Fuzzy Difference Equations in Diagnoses of Glaucoma from Retinal Images Using Deep Learning

The intuitive fuzzy set has found important application in decision-making and machine learning.To enrich and utilize the intuitive fuzzy set,this study designed and developed a deep neural network-based glaucoma eye detection using fuzzy difference equations in the domain where the retinal images converge.Retinal image detections are categorized as normal eye recognition,suspected glaucomatous eye recognition,and glaucomatous eye recognition.Fuzzy degrees associated with weighted values are calculated to determine the level of concentration between the fuzzy partition and the retinal images.The proposed model was used to diagnose glaucoma using retinal images and involved utilizing the Convolutional Neural Network(CNN)and deep learning to identify the fuzzy weighted regularization between images.This methodology was used to clarify the input images and make them adequate for the process of glaucoma detection.The objective of this study was to propose a novel approach to the early diagnosis of glaucoma using the Fuzzy Expert System(FES)and Fuzzy differential equation(FDE).The intensities of the different regions in the images and their respective peak levels were determined.Once the peak regions were identified,the recurrence relationships among those peaks were then measured.Image partitioning was done due to varying degrees of similar and dissimilar concentrations in the image.Similar and dissimilar concentration levels and spatial frequency generated a threshold image from the combined fuzzy matrix and FDE.This distinguished between a normal and abnormal eye condition,thus detecting patients with glaucomatous eyes.

Robust Facial Biometric Authentication System Using Pupillary Light Reflex for Liveness Detection of Facial Images

Pupil dynamics are the important characteristics of face spoofing detection.The face recognition system is one of the most used biometrics for authenticating individual identity.The main threats to the facial recognition system are different types of presentation attacks like print attacks,3D mask attacks,replay attacks,etc.The proposed model uses pupil characteristics for liveness detection during the authentication process.The pupillary light reflex is an involuntary reaction controlling the pupil’s diameter at different light intensities.The proposed framework consists of two-phase methodologies.In the first phase,the pupil’s diameter is calculated by applying stimulus(light)in one eye of the subject and calculating the constriction of the pupil size on both eyes in different video frames.The above measurement is converted into feature space using Kohn and Clynes model-defined parameters.The Support Vector Machine is used to classify legitimate subjects when the diameter change is normal(or when the eye is alive)or illegitimate subjects when there is no change or abnormal oscillations of pupil behavior due to the presence of printed photograph,video,or 3D mask of the subject in front of the camera.In the second phase,we perform the facial recognition process.Scale-invariant feature transform(SIFT)is used to find the features from the facial images,with each feature having a size of a 128-dimensional vector.These features are scale,rotation,and orientation invariant and are used for recognizing facial images.The brute force matching algorithm is used for matching features of two different images.The threshold value we considered is 0.08 for good matches.To analyze the performance of the framework,we tested our model in two Face antispoofing datasets named Replay attack datasets and CASIA-SURF datasets,which were used because they contain the videos of the subjects in each sample having three modalities(RGB,IR,Depth).The CASIA-SURF datasets showed an 89.9%Equal Error Rate,while the Replay Attack datasets showed a 92.1%Equal Error Rate.

Prediction of Geopolymer Concrete Compressive Strength Using Convolutional Neural Networks

Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental problems.Its attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation infrastructure.In this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering field.To achieve this goal,a new approach using convolutional neural networks(CNNs)has been adopted.This study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly ash.The selection of optimal input parameters is guided by two distinct criteria.The first criterion leverages insights garnered from previous research on the influence of individual features on compressive strength.The second criterion scrutinizes the impact of these features within the model’s predictive framework.Key to enhancing the CNN model’s performance is the meticulous determination of the optimal hyperparameters.Through a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s robustness.The model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score analyses.Furthermore,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction methods.To unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rate.The study culminates in compelling findings that underscore the CNN model’s accurate prediction of geopolymer concrete compressive strength.To maximize the dataset’s potential,the application of bivariate plots unveils nuanced trends and interactions among variables,fortifying the consistency with earlier research.Evidenced by promising prediction accuracy,the study’s outcomes hold significant promise in guiding the development of innovative geopolymer concrete formulations,thereby reinforcing its role as an eco-conscious and robust construction material.The findings prove that the CNN model accurately estimated geopolymer concrete’s compressive strength.The results show that the prediction accuracy is promising and can be used for the development of new geopolymer concrete mixes.The outcomes not only underscore the significance of leveraging technology for sustainable construction practices but also pave the way for innovation and efficiency in the field of civil engineering.

A critical review of self-diverting acid treatments applied to carbonate oil and gas reservoirs

Carbonate reservoirs generally achieved relatively low primary resource recovery rates.It is therefore often necessary to clean those reservoirs up and/or stimulate them post drilling and later in their production life.A common and basic carbonate reservoir cleanup technique to remove contaminating material from the wellbore is acidizing.The efficiency of acid treatments is determined by many factors,including:the type and quantity of the acid used;the number of repeated treatments performed,heterogeneity of the reservoir,water cut of the reservoir fluids,and presence of idle zones and interlayers.Post-treatment production performance of such reservoirs frequently does not meet design expectations.There is therefore much scope to improve acidizing technologies and treatment designs to make them more reliable and effective.This review considers acid treatment technologies applied to carbonate reservoirs at the laboratory scale and in field-scale applications.The range of acid treatment techniques commonly applied are compared.Differences between specific acid treatments,such as foamed acids,acid emulsions,gelled and thickened acid systems,targeted acid treatments,and acid hydraulic fracturing are described in terms of the positive and negative influences they have on carbonate oil production rates and recovery.Opportunities to improve acid treatment techniques are identified,particularly those involving the deployment of nanoparticles(NPs).Due consideration is also given to the potential environmental impacts associated with carbonate reservoir acid treatment.Recommendations are made regarding the future research required to overcome the remaining challenges pertaining to acid treatment applications.

Magnetic Field Effect and Heat Transfer of Nanofluids within Waveform Microchannel

In this research,a numerical study of mixed convection of non-Newtonian fluid and magnetic field effect along a vertical wavy surface was investigated.A simple coordinate transformation to transform wavy surface to a flat surface is employed.A cubic spline collocation numerical method is employed to analyze transformed equations.The effect of various parameters such as Reynolds number,volume fraction 0-,Hartmann number,and amplitude of wave length was evaluated in improving the performance of a wavy microchannel.According to the presented results,the sinusoidal shape of the microchannel has a direct impact on heat transfer.By increasing the microchannel wave amplitude,the Nusselt number has risen.On the other hand,increasing the heat transfer in the higher wavelength ratio corrugated channel is seen as an effective method of increasing the heat transfer,especially at higher Reynolds numbers.The results showed that with increasing Hartmann numbers,the flow line near thewall becomesmore regular and,according to the temperature gradient created,theNusselt number growth.

Self-perceived burden and influencing factors in patients with cervical cancer administered with radiotherapy

BACKGROUND Cervical cancer is the fourth commonest malignancy in women around the world.It represents the second most commonly diagnosed cancer in South East Asian women,and an important cancer death cause in women of developing nations.Data collected in 2018 revealed 5690000 cervical cancer cases worldwide,85%of which occurred in developing countries.AIM To assess self-perceived burden(SPB)and related influencing factors in cervical cancer patients undergoing radiotherapy.METHODS Patients were prospectively included by convenient sampling at The Fifth Affiliated Hospital of Sun Yat-Sen University,China between March 2018 and March 2019.The survey was completed using a self-designed general information questionnaire,the SPB scale for cancer patients,and the self-care self-efficacy scale,Strategies Used by People to Promote Health,which were delivered to patients with cervical cancer undergoing radiotherapy.Measurement data are expressed as the mean±SD.Enumeration data are expressed as frequencies or percentages.Caregivers were the spouse,offspring,and other in 46.4,40.9,and 12.7%,respectively,and the majority were male(59.1%).As for pathological type,90 and 20 cases had squamous and adenocarcinoma/adenosquamous carcinomas,respectively.Stage IV disease was found in 12(10.9%)patients.RESULTS A total of 115 questionnaires were released,and five patients were excluded for too long evaluation time(n=2)and the inability to confirm the questionnaire contents(n=3).Finally,a total of 110 questionnaires were collected.They were aged 31-79 years,with the 40-59 age group being most represented(65.4%of all cases).Most patients were married(91.8%)and an overwhelming number had no religion(92.7%).Total SPB score was 43.13±16.65.SPB was associated with the place of residence,monthly family income,payment method,transfer status,the presence of radiotherapy complications,and the presence of pain(P<0.05).The SPB and self-care self-efficacy were negatively correlated(P<0.01).In multivariate analysis,self-care self-efficacy,place of residence,monthly family income,payment method,degree of radiation dermatitis,and radiation proctitis were influencing factors of SPB(P<0.05).CONCLUSION Patients with cervical cancer undergoing radiotherapy often have SPB.Self-care self-efficacy scale,place of residence,monthly family income,payment method,and radiation dermatitis and proctitis are factors independently influencing SPB.

广域保护中基于能量守恒原理的母线及输电线差动保护(英文)

提出一种基于能量守恒原理的纵联差动保护,对线路两端或母线各端的有功功率进行比较,若各端功率的和超出整定值,则判定为区内故障。在典型400 kV输电系统模型上,运用MATLAB仿真软件对所提出的原理进行了仿真验证。仿真结果表明,该方法比传统电流比率差动保护更加可靠且计算量小,因而可缩短动作时间。该方法能检测所有故障类型,适应于超高压远距离输电线路。

Reduction behavior and kinetics of vanadium–titanium sinters under high potential oxygen enriched pulverized coal injection

In this work, the reduction behavior of vanadium–titanium sinters was studied under five different sets of conditions of pulverized coal injection with oxygen enrichment. The modified random pore model was established to analyze the reduction kinetics. The results show that the reduction rate of sinters was accelerated by an increase of CO and H2contents. Meanwhile, with the increase in CO and H2contents, the increasing range of the medium reduction index (MRE) of sinters decreased. The increasing oxygen enrichment ratio played a diminishing role in improving the reduction behavior of the sinters. The reducing process kinetic parameters were solved using the modified random role model. The results indicated that, with increasing oxygen enrichment, the contents of CO and H2in the reducing gas increased. The reduction activation energy of the sinters decreased to between 20.4 and 23.2 kJ/mol. ? 2017, The Author(s).

Experimental and computational chemical studies on the cationic furanylnicotinamidines as novel corrosion inhibitors in aqueous solutions

The corrosion inhibition action of three newly synthesized furanylnicotinamidine derivatives namely: 6-[5-{4(dimethylamino)phenyl}furan-2-yl]nicotinamidine(MA-1256), 6-[5-(4-chlorophenyl)furan-2-yl]nicotinamidine(MA-1266), and 6-[5-{4-(dimethylamino)phenyl}furan-2-yl]nicotinonitrile(MA-1250) on carbon steel(C-steel) was investigated in 1.0 mol·L-1 HCl solution by weight loss(WL), potentiodynamic polarization(PP), electrochemical impedance spectroscopy(EIS), and electrochemical frequency modulation(EFM)techniques. Morphological analysis was performed on the uninhibited and inhibited C-steel using atomic force microscope(AFM) and Infrared Spectroscopy(ATR-IR) methods. The effect of temperature was studied and discussed. Inspection of experimental results revealed that the inhibition efficiency(IE) increases with the incremental addition of inhibitors and with elevating the temperature of the acid media. The adsorption of furanylnicotinamidine derivatives on C-steel follows Temkin’s isotherm. PP studies indicated that the investigated compounds act as mixed-type inhibitors and showed that p-dimethylaminophenyl furanylnicotinamidine derivative(MA-1256) was the most efficient inhibitor among the other studied derivatives with IE reached(95%)at 21 × 10-6 mol·L-1. MA-1266 is highly soluble in aqueous solution and has non-toxicity profile with LC50 N 37 mg·L-1. Thus, MA-1266 can be a promising green corrosion inhibitor candidate with IE N 91% at 21× 10-6 mol·L-1. The experiments were coupled with computational chemical theories such as quantum chemical and molecular dynamic methods. The experimental results were in good agreement with the computational outputs.

The synergistic effect of honey and cinnamon against Streptococcus mutans bacteria

Objective: To investigate the effect of Iranian honey, cinnamon and their combination against Streptococcus mutans bacteria.Methods: Nine experimental solutions were examined in this study, including two types of honey(pasteurized and sterilized), two types of cinnamon extract(dissolved in distilled water or dimethyl sulfoxide) and five different mixtures of cinnamon in honey(prepared by admixing 1%–5% w/w of cinnamon extract into 99%–95% w/w of honey, respectively).Meanwhile, each of mentioned agent was considered as the first solution while it was diluted into seven serially two-fold dilutions(from 1:2 to 1:128 v/v).Therefore, eight different concentrations of each agent were tested.The antibacterial tests were performed through blood agar well diffusion method, and the minimum inhibitory concentration(MIC) was determined.Ultimately, the data were subjected to statistical analysis incorporating Two-way ANOVA and Bonferroni post hoc tests(a = 0.01).Results: The highest zone of inhibition was recorded for the mixtures of honey and cinnamon while all the subgroups containing 95%–99% v/v of honey were in the same range(P < 0.01).The MIC for both honey solutions were obtained as 500 mg/mL whereas it was 50 mg/m L for both cinnamon solutions.Moreover, the MIC related to all honey/cinnamon mixtures were 200 mg/mL.Conclusions: A profound synergistic effect of honey and cinnamon was observed against Streptococcus mutans while there was no significant difference among extracts containing 99%–95% v/v of honey admixing with 1%–5% v/v of cinnamon, respectively.

A Lightweight Sentiment Analysis Method

The emergence of big data leads to an increasing demand for data processing methods.As the most influential media for Chinese domestic movie ratings,Douban contains a huge amount of data and one can understand users'perspectives towards these movies by analyzing these data.In this article,we study movie's critics from the Douban website,perform sentiment analysis on the data obtained by crawling,and visualize the results with a word cloud.We propose a lightweight sentiment analysis method which is free from heavy training and visualize the results in a more conceivable way.

Design of hybrid damping controller based on multi-target gravitational search optimization algorithm in a multi-machine power system with high penetration of PV park

A mathematical approach was proposed to investigate the impact of high penetration of large-scale photovoltaic park(LPP) on small-signal stability of a power network and design of hybrid controller for these units.A systematic procedure was performed to obtain the complete model of a multi-machine power network including LPP.For damping of oscillations focusing on inter-area oscillatory modes,a hybrid controller for LPP was proposed.The performance of the suggested controller was tested using a 16-machine 5-area network.The results indicate that the proposed hybrid controller for LPP provides sufficient damping to the low-frequency modes of power system for a wide range of operating conditions.The method presented in this work effectively indentifies the impact of increased PV penetration and its controller on dynamic performance of multi-machine power network containing LPP.Simulation results demonstrate that the model presented can be used in designing of essential controllers for LPP.

Calcitonin and vitamin D3 have high therapeutic potential for improving diabetic mandibular growth

The goal of this study was to assess the effect of the intermittent combination of an antiresorptive agent （calcitonin） and an anabolic agent （vitamin D3） on treating the detrimental effects of Type 1 diabetes mellitus （DM） on mandibular bone formation and growth. Forty 3-week-old male Wistar rats were divided into four groups： the control group （normal rats）, the control C＋D group （normal rats injected with calcitonin and vitamin D3）, the diabetic C＋D group （diabetic rats injected with calcitonin and vitamin D3） and the diabetic group （uncontrolled diabetic rats）. An experimental DM condition was induced in the male Wistar rats in the diabetic and diabetic C＋ D groups using a single dose of 60 mg.kg-1 body weight of streptozotocin. Calcitonin and vitamin D3 were simultaneously injected in the rats of the control C＋D and diabetic C＋D groups. All rats were killed after 4 weeks, and the right mandibles were evaluated by micro-computed tomography and histomorphometric analysis. Diabetic rats showed a significant deterioration in bone quality and bone formation （diabetic group）. By contrast, with the injection of calcitonin and vitamin D3, both bone parameters and bone formation significantly improved （diabetic C＋ D group） （P 〈 0.05）. These findings suggest that these two hormones might potentially improve various bone properties.

An Optimized Ensemble Model for Prediction the Bandwidth of Metamaterial Antenna

Metamaterial Antenna is a special class of antennas that uses metamaterial to enhance their performance.Antenna size affects the quality factor and the radiation loss of the antenna.Metamaterial antennas can overcome the limitation of bandwidth for small antennas.Machine learning(ML)model is recently applied to predict antenna parameters.ML can be used as an alternative approach to the trial-and-error process of finding proper parameters of the simulated antenna.The accuracy of the prediction depends mainly on the selected model.Ensemble models combine two or more base models to produce a better-enhanced model.In this paper,a weighted average ensemble model is proposed to predict the bandwidth of the Metamaterial Antenna.Two base models are used namely:Multilayer Perceptron(MLP)and Support Vector Machines(SVM).To calculate the weights for each model,an optimization algorithm is used to find the optimal weights of the ensemble.Dynamic Group-Based Cooperative Optimizer(DGCO)is employed to search for optimal weight for the base models.The proposed model is compared with three based models and the average ensemble model.The results show that the proposed model is better than other models and can predict antenna bandwidth efficiently.

Corrosion inhibition of carbon steel in hydrochloric acid by cationic arylthiophenes as new eco-friendly inhibitors: Experimental and quantum chemical study

This study describes the adsorption behavior of three arylthiophene derivatives namely:2-(4-amidino-3-fluorophenyl)-5-[4-methoxy phenyl] thiophene dihydrochloride salt(MA-1217),2-(4-amidinophenyl)-5-[4-chlorophenyll thiophene dihydrochloride salt(MA-1316) and 2-(4-amidino-3-fluorophenyl)-5-[4-ch lorophenyllthiophene dihydrochloride salt(MA-1312) at C-steel in 1.0 mol·L^(-1) HCl interface using experimental and theoretical studies.Electrochemical and mass loss measurements showed that the inhibition efficiency(IE) of the arylthiophene derivatives increases with increasing concentrations and exhibited maximum efficiency 89% at 21×10^(-6) mol·L^(-1)(MA-1217) by mass loss method.The investigated arylthiophene derivatives obey the Langmuir adsorption isotherm.From polarization studies the arylthiophene derivatives act as mixed-type inhibitors.Surface analysis were carried out and discussed.The mode of orientation and adsorption of inhibitor molecules on C-steel surface was studied using molecular dynamics(MD) simulations.Quantum chemical parameters as well as the radial distribution function indices and binding energies confirm the experimental results.

手术室外有创操作安全核查方案的制订及应用研究

目的制订并应用手术室外有创操作安全核查方案,并评价其应用效果。方法2017年,医院质量和安全委员会为了提高在手术室外进行有创操作时患者的安全,成立有创操作安全委员会,采取制订工作计划、修改安全核查制度和表格、对相关医务人员进行培训等一系列措施。对比方案实施前(2017年4-9月)、方案实施后2个阶段(2018年4-9月;2019年4-9月)有创操作安全核查表使用率、签名符合率及签名时间的准确率。结果方案实施后,手术室外有创操作安全核查表的使用率由实施前的41.7%(521/1,249)提升至实施后90.4%(3,572/3,950)(2018年4-9月),签名符合率从41.9%(218/521)提升至99.0%(4,423/4,465)(2019年4-9月),签名时间的正确率从34.4%(179/521)提升至98.5%(4,401/4,465)(2019年4-9月),差异有统计学意义(P<0.01)。结论此方案可有效提升手术室外有创操作患者安全,起到监管作用,同时也提升了医务人员对手术室外有创操作安全核查的重视及团队间的合作与沟通。

Asymptotic behavior for sums of non-identically distributed random variables

For any given positive integer m, let X_i, 1 ≤ i ≤ m be m independent random variables with distributions F_i, 1 ≤ i ≤ m. When all the summands are nonnegative and at least one of them is heavy-tailed, we prove that the lower limit of the ratio ■equals 1 as x →∞. When the summands are real-valued, we also obtain some asymptotic results for the tail probability of the sums. Besides, a local version as well as a density version of the above results is also presented.

An Artificial Intelligence Approach for Solving Stochastic Transportation Problems

Recent years witness a great deal of interest in artificial intelligence(AI)tools in the area of optimization.AI has developed a large number of tools to solve themost difficult search-and-optimization problems in computer science and operations research.Indeed,metaheuristic-based algorithms are a sub-field of AI.This study presents the use of themetaheuristic algorithm,that is,water cycle algorithm(WCA),in the transportation problem.A stochastic transportation problem is considered in which the parameters supply and demand are considered as random variables that follow the Weibull distribution.Since the parameters are stochastic,the corresponding constraints are probabilistic.They are converted into deterministic constraints using the stochastic programming approach.In this study,we propose evolutionary algorithms to handle the difficulties of the complex high-dimensional optimization problems.WCA is influenced by the water cycle process of how streams and rivers flow toward the sea(optimal solution).WCA is applied to the stochastic transportation problem,and obtained results are compared with that of the new metaheuristic optimization algorithm,namely the neural network algorithm which is inspired by the biological nervous system.It is concluded that WCA presents better results when compared with the neural network algorithm.

Gaining-Sharing Knowledge Based Algorithm for Solving Stochastic Programming Problems

This paper presents a novel application of metaheuristic algorithmsfor solving stochastic programming problems using a recently developed gaining sharing knowledge based optimization (GSK) algorithm. The algorithmis based on human behavior in which people gain and share their knowledgewith others. Different types of stochastic fractional programming problemsare considered in this study. The augmented Lagrangian method (ALM)is used to handle these constrained optimization problems by convertingthem into unconstrained optimization problems. Three examples from theliterature are considered and transformed into their deterministic form usingthe chance-constrained technique. The transformed problems are solved usingGSK algorithm and the results are compared with eight other state-of-the-artmetaheuristic algorithms. The obtained results are also compared with theoptimal global solution and the results quoted in the literature. To investigatethe performance of the GSK algorithm on a real-world problem, a solidstochastic fixed charge transportation problem is examined, in which theparameters of the problem are considered as random variables. The obtainedresults show that the GSK algorithm outperforms other algorithms in termsof convergence, robustness, computational time, and quality of obtainedsolutions.

Modeling of D-STATCOM in distribution systems load flow

This paper presents modeling of Distribution STATCOM (D-STATCOM) in load flow calculations for the steady- state voltage compensation. An accurate model for D-STATCOM is derived to use in load flow calculations. The rating of this device as well as the direction of required reactive power injection for voltage compensation in the desired value (1 p.u.) is de- rived and discussed analytically and mathematically by the phasor diagram method. Furthermore, an efficient method for node and line identification used in load flow calculations is presented. The validity of the proposed model is examined by using two standard distribution systems consisting of 33 and 69 nodes, respectively. The best location of D-STATCOM for under voltage problem mitigation approach in the distribution networks is determined. The results validate the proposed model for D- STATCOM in large distribution systems.