Characteristic Curve Research Articles

Mechanical characterization of large diameter UHMWPE ropes under quasi-static tensile loading: An experimental study

The Large diameter UHMWPEFR (Ultra High Molecular Weight Polyethylene Fiber Rope) are ideal for heavy lifting and slinging, with their mechanical properties and behaviour characterisation are key to the application.Therefore, this study conducts uniaxial tensile tests on a 115 mm diameter rope (FR-115) to investigate its mechanical properties under quasi-static tensile conditions. considering the anisotropic structural characteristics of UHMWPEFR, a method and criteria for eliminating inter-strand gaps and uneven force through pre-tensioning were proposed. Subsequently, tensile tests were carried out to examine the mechanical properties and constitutive relationship of the rope. The experimental results indicate that pre-tensioning can effectively eliminate the influence of non-elastic deformation of the rope, meeting the formal tensioning requirements based on the stability criteria of the rope structure constructed from mechanical and physical parameters. The stress-strain curve of FR-115 shows a linear bilinear shape without a yield segment during the tensioning process, presenting a typical brittle failure mode. The mechanical behavior is closely related to the historical load effects and loading speed, with the rope transitioning from soft and tough to hard and brittle, exhibiting hysteresis. Based on the above analysis, the graded load and time-varying modulus parameter are fixed, and the internal force is more sensitive to the quasi-static tensile velocity; a two-stage principal model is established based on the analysis of curve characteristics and time-dependent correlation, with 50 % MBS(Minimum Break Strength) as the characterization point, and the UHMWPEFR principal relationship in the pre-period is linear-elastic, and the strain-rate effect is taken into account for the dense section, and the rate correlation and nonlinear characteristics of the dense section of the rope can be accurately portrayed with the introduction of Cowper-Symonds.

Development of a Large-Scale Horizontal Curve Inventory for Safety Analysis Using Open GIS Data

Abstract Horizontal curves are crucial for the safety and mobility of roadways. To enhance the understanding of these curves, transportation agencies are investigating methods to accurately determine their spatial locations, geometric attributes, and conditions within their extensive road networks. Leveraging the widespread availability of Global Positioning System (GPS) and Geographic Information System (GIS) data, significant advancements have been made in developing automated algorithms that identify horizontal curves from GPS trajectories and GIS basemaps. This study introduces a novel dataset—the Horizontal Curve Inventory—that encapsulates detailed features of horizontal curves. We employed a modified automated cycle regression algorithm designed for large-scale road network analysis to extract horizontal curve data. This method was applied nationally to process an expansive network utilizing open-source GIS basemaps, encompassing over 300,000 miles across interstate, U.S. highway, and state highway systems. The inventory includes comprehensive data on curve radius, rotation angle, start and end point coordinates, and the curve’s center points. These data are essential for supporting safety research pertaining to road alignment. Our approach demonstrated robust performance across various open GIS data sources and provides the first accurate, efficient, and comprehensive inventory of horizontal curves for all major highway systems in the United States. The dataset is available at http://aichengbo.com/research/national-horizontal-curve-inventory/.

Advances in Gas Detection of Pattern Recognition Algorithms for Chemiresistive Gas Sensor

Gas detection and monitoring are critical to protect human health and safeguard the environment and ecosystems. Chemiresistive sensors are widely used in gas monitoring due to their ease of fabrication, high customizability, mechanical flexibility, and fast response time. However, with the rapid development of industrialization and technology, the main challenges faced by chemiresistive gas sensors are poor selectivity and insufficient anti-interference stability in complex application environments. In order to overcome these shortcomings of chemiresistive gas sensors, the pattern recognition method is emerging and is having a great impact in the field of sensing. In this review, we focus systematically on the advancements in the field of data processing methods for feature extraction, such as the methods of determining the characteristics of the original response curve, the curve fitting parameters, and the transform domain. Additionally, we emphasized the developments of traditional recognition algorithms and neural network algorithm in gas discrimination and analyzed the advantages through an extensive literature review. Lastly, we summarized the research on chemiresistive gas sensors and provided prospects for future development.

The fast rise of the unusual type IIL/IIb SN 2018ivc

We present an analysis of the photometric and spectroscopic dataset of the type II supernova (SN) 2018ivc in the nearby (10 Mpc) galaxy Messier 77. Thanks to our high-cadence data, we observed the SN rising very rapidly by nearly three magnitudes in five hours (or 18 mag d$^ $). The $r$-band light curve presents four distinct phases: the maximum light, which was reached in just one day, followed by a first, rapid linear decline and a short-duration plateau. Finally, the long, slower linear decline lasted for one year. Thanks to the ensuing radio re-brightening, we were able to detect SN 2018ivc four years after the explosion. The early spectra show a blue, nearly featureless continuum, but the spectra go on to evolve rapidly; after about ten days, a prominent Halpha line starts to emerge, characterised by a peculiar profile. However, the spectra are heavily contaminated by emission lines from the host galaxy. The He I lines, namely $ are also strong. In addition, strong absorption from the Na I doublet is evident and indicative of a non-negligible internal reddening. From its equivalent width, we derived a lower limit on the host reddening of $A_V mag. From the Balmer decrement and a match of the $B-V$ colour curve of SN 2018ivc to that of the comparison objects, we obtained a host reddening of $A_V mag. The spectra are similar to those of SNe II, but with strong He lines. Given the peculiar light curve and spectral features, we suggest SN 2018ivc could be a transitional object between the type IIL and type IIb SNe classes. In addition, we found signs of an interaction with the circum-stellar medium (CSM) in the light curve, also making SN 2018ivc an interacting event. Finally, we modelled the early multi-band light curves and photospheric velocity of SN 2018ivc to estimate the physical parameters of the explosion and CSM.

Vuv/vis absorption spectroscopy of different PAHs

AbstractThe present study provides new solid-phase absorption data of different Polycyclic Aromatic Hydrocarbon (PAHs) molecules in the Far Ultraviolet (FUV) and UV/Visible regions. Two features of interstellar extinction curves fall in this region: the FUV rise and the UV bump. Here, thin films of PAHs were prepared by means of the spin coating method onto a transparent substrate, and their solid phase absorption spectra were recorded. Spectra of isolated molecules simulated by Time-Dependent Density Functional Theory (TD-DFT) calculations were also produced for comparison. The results fit quite well with the FUV rise but not with the UV bump. Given the scarcity of FUV experimental absorption data of PAHs in the literature and especially in solid phase, these results are also interesting for reference for other studies ranging from thin films applications to interstellar medium models.

Cooling-heating properties of the FRW universe in gravity with a generalized conformal scalar field

In this paper, within the framework of modified gravity involving a conformal scalar field, we investigate the Joule-Thomson expansion of the Friedmann-Robertson-Walker (FRW) universe to identify cooling and heating regions. We observe a divergence in the Joule-Thomson coefficient (μ), precisely at the apparent horizon (RA=−2α) of the FRW universe, under conditions of negative gravitational coupling (α<0), which aligns with a thermodynamic singularity. Furthermore, we determine the inversion temperature and pressure for the FRW universe, and elucidate the salient features of inversion and isenthalpic curves in the temperature-pressure (T-P) plane. We compare these findings with results obtained under Einstein gravity, discussing the influence of the modification term on the cooling and heating properties of the FRW universe. This work presents insights into the formation of cooling and heating regions within the FRW universe, contributing to a deeper understanding of the physical mechanisms behind cosmic expansion history.

Research on the NI-MLA Method for Enhancing the Spot Position Detection Accuracy of Quadrant Detectors Under Atmospheric Turbulence

The distorted spots induced by atmospheric turbulence significantly degrade the spot position detection accuracy of the quadrant detector (QD). In this paper, we utilize angular measurement and homogenization characteristics of non-imaging microlens array (NI-MLA) systems, effectively reducing the distortion degree of the spots received on the QD target surface, thereby significantly enhancing the spot detection accuracy of the QD. First, based on the principles of geometric optics and Fourier optics, it is proved that the NI-MLA system possesses the angular measurement characteristic (AMC) within the paraxial region while deriving and verifying the focal length of the system. Then, the QD computation curve characteristics of the system under non-turbulence are explored. This study further elucidates the mathematical principle of the NI-MLA system for mitigating the spot position detection random error of QD (SPDRE-QD) and discusses in depth the relationship between the NI-MLA system’s capability to mitigate the SPDRE-QD and the system’s parameters under various turbulence intensities. Finally, it is experimentally verified that the root-mean-square error (RMSE) of the QD computation values using the NI-MLA system are reduced by a significant improvement of at least 2.44 times and up to 17.36 times compared with that of the conventional optical system of QD (COS-QD) under turbulence conditions ranging from weak to strong.

Diagnostic Value of sLR11 and sIL-2R in the Cerebrospinal Fluid for Malignant Central Nervous System Lymphoma.

Objective We previously reported that patients with acute leukemia and malignant lymphoma (ML) demonstrated significantly increased serum soluble LR11 (sLR11) levels compared to normal controls. Accurately diagnosing ML of the central nervous system (CNS ML) using cytology is frequently difficult. Therefore, we evaluated the use of cerebrospinal fluid (CSF) sLR11 and soluble interleukin-2 receptor (sIL-2R) as diagnostic and treatment response markers for CNS ML. Methods We retrospectively evaluated the CSF results for CNS ML using clinical data at our institution, and then analyzed the usefulness of sLR11 and sIL-2R in CSF for both the diagnosis and as surrogate markers that reflect the therapeutic effect. Patients We enrolled patients with CNS ML who received intrathecal anticancer drugs between 2017 and 2023. We analyzed the sLR11 and sIL-2R levels in CSF and cytological malignant grades. We studied 22 patients, including 17 with central nervous system (CNS) clinical conditions and five who received prevention treatment. Results The CSF sLR11 levels were significantly and positively correlated with CSF sIL-2R levels. The CSF sLR11 and sIL-2R levels in patients with CNS ML were significantly higher than those in the prevention group. A receiver operating characteristic (ROC) curve analysis showed the cut-off value of sLR11 for CNS invasion to be 21.7 ng/mL. Moreover, the chemotherapy-responder group demonstrated significantly decreased CSF sLR11 and sIL-2R levels after treatment. Conclusion CSF sLR11 and sIL-2R of CSF were found to be useful biomarkers for the diagnostic and treatment response evaluation in patients with CNS ML.

Medium- and Long-Term Power System Planning Method Based on Source-Load Uncertainty Modeling

In order to consider the impact of source-load uncertainty on traditional power system planning methods, a medium- and long-term optimization planning method based on source-load uncertainty modeling and time-series production simulation is proposed. First, a new energy output probability model is developed using non-parametric kernel density estimation, and the spatial correlation of the new energy output is described using pair-copula theory to model the uncertainty analysis of the new energy output. Secondly, a large number of source-load scenarios are generated using the Markov chain Monte Carlo simulation method, and the optimal selection method for discrete state numbers is provided, and then the scenario reduction is carried out using the fast forward elimination technology. Finally, the typical time-series curves of the source-load uncertainty characteristics obtained are incorporated into the optimization planning method together with various flexible resources, such as the demand-side response and energy storage, and the rationality of the planning scheme is judged and optimized based on key indicators such as the cost, wind–light abandonment rate, and loss-of-load rate. Based on the above methods, this paper offers an example of the power supply planning scheme for a certain region in the next 30 years, providing effective guidance for the development of new energy in the region.

A fault-tolerant algorithm of AUV formation based on reconfiguration map

Aiming at the fault occurrence of AUV formation members during sailing, a fault tolerance algorithm of AUV formation based on reconfiguration was proposed. Firstly, the cost matrix is designed based on the reconfiguration and Hungarian algorithm to solve the problem of assigning target points under the fault condition. Then, based on Bezier curves, the dynamic constraints of AUV are designed parametrically. The nonlinear optimization problem satisfies the constraints through the characteristics of spline curves, and the adaptive particle swarm optimization algorithm is used to solve the tracking trajectory. The comparison and simulation experiment of a particle swarm algorithm based on a reconfiguration map (RMPSO) was carried out. The simulation results show that under the same planned path, the total tracking path of the following AUV is shorter, the turning radius is smaller, and the heading Angle is more stable. Finally, field tests are carried out in Qiandao Lake to verify the effectiveness of the algorithm for formation faults.

A Leakage Safety Discrimination Model and Method for Saline Aquifer CCS Based on Pressure Dynamics

The saline aquifer CCS is a crucial site for carbon storage. Safety monitoring is a key technology for saline aquifer CCS. Current CO2 leakage detection methods include microseismic, electromagnetic, and well-logging techniques. However, these methods face challenges, such as difficulties in determining CO2 migration fronts and predicting potential leakage events; as a result, the formulation of test timing and methods for these safety monitoring techniques are somewhat arbitrary. This study establishes a gas–water two-phase seepage model and solves it using a semi-analytical method to obtain the injection pressure and the derivative curve characteristics of the injection well. The pressure derivative curve can reflect the physical properties of the reservoir through which CO2 flows underground, and it can also be used to determine whether CO2 leakage has occurred, as well as the timing and amount of leakage, based on boundary responses. This study conducted sensitivity analyses on eight parameters to determine the impact of each parameter on the bottom-hole pressure and its derivatives, thereby obtaining the influence of its parameters on different flow stages. The research indicates that, when a steady-state flow characteristic appears at the outer boundary, CO2 leakage will occur. Additionally, the leakage location can be determined by calculating the distance from the injection well. This can guide the placement and measurement of safety monitoring methods for saline aquifer CCS. The method proposed in this paper can effectively monitor the timing, location, and amount of leakage, providing a technical safeguard for promoting CCS technology.

Significance of magnetic field and Darcy–Forchheimer law on dynamics of non-Newtonian hybrid nanofluid flow over a spinning disc with Arrhenius activation energy and shape factor

Purpose The purpose of the study is to explore the three-dimensional heat and mass transport dynamics of the magneto-hydrodynamic non-Newtonian (Casson fluid) hybrid nanofluid flow comprised of − as nanoparticles suspended in base liquid water as it passes through a flexible spinning disc. The influence of a magnetic field, rotation parameter, porosity, Darcy−Forchheimer, Arrhenius’s activation energy, chemical reaction, Schmidt number and nanoparticle shape effects are substantial physical features of the investigation. Furthermore, the influence of hybrid nanofluid on Brownian motion and thermophoresis features has been represented using the Buongiorno model. The novelty of the work is intended to contribute to a better understanding of Casson non-Newtonian fluid boundary layer flow. Design/methodology/approach The governing mathematical equations that explain the flow and heat and mass transport phenomena for fluid domains include the Navier−Stokes equation, the thermal energy equation and the solutal concentration equations. The governing equations are expressed as partial differential equations, which are then converted into a suitable set of non-linear ordinary differential equations by using the necessary similarity variables. The ordinary differential equations are computed by combining the shooting operation with the three-stage Lobatto BVP4c technique. Findings Graphs and tables are used in the process of analysing the characteristics of velocity distributions, temperature profiles and solutal curves at varying values of the parameters, along with friction drag, heat transfer rate and Sherwood number. It has been revealed that the radial and axial velocities decrease when the Casson parameter value increases and that the rate of heat transmission is higher in hybrid nanofluids with nanoparticles in the shape of a blade. The increase in Brownian motion and thermophoresis parameters causes a rise in the temperature profile. Also, an increase in the activation energy parameter improves the solutal curve. The use of nanoparticles was shown to improve extrusion properties, the rotary heat process and biofuel generation. Originality/value All results are presented graphically and all physical quantities are computed and tabulated. The current results are compared to previous investigations and found to agree significantly with them.

Forest point cloud denoising method based on curvature and density

ABSTRACT When LiDAR scans woodlands, the three-dimensional (3D) point cloud data of woodlands directly collected usually contain a large number of noise points, which is caused by equipment performance, environmental interference, or other factors. The poor data quality of the point cloud data affects the accuracy of the extracted individual tree feature parameters and forest 3D reconstruction. To improve the quality of forest 3D point cloud data, a curvature- and density-based denoising method has been proposed in this study. First, the surface curvature features were calculated for the noise-dense trunk area of the forest for primary denoising, and the initial denoising results were used to construct the K-dimensional-Tree (KD-Tree) space index to search the spherical neighbourhood. The denoising was completed according to the proposed density calculation method (SN-NN, Spherical Neighbourhood and Nearest Neighbour). To evaluate the comprehensive performance of the proposed denoising method, a new denoising evaluation index has been designed by integrating the denoising precision and effective data retention degree of the forest point cloud data. The proposed method was validated based on forest point cloud data collected using Terrestrial LiDAR. The results show that the denoising precision and effective data retention degree of the proposed method are both preferable, which can effectively improve the quality of forest point cloud data.

Exploring the Potential Molecular Mechanism of the Shugan Jieyu Capsule in the Treatment of Depression through Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulation.

Shugan Jieyu Capsule (SJC) is a pure Chinese medicine compound prepared with Hypericum perforatum and Acanthopanacis senticosi. SJC has been approved for the clinical treatment of depression, but the mechanism of action is still unclear. Network pharmacology, molecular docking, and molecular dynamics simulation (MDS) were applied in the present study to explore the potential mechanism of SJC in the treatment of depression. TCMSP, BATMAN-TCM, and HERB databases were used, and related literature was reviewed to screen the effective active ingredients of Hypericum perforatum and Acanthopanacis senticosi. TCMSP, BATMAN-TCM, HERB, and STITCH databases were used to predict the potential targets of effective active ingredients. GeneCards database, DisGeNET database, and GEO data set were used to obtain depression targets and clarify the intersection targets of SJC and depression. STRING database and Cytoscape software were used to build a protein-protein interaction (PPI) network of intersection targets and screen the core targets. The enrichment analysis on the intersection targets was conducted. Then the receiver operator characteristic (ROC) curve was constructed to verify the core targets. The pharmacokinetic characteristics of core active ingredients were predicted by SwissADME and pkCSM. Molecular docking was performed to verify the docking activity of the core active ingredients and core targets, and molecular dynamics simulations were performed to evaluate the accuracy of the docking complex. We obtained 15 active ingredients and 308 potential drug targets with quercetin, kaempferol, luteolin, and hyperforin as the core active ingredients. We obtained 3598 targets of depression and 193 intersection targets of SJC and depression. A total of 9 core targets (AKT1, TNF, IL6, IL1B, VEGFA, JUN, CASP3, MAPK3, PTGS2) were screened with Cytoscape 3.8.2 software. A total of 442 GO entries and 165 KEGG pathways (p <0.01) were obtained from the enrichment analysis of the intersection targets, mainly enriched in IL-17, TNF, and MAPK signaling pathways. The pharmacokinetic characteristics of the 4 core active ingredients indicated that they could play a role in SJC antidepressants with fewer side effects. Molecular docking showed that the 4 core active components could effectively bind to the 8 core targets (AKT1, TNF, IL6, IL1B, VEGFA, JUN, CASP3, MAPK3, PTGS2), which were related to depression by the ROC curve. MDS showed that the docking complex was stable. SJC may treat depression by using active ingredients such as quercetin, kaempferol, luteolin, and hyperforin to regulate targets such as PTGS2 and CASP3 and signaling pathways such as IL-17, TNF, and MAPK, and participate in immune inflammation, oxidative stress, apoptosis, neurogenesis, etc.

Intraoperative Hypotension Prediction Based on Features Automatically Generated Within an Interpretable Deep Learning Model.

The monitoring of arterial blood pressure (ABP) in anesthetized patients is crucial for preventing hypotension, which can lead to adverse clinical outcomes. Several efforts have been devoted to develop artificial intelligence-based hypotension prediction indices. However, the use of such indices is limited because they may not provide a compelling interpretation of the association between the predictors and hypotension. Herein, an interpretable deep learning model is developed that forecasts hypotension occurrence 10 min before a given 90-s ABP record. Internal and external validations of the model performance show the area under the receiver operating characteristic curves of 0.9145 and 0.9035, respectively. Furthermore, the hypotension prediction mechanism can be physiologically interpreted using the predictors automatically generated from the proposed model for representing ABP trends. Finally, the applicability of a deep learning model with high accuracy is demonstrated, thus providing an interpretation of the association between ABP trends and hypotension in clinical practice.

Application of a Machine Learning Predictive Model for Recurrent Acute Pancreatitis.

Acute pancreatitis is the main cause of hospitalization for pancreatic disease. Some patients tend to have recurrent episodes after experiencing an episode of acute pancreatitis. This study aimed to construct predictive models for recurrent acute pancreatitis (RAP). A total of 531 patients who were hospitalized for the first episode of acute pancreatitis at the Affiliated Hospital of Southwest Medical University from January 2018 to December 2019 were enrolled in the study. We confirmed whether the patients had a second episode until December 31, 2021, through an electronic medical record system and telephone or WeChat follow-up. Clinical and follow-up data of patients were collected and randomly allocated to the training and test sets at a ratio of 7:3. The training set was used to select the best model, and the selected model was tested with the test set. The area under the receiver operating characteristic curves, sensitivity, specificity, positive predictive value, negative predictive value, accuracy, decision curve, and calibration plots were used to assess the efficacy of the models. Shapley additive explanation values were used to explain the model. Considering multiple indices, XGBoost was the best model. The area under the receiver operating characteristic curves, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model in the test set were 0.779, 0.763, 0.883, 0.647, 0.341, and 0.922, respectively. According to the Shapley additive explanation values, drinking, smoking, higher levels of triglyceride, and the occurrence of ANC are associated with RAP. The XGBoost model shows good performance in predicting RAP, which may help identify high-risk patients.

Audiological Characterization of Individuals with Cornelia de Lange Syndrome.

Introduction Cornelia de Lange Syndrome (CdLS) is a genetic disorder in which individuals may present sensorineural and/or conductive hearing loss, and the results of behavioral auditory assessments are not accurate. Objective To characterize the audiological profile of individuals with CdLS through behavioral, electroacoustic, and electrophysiological audiological assessments. Methods The study included 13 individuals of both sexes, aged between 3 and 26 years, with diagnoses confirmed through genetic studies. The following procedures were performed: medical history survey, otoscopy (pure-tone audiometry [PTA], speech audiometry, and acoustic immittance measures), and auditory brainstem response (ABR). Results In total 62.50% of the participants who underwent PTA had abnormal results (all of which were mild), with a predominance of bilateral conductive hearing loss (60%). Regarding tympanometry, 76.93% had abnormal results, most frequently type B (85.72% on the right and 88.89% on the left ear). Acoustic reflexes showed results compatible with tympanometry changes. Changes in ABR latency values compatible with middle-ear impairment were found in 8 of them (66.66%) - 3 had bilateral (37.50%), and 5 had unilateral impairments (62.50%). Conclusion Mild hearing loss was identified in 62.5% of the individuals with CdLS who underwent the behavioral audiological assessment. In the acoustic immittance measures, 76.9% of the participants presented a tympanometry curve characteristic of middle-ear changes. Acoustic reflexes were absent in 84.6% of the subjects. In the ABR, no changes were identified in auditory pathway integrity. On the other hand, changes in the absolute latency values were found, which are characteristic of conductive hearing loss.

Experimental analysis and development of novel drying kinetics model for drying grapes in a double slope solar dryer

This experimental study aimed to evaluate the efficacy of natural and forced convective drying techniques in reducing the moisture content of the grapes in comparison with the conventional open sun drying method. Moisture effective diffusivity and activation energy were graphically determined using the Arrhenius equation. In the initial three-day period, forced convection drying significantly reduced moisture from 2000 g to 353 g, with a minimum loss of 14 g. In the next three days, natural convection reduced moisture from 2787 g to 468 g, with a minimum loss of 11 g. These outcomes were then compared to the results of open-sun drying. The investigation showed that open drying and natural convection methods eliminated 25.05 % and 82.35 % of the moisture content, respectively. However, for three days, open-sun drying removed 30.5 % of grape moisture, while forced convection achieved an impressive 83.21 % reduction. The study's innovative mathematical model explained drying curve characteristics, supported by correlation coefficients and parity plots. The comparison shows that the experimental moisture ratios and those predicted by the new correlation exhibit R2 values ranging from 0.984 to 0.994.

Clinical Application of Equivalent Uniform Dose in Intensity-Modulated Rotational Radiotherapy Based on Eclipse TPS

To investigate the application of equivalent uniform dose (EUD) in intensity-modulated rotational radiotherapy and to explore optimization methods for improving the quality of modulated treatment plans. The impact of the parameter a in the EUD formula on the characteristics of the EUD curve was analyzed using Python. Thirty cases of head and neck tumors, thoracic tumors, and pelvic tumors were randomly selected for treatment planning. Dose optimization for the target area and organs at risk were performed using a physics-based optimization approach or an optimization approach that combines physical constraints with the EUD function. The dose distribution and compliance with constraints of the two groups of plans were compared, while also observing the effect of different values of a on the planning outcomes. The impact of the value of a on the changes in EUD curve characteristics was consistent with its impact on the results of EUD plan optimization. When -15≤ a≤-5, the dose distribution in the target area was more uniform; when 1≤ a≤7, the effect on the uniform dose and low-dose regions in organs at risk was more noticeable; when 10≤ a≤30, the effect of constraining the high-dose regions in organs at risk was more pronounced, with the EUD for the target area and organs at risk exhibiting different expressions under different a values. The study also found that the target dose distribution and the protection of organs at risk in the EUD optimization group were better than those in the physical optimization group only. The a-value has a significant impact on the, the dose distribution in the target area and the organ at risk, providing a reference for the setting of a-value while using EUD to optimize the intensity modulation plan. The using of EUD optimization method can not only achieve excellent dose distribution in the target area, but also significantly reduce the normal tissue dose and the probability of complications, which has certain clinical application value.

Identification of dominant instability modes in power systems based on spatial‐temporal feature mining and TSOA optimization

AbstractThe recognition of the transient dominant instability mode is of great significance for rapidly and accurately formulating transient emergency decisions in power systems. In response to the challenge of accurately distinguishing between angle instability and voltage instability, which are coupled in actual power grids, this paper explores the mapping relationship between simulation data and the stable state of the system, as well as the dominant instability mode. The method enables real‐time identification of the dominant instability mode, which bypasses complex physical mechanisms. Firstly, spatio‐temporal feature mining is conducted, where convolutional neural networks are employed to learn crucial local features of transient curves, and bidirectional gated recurrent unit s utilized to learn transient features over time sequences. Next, a multihead attention mechanism is introduced to enhance sensitivity to important time steps in the sequence data. Finally, the transit search optimization algorithm optimizes the global model parameters, further increasing the accuracy of the model. Using the IEEE 10‐machine and 39‐node system as an example for simulation, the results validate that the proposed method exhibits significant advantages in terms of accuracy and applicability compared with other machine learning methods.