Structural failure analysis with CMS-based ground motion selection using innovative cost function and weight factors

The selection and scaling of ground motion records is considered a primary and essential task in performing structural analysis and design.Conventional methods involve using ground motion models and a conditional spectrum to select ground motion records based on the target spectrum.This research demonstrates the influence of adopting different weighted factors for various period ranges during matching selected ground motions with the target hazard spectrum.The event data from the Next Generation Attenuation West 2(NGA-West 2)database is used as the basis for ground motion selection,and hazard de-aggregation is conducted to estimate the event parameters of interest,which are then used to construct the target intensity measure(IM).The target IMs are then used to select ground motion records with different weighted vector-valued objective functions.The weights are altered to account for the relative importance of IM in accordance with the structural analysis application of steel moment resisting frame(SMRF)buildings.Instead of an ordinary objective function for the matching spectrum,a novel model is introduced and compared with the conventional cost function.The results indicate that when applying the new cost function for ground motion selection,it places higher demands on structures compared to the conventional cost function.Moreover,submitting more weights to the first-mode period of structures increases engineering demand parameters.Findings demonstrate that weight factors allocated to different period ranges can successfully account for period elongation and higher mode effects.

Prioritizing Indicators for Rapid Response in Global Health Security:A Bayesian Network Approach

This study explored a Bayesian belief networks(BBNs)approach,developing two distinct models for prioritizing the seven indicators related to the“rapid response to and mitigation of the spread of an epidemic”category within the context of both the specifc category and the Global Health Security Index(GHS index).Utilizing data from the 2021 GHS index,the methodology involves rigorous preprocessing,the application of the augmented naive Bayes algorithm for structural learning,and k-fold cross-validation.Key fndings show unique perspectives in both BBN models.In the mutual value of information analysis,“linking public health and security authorities”emerged as the key predictor for the“rapid response to and mitigation of the spread of an epidemic”category,while“emergency preparedness and response planning”assumed precedence for the GHS index.Sensitivity analysis highlighted the critical role of“emergency preparedness and response planning”and“linking public health and security authorities”in extreme performance states,with“access to communications infrastructure”and“trade and travel restrictions”exhibiting varied signifcance.The BBN models exhibit high predictive accuracy,achieving 83.3%and 82.3%accuracy for extreme states in“rapid response to and mitigation of the spread of an epidemic”and the GHS index,respectively.This study contributes to the literature on GHS by modeling the dependencies among various indicators of the rapid response dimension of the GHS index and highlighting their relative importance based on the mutual value of information and sensitivity analyses.

Validity,Reliability,and Measurement Invariance of the Thai Smartphone Application-Based Addiction Scale and Bergen Social Media Addiction Scale

Background:In recent years,there has been increased research interest in both smartphone addiction and social media addiction as well as the development of psychometric instruments to assess these constructs.However,there is a lack of psychometric evaluation for instruments assessing smartphone addiction and social media addiction in Thailand.The present study evaluated the psychometric properties and gender measurement invariance of the Thai version of the Smartphone Application-Based Addiction Scale(SABAS)and Bergen Social Media Addiction Scale(BSMAS).Method:A total of 801 Thai university students participated in an online survey from January 2022 to July 2022 which included demographic information,SABAS,BSMAS,and the Internet Gaming Disorder Scale-Short Form(IGDS9-SF).Results:Confirmatory Factor Analyses(CFAs)found that both the SABAS and BSMAS had a one-factor structure.Findings demonstrated adequate psychometric properties of both instruments and also supported measurement invariance across genders.Moreover,scores on the SABAS and BSMAS were correlated with scores on the IGDS9-SF.Conclusion:The results indicated that the SABAS and BSMAS are useful psychometric instruments for assessing the risk of smartphone addiction and social media addiction among Thai young adults.

Machine Learning Prediction of Fetal Health Status from Cardiotocography Examination in Developing Healthcare Contexts

Reducing neonatal mortality is a critical global health objective,especially in resource-constrained developing countries.This study employs machine learning(ML)techniques to predict fetal health status based on cardiotocography(CTG)examination findings,utilizing a dataset from the Kaggle repository due to the limited comprehensive healthcare data available in developing nations.Features such as baseline fetal heart rate,uterine contractions,and waveform characteristics were extracted using the RFE wrapper feature engineering technique and scaled with a standard scaler.Six ML models—Logistic Regression(LR),Decision Tree(DT),Random Forest(RF),Gradient Boosting(GB),Categorical Boosting(CB),and Extended Gradient Boosting(XGB)—are trained via cross-validation and evaluated using performance metrics.The developed models were trained via cross-validation and evaluated using ML performance metrics.Eight out of the 21 features selected by GB returned their maximum Matthews Correlation Coefficient(MCC)score of 0.6255,while CB,with 20 of the 21 features,returned the maximum and highest MCC score of 0.6321.The study demonstrated the ability of ML models to predict fetal health conditions from CTG exam results,facilitating early identification of high-risk pregnancies and enabling prompt treatment to prevent severe neonatal outcomes.

Investigation into Impedance Measurements for Rapid Capacity Estimation of Lithium-ion Batteries in Electric Vehicles

With the dramatic increase in electric vehicles(EVs)globally,the demand for lithium-ion batteries has grown dramatically,resulting in many batteries being retired in the future.Developing a rapid and robust capacity estimation method is a challenging work to recognize the battery aging level on service and provide regroup strategy of the retied batteries in secondary use.There are still limitations on the current rapid battery capacity estimation methods,such as direct current internal resistance(DCIR)and electrochemical impedance spectroscopy(EIS),in terms of efficiency and robustness.To address the challenges,this paper proposes an improved version of DCIR,named pulse impedance technique(PIT),for rapid battery capacity estimation with more robustness.First,PIT is carried out based on the transient current excitation and dynamic voltage measurement using the high sampling frequency,in which the coherence analysis is used to guide the selection of a reliable frequency band.The battery impedance can be extracted in a wide range of frequency bands compared to the traditional DCIR method,which obtains more information on the battery capacity evaluation.Second,various statistical variables are used to extract aging features,and Pearson correlation analysis is applied to determine the highly correlated features.Then a linear regression model is developed to map the relationship between extracted features and battery capacity.To validate the performance of the proposed method,the experimental system is designed to conduct comparative studies between PIT and EIS based on the two 18650 batteries connected in series.The results reveal that the proposed PIT can provide comparative indicators to EIS,which contributes higher estimation accuracy of the proposed PIT method than EIS technology with lower time and cost.

探讨机械设计制造及其自动化的节能设计思想

社会经济的快速发展,相应提升了机械制造行业的技术水平。在技术提升中,需要关注机械制造与自动化问题。由于能源消耗会影响机械制造,为了降低能耗,实现工业绿色化、节能化发展,需要深入分析设计理念与方式。本文研究中,围绕机械设计制造及其自动化,提出节能设计思想,仅供参考。

Which return regime induces overconfidence behavior?Artificial intelligence and a nonlinear approach

Overconfidence behavior,one form of positive illusion,has drawn considerable attention throughout history because it is viewed as the main reason for many crises.Investors’overconfidence,which can be observed as overtrading following positive returns,may lead to inefficiencies in stock markets.To the best of our knowledge,this is the first study to examine the presence of investor overconfidence by employing an artificial intelligence technique and a nonlinear approach to impulse responses to analyze the impact of different return regimes on the overconfidence attitude.We examine whether investors in an emerging stock market(Borsa Istanbul)exhibit overconfidence behavior using a feed-forward,neural network,nonlinear Granger causality test and nonlinear impulseresponse functions based on local projections.These are the first applications in the relevant literature due to the novelty of these models in forecasting high-dimensional,multivariate time series.The results obtained from distinguishing between the different market regimes to analyze the responses of trading volume to return shocks contradict those in the literature,which is the key contribution of the study.The empirical findings imply that overconfidence behavior exhibits asymmetries in different return regimes and is persistent during the 20-day forecasting horizon.Overconfidence is more persistent in the low-than in the high-return regime.In the negative interest-rate period,a high-return regime induces overconfidence behavior,whereas in the positive interest-rate period,a low-return regime induces overconfidence behavior.Based on the empirical findings,investors should be aware that portfolio gains may result in losses depending on aggressive and excessive trading strategies,particularly in low-return regimes.

环式静电传感器空间灵敏度的模型优化与验证

在气固两相流中,流动参数的精确测量是非常重要的,且这种流动必须在散装固体颗粒运输的过程中得到有效的解决。环式静电传感器是用于气固两相流流量测量的传感器之一。首先采用有限元法建立了该传感器的数学模型,对传感器的空间灵敏度特性进行了研究;并采用非线性最小二乘法及迭代法对解析模型进行了优化处理。最后通过实验结果与优化处理之后结果之间的相关系数进行对比分析;并完善了该模型的数学表达式,验证了该优化模型的可行性和实用性。

Association between fundamental motor skills and physical activity in the early years:A systematic review and meta-analysis

Background:Physical activity(PA)in the early years is associated with a range of positive health outcomes.Fundamental motor skill(FMS)competence is associated with PA and is theorized to be driven by PA in the early years and vice versa in mid to late childhood.However,to date,no studies have meta-analyzed the association between PA and FMS in the early years.Methods:Six electronic databases were searched for articles published up to April 2019.Cross-sectional and longitudinal studies were included if they targeted children(ages 3-6 year)as the population of the study and assessed the association between objectively measured PA and FMS.Total FMS,total physical activity(TPA),and moderate-to-vigorous physical activity(MVPA)data were meta-analyzed using a random effects model.Results:We identified 24,815 titles and abstracts.In total,19 studies met the inclusion criteria,including 14 cross-sectional and 4 longitudinal studies,as well as 1 study with cross-sectional and longitudinal analysis.There was a significant but small positive association between FMS and MVPA(r=0.20,95%confidence interval(CI):0.13-0.26)and TPA(r=0.20,95%CI:0.12-0.28).Findings from longitudinal studies revealed that PA drives FMS in early childhood.Mediation was explored in 1 study,which found that perceived motor competence did not mediate the association between FMS and PA.Conclusion:Using a meta-analysis,this study is the first to show a positive association between FMS,MVPA,and TPA in the early years of childhood,suggesting that the association begins at an early age.Limited evidence from longitudinal studies supports the theory that PA drives FMS in the early years of childhood.More evidence is needed from large studies to track PA and FMS until mid to late childhood and to explore the mediators of this association.

玉米秸秆生物炭对水中对苯醌的吸附性能研究

资源环境问题一直受到人们的广泛关注,农业废弃物玉米秸秆可以用来提取油气,副产物生物炭预期能够用于环境治理。在600℃条件下,通过简单的限氧热解法制备得到玉米秸秆生物炭,将其用于吸附去除在高级氧化过程中产生的中间氧化产物——对苯醌,研究了玉米秸秆生物炭的投加量和溶液初始pH值对吸附效果的影响,玉米秸秆生物炭吸附对苯醌的动力学、热力学作用机理,玉米秸秆生物炭的再生利用性能等。研究结果表明:①玉米秸秆生物炭能够高效吸附去除对苯醌,投加量为0.8 g/L时的去除率可达到97.2%。②在较宽的pH值变化范围内(3.0-11.0)均表现出了较高的去除能力,且生物炭和对苯醌之间产生的∏-∏电子供体-受体(∏-∏electron-donor-acceptor interaction)相互作用占主导。③Elovich和准二级动力学模型均能较好地拟合试验动力学数据,表明玉米秸秆生物炭与对苯醌之间可能存在扩散控制的化学吸附过程。④基于Langmuir模型的玉米秸秆生物炭对对苯醌的最大吸附量qmax为94.6 mg/g;与Langmuir模型相比,Freundlich模型能够更好地拟合试验热力学数据,且玉米秸秆生物炭对对苯醌的吸附在很大程度上是多分子层吸附过程。⑤多次循环再生后的玉米秸秆生物炭仍具有较好的吸附去除率。

Exploring Barriers and Opportunities in Adopting Crowdsourcing Based New Product Development in Manufacturing SMEs

Crowdsourcing is an innovative business practice of obtaining needed services, ideas, or content or even funds by soliciting contributions from a large group of people （the ＇Crowd＇）. The potential benefits of utilizing crowdsourcing in product design are well-documented, but little research exists on what are the barriers and opportunities in adopting crowdsourcing in new product development （NPD） of manufacturing SMEs. In order to answer the above questions, a Proof of Market study is carried out on crowdsourcing-based product design under an Innovate UK funded Smart project, which aims at identifying the needs, challenges and future development opportunities associated with adopting crowdsourcing strategies for NPD. The research findings from this study are reported here and can be used to guide future development of crowdsourcing-based collaborative design methods and tools and provide some practical references for industry to adopt this new and emerging collaborative design method in their business.

Energy Theft Detection in Smart Grids:Taxonomy,Comparative Analysis,Challenges,and Future Research Directions

Electricity theft is one of the major issues in developing countries which is affecting their economy badly.Especially with the introduction of emerging technologies,this issue became more complicated.Though many new energy theft detection(ETD)techniques have been proposed by utilising different data mining(DM)techniques,state&network(S&N)based techniques,and game theory(GT)techniques.Here,a detailed survey is presented where many state-of-the-art ETD techniques are studied and analysed for their strengths and limitations.Three levels of taxonomy are presented to classify state-of-the-art ETD techniques.Different types and ways of energy theft and their consequences are studied and summarised and different parameters to benchmark the performance of proposed techniques are extracted from literature.The challenges of different ETD techniques and their mitigation are suggested for future work.It is observed that the literature on ETD lacks knowledge management techniques that can be more effective,not only for ETD but also for theft tracking.This can help in the prevention of energy theft,in the future,as well as for ETD.

Augmented reality-based visual-haptic modeling for thoracoscopic surgery training systems

Background Compared with traditional thoracotomy,video-assisted thoracoscopic surgery(VATS)has less minor trauma,faster recovery,higher patient compliance,but higher requirements for surgeons.Virtual surgery training simulation systems are important and have been widely used in Europe and America.Augmented reality(AR)in surgical training simulation systems significantly improve the training effect of virtual surgical training,although AR technology is still in its initial stage.Mixed reality has gained increased attention in technology-driven modern medicine but has yet to be used in everyday practice.Methods This study proposed an immersive AR lobectomy within a thoracoscope surgery training system,using visual and haptic modeling to study the potential benefits of this critical technology.The content included immersive AR visual rendering,based on the cluster-based extended position-based dynamics algorithm of soft tissue physical modeling.Furthermore,we designed an AR haptic rendering systems,whose model architecture consisted of multi-touch interaction points,including kinesthetic and pressure-sensitive points.Finally,based on the above theoretical research,we developed an AR interactive VATS surgical training platform.Results Twenty-four volunteers were recruited from the First People's Hospital of Yunnan Province to evaluate the VATS training system.Face,content,and construct validation methods were used to assess the tactile sense,visual sense,scene authenticity,and simulator performance.Conclusions The results of our construction validation demonstrate that the simulator is useful in improving novice and surgical skills that can be retained after a certain period of time.The video-assisted thoracoscopic system based on AR developed in this study is effective and can be used as a training device to assist in the development of thoracoscopic skills for novices.

Email Filtering Using Hybrid Feature Selection Model

Undoubtedly,spam is a serious problem,and the number of spam emails is increased rapidly.Besides,the massive number of spam emails prompts the need for spam detection techniques.Several methods and algorithms are used for spam filtering.Also,some emergent spam detection techniques use machine learning methods and feature extraction.Some methods and algorithms have been introduced for spam detecting and filtering.This research proposes two models for spam detection and feature selection.The first model is evaluated with the email spam classification dataset,which is based on reducing the number of keywords to its minimum.The results of this model are promising and highly acceptable.The second proposed model is based on creating features for spam detection as a first stage.Then,the number of features is reduced using three well-known metaheuristic algorithms at the second stage.The algorithms used in the second model are Artificial Bee Colony(ABC),Ant Colony Optimization(ACO),and Particle Swarm Optimization(PSO),and these three algorithms are adapted to fit the proposed model.Also,the authors give it the names AABC,AACO,and APSO,respectively.The dataset used for the evaluation of this model is Enron.Finally,well-known criteria are used for the evaluation purposes of this model,such as true positive,false positive,false negative,precision,recall,and F-Measure.The outcomes of the second proposed model are highly significant compared to the first one.

Bio-inspired Hybrid Feature Selection Model for Intrusion Detection

Intrusion detection is a serious and complex problem.Undoubtedly due to a large number of attacks around the world,the concept of intrusion detection has become very important.This research proposes a multilayer bioinspired feature selection model for intrusion detection using an optimized genetic algorithm.Furthermore,the proposed multilayer model consists of two layers(layers 1 and 2).At layer 1,three algorithms are used for the feature selection.The algorithms used are Particle Swarm Optimization(PSO),Grey Wolf Optimization(GWO),and Firefly Optimization Algorithm(FFA).At the end of layer 1,a priority value will be assigned for each feature set.At layer 2 of the proposed model,the Optimized Genetic Algorithm(GA)is used to select one feature set based on the priority value.Modifications are done on standard GA to perform optimization and to fit the proposed model.The Optimized GA is used in the training phase to assign a priority value for each feature set.Also,the priority values are categorized into three categories:high,medium,and low.Besides,the Optimized GA is used in the testing phase to select a feature set based on its priority.The feature set with a high priority will be given a high priority to be selected.At the end of phase 2,an update for feature set priority may occur based on the selected features priority and the calculated F-Measures.The proposed model can learn and modify feature sets priority,which will be reflected in selecting features.For evaluation purposes,two well-known datasets are used in these experiments.The first dataset is UNSW-NB15,the other dataset is the NSL-KDD.Several evaluation criteria are used,such as precision,recall,and F-Measure.The experiments in this research suggest that the proposed model has a powerful and promising mechanism for the intrusion detection system.

Pathology and genetic connectedness of the mangrove crab (Aratus pisonii) – a foundation for understanding mangrove disease ecology

Mangrove forests are productive ecosystems,acting as a sink for CO_(2),a habitat for a diverse array of terrestrial and marine species,and as a natural barrier to coastline erosion.The species that reside within mangrove ecosystems have important roles to play,including litter decomposition and the recycling of nutrients.Crustacea are important detritivores in such ecosystems and understanding their limitations(i.e.disease)is an important endeavour when considering the larger ecological services provided.Histology and metagenomics were used to identify viral(Nudiviridae,Alphaflexiviridae),bacterial(Paracoccus sp.,'Candidatus Gracilibacteria sp.’,and Pseudoalteromonas sp.),protozoan,fungal,and metazoan diversity that compose the symbiome of the mangrove crab,Aratus pisonii.The symbiotic groups were observed at varying prevalence under histology:nudivirus(6.5%),putative gut epithelial virus(3.2%),ciliated protozoa(35.5%),gonad fungus(3.2%),gill ectoparasitic metazoan(6.5%).Metagenomic analysis of one specimen exhibiting a nudivirus infection provided the complete host mitochondrial genome(15,642 bp),nudivirus genome(108,981 bp),and the genome of a Cassava common mosaic virus isolate(6387 bp).Our phylogenetic analyses group the novel nudivirus with the Gammanudivirus and protein similarity searches indicate that Carcinus maenas nudivrius is the most similar to the new isolate.The mitochondrial genome were used to mine short fragments used in population genetic studies to gauge an idea of diversity in this host species across the USA,Caribbean,and central and southern America.This study report several new symbionts based on their pathology,taxonomy,and genomics(where available)and discuss what effect they may have on the crab population.The role of mangrove crabs from a OneHealth perspective were explored,since their pathobiome includes cassava-infecting viruses.Finally,given that this species is abundant in mangrove forests and now boasts a well-described pathogen profile,we posit that A.pisonii is a valuable model system for understanding mangrove disease ecology.

Model improvement and verification of ring electrostatic sensors

Accurate measurement of flow parameters is important in gas-solid two-phase flow,and such flow has to be dealt with in many processes involving bulk solids handling and transportation.The circular electrostatic sensor is one of those used for gas-solid flow measurement.In this paper,the finite element method(FEM)is used to establish the mathematical model of the sensor,the spatial sensitivity characteristics of the sensors is analyzed,and the analytic model is improved by the nonlinear least square method and the iterative method.Finally,the correlation coefficients between the experimental results and the improved processing are compared and analyzed,and the mathematical expression of the model is improved.The feasibility and practicability of the improved model are verified.

Non-Linear Tank Level Control for Industrial Applications

Tank level control is ubiquitous in industry. The focus of this paper is on accurate liquid level control in single tank systems which can be actuated continuously and modulation of the level setpoint is also required, for example in cascade control loops or supervisory Model Predictive Control (MPC) applications. To avoid common problems encountered when using fixed gain or adaptive/gain scheduled schemes, an accurate technique based around feedback linearization and Proportional Integral (PI) control is introduced. This simple controller can maintain linear performance over the full operating range of a uniform tank. As will be demonstrated, the implementation overhead compared to a regular PI controller is negligible, making it ideal for industrial implementation. Implementation details and parameter identification for adaptive implementation are discussed. Simulations coupled with experimental results using a large-scale laboratory level control system using commercial industrial control equipment validate the approach, and illustrate its effectiveness for both level tracking and disturbance rejection.

Evaluation of the Suitability of International Financial Reporting Standards （IFRSs） for Application in Emerging North African Countries： A Literature Review and a Research Agenda

Despite the progress of international accounting harmonization, there remain a number of countries which have not adopted International Financial Reporting Standards （IFRSs） but continue to adhere to their own accounting laws or standards, including Libya and some surrounding countries. This paper examines the arguments surrounding the appropriateness of accounting harmonization and the obstacles to achieve it and seeks to apply these arguments in the case of Libya. The conclusion is that although harmonization with IFRSs is not precluded by any cultural considerations, historical factors and accounting education deficiencies may make the adoption of IFRSs more difficult, while the absence of an active stock market may make it less desirable.