mlsa-iai-msu-lab/ru_sci_bench_zho_cite_reranking huggingface dataset:
微量注射进样火焰原子吸收法测定耳血全血锌铜及幼儿正常参考值的获得

介绍了在国产WFX-IF2型原子吸收分光光度计上, 应用微量注射进样技术和火焰原子吸收法测定人耳血全血锌铜的分析方法。以间断(小体积样品)喷雾取代连续喷雾技术, 使样品溶液进样体积降至10～20μl。图6表11参15
大熊猫颅骨、下颌骨及牙齿特征在进化上的意义

对大熊猫颅骨形态及牙齿结构进行了研究,认为大熊猫与始熊猫的裔征不同于熊科成员,应单列一大熊猫科(Ailuropodidae)。在大熊猫种系进化中,它们的形态与机能是统一的。不同种类的形态特征,又是在适应环境中逐渐产生的。根据这个认识,把大熊猫的进化历程划分为始发期、成长期、鼎盛期和衰败期。
甲壳质、甲壳胺衍生物保护的遗金属胶体

用甲壳质、甲壳胺的可溶性衍生物即O-羧甲基甲壳质钠盐及甲壳胺乙酸盐制备了保护铂、铑金属胶体。探讨了其催化性能, 以及高分子保护剂与胶体金属间的相互作用及其保护机制。
聚乙烯燃烧和阻燃的热分析研究

采用DSC和TG两种热分析方法研究了聚乙烯的燃烧过程及几种含卤化合物与三氧化二锑并用体系对聚乙烯的阻燃行为, 提出了一种评估阻燃剂对聚乙烯的阻燃效果的方法。
一种温敏萃取凝胶

本工作合成了一种温敏萃取凝胶, 即N-异丙基丙烯酰胺与甲基丙烯酸钠的共聚物凝胶。分别考察了在合成凝胶时, 离子剂用量和交联剂用量对凝胶溶胀性能的影响。还考察了在不同溶剂中和在酸、碱、盐存在下凝胶的溶胀情况, 以及凝胶的再生性能。此种新温敏凝胶, 于室温下在水中的溶胀比明显高于文献中报道的其他温敏凝胶, 而且其再生性能良好, 在水中溶胀皱缩反复到第10次时, 其吸水性能无什么变化。图5表4参6
最佳一致逼近问题的并行交换算法

介绍了区间上最佳一致逼近解的新方法。该并行交换算法本质上是线性规划类算法，但线性规划的规模不随着精度要求的提高而增大，而且适用于区域上多元最佳一致逼近问题。二节给出算法，三节讨论算法的性质。
二维凝聚体系中单轴取向分子的偏振Raman散射强度

分析了薄膜体系几种常用测量坐标下的偏振Raman散射强度表达式, 发现当测量坐标与膜坐标重叠时, 各种偏振Raman散射强度只需用膜坐标中的Raman散射活性来表达。如果这两个坐标不重叠, 需进一步求出膜坐标中Raman张量元二次交叉项的平均值。本文给出了单轴唯一角取向模型下这些二次交叉项的平均值表达式及取向分布模型下膜坐标中所有Raman张量元二次项平均值。
三-（2-甲基氮丙啶）氧化膦异构体的核磁共振和量子化学研究

三-(2-甲基氨丙啶)氧化膦(MAPO)为黄色油状液体, 其化学性质活泼, 可用作塑料、橡产及造纸业中高分子化合物的交联剂等。本文对MAPO进行了1~H、13~C、15~N、31~P和17~O的核磁共振研究及量子化学计算。结果表明, 该化合物存在4种异构体, 其中2种为对映体, 并得到了这4种异构体的几何构型。
碳管笼烯的稳定性问题

碳笼烯C60及C70等的发现启示人们去探索合成更多的新型封闭型碳烯化合物~[1_5], 可以设想, 由六元碳环构成的石墨层平面可以卷成顶和底为p元环、侧面为紧接着顶底的p个五元环、中间k层p个六元环围成的碳管笼烯C_m(n=2p(k+2)),其结构见图1。笼烯的每个C原子都与3个相邻的C原子成键, 并有一个与笼面垂直的单电子占据轨道, 可以相互作用构成笼面共轭大π键, 我们在HMO近似下, 利用群论和图论方法讨论了其π电子能级的本征多项式的一般表达式, 据此计算了4≤p≤12, 1≤k≤12的分子能级, 讨论了其能级分布的一般特征及分子稳定性问题。
吡唑类化合物的研究（Ⅵ）

合成了14种1-苯基-3-氨基-5-氧代-4-取代腙吡唑及其关环产物吡唑[3,4-e]并-1,2,4-三嗪和吡唑[3,4-e]并-1,2,3,4-四嗪衍生物, 经元素分析、IR、~1H NMR和MS确定了其结构, 并讨论了一些化合物的IR和~1H NMR波谱性质。
分级时效工艺对12Cr2MoWVTiB钢组织和力学性能的影响

研究了分级时效工艺对12Cr2MoWVTiB钢组织和力学性能的影响, 研究结果表明, 660℃+780℃分级时效, 不但使主要强化相(V, Ti)C细化, 而且使马氏体-奥氏体岛的分解产物改善, 从而显著提高钢的屈服强度和高温硬度。
负载Ru催化剂的CO吸附态的研究

本文运用原位FT-IR光谱系统研究了各种活化处理因素对负载Ru催化剂的CO吸附态的影响。发现2135±5(HF)和2075±5(MF)cm~-~1IR谱峰产生于金属与载体接触的周边部位, 反映了以多重态吸附的CO对称和反对称振动。金属载体间的相互作用的增强, HF和MF强度大幅度衰减。经纯O2处理的催化剂, HF和MF锋位置没有发生变化,而峰强度随吸附时间的相对增长速率有很大的差异。对HF和MF作了归属, 提出了6配位18电子紧壳层稳定结构的表面锚合的羟基氧桥或卤桥的多聚羰基化合物种CO吸附模式。
2,9-二氯-1,10-邻--啉碳糊电极测定痕量银

合成2,9-二氯-1,10-邻菲 啉,并以此化合物研制了对银有灵敏响应的碳糊电极。在硝酸介质中,银离子浓度在1×10_-9_5×10_-7mol/L范围内与阳极溶出峰电流呈良好的线性关系,线性相关系数0.996。电极用于电镀废水中银的测定,结果令人满意。图1表2参7
国际河流研究的意义与发展

由于世界各国经济的发展,人口的增加和城市化的发展,全球性的缺水日益严重,因此,国际河流开发与水资源的合理分配将成为本世纪末和下世纪初国际上一个重大的前沿科学问题.该文综述了国际河流研究的迫切性和开发的主要方式及产生的问题,对开发国际河流的主要法规和水资源的合理分配进行了分析和论证,阐明了所要研究的主要内容、方法和手段以及加强这一研究对维护我国权益,发展边疆地区经济,推动地理分支学科发展的意义.
担体对Fe-MnO催化剂CO加氢合成烯烃性能影响的TPSR表征

研究了不同担体担载的Fe-MnO催化剂上CO加氢合成燃烃的反应, 担体为碱性的催化剂有强吸附CO的能力, 且生成的烯烃不易发生二次反应, 故烯烃选择性高, 担体为酸性的催化剂吸附CO能力弱, 吸附H2能力较强, 且生成烯烃的发生显著的二次反应。其它担体的催化剂的结果介于以上二者之间。图8表1参9
阳离子改性HZSM-5沸石上低碳醇反应历程的TPSR-MS研究

发现Zn-ZSM-5和Ga-ZSM-5有两种芳枸化活性中心, 即B酸和Zn, Ga活性物种。甲醇在Cu-ZSM-5上面生成二甲醚、CO和CO2, 提出了低碳醇在各改性沸石上转化的反应历程。图2参9
外周单核细胞源性巨噬细胞在脊髓损伤修复中作用的研究进展

脊髓损伤和修复是一个涉及多种细胞相互作用的复杂病理生理过程,巨噬细胞在其中起关键作用。既往研究发现,巨噬细胞在脊髓损伤发病过程的所有阶段都发挥作用。已经证明巨噬细胞是多来源细胞(来源于脾脏、骨髓以及局部自我更新),不同来源巨噬细胞在脊髓损伤修复中发挥的作用不同。外周浸润单核细胞衍生的巨噬细胞与脊髓常驻小胶质细胞具有不同的生理功能,前者在环境信号(例如影响巨噬细胞极化和募集的特定细胞因子和趋化因子)影响下通过极化为不同状态在脊髓损伤后介导有害或有益的作用。阐明巨噬细胞来源,巨噬细胞在脊髓损伤、修复中的作用以及机制,可为探索脊髓损伤潜在的治疗方法提供新思路、新策略。
机器学习在力学模拟与控制中的应用专题序

近几年来,随着高性能计算机和大数据科学的快速发展,机器学习方法在各个领域得到了越来越多的应用.力学学科在过去几十年积累了大量的数值模拟数据、实验测量数据和现场监测数据,这些大规模、高维度的数据蕴含了丰富的物理特征,但传统方法无法有效地处理这些庞大的数据群.机器学习方法可以从巨量的数据海洋中挖掘有用的信息,并能为总结新的物理规律提供有效的指导.另一方面,机器学习方法存在着可解释性差、泛化能力弱、容易过拟合等问题.针对基于第一性原理的力学问题开展机器学习研究,并和已知的物理规律相对照,有助于更深入地理解机器学习方法.因此,与机器学习的交叉融合,将有力地促进力学学科研究范式的创新,并不断拓展该学科的研究深度和应用范围.
以第一跖背动脉为蒂的趾皮瓣与第二足趾组合移植再造手指缺损

目的探讨以第一跖背动脉为蒂的趾皮瓣与第二足趾组合移植再造手指缺损的临床疗效。方法自2017年6月至2020年4月,采用以第一跖背动脉为蒂的趾皮瓣与第二足趾组合移植再造手指缺损6例,其中拇指掌骨基底水平缺损1例,拇指掌指关节缺损1例,拇指近节基底缺损1例,示指近节基底水平缺损1例,拇指皮肤缺损伴虎口小部分皮肤缺损2例。移植组织动脉与桡动脉深支通过直接吻合或血管桥接建立血供,大隐静脉与头静脉直接吻合建立回流。结果术后6例再造指全部存活,未发生动脉危象及静脉危象。随访5 ~20个月,再造手指外形及功能满意,按中华医学会手外科学会上肢部分功能评定试用标准评定:优4例,良2例。供区恢复良好,功能无明显障碍。结论以第一跖背动脉为蒂的趾皮瓣与第二足趾组合移植再造手指具有外形好、功能满意、供区损失小特点,是修复手指缺损的良好方法。
Separator Wettability Enhanced by Electrolyte Additive to Boost the Electrochemical Performance of Lithium Metal Batteries

Lithium (Li) metal has been regarded as one of the most promising candidates to replace graphite anode due to its high theoretical specific capacity and the lowest electrochemical potential [1–3]. However, the immoderate growth of Li dendrite during Li plating/stripping causes serious safety problem and poor performance that severely impedes the practical application of lithium metal batteries (LMBs) [4–6]. Until now, there have been numerous kinds of strategies be proposed to inhibit Li dendrites growth and protect lithium metal anode such as high concentration electrolytes [7], construction of the solid electrolyte interface layer [8], structural design of anode materials [9], regulation of Li+ solvation [10], and solid-state electrolytes [11]. As an important part of battery structure, separator plays a vital role in the performance of battery [12]. The main function of separator is to divide the anode and cathode that prevents internal short circuit caused by direct contact between anode and cathode. So, the separator needs to be electrically insulated. At the same time, the separator also needs to ensure that the electrolyte is conductive between anode and cathode [13]. Therefore, it is necessary to render the separator fully wetted. Nevertheless, there are few researches on enhancing the wettability of the separator especially functional electrolyte additives.
Hybrid Triboelectric-Electromagnetic Nanogenerators for Mechanical Energy Harvesting: A Review

Motion-driven electromagnetictriboelectric energy generators (E-TENGs) hold a great potential to provide higher voltages, higher currents and wider operating bandwidths than both electromagnetic and triboelectric generators standing alone. Therefore, they are promising solutions to autonomously supply a broad range of highly sophisticated devices. This paper provides a thorough review focused on major recent breakthroughs in the area of electromagnetic-triboelectric vibrational energy harvesting. A detailed analysis was conducted on various architectures including rotational, pendulum, linear, sliding, cantilever, flexible blade, multidimensional and magnetoelectric, and the following hybrid technologies. They enable highly efficient ways to harvest electric energy from many forms of vibrational, rotational, biomechanical, wave, wind and thermal sources, among others. Open-circuit voltages up to 75 V, short-circuit currents up to 60 mA and instantaneous power up to 144 mW were already achieved by these nanogenerators. Their transduction mechanisms, including proposed models to make intelligible the involved physical phenomena, are also overviewed here. A comprehensive analysis was performed to compare their respective construction designs, external excitations and electric outputs. The results highlight the potential of hybrid E-TENGs to convert unused mechanical motion into electric energy for both large- and small-scale applications. Finally, this paper proposes future research directions toward optimization of energy conversion efficiency, power management, durability and stability, packaging, energy storage, operation input, research of transduction mechanisms, quantitative standardization, system integration, miniaturization and multi-energy hybrid cells.
LSTM-based Energy Management for Electric Vehicle Charging in Commercial-building Prosumers

As typical prosumers, commercial buildings equipped with electric vehicle (EV) charging piles and solar photovoltaic panels require an effective energy management method. However, the conventional optimization-model-based building energy management system faces significant challenges regarding prediction and calculation in online execution. To address this issue, a long short-term memory (LSTM) recurrent neural network (RNN) based machine learning algorithm is proposed in this paper to schedule the charging and discharging of numerous EVs in commercial-building prosumers. Under the proposed system control structure, the LSTM algorithm can be separated into offline and online stages. At the offline stage, the LSTM is used to map states (inputs) to decisions (outputs) based on the network training. At the online stage, once the current state is input, the LSTM can quickly generate a solution without any additional prediction. A preliminary data processing rule and an additional output filtering procedure are designed to improve the decision performance of LSTM network. The simulation results demonstrate that the LSTM algorithm can generate near-optimal solutions in milliseconds and significantly reduce the prediction and calculation pressures compared with the conventional optimization algorithm.
Fault Feeder Identification in Non-effectively Grounded Distribution Network with Secondary Earth Fault

Secondary earth faults occur frequently in power distribution networks under harsh weather conditions. Owing to its characteristics, a secondary earth fault is typically hidden within the transient of the first fault. Therefore, most researchers tend to focus on a feeder with single fault while disregarding secondary faults. This paper presents a fault feeder identification method that considers secondary earth faults in a non-effectively grounded distribution network. First, the wavelet singular entropy method is used to detect a secondary fault event. This method can identify the moment at which a secondary fault occurs. The zero-sequence current data can be categorized into two fault stages. The first and second fault stages correspond to the first and secondary faults, respectively. Subsequently,a similarity matrix containing the time-frequency transient information of the zero-sequence current at the two fault stages is defined to identify the fault feeders. Finally, to confirm the effectiveness and reliability of the proposed method, we conduct simulation experiments and an adaptability analysis based on an electromagnetic transient program.
Optimal Day-ahead Scheduling of Islanded Microgrid Considering Risk-based Reserve Decision

Due to the lack of support from the main grid, the intermittency of renewable energy sources (RESs) and the fluctuation of load will derive uncertainties to the operation of islanded microgrids (IMGs). It is crucial to allocate appropriate reserve capacity for the economic and reliable operation of IMGs. With the high penetration of RESs, it faces both economic and environmental challenges if we only use spinning reserve for reserve support. To solve these problems, a multi-type reserve scheme for IMGs is proposed according to different operation characteristics of generation, load, and storage. The operation risk due to reserve shortage is modeled by the conditional value-at-risk (CVaR) method. The correlation of input variables is considered for the forecasting error modeling of RES and load, and Latin hypercube sampling (LHS) is adopted to generate the random scenarios of the forecasting error, so as to avoid the dimension disaster caused by conventional large-scale scenario sampling approaches. Furthermore, an optimal day-ahead scheduling model of joint energy and reserve considering riskbased reserve decision is established to coordinate the security and economy of the operation of IMGs. Finally, the comparison of numerical results of different schemes demonstrate the rationality and effectiveness of the proposed scheme and model.
Power Flow Based Volt/var Optimization Under Uncertainty

Volt/var optimization (VVO) is a control function that is employed in distribution systems to keep the load voltages within the standard limits, and it includes secondary objectives such as loss minimization. The power flow based VVO is the way of choice in practical applications because it can handle a variety of objective functions and provides a solution even for large-scale network instances. This paper extends the power flow based VVO to account for uncertainty in both the load values and the power generation from photovoltaic sources. The proposed method employs circular arithmetic in complex variables to compute VVO settings that guard against load uncertainty and an optimized linear decision rule that modulates the reactive power of photovoltaic inverter in function of its active power. Finally, the proposed method is tested on distribution networks with up to 3146 nodes and is shown to produce optimal solutions that are robust against power variations.
Deep Reinforcement Learning Based Approach for Optimal Power Flow of Distribution Networks Embedded with Renewable Energy and Storage Devices

This study proposes a deep reinforcement learning (DRL) based approach to analyze the optimal power flow (OPF) of distribution networks (DNs) embedded with renewable energy and storage devices. First, the OPF of the DN is formulated as a stochastic nonlinear programming problem. Then,the multi-period nonlinear programming decision problem is formulated as a Markov decision process (MDP), which is composed of multiple single-time-step sub-problems. Subsequently,the state-of-the-art DRL algorithm, i.e., proximal policy optimization (PPO), is used to solve the MDP sequentially considering the impact on the future. Neural networks are used to extract operation knowledge from historical data offline and provide online decisions according to the real-time state of the DN. The proposed approach fully exploits the historical data and reduces the influence of the prediction error on the optimization results. The proposed real-time control strategy can provide more flexible decisions and achieve better performance than the pre-determined ones. Comparative results demonstrate the effectiveness of the proposed approach.
Bi-level Coordinated Planning of Active Distribution Network Considering Demand Response Resources and Severely Restricted Scenarios

Due to the uncertainty and fluctuation of distributed generation (DG) and load, the operation of active distribution network (ADN) is affected by multi-dimension factors which are described by massive operation scenarios. Efficient and accurate screening of severely restricted scenarios (SRSs) has become a new challenge in ADN planning. In this paper, a novel bi-level coordinated planning model which combines the short-time-scale operation problem with the long-time-scale planning problem is proposed. At the upper level, the demand response (DR) resource, an effective non-component planning resource characterized by low capacity price, high energy price,and short contract term, is co-optimized with the configuration of lines and energy storage systems (ESSs) to achieve the economic trade-off between the investment cost and the operation cost under SRSs. At the lower level, with the planning scheme obtained from the upper level, massive operation problems are optimized to minimize the daily operation cost;and the SRSs are provided to the upper level through a shadow-price-based scenario screening method, which simulates the planning information (i.e., the restricted degrees of operation scenarios) feedback process from ADN operators to ADN planners. Case studies on a 62-node distribution system in Jianshan New District,Zhejiang Province, China, illustrate the effectiveness of the proposed bi-level coordinated planning model considering DR resources and SRSs.
Accelerated solution of the transmission maintenance schedule problem:a Bayesian optimization approach

To maximize the maintenance willingness of the owner of transmission lines,this study presents a transmission maintenance scheduling model that considers the energy constraints of the power system and the security constraints of onsite maintenance operations.Considering the computational complexity of the mixed integer programming (MIP) problem,a machine learning (ML) approach is presented to solve the transmission maintenance scheduling model efficiently.The value of the branching score factor value is optimized by Bayesian optimization (BO) in the proposed algorithm,which plays an important role in the size of the branch-and-bound search tree in the solution process.The test case in a modified version of the IEEE 30-bus system shows that the proposed algorithm can not only reach the optimal solution but also improve the computational efficiency.
Optimal configuration for photovoltaic storage system capacity in 5G base station microgrids

Base station operators deploy a large number of distributed photovoltaics to solve the problems of high energy consumption and high electricity costs of 5G base stations.In this study,the idle space of the base station's energy storage is used to stabilize the photovoltaic output,and a photovoltaic storage system microgrid of a 5G base station is constructed.Aiming at the capacity planning problem of photovoltaic storage systems,a two-layer optimal configuration method is proposed.The inner layer optimization considers the energy sharing among the base station microgrids,combines the communication characteristics of the 5G base station and the backup power demand of the energy storage battery,and determines an economic scheduling strategy for each photovoltaic storage system with the goal of minimizing the daily operation cost of the base station microgrid.The outer model aims to minimize the annual average comprehensive revenue of the 5G base station microgrid,while considering peak clipping and valley filling,to optimize the photovoltaic storage system capacity.The CPLEX solver and a genetic algorithm were used to solve the two-layer models.Considering the construction of the 5G base station in a certain area as an example,the results showed that the proposed model can not only reduce the cost of the 5G base station operators,but also reduce the peak load of the power grid and promote the local digestion of photovoltaic power.
Robust transmission expansion planning model considering multiple uncertainties and active load

Uncertainty must be well addressed in transmission expansion planning (TEP) problem,and it significantly affects the reliability and cost-effectiveness of power systems.Owing to the complex operating environment of power systems,it is crucial to consider different types of uncertainties during the planning stage.In this paper,a robust TEP model is proposed by considering multiple uncertainties and active load.Specifically,in this model,the uncertainties of wind power output and contingency probability are considered simultaneously.The uncertainties are described by scenario and interval,and the Benders decomposition technique is applied to solve the model.The feasibility and effectiveness of the proposed model are illustrated using the IEEE RTS and IEEE 118-node systems.
Genetic diversity and population structure analysis of 161 broccoli cultivars based on SNP markers

To better understand the genetic diversity and population structure of broccoli cultivars planted in China, a total of 161 representative broccoli cultivars in the past 25 years were collected and analysed based on single nucleotide polymorphism(SNP) markers. Ten pairs of primers with good polymorphism and high resolution were screened from 315 pairs of SNP primers by 3 broccoli accessions(inbred lines) with different phenotypes and maturity. The 10 pairs of SNP primers were selected, producing 78 alleles. The diversity analysis indicated that the polymorphism information content(PIC) of SNP primer ranged from 0.64 to 0.90. The observed number of alleles(Na) was 2.00, the effective number of alleles(Ne) was 1.11-2.00, the Nei's gene diversity(H) was 0.10-0.50, and Shannon information index(I) was 0.20-0.70 using PopGene32 software. The clustering results showed that the 161 broccoli cultivars could be divided into 4 major subgroups(A, B, C and D), foreign cultivars were all assigned to subgroup A, and domestic cultivars were assigned to 3 subgroups of B, C, and D. This study indicated that some domestic cultivars and foreign cultivars were similar in genetic background, but most domestic cultivars were still different from the Japanese cultivars. When K = 2, the population structure result presented that 161 broccoli cultivars could be divided into 1 simple group(2 groups) and 1 mixed group. When Q≥0.6,143(88.82%) broccoli cultivars belonged to the simple groups. In simple groups 68(42.24%) broccoli cultivars of group 1 were derived from Japan, the United States, Switzerland, the Netherlands, China-Taiwan, and China-Mainland;75(46.58%) broccoli cultivars belonged to group 2;when Q <0.6,18(11.18%) broccoli cultivars belonged to the mixed groups. This study is helpful to understand the diversity and resolution of broccoli cultivars from worldwide, which is beneficial to plant breeding and materials innovation. And meanwhile, this result is also used for construction of broccoli fingerprint serving for cultivar identification.
A review for the breeding of orchids: Current achievements and prospects

Orchids are horticultural plants with significant ornamental and economic value. Increasing market circulation and rising trade in orchids is forcing breeders to develop varieties with unique characteristics, including flower color, morphology, and resistance using a range of approaches,including traditional and molecular breeding. Advances in high-throughput technologies have generated extensive data sets with greater sequencing depths and broader coverage, providing the potential for discovering new genes/pathways that give rise to key traits. Several attempts have been made to use emerging molecular and omics methods to accelerate the breeding process in certain of the commercially valuable orchids. This review consolidates current approaches and achievements in orchid breeding and discusses their future applications for improving the resistance, ornamental, and other valuable characteristics of these plants.
Robust Segmentation Method for Noisy Images Based on an Unsupervised Denosing Filter

Level-set-based image segmentation has been widely used in unsupervised segmentation tasks. Researchers have recently alleviated the influence of image noise on segmentation results by introducing global or local statistics into existing models. Most existing methods are based on the assumption that the distribution of image noise is known or observable. However, real-time images do not meet this assumption. To bridge this gap, we propose a novel level-set-based segmentation method with an unsupervised denoising mechanism. First, a denoising filter is acquired under the unsupervised learning paradigm. Second, the denoising filter is integrated into the level-set framework to separate noise from the noisy image input. Finally, the level-set energy function is minimized to acquire segmentation contours. Extensive experiments demonstrate the robustness and effectiveness of the proposed method when applied to noisy images.