An Optimized System of Random Forest Model by Global Harmony Search with Generalized Opposition-Based Learning for Forecasting TBM Advance Rate

As massive underground projects have become popular in dense urban cities,a problem has arisen:which model predicts the best for Tunnel Boring Machine(TBM)performance in these tunneling projects?However,performance level of TBMs in complex geological conditions is still a great challenge for practitioners and researchers.On the other hand,a reliable and accurate prediction of TBM performance is essential to planning an applicable tunnel construction schedule.The performance of TBM is very difficult to estimate due to various geotechnical and geological factors and machine specifications.The previously-proposed intelligent techniques in this field are mostly based on a single or base model with a low level of accuracy.Hence,this study aims to introduce a hybrid randomforest(RF)technique optimized by global harmony search with generalized oppositionbased learning(GOGHS)for forecasting TBM advance rate(AR).Optimizing the RF hyper-parameters in terms of,e.g.,tree number and maximum tree depth is the main objective of using the GOGHS-RF model.In the modelling of this study,a comprehensive databasewith themost influential parameters onTBMtogetherwithTBM AR were used as input and output variables,respectively.To examine the capability and power of the GOGHSRF model,three more hybrid models of particle swarm optimization-RF,genetic algorithm-RF and artificial bee colony-RF were also constructed to forecast TBM AR.Evaluation of the developed models was performed by calculating several performance indices,including determination coefficient(R2),root-mean-square-error(RMSE),and mean-absolute-percentage-error(MAPE).The results showed that theGOGHS-RF is a more accurate technique for estimatingTBMAR compared to the other applied models.The newly-developedGOGHS-RFmodel enjoyed R2=0.9937 and 0.9844,respectively,for train and test stages,which are higher than a pre-developed RF.Also,the importance of the input parameters was interpreted through the SHapley Additive exPlanations(SHAP)method,and it was found that thrust force per cutter is the most important variable on TBMAR.The GOGHS-RF model can be used in mechanized tunnel projects for predicting and checking performance.

Constructing long-cycling crystalline C_(3)N_(4)-based carbonaceous anodes for sodium-ion battery via N configuration control

Carbon nitrides with two-dimensional layered structures and high theoretical capacities are attractive as anode materials for sodium-ion batteries while their low crystallinity and insufficient structural stability strongly restrict their practical applications.Coupling carbon nitrides with conductive carbon may relieve these issues.However,little is known about the influence of nitrogen(N)configurations on the interactions between carbon and C_(3)N_(4),which is fundamentally critical for guiding the precise design of advanced C_(3)N_(4)-related electrodes.Herein,highly crystalline C_(3)N_(4)(poly(triazine imide),PTI)based all-carbon composites were developed by molten salt strategy.More importantly,the vital role of pyrrolic-N for enhancing charge transfer and boosting Na+storage of C_(3)N_(4)-based composites,which was confirmed by both theoretical and experimental evidence,was spot-highlighted for the first time.By elaborately controlling the salt composition,the composite with high pyrrolic-N and minimized graphitic-N content was obtained.Profiting from the formation of highly crystalline PTI and electrochemically favorable pyrrolic-N configurations,the composite delivered an unusual reverse growth and record-level cycling stability even after 5000 cycles along with high reversible capacity and outstanding full-cell capacity retention.This work broadens the energy storage applications of C_(3)N_(4) and provides new prospects for the design of advanced all-carbon electrodes.

Optimized sequential therapy vs 10- and 14-d concomitant therapy for eradicating Helicobacter pylori: A randomized clinical trial

BACKGROUND A cure for Helicobacter pylori(H.pylori)remains a problem of global concern.The prevalence of antimicrobial resistance is widely rising and becoming a challenging issue worldwide.Optimizing sequential therapy seems to be one of the most attractive strategies in terms of efficacy,tolerability and cost.The most common sequential therapy consists of a dual therapy[proton-pump inhibitors(PPIs)and amoxicillin]for the first period(5 to 7 d),followed by a triple therapy for the second period(PPI,clarithromycin and metronidazole).PPIs play a key role in maintaining a gastric pH at a level that allows an optimal efficacy of antibiotics,hence the idea of using new generation molecules.This open-label prospective study randomized 328 patients with confirmed H.pylori infection into three groups(1:1:1):The first group received quadruple therapy consisting of twice-daily(bid)omeprazole 20 mg,amoxicillin 1 g,clarith-romycin 500 mg and metronidazole 500 mg for 10 d(QT-10),the second group received a 14 d quadruple therapy following the same regimen(QT-14),and the third group received an optimized sequential therapy consisting of bid rabe-prazole 20 mg plus amoxicillin 1 g for 7 d,followed by bid rabeprazole 20 mg,clarithromycin 500 mg and metronidazole 500 mg for the next 7 d(OST-14).AEs were recorded throughout the study,and the H.pylori eradication rate was determined 4 to 6 wk after the end of treatment,using the 13C urea breath test.RESULTS In the intention-to-treat and per-protocol analysis,the eradication rate was higher in the OST-14 group compared to the QT-10 group:(93.5%,85.5%P=0.04)and(96.2%,89.5%P=0.03)respectively.However,there was no statist-ically significant difference in eradication rates between the OST-14 and QT-14 groups:(93.5%,91.8%P=0.34)and(96.2%,94.4%P=0.35),respectively.The overall incidence of AEs was significantly lower in the OST-14 group(P=0.01).Furthermore,OST-14 was the most cost-effective among the three groups.CONCLUSION The optimized 14-d sequential therapy is a safe and effective alternative.Its eradication rate is comparable to that of the 14-d concomitant therapy while causing fewer AEs and allowing a gain in terms of cost.

Optimal synthesis of heat-integrated distillation configurations using the two-column superstructure

In the realm of the synthesis of heat-integrated distillation configurations,the conventional approach for exploring more heat integration possibilities typically entails the splitting of a single column into a twocolumn configuration.However,this approach frequently necessitates tedious enumeration procedures,resulting in a considerable computational burden.To surmount this formidable challenge,the present study introduces an innovative remedy:The proposition of a superstructure that encompasses both single-column and multiple two-column configurations.Additionally,a simultaneous optimization algorithm is applied to optimize both the process parameters and heat integration structures of the twocolumn configurations.The effectiveness of this approach is demonstrated through a case study focusing on industrial organosilicon separation.The results underscore that the superstructure methodology not only substantially mitigates computational time compared to exhaustive enumeration but also furnishes solutions that exhibit comparable performance.

Advanced Optimized Anomaly Detection System for IoT Cyberattacks Using Artificial Intelligence

While emerging technologies such as the Internet of Things(IoT)have many benefits,they also pose considerable security challenges that require innovative solutions,including those based on artificial intelligence(AI),given that these techniques are increasingly being used by malicious actors to compromise IoT systems.Although an ample body of research focusing on conventional AI methods exists,there is a paucity of studies related to advanced statistical and optimization approaches aimed at enhancing security measures.To contribute to this nascent research stream,a novel AI-driven security system denoted as“AI2AI”is presented in this work.AI2AI employs AI techniques to enhance the performance and optimize security mechanisms within the IoT framework.We also introduce the Genetic Algorithm Anomaly Detection and Prevention Deep Neural Networks(GAADPSDNN)sys-tem that can be implemented to effectively identify,detect,and prevent cyberattacks targeting IoT devices.Notably,this system demonstrates adaptability to both federated and centralized learning environments,accommodating a wide array of IoT devices.Our evaluation of the GAADPSDNN system using the recently complied WUSTL-IIoT and Edge-IIoT datasets underscores its efficacy.Achieving an impressive overall accuracy of 98.18%on the Edge-IIoT dataset,the GAADPSDNN outperforms the standard deep neural network(DNN)classifier with 94.11%accuracy.Furthermore,with the proposed enhancements,the accuracy of the unoptimized random forest classifier(80.89%)is improved to 93.51%,while the overall accuracy(98.18%)surpasses the results(93.91%,94.67%,94.94%,and 94.96%)achieved when alternative systems based on diverse optimization techniques and the same dataset are employed.The proposed optimization techniques increase the effectiveness of the anomaly detection system by efficiently achieving high accuracy and reducing the computational load on IoT devices through the adaptive selection of active features.

Product quality prediction based on RBF optimized by firefly algorithm

With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used effectively.The traditional product quality prediction models have many disadvantages,such as high complexity and low accuracy.To overcome the above problems,we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model:radial basis function model optimized by the firefly algorithm with Levy flight mechanism(RBFFALM).First,the new data equalization method is introduced to pre-process the dataset,which reduces the dimension of the data,removes redundant features,and improves the data distribution.Then the RBFFALFM is used to predict product quality.Comprehensive expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality.

Bound and Resonating Multiquark Configurations

We review the predictions of quark models for multiquark configurations that are bound or resonant states,and compare different methods for estimating the properties of resonances.

Optimization of Wetland Plant Selection and Configuration in Urban Water Environment Ecological Restoration Projects

In urban water ecological restoration projects,the selection and configuration of wetland plants are crucial for water quality improvement,ecological diversity enhancement,and landscape beautification.Different plants have different characteristics,and a scientific and rational selection and optimization of plant species is needed.This paper proposes an optimized plant selection and configuration scheme for urban water ecological restoration based on the ecological characteristics and pollutant removal performance of wetland plants.It analyzes the diversity,removal mechanisms,and configuration modes of wetland plants,taking into account ecology,aesthetics,and cost-effectiveness,to provide scientific evidence for wetland plant configuration and support water environment management decision-making.

Engineering of geometrical configurations in dual-atom catalysts for electrocatalytic applications

Geometrical configurations play a crucial role in dual-atom catalysts(DACs)for electrocatalytic applications.Significant progress has been made to design DACs electrocatalysts with various geometri-cal configurations,but in-depth understanding the relationship between geometrical configurations and metal-metal interaction mechanisms for designing targeted DACs is still required.In this review,the recent progress in engineering of geometrical configurations of DACs is systematically summarized.Based on the polarity of geometrical configuration,DACs can be classified into two different types that are homonuclear and heteronuclear DACs.Furthermore,with regard to the geometrical configurations of the active sites,homonuclear DACs are identified into adjacent and bridged configurations,and heteronuclear DACs can be classified into adjacent,bridged,and separated configurations.Subsequently,metal-metal interactions in DACs with different geometrical configurations are introduced.Additionally,the applications of DACs in different electrocatalytic reactions are discussed,including the oxygen reduction reaction(ORR),oxygen evolution reaction(OER),hydrogen evolution reaction(HER),and other catalysis.Finally,the future challenges and perspectives for advancements in DACs are high-lighted.This review aims to provide inspiration for the design of highly effcient DACs towards energy relatedapplications.

Review of the Configuration and Transient Stability of Large-scale Renewable Energy Generation through Hybrid DC Transmission

Based on the complementary advantages of Line Commutated Converter(LCC)and Modular Multilevel Converter(MMC)in power grid applications,there are two types of hybrid DC system topologies:one is the parallel connection of LCC converter stations and MMC converter stations,and the other is the series connection of LCC and MMC converter stations within a single station.The hybrid DC transmission system faces broad application prospects and development potential in large-scale clean energy integration across regions and the construction of a new power system dominated by new energy sources in China.This paper first analyzes the system forms and topological characteristics of hybrid DC transmission,introducing the forms and topological characteristics of converter-level hybrid DC transmission systems and system-level hybrid DC transmission systems.Next,it analyzes the operating characteristics of LCC and MMC inverter-level hybrid DC transmission systems,provides insights into the transient stability of hybrid DC transmission systems,and typical fault ride-through control strategies.Finally,it summarizes the networking characteristics of the LCC-MMC series within the converter station hybrid DC transmission system,studies the transient characteristics and fault ridethrough control strategies under different fault types for the LCC-MMC series in the receiving-end converter station,and investigates the transient characteristics and fault ride-through control strategies under different fault types for the LCC-MMC series in the sending-end converter station.

Research on the Mechanism of Multi-Sensor Fusion Configuration Based on the Optimal Principle of the Vehicle

In order to address the issue of sensor configuration redundancy in intelligent driving,this paper constructs a multi-objective optimization model that considers cost,coverage ability,and perception performance.And then,combining a specific set of parameters,the NSGA-II algorithm is used to solve the multi-objective model established in this paper,and a Pareto front containing 24 typical configuration schemes is extracted after considering empirical constraints.Finally,using the decision preference method proposed in this paper that combines subjective and objective factors,decision scores are calculated and ranked for various configuration schemes from both cost and performance preferences.The research results indicate that the multi-objective optimization model established in this paper can screen and optimize various configuration schemes from the optimal principle of the vehicle,and the optimized configuration schemes can be quantitatively ranked to obtain the decision results for the vehicle under different preference tendencies.

Seismic fragility analysis of three-tower cable-stayed bridges with different connection configurations

Seismic fragility analysis of three-tower cable-stayed bridges with three different structural systems,including rigid system(RS),floating system(FS),and passive energy dissipation system(PEDS),is conducted to study the effects of connection configurations on seismic responses and fragilities.Finite element models of bridges are established using OpenSees.A new ground motion screening method based on the statistical characteristic of the predominant period is proposed to avoid irregular behavior in the selection process of ground motions,and incremental dynamic analysis(IDA)is performed to develop components and systems fragility curves.The effects of damper failure on calculated results for PEDS are examined in terms of seismic response and fragility analysis.The results show that the bridge tower is the most affected component by different structural systems.For RS,the fragility of the middle tower is significantly higher than other components,and the bridge failure starts from the middle tower,exhibiting a characteristic of local failure.For FS and PEDS,the fragility of the edge tower is higher than the middle tower.The system fragility of RS is higher than FS and PEDS.Taking the failure of dampers into account is necessary to obtain reliable seismic capacity of cable-stayed bridges.

Sliding and damming properties of granular debris with different geometric configurations and grain size distributions

Granular debris plays a significant role in determining damming deposit characteristics. An indepth understanding of how variations in grain size distribution(GSD) and geometric configurations impact the behavior of granular debris during the occurrence of granular debris is essential for precise assessment and effective mitigation of landslide hazards in mountainous terrains. This research aims to investigate the impact of GSD and geometric configurations on sliding and damming properties through laboratory experiments. The geometric configurations were categorized into three categories based on the spatial distribution of maximum volume: located at the front(Type Ⅰ), middle(Type Ⅱ), and rear(Type Ⅲ) of the granular debris. Our experimental findings highlight that the sliding and damming processes primarily depend on the interaction among the geometric configuration, grain size, and GSD in granular debris. Different sliding and damming mechanisms across various geometric configurations induce variability in motion parameters and deposition patterns. For Type Ⅰ configurations, the front debris functions as the critical and primary driving component, with energy dissipation primarily occurring through inter-grain interactions. In contrast, Type Ⅱ configurations feature the middle debris as the dominant driving component, experiencing hindrance from the front debris and propulsion from the rear, leading to complex alterations in sliding motion. Here, energy dissipation arises from a combination of inter-grain and grain-substrate interactions. Lastly, in Type Ⅲ configurations, both the middle and rear debris serve as the main driving components, with the rear sliding debris impeded by the front. In this case, energy dissipation predominantly results from grainsubstrate interaction. Moreover, we have quantitatively demonstrated that the inverse grading in damming deposits, where coarse grain moves upward and fine grain moves downward, is primarily caused by grain sorting due to collisions among the grains and between the grain and the base. The impact of grain on the horizontal channel further aids grain sorting and contributes to inverse grading. The proposed classification of three geometric configurations in our study enhances the understanding of damming properties from the view of mechanism, which provides valuable insights for related study about damming granular debris.

Network Configuration Entity Extraction Method Based on Transformer with Multi-Head Attention Mechanism

Nowadays,ensuring thequality of networkserviceshas become increasingly vital.Experts are turning toknowledge graph technology,with a significant emphasis on entity extraction in the identification of device configurations.This research paper presents a novel entity extraction method that leverages a combination of active learning and attention mechanisms.Initially,an improved active learning approach is employed to select the most valuable unlabeled samples,which are subsequently submitted for expert labeling.This approach successfully addresses the problems of isolated points and sample redundancy within the network configuration sample set.Then the labeled samples are utilized to train the model for network configuration entity extraction.Furthermore,the multi-head self-attention of the transformer model is enhanced by introducing the Adaptive Weighting method based on the Laplace mixture distribution.This enhancement enables the transformer model to dynamically adapt its focus to words in various positions,displaying exceptional adaptability to abnormal data and further elevating the accuracy of the proposed model.Through comparisons with Random Sampling(RANDOM),Maximum Normalized Log-Probability(MNLP),Least Confidence(LC),Token Entrop(TE),and Entropy Query by Bagging(EQB),the proposed method,Entropy Query by Bagging and Maximum Influence Active Learning(EQBMIAL),achieves comparable performance with only 40% of the samples on both datasets,while other algorithms require 50% of the samples.Furthermore,the entity extraction algorithm with the Adaptive Weighted Multi-head Attention mechanism(AW-MHA)is compared with BILSTM-CRF,Mutil_Attention-Bilstm-Crf,Deep_Neural_Model_NER and BERT_Transformer,achieving precision rates of 75.98% and 98.32% on the two datasets,respectively.Statistical tests demonstrate the statistical significance and effectiveness of the proposed algorithms in this paper.

Determination of the Pile Drivability Using Random Forest Optimized by Particle Swarm Optimization and Bayesian Optimizer

Driven piles are used in many geological environments as a practical and convenient structural component.Hence,the determination of the drivability of piles is actually of great importance in complex geotechnical applications.Conventional methods of predicting pile drivability often rely on simplified physicalmodels or empirical formulas,whichmay lack accuracy or applicability in complex geological conditions.Therefore,this study presents a practical machine learning approach,namely a Random Forest(RF)optimized by Bayesian Optimization(BO)and Particle Swarm Optimization(PSO),which not only enhances prediction accuracy but also better adapts to varying geological environments to predict the drivability parameters of piles(i.e.,maximumcompressive stress,maximum tensile stress,and blow per foot).In addition,support vector regression,extreme gradient boosting,k nearest neighbor,and decision tree are also used and applied for comparison purposes.In order to train and test these models,among the 4072 datasets collected with 17model inputs,3258 datasets were randomly selected for training,and the remaining 814 datasets were used for model testing.Lastly,the results of these models were compared and evaluated using two performance indices,i.e.,the root mean square error(RMSE)and the coefficient of determination(R2).The results indicate that the optimized RF model achieved lower RMSE than other prediction models in predicting the three parameters,specifically 0.044,0.438,and 0.146;and higher R^(2) values than other implemented techniques,specifically 0.966,0.884,and 0.977.In addition,the sensitivity and uncertainty of the optimized RF model were analyzed using Sobol sensitivity analysis and Monte Carlo(MC)simulation.It can be concluded that the optimized RF model could be used to predict the performance of the pile,and it may provide a useful reference for solving some problems under similar engineering conditions.

A numerical survey of parameters to reach ignition condition for axial compression of a large-sized field reversed configuration

Field reversed configuration(FRC)is widely considered as an ideal target plasma for magnetoinertial fusion.However,its confinement and stability,both proportional to the radius,will deteriorate inevitably during radial compression.Hence,we propose a new fusion approach based on axial compression of a large-sized FRC.The axial compression can be made by plasma jets or plasmoids converging onto the axial ends of the FRC.The parameter space that can reach the ignition condition while preserving the FRC's overall quality is studied using a numerical model based on different FRC confinement scalings.It is found that ignition is possible for a large FRC that can be achieved with the current FRC formation techniques if compression ratio is greater than 50.A more realistic compression is to combine axial with moderate radial compression,which is also presented and calculated in this work.

Configuration Synthesis and Grasping Performance Analysis of Multi-Loop Coupling Capture Mechanism for Launch and Recovery of Torpedo-Shaped Autonomous Underwater Vehicle

Research of capture mechanisms with strong capture adaptability and stable grasp is important to solve the problem of launch and recovery of torpedo-shaped autonomous underwater vehicles(AUVs).A multi-loop coupling capture mechanism with strong adaptability and high retraction rate has been proposed for the launch and recovery of torpedo-shaped AUVs with different morphological features.Firstly,the principle of capturing motion retraction is described based on the appearance characteristics of torpedo-shaped AUVs,and the configuration synthesis of the capture mechanism is carried out using the method of constrained chain synthesis.Secondly,the screw theory is employed to analyze the degree of freedom(DoF)of the capture mechanism.Then,the 3D model of the capture mechanism is established,and the kinematics and dynamics simulations are carried out.Combined with the capture orientation requirements of the capture mechanism,the statics and vibration characteristics analyses are carried out.Furthermore,considering the capture process and the underwater working environment,the motion characteristics and hydraulics characteristics of the capture mechanism are analyzed.Finally,a principle prototype is developed and the torpedo-shaped AUVs capture experiment is completed.The work provides technical reserves for the research and development of AUV capture special equipment.

Spatiotemporal variations,influencing factors,and configurational promotion paths of energy efficiency in China’s cities

The promotion of energy efficiency(EE)helps address energy constraints and promote environmental sustainability.This study comprehensively explores the spatiotemporal variations,influencing factors,and configuration promotion paths of EE in 284 Chinese cities during 2003‒2019 using the global super-efficiency minimum distance to strong efficient frontier(G-S-MinDS),exploratory spatial data analysis(ESDA),multiscale geographically weighted regression(MGWR),and fuzzy set qualitative comparative analysis(fsQCA)methods.The findings are:①China’s cities have an annual average EE of 0.658 with a growth rate of 0.53%,showing considerable promotion potential.②Industrial structure optimization,population agglomeration,economic development,and increased green coverage contribute positively,while government intervention and openness hinder China’s urban EE.③Four configurational promotion paths for enhancing China’s urban EE are identified,where among those paths population density is a core condition,while government intervention is not.This study provides valuable insights into substantially improving urban EE,emphasizing the need for targeted policies to address energy and environmental crises in China.

Retraction:Optimized Design of Bio-inspired Wind Turbine Blades

The published article titled“Optimized Design of Bio-inspired Wind Turbine Blades”has been retracted from Fluid Dynamics&Materials Processing.

Optimized Phishing Detection with Recurrent Neural Network and Whale Optimizer Algorithm

Phishing attacks present a persistent and evolving threat in the cybersecurity land-scape,necessitating the development of more sophisticated detection methods.Traditional machine learning approaches to phishing detection have relied heavily on feature engineering and have often fallen short in adapting to the dynamically changing patterns of phishingUniformResource Locator(URLs).Addressing these challenge,we introduce a framework that integrates the sequential data processing strengths of a Recurrent Neural Network(RNN)with the hyperparameter optimization prowess of theWhale Optimization Algorithm(WOA).Ourmodel capitalizes on an extensive Kaggle dataset,featuring over 11,000 URLs,each delineated by 30 attributes.The WOA’s hyperparameter optimization enhances the RNN’s performance,evidenced by a meticulous validation process.The results,encapsulated in precision,recall,and F1-score metrics,surpass baseline models,achieving an overall accuracy of 92%.This study not only demonstrates the RNN’s proficiency in learning complex patterns but also underscores the WOA’s effectiveness in refining machine learning models for the critical task of phishing detection.