Model prediction of inactivation of Aeromonas salmonicida grown on poultry in situ by intense pulsed light

The aim of this study was to evaluate the factors influencing the inactivation effect of intense pulsed light(IPL)on Aeromonas salmonicida grown on chicken meat and skin,and to further develop prediction models of inactivation.In this work,chicken meat and skin inoculated with meat-borne A.salmonicida isolates were subjected to IPL treatments under different conditions.The results showed that IPL had obvious bactericidal effect in the chicken skin and thickness groups when the treatment voltage and time were 7 V combined with 5 s.In addition,the lethality curves of A.salmonicida were fitted under IPL conditions of 3.5-7.5 V.The comparison of statistical parameters revealed that the Weibull model could best fit the mortality curves and could accurately predict the mortality dynamic of A.salmonicida grown on chicken skin.And further a secondary model between the scale factor b and the treatment voltage in Weibull model was established using linear equations,which determined that the secondary model could accurately predict the inactivation of A.salmonicida.This study provides a theoretical basis for future prediction models of Aeromonas,and also provides new ideas for sterilization approaches of meat-borne Aeromonas.

Evaluating Privacy Leakage and Memorization Attacks on Large Language Models (LLMs) in Generative AI Applications

The recent interest in the deployment of Generative AI applications that use large language models (LLMs) has brought to the forefront significant privacy concerns, notably the leakage of Personally Identifiable Information (PII) and other confidential or protected information that may have been memorized during training, specifically during a fine-tuning or customization process. We describe different black-box attacks from potential adversaries and study their impact on the amount and type of information that may be recovered from commonly used and deployed LLMs. Our research investigates the relationship between PII leakage, memorization, and factors such as model size, architecture, and the nature of attacks employed. The study utilizes two broad categories of attacks: PII leakage-focused attacks (auto-completion and extraction attacks) and memorization-focused attacks (various membership inference attacks). The findings from these investigations are quantified using an array of evaluative metrics, providing a detailed understanding of LLM vulnerabilities and the effectiveness of different attacks.

Analytical and NumericalMethods to Study the MFPT and SR of a Stochastic Tumor-Immune Model

The Mean First-Passage Time (MFPT) and Stochastic Resonance (SR) of a stochastic tumor-immune model withnoise perturbation are discussed in this paper. Firstly, considering environmental perturbation, Gaussian whitenoise and Gaussian colored noise are introduced into a tumor growth model under immune surveillance. Asfollows, the long-time evolution of the tumor characterized by the Stationary Probability Density (SPD) and MFPTis obtained in theory on the basis of the Approximated Fokker-Planck Equation (AFPE). Herein the recurrenceof the tumor from the extinction state to the tumor-present state is more concerned in this paper. A moreefficient algorithmof Back-Propagation Neural Network (BPNN) is utilized in order to testify the correction of thetheoretical SPDandMFPT.With the existence of aweak signal, the functional relationship between Signal-to-NoiseRatio (SNR), noise intensities and correlation time is also studied. Numerical results show that both multiplicativeGaussian colored noise and additive Gaussian white noise can promote the extinction of the tumors, and themultiplicative Gaussian colored noise can lead to the resonance-like peak on MFPT curves, while the increasingintensity of the additiveGaussian white noise results in theminimum of MFPT. In addition, the correlation timesare negatively correlated with MFPT. As for the SNR, we find the intensities of both the Gaussian white noise andthe Gaussian colored noise, as well as their correlation intensity can induce SR. Especially, SNR is monotonouslyincreased in the case ofGaussian white noisewith the change of the correlation time.At last, the optimal parametersin BPNN structure are analyzed for MFPT from three aspects: the penalty factors, the number of neural networklayers and the number of nodes in each layer.

Security Vulnerability Analyses of Large Language Models (LLMs) through Extension of the Common Vulnerability Scoring System (CVSS) Framework

Large Language Models (LLMs) have revolutionized Generative Artificial Intelligence (GenAI) tasks, becoming an integral part of various applications in society, including text generation, translation, summarization, and more. However, their widespread usage emphasizes the critical need to enhance their security posture to ensure the integrity and reliability of their outputs and minimize harmful effects. Prompt injections and training data poisoning attacks are two of the most prominent vulnerabilities in LLMs, which could potentially lead to unpredictable and undesirable behaviors, such as biased outputs, misinformation propagation, and even malicious content generation. The Common Vulnerability Scoring System (CVSS) framework provides a standardized approach to capturing the principal characteristics of vulnerabilities, facilitating a deeper understanding of their severity within the security and AI communities. By extending the current CVSS framework, we generate scores for these vulnerabilities such that organizations can prioritize mitigation efforts, allocate resources effectively, and implement targeted security measures to defend against potential risks.

蓖麻Lm型雌性系及其杂交组合的杂种优势分析

为了解Lm型雌性系蓖麻的配合力遗传基础,以5个蓖麻Lm型雌性系为母本,3个蓖麻两性系为父本,根据NC-Ⅱ不完全双列杂交组配15个杂交组合,对蓖麻杂交组合的配合力、遗传力以及杂种优势进行分析。结果表明:雌性系A1和两性系B2的GCA和杂交组合SCA在蓖麻大部分农艺性状中均表现优秀,可在提高蓖麻产量的亲本选育中作为优异亲本进行杂交组合的构建。对亲本杂种优势研究发现,两性系B2参与组配的杂交组合在多个农艺性状上表现为强杂种优势,而Lm型雌性系则表现为在单个性状上具有较强的杂种优势。从遗传力分析结果来看,蓖麻的主茎蒴果数、一级分枝蒴果数、主茎穗长、主茎节数、一级分枝百粒质量和产量受加性效应和非加性效应共同影响,且受环境影响较小,适宜在早代进行选择。

基于LM优化的车载全景影像绝对测量方法

【目的】为解决车载全景影像在智慧城市、智慧城管、智慧交通等领域中应用不足的问题,提出了一种基于LM优化的车载序列全景影像绝对测量的方法。【方法】首先,对待测目标点在全景影像上的成像过程进行分析,详细推导了目标点绝对坐标测量的理论模型。其次,为解决该理论模型的优化求解问题,引入了对初值不敏感且收敛速度较快的LM算法,并对LM算法中的初值获取、函数构建、参数设置等问题进行了设计。最后,结合实际车载全景影像和目标点位实测数据,对所提的方法进行验证。【结果】该方法获得的目标点绝对坐标平面中误差和高程中误差分别为0.123 m和0.118 m,符合《城市测量规范》(CJJ/T 8—2011)中的相关要求。【结论】该方法能够有效获取全景影像范围内待测目标点的绝对坐标,从而为全景影像在城市部件普查、交通基础设施测量等领域的应用提供了可能。

采用STAMP-24Model的多组织事故分析

安全生产事故往往由多组织交互、多因素耦合造成,事故原因涉及多个组织。为预防和遏制多组织生产安全事故的发生,基于系统理论事故建模与过程模型(Systems-Theory Accident Modeling and Process,STAMP)、24Model,构建一种用于多组织事故分析的方法,并以青岛石油爆炸事故为例进行事故原因分析。结果显示:STAMP-24Model可以分组织,分层次且有效、全面、详细地分析涉及多个组织的事故原因,探究多组织之间的交互关系;对事故进行动态演化分析,可得到各组织不安全动作耦合关系与形成的事故失效链及管控失效路径,进而为预防多组织事故提供思路和参考。

基于改进变步长LMS算法的谐波电流检测法

针对传统基于瞬时无功功率理论谐波电流检测中的二阶低通滤波器无法同时满足检测精度和响应时间的问题,提出了基于改进的双曲正切函数变步长LMS算法(IVSSLMS)的低通滤波器提取p轴和q轴中直流分量的方法;将两个谐波电流的间隔时间通过适当的调节采样间隔次数来延长,目的是为了达到高次谐波的自相关性能迅速降为零。从理论上分析了算法性能的优越,给出了参数的取值,并在simulink中搭建了仿真模型,仿真实验结果表明,改进后的低通滤波器直流分量的跟踪速度提升了一倍,得到基波正弦波形的THD值下降了0.37%。并得出以下结论:在同样输电条件下,采用改进变步长LMS算法构成低通滤波器加快了直流量的跟踪,提取的基波分量更加接近于理想波形,提升了谐波检测的瞬时性和准确性。

基于改进24Model-ISM-SNA建筑工人不安全行为关联路径研究

建筑施工现场环境复杂,为有效控制不安全行为发生,基于行为安全“2-4”模型对360份具有代表性的建筑安全事故调查报告进行分析,提取出22个不安全行为的主要影响因素。利用灰色关联分析方法(GRA)改进的集成ISM-SNA模型,将不安全行为风险因素划分为表层、过渡层与深层,然后对风险因素进行可视化分析、中心度分析及凝聚子群分析,揭示了各致因因素间的关联关系和传导路径。结果表明,建筑工人不安全行为影响因素可划分成7级3阶的多级递阶结构,安全意识、现场监管、外部环境是建筑工人不安全行为的关键影响因素,同时现场监管和隐患排查到位能有效降低不安全行为的发生。

基于24Model-D-ISM的地铁站火灾疏散影响因素研究

为预防地铁站火灾事故,深入了解地铁站火灾人员疏散影响因素间的内在联系与层次结构,基于第6版“2-4”模型(24Model)分析63起地铁站火灾疏散事故,充分考虑各个因素之间的交互作用,提取19个影响地铁站人员疏散的关键因素,建立地铁站火灾人员疏散影响因素指标体系;采用算子客观赋权法(C-OWA)改进决策试验与评价实验法(DEMATEL),确定地铁站火灾人员疏散的重要影响因素;在此基础上,采用解释结构模型(ISM)分析各个因素间的层次结构及相互作用路径,构建地铁站火灾人员疏散影响因素的多级递阶结构模型。研究结果表明:疏散引导、恐慌从众行为、人员拥挤为地铁站火灾人员疏散的关键影响因素;地铁站火灾人员疏散受表层因素、中间层因素、深层因素共同作用的影响,其中,疏散教育与培训、设施维护与检查、疏散预案等因素是根源影响因素,重视根源影响因素的改善有利于从本质上预防和控制事故的发生。

基于x-LMS算法的直升机主动消振电力作动器系统研究

直升机前飞时,气动环境会导致不同方位角的桨叶出现气动载荷的瞬间不对称,通过基础结构传递会在机身形成大幅度的低频振动。为了消除多方向幅值变化的振动力,利用结构响应主动控制原理,设计了基于x-LMS算法的主动消振电力作动器系统,并进行了减振实验。首先,通过比较确定了单台作动器中两台电机同向旋转的方案。通过两台作动器的组合使用,推导出输出力的数学模型。其次,采用负载相位差交叉耦合的控制策略设计系统控制框图。针对存在耦合的相位外环,通过回差阵特征值法确定满足系统稳定裕度要求的参数范围,再根据灵敏度函数和输入跟踪性能在所得的参数稳定域内寻找最优解。然后,提出了基于x-LMS算法的直升机主动振动控制系统,并通过仿真验证了该系统的减振效果。最后,研制的实验样机进行了动稳态实验以及减振实验,验证了系统的实际减振效果。

基于Adam算法的LMS优化

虽然传统的LMS算法简单有效,但它的收敛速度较慢,容易受到初始权值选择和步长设置的影响。为了解决这个问题,我们引入了Adam算法的自适应学习率特性到LMS算法中。Adam算法通过一阶矩估计(mean)和二阶矩估计(variance)来估计梯度的均值和方差,并根据这些估计值来调整学习率。这样可以对梯度下降的方向和速度进行更精细地控制。通过Adam算法对LMS优化,LMS算法在更新权重时能够更加高效地利用梯度信息,从而提高训练速度和收敛性能。此外,优化后的算法还具有一些超参数,如beta1、beta2和eps等,需要根据具体的应用场景和数据特点进行调节,以获得最佳的优化效果。仿真结果表明优化方案比传统LMS算法在收敛过程中的震荡要更小。

A novel pulmonary fibrosis murine model with immune-related liver injury

Idiopathic pulmonary fibrosis(IPF),characterized by aggravated alveolar destruc-tion and fibrotic matrix deposition,tendentiously experiences the stage called acute exacerbation IPF(AE-IPF)and progresses to multiple organ damage,especially liver injury.Recent studies have found a variety of immune microenvironment disorders associated with elevated IPF risk and secondary organ injury,whereas current animal models induced with bleomycin(BLM)could not completely reflect the pathologi-cal manifestations of AE-IPF patients in clinic,and the exact underlying mechanisms are not yet fully explored.In the current study,we established an AE-IPF model by tracheal administration of a single dose of BLM and then repeated administrations of lipopolysaccharide in mice.This mouse model successfully recapitulated the clinical features of AE-IPF,including excessive intrapulmonary inflammation and fibrosis and extrapulmonary manifestations,as indicated by significant upregulation of Il6,Tnfa,Il1b,Tgfb,fibronectin,and Col1a1 in both lungs and liver and elevated serum aspartate transaminase and alanine transaminase levels.These effects might be attributed to the regulation of Th17 cells.By sharing this novel murine model,we expect to pro-vide an appropriate experimental platform to investigate the pathogenesis of AE-IPF coupled with liver injury and contribute to the discovery and development of targeted interventions.

基于LMDI和Tapio脱钩模型的甘肃省碳排放研究

在定量核算2005—2021年甘肃省碳排放量和强度的基础上,预测分析基准情景、高排放情景和低排放情景3种发展情景下甘肃省碳达峰时间及路径.研究表明,甘肃省整体脱钩状态较为理想,贡献主要来源于煤炭碳排放量与经济增长长期处于弱脱钩;经济增长具有增排效应,能源强度、能源结构和人口规模具有节能减排效应,尤其是能源强度.从情景预测分析看,基准情景下,甘肃省将在2035年碳排放达到峰值,2035年之后经济与碳排放处于强脱钩状态,很难在2060年之前实现碳中和;高排放情景下,将在2045达到峰值,经济与碳排放一直处于弱脱钩状态,后续实现碳达峰的难度较大;低排放情景下,2030年后经济与碳排放一直处于强脱钩状态,碳排放量下降幅度较大,在2060年之前实现碳中和较为容易.

水稻类病变突变体lms1的表型鉴定与抗病分子机制分析

类病变突变体是研究植物细胞死亡和抗病分子机制的重要遗传材料。通过辐射诱变粳稻品种武运粳7号,获得一个少见的抗病性下降的类病变突变体lms1(lesion mimic and disease susceptible mutant 1)。与野生型相比,该突变体叶片自发出现红褐色斑点,其株高、穗长、每穗粒数以及单株产量降低,但千粒重增加;对水稻白叶枯病的抗性显著下降,组织染色表明突变体的叶片中存在明显的细胞死亡以及活性氧的过量积累。遗传分析表明,lms1突变体的表型受单隐性核基因控制,利用图位克隆技术将lms1基因精细定位于水稻9号染色体Indel7和Indel8两个分子标记间,物理距离为62 kb。定位区间内候选基因的PCR扩增测序结果表明,其中一个编码泛素羧基末端水解酶的OsLMP1(Lesion Mimic Phenotype 1)基因第1个外显子中插入了一段长度为654 bp的序列,导致其蛋白翻译提前终止。利用蛋白组学技术分析了lms1突变体与野生型对照的蛋白积累水平,共鉴定到19个差异蛋白(7个上调、12个下调),主要参与氧化还原、叶绿素合成、光合作用等代谢途径。上述结果为进一步研究OsLMP1基因的功能及其调控细胞程序性死亡与抗病性的分子机制提供借鉴。

一种LM-BP加速搜索的周跳探测与修复方法

针对传统三频周跳探测与修复方法中的不敏感、漏检以及效率较低等问题,提出一种基于莱文伯格-马夸特(LM)-反向传播(BP)神经网络加速搜索法的伪距相位组合与电离层残差组合联合周跳探测与修复方法:利用2个伪距相位组合以减少不敏感周跳数量,利用1个电离层残差组合以提高小周跳探测敏感度;在构成3个线性无关的组合观测值后,使用LM-BP加速搜索算法进行周跳探测与修复。实验结果表明,相对常规的伪距相位组合与电离层残差组合联合方法,该方法能够提高周跳探测与修复性能,可探测小至1个的周跳,探测与修复整体时效有较大提升。

State of the art in applications of machine learning in steelmaking process modeling

With the development of automation and informatization in the steelmaking industry,the human brain gradually fails to cope with an increasing amount of data generated during the steelmaking process.Machine learning technology provides a new method other than production experience and metallurgical principles in dealing with large amounts of data.The application of machine learning in the steelmaking process has become a research hotspot in recent years.This paper provides an overview of the applications of machine learning in the steelmaking process modeling involving hot metal pretreatment,primary steelmaking,secondary refining,and some other aspects.The three most frequently used machine learning algorithms in steelmaking process modeling are the artificial neural network,support vector machine,and case-based reasoning,demonstrating proportions of 56%,14%,and 10%,respectively.Collected data in the steelmaking plants are frequently faulty.Thus,data processing,especially data cleaning,is crucially important to the performance of machine learning models.The detection of variable importance can be used to optimize the process parameters and guide production.Machine learning is used in hot metal pretreatment modeling mainly for endpoint S content prediction.The predictions of the endpoints of element compositions and the process parameters are widely investigated in primary steelmaking.Machine learning is used in secondary refining modeling mainly for ladle furnaces,Ruhrstahl–Heraeus,vacuum degassing,argon oxygen decarburization,and vacuum oxygen decarburization processes.Further development of machine learning in the steelmaking process modeling can be realized through additional efforts in the construction of the data platform,the industrial transformation of the research achievements to the practical steelmaking process,and the improvement of the universality of the machine learning models.

基于LM-BP神经网络的办公建筑逐时空调能耗预测

利用LM算法对BP神经网络进行改进,以提高模型收敛速度,减少迭代次数,并分别对夏季、冬季空调能耗模拟数据进行样本训练和测试,构建了LM-BP神经网络的办公建筑逐时空调能耗预测模型。结果表明夏季模型的预测值和模拟值之间的MAPE误差平均为5.74%,冬季模型的MAPE误差平均为5%,其误差均在10%以内,说明LM-BP神经网络在建筑空调能耗预测方面的可行性。并以成都地区某办公建筑实际空调能耗数据为基础对该模型进行了验证,其预测值与实际值平均相对误差为6.6%,说明建立的能耗预测模型能够满足实际工程的需要,准确的用电预测数据可为办公建筑用电侧进行实际电力市场交易提供科学的决策依据。

耦合优化蚁群算法与P-Median model的选址模型设计

为节省城建部门对于公共体育设施的投入成本以及提高城市人民生活质量,以运动场所优化选址为例,提出一种新型设施选址模型。该模型主要基于P-Median model(最小化阻抗模型)根据需求点数量从全部候选设施选址中选择设施空间位置,让用户达到离自己最近设施距离成本总和最小的目的,对选址的基本原则和实际情况提出要求,构造目标函数用于优化后蚁群算法求解进行选址工作。优化蚁群算法实现基于Python语言模块,通过改进蚁群原始信息素,提升原有算法的收敛速度,求出目标函数最优解,可以很好地模拟对于运动场所的选址。用二者耦合进行优势互补所设计的选址模型来搜寻研究区蚁群信息素浓度残留最大的栅格像元,从而确定未被已有设施点服务半径覆盖的最佳设施点建立位置。实验结果表明,该新型选址模型相较于最小化阻抗模型与最大化覆盖模型,新增优化设施点使整体服务半径覆盖率分别高出10.42%和6.95%,适合求解较为精确且小规模空间下的选址问题。

光伏发电企业运营效率及影响因素研究——基于DEA-Malmquist-Tobit模型的分析

在“双碳”目标的引领下,科学评价光伏发电企业的运营效率及其影响因素,对促进光伏企业及产业的高质量发展有着重要意义。基于2014—2022年光伏发电上市公司的面板数据,运用DEA-Malmquist-Tobit模型测度了光伏企业的运营效率及其综合效率的动态变化,实证分析了运营效率的主要影响因素。结果表明,9年间光伏发电企业的全要素生产率呈1.8%增长率的小幅增长趋势,股权结构、每股收益等5个变量对光伏发电企业的运营效率有显著性影响。