Static Parameters Research Articles

Kalman tracking and parameter estimation of continuous gravitational waves with a pulsar timing array

Abstract Continuous nanohertz gravitational waves from individual supermassive black hole binaries may be detectable with pulsar timing arrays. A novel search strategy is developed, wherein intrinsic achromatic spin wandering is tracked simultaneously with the modulation induced by a single gravitational wave source in the pulse times of arrival. A two-step inference procedure is applied within a state-space framework, such that the modulation is tracked with a Kalman filter, which then provides a likelihood for nested sampling. The procedure estimates the static parameters in the problem, such as the sky position of the source, without fitting for ensemble-averaged statistics such as the power spectral density of the timing noise, and therefore complements traditional parameter estimation methods. It also returns the Bayes factor relating a model with a single gravitational wave source to one without, complementing traditional detection methods. It is shown via astrophysically representative software injections in Gaussian measurement noise that the procedure distinguishes a gravitational wave from pure noise down to a characteristic wave strain of h0 ≈ 2 × 10−15. Full posterior distributions of model parameters are recovered and tested for accuracy. There is a bias of ≈0.3 rad in the marginalised one-dimensional posterior for the orbital inclination ι, introduced by dropping the so-called ‘pulsar terms’. Smaller biases $\lesssim 10~{{\%}}$ are also observed in other static parameters.

DFT insights into the nonlinear optical properties of azulene-stilbene dyads for photonics and optoelectronics

The present study aims to investigate the nonlinear optical characteristics of a series of azulene-stilbene chromophores through the utilization of density functional theory (DFT). Various acceptor substitution techniques were used to enhance first, second, and third order static and dynamic polarizabilities of the parent azulene-stilbene dyad (AS). The natural transition orbitals and UV-Visible absorption spectra were analyzed using time dependent-density functional theory (TD-DFT). Theoretical computations were performed to analyze the structural, electronic, and charge transfer properties of all designed compounds. The energy gap separating the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) of the designed moieties was observed to decrease in the new compounds compared to the parent compound. Analysis of the nonlinear optical (NLO) data among the designed compounds (AS1-AS10) revealed that AS2, AS3, AS4, and AS10 exhibit promising second and third order NLO characteristics, with AS6 exhibits suitability as a second order NLO material and AS8 and AS9 display third order NLO responses. The computed static and dynamic NLO parameters and maximum absorption wavelength closely match the experimentally reported data in the literature, suggesting these derivatives are excellent candidates for photonic and optoelectronic applications in the future.

Associations of static and dynamic iris parameters in healthy Chinese individuals: the Handan Eye Study.

To determine the associations between the demographic factors (age and sex) and physiological dynamic iris changes and explore the associated factors for iris cross-sectional area (IA) change in healthy Chinese individuals. This cross-sectional study included individuals aged ≥40 years with an open angle and underwent anterior segment optical coherence tomography under light and dark conditions from the follow-up cohort of the Handan Eye Study. Ocular data from the right eye were analyzed. The Pearson correlation coefficient was used to assess the relationship between age and iris parameters, including iris thickness (IT), IA, and iris curvature (IC), as well as the pupil diameter (PD) in the dark, and their changes from light to dark conditions. Linear regression analysis was performed to identify the potential factors associated with IA change. The final analysis included 465 healthy individuals. PD in dark, IA change and PD change decreased with age (P < 0.001), whereas IC increased with age (P < 0.001). IT and IT change were smaller, and IC was larger in women than that in men (P = 0.021, 0.007, and 0.010, respectively). Older age (P = 0.041), larger lens thickness (P = 0.013), larger IC change (P < 0.001), and smaller PD change (P < 0.001) were significantly associated with a smaller IA change. Our study demonstrated the associations of static and dynamic iris parameters in healthy Chinese individuals. The findings provided a possible explanation for the higher prevalence of primary angle closure disease in elderly and female populations.

A state-space relational event modeling approach for learning dynamic social interaction behavior

Relational event models (REMs) are the primary choice for the analysis of relational-event network data. However, the standard REM assumes static parameters, which hinders the modeling of time-varying dynamics. This assumption might be too restrictive in real-life scenarios, making a model that allows for time-varying parameters more valuable. We introduce a state-space extension of the relational event model as a way to tackle this problem. The model has three main attributes. First, it provides a statistical framework of the temporal change of the parameters. Second, it enables the forecasting of future parameter values (which can be utilized to simulate new networks that can account for temporal dynamics in out-of-sample predictions). Third, it requires smaller data structures to be loaded into computer memory compared to the standard REM; this makes the model easily scalable to large networks. We conduct empirical analyses on bike-sharing data, corporate communications, and interactions among socio-political actors to illustrate model usage and applicability.

Optimization Design of Redundant Parallel Posture Adjustment Mechanism for Solar Wing Docking Based on Response Surface Methodology

The parallel mechanism exhibits high stiffness and excellent dynamic response, making it ideal for high-precision applications. In our early work, a novel 6-DOF redundant parallel posture mechanism with four limbs for solar wing docking has been proposed; each limb consists of three links and four joints. This paper primarily focuses on optimization design of the mechanism. The calculation of workspace volume reveals that factors influencing the range of posture adjustment include dynamic platform parameters, static platform parameters, the drive trajectory of each kinematic pair, and the angles between each kinematic pair. A sensitivity analysis was conducted to examine the impact of each parameter on the range of posture adjustment. To reduce computational complexity and improve analysis efficiency, a combined approach of single-factor analysis and response surface methodology (RSM) is used in the paper. Single-factor analysis is utilized to evaluate the effect of each parameter on the posture adjustment range. Based on these results, RSM is used to establish a regression model for parameters; thereby, the optimal parameter combination for the mechanism is determined. The regression coefficient R2 = 0.9374 attests to the validity of the proposed model. Finally, a comparison of the posture adjustment range before and after optimization is presented, providing a foundation for the practical application of the redundant parallel mechanism. This paper introduces a novel structural design concept aimed at resolving the conflict between heavy loads and compact sizes in redundant parallel mechanisms while providing valuable insights for miniaturized design.

Blood pressure variability in elderly and senior hypertensive patients

Background. In recent years the deterioration of the demographic situation has been noted in most countries of the world that is due to the steady increase in population of arterial hypertension and the aging of the population. According to the statistical forecast of the UN by 2025, the number of people over 60 will exceed 1 billion which is 15% of the entire global population. In Ukraine, according to epidemiological studies, the prevalence of hypertension among circulatory system diseases in adults (18 years and older) is 46.8% and almost half of patients with circulatory system diseases have elevated blood pressure. In patients aged 65 years and older, the prevalence of hypertension varies in the range of 53-72%. It has long been proven that the risk of cardiovascular and cerebrovascular complications in hypertensive patients depends not only on the absolute blood pressure level but also on fluctuations in blood pressure over different periods that is blood pressure variability. Blood pressure is not a static parameter but rather undergoes continuous fluctuations over time due to the interaction between environmental factors and behaviour on the one hand and the internal regulatory mechanisms of the cardiovascular system on the other hand. Elevated blood pressure may indicate cardiovascular dysregulation and itself may be a cardiovascular risk factor associated with increased all-cause mortality and cardiovascular mortality, stroke, coronary artery disease, heart failure, end-stage renal disease and incidence of dementia. Aim. The study aimed to improve the system of prevention and diagnosis of elderly and senior hypertensive patients at the ambulatory-polyclinic stage due to the study of the contribution of blood pressure variability. Materials and methods. The group of examinees was formed taking into account 27 elderly and senile hypertensive patients including those combined with coronary artery disease. For this purpose, ambulatory blood pressure monitoring was used. Results. Systolic blood pressure variability (SD) in hypertensive patients was higher than in the control group (p&lt;0.001). So, during the study, SD was 17.9±7.1 mm Hg. while in the control group 12.1±2.6 mm Hg. The frequency of high blood pressure variability detection in elderly and senile hypertensive patients was 51.9% (n=14). High blood pressure variability compared to low blood pressure variability and control was associated with a more pronounced systolic blood pressure variability (during the active monitoring period), and it was equalled 23.7±5.2 mm Hg compared to 14.8±6.5 mm Hg (p&lt;0.001) and 12.1±2.6 mm Hg. (p&lt;0.001). In elderly and senile hypertensive patients with high and low blood pressure variability, the non-dipper group prevailed over the dipper group in the structure of daily blood pressure rhythm (71.4% vs. 28.6% and 84.6% vs. 15.4% respectively). According to the ambulatory blood pressure monitoring data high blood pressure variability group compared to low blood pressure variability and controls was characterised by higher average values of the number of blood pressure indicators. In the group of high blood pressure variability patients, the average daily systolic blood pressure exceeded the control value by 10% (р&lt;0.05). The daily index of systolic blood pressure in the group of high blood pressure variability patients was 111% higher than in patients with low blood pressure variability (p&lt;0.05). The daily index of diastolic blood pressure in the group of high blood pressure variability patients was 140% higher than in patients with low blood pressure variability (p&lt;0.05). The daily index of average blood pressure in the group of high blood pressure variability patients was 191% higher than in patients with low blood pressure variability (p&lt;0.01). Conclusions. 1. High blood pressure variability has occurred in 16 out of 27 (51.9%) hypertensive elderly and senile patients. 2. The non-dipper group has predominated in the structure of blood pressure diurnal rhythm in elderly and senile hypertensive patients with high and low blood pressure variability patients. _________________________________________________________________________________________ Keywords: arterial hypertension; essential hypertension; blood pressure variability; high blood pressure variability; low blood pressure variability; elderly and senile hypertensive patients

Cumulative thermal coupling modeling and analysis of oil-immersed motor-pump assembly for electro–hydrostatic actuator

The Electro–Hydrostatic Actuator (EHA) is applied to drive the control surface in flight control system of more electric aircraft. In EHA, the Oil-Immersed Motor Pump (OMP) serves as the core as a power assembly. However, the compact integration of the OMP presents challenges in efficiently dissipating internal heat, leading to a performance degradation of the EHA due to elevated temperatures. Therefore, accurately modeling and predicting the internal thermal dynamics of the OMP hold considerable significance for monitoring the operational condition of the EHA. In view of this, a modeling method considering cumulative thermal coupling was hereby proposed. Based on the proposed method, the thermal models of the motor and the pump were established, taking into account heat accumulation and transfer. Taking the leakage oil as the heat coupling point between the motor and the pump, the dynamic thermal coupling model of the OMP was developed, with the thermal characteristics of the oil considered. Additionally, the comparative experiments were conducted to illustrate the efficiency of the proposed model. The experimental results demonstrate that the proposed dynamic thermal coupling model accurately captured the thermal behavior of OMP, outperforming the static thermal parameter model. Overall, this advancement is crucial for effectively monitoring the health of EHA and ensuring flight safety.

Modelling the long-term health impact of COVID-19 using Graphical Chain Models

BackgroundLong-term sequelae of SARS-CoV-2 infection, namely long COVID syndrome, affect about 10% of severe COVID-19 survivors. This condition includes several physical symptoms and objective measures of organ dysfunction resulting from a complex interaction between individual predisposing factors and the acute manifestation of disease. We aimed at describing the complexity of the relationship between long COVID symptoms and their predictors in a population of survivors of hospitalization for severe COVID-19-related pneumonia using a Graphical Chain Model (GCM).Methods96 patients with severe COVID-19 hospitalized in a non-intensive ward at the “Santa Maria” University Hospital, Terni, Italy, were followed up at 3–6 months. Data regarding present and previous clinical status, drug treatment, findings recorded during the in-hospital phase, presence of symptoms and signs of organ damage at follow-up were collected. Static and dynamic cardiac and respiratory parameters were evaluated by resting pulmonary function test, echocardiography, high-resolution chest tomography (HRCT) and cardiopulmonary exercise testing (CPET).ResultsTwelve clinically most relevant factors were identified and partitioned into four ordered blocks in the GCM: block 1 - gender, smoking, age and body mass index (BMI); block 2 - admission to the intensive care unit (ICU) and length of follow-up in days; block 3 - peak oxygen consumption (VO2), forced expiratory volume at first second (FEV1), D-dimer levels, depression score and presence of fatigue; block 4 - HRCT pathological findings. Higher BMI and smoking had a significant impact on the probability of a patient’s admission to ICU. VO2 showed dependency on length of follow-up. FEV1 was related to the self-assessed indicator of fatigue, and, in turn, fatigue was significantly associated with the depression score. Notably, neither fatigue nor depression depended on variables in block 2, including length of follow-up.ConclusionsThe biological plausibility of the relationships between variables demonstrated by the GCM validates the efficacy of this approach as a valuable statistical tool for elucidating structural features, such as conditional dependencies and associations. This promising method holds potential for exploring the long-term health repercussions of COVID-19 by identifying predictive factors and establishing suitable therapeutic strategies.

Dual-Modal Optical Imaging of Tissue Perfusion in Response to Cooling Stimulation Facilitates Early Detection of Pressure Ulcer.

Pressure ulcers present a significant human and economic challenge, lacking a reliable method for early detection. To address this, we developed a system capable of early detection by using cooling stimulation and dynamic data acquisition techniques to monitor blood perfusion and skin temperature. The system consists of laser speckle perfusion imaging and thermal imaging. And we performed simulations to demonstrate that the system is capable of detect tissue damage across multiple layers, from superficial to deep. Testing on a rabbit ear model demonstrated that this approach, which combines dynamic perfusion and temperature parameters, effectively distinguishes early pressure ulcer areas from normal skin with a significant p value of 0.0015. This distinction was more precise compared to methods relying solely on static parameters or one parameter. Our study thereby offers a promising advancement in the proactive management and prevention of pressure ulcers.

How does a dynamic surface roughness affect snowpack modeling?

The SNOWPACK model is a cryosphere model which incorporates several environmental model parameters, one of which being the aerodynamic roughness length (z0). The z0 is considered a static parameter, however, research has shown that the z0 of the surface is variable due to the changing nature of the snowpack surface throughout the winter season. This study highlights the sensitivity of the z0 within the SNOWPACK model based on the outputs of sublimation, SWE, and sensible heat. The z0 values were calculated in two ways, anemometrically (z0-A), using a wind profile, and geometrically (z0-G), measuring surface geometry. Calculated z0-A values were between 1.03 × 10−6 to 0.12 m. The z0-G values were calculated from a terrestrial lidar scan using various resolution values of post-process resolutions. These resolutions of 0.01, 0.1, and 1 m resulted in z0-G values of 0.26, 0.08, and 0.01 m, respectively. Therefore, as the resolution coarsened, the z0-G values decreased. Lastly, these calculated z0-G values, a variable run, using weekly measured z0-G values, and 0.002 (SNOWPACK default), 0.02, and 0.2 m values were incorporated into the SNOWPACK model. When applied, cumulative sublimation, SWE, and sensible heat outputs varied by 131%, −71%, and −49%, when compared to the default z0 value used within the model.

Pre-Compensation Strategy for Tracking Error and Contour Error by Using Friction and Cross-Coupled Control

This paper focuses on improving the tracking accuracy for servo systems and increasing the contouring performance of precision machining. The dynamic friction during precision machining is analyzed using the LuGre model. The dynamic and static parameters in the friction model are efficiently and accurately identified using the improved Drosophila swarm algorithm based on cross-mutation. The friction tracking error can be deduced from the friction state space and an expression is derived. To compensate for the tracking error caused by friction, a feedforward compensation control is designed to avoid signal lag in traditional friction controllers. Furthermore, the factors of multi-axis parameter mismatching that impact the machining profile accuracy are analyzed for multi-axis control. An adaptive cross-coupled control-based pre-compensation strategy of contour error is designed to reduce both the tracking error and the contour error. The effectiveness of the proposed method is validated through several experiments, which demonstrate a remarkable improvement in tracking performance and contour accuracy.

A dynamic hybrid model of supercritical once-through boiler-turbine unit including recirculation mode and once-through mode

Supercritical once-through boiler-turbine units play a paramount role in maintaining grid stability owing to their exceptional efficiency and flexibility. Nevertheless, achieving precise modeling of this device under various operational conditions remains a significant challenge. This study conducts modeling and simulation of an actual supercritical once-through coal-fired power plant. The findings signify that the hybrid model proposed herein adeptly encompasses all operational modes of supercritical once-through units, including start-up recirculation mode and once-through mode. To bolster model precision, an optimization algorithm grounded on weighted mean of vectors (INFO) has been employed for parameter identification, while the INFO-XGBoost was utilized to fashion a machine learning model for static parameters. It has been validated that the developed model accurately captures the dynamic characteristics of the unit. Furthermore, the utilization of the INFO-XGboost to model static parameters has been proved to enable the errors in principal outputs of coordinated control system to be within 0.35 % of MAPE across the entire operational process, thus facilitating the design of more refined and intelligent control systems.

Explosive welding of TA2-SiC-AW5083 composite armor

This study investigates the use of explosive welding to encapsulate SiC between TA2 and 5083, aiming to achieve large-area, high-efficiency, and low-cost fabrication of ceramic composite armor. A new weldability window was established, and six configurations of ceramic composite armor were devised. Ten sets of experiments were conducted to explore the influence of explosive welding parameters, ceramic fracture, and different configurations of composite complate on mechanical properties, macro-deformation, welding interface morphology, and energy absorption and conversion. The research revealed that the fracture of ceramic influences the welding quality, the propagation of the blast wave, and the kinetic energy utilization of the flying plate, while the crevices between the ceramic and the groove on the base plate do not influence the welding quality. When the ceramic remains intact, the kinetic energy utilization rate of the flying plate can be calculated using the η = E1(1-A1/A)/Ef, and when the ceramic penetratively fractures, the kinetic energy utilization rate of the plate can be calculated using the η=(E1-E4) (1-A1/A)/Ef. Finally, we recommend adding a suitable interlayer as a buffer between the flying plate and the ceramic, and the static parameters for explosive welding should strictly adhere to the “Lower bound principle."By analyzing the energy conversion and absorption during the explosive welding process, the macro-deformation and mechanical properties of the composites, the interface morphology, and the level and characteristics of the ceramic fracture, the energy source of the ceramic fracture was found, and the effects of ceramic fracture and structural characteristics of composites on gap airflow, kinetic energy utilization of face plate, explosive wave propagation path, and welding quality were analyzed. Finally, we propose that the explosive welding static parameters of ceramic composite armor should strictly adhere to the " The principle of lower explosives limit."

Enhancing battery electrochemical-thermal model accuracy through a hybrid parameter estimation framework

Electrochemical-thermal models offer profound insight into the internal state of batteries, demonstrating significant potential in energy storage. However, model complexity makes accurate parameter acquisition challenging. This study proposes a novel hybrid parameter estimation framework for effective parameterisation of battery electrochemical-thermal models. A high-fidelity reduced-order electrochemical-thermal model was established to accelerate the simulation of the entire time-domain process. A global sensitivity analysis of the model parameters was conducted, comprehensively evaluating their impact on simulated voltage and temperature. Highly sensitive parameters were categorised into static and time-dependent dynamic estimation groups for hierarchical estimation. A deep learning method was applied to generate preliminary parameter estimates to accelerate the convergence of estimation process. Subsequently, a genetic algorithm was employed to optimise the preliminary estimates of static parameters. A full temporal domain estimation was conducted using Markov Chain Monte Carlo methods for time-dependent dynamic parameters. The experimental data covers six dynamic and nine constant current conditions to validate the proposed method's feasibility. After parameter estimation, the proposed method reduces the comprehensive normalised root mean square error of voltage and temperature by up to 80.36% compared to four conventional methods. The minimum average absolute errors of voltage and temperature under constant and dynamic current conditions are 10.63 mV and 0.10 °C, respectively. The proposed method provides new insights for the parameterisation of battery models.

Determining the geomechanical units using rock physics methods

The stability of the drilled wellbores in carbonate formations is of great importance. This paper is a new approach to determine the elastic properties of the wellbore based on rock physics methods using the Hashin-Shtrikman bounds to determine the wellbore stability. Initially, the static elastic parameters were determined by two different methods, namely the rock physics method and DSI log, followed by clustering through Multi-Resolutional Graph-Based clustering (MRGC) and calculating geomechanical-units (GMUs) of each method separately. The obtained results from DSI log and rock physic methods were then compared followed by determining the wellbore stability. The results showed that the correlation between shear and compressional wave velocity obtained from the petrophysical method with the measured values of shear and pressure wave velocity in the well was 0.94 and 0.90, respectively, which shows that the petrophysical method can estimate these two logs with high accuracy, and it can be a suitable alternative instead of using the Dipole Shear Sonic Imager (DSI). It was observed that the geomechanical units of elastic parameters calculated by proposed rock physics method are in good agreement with those obtained from DSI log. Rock-physic method can be a good alternative for when expensive DSI logs are missing.

Application of silicon nanowires in sensors of temperature, light and humidity

Resistive and capacitive types of sensors based on silicon nanowires (SiNWs) for measuring of physical quantities have been manufactured. Silicon nanowires were obtained through a two-step metal-assisted chemical etching (MACE) method. The surface morphology of the silicon nanowire array was investigated using atomic force microscopy (AFM) and scanning electron microscopy (SEM). The impact of SiNWs synthesis parameters on sensor sensitivity has been investigated. In particular, the influence of deposition time of AgNPs and etching time of silicon, as well as the content of H2O2 in the etchant, preliminary texturing of the substrate, and additional post-processing in isotropic/anisotropic etchant after the MACE process, on the characteristics of physical quantity sensors has been determined. Static and dynamic parameters were calculated for the obtained sensors. To compare the obtained sensors, physical sensors without SiNWs were manufactured. The highest response value for temperature sensors was 132, which is two orders of magnitude better than for sensors without SiNWs. The presence of SiNWs on the surface of the sensor led to an increase in the response value by about two orders of magnitude for all types of sensors. The speed of humidity sensors improved by 3–4 times, and for temperature and light sensors by an order of magnitude. Further device applications include of multi-parameter sensors, human breath sensors, and solar radiation power sensors.

Relationship between fatigue crack threshold K max,th and K 1SCC

Abstract This article describes the relationship that relates the static threshold parameter of K 1SCC (at ∼10−10 m/s) to ΔK th and K max,th (at ∼10−10 m/cycle) under fatigue load at high R-ratio like R = 0.85. At these high R-ratios (&gt;0.75), applied K max &gt; K 1SCC and the fracture path has a larger component of static fracture. 5083 Al alloy is adapted as an example to demonstrate this relationship at room temperature their relationship with and without GB-β anodic precipitates in inert and a 3.5 % NaCl solution environments. We are interested in the mechanical cyclic ΔK th and K max,th and quasi-static K ISCC driving forces affecting the damage in the presence of a chemical environment due to anodic dissolution of the GB-β precipitates. Detailed discussions involve how these parameters are interrelated.

The value of net influx constant based on FDG PET/CT dynamic imaging in the differential diagnosis of metastatic from non-metastatic lymph nodes in lung cancer.

This study aims to evaluate the value of the dynamic and static quantitative metabolic parameters derived from 18F-fluorodeoxyglucose (FDG)-positron emission tomography/CT (PET/CT) in the differential diagnosis of metastatic from non-metastatic lymph nodes (LNs) in lung cancer and to validate them based on the results of a previous study. One hundred and twenty-one patients with lung nodules or masses detected on chest CT scan underwent 18F-FDG PET/CT dynamic + static imaging with informed consent. A retrospective collection of 126 LNs in 37 patients with lung cancer was pathologically confirmed. Static image analysis parameters include LN-SUVmax and LN-SUVmax/primary tumor SUVmax (LN-SUVmax/PT-SUVmax). Dynamic metabolic parameters including the net influx rate (Ki) and the surrogate of perfusion (K1) and of each LN were obtained by applying the irreversible two-tissue compartment model using in-house Matlab software. Ki/K1 was then calculated as a separate marker. Based on the pathological findings, we divided into a metastatic group and a non-metastatic group. The χ2 test was used to evaluate the agreement of the individual and combined diagnosis of each metabolic parameter with the gold standard. The receiver-operating characteristic (ROC) analysis was performed for each parameter to determine the diagnostic efficacy in differentiating non-metastatic from metastatic LNs with high FDG-avid. P < 0.05 was considered statistically significant. Among the 126 FDG-avid LNs confirmed by pathology, 70 LNs were metastatic, and 56 LNs were non-metastatic. For ROC analysis, in separate assays, the dynamic metabolic parameter Ki [sensitivity (SEN) of 84.30%, specificity (SPE) of 94.60%, accuracy of 88.89%, and AUC of 0.895] had a better diagnostic value than the static metabolic parameter SUVmax (SEN of 82.90%, SPE of 62.50%, accuracy of 74.60%, and AUC of 0.727) in differentiating between metastatic from non-metastatic LNs groups, respectively. In the combined diagnosis group, the combined SUVmax + Ki diagnosis had a better diagnostic value in the differential diagnosis of metastatic from non-metastatic LNs, with SEN, SPE, accuracy, and AUC of 84.3%, 94.6%, 88.89%, and 0.907, respectively. When the cutoff value of Ki was 0.022ml/g/min, it had a high diagnostic value in the differential diagnosis between metastasis and non-metastasis in FDG-avid LNs of lung cancer, especially in improving the specificity. The combination of SUVmax and Ki is expected to be a reliable metabolic parameter for N-staging of lung cancer.

Results of the cyclic triaxial testing on mechanically-biologically treated waste.

Accurate assessment of the dynamic strength characteristics of mechanically-biologically treated (MBT) waste is crucial for the construction and safe operation of landfill sites. Herein, samples of MBT waste from the Hangzhou Tianziling landfill were collected and subjected to consolidated undrained cyclic triaxial tests under four confinement levels and six cyclic stress ratios (CSRs). Under cyclic loading, the MBT waste exhibited a critical CSR. If the CSR exceeds the critical value, the MBT waste specimen rapidly undergoes deformation and failure. Dynamic strength of MBT waste decreases with an increase in the number of cyclic vibrations and increases with an increase in confining pressure. Considering the influence of cyclic vibrations and confining pressure, a formula for dynamic strength in terms of cyclic vibrations and confining pressure has been established. The dynamic shear strength parameter ranges for MBT waste were obtained under different seismic magnitudes. We compared the dynamic and static shear strength parameters of MBT waste and municipal solid waste. These study findings can serve as a reference for the dynamic stability analysis of MBT waste landfills.

Iterative Tomographic Image Reconstruction Algorithm Based on Extended Power Divergence by Dynamic Parameter Tuning.

Computed tomography (CT) imaging plays a crucial role in various medical applications, but noise in projection data can significantly degrade image quality and hinder diagnosis accuracy. Iterative algorithms for tomographic image reconstruction outperform transform methods, especially in scenarios with severe noise in projections. In this paper, we propose a method to dynamically adjust two parameters included in the iterative rules during the reconstruction process. The algorithm, named the parameter-extended expectation-maximization based on power divergence (PXEM), aims to minimize the weighted extended power divergence between the measured and forward projections at each iteration. Our numerical and physical experiments showed that PXEM surpassed conventional methods such as maximum-likelihood expectation-maximization (MLEM), particularly in noisy scenarios. PXEM combines the noise suppression capabilities of power divergence-based expectation-maximization with static parameters at every iteration and the edge preservation properties of MLEM. The experimental results demonstrated significant improvements in image quality in metrics such as the structural similarity index measure and peak signal-to-noise ratio. PXEM improves CT image reconstruction quality under high noise conditions through enhanced optimization techniques.