Trajectory tracking guidance of interceptor via prescribed performance integral sliding mode with neural network disturbance observer

This paper investigates interception missiles’trajectory tracking guidance problem under wind field and external disturbances in the boost phase.Indeed,the velocity control in such trajectory tracking guidance systems of missiles is challenging.As our contribution,the velocity control channel is designed to deal with the intractable velocity problem and improve tracking accuracy.The global prescribed performance function,which guarantees the tracking error within the set range and the global convergence of the tracking guidance system,is first proposed based on the traditional PPF.Then,a tracking guidance strategy is derived using the integral sliding mode control techniques to make the sliding manifold and tracking errors converge to zero and avoid singularities.Meanwhile,an improved switching control law is introduced into the designed tracking guidance algorithm to deal with the chattering problem.A back propagation neural network(BPNN)extended state observer(BPNNESO)is employed in the inner loop to identify disturbances.The obtained results indicate that the proposed tracking guidance approach achieves the trajectory tracking guidance objective without and with disturbances and outperforms the existing tracking guidance schemes with the lowest tracking errors,convergence times,and overshoots.

Safety Performance Functions for Traffic Signals:Phasing and Geometry

A significant proportion of urban crashes,especially serious and fatal crashes,occur at traffic signals.Many of the black-spots in both Australia and New Zealand cities occur at high volume and/or high-speed traffic signals.Given this,crash reduction studies often focus on the major signalised intersections.However,there is limited information that links the phasing configuration,degree of saturation and overall cycle time to crashes.While a number of analysis tools are available for assessing the efficiency of intersections,there are very few tools that can assist engineers in assessing the safety effects of intersection upgrades and new intersections.Safety performance functions have been developed to help quantify the safety impact of various traffic signal phasing configurations and level of intersection congestion at low and high-speed traffic signals in New Zealand and Australia.Data from 238 signalised intersection sites in Auckland,Wellington,Christchurch,Hamilton,Dunedin and Melbourne was used to develop crash prediction models for key crash-causing movements at traffic signals.Different variables(road features)effect each crash type.The models indicate that the safety of intersections can be improved by longer cycle times and longer lost inter-green times,especially all-red time,using fully protected right turns and by extending the length of right turn bays.The exception is at intersections with lots of pedestrians where shorter cycle times are preferred as pedestrian crashes increase with longer wait times.A number of factors have a negative impact on safety including,free left turns,more approach lanes,intersection arms operating near or over capacity in peak periods and higher speed limits.

RELATIONS BETWEEN PERFORMANCE POTENTIALS AND INFINITESIMAL REALIZATION FACTORS IN CLOSED QUEUEING NETWORKS

In this paper,the concept of the infinitesimal realization factor is extended to the parameter dependent performance functions in closed queueing networks.Then the concepts of realization matrix (its elements are called realization factors) and performance potential are introduced,and the relations between infinitesimal realization factors and these two quantities are discussed.This provides a united framework for both IPA and non IPA approaches.Finally,another physical meaning of the service rate is given.

Structural Reliability Analysis for Implicit Performance with Legendre Orthogonal Neural Network Method

In order to evaluate the failure probability of a complicated structure,the structural responses usually need to be estimated by some numerical analysis methods such as finite element method( FEM). The response surface method( RSM) can be used to reduce the computational effort required for reliability analysis when the performance functions are implicit. However,the conventional RSM is time-consuming or cumbersome if the number of random variables is large. This paper proposes a Legendre orthogonal neural network( LONN)-based RSM to estimate the structural reliability. In this method,the relationship between the random variables and structural responses is established by a LONN model. Then the LONN model is connected to a reliability analysis method,i.e. first-order reliability methods( FORM) to calculate the failure probability of the structure.Numerical examples show that the proposed approach is applicable to structural reliability analysis,as well as the structure with implicit performance functions.

Preliminary evaluation of the VIsion PERformance(VIPER) simulator

Background:This study evaluated the VIsion PERformance(VIPER)simulator’s ability to assess the functional visual performance in warfighters conducting civilian and military tasks.Methods:Thirty service members,aged 25–35 years old with a best corrected distance visual acuity(VA)better than or equal to 20/20 or logarithm of the minimum angle of resolution(logMAR)0.00,were randomized to locate and identify road signs and mock improvised explosive devices(IEDs)under either daytime conditions or with infrared imagery,with(cc)and without(sc)wearing their habitual correction.Participants also underwent binocular uncorrected(UDVA)and corrected(CDVA)visual assessment,refraction,contrast sensitivity testing and wavefront aberrometry.Results:The mean age was 28.47 years.The manifest spherical equivalent was–3.16±1.75 diopters(D),the UDVA in both eyes(OU)was logMAR 0.83±0.47,and the CDVA OU was–0.11±0.06.For VIPER,the mean difference in the detection distance(DD)for road signs ccDD vs.scDD was(76.7±52.8)m(P<0.001).The average difference in identification distance(ID)ccID vs.scID was(13.9±6.3)m(P<0.001).The mean accuracies were 83.5%and 27.9%for cc and sc,respectively(P<0.001).The regression analysis indicated that a 1.6 m change in the distance accounts for a 1%change in the accuracy(P=0.002).Without correction,a 4.1 m change accounts for a 1%change in the accuracy(P<0.001).The average IED ccDD was(29.9±8.2)m,and that for scDD was(13.2±13.6)m(P<0.001).The average IED ccID was(32.2±6.2)m and that for the scID was(7.4±10.3)m(P<0.001).The mean IED identification accuracy was 46.7 and 11.4%for cc and sc,respectively(P<0.001).Conclusions:The preliminary results reflect VIPER’s ability to assess functional visual performance when detecting and identifying signs and IEDs.Furthermore,VIPER is able to detect performance changes with and without correction.

Impact of Arsenic Related Defects on Electronic Performance of ZrO2/GaAs:Density Functional Theory Calculations

Arsenic can diffuse into high-κ dielectrics during OaAs-based metal oxide semiconductor transistor process, which causes the degradation of gate dielectrics. To explore the origins of the degradation, we employ nonlocal B3LYP hybrid functional to study arsenic related defects in ZrO2. Via band alignments between the OaAs and ZrO2, we are able to determine the defect formation energy in the GaAs relative to the ZrO2 band gap and assess how they will affect the device performance. Arsenic at the interstitial site serves as a source of positive fixed charge while at the oxygen or zirconium substitutional site changes its charge state within the band gap of GaAs. Moreover, it is found that arsenic related defects produce conduction band offset reduction and gap states, which will increase the gate leakage current.

Self-management of osteoarthritis while waiting for total knee arthroplasty during the COVID-19 pandemic among older Malaysians

BACKGROUND The study sought to understand the self-management strategies used by patients during the postponement of their total knee arthroplasty(TKA)procedure,as well as the associations between the length of waiting time,pain,and physical frailty and function.The study focused on individuals aged 50 years and above,as they are known to be more vulnerable to the negative impacts of delayed elective surgery and rehabilitation.This study hypothesizes that delayed TKR due to coronavirus disease 2019(COVID-19)will bear negative effect in self-management,pain,and physical frailty and function in older adults.AIM To investigate the effects of COVID-19 pandemic on self-management,pain,and physical function in older adults awaiting TKA in Malaysia.METHODS This cross-sectional study has the data of participants,who matched the criteria and scheduled for TKA for the first time,extracted from the TKA registry in the Department of Orthopaedics and Traumatology,Hospital Canselor Tuanku Mukhriz.Data on pain status,and self-management,physical frailty,and instrumental activities daily living were also collected.Multiple linear regression analysis with a significant level of 0.05 was used to identify the association between waiting time and pain on physical frailty and functional performance.RESULTS Out of 180 had deferred TKA,50%of them aged 50 years old and above,80%were women with ethnic distribution Malay(66%),Chinese(22%),Indian(10%),and others(2%)respectively.Ninety-two percent of the participants took medication to manage their pain during the waiting time,while 10%used herbs and traditional supplements,and 68%did exercises as part of their osteoarthritis(OA)self-management.Thirty-six participants were found to have physical frailty(strength,assistance with walking,rising from a chair,climbing stairs,and falls questionnaire score>4)which accounted for 72%.Increased pain was associated with physical frailty with odds ratio,odds ratio(95%confidence interval):1.46(1.04-2.05).This association remained significant even after the adjustment according to age and self-management.CONCLUSION While deferring TKA during a pandemic is unavoidable,patient monitoring for OA treatment during the waiting period is important in reducing physical frailty,ensuring the older patients’independence.

Responsiveness of functional performance and muscle strength,power,and size to resistance training:A systematic review

There is a recent and growing interest in assessing differential responders to resistance training(RT)for diverse outcomes.Thus,the individual ability to respond to an intervention for a specific measurement,called responsiveness,remains to be better understood.Thus,the current study aimed to summarize the available information about the effects of RT on functional performance and muscle strength,power,and size in healthy adults,through the prevalence rate in different responsiveness classifications models.A systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)and was registered at the International Prospective Register of Systematic Reviews(PROSPERO,CRD42021265378).PubMed/MEDLINE,Scopus,and Embase databases were systematically searched in October 2023.A total of 13 studies were included,totaling 921 subjects.Only two studies presented a low risk of bias.Regarding the effectiveness of RT,the prevalence rate for non-responders ranged from 0%to 44%for muscle strength,from 0%to 84%for muscle size,and from 0%to 42%for functional performance,while for muscle power,the only study found showed a responsiveness rate of 37%.In conclusion,a wide range of differential responders is described for all variables investigated.However,the evidence summarized in this systematic review suggested some caution while interpreting the findings,since the body of evidence found seems to be incipient,and widely heterogeneous in methodological and statistical aspects.

Construction of crash prediction model of freeway basic segment based on interactive influence of explanatory variables

In order to improve the prediction precision of the safety performance function （SPF） of freeway basic segments, design and crash data of 640 segments are collected from different institutions. Three negative binomial （NB） regression models and three generalized negative binomial （GNB） regression models are built to prove that the interactive influence of explanatory variables plays an important role in fitting goodness. The effective use of the GNB model in analyzing the interactive influence of explanatory variables and predicting freeway basic segments is demonstrated. Among six models, the two models （one is the NB model and the other is the GNB model. ） which consider the interactive influence of the annual average daily traffic （AADT） and length are more reasonable for predicting results. Furthermore, a comprehensive study is carried out to prove that when considering the interactive influence, the NB and GNB models have almost the same fitting performance in estimating the crashes, among which the GNB model is slightly better for prediction performance.

Dynamic cluster member selection method for multi-target tracking in wireless sensor network

Multi-target tracking(MTT) is a research hotspot of wireless sensor networks at present.A self-organized dynamic cluster task allocation scheme is used to implement collaborative task allocation for MTT in WSN and a special cluster member(CM) node selection method is put forward in the scheme.An energy efficiency model was proposed under consideration of both energy consumption and remaining energy balance in the network.A tracking accuracy model based on area-sum principle was also presented through analyzing the localization accuracy of triangulation.Then,the two models mentioned above were combined to establish dynamic cluster member selection model for MTT where a comprehensive performance index function was designed to guide the CM node selection.This selection was fulfilled using genetic algorithm.Simulation results show that this method keeps both energy efficiency and tracking quality in optimal state,and also indicate the validity of genetic algorithm in implementing CM node selection.

SUPPORT VECTOR MACHINE FOR STRUCTURAL RELIABILITY ANALYSIS

Support vector machine （SVM） was introduced to analyze the reliability of the implicit performance function, which is difficult to implement by the classical methods such as the first order reliability method （FORM） and the Monte Carlo simulation （MCS）. As a classification method where the underlying structural risk minimization inference rule is employed, SVM possesses excellent learning capacity with a small amount of information and good capability of generalization over the complete data. Hence, two approaches, i.e., SVM-based FORM and SVM-based MCS, were presented for the structural reliability analysis of the implicit limit state function. Compared to the conventional response surface method （RSM） and the artificial neural network （ANN）, which are widely used to replace the implicit state function for alleviating the computation cost, the more important advantages of SVM are that it can approximate the implicit function with higher precision and better generalization under the small amount of information and avoid the ＂curse of dimensionality＂. The SVM-based reliability approaches can approximate the actual performance function over the complete sampling data with the decreased number of the implicit performance function analysis （usually finite element analysis）, and the computational precision can satisfy the engineering requirement, which are demonstrated by illustrations.

Subinterval Decomposition-Based Interval Importance Analysis Method

The importance analysis method represents a powerful tool for quantifying the impact of input uncertainty on the output uncertainty.When an input variable is described by a specific interval rather than a certain probability distribution,the interval importance measure of input interval variable can be calculated by the traditional non-probabilistic importance analysis methods.Generally,the non-probabilistic importance analysis methods involve the Monte Carlo simulation(MCS)and the optimization-based methods,which both have high computational cost.In order to overcome this problem,this study proposes an interval important analytical method avoids the time-consuming optimization process.First,the original performance function is decomposed into a combination of a series of one-dimensional subsystems.Next,the interval of each variable is divided into several subintervals,and the response value of each one-dimensional subsystem at a specific input point is calculated.Then,the obtained responses are taken as specific values of the new input variable,and the interval importance is calculated by the approximated performance function.Compared with the traditional non-probabilistic importance analysis method,the proposed method significantly reduces the computational cost caused by the MCS and optimization process.In the proposed method,the number of function evaluations is equal to one plus the sum of the subintervals of all of the variables.The efficiency and accuracy of the proposed method are verified by five examples.The results show that the proposed method is not only efficient but also accurate.

A COMPUTATIONAL METHOD FOR FIRST-EXCURSION RELIABILITY

An efficient computational method is suggested for the first-excursion reliability assessment of nonstationary process. In the proposed method, the nonlinear performance function is Linearized at the Hasofer-Lind point obtained by an iterative algorithm. The problem of the nonstationary processes is solved by the discrete-time method, in which the precision can be controlled by choosing the steps of discretization. The derived formulae can be conveniently degraded to calculate both the first-excursion reliability with linear performance function of stationary processes and the time-independent reliability. The suggested method is useful for the analysis of components and systems with nonstationary responses in structural design where some uncertainties are represented by a vector of nonstationary processes. Examples are given to demonstrate the fast convergency and effectiveness of the presented method.

Structural Reliability Analysis Based on Support Vector Machine and Dual Neural Network Direct Integration Method

Aiming at the reliability analysis of small sample data or implicit structural function,a novel structural reliability analysis model based on support vector machine(SVM)and neural network direct integration method(DNN)is proposed.Firstly,SVM with good small sample learning ability is used to train small sample data,fit structural performance functions and establish regular integration regions.Secondly,DNN is approximated the integral function to achieve multiple integration in the integration region.Finally,structural reliability was obtained by DNN.Numerical examples are investigated to demonstrate the effectiveness of the present method,which provides a feasible way for the structural reliability analysis.

Safety Management of Non-urban Roads in Israel： An Application of Empirical Bayes Evaluation

A safety management system was established to provide for continuously improved safety levels of the non-urban roads in Israel. One of the main functions of the system lies in the identification and treatment of HL （hazardous locations） on existing roads. In line with the state-of-the art in road safety, the HL identification is based on an empirical Bayes evaluation, where an HL is recognized using a high positive difference between the number of accidents expected at the site and that predicted for similar sites. The latter is estimated using safety performance functions that were developed for local conditions, including single- and dual-carriageway road sections, and various types of intersections： signalized/non-signalized, three- and four-legged. The procedure of HL identification is applied annually, serving as a basis for the working programs on road infrastructure improvements. Positive safety effects of such improvements were recently reported in the country. These activities comply with the Road Infrastructure Safety Directive that was recently introduced in the European Union.

Microstructures and Mechanical,Electrical,High-temperature Properties of Cu/Ti_2AlC FGM Fabricated by Hot-pressing

The microstructure and the electrical, thermal, friction, and mechanical properties of Cu/Ti_2AlC fabricated by hot-pressing at 900 ℃ for 1 h were investigated in the present work. Microstructural observations have shown that the plate-like Ti_2AlC grains distribute irregularly in the network of Cu grains, and well-structured, crack-free bonds between the layers. With the increase in the content of Ti_2AlC from layer A to layer D, the electrical resistivity increases from 1.381×10^（-7）Ω·m to 1.918 ×10^（-7）Ω·m, the hardness increases from about 980.27 MPa to about 2196.01 MPa, and the friction coefficient from above 0.20 reduces to about 0.15. Oxidation rate increases with the increases of temperature. Exfoliation was obviously observed on the surface of oxidation layer A. The surface of layer D was still intact and the spalling and other defects were not found. The mass decreases in the acid solution, and increases in the alkaline solution. The largest corrosion rate is found in 6.5% HNO_3 or 4% Na OH solution.

To Actively Perform the Judicial Administration Function and Promote the Development of Cause of Human Rights

Respect for human rights and protection of human rights are significant rules in the Constitution of the People’s Republic of China.In 2015,judicial administration departments at all levels legally exercised their duties,implemented the principles and rules in constitution,and kept strengthening propagation of human rights through creation of contents and methods which had acquired great effects.What they have done contributes significantly toward the development of human rights in China.

Biological and bioinspired materials: Structure leading to functional and mechanical performance

Nature has achieved materials with properties and mechanisms that go far beyond the current know-how of the engineering-materials industry.The remarkable efficiency of biological materials,such as their exceptional properties that rely on weak constituents,high performance per unit mass,and diverse functionalities in addition to mechanical properties,has been mostly attributed to their hierarchical structure.Key strategies for bioinspired materials include formulating the fundamental understanding of biological materials that act as inspiration,correlating this fundamental understanding to engineering needs/problems,and fabricating hierarchically structured materials with enhanced properties accordingly.The vast,existing literature on biological and bioinspired materials can be discussed in terms of functional and mechanical aspects.Through essential representative properties and materials,the development of bioinspired materials utilizes the design strategies from biological systems to innovatively augment material performance for various practical applications,such as marine,aerospace,medical,and civil engineering.Despite the current challenges,bioinspired materials have become an important part in promoting innovations and breakthroughs in the modern materials industry.

International transferability of macro-level safety performance functions: a case study of the United States and Italy

Safety performance functions(SPFs),or crash-prediction models,have played an important role in identifying the factors contributing to crashes,predicting crash counts and identifying hotspots.Since a great deal of time and effort is needed to estimate an SPF,previous studies have sought to determine the transferability of particular SPFs;that is,the extent to which they can be applied to data from other regions.Although many efforts have been made to examine micro-level SPF transferability,few studies have focused on macro-level SPF transferability.There has been little transferability analysis of macro-level SPFs in the international context,especially between western countries.This study therefore evaluates the transferability of SPFs for several states in the USA(Illinois,Florida and Colorado)and for Italy.The SPFs were developed using data from counties in the United States and provincias in Italy,and the results revealed multiple common significant variables between the two countries.Transferability indexes were then calculated between the SPFs.These showed that the Italy SPFs for total crashes and bicycle crashes were transferable to US data after calibration factors were applied,whereas the US SPFs for total and bicycle crashes,with the exception of the Colorado SPF,could not be transferred to the Italian data.On the other hand,none of the pedestrian SPFs developed was transferable to other countries.This paper provides insights into the applicability of macro-level SPFs between the USA and Italy,and shows a good potential for international SPF transferability.Nevertheless,further investigation is needed of SPF transferability between a wider range of countries.

Investigating the influence of segmentation in estimating safety performance functions for roadway sections

Safety performance functions（SPFs） are crucial to science-based road safety management.Success in developing and applying SPFs, apart data quality and availability, depends fundamentally on two key factors： the validity of the statistical inferences for the available data and on how well the data can be organized into distinct homogeneous entities. The latter aspect plays a key role in the identification and treatment of road sections or corridors with problems related to safety. Indeed, the segmentation of a road network could be especially critical in the development of SPFs that could be used in safety management for roadway types, such as motorways（freeways in North America）, which have a large number of variables that could result in very short segments if these are desired to be homogeneous. This consequence, from an analytical point of view, can be a problem when the location of crashes is not precise and when there is an overabundance of segments with zero crashes. Lengthening the segments for developing and applying SPFs can mitigate this problem, but at a sacrifice of homogeneity. This paper seeks to address this dilemma by investigating four approaches for segmentation for motorways, using sample data from Italy. The best results were obtained for the segmentation based on two curves and two tangents within a segment and with fixed length segments. The segmentation characterized by a constant value of all original variables inside each segment was the poorest approach by all measures.