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 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
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
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 hone...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.展开更多
Graphs are used in various disciplines such as telecommunication,biological networks,as well as social networks.In large-scale networks,it is challenging to detect the communities by learning the distinct properties o...Graphs are used in various disciplines such as telecommunication,biological networks,as well as social networks.In large-scale networks,it is challenging to detect the communities by learning the distinct properties of the graph.As deep learning hasmade contributions in a variety of domains,we try to use deep learning techniques to mine the knowledge from large-scale graph networks.In this paper,we aim to provide a strategy for detecting communities using deep autoencoders and obtain generic neural attention to graphs.The advantages of neural attention are widely seen in the field of NLP and computer vision,which has low computational complexity for large-scale graphs.The contributions of the paper are summarized as follows.Firstly,a transformer is utilized to downsample the first-order proximities of the graph into a latent space,which can result in the structural properties and eventually assist in detecting the communities.Secondly,the fine-tuning task is conducted by tuning variant hyperparameters cautiously,which is applied to multiple social networks(Facebook and Twitch).Furthermore,the objective function(crossentropy)is tuned by L0 regularization.Lastly,the reconstructed model forms communities that present the relationship between the groups.The proposed robust model provides good generalization and is applicable to obtaining not only the community structures in social networks but also the node classification.The proposed graph-transformer shows advanced performance on the social networks with the average NMIs of 0.67±0.04,0.198±0.02,0.228±0.02,and 0.68±0.03 on Wikipedia crocodiles,Github Developers,Twitch England,and Facebook Page-Page networks,respectively.展开更多
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 ...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.展开更多
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&#...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.展开更多
Parkinson’s disease(PD),one of whose symptoms is dysphonia,is a prevalent neurodegenerative disease.The use of outdated diagnosis techniques,which yield inaccurate and unreliable results,continues to represent an obs...Parkinson’s disease(PD),one of whose symptoms is dysphonia,is a prevalent neurodegenerative disease.The use of outdated diagnosis techniques,which yield inaccurate and unreliable results,continues to represent an obstacle in early-stage detection and diagnosis for clinical professionals in the medical field.To solve this issue,the study proposes using machine learning and deep learning models to analyze processed speech signals of patients’voice recordings.Datasets of these processed speech signals were obtained and experimented on by random forest and logistic regression classifiers.Results were highly successful,with 90%accuracy produced by the random forest classifier and 81.5%by the logistic regression classifier.Furthermore,a deep neural network was implemented to investigate if such variation in method could add to the findings.It proved to be effective,as the neural network yielded an accuracy of nearly 92%.Such results suggest that it is possible to accurately diagnose early-stage PD through merely testing patients’voices.This research calls for a revolutionary diagnostic approach in decision support systems,and is the first step in a market-wide implementation of healthcare software dedicated to the aid of clinicians in early diagnosis of PD.展开更多
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 mel...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.展开更多
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 s...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.展开更多
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.
基金Supported by Dental Research Center of Shahed Dental School,Tehran,Iran(Grant No.41/41)
文摘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.
基金The research is funded by the Researchers Supporting Project at King Saud University(Project#RSP-2021/305).
文摘Graphs are used in various disciplines such as telecommunication,biological networks,as well as social networks.In large-scale networks,it is challenging to detect the communities by learning the distinct properties of the graph.As deep learning hasmade contributions in a variety of domains,we try to use deep learning techniques to mine the knowledge from large-scale graph networks.In this paper,we aim to provide a strategy for detecting communities using deep autoencoders and obtain generic neural attention to graphs.The advantages of neural attention are widely seen in the field of NLP and computer vision,which has low computational complexity for large-scale graphs.The contributions of the paper are summarized as follows.Firstly,a transformer is utilized to downsample the first-order proximities of the graph into a latent space,which can result in the structural properties and eventually assist in detecting the communities.Secondly,the fine-tuning task is conducted by tuning variant hyperparameters cautiously,which is applied to multiple social networks(Facebook and Twitch).Furthermore,the objective function(crossentropy)is tuned by L0 regularization.Lastly,the reconstructed model forms communities that present the relationship between the groups.The proposed robust model provides good generalization and is applicable to obtaining not only the community structures in social networks but also the node classification.The proposed graph-transformer shows advanced performance on the social networks with the average NMIs of 0.67±0.04,0.198±0.02,0.228±0.02,and 0.68±0.03 on Wikipedia crocodiles,Github Developers,Twitch England,and Facebook Page-Page networks,respectively.
文摘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.
文摘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.
文摘Parkinson’s disease(PD),one of whose symptoms is dysphonia,is a prevalent neurodegenerative disease.The use of outdated diagnosis techniques,which yield inaccurate and unreliable results,continues to represent an obstacle in early-stage detection and diagnosis for clinical professionals in the medical field.To solve this issue,the study proposes using machine learning and deep learning models to analyze processed speech signals of patients’voice recordings.Datasets of these processed speech signals were obtained and experimented on by random forest and logistic regression classifiers.Results were highly successful,with 90%accuracy produced by the random forest classifier and 81.5%by the logistic regression classifier.Furthermore,a deep neural network was implemented to investigate if such variation in method could add to the findings.It proved to be effective,as the neural network yielded an accuracy of nearly 92%.Such results suggest that it is possible to accurately diagnose early-stage PD through merely testing patients’voices.This research calls for a revolutionary diagnostic approach in decision support systems,and is the first step in a market-wide implementation of healthcare software dedicated to the aid of clinicians in early diagnosis of PD.
基金the National Plan for Science,Technology and Innovation(MAARIFAH)-King Abdulaziz City for Science Technology-the Kingdom of Saudi Arabia award number(12-MED2735-03)Science and Technology Unit,King Abdulaziz University for technical support
文摘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.
文摘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.
