The wheel wear of light rail trains is difficult to predict due to poor information and small data samples.However,the amount of wear gradually increases with the running mileage.The grey future prediction model is su...The wheel wear of light rail trains is difficult to predict due to poor information and small data samples.However,the amount of wear gradually increases with the running mileage.The grey future prediction model is supposed to deal with this problem effectively.In this study,we propose an improved non-equidistant grey model GM(1,1)with background values optimized by a genetic algorithm(GA).While the grey model is not good enough to track data series with features of randomness and nonlinearity,the residual error series of the GA-GM(1,1)model is corrected through a back propagation neural network(BPNN).To further improve the performance of the GA-GM(1,1)-BPNN model,a particle swarm optimization(PSO)algorithm is implemented to train the weight and bias in the neural network.The traditional non-equidistant GM(1,1)model and the proposed GA-GM(1,1),GA-GM(1,1)-BPNN,and GA-GM(1,1)-PSO-BPNN models were used to predict the wheel diameter and wheel flange wear of the Changchun light rail train and their validity and rationality were verified.Benefitting from the optimization effects of the GA,neural network,and PSO algorithm,the performance ranking of the four methods from highest to lowest was GA-GM(1,1)-PSO-BPNN>GA-GM(1,1)-BPNN>GA-GM(1,1)>GM(1,1)in both the fitting and prediction zones.The GA-GM(1,1)-PSO-BPNN model performed best,with the lowest fitting and forecasting maximum relative error,mean absolute error,mean absolute percentage error,and mean squared error of all four models.Therefore,it is the most effective and stable model in field application of light rail train wheel wear prediction.展开更多
The development of artificial intelligence has brought tremendous changes to enterprises and also pose higher demands on financial professionals.Through literature research,this paper explores the viewpoints of domest...The development of artificial intelligence has brought tremendous changes to enterprises and also pose higher demands on financial professionals.Through literature research,this paper explores the viewpoints of domestic and foreign scholars and industry experts on the impact of Artificial Intelligence(AI)on corporate financial management and the role transformation of financial professionals.It analyzes the current application status of AI technology in finance.The results indicate that AI will replace some repetitive and highly procedural tasks,such as simple data entry and bookkeeping.AI can improve the processing speed and accuracy of corporate financial data.With its learning capabilities,AI can assist financial professionals in addressing knowledge gaps.However,AI cannot completely replace human thinking,judgment,and decision-making,especially in areas like emotional communication and aesthetic experience.This requires financial professionals to continuously improve their overall qualities,leverage their strengths,and achieve complementary advantages with machines,jointly promoting innovative financial development in the era of artificial intelligence.展开更多
Metallurgical slag is a waste or by-product of the metallurgical process,and its improper disposal can pose negative environmental impacts,including groundwater and soil contamination.The composition and properties of...Metallurgical slag is a waste or by-product of the metallurgical process,and its improper disposal can pose negative environmental impacts,including groundwater and soil contamination.The composition and properties of metallurgical slag are complex,which is usually difficult to use or process directly and requires special treatment and utilization methods.Taking converter slag and blast furnace slag as examples,the research frontiers and development potential were primarily discussed and analyzed in three aspects:the recycling within and outside metallurgical slag plants,the extraction and utilization of thermal energy from metallurgical slag,and the functionalization and social application of metallurgical slag.The metallurgical slag waste heat recovery includes chemical methods and physical methods.Among them,the physical method currently most used was centrifugal granulation to recover heat.Chemical laws could recover hydrogen through the waste heat of metallurgical slag,which could save fuel and reduce CO_(2) generated by fuel combustion.Metallurgical slag is rich in alkaline metal oxides,which can undergo a carbonation reaction with CO_(2) to achieve carbon sequestration in metallurgical slag.Elements such as iron,phosphorus,and silicon contained in metallurgical slag could be used in soil conditioners,cement raw materials,and wastewater treatment.For example,the phosphorus element in the slag could be extracted by melt modification followed by acid leaching and used as a raw material for phosphate fertilizer.Therefore,under the background of China’s carbon neutrality goal,it is important to develop the key technologies of waste heat utilization of metallurgical slag and carbon sequestration of metallurgical slag.展开更多
Multi-objective optimization is critical for problem-solving in engineering,economics,and AI.This study introduces the Multi-Objective Chef-Based Optimization Algorithm(MOCBOA),an upgraded version of the Chef-Based Op...Multi-objective optimization is critical for problem-solving in engineering,economics,and AI.This study introduces the Multi-Objective Chef-Based Optimization Algorithm(MOCBOA),an upgraded version of the Chef-Based Optimization Algorithm(CBOA)that addresses distinct objectives.Our approach is unique in systematically examining four dominance relations—Pareto,Epsilon,Cone-epsilon,and Strengthened dominance—to evaluate their influence on sustaining solution variety and driving convergence toward the Pareto front.Our comparison investigation,which was conducted on fifty test problems from the CEC 2021 benchmark and applied to areas such as chemical engineering,mechanical design,and power systems,reveals that the dominance approach used has a considerable impact on the key optimization measures such as the hypervolume metric.This paper provides a solid foundation for determining themost effective dominance approach and significant insights for both theoretical research and practical applications in multi-objective optimization.展开更多
Objective:This study aimed to describe the implementation of the surgical safety check policy and the surgical safety checklist for invasive procedures outside the operating room(OR)and evaluate its effectiveness.Meth...Objective:This study aimed to describe the implementation of the surgical safety check policy and the surgical safety checklist for invasive procedures outside the operating room(OR)and evaluate its effectiveness.Methods:In 2017,to improve the safety of patients who underwent invasive procedures outside of the OR,the hospital quality and safety committee established the surgery safety check committee responsible for developing a new working plan,revise the surgery safety check policy,surgery safety check Keywords:Invasive procedures outside the operating room Safety management Surgical safety checklist Patient safety form,and provide training to the related staff,evaluated their competency,and implemented the updated surgical safety check policy and checklist.The study compared the data of pre-implementation(Apr to Sep 2017)and two post-implementation phases(Apr to Sep 2018,Apr to Sep 2019).It also evaluated the number of completed surgery safety checklist,correct signature,and correct timing of signature.Results:The results showed an increase in the completion rate of the safety checklist after the program implementation from 41.7%(521/1,249)to 90.4%(3,572/3,950),the correct rates of signature from 41.9%(218/521)to 99.0%(4,423/4,465),and the correct timing rates of signature from 34.4%(179/521)to 98.5%(4,401/4,465),with statistical significance(P<0.01).Conclusion:Implementing the updated surgery safety check significantly is a necessary and effective measure to ensure patient safety for those who underwent invasive procedures outside the OR.Implementing surgical safety checks roused up the clinical staff's compliance in performing safety checks,and enhanced team collaboration and communication.展开更多
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 ...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.展开更多
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 recognit...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.展开更多
Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization problems.This approach aims to leverage the strengt...Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization problems.This approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization tasks.In this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)optimizer.The proposed hybrid algorithm will be referred to as BHJO.Through this fusion,the BHJO algorithm aims to leverage the strengths of each optimizer.Before this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and exploitation.This meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization performance.In addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid algorithm.Moreover,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem domains.Similarly,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation metrics.This rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedding light on its effectiveness and reliability in solving optimization problems.Finally,the obtained numerical statistics underwent rigorous analysis using the Friedman post hoc Dunn’s test.The resulting numerical values revealed the BHJO algorithm’s competitiveness in tackling intricate optimization problems,affirming its capability to deliver favorable outcomes in challenging scenarios.展开更多
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 conventio...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.展开更多
Neurological disorders,including headaches(tension-type headaches,medication-overuse headaches,and migraines)and dementias that include Alzheimer’s disease,are among the most prevalent and debilitating global conditi...Neurological disorders,including headaches(tension-type headaches,medication-overuse headaches,and migraines)and dementias that include Alzheimer’s disease,are among the most prevalent and debilitating global conditions.In 2016,these disorders affected 276 million people worldwide and were the second leading cause of death that year[1].This highlights the urgent need for effective prevention,treatment,and support strategies.The etiology of neurological disorders is multifaceted and involves genetic,environmental,physiological,and social factors[2].展开更多
Background:In a study conducted from March to September 2021,124 cancer patients undergoing chemotherapy at our hospital were divided into two groups.The control group received routine inpatient nursing care,while the...Background:In a study conducted from March to September 2021,124 cancer patients undergoing chemotherapy at our hospital were divided into two groups.The control group received routine inpatient nursing care,while the observation group received Traditional Chinese Medicine(TCM)nursing interventions in addition to routine care.Data analysis was conducted to compare the incidence of clinical adverse reactions,constipation scores,and changes in anxiety levels between the two groups.The results showed that the observation group,receiving TCM nursing interventions,had lower incidence of clinical adverse reactions and lower constipation scores compared to the control group.Additionally,anxiety levels were found to decrease significantly in the observation group post-intervention.These findings suggest that incorporating TCM nursing interventions in the care of cancer patients undergoing chemotherapy may help in reducing the occurrence of adverse reactions,alleviating constipation,and managing anxiety levels.Further research is needed to explore the full potential of integrating TCM into conventional nursing care for cancer patients.Methods:Following interventions,both groups experienced varying degrees of clinical adverse reactions,with the observation group demonstrating a significantly lower total incidence(29.03%)compared to the control group.This disparity was statistically significant(P<0.05).Furthermore,improvements were observed in defecation time(0.53±0.18)points and defecation frequency(1.17±0.25)points post-intervention.These findings suggest that the intervention had a positive impact on reducing adverse reactions and improving defecation patterns.Results:In a recent study,researchers found that individuals in the observation group experienced lower levels of difficulty with defecation and had a more regular defecation form compared to those in the control group.The results showed a significant difference in defecation difficulty and form,with the observation group scoring lower in both aspects.Interestingly,there was no significant difference in anxiety levels between the two groups prior to the intervention.However,after the intervention,both groups experienced a decrease in anxiety levels,with the observation group showing a greater reduction compared to the control group.This suggests that the intervention had a positive impact on reducing anxiety levels,particularly in the observation group,where anxiety scores were significantly lower.These findings highlight the possible benefits of certain interventions in improving both physical and psychological well-being.Conclusion:TCM nursing interventions have shown to be beneficial in reducing anxiety and improving constipation symptoms in cancer patients.These methods not only enhance the quality of life for patients but also offer a promising approach in clinical cancer treatment.The efficacy of TCM nursing highlights its value and encourages further promotion and application in future cancer care strategies.TCM nursing helps cancer patients undergoing chemotherapy with constipation and anxiety.展开更多
In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects adults.Treatment depends on the type of leukemia...In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects adults.Treatment depends on the type of leukemia and the extent to which cancer has established throughout the body.Identifying leukemia in the initial stage is vital to providing timely patient care.Medical image-analysis-related approaches grant safer,quicker,and less costly solutions while ignoring the difficulties of these invasive processes.It can be simple to generalize Computer vision(CV)-based and image-processing techniques and eradicate human error.Many researchers have implemented computer-aided diagnosticmethods andmachine learning(ML)for laboratory image analysis,hopefully overcoming the limitations of late leukemia detection and determining its subgroups.This study establishes a Marine Predators Algorithm with Deep Learning Leukemia Cancer Classification(MPADL-LCC)algorithm onMedical Images.The projectedMPADL-LCC system uses a bilateral filtering(BF)technique to pre-process medical images.The MPADL-LCC system uses Faster SqueezeNet withMarine Predators Algorithm(MPA)as a hyperparameter optimizer for feature extraction.Lastly,the denoising autoencoder(DAE)methodology can be executed to accurately detect and classify leukemia cancer.The hyperparameter tuning process using MPA helps enhance leukemia cancer classification performance.Simulation results are compared with other recent approaches concerning various measurements and the MPADL-LCC algorithm exhibits the best results over other recent approaches.展开更多
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...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.展开更多
To solve the problem of difficult utilization of steel slag,the liquid steel slag was modified and the air-quenching granulation process was carried out to make steel slag into a value-added end product:air-quenching ...To solve the problem of difficult utilization of steel slag,the liquid steel slag was modified and the air-quenching granulation process was carried out to make steel slag into a value-added end product:air-quenching granulated steel slag.The granulated slag was tested to analyze the variation rule of slag properties under different modification conditions.Based on the phase diagram of CaO–Si_(2)O–FeO–MgO–Al2O3 slag system,the feasibility of blast furnace(BF)slag as modifier was determined.When the addition of BF slag was increased from 0%to 35%,following results were obtained.The slag fluidity was improved,and the air-quenching temperature range was expanded.Then,the yield of air-quenched steel slag increased,while the granulation rate,the degree of sphericity,the compactness were decreased.Furthermore,the air-quenching granulation process could substantially improve the stability and the amorphous content of steel slag.The maximum removal rate of free CaO was above 80%and the amorphous content was up to 95%.Taking the factors of yield and properties of granulated steel slag into full consideration,the optimum proportion of BF slag is around 15%.展开更多
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 lif...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.展开更多
Driving behavior is one of the main reasons that causes bottleneck on the freeway or restricts the capacity of signalized intersections.This paper proposes a car-following scheme in a model predictive control(MPC)fram...Driving behavior is one of the main reasons that causes bottleneck on the freeway or restricts the capacity of signalized intersections.This paper proposes a car-following scheme in a model predictive control(MPC)framework to improve the traffic flow behavior,particularly in stopping and speeding up of individual vehicles in dense urban traffic under a connected vehicle(CV)environment.Using information received through vehicle-to-vehicle(V2V)communication,the scheme predicts the future states of the preceding vehicle and computes the control input by solving a constrained optimization problem considering a finite future horizon.The objective function is to minimize the weighted costs due to speed deviation,control input,and unsafe gaps.The scheme shares the planned driving information with the following vehicles so that they can make better cooperative driving decision.The proposed car-following scheme is simulated in a typical driving scenario with multiple vehicles in dense traffic that has to stop at red signals in multiple intersections.The speeding up or queue clearing and stopping characteristics of the traffic using the proposed scheme is compared with the existing car-following scheme through numerical simulation.展开更多
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-1...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.展开更多
Machine learning(ML)has taken the world by a tornado with its prevalent applications in automating ordinary tasks and using turbulent insights throughout scientific research and design strolls.ML is a massive area wit...Machine learning(ML)has taken the world by a tornado with its prevalent applications in automating ordinary tasks and using turbulent insights throughout scientific research and design strolls.ML is a massive area within artificial intelligence(AI)that focuses on obtaining valuable information out of data,explaining why ML has often been related to stats and data science.An advanced meta-heuristic optimization algorithm is proposed in this work for the optimization problem of antenna architecture design.The algorithm is designed,depending on the hybrid between the Sine Cosine Algorithm(SCA)and the Grey Wolf Optimizer(GWO),to train neural networkbased Multilayer Perceptron(MLP).The proposed optimization algorithm is a practical,versatile,and trustworthy platform to recognize the design parameters in an optimal way for an endorsement double T-shaped monopole antenna.The proposed algorithm likewise shows a comparative and statistical analysis by different curves in addition to the ANOVA and T-Test.It offers the superiority and validation stability evaluation of the predicted results to verify the procedures’accuracy.展开更多
基金supported by the National Natural Science Foundation of China(No.52178436)the Shanghai Collaborative Innovation Research Center for Multi-network&Multi-modal Rail Transit,China.
文摘The wheel wear of light rail trains is difficult to predict due to poor information and small data samples.However,the amount of wear gradually increases with the running mileage.The grey future prediction model is supposed to deal with this problem effectively.In this study,we propose an improved non-equidistant grey model GM(1,1)with background values optimized by a genetic algorithm(GA).While the grey model is not good enough to track data series with features of randomness and nonlinearity,the residual error series of the GA-GM(1,1)model is corrected through a back propagation neural network(BPNN).To further improve the performance of the GA-GM(1,1)-BPNN model,a particle swarm optimization(PSO)algorithm is implemented to train the weight and bias in the neural network.The traditional non-equidistant GM(1,1)model and the proposed GA-GM(1,1),GA-GM(1,1)-BPNN,and GA-GM(1,1)-PSO-BPNN models were used to predict the wheel diameter and wheel flange wear of the Changchun light rail train and their validity and rationality were verified.Benefitting from the optimization effects of the GA,neural network,and PSO algorithm,the performance ranking of the four methods from highest to lowest was GA-GM(1,1)-PSO-BPNN>GA-GM(1,1)-BPNN>GA-GM(1,1)>GM(1,1)in both the fitting and prediction zones.The GA-GM(1,1)-PSO-BPNN model performed best,with the lowest fitting and forecasting maximum relative error,mean absolute error,mean absolute percentage error,and mean squared error of all four models.Therefore,it is the most effective and stable model in field application of light rail train wheel wear prediction.
文摘The development of artificial intelligence has brought tremendous changes to enterprises and also pose higher demands on financial professionals.Through literature research,this paper explores the viewpoints of domestic and foreign scholars and industry experts on the impact of Artificial Intelligence(AI)on corporate financial management and the role transformation of financial professionals.It analyzes the current application status of AI technology in finance.The results indicate that AI will replace some repetitive and highly procedural tasks,such as simple data entry and bookkeeping.AI can improve the processing speed and accuracy of corporate financial data.With its learning capabilities,AI can assist financial professionals in addressing knowledge gaps.However,AI cannot completely replace human thinking,judgment,and decision-making,especially in areas like emotional communication and aesthetic experience.This requires financial professionals to continuously improve their overall qualities,leverage their strengths,and achieve complementary advantages with machines,jointly promoting innovative financial development in the era of artificial intelligence.
基金supported by the following funds:Guizhou Science and Technology Support Program Project[Grant No.Guizhou Science and Technology Cooperation Support(2025)General 079]Guizhou Provincial Department of Education’s"Top 100 Schools and Thousand Enterprises in Science andTechnology Research and Development"Project in 2025(Contract Number:Guizhou Education and Technology[2025]No.009)+6 种基金Hebei Province Innovation Ability Improvement Plan(No.23561001D)Hebei Provincial Natural Science Foundation(No.H2022209089)Open Fund Project of the Key Laboratory for Ferrous Metallurgy and Resources Utilization of Ministry of Education(Grant No.FMRUlab23-03)the National Natural Science Foundation of China(No.52074128)Basic Scientific Research Business Expenses of Colleges and Universities in Hebei Province(Nos.JYG2022001 and JQN2023008)Tangshan Talent Funding Project(No.A202202007),Natural Science Foundation of Hebei Province(No.E2023209107)Foundation of Tangshan Science and Technology Bureau(No.23150219A).
文摘Metallurgical slag is a waste or by-product of the metallurgical process,and its improper disposal can pose negative environmental impacts,including groundwater and soil contamination.The composition and properties of metallurgical slag are complex,which is usually difficult to use or process directly and requires special treatment and utilization methods.Taking converter slag and blast furnace slag as examples,the research frontiers and development potential were primarily discussed and analyzed in three aspects:the recycling within and outside metallurgical slag plants,the extraction and utilization of thermal energy from metallurgical slag,and the functionalization and social application of metallurgical slag.The metallurgical slag waste heat recovery includes chemical methods and physical methods.Among them,the physical method currently most used was centrifugal granulation to recover heat.Chemical laws could recover hydrogen through the waste heat of metallurgical slag,which could save fuel and reduce CO_(2) generated by fuel combustion.Metallurgical slag is rich in alkaline metal oxides,which can undergo a carbonation reaction with CO_(2) to achieve carbon sequestration in metallurgical slag.Elements such as iron,phosphorus,and silicon contained in metallurgical slag could be used in soil conditioners,cement raw materials,and wastewater treatment.For example,the phosphorus element in the slag could be extracted by melt modification followed by acid leaching and used as a raw material for phosphate fertilizer.Therefore,under the background of China’s carbon neutrality goal,it is important to develop the key technologies of waste heat utilization of metallurgical slag and carbon sequestration of metallurgical slag.
基金funded by Researchers Supporting Programnumber(RSPD2024R809),King Saud University,Riyadh,Saudi Arabia.
文摘Multi-objective optimization is critical for problem-solving in engineering,economics,and AI.This study introduces the Multi-Objective Chef-Based Optimization Algorithm(MOCBOA),an upgraded version of the Chef-Based Optimization Algorithm(CBOA)that addresses distinct objectives.Our approach is unique in systematically examining four dominance relations—Pareto,Epsilon,Cone-epsilon,and Strengthened dominance—to evaluate their influence on sustaining solution variety and driving convergence toward the Pareto front.Our comparison investigation,which was conducted on fifty test problems from the CEC 2021 benchmark and applied to areas such as chemical engineering,mechanical design,and power systems,reveals that the dominance approach used has a considerable impact on the key optimization measures such as the hypervolume metric.This paper provides a solid foundation for determining themost effective dominance approach and significant insights for both theoretical research and practical applications in multi-objective optimization.
文摘Objective:This study aimed to describe the implementation of the surgical safety check policy and the surgical safety checklist for invasive procedures outside the operating room(OR)and evaluate its effectiveness.Methods:In 2017,to improve the safety of patients who underwent invasive procedures outside of the OR,the hospital quality and safety committee established the surgery safety check committee responsible for developing a new working plan,revise the surgery safety check policy,surgery safety check Keywords:Invasive procedures outside the operating room Safety management Surgical safety checklist Patient safety form,and provide training to the related staff,evaluated their competency,and implemented the updated surgical safety check policy and checklist.The study compared the data of pre-implementation(Apr to Sep 2017)and two post-implementation phases(Apr to Sep 2018,Apr to Sep 2019).It also evaluated the number of completed surgery safety checklist,correct signature,and correct timing of signature.Results:The results showed an increase in the completion rate of the safety checklist after the program implementation from 41.7%(521/1,249)to 90.4%(3,572/3,950),the correct rates of signature from 41.9%(218/521)to 99.0%(4,423/4,465),and the correct timing rates of signature from 34.4%(179/521)to 98.5%(4,401/4,465),with statistical significance(P<0.01).Conclusion:Implementing the updated surgery safety check significantly is a necessary and effective measure to ensure patient safety for those who underwent invasive procedures outside the OR.Implementing surgical safety checks roused up the clinical staff's compliance in performing safety checks,and enhanced team collaboration and communication.
基金funding the publication of this research through the Researchers Supporting Program (RSPD2023R809),King Saud University,Riyadh,Saudi Arabia.
文摘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.
基金funded by Researchers Supporting Program at King Saud University (RSPD2023R809).
文摘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.
基金funded by the Researchers Supporting Program at King Saud University(RSPD2024R809).
文摘Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization problems.This approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization tasks.In this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)optimizer.The proposed hybrid algorithm will be referred to as BHJO.Through this fusion,the BHJO algorithm aims to leverage the strengths of each optimizer.Before this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and exploitation.This meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization performance.In addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid algorithm.Moreover,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem domains.Similarly,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation metrics.This rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedding light on its effectiveness and reliability in solving optimization problems.Finally,the obtained numerical statistics underwent rigorous analysis using the Friedman post hoc Dunn’s test.The resulting numerical values revealed the BHJO algorithm’s competitiveness in tackling intricate optimization problems,affirming its capability to deliver favorable outcomes in challenging scenarios.
基金funded by the Researchers Supporting Program at King Saud University(RSPD2023R809).
文摘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.
基金supported by the National Key Research and Development Program of China[2018YFE0206900].
文摘Neurological disorders,including headaches(tension-type headaches,medication-overuse headaches,and migraines)and dementias that include Alzheimer’s disease,are among the most prevalent and debilitating global conditions.In 2016,these disorders affected 276 million people worldwide and were the second leading cause of death that year[1].This highlights the urgent need for effective prevention,treatment,and support strategies.The etiology of neurological disorders is multifaceted and involves genetic,environmental,physiological,and social factors[2].
基金supported by the Special Fund for Construction Projects of Major Weak Disciplines of Shanghai Pudong New District Health System(No.PWZbr2022-04).
文摘Background:In a study conducted from March to September 2021,124 cancer patients undergoing chemotherapy at our hospital were divided into two groups.The control group received routine inpatient nursing care,while the observation group received Traditional Chinese Medicine(TCM)nursing interventions in addition to routine care.Data analysis was conducted to compare the incidence of clinical adverse reactions,constipation scores,and changes in anxiety levels between the two groups.The results showed that the observation group,receiving TCM nursing interventions,had lower incidence of clinical adverse reactions and lower constipation scores compared to the control group.Additionally,anxiety levels were found to decrease significantly in the observation group post-intervention.These findings suggest that incorporating TCM nursing interventions in the care of cancer patients undergoing chemotherapy may help in reducing the occurrence of adverse reactions,alleviating constipation,and managing anxiety levels.Further research is needed to explore the full potential of integrating TCM into conventional nursing care for cancer patients.Methods:Following interventions,both groups experienced varying degrees of clinical adverse reactions,with the observation group demonstrating a significantly lower total incidence(29.03%)compared to the control group.This disparity was statistically significant(P<0.05).Furthermore,improvements were observed in defecation time(0.53±0.18)points and defecation frequency(1.17±0.25)points post-intervention.These findings suggest that the intervention had a positive impact on reducing adverse reactions and improving defecation patterns.Results:In a recent study,researchers found that individuals in the observation group experienced lower levels of difficulty with defecation and had a more regular defecation form compared to those in the control group.The results showed a significant difference in defecation difficulty and form,with the observation group scoring lower in both aspects.Interestingly,there was no significant difference in anxiety levels between the two groups prior to the intervention.However,after the intervention,both groups experienced a decrease in anxiety levels,with the observation group showing a greater reduction compared to the control group.This suggests that the intervention had a positive impact on reducing anxiety levels,particularly in the observation group,where anxiety scores were significantly lower.These findings highlight the possible benefits of certain interventions in improving both physical and psychological well-being.Conclusion:TCM nursing interventions have shown to be beneficial in reducing anxiety and improving constipation symptoms in cancer patients.These methods not only enhance the quality of life for patients but also offer a promising approach in clinical cancer treatment.The efficacy of TCM nursing highlights its value and encourages further promotion and application in future cancer care strategies.TCM nursing helps cancer patients undergoing chemotherapy with constipation and anxiety.
基金funded by Researchers Supporting Program at King Saud University,(RSPD2024R809).
文摘In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects adults.Treatment depends on the type of leukemia and the extent to which cancer has established throughout the body.Identifying leukemia in the initial stage is vital to providing timely patient care.Medical image-analysis-related approaches grant safer,quicker,and less costly solutions while ignoring the difficulties of these invasive processes.It can be simple to generalize Computer vision(CV)-based and image-processing techniques and eradicate human error.Many researchers have implemented computer-aided diagnosticmethods andmachine learning(ML)for laboratory image analysis,hopefully overcoming the limitations of late leukemia detection and determining its subgroups.This study establishes a Marine Predators Algorithm with Deep Learning Leukemia Cancer Classification(MPADL-LCC)algorithm onMedical Images.The projectedMPADL-LCC system uses a bilateral filtering(BF)technique to pre-process medical images.The MPADL-LCC system uses Faster SqueezeNet withMarine Predators Algorithm(MPA)as a hyperparameter optimizer for feature extraction.Lastly,the denoising autoencoder(DAE)methodology can be executed to accurately detect and classify leukemia cancer.The hyperparameter tuning process using MPA helps enhance leukemia cancer classification performance.Simulation results are compared with other recent approaches concerning various measurements and the MPADL-LCC algorithm exhibits the best results over other recent approaches.
文摘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.
基金supported by the Key Research and Development Program of Hebei Province(Grant Number 19273806D)the Project of Hebei Provincial Department of Education(Grant Number JQN2020042).
文摘To solve the problem of difficult utilization of steel slag,the liquid steel slag was modified and the air-quenching granulation process was carried out to make steel slag into a value-added end product:air-quenching granulated steel slag.The granulated slag was tested to analyze the variation rule of slag properties under different modification conditions.Based on the phase diagram of CaO–Si_(2)O–FeO–MgO–Al2O3 slag system,the feasibility of blast furnace(BF)slag as modifier was determined.When the addition of BF slag was increased from 0%to 35%,following results were obtained.The slag fluidity was improved,and the air-quenching temperature range was expanded.Then,the yield of air-quenched steel slag increased,while the granulation rate,the degree of sphericity,the compactness were decreased.Furthermore,the air-quenching granulation process could substantially improve the stability and the amorphous content of steel slag.The maximum removal rate of free CaO was above 80%and the amorphous content was up to 95%.Taking the factors of yield and properties of granulated steel slag into full consideration,the optimum proportion of BF slag is around 15%.
基金supported by the Tomsk Polytechnic University development program.
文摘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.
文摘Driving behavior is one of the main reasons that causes bottleneck on the freeway or restricts the capacity of signalized intersections.This paper proposes a car-following scheme in a model predictive control(MPC)framework to improve the traffic flow behavior,particularly in stopping and speeding up of individual vehicles in dense urban traffic under a connected vehicle(CV)environment.Using information received through vehicle-to-vehicle(V2V)communication,the scheme predicts the future states of the preceding vehicle and computes the control input by solving a constrained optimization problem considering a finite future horizon.The objective function is to minimize the weighted costs due to speed deviation,control input,and unsafe gaps.The scheme shares the planned driving information with the following vehicles so that they can make better cooperative driving decision.The proposed car-following scheme is simulated in a typical driving scenario with multiple vehicles in dense traffic that has to stop at red signals in multiple intersections.The speeding up or queue clearing and stopping characteristics of the traffic using the proposed scheme is compared with the existing car-following scheme through numerical simulation.
文摘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.
文摘Machine learning(ML)has taken the world by a tornado with its prevalent applications in automating ordinary tasks and using turbulent insights throughout scientific research and design strolls.ML is a massive area within artificial intelligence(AI)that focuses on obtaining valuable information out of data,explaining why ML has often been related to stats and data science.An advanced meta-heuristic optimization algorithm is proposed in this work for the optimization problem of antenna architecture design.The algorithm is designed,depending on the hybrid between the Sine Cosine Algorithm(SCA)and the Grey Wolf Optimizer(GWO),to train neural networkbased Multilayer Perceptron(MLP).The proposed optimization algorithm is a practical,versatile,and trustworthy platform to recognize the design parameters in an optimal way for an endorsement double T-shaped monopole antenna.The proposed algorithm likewise shows a comparative and statistical analysis by different curves in addition to the ANOVA and T-Test.It offers the superiority and validation stability evaluation of the predicted results to verify the procedures’accuracy.
