A Superlinerly Convergent ODE-type Trust Region Algorithm for LC^1 Optimization Problems

In this paper,a new trust region algorithm for unconstrained LC^1 optimization problems is given.Compare with those existing trust regiion methods,this algorithm has a different feature:it obtains a stepsize at each iteration not by soloving a quadratic subproblem with a trust region bound,but by solving a system of linear equations.Thus it reduces computational complexity and improves computation efficlency,It is proven that this algorithm is globally convergent and locally superlinear under some conditions.

A GLOBALLY AND SUPERLINEARLY CONVERGENT TRUST REGION METHOD FOR LC^1 OPTIMIZATION PROBLEMS

A new trust region algorithm for solving convex LC 1 optimization problem is presented.It is proved that the algorithm is globally convergent and the rate of convergence is superlinear under some reasonable assumptions.

Pulmonary Edema and Pleural Effusion Detection Using Efficient Net-V1-B4 Architecture and AdamW Optimizer from Chest X-Rays Images

This paper presents a novelmulticlass systemdesigned to detect pleural effusion and pulmonary edema on chest Xray images,addressing the critical need for early detection in healthcare.A new comprehensive dataset was formed by combining 28,309 samples from the ChestX-ray14,PadChest,and CheXpert databases,with 10,287,6022,and 12,000 samples representing Pleural Effusion,Pulmonary Edema,and Normal cases,respectively.Consequently,the preprocessing step involves applying the Contrast Limited Adaptive Histogram Equalization(CLAHE)method to boost the local contrast of the X-ray samples,then resizing the images to 380×380 dimensions,followed by using the data augmentation technique.The classification task employs a deep learning model based on the EfficientNet-V1-B4 architecture and is trained using the AdamW optimizer.The proposed multiclass system achieved an accuracy(ACC)of 98.3%,recall of 98.3%,precision of 98.7%,and F1-score of 98.7%.Moreover,the robustness of the model was revealed by the Receiver Operating Characteristic(ROC)analysis,which demonstrated an Area Under the Curve(AUC)of 1.00 for edema and normal cases and 0.99 for effusion.The experimental results demonstrate the superiority of the proposedmulti-class system,which has the potential to assist clinicians in timely and accurate diagnosis,leading to improved patient outcomes.Notably,ablation-CAM visualization at the last convolutional layer portrayed further enhanced diagnostic capabilities with heat maps on X-ray images,which will aid clinicians in interpreting and localizing abnormalities more effectively.

A SUPERLINEARLY CONVERGENT TRUST REGION ALGORITHM FOR LC^1 CONSTRAINED OPTIMIZATION PROBLEMS

In this paper, a new trust region algorithm for nonlinear equality constrained LC1 optimization problems is given. It obtains a search direction at each iteration not by solving a quadratic programming subprobiem with a trust region bound, but by solving a system of linear equations. Since the computational complexity of a QP-Problem is in general much larger than that of a system of linear equations, this method proposed in this paper may reduce the computational complexity and hence improve computational efficiency. Furthermore, it is proved under appropriate assumptions that this algorithm is globally and super-linearly convergent to a solution of the original problem. Some numerical examples are reported, showing the proposed algorithm can be beneficial from a computational point of view.

A FILTER-TRUST-REGION METHOD FOR LC^1 UNCONSTRAINED OPTIMIZATION AND ITS GLOBAL CONVERGENCE

In this paper we present a filter-trust-region algorithm for solving LC1 unconstrained optimization problems which uses the second Dini upper directional derivative. We establish the global convergence of the algorithm under reasonable assumptions.

干酪乳杆菌LK-1的发酵特性及其在菠萝果汁中的应用

为利用干酪乳杆菌制备发酵菠萝果汁饮品,本研究对干酪乳杆菌LK-1(Lactobacillus casei LK-1,L. casei LK-1)的生长曲线、产酸、耐酸和耐糖等特性进行研究分析,并以活菌数和总酸为指标,通过单因素实验及响应面法考察L. casei LK-1发酵菠萝果汁的最佳条件。结果表明,L. casei LK-1在pH5~7和0%~10%质量分数葡萄糖环境下,具备良好的生长繁殖能力;在酸性(pH3)和高糖(40%质量分数葡萄糖)环境中培养3 h,其存活率分别为76%和71%;在MRS培养基中培养48 h,产酸量为5.35 g/kg,产酸能力良好,适用于果汁的发酵。L. casei LK-1发酵菠萝果汁的最佳条件为:初始pH6.8,发酵温度37℃,接种量1%(v/v),发酵时间30 h,此条件下制备的发酵菠萝果汁饮品活菌数为8.99±0.04 lg CFU/mL,总酸含量为7.16±0.26 g/kg,且色泽鲜亮,酸甜适口,风味较好,为L. casei LK-1在发酵食品中的进一步应用提供了理论和技术依据。

Optimization of Fermentation Conditions for Xylanase Production by Aspergillus niger NS-1

In this study, a xylanase-produeing Aspergillus niger strain, NS-1, was screened and isolated from agricultural and forestry wastes. Based on single-fac- tor experiments, the effects of different carbon sources, composite carbon sources, nitrogen sources, calcium carbonate concentrations, initial pH and surfactants on xylanase production by A. niger NS-1 were investigated. The results indicated that the most appropriate carbon source was corncobs ; the best composite carbon source was corncobs ＋ xylan, which was conducive to xylanase secretion; the most suitable nitrogen source was ammonium sulfate. Xylanase activity reached the highest in the medium added with 1.5% calcium carbonate and SDS as a surfactant with an initial pH of 5.0. This study provided the basis for the production of high-activity xylanase.

A NEURAL-BASED NONLINEAR L_1-NORM OPTIMIZATION ALGORITHM FOR DIAGNOSIS OF NETWORKS*

Based on exact penalty function, a new neural network for solving the L1-norm optimization problem is proposed. In comparison with Kennedy and Chua’s network(1988), it has better properties.Based on Bandler’s fault location method(1982), a new nonlinearly constrained L1-norm problem is developed. It can be solved with less computing time through only one optimization processing. The proposed neural network can be used to solve the analog diagnosis L1 problem. The validity of the proposed neural networks and the fault location L1 method are illustrated by extensive computer simulations.

Design and optimization of purification process of sinomenine hydrochloride

Sinomenine hydrochloride is generally produced from Caulis Sinomenii. At present, the purification process in industrial production suffers from large amount of solid waste, high solvent toxicity, and low sinomenine hydrochloride yield. In this study, a new purification process for sinomenine hydrochloride was proposed by using the extract obtained from acid extraction of Caulis Sinomenii as the starting material.The process included the following steps: alkalization, extraction, water washing, acid–water stripping,drying, and crystallization. 1-Heptanol was used as the extractant. The distribution coefficients of sinomenine and sinomenine hydrochloride in 1-heptanol–water system were 27.4 and 0.0167, respectively.The dissociation constants of sinomenine hydrochloride were 8.27 and 11.24, respectively. Process parameters of the new purification process were optimized with experimental design. The extractant1-heptanol and sinomenine hydrochloride in the crystallization mother solution can be recycled in the new process. The purity of the obtained sinomenine hydrochloride crystals exceeded 85%, and the yield was about 70%. Compared with current industrial processes, safer extractant, less solid waste, and higher sinomenine hydrochloride yield can be achieved using the new purification process of sinomenine hydrochloride provided in this study.

乙型肝炎相关性肝硬化患者血清circMTO1表达水平及其与肝纤维化程度、预后的相关性

目的分析乙型肝炎相关性肝硬化患者血清环状RNA线粒体tRNA翻译优化因子1(circMTO1)表达水平及其与肝纤维化程度、预后的关系。方法纳入115例乙型肝炎相关性肝硬化患者(乙肝肝硬化组)及120例慢性乙型肝炎患者(慢乙肝组)作为研究对象。根据治疗后短期预后情况,将乙肝肝硬化组患者分为预后良好组(n=84)与预后不良组(n=31)。比较乙肝肝硬化组与慢乙肝组之间、预后良好组与预后不良组之间的血清circMTO1表达水平。分析乙肝肝硬化组血清circMTO1表达水平与肝纤维化分级的相关性。采用受试者工作特征曲线分析血清circMTO1表达水平对乙肝肝硬化组患者不良预后的预测价值。结果乙肝肝硬化组患者的血清circMTO1表达水平低于慢乙肝组,预后不良组患者的circMTO1表达水平低于预后良好组(P<0.05)。乙肝肝硬化组患者血清circMTO1表达水平与肝纤维化分级呈负相关(P<0.05)。血清circMTO1表达水平预测乙肝肝硬化组患者预后不良的曲线下面积为0.747,敏感度为77.40%,特异度为77.40%,最佳截断值为0.332。结论与未发生肝硬化的慢性乙型肝炎患者相比,乙型肝炎相关性肝硬化患者的血清circMTO1表达水平降低,且其与肝纤维化程度相关。circMTO1或可作为评估乙型肝炎相关性肝硬化患者预后的分子标志物。

Weed Classification Using Particle Swarm Optimization and Deep Learning Models

Weed is a plant that grows along with nearly allfield crops,including rice,wheat,cotton,millets and sugar cane,affecting crop yield and quality.Classification and accurate identification of all types of weeds is a challenging task for farmers in earlier stage of crop growth because of similarity.To address this issue,an efficient weed classification model is proposed with the Deep Convolutional Neural Network(CNN)that implements automatic feature extraction and performs complex feature learning for image classification.Throughout this work,weed images were trained using the proposed CNN model with evolutionary computing approach to classify the weeds based on the two publicly available weed datasets.The Tamil Nadu Agricultural University(TNAU)dataset used as afirst dataset that consists of 40 classes of weed images and the other dataset is from Indian Council of Agriculture Research–Directorate of Weed Research(ICAR-DWR)which contains 50 classes of weed images.An effective Particle Swarm Optimization(PSO)technique is applied in the proposed CNN to automa-tically evolve and improve its classification accuracy.The proposed model was evaluated and compared with pre-trained transfer learning models such as GoogLeNet,AlexNet,Residual neural Network(ResNet)and Visual Geometry Group Network(VGGNet)for weed classification.This work shows that the performance of the PSO assisted proposed CNN model is significantly improved the success rate by 98.58%for TNAU and 97.79%for ICAR-DWR weed datasets.

基于BP神经网络模型优化Fe_(1-x)O基氨合成催化剂

运用BP神经网络建立了助催化剂含量与催化剂活性之间的预测模型,对Fe_(1-x)O基氨合成催化剂的助催化剂进行优化。首先将前期实验数据整理归纳为含有3、4、5、6和7个助催化剂等5类催化剂,以助催化剂含量(体积分数)为输入变量,以425℃反应器出口氨浓度(活性)为输出变量,对助催化剂进行优化。结果表明,BP神经网络预测模型拟合值均方误差最高为0.2784,预测值均方误差最高为0.1592,构建的BP神经网络模型准确度较高。在该模型的基础上,运用多种群遗传算法进行极值寻优,求解最优的催化剂配方,并进行实验验证。结果表明,根据优化结果制备5个样品的实验测定值与预测值的相对误差最高为2.88%,优化结果较为准确;含有7个助催化剂的催化剂活性最高为18.83%,比原样本的统计平均活性值(17.52%)高1.31%,相对提高7.48%,助催化剂含量优化取得满意的结果。

增强型群论优化算法求解折扣{0-1}背包问题

群论优化算法(GTOA)是基于群论方法提出的一个离散演化算法,非常适于求解以整型向量为可行解的组合优化问题。为了进一步提高GTOA求解折扣{0-1}背包问题(D{0-1}KP)的性能,首先指出了它的随机线性组合算子(RLCO)未能充分考虑当前个体位置信息的不足,基于个体基因保留策略对其进行改进。然后,在随机反向变异算子(IRMO)中引入增强0分量变异策略,用于处理因个体0分量无法及时变异而导致的解的质量下降、种群多样性降低等问题。在改进上述两个算子的基础上,提出了增强型GTOA(EGTOA),并基于它给出求解D{0-1}KP的新方法。随后,将改进策略应用于二进制GTOA(GTOA-2),提出了增强型GTOA-2(EGTOA-2)及其求解D{0-1}KP的新方法。为了验证EGTOA和EGTOA-2的性能提高程度与优异性,分别利用它们求解四类大规模D{0-1}KP实例,通过与GTOA、GTOA-2以及求解D{0-1}KP的已有8个最先进算法的比较表明:EGTOA和EGTOA-2求得最优解的能力比GTOA和GTOA-2提高了至少1.14倍,比8个最先进算法提高了5%~60%,它们的平均性能比GTOA、GTOA-2以及8个最先进算法的性能更佳。因此,EGTOA和EGTOA-2是当前求解D{0-1}KP的最佳算法。

考虑源荷不确定性和N-1安全约束的电力系统优化调度

大规模风电并网增加了系统运行的不确定因素,文章利用快速可调资源平抑风电和负荷波动引发的功率不平衡,并考虑备用调用过程减少对系统潮流的影响。首先分析了风电和负荷预测误差的概率分布模型;然后,建立含N-1网络安全机会约束和旋转备用机会约束的电力系统优化调度模型,以系统调度总成本最小为目标优化机组出力;最后,基于起作用整数变量的识别方法的Benders分解法处理N-1网络安全约束,提高求解效率。利用修正的IEEE30节点系统,验证了所提模型能够保障系统备用可用性、经济性和安全性,充分挖掘抽水蓄能的快速可调能力。

固定化脂肪酶ANL-MARE催化酸解合成1-油酸-2-棕榈酸-3-亚油酸甘油三酯

1-油酸-2-棕榈酸-3-亚油酸甘油三酯(OPL)是人乳中主要的脂质组成,为满足婴幼儿配方食品母乳化需求,该试验探究了新型固定化脂肪酶ANL-MARE催化制备OPL的方法。试验成功制备和表征了固定化酶ANLMARE,并以ANL-MARE为生物催化剂,建立了一种用三棕榈酸甘油三酯(PPP)、油酸(OA)和亚油酸(LA)高效酶催化制备富含OPL结构脂的方法。经单因素和响应面试验优化,获得OPL最优合成工艺为:PPP与总脂肪酸的摩尔比1:14.27,OA与LA摩尔比为1:0.76,脂肪酶添加量12.70%,反应温度50℃,反应4 h,此条件下产物中OPL相对含量为47.93%,2位棕榈酸(sn-2 PA)占总棕榈酸(PA)的质量分数(sn-2 PA相对含量)为71.69%。此外,固定化酶ANL-MARE与商业脂肪酶相比,表现出较好的催化合成OPL的活性。综上所述,固定化酶ANL-MARE具有催化制备OPL结构脂的重大潜力,为人乳脂替代脂的高效制备提供了新策略和理论基础。

Performance Optimization of Torque Converters Based on Modified 1D Flow Model

A methodology for performance optimization of torque converters is put forward based on the one-dimensional (1D) flow model. It is found that the inaccuracy of 1D flow model for predicting hydraulic performance at the low speed ratio is mainly caused by the separation phenomenon at the stator cascade which is induced by large flow impinging at the pressure side of the stator blades. A semi-empirical separation model is presented and incorporated to the original 1D flow model. It is illustrated that the improved model is able to predict the circumferential velocity components accurately, which can be applied to performance optimization. Then, the Pareto front is obtained by using the genetic algorithm (GA) in order to inspect the coupled relationship among stalling impeller torque capacity, stalling torque ratio and efficiency. The efficiency is maximized on the premise that a target stalling impeller torque capacity and torque ratio are achieved. Finally, the optimized result is verified by the computational fluid dynamics(CFD) simulation, which indicates that the maximal efficiency is increased by 0.96%.

Modulating Co-Co bonds average length in Co_(0.85)Se_(1-x)S_(x) to enhance conversion reaction for potassium storage

While alloying transition metal chalcogenides(TMCs)with other chalcogen elements can effectively improve their conductivity and electrochemical properties,the optimal alloying content is still uncertain.In this study,we study the influence of dopant concentration on the chemical bonds in TMC and reveal the associated stepwise conversion reaction mechanism for potassium ion storage.According to density function theory calculations,appropriate S-doping in Co0.85Se(Co_(0.85)Se_(1-x)S_(x))can reduce the average length of Co-Co bonds because of the electronegativity variation,which is thermodynamically favourable to the phase transition reactions.The optimal Se/S ratio(x=0.12)for the conductivity has been obtained from experimental results.When assembled as an anode in potassium-ion batteries(PIBs),the sample with optimized Se/S ratio exhibits extraordinary electrochemical performance.The rate performance(229.2 mA h g^(-1)at 10 A g^(-1))is superior to the state-of-the-art results.When assembled with Prussian blue(PB)as a cathode,the pouch cell exhibits excellent performance,demonstrating its great potential for applications.Moreover,the stepwise K+storage mechanism caused by the coexistence of S and Se is revealed by in-situ X-ray diffraction and ex-situ transmission electron microscopy techniques.Hence,this work not only provides an effective strategy to enhance the electrochemical performance of transition metal chalcogenides but also reveals the underlying mechanism for the construction of advanced electrode materials.

Approximation methods of mixed l_1/H_2 optimization problems for MIMO discrete-time systems

The mixed l1/H2 optimization problem for MIMO (multiple input-multiple output) discrete-time systems is considered. This problem is formulated as minimizing the l1-norm of a closed-loop transfer matrix while maintaining the H2-norm of another closed-loop transfer matrix at prescribed level. The continuity property of the optimal value in respect to changes in the H2-norm constraint is studied. The existence of the optimal solutions of mixed l1/H2 problem is proved. Because the solution of the mixed l1/H2 problem is based on the scaled-Q method, it avoids the zero interpolation difficulties. The convergent upper and lower bounds can be obtained by solving a sequence of finite dimensional nonlinear programming for which many efficient numerical optimization algorithms exist.

Optimization on Extraction Conditions of Flavonoids from Edgeworthia chrysantha Lind1.

[Objectives]The extraction conditions of flavonoids from flowers of Edgeworthia chrysantha Lind1.were optimized.[Methods]With ethanol as an extraction agent,firstly by single-factor tests,the best levels of ethanol concentration,material-to-liquid ratio,extraction temperature and extraction time were determined.Then the optimal extraction conditions were determined through the quadratic orthogonal regression rotatable design.[Results]The optimal extraction conditions for flavonoids from E.chrysantha were∶material-to-liquid ratio 1∶15.4,extraction temperature 75℃,extraction time 120 min and ethanol concentration 75%,with which the highest yield of flavonoids was 6.02 mg/g.[Conclusions]This study provides a theoretical basis for the further development and utilization of E.chrysantha flowers.

丁烯-1装置运行分析及工艺优化

为实现清晰分离MTBE合成装置醚后碳四资源,减少资源浪费,新建丁烯-1装置。丁烯-1装置开工以来,存在丁烯-1产品质量低、收率低的问题。通过对丁烯-1装置运行情况进行分析,从调整丁烯-1装置反应加氢量和反应温度、选择塔灵敏板位置、调整塔灵敏板温度方面入手,给出了相应的工艺优化对策,从而满足下游装置需求,实现丁烯-1产品自给自足,降低生产成本。