10 years apart and partitioning net forest biomass change (FBC) into growth and mortality. Using >10 000 samples, we revealed that naturally regenerated forests have large spatial heterogeneity in net biomass change, and 3/4 of the photosynthetically acquired carbon stock moved to necromass even without anthropogenic disturbances. Actual carbon residence time as living tree biomass was estimated by dividing biomass by growth or mortality rates. The residence time was 107 and 106 years, respectively with large spatial variation among stands (48 and 42 years, respectively, as the difference between 25 and 75 percentile), although studied forest stands have small variation in the forest functional type in a landscape-scale. The best predictors of subsequent decadal biomass changes were two forest structural factors, mean canopy height and canopy height variation in addition to one environmental factor, elevation. Considering the long lifetime of trees, these structural factors may be an indicator of forest soundness rather than a cause of forest growth or mortality. However, in any cases, these structural factors can be powerful predictors of subsequent FBC.

A novel configuration optimization approach for IES considering exergy-degradation and non-energy costs of equipment

Improving the exergy utilization and economic performance is the core goal for the configuration optimization of the integrated energy system (IES). Achieving such a goal requires accurate evaluation of the exergy efficiency and economy. However, IES is a multisource heterogeneous and strongly coupled system. The development of reasonable exergy efficiency-economy characteristic evaluation indicators is a complex issue that limits its application in IES configuration. This article proposes a novel IES configuration optimization approach based on the concept of the equipment exergyeconomic cost coefficient. The exergyeconomic cost coefficient consists of the exergy-degradation and non-energy costs, which can quantitatively characterize the exergy efficiency and economy of the IES under a unified benchmark. A mechanism model for the equipment exergyeconomic cost coefficient, output of various equipment and total exergyeconomic cost is established. Furthermore, exergy flow attribute classification method is proposed to obtain the equipment exergyeconomic cost coefficient in the IES. The proposed method is verified in a real-world study case located in Yancheng, China. Compared to the conventional configuration method, the capacity of the renewable energy and combined heat and power generation (CHP) are enhanced, while the capacity of purchased electricity and electric boiler (EB) are reduced. It can obtain the minimum total exergyeconomic cost (TEC) for the IES. This study offers a new direction for the optimal configuration of IES.

Low-Frequency Magnetic Incremental Permeability for the Non-destructive Evaluation of Hardness Profile After Carburization Treatment with Large Case Hardening Depth

Carburization treatment with large case hardening depth is a technical process to enhance steel parts' surface hardness and wear resistance. Accurate evaluation of this metallurgical treatment is crucial to prevent critical mechanical failures. Low-frequency magnetic incremental permeability (LF-MIP) emerges as a non-destructive surface technique well-suited for this purpose in the case of ferromagnetic parts. Although correlations between magnetic indicators obtained through LF-MIP characterization and deep carburization treatment have been demonstrated, they remain qualitative. In this study, we propose an innovative method to assess the entire hardness profile based on LF-MIP characterization. Experimental results and simulation data are integrated into a reference chart used for post-processing, enabling the prediction of hardness profiles for specimens in a blind test. With a relative Euclidean distance of less than 6% between the method's predictions and destructive tests conducted on specimens treated with medium, deep, and intense intensities, we consider the method validated.

Greenhouse gas emission and denitrification kinetics of woodchip bioreactors treating onsite wastewater

The accurate evaluation of denitrification rate and greenhouse gas (GHG) emission in field-scale woodchip bioreactors for onsite wastewater treatment are problematic due to inevitably varied environmental conditions and underestimated GHG production with limited analysis of dissolved gas in field samples. To address these problems, batch incubation experiments were conducted with controlled conditions to precisely evaluate the denitrification kinetics and N2O and CH4 emission of both gaseous and dissolved phases in fresh (6 months) and aged (5 years) woodchip bioreactors treating onsite wastewater at high (1–3 mg L-1) and no (0 mg L-1) dissolved oxygen (DO) levels. NO3- removal rate decreased from 37.5–119.0 g NO3--N m-3d-1 at no DO to 8.8–16.6 g NO3--N m-3d-1 at high DO (1–3 mg L-1) due to the growth suppression of NO2- reducing microorganisms (37–55 % lower nirS+nirK abundance). However, the presence of high DO increased N2O emission level from 5.6–6.9 mg N2ON m-3 at no DO to 179.5–273.6 mg N2ON m-3) due to the enhanced growth of NO reducing microorganisms (1–7 times higher norB levels) and the decreased abundance of N2O reducing microorganisms (53–75 % lower nosZ abundance). On the other hand, increased DO level negatively correlated with CH4 production (1.0–3.9 g CH4-C m-3d-1) in fresh woodchips, while showed insignificant impact on CH4 production (0.1–1.4 g CH4-C m-3d-1) in aged woodchips. Woodchip age increase (5 years) negatively impacted the NO3- removal rate (75–85 % lower than fresh woodchips) and CH4 production rate (>3 times lower than fresh woodchips), probably due to the reduced biomass density of NO2- reducing microorganisms (52–58 % lower nirS+nirK abundance) and methanogens (95–98 % lower mcrA levels). The incubation results suggested that long hydraulic retention time (>2–5 days) and anaerobic/anoxic condition are preferred for the optimal NO3- removal and low N2O emission potential of woodchip bioreactors treating onsite wastewater.

Applicability of ensemble learning in total organic carbon and porosity evaluation of shales

Accurate evaluation of total organic carbon (TOC) content and porosity is of paramount significance for assessment and target interval selection for shale reservoirs. This study takes shales from the western Chongqing area as an exemplary case to delve into the applicability and reliability of ensemble learning in evaluating TOC content and porosity. The results indicate that although both Light Gradient Boosting Machine (LightGBM) and Random Forest (RF) algorithms are suitable for evaluating TOC content and porosity in shales, LightGBM algorithm is preferred due to its comprehensive advantages, including higher accuracy, stronger generalization capability, and faster operating speed. For TOC content evaluation, the four most important logging parameters identified by LightGBM and RF are consistent, but exhibit different orders: DEN (compensated density) > GR (gamma ray) > U (uranium) > CNL (compensated neutron) and DEN > U > GR > CNL, respectively. For porosity evaluation, LightGBM and RF identify the same type and order of the three most important logging parameters: AC (acoustic transit time) > DEN > U. This similarity may be attributed to the fact that both algorithms utilize Classification and Regression Tree (CART) as base learners. The dependence plots between SHAP (SHapley Additive exPlanations) values and logging parameters reveal that the role of each logging parameter in the evaluation model is segmented, rather than exhibiting a continuous linear contribution. In conclusion, given the exceptional performance of ensemble learning algorithms, they, especially LightGBM algorithm, are highly recommended for shale evaluation.

Classification of Neoadjuvant Therapy Response in Patients With Colorectal Liver Metastases Using Contrast-Enhanced Ultrasound—With Histological Pathology as the Gold Standard

ObjectiveTo evaluate the response to neoadjuvant therapy in patients with colorectal liver metastases (CRLMs) using ultrasound(US) and contrast-enhanced ultrasound(CEUS), with correction to the tumor regression grade (TRG) of pathological results. MethodsThis study included patients with resectable CRLMs admitted from February to December 2022. After at least 4 cycles neoadjuvant therapy, all the patients received US and CEUS examinations within two weeks before hepatectomy. CEUS clips were postprocessed with color parameter imaging (CPI) and microflow imaging (MFI) analysis. Logistic regression analyses were used to develop an evaluation Nomogram. Ultrasound-based model was constructed to discriminate between the response (TRG1/2/3) and nonresponse (TRG4/5) groups at the lesion level. The model's predictive ability was evaluated using the C index and calibration curve, with decision curve analysis assessing the Nomogram's added value. ResultsThe study analyzed 105 CRLM lesions (the lesion with the highest diameter analyzed for each patient), with 43.8% showing a response to therapy. Univariate analysis identified calcification on US (p = 0.039), CEUS enhancement degree (p < 0.001), CEUS enhancement pattern (p<0.001), CEUS washout type (p < 0.001), CEUS necrosis (p < 0.001), CPI feeding artery (p = 0.003) and MFI pattern (p < 0.001) were significantly associated with TRG. The multivariate analysis showed CEUS enhancement pattern (p = 0.026), CEUS washout type (p = 0.018) and CEUS necrosis (p = 0.005) were independently associated with the neoadjuvant therapy response. A Nomogram with the three independent predictors was developed, with an AUC of 0.898. ConclusionThe ultrasound-based model provided accurate evaluation of pathological tumor response to preoperative chemotherapy in patients with CRLM, and may help to decide the individualized treatment strategy.

Shaking table study on the seismic dynamic behavior of high-speed railway subgrade with pile network composite-reinforced soil

Pile network composite structures are used in the construction of high-speed railway subgrades. There have been few studies on their seismic dynamic response, however, which has restricted the accurate evaluation of their seismic performance. In this study, a series of shaking table tests on a pile network composite-reinforced soil high-speed railway subgrade were conducted. Particle image velocimetry was used to analyze the slope motion and sensor data to evaluate the overall dynamic response characteristics of the subgrade. Findings indicate that seismic activity causes subsidence throughout the subgrade, with deformations occurring in three distinct phases depending on the input seismic amplitude from 0.1 to 0.4 g (slow increasing), 0.4–0.6 g (faster increasing), and 0.6–1.0 g (rapidly increasing). The inclusion of geogrids aids in dissipating seismic energy, thereby reducing the peak acceleration amplification along the elevation. The increased dynamic soil pressure of the subgrade is mitigated by the geogrid reinforcement, which improves local stability. Moreover, the geogrid strain escalated with greater seismic wave amplitudes, resulting in the progressive expansion of the unstable zone from the slope towards the center of the subgrade as the elevation increased.