Systematic comparison of Kawasaki disease Z-score formulas:Enhancing diagnostic accuracy and clinical applicability across populations

This study aims to evaluate the performance of six distinct Z-score formulas in diagnosing coronary artery dilation in Kawasaki disease(KD)patients,with a focus on their clinical applicability across diverse populations.A retrospective analysis was conducted using comparative statistical methods to assess the sensitivity and specificity of each formula.The Kobayashi and Dallaire formulas demonstrated the highest sensitivity in China,particularly in younger patients with prolonged fever duration.Our findings provide evidence-based recommend-ations for selecting Z-score formulas to optimize diagnostic precision in KD patients.

基于Z-Score动态压缩的高效联邦学习算法

联邦学习作为一种具有隐私保护的新兴分布式计算范式,在一定程度上保护了用户隐私和数据安全。然而,由于联邦学习系统中客户端与服务器需要频繁地交换模型参数,造成了较大的通信开销。在带宽有限的无线通信场景中,这成为了限制联邦学习发展的主要瓶颈。针对这一问题,提出了一种基于Z-Score的动态稀疏压缩算法。通过引入Z-Score,对局部模型更新进行离群点检测,将重要的更新值视为离群点,从而将其挑选出来。在不需要复杂的排序算法以及原始模型更新的先验知识的情况下,实现模型更新的稀疏化。同时随着通信轮次的增加,根据全局模型的损失值动态地调整稀疏率,从而在保证模型精度的前提下最大程度地减少总通信量。通过实验证明,在I.I.D.数据场景下,该算法与联邦平均(FedAvg)算法相比可以降低95%的通信量,精度损失仅仅为1.6%,与FTTQ算法相比可以降低40%~50%的通信量,精度损失仅为1.29%,证明了该方法在保证模型性能的同时显著降低了通信成本。

基于Z-score模型的格力电器财务风险分析

随着居民生活水平的提高,人们对家电的要求逐渐从看重价格到看重品质。随着科学技术的不断进步,家电企业之间关于科技的较量不断加剧,产品竞争越发激烈,如何得到消费者青睐是每个家电企业应该思考的问题。本文以格力电器为例,选择其2018—2022年的财务数据,进行具体的财务指标分析,并利用Z-score模型对格力电器进行横向和纵向的综合分析,发现格力电器存在一定的财务风险,结合风险提出相应发展策略,希望能对格力电器健康持续发展有所帮助。

基于Z-Score模型的上市公司财务风险评价——来自瑞幸咖啡公司2017-2022年财务数据的证据

财务风险评价是制约公司经营效率和经营成本的关键因素。文章以瑞幸咖啡公司为案例,选取2017—2022年财务报表数据,采用偿债能力、盈利能力、营运能力和Z-Score分数模型对其财务风险进行综合评价。结果表明:一是瑞幸咖啡公司在财务舞弊前、中、后三个时期财务风险呈现出不同的发展趋势,在财务舞弊前财务风险能力较低,在舞弊时期呈现出不正常现象,在舞弊后回归正常;二是Z-Score模型发现瑞幸咖啡公司在2020年Z值为历年最低,2021—2022年Z值数据稳步上升,虽然仍低于判别值1.81,存在较大的财务风险,但是可预见瑞幸咖啡公司正在朝着正确的道路前进。文章为瑞幸咖啡公司提高财务风险应对能力提供建议,同时为我国上市公司进行财务风险评价和防范提供参考。

Robust Information Hiding Based on Neural Style Transfer with Artificial Intelligence

This paper proposes an artificial intelligence-based robust information hiding algorithm to address the issue of confidential information being susceptible to noise attacks during transmission.The algorithm we designed aims to mitigate the impact of various noise attacks on the integrity of secret information during transmission.The method we propose involves encoding secret images into stylized encrypted images and applies adversarial transfer to both the style and content features of the original and embedded data.This process effectively enhances the concealment and imperceptibility of confidential information,thereby improving the security of such information during transmission and reducing security risks.Furthermore,we have designed a specialized attack layer to simulate real-world attacks and common noise scenarios encountered in practical environments.Through adversarial training,the algorithm is strengthened to enhance its resilience against attacks and overall robustness,ensuring better protection against potential threats.Experimental results demonstrate that our proposed algorithm successfully enhances the concealment and unknowability of secret information while maintaining embedding capacity.Additionally,it ensures the quality and fidelity of the stego image.The method we propose not only improves the security and robustness of information hiding technology but also holds practical application value in protecting sensitive data and ensuring the invisibility of confidential information.

Adaptive Robust Servo Control for Vertical Electric Stabilization System of Tank and Experimental Validation

A tracking stability control problem for the vertical electric stabilization system of moving tank based on adaptive robust servo control is addressed.This paper mainly focuses on two types of possibly fast timevarying but bounded uncertainty within the vertical electric stabilization system:model parameter uncertainty and uncertain nonlinearity.First,the vertical electric stabilization system is constructed as an uncertain nonlinear dynamic system that can reflect the practical mechanics transfer process of the system.Second,the dynamical equation in the form of state space is established by designing the angular tracking error.Third,the comprehensive parameter of system uncertainty is designed to estimate the most conservative effects of uncertainty.Finally,an adaptive robust servo control which can effectively handle the combined effects of complex nonlinearity and uncertainty is proposed.The feasibility of the proposed control strategy under the practical physical condition is validated through the tests on the experimental platform.This paper pioneers the introduction of the internal nonlinearity and uncertainty of the vertical electric stabilization system into the settlement of the tracking stability control problem,and validates the advanced servo control strategy through experiment for the first time.

Robust adaptive leaderless consensus of unknown non-minimum phase linear multi-agent systems subject to disturbances and/or unmodeled dynamics

This article investigates the problem of robust adaptive leaderless consensus for heterogeneous uncertain nonminimumphase linear multi-agent systems over directed communication graphs. Each agent is assumed tobe of unknown nominal dynamics and also subject to external disturbances and/or unmodeled dynamics. Anovel distributed robust adaptive control strategy is proposed. It is shown that the robust adaptive leaderlessconsensus problem is solved with the proposed control strategy under some sufficient conditions. Two examplesare provided to demonstrate the efficacy of the proposed control strategy.

Robustα-Fe_(2)O_(3)/Epoxy Resin Superhydrophobic Coatings for Anti-icing Property

α-Fe_(2)O_(3)/epoxy resin composite superhydrophobic coating was prepared withα-Fe_(2)O_(3) nanoparticles and epoxy resin by spin coating method.The coating without epoxy resin has higher contact angle(CA)and lower ice adhesion strength(IAS),but the mechanical properties are poor.Theα-Fe_(2)O_(3)/epoxy resin composite superhydrophobic coating exhibits good mechanical durability.In addition,compared with the bare aluminum substrate,the Ecorr of the composite coating is positive and the Jcorr is lower.The inhibition efficiency of the composite coating is as high as 99.98%in 3.5 wt%NaCl solution.The difference in the microstructure caused by the two preparation methods leads to the changes in mechanical properties and corrosion resistance of composite superhydrophobic coating.

Robustness Study and Superior Method Development and Validation for Analytical Assay Method of Atropine Sulfate in Pharmaceutical Ophthalmic Solution

Background: The robustness is a measurement of an analytical chemical method and its ability to contain unaffected by little with deliberate variation of analytical chemical method parameters. The analytical chemical method variation parameters are based on pH variability of buffer solution of mobile phase, organic ratio composition changes, stationary phase (column) manufacture, brand name and lot number variation;flow rate variation and temperature variation of chromatographic system. The analytical chemical method for assay of Atropine Sulfate conducted for robustness evaluation. The typical variation considered for mobile phase organic ratio change, change of pH, change of temperature, change of flow rate, change of column etc. Purpose: The aim of this study is to develop a cost effective, short run time and robust analytical chemical method for the assay quantification of Atropine in Pharmaceutical Ophthalmic Solution. This will help to make analytical decisions quickly for research and development scientists as well as will help with quality control product release for patient consumption. This analytical method will help to meet the market demand through quick quality control test of Atropine Ophthalmic Solution and it is very easy for maintaining (GDP) good documentation practices within the shortest period of time. Method: HPLC method has been selected for developing superior method to Compendial method. Both the compendial HPLC method and developed HPLC method was run into the same HPLC system to prove the superiority of developed method. Sensitivity, precision, reproducibility, accuracy parameters were considered for superiority of method. Mobile phase ratio change, pH of buffer solution, change of stationary phase temperature, change of flow rate and change of column were taken into consideration for robustness study of the developed method. Results: The limit of quantitation (LOQ) of developed method was much low than the compendial method. The % RSD for the six sample assay of developed method was 0.4% where the % RSD of the compendial method was 1.2%. The reproducibility between two analysts was 100.4% for developed method on the contrary the compendial method was 98.4%.

Z-Score in Fetal Echocardiography–Is there Still Room for New Studies?

Congenital heart disease(CHD)is the most common type of birth defect,representing a significant cause of peri-natal morbidity and mortality.Early diagnosis of such anomalies is crucial for improving outcomes.Current pro-tocols recommend a qualitative assessment of cardiac structures using two-dimensional ultrasound(2DUS)and color Doppler imaging.In cases of suspected abnormalities,quantitative assessments through cardiac structure measurements and reference curves can aid in accurate diagnosis.Similar to centiles widely employed in obste-trics,Z-scores provide more precise quantification of various cardiac structures,particularly at the extremes of the curve.While the development of reference curves and Z-scores has progressed over the past two decades,a lack of standardization in measurements and statistical methodology for their determination is evident.Establishing reference curves requires adherence to specific recommendations to improve their accuracy.The purpose of this study is to provide a narrative review of the major studies that have generated reference values for cardiac struc-tures using 2DUS and Z-scores,to evaluate their methodology,and to provide a summary of the results.

Image Hiding with High Robustness Based on Dynamic Region Attention in the Wavelet Domain

Hidden capacity,concealment,security,and robustness are essential indicators of hiding algorithms.Currently,hiding algorithms tend to focus on algorithmic capacity,concealment,and security but often overlook the robustness of the algorithms.In practical applications,the container can suffer from damage caused by noise,cropping,and other attacks during transmission,resulting in challenging or even impossible complete recovery of the secret image.An image hiding algorithm based on dynamic region attention in the multi-scale wavelet domain is proposed to address this issue and enhance the robustness of hiding algorithms.In this proposed algorithm,a secret image of size 256×256 is first decomposed using an eight-level Haar wavelet transform.The wavelet transform generates one coefficient in the approximation component and twenty-four detail bands,which are then embedded into the carrier image via a hiding network.During the recovery process,the container image is divided into four non-overlapping parts,each employed to reconstruct a low-resolution secret image.These lowresolution secret images are combined using densemodules to obtain a high-quality secret image.The experimental results showed that even under destructive attacks on the container image,the proposed algorithm is successful in recovering a high-quality secret image,indicating that the algorithm exhibits a high degree of robustness against various attacks.The proposed algorithm effectively addresses the robustness issue by incorporating both spatial and channel attention mechanisms in the multi-scale wavelet domain,making it suitable for practical applications.In conclusion,the image hiding algorithm introduced in this study offers significant improvements in robustness compared to existing algorithms.Its ability to recover high-quality secret images even in the presence of destructive attacksmakes it an attractive option for various applications.Further research and experimentation can explore the algorithm’s performance under different scenarios and expand its potential applications.

Gates joint locally connected network for accurate and robust reconstruction in optical molecular tomography

Optical molecular tomography(OMT)is a potential pre-clinical molecular imaging technique with applications in a variety of biomedical areas,which can provide non-invasive quantitative three-dimensional(3D)information regarding tumor distribution in living animals.The construction of optical transmission models and the application of reconstruction algorithms in traditional model-based reconstruction processes have affected the reconstruction results,resulting in problems such as low accuracy,poor robustness,and long-time consumption.Here,a gates joint locally connected network(GLCN)method is proposed by establishing the mapping relationship between the inside source distribution and the photon density on surface directly,thus avoiding the extra time consumption caused by iteration and the reconstruction errors caused by model inaccuracy.Moreover,gates module was composed of the concatenation and multiplication operators of three different gates.It was embedded into the network aiming at remembering input surface photon density over a period and allowing the network to capture neurons connected to the true source selectively by controlling three different gates.To evaluate the performance of the proposed method,numerical simulations were conducted,whose results demonstrated good performance in terms of reconstruction positioning accuracy and robustness.

Physics-Constrained Robustness Enhancement for Tree Ensembles Applied in Smart Grid

With the widespread use of machine learning(ML)technology,the operational efficiency and responsiveness of power grids have been significantly enhanced,allowing smart grids to achieve high levels of automation and intelligence.However,tree ensemble models commonly used in smart grids are vulnerable to adversarial attacks,making it urgent to enhance their robustness.To address this,we propose a robustness enhancement method that incorporates physical constraints into the node-splitting decisions of tree ensembles.Our algorithm improves robustness by developing a dataset of adversarial examples that comply with physical laws,ensuring training data accurately reflects possible attack scenarios while adhering to physical rules.In our experiments,the proposed method increased robustness against adversarial attacks by 100%when applied to real grid data under physical constraints.These results highlight the advantages of our method in maintaining efficient and secure operation of smart grids under adversarial conditions.

Robust optical mode converter based on topological waveguide arrays

Optical mode converters are essential for enhancing the capacity of optical communication systems. However, fabrication errors restrict the further improvement of conventional mode converters. To address this challenge, we have designed an on-chip TE0–TE1mode converter based on topologically protected waveguide arrays. The simulation results demonstrate that the converter exhibits a mode coupling efficiency of 93.5% near 1550 nm and can tolerate a relative fabrication error of 30%. Our design approach can be extended to enhance the robustness for other integrated photonic devices, beneficial for future development of optical network systems.

Constructive Robust Steganography Algorithm Based on Style Transfer

Traditional information hiding techniques achieve information hiding by modifying carrier data,which can easily leave detectable traces that may be detected by steganalysis tools.Especially in image transmission,both geometric and non-geometric attacks can cause subtle changes in the pixels of the image during transmission.To overcome these challenges,we propose a constructive robust image steganography technique based on style transformation.Unlike traditional steganography,our algorithm does not involve any direct modifications to the carrier data.In this study,we constructed a mapping dictionary by setting the correspondence between binary codes and image categories and then used the mapping dictionary to map secret information to secret images.Through image semantic segmentation and style transfer techniques,we combined the style of secret images with the content of public images to generate stego images.This type of stego image can resist interference during public channel transmission,ensuring the secure transmission of information.At the receiving end,we input the stego image into a trained secret image reconstruction network,which can effectively reconstruct the original secret image and further recover the secret information through a mapping dictionary to ensure the security,accuracy,and efficient decoding of the information.The experimental results show that this constructive information hiding method based on style transfer improves the security of information hiding,enhances the robustness of the algorithm to various attacks,and ensures information security.

Constructing high-toughness polyimide binder with robust polarity and ion-conductive mechanisms ensuring long-term operational stability of silicon-based anodes

Silicon-based materials have demonstrated remarkable potential in high-energy-density batteries owing to their high theoretical capacity.However,the significant volume expansion of silicon seriously hinders its utilization as a lithium-ion anode.Herein,a functionalized high-toughness polyimide(PDMI) is synthesized by copolymerizing the 4,4'-Oxydiphthalic anhydride(ODPA) with 4,4'-oxydianiline(ODA),2,3-diaminobenzoic acid(DABA),and 1,3-bis(3-aminopropyl)-tetramethyl disiloxane(DMS).The combination of rigid benzene rings and flexible oxygen groups(-O-) in the PDMI molecular chain via a rigidness/softness coupling mechanism contributes to high toughness.The plentiful polar carboxyl(-COOH) groups establish robust bonding strength.Rapid ionic transport is achieved by incorporating the flexible siloxane segment(Si-O-Si),which imparts high molecular chain motility and augments free volume holes to facilitate lithium-ion transport(9.8 × 10^(-10) cm^(2) s^(-1) vs.16 × 10^(-10) cm^(2) s~(-1)).As expected,the SiO_x@PDMI-1.5 electrode delivers brilliant long-term cycle performance with a remarkable capacity retention of 85% over 500 cycles at 1.3 A g^(-1).The well-designed functionalized polyimide also significantly enhances the electrochemical properties of Si nanoparticles electrode.Meanwhile,the assembled SiO_x@PDMI-1.5/NCM811 full cell delivers a high retention of 80% after 100 cycles.The perspective of the binder design strategy based on polyimide modification delivers a novel path toward high-capacity electrodes for high-energy-density batteries.

Nonlinear robust adaptive control for bidirectional stabilization system of all-electric tank with unknown actuator backlash compensation and disturbance estimation

Since backlash nonlinearity is inevitably existing in actuators for bidirectional stabilization system of allelectric tank,it behaves more drastically in high maneuvering environments.In this work,the accurate tracking control for bidirectional stabilization system of moving all-electric tank with actuator backlash and unmodeled disturbance is solved.By utilizing the smooth adaptive backlash inverse model,a nonlinear robust adaptive feedback control scheme is presented.The unknown parameters and unmodelled disturbance are addressed separately through the derived parametric adaptive function and the continuous nonlinear robust term.Because the unknown backlash parameters are updated via adaptive function and the backlash effect can be suppressed successfully by inverse operation,which ensures the system stability.Meanwhile,the system disturbance in the high maneuverable environment can be estimated with the constructed adaptive law online improving the engineering practicality.Finally,Lyapunov-based analysis proves that the developed controller can ensure the tracking error asymptotically converges to zero even with unmodeled disturbance and unknown actuator backlash.Contrast co-simulations and experiments illustrate the advantages of the proposed approach.

Data-driven Wasserstein distributionally robust chance-constrained optimization for crude oil scheduling under uncertainty

Crude oil scheduling optimization is an effective method to enhance the economic benefits of oil refining.But uncertainties,including uncertain demands of crude distillation units(CDUs),might make the production plans made by the traditional deterministic optimization models infeasible.A data-driven Wasserstein distributionally robust chance-constrained(WDRCC)optimization approach is proposed in this paper to deal with demand uncertainty in crude oil scheduling.First,a new deterministic crude oil scheduling optimization model is developed as the basis of this approach.The Wasserstein distance is then used to build ambiguity sets from historical data to describe the possible realizations of probability distributions of uncertain demands.A cross-validation method is advanced to choose suitable radii for these ambiguity sets.The deterministic model is reformulated as a WDRCC optimization model for crude oil scheduling to guarantee the demand constraints hold with a desired high probability even in the worst situation in ambiguity sets.The proposed WDRCC model is transferred into an equivalent conditional value-at-risk representation and further derived as a mixed-integer nonlinear programming counterpart.Industrial case studies from a real-world refinery are conducted to show the effectiveness of the proposed method.Out-of-sample tests demonstrate that the solution of the WDRCC model is more robust than those of the deterministic model and the chance-constrained model.

MarkINeRV: A Robust Watermarking Scheme for Neural Representation for Videos Based on Invertible Neural Networks

Recent research advances in implicit neural representation have shown that a wide range of video data distributions are achieved by sharing model weights for Neural Representation for Videos(NeRV).While explicit methods exist for accurately embedding ownership or copyright information in video data,the nascent NeRV framework has yet to address this issue comprehensively.In response,this paper introduces MarkINeRV,a scheme designed to embed watermarking information into video frames using an invertible neural network watermarking approach to protect the copyright of NeRV,which models the embedding and extraction of watermarks as a pair of inverse processes of a reversible network and employs the same network to achieve embedding and extraction of watermarks.It is just that the information flow is in the opposite direction.Additionally,a video frame quality enhancement module is incorporated to mitigate watermarking information losses in the rendering process and the possibility ofmalicious attacks during transmission,ensuring the accurate extraction of watermarking information through the invertible network’s inverse process.This paper evaluates the accuracy,robustness,and invisibility of MarkINeRV through multiple video datasets.The results demonstrate its efficacy in extracting watermarking information for copyright protection of NeRV.MarkINeRV represents a pioneering investigation into copyright issues surrounding NeRV.

Robust optimal dispatch strategy of integrated energy system considering CHP-P2G-CCS

Integrated energy systems(IESs)can improve energy efficiency and reduce carbon emissions,essential for achieving peak carbon emissions and carbon neutrality.This study investigated the characteristics of the CHP model considering P2G and carbon capture systems,and a two-stage robust optimization model of the electricity-heat-gascold integrated energy system was developed.First,a CHP model considering the P2G and carbon capture system was established,and the electric-thermal coupling characteristics and P2G capacity constraints of the model were derived,which proved that the model could weaken the electric-thermal coupling characteristics,increase the electric power regulation range,and reduce carbon emissions.Subsequently,a two-stage robust optimal scheduling model of an IES was constructed,in which the objective function in the day-ahead scheduling stage was to minimize the start-up and shutdown costs.The objective function in the real-time scheduling stage was to minimize the equipment operating costs,carbon emission costs,wind curtailment,and solar curtailment costs,considering multiple uncertainties.Finally,after the objective function is linearized with a ψ-piecewise method,the model is solved based on the C&CG algorithm.Simulation results show that the proposed model can effectively absorb renewable energy and reduce the total cost of the system.