Online identification and extraction method of regional large-scale adjustable load-aggregation characteristics

This article introduces the concept of load aggregation,which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems.The online identification method is a computer-involved approach for data collection,processing,and system identification,commonly used for adaptive control and prediction.This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration,aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods.The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics,economic efficiency,and comfort.The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes,the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57,indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term.Overall,the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective.

Straw return increases crop production by improving soil organic carbon sequestration and soil aggregation in a long-term wheat-cotton cropping system

Straw return is a promising strategy for managing soil organic carbon(SOC)and improving yield stability.However,the optimal straw return strategy for sustainable crop production in the wheat(Triticum aestivum L.)-cotton(Gossypium hirsutum L.)cropping system remains uncertain.The objective of this study was to quantify the long-term(10 years)impact of carbon(C)input on SOC sequestration,soil aggregation and crop yields in a wheat-cotton cropping system in the Yangtze River Valley,China.Five treatments were arranged with a single-factor randomized design as follows:no straw return(Control),return of wheat straw only(Wt),return of cotton straw only(Ct),return of 50%wheat and 50%cotton straw(Wh-Ch)and return of 100%wheat and 100%cotton straw(Wt-Ct).In comparison to the Control,the SOC content increased by 8.4 to 20.2%under straw return.A significant linear positive correlation between SOC sequestration and C input(1.42-7.19 Mg ha^(−1)yr^(−1))(P<0.05)was detected.The percentages of aggregates of sizes>2 and 1-2 mm at the 0-20 cm soil depth were also significantly elevated under straw return,with the greatest increase of the aggregate stability in the Wt-Ct treatment(28.1%).The average wheat yields increased by 12.4-36.0%and cotton yields increased by 29.4-73.7%,and significantly linear positive correlations were also detected between C input and the yields of wheat and cotton.The average sustainable yield index(SYI)reached a maximum value of 0.69 when the C input was 7.08 Mg ha^(−1)yr^(−1),which was close to the maximum value(SYI of 0.69,C input of 7.19 Mg ha^(−1)yr^(-1))in the Wt-Ct treatment.Overall,the return of both wheat and cotton straw was the best strategy for improving SOC sequestration,soil aggregation,yields and their sustainability in the wheat-cotton rotation system.

A novel approach to Parkinson's disease treatment with a potentially dual-acting therapeutic agent that targetsα-synuclein aggregation and neuron death

Parkinson's disease(PD),a prevalent neurodegenerative disorder,is chara cterized by the loss of dopaminergic neurons and the aggregation ofα-synuclein protein into Lewy bodies.While the current standards of therapy have been successful in providing some symptom relief,they fail to address the underlying pathophysiology of PD and as a result,they have no effect on disease progression.

RBC aggregation,deformation and adhesion to endothelium:Role of nitric oxide derived from L-Arginine and sodium nitroprusside

Red blood cells(RBCs)are the most abundant human blood cells.RBC aggregation and deformation strongly determine blood viscosity which impacts hemorheology and microcirculation.In turn,RBC properties depend on di®erent endogenous and exogenous factors.One such factor is nitric oxide(NO),which is mainly produced by endothelial cells(EC)from L-arginine amino acid in the circulatory system.Since the mechanisms of the RBC-endothelium interplay are not clear up to date and considering its possible clinical importance,the aims of this study are to investigate in vitro:(1)The effect of L-arginine induced NO on RBC aggregation and adhesion to endothelium;(2)the NO e®ect on RBC aggregation and deformation induced by L-arginine and sodium nitroprusside without the presence of endothelium in the samples.The RBC aggregation and adhesion to a monolayer of EC were studied using optical tweezers(OT).The RBC deformability and aggregation without endothelium in the samples were studied using the flow chamber method and Myrenne aggregometer.We confirmed that NO increases deformability and decreases aggregation of RBCs.We showed that the soluble guanylate cyclase pathway appears to be the only NO signaling pathway involved.In the samples with the endothelium,the "bell-shaped"dependence of RBC aggregation force on L-arginine concentration was observed,which improves our knowledge about the process of NO production by endothelium.Additionally,data related to L-arginine accumulation by endothelium were obtained:Necessity of the presence of extracellular L-arginine stated by other authors was put under question.In our study,NO decreased the RBC-endothelium adhesion,however,the tendency appeared to be weak and was not confirmed in another set of experiments.To our knowledge,this is the first attempt to measure the forces of RBC adhesion to endothelium monolayer with OT.

Impacts of Aggregation Methods and Trophospecies Number on the Structure and Function of Marine Food Webs

Aggregation of species with similar ecological properties is one of the effective methods to simplify food web researches.However,species aggregation will affect not only the complexity of modeling process but also the accuracy of models’outputs.Selection of aggregation methods and the number of trophospecies are the keys to study the simplification of food web.In this study,three aggregation methods,including taxonomic aggregation(TA),structural equivalence aggregation(SEA),and self-organizing maps(SOM),were analyzed and compared with the linear inverse model–Markov Chain Monte Carlo(LIM-MCMC)model.Impacts of aggregation methods and trophospecies number on food webs were evaluated based on the robustness and unitless of ecological net-work indices.Results showed that aggregation method of SEA performed better than the other two methods in estimating food web structure and function indices.The effects of aggregation methods were driven by the differences in species aggregation principles,which will alter food web structure and function through the redistribution of energy flow.According to the results of mean absolute percentage error(MAPE)which can be applied to evaluate the accuracy of the model,we found that MAPE in food web indices will increase with the reducing trophospecies number,and MAPE in food web function indices were smaller and more stable than those in food web structure indices.Therefore,trade-off between simplifying food webs and reflecting the status of ecosystem should be con-sidered in food web studies.These findings highlight the importance of aggregation methods and trophospecies number in the analy-sis of food web simplification.This study provided a framework to explore the extent to which food web models are affected by dif-ferent species aggregation,and will provide scientific basis for the construction of food webs.

Small molecules to target tau amyloid aggregation

Protein aggregation has been linked with many neurodegenerative diseases,such as Alzheimer’s disease(AD)or Parkinson’s disease.AD belongs to a group of heterogeneous and incurable neurodegenerative disorders collectively known as tauopathies.They comprise frontotemporal dementia,Pick’s disease,or corticobasal degeneration,among others.The symptomatology varies with the specific tau protein variant involved and the affected brain region or cell type.However,they share a common neuropathological hallmark-the formation of proteinaceous deposits named neurofibrillary tangles.Neurofibrillary tangles,primarily composed of aggregated tau(Zhang et al.,2022),disrupt normal neuronal functions,leading to cell death and cognitive decline.

Aggregation-regulated bioreduction process of graphene oxide by Shewanella bacteria

The bioreduction of graphene oxide(GO)using environmentally functional bacteria such as Shewanella represents a green approach to produce reduced graphene oxide(rGO).This process differs from the chemical reduction that involves instantaneous molecular reactions.In bioreduction,the contact of bacterial cells and GO is considered the rate-limiting step.To reveal how the bacteria-GO integration regulates rGO production,the comparative experiments of GO and three Shewanella strains were carried out.Fourier-transform infrared spectroscopy,X-ray photoelectron spectroscopy,Raman spectroscopy,and atomic force microscopy were used to characterize the reduction degree and the aggregation degree.The results showed that a spontaneous aggregation of GO and Shewanella into the condensed entity occurred within 36 h.A positive linear correlation was established,linking three indexes of the aggregation potential,the bacterial reduction ability,and the reduction degree(ID/IG)comprehensively.

Differentially Private Support Vector Machines with Knowledge Aggregation

With the widespread data collection and processing,privacy-preserving machine learning has become increasingly important in addressing privacy risks related to individuals.Support vector machine(SVM)is one of the most elementary learning models of machine learning.Privacy issues surrounding SVM classifier training have attracted increasing attention.In this paper,we investigate Differential Privacy-compliant Federated Machine Learning with Dimensionality Reduction,called FedDPDR-DPML,which greatly improves data utility while providing strong privacy guarantees.Considering in distributed learning scenarios,multiple participants usually hold unbalanced or small amounts of data.Therefore,FedDPDR-DPML enables multiple participants to collaboratively learn a global model based on weighted model averaging and knowledge aggregation and then the server distributes the global model to each participant to improve local data utility.Aiming at high-dimensional data,we adopt differential privacy in both the principal component analysis(PCA)-based dimensionality reduction phase and SVM classifiers training phase,which improves model accuracy while achieving strict differential privacy protection.Besides,we train Differential privacy(DP)-compliant SVM classifiers by adding noise to the objective function itself,thus leading to better data utility.Extensive experiments on three high-dimensional datasets demonstrate that FedDPDR-DPML can achieve high accuracy while ensuring strong privacy protection.

A non-probabilistic reliability topology optimization method based on aggregation function and matrix multiplication considering buckling response constraints

A non-probabilistic reliability topology optimization method is proposed based on the aggregation function and matrix multiplication.The expression of the geometric stiffness matrix is derived,the finite element linear buckling analysis is conducted,and the sensitivity solution of the linear buckling factor is achieved.For a specific problem in linear buckling topology optimization,a Heaviside projection function based on the exponential smooth growth is developed to eliminate the gray cells.The aggregation function method is used to consider the high-order eigenvalues,so as to obtain continuous sensitivity information and refined structural design.With cyclic matrix programming,a fast topology optimization method that can be used to efficiently obtain the unit assembly and sensitivity solution is conducted.To maximize the buckling load,under the constraint of the given buckling load,two types of topological optimization columns are constructed.The variable density method is used to achieve the topology optimization solution along with the moving asymptote optimization algorithm.The vertex method and the matching point method are used to carry out an uncertainty propagation analysis,and the non-probability reliability topology optimization method considering buckling responses is developed based on the transformation of non-probability reliability indices based on the characteristic distance.Finally,the differences in the structural topology optimization under different reliability degrees are illustrated by examples.

Prediction Model-based Multi-objective Optimization for Mix-ratio Design of Recycled Aggregate Concrete

The prediction model for mechanical properties of RAC was established through the Bayesian optimization-based Gaussian process regression(BO-GPR)method,where the input variables in BO-GPR model depend on the mix ratio of concrete.Then the compressive strength prediction model,the material cost,and environmental factors were simultaneously considered as objectives,while a multi-objective gray wolf optimization algorithm was developed for finding the optimal mix ratio.A total of 730 RAC datasets were used for training and testing the predication model,while the optimal design method for mix ratio was verified through RAC experiments.The experimental results show that the predicted,testing,and expected compressive strengths are nearly consistent,illustrating the effectiveness of the proposed method.

Cloud-Edge Collaborative Federated GAN Based Data Processing for IoT-Empowered Multi-Flow Integrated Energy Aggregation Dispatch

The convergence of Internet of Things(IoT),5G,and cloud collaboration offers tailored solutions to the rigorous demands of multi-flow integrated energy aggregation dispatch data processing.While generative adversarial networks(GANs)are instrumental in resource scheduling,their application in this domain is impeded by challenges such as convergence speed,inferior optimality searching capability,and the inability to learn from failed decision making feedbacks.Therefore,a cloud-edge collaborative federated GAN-based communication and computing resource scheduling algorithm with long-term constraint violation sensitiveness is proposed to address these challenges.The proposed algorithm facilitates real-time,energy-efficient data processing by optimizing transmission power control,data migration,and computing resource allocation.It employs federated learning for global parameter aggregation to enhance GAN parameter updating and dynamically adjusts GAN learning rates and global aggregation weights based on energy consumption constraint violations.Simulation results indicate that the proposed algorithm effectively reduces data processing latency,energy consumption,and convergence time.

Fake News Detection Based on Cross-Modal Message Aggregation and Gated Fusion Network

Social media has become increasingly significant in modern society,but it has also turned into a breeding ground for the propagation of misleading information,potentially causing a detrimental impact on public opinion and daily life.Compared to pure text content,multmodal content significantly increases the visibility and share ability of posts.This has made the search for efficient modality representations and cross-modal information interaction methods a key focus in the field of multimodal fake news detection.To effectively address the critical challenge of accurately detecting fake news on social media,this paper proposes a fake news detection model based on crossmodal message aggregation and a gated fusion network(MAGF).MAGF first uses BERT to extract cumulative textual feature representations and word-level features,applies Faster Region-based ConvolutionalNeuralNetwork(Faster R-CNN)to obtain image objects,and leverages ResNet-50 and Visual Geometry Group-19(VGG-19)to obtain image region features and global features.The image region features and word-level text features are then projected into a low-dimensional space to calculate a text-image affinity matrix for cross-modal message aggregation.The gated fusion network combines text and image region features to obtain adaptively aggregated features.The interaction matrix is derived through an attention mechanism and further integrated with global image features using a co-attention mechanism to producemultimodal representations.Finally,these fused features are fed into a classifier for news categorization.Experiments were conducted on two public datasets,Twitter and Weibo.Results show that the proposed model achieves accuracy rates of 91.8%and 88.7%on the two datasets,respectively,significantly outperforming traditional unimodal and existing multimodal models.

Aggregation Operators for Decision Making Based on q-Rung Orthopair Fuzzy Hypersoft Sets:An Application in Real Estate Project

In this paper,a decision-making problem with a q-rung orthopair fuzzy hypersoft environment is developed,and two operators of ordered weighted average and induced ordered weighted average are developed.Several fundamental features are also derived.The induced ordered weighted average operator is essential in a q-ROFH environment as the induced ordered aggregation operators are special cases of the existing aggregation operators that already exist in q-ROFH environments.The main function of these operators is to help decision-makers gain a complete understanding of uncertain facts.The proposed aggregation operator is applied to a decision-making problem,with the aim of selecting the most promising real estate project for investment.

思维导图联合视频回授法在泌尿系结石留置双J管患者健康教育中的应用效果

目的探讨思维导图联合视频回授法在泌尿系结石术后留置双J管患者中的应用效果。方法选取300例泌尿系结石术后留置双J管患者作为研究对象,按随机数字表法将其分为观察组和对照组,各150例。对照组患者给予常规护理及健康指导,观察组患者在对照组的基础上,运用思维导图联合视频回授法进行健康教育。比较两组患者留置双J管知信行水平、并发症发生情况、生活质量及满意度。结果(1)出院后第15天,观察组患者留置双J管相关知识、信念与态度、行为管理得分均高于对照组(均P<0.05)。(2)出院后第15天至拔管前,观察组患者腰部不适、血尿、膀胱刺激征发生率均低于对照组(均P<0.05),且无附壁结石、双J管移位/脱落病例。(3)出院后第15天,观察组患者的各项生活质量评分及总分均高于对照组(均P<0.05)。(4)观察组患者的满意度高于对照组(P<0.05)。结论应用思维导图联合视频回授法对泌尿系结石术后留置双J管患者进行健康教育,可提高患者留置双J管知信行水平,降低并发症发生率,提高患者生活质量和满意度。

输尿管软镜钬激光碎石术治疗输尿管上段复杂性结石后双J管附壁结石形成的原因分析

目的:分析输尿管软镜钬激光碎石术治疗输尿管上段复杂性结石后双J管附壁结石形成的原因。方法:选择2015年1月至2023年8月于首都医科大学附属北京潞河医院收治的105例输尿管上段复杂性结石患者,其中男58例,女47例,根据结石评估结果分为结石组(n=40)与非结石组(n=65)。通过随机森林算法分析患者双J管附壁结石形成的影响因素。通过单因素和多因素分析双J管附壁结石形成的独立影响因素,并构建分类树模型。结果:结石组和非结石组CT值、术中出血量、双J管留置时间、白细胞计数(WBC)、C反应蛋白(CRP)、尿酸(UA)、血钙、尿pH值、尿蛋白、尿红细胞、尿白细胞、尿结晶比较差异均有统计学意义(t/χ^(2)值分别为4.361、2.502、6.815、12.176、24.398、15.595、6.517、2.069、6.881、4.524、3.883、4.699,均P<0.05)。双J管留置时间、尿蛋白阳性、尿结晶阳性、UA为双J管附壁结石形成的独立危险因素(P<0.05)。分类树模型显示,双J管留置时间是双J管附壁结石形成的重要预测因素,收益图和索引图显示模型预测良好。结论:双J管留置时间、尿蛋白阳性、尿结晶阳性、UA为双J管附壁结石形成的独立危险因素。临床应重点关注相关因素,及时制定预防策略,以期降低双J管附壁结石的发生风险。

J型针刀联合青藤碱治疗骶髂关节炎的临床疗效观察

目的探究J型针刀联合青藤碱治疗骶髂关节炎患者的临床疗效。方法将医院2022年5月至2023年12月收治的40例骶髂关节炎患者按照随机数字表法分为观察组和对照组,各20例。观察组采用J型针刀联合青藤碱治疗,对照组给予双氯芬酸钠片治疗,两组均连续治疗16 d。比较两组临床疗效、视觉模拟评分法(VAS)、Oswestry功能障碍指数(ODI)、巴氏强直性脊柱炎功能指数(BASFI)评分、症状积分及不良反应发生情况。结果观察组治疗总有效率高于对照组(P<0.05);治疗后两组VAS、ODI、BASFI评分均低于治疗前(P<0.05),但组间比较差异无统计学意义(P>0.05);治疗后两组症状积分均低于治疗前(P<0.05),且观察组关节活动障碍度、晨僵及症状总积分均低于对照组(P<0.05);两组均未见严重不良反应。结论J型针刀联合青藤碱和口服双氯芬酸钠片均对骶髂关节炎治疗有效,J型针刀联合青藤碱治疗效果更佳,在改善关节活动度、提高患者生活质量方面更具有优势。

吴氏理筋手法结合J型针刀治疗术后肘关节僵硬的临床研究

目的 探讨吴氏理筋手法结合J型针刀治疗术后肘关节僵硬的临床应用价值。方法 选取江西省南昌市洪都中医院康复科2020年6月—2021年6月收治的60例术后肘关节僵硬患者,以单盲随机法分为对照组(关节松动术)与治疗组(吴氏理筋手法结合J型针刀),各30例。治疗4周后,比较两组Mayo肘关节功能评分(MEPS)与视觉模拟量表(VAS)评分,同时对疗效开展评估。结果 治疗后,与对照组相比,治疗组MEPS评分较高,而VAS评分较低(P<0.05);且治疗组总有效率为100.00%(30/30),明显高于对照组的83.33%(25/30)(P<0.05)。结论 吴氏理筋手法结合J型针刀治疗术后肘关节僵硬患者,能实现肘关节功能的恢复,并减轻患者疼痛,效果确切,具有较高的推广价值。

基于正交切削逆识别井下管柱的Johnson-Cook本构模型

在有限元仿真金属切削过程中,本构模型是仿真的基础,目前井下生产管柱J55油管材料的本构模型参数尚不明确,为此,提出一种基于正交切削理论逆识别油管材料Johnson-Cook(J-C)本构模型参数的方法。首先,通过准静态拉伸试验得到油管材料的应力-应变曲线,根据曲线拟合确定J55油管的初始屈服强度A、应变硬化系数B和应变硬化因子n;其次,通过正交切削试验获得切削力以及进给力,根据正交切削试验和平行剪切区模型,计算正交切削过程主剪切区中的流动应力、应变、应变率以及切削温度,并将这些参数与计算出的A、B、n代入J-C模型中,得到多组应变率敏感性参数C以及热软化效应参数m,从而确定其约束范围;第三,采用自适应权重粒子群算法在约束范围中搜索出参数C和m的最优值,进而确定J55油管材料本构模型参数;最后,使用逆识别法得出的J-C本构模型进行有限元仿真,将仿真结果与试验结果进行了对比。研究结果表明:与试验相比,有限元仿真的切削力和进给力最大相对误差分别为14.4%和13.64%,有限元仿真模拟温度与实际测量温度的最大相对误差为10.57%,仿真模型和实际切削过程在切屑形态上表现出了一致规律,验证了逆识别方法可行性和本构模型的可靠性。

耳穴揿针联合坦索罗辛对输尿管镜碎石术后留置双J管患者USSQ评分及满意度的影响

目的:观察耳穴揿针联合坦索罗辛对输尿管镜碎石术后留置双J管患者输尿管支架相关症状调查问卷(USSQ)评分及满意度的影响。方法:选取74例输尿管镜碎石术后留置双J管患者为观察对象,按随机数字表法分为观察组及对照组各37例。观察组给予常规护理、口服坦索罗联合耳穴揿针治疗,对照组给予常规护理、口服坦索罗联合耳穴压豆治疗。疗程均为6 d,比较2组治疗后USSQ评分及治疗满意度。结果:治疗前,2组USSQ评分比较,差异无统计学意义(P>0.05)。治疗6 d后,2组USSQ评分均较治疗前及治疗3 d下降(P<0.05),并呈明显下降趋势(P<0.05);且观察组相同时间点USSQ评分均低于对照组(P<0.05)。治疗结束后,观察组满意度为97.30%,对照组为83.78%,2组比较,差异有统计学意义(P<0.05)。结论:耳穴揿针联合坦索罗辛可有效改善输尿管镜碎石术后留置双J管患者相关症状和生活质量,提高其治疗满意度。

基于PACAP-cAMP-PKA信号通路探讨J型针刀调节神经源性膀胱逼尿肌的效应机制

目的:探讨J型针刀通过PACAP-cAMP-PKA信号通路改善脊髓损伤后神经源性膀胱大鼠逼尿肌的作用机制。方法:36只SD雄性大鼠随机分为6组:空白组、假手术组、模型组、J型针刀治疗组、治疗+Bupivacaine抑制剂组、治疗+H-89抑制剂组,各6只。采用脊髓横断法制作神经源性膀胱模型。空白组为正常SD大鼠;假手术组为切开相关部位组织,无脊髓切断;其余4组予以手术脊髓切断造模。J型针刀治疗组造模后第19天予J型针刀针刺大鼠次髎穴,2 d 1次,共治疗7次。干预结束后各组行HE染色观察膀胱逼尿肌组织形态变化,Elisa检测膀胱逼尿肌组织中c AMP、PKA含量,免疫组化检测膀胱逼尿肌组织中PACAP38蛋白表达,Western blot检测膀胱逼尿肌组织中PKA、p-MLC蛋白表达。结果:HE染色结果显示,与空白组相比,模型组中膀胱逼尿肌组织严重坏死,可见大量炎症细胞浸润;与模型组相比,J型针刀治疗组、治疗+Bupivacaine抑制剂组及治疗+H-89抑制剂组均有不同程度坏死组织修复情况。免疫组化检测PACAP38蛋白表达在模型组中表达量最低,针刀治疗之后,PACAP38表达量均不同程度上调;与J型针刀治疗组相比,治疗+Bupivacaine抑制剂组及治疗+H-89抑制剂组PACAP38蛋白表达均不同程度下调(P<0.05),但高于模型组(P<0.05)。Elisa检测结果显示,cAMP、PKA表达量模型组显著下调(P<0.05),J型针刀治疗组与模型组比显著上调(P<0.05),治疗+Bupivacaine抑制剂组及治疗+H-89抑制剂组比无显著差异(P>0.05)。WB结果显示:与空白组相比,模型组PKA、p-MLC蛋白表达上调;针刀治疗组表达量下调,其中PKA蛋白表达有显著性(P<0.05),p-MLC蛋白表达无显著性(P>0.05)。与J型针刀治疗组比,PKA蛋白表达量在治疗+Bupivacaine抑制剂组及治疗+H-89抑制剂组中无显著差异(P>0.05),p-MLC蛋白表达量显著下降(P<0.05)。结论:J型针刀针刺次髎穴可改善脊髓损伤后神经源性膀胱大鼠膀胱功能,其机制与J型针刀上调膀胱逼尿肌中PACAP38、cAMP、PKA表达,激活PACAP-cAMP-PKA信号通路,从而促进逼尿肌舒张有关。