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%.

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.

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.

Dynamic Hypergraph Modeling and Robustness Analysis for SIoT

The Social Internet of Things(SIoT)integrates the Internet of Things(IoT)and social networks,taking into account the social attributes of objects and diversifying the relationship between humans and objects,which overcomes the limitations of the IoT’s focus on associations between objects.Artificial Intelligence(AI)technology is rapidly evolving.It is critical to build trustworthy and transparent systems,especially with system security issues coming to the surface.This paper emphasizes the social attributes of objects and uses hypergraphs to model the diverse entities and relationships in SIoT,aiming to build an SIoT hypergraph generation model to explore the complex interactions between entities in the context of intelligent SIoT.Current hypergraph generation models impose too many constraints and fail to capture more details of real hypernetworks.In contrast,this paper proposes a hypergraph generation model that evolves dynamically over time,where only the number of nodes is fixed.It combines node wandering with a forest fire model and uses two different methods to control the size of the hyperedges.As new nodes are added,the model can promptly reflect changes in entities and relationships within SIoT.Experimental results exhibit that our model can effectively replicate the topological structure of real-world hypernetworks.We also evaluate the vulnerability of the hypergraph under different attack strategies,which provides theoretical support for building a more robust intelligent SIoT hypergraph model and lays the foundation for building safer and more reliable systems in the future.

Data-driven diagnosis of high temperature PEM fuel cells based on the electrochemical impedance spectroscopy: Robustness improvement and evaluation

Utilizing machine learning techniques for data-driven diagnosis of high temperature PEM fuel cells is beneficial and meaningful to the system durability. Nevertheless, ensuring the robustness of diagnosis remains a critical and challenging task in real application. To enhance the robustness of diagnosis and achieve a more thorough evaluation of diagnostic performance, a robust diagnostic procedure based on electrochemical impedance spectroscopy (EIS) and a new method for evaluation of the diagnosis robustness was proposed and investigated in this work. To improve the diagnosis robustness: (1) the degradation mechanism of different faults in the high temperature PEM fuel cell was first analyzed via the distribution of relaxation time of EIS to determine the equivalent circuit model (ECM) with better interpretability, simplicity and accuracy;(2) the feature extraction was implemented on the identified parameters of the ECM and extra attention was paid to distinguishing between the long-term normal degradation and other faults;(3) a Siamese Network was adopted to get features with higher robustness in a new embedding. The diagnosis was conducted using 6 classic classification algorithms—support vector machine (SVM), K-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), random forest (RF), and Naive Bayes employing a dataset comprising a total of 1935 collected EIS. To evaluate the robustness of trained models: (1) different levels of errors were added to the features for performance evaluation;(2) a robustness coefficient (Roubust_C) was defined for a quantified and explicit evaluation of the diagnosis robustness. The diagnostic models employing the proposed feature extraction method can not only achieve the higher performance of around 100% but also higher robustness for diagnosis models. Despite the initial performance being similar, the KNN demonstrated a superior robustness after feature selection and re-embedding by triplet-loss method, which suggests the necessity of robustness evaluation for the machine learning models and the effectiveness of the defined robustness coefficient. This work hopes to give new insights to the robust diagnosis of high temperature PEM fuel cells and more comprehensive performance evaluation of the data-driven method for diagnostic application.

An Affordable and Easily Accessible Approach for Acrylonitrile Monomer Purification through a Simple Column Technique

This manuscript presents a dataset detailing a method for purifying monomers. Purification plays a crucial role in every chemical process, as it leads to an improvement in product quality through the removal of impurities. The primary method for monomer purification, like acrylonitrile (AN), is the distillation technique. However, this technique is unsafe and hard to set up or handle. A straightforward, risk-free, low-cost method like the column technique resolves these issues. A simple column technique demonstrated the successful execution of purifying AN. Fourier transform infrared (FTIR) and nuclear magnetic resonance (NMR) analyses confirmed that AN was successfully purified, with purity reaching 99.8%. FTIR spectra revealed changes in the position and intensity of the stretching vibration peaks after purification. Also, the functional groups of the inhibitor monomethyl ether of hydroquinone (MeHQ) were undetected after purification. Furthermore, after purification, NMR spectra revealed the absence of aromatic protons and carbons associated with MeHQ. In conclusion, the column technique is a successful and inexpensive way to purify AN monomers. This makes it useful for a wide range of applications, especially in polymerization reactions where MeHQ needs to be removed to prevent self-polymerization during the initiation process.

Adversarial Attack-Based Robustness Evaluation for Trustworthy AI

Artificial Intelligence(AI)technology has been extensively researched in various fields,including the field of malware detection.AI models must be trustworthy to introduce AI systems into critical decisionmaking and resource protection roles.The problem of robustness to adversarial attacks is a significant barrier to trustworthy AI.Although various adversarial attack and defense methods are actively being studied,there is a lack of research on robustness evaluation metrics that serve as standards for determining whether AI models are safe and reliable against adversarial attacks.An AI model’s robustness level cannot be evaluated by traditional evaluation indicators such as accuracy and recall.Additional evaluation indicators are necessary to evaluate the robustness of AI models against adversarial attacks.In this paper,a Sophisticated Adversarial Robustness Score(SARS)is proposed for AI model robustness evaluation.SARS uses various factors in addition to the ratio of perturbated features and the size of perturbation to evaluate robustness accurately in the evaluation process.This evaluation indicator reflects aspects that are difficult to evaluate using traditional evaluation indicators.Moreover,the level of robustness can be evaluated by considering the difficulty of generating adversarial samples through adversarial attacks.This paper proposed using SARS,calculated based on adversarial attacks,to identify data groups with robustness vulnerability and improve robustness through adversarial training.Through SARS,it is possible to evaluate the level of robustness,which can help developers identify areas for improvement.To validate the proposed method,experiments were conducted using a malware dataset.Through adversarial training,it was confirmed that SARS increased by 70.59%,and the recall reduction rate improved by 64.96%.Through SARS,it is possible to evaluate whether an AI model is vulnerable to adversarial attacks and to identify vulnerable data types.In addition,it is expected that improved models can be achieved by improving resistance to adversarial attacks via methods such as adversarial training.

A New Quenching Process and Tower to Improve the Recovery of Acrylonitrile

Quenching process and design of the quenching tower in acrylonitrile production in China were studied in order to decrease the polymerization loss of acrylonitrile in the quenching tower. Based on the research of acrylonitrile polymerization in the quenching tower, a new quenching process was proposed to avoid the disadvantages of the original process. Two kinds of internals were installed to improve the performance of the quenching tower. Through a series of air-flow and real-flow model experiments, the new quenching process and new design were showed to be successful in enhancing the mass and heat transfer in the vapor-liquid system and decreasing the loss of acrylonitrile.Industrial application showed satisfactory results of decrease of the acrylonitrile loss in the quenching tower by about 4.5% and increase of the acrylonitrile recovery of the whole plant by more than 4%.

浅议structural robustness在结构工程中的定名

structural robustness的意义在结构工程领域已受到广泛认可,但在国内还没有公认合适的译名,这不利于国内技术人员对此概念的理解和交流。通过简要回顾structural robustness的产生思路,探讨了结构鲁棒性等几种定名。

Optimization of Uncertain Structures with Interval Parameters Considering Objective and Feasibility Robustness

For the purpose of improving the mechanical performance indices of uncertain structures with interval parameters and ensure their robustness when fluctuating under interval parameters, a constrained interval robust optimization model is constructed with both the center and halfwidth of the most important mechanical performance index described as objective functions and the other requirements on the mechanical performance indices described as constraint functions. To locate the optimal solution of objective and feasibility robustness, a new concept of interval violation vector and its calculation formulae corresponding to different constraint functions are proposed. The math?ematical formulae for calculating the feasibility and objective robustness indices and the robustness?based preferential guidelines are proposed for directly ranking various design vectors, which is realized by an algorithm integrating Kriging and nested genetic algorithm. The validity of the proposed method and its superiority to present interval optimization approaches are demonstrated by a numerical example. The robust optimization of the upper beam in a high?speed press with interval material properties demonstrated the applicability and effectiveness of the proposed method in engineering.

Amidoxime-based adsorbents prepared by cografting acrylic acid with acrylonitrile onto HDPE fiber for the recovery of uranium from seawater

An amidoxime-based polymeric adsorbent was prepared by pre-irradiation grafting of acrylonitrile and acrylic acid onto high-density polyethylene fibers using electron beams,followed by amidoximation.Quantitative recovery of uranium was investigated by flow-through experiment using simulated seawater and marine test in natural seawater.The maximum amount of uranium uptake was 2.51 mg/g-ads after 42 days of contact with simulated seawater and 0.13 mg/g-ads for 15 days of contact with natural seawater.A lower uranium uptake in marine test can be attributed to the short adsorption time and the contamination of marine microorganisms and iron.However,the high selectivity toward uranium against vanadium may be beneficial to harvest uranyl ion onto adsorbents and the economic feasibility for recovery of uranium from seawater.

Polymerization of Acrylonitrile Initiated by Di（2，6－di－tert－butyl－4－methylphenoxo） Samarium Complex

The polymerization of acrylonitrile initiated by organolanthanide complexes alone is studied for the first time. The effect of polymerization conditions on catalytic activity of the title complex and molecular weight of the polymers produced have been studied.

Preparation and Characterization of Carboxyl-terminated Poly(butadiene-co-acrylonitrile)-epoxy Resin Prepolymers for Fusion-bonded-epoxy Powder Coating

Liquid carboxyl-terminated poly（butadiene-co-acrylonitrile）（CTBN）-epoxy resin（EP） prepolymers were prepared with different contents of CTBN.The chemical reactions between EP and CTBN were characterized by Fourier ransform infrared（FTIR） spectroscopy and gel permeation chromatography（GPC）.The scanning electron micrograph（SEM） and dynamic mechanical analysis（DMA） of curing films showed phase separation,and the rubber particles were finely dispersed in the epoxy matrix.Mechanical properties analysis of curing films showed that impact strength and elongation at break increased significantly upon the addition of CTBN,indicating good toughness of the modified epoxy resins.Thermogravimetric analysis（TGA） showed that the incorporation of CTBN had little effect on the thermal stability of EP.Fusion-bonded-epoxy（FBE） powder coatings modified with CTBN-EP prepolymers were prepared.The experimental results demonstrate the ability of CTBN-EP prepolymers,toughening technology to dramatically enhance the flexibility and impact resistance of FBE coatings without compromising other key properties such as corrosion protection.

Formation and Characteristics of Acrylonitrile/Urea Inclusion Compound

The formation process and composition of the acrylonitrile/urea inclusion compounds （AN/UIC） with different aging times and AN/urea molar feed ratios are studied by differential scanning calorimetry （DSC） and X-ray diffraction （XRD）. It is suggested that DSC can determine the guest/host ratio and the heat of decomposition. Meanwhile, the guest/host ratio and heat of decomposition are obtained, which are 1.17 and 5361.53 J/mol, respec- tively. It is suggested AN molecules included in urea canal lattice may be packed flat against each other. It is found that the formation of AN/UIC depends on the aging time. XRD results reveal that once AN molecules enter urea lattice, AN/UIC are formed, which possess the final structure. When AN molecules are sufficient, the length of AN molecular arrays in urea canals increases as aging time prolonging until urea tunnels are saturated by AN.

Synthesis and Characterization of the Polyacrylonitrile-block- poly(methyl acrylate) by RAFT Technique

Polyacrylonitrile-block-poly（methyl acrylate）（P（AN-b-MA）） was synthesized by reversible addition-fragmentation chain transfer （RAFT） polymerization employing macro-RAFT agent （PAN-RAFT） as the chain transfer agent and azobis（isobutyronitrile） （AIBN） as the initiator. A linear relationship between ln（[M]0/[M]1） and reaction time was observed. The molecular structure of P（AN-b-MA） was characterized by ^1H-NMR, element analysis, FTIR and SEC. The molecular weight distribution （MWD） was less than 1.40, the Mn could be controled from 0.733 to 4.834×10^4, and the molar content of MA in P（AN-b-MA） were from 15.6 to 75.0 percentage, respectively.

REDUCING NONSELECTIVE PROTEIN ADSORPTION AND CELL ADHESION ON POLYACRYLONITRILE FILMS BY COPOLYMERIZATION OF ACRYLONITRILE WITH α-ALLYL GLUCOSIDE

In this work, the surface properties of novel sugar-containing polymers, α-allyl glucoside (AG)/acrylonitrile (AN)copolymers, were studied by contact angle, protein adsorption and cell adhesion measurements. It was found that the contactangle of the copolymer films decreased from 68° to 30° with the increase of AG content in the copolymer. The adsorptionamount of bovine serum albumin (BSA) and the adhesive macrophage onto the film surface also decreased significantly withincreasing α-allyl glucoside content from 0 to 42 wt% in the copolymer. These preliminary results reveal that both thehydrophilicity and the biocompatibility of polyacrylonitrile-based membranes could be improved by copolymerizin gacrylonitrile with vinyl carbohydrates.

Optimization of robustness of network controllability against malicious attacks

As the controllability of complex networks has attracted much attention recently, how to design and optimize the robustness of network controllability has become a common and urgent problem in the engineering field. In this work, we propose a method that modifies any given network with strict structural perturbation to effectively enhance its robustness against malicious attacks, called dynamic optimization of controllability. Unlike other structural perturbations, the strict perturbation only swaps the links and keeps the in- and out-degree unchanged. A series of extensive experiments show that the robustness of controllability and connectivity can be improved dramatically. Furthermore, the effectiveness of our method is explained from the views of underlying structure. The analysis results indicate that the optimization algorithm makes networks more homogenous and assortative.

Random Fuzzy Chance-constrained Programming Based on Adaptive Chaos Quantum Honey Bee Algorithm and Robustness Analysis

This paper proposes an adaptive chaos quantum honey bee algorithm （CQHBA） for solving chance-constrained program- ming in random fuzzy environment based on random fuzzy simulations. Random fuzzy simulation is designed to estimate the chance of a random fuzzy event and the optimistic value to a random fuzzy variable. In CQHBA, each bee carries a group of quantum bits representing a solution. Chaos optimization searches space around the selected best-so-far food source. In the marriage process, random interferential discrete quantum crossover is done between selected drones and the queen. Gaussian quantum mutation is used to keep the diversity of whole population. New methods of computing quantum rotation angles are designed based on grads. A proof of con- vergence for CQHBA is developed and a theoretical analysis of the computational overhead for the algorithm is presented. Numerical examples are presented to demonstrate its superiority in robustness and stability, efficiency of computational complexity, success rate, and accuracy of solution quality. CQHBA is manifested to be highly robust under various conditions and capable of handling most random fuzzy programmings with any parameter settings, variable initializations, system tolerance and confidence level, perturbations, and noises.

THE PRINCIPLE OF ROBUSTNESS IN GENERALIZED PREDICTIVE CONTROL

This paper deeply analyzes the closed-loop nature ofGPCin the fram ework ofinter- nalm odelcontrol(IMC) theory. A new sort ofrelation lies in the feedback structure so that robustreason can be satisfactorily explained. The resultissignificantbecause the previous con- clusions are only applied to open-loop stable plant(orm odel).

ON THE ROBUSTNESS OF A GENERALIZED PREDICTIVE CONTROLLER

In this paper, we present a quantitative analysis of the robustness of a generalized predictive controller. The result of stability analysis shows that, under a specific bounded modelling error, the closed-loop system is BIBO stable in the presence of unmodelled dynamics.