Time-sensitivity of the Kaiser effect of acoustic emission in limestone and its application to measurements of in-situ stress

Measuring in-situ stress by using the Kaiser effect in rocks has such advantages as timeefficiency, low cost and little limitation, but the precision of the method is dependent on rock properties and delay time of the measurement. In this paper, experiments on the Kaiser effect in limestones were performed, and it was found that the limestones had good ability to retain a memory of their recent stress history and high time-sensitivity. The longer the experiment was delayed from the extraction of the stone, the larger the Felicity ratio was. As the Felicity ratio approached l, significant Kaiser effect was observed. In-situ stress should be determined by the limestone measurements when the delay time was 40-120 days. Finally, the in-situ stress in a limestone formation could be successfully measured in practice.

Recent Trends of In-Vehicle Time Sensitive Networking Technologies, Applications and Challenges

With the vigorous development of automobile industry,in-vehicle network is also constantly upgraded to meet data transmission requirements of emerging applications.The main transmission requirements are low latency and certainty especially for autonomous driving.Time sensitive networking(TSN)based on Ethernet gives a possible solution to these requirements.Previous surveys usually investigated TSN from a general perspective,which referred to TSN of various application fields.In this paper,we focus on the application of TSN to the in-vehicle networks.For in-vehicle networks,we discuss all related TSN standards specified by IEEE 802.1 work group up to now.We further overview and analyze recent literature on various aspects of TSN for automotive applications,including synchronization,resource reservation,scheduling,certainty,software and hardware.Application scenarios of TSN for in-vehicle networks are analyzed one by one.Since TSN of in-vehicle network is still at a very initial stage,this paper also gives insights on open issues,future research directions and possible solutions.

Impact of the Time Scale of Model Sensitivity Response on Coupled Model Parameter Estimation

That a model has sensitivity responses to parameter uncertainties is a key concept in implementing model parameter es- timation using filtering theory and methodology. Depending on the nature of associated physics and characteristic variability of the fluid in a coupled system, the response time scales of a model to parameters can be different, from hourly to decadal. Unlike state estimation, where the update frequency is usually linked with observational frequency, the update frequency for parameter estimation must be associated with the time scale of the model sensitivity response to the parameter being esti- mated. Here, with a simple coupled model, the impact of model sensitivity response time scales on coupled model parameter estimation is studied. The model includes characteristic synoptic to decadal scales by coupling a long-term varying deep ocean with a slow-varying upper ocean forced by a chaotic atmosphere. Results show that, using the update frequency deter- mined by the model sensitivity response time scale, both the reliability and quality of parameter estimation can be improved significantly, and thus the estimated parameters make the model more consistent with the observation. These simple model results provide a guideline for when real observations are used to optimize the parameters in a coupled general circulation model for improving climate analysis and prediction initialization.

Age-Driven Joint Sampling and Non-Slot Based Scheduling for Industrial Internet of Things

Effective control of time-sensitive industrial applications depends on the real-time transmission of data from underlying sensors.Quantifying the data freshness through age of information(AoI),in this paper,we jointly design sampling and non-slot based scheduling policies to minimize the maximum time-average age of information(MAoI)among sensors with the constraints of average energy cost and finite queue stability.To overcome the intractability involving high couplings of such a complex stochastic process,we first focus on the single-sensor time-average AoI optimization problem and convert the constrained Markov decision process(CMDP)into an unconstrained Markov decision process(MDP)by the Lagrangian method.With the infinite-time average energy and AoI expression expended as the Bellman equation,the singlesensor time-average AoI optimization problem can be approached through the steady-state distribution probability.Further,we propose a low-complexity sub-optimal sampling and semi-distributed scheduling scheme for the multi-sensor scenario.The simulation results show that the proposed scheme reduces the MAoI significantly while achieving a balance between the sampling rate and service rate for multiple sensors.

Cloud control for IIoT in a cloud-edge environment

The industrial Internet of Things(IIoT)is a new indus-trial idea that combines the latest information and communica-tion technologies with the industrial economy.In this paper,a cloud control structure is designed for IIoT in cloud-edge envi-ronment with three modes of 5G.For 5G based IIoT,the time sensitive network(TSN)service is introduced in transmission network.A 5G logical TSN bridge is designed to transport TSN streams over 5G framework to achieve end-to-end configuration.For a transmission control protocol(TCP)model with nonlinear disturbance,time delay and uncertainties,a robust adaptive fuzzy sliding mode controller(AFSMC)is given with control rule parameters.IIoT workflows are made up of a series of subtasks that are linked by the dependencies between sensor datasets and task flows.IIoT workflow scheduling is a non-deterministic polynomial(NP)-hard problem in cloud-edge environment.An adaptive and non-local-convergent particle swarm optimization(ANCPSO)is designed with nonlinear inertia weight to avoid falling into local optimum,which can reduce the makespan and cost dramatically.Simulation and experiments demonstrate that ANCPSO has better performances than other classical algo-rithms.

Assessing the Impact of the Lead/Lag Times on the Project Duration Estimates in Highway Construction

The literature mentions multiple factors that can affect the accuracy of estimating the project duration in highway construction,such as weather,location,and soil conditions.However,there are other factors that have not been explored,yet they can have significant impact on the accuracy of the project time estimate.Recently,TxDOT raised a concern regarding the importance of the proper estimating of the lead/lag times in project schedules.These lead/lag times are often determined based on the engineer’s experience.However,inaccurate estimates of the lead/lag time can result in unrealistic project durations.In order to investigate this claim,the study utilizes four time sensitivity measures(TSM),namely the Criticality Index(CI),Significance Index(SI),Cruciality Index(CRI),and the Schedule Sensitivity Index(SSI)to statistically analyze and draw conclusions regarding the impact of the lead/lag time estimates on the total duration in highway projects.An Excel-based scheduling software was developed with Monte Carlo simulation capabilities to calculate these TSM.The results from this paper show that the variability of some lead/lag times can significantly impact the accuracy of the estimated total project duration.It was concluded that the current practices used for estimating the lead/lag times are insufficient.As such,it is recommended to utilize more robust methods,such as the time sensitivity measures,to accurately estimate the lead/lad times in the projects scheduled.

Joint Algorithm of Message Fragmentation and No-Wait Scheduling for Time-Sensitive Networks

Time-sensitive networks(TSNs)support not only traditional best-effort communications but also deterministic communications,which send each packet at a deterministic time so that the data transmissions of networked control systems can be precisely scheduled to guarantee hard real-time constraints.No-wait scheduling is suitable for such TSNs and generates the schedules of deterministic communications with the minimal network resources so that all of the remaining resources can be used to improve the throughput of best-effort communications.However,due to inappropriate message fragmentation,the realtime performance of no-wait scheduling algorithms is reduced.Therefore,in this paper,joint algorithms of message fragmentation and no-wait scheduling are proposed.First,a specification for the joint problem based on optimization modulo theories is proposed so that off-the-shelf solvers can be used to find optimal solutions.Second,to improve the scalability of our algorithm,the worst-case delay of messages is analyzed,and then,based on the analysis,a heuristic algorithm is proposed to construct low-delay schedules.Finally,we conduct extensive test cases to evaluate our proposed algorithms.The evaluation results indicate that,compared to existing algorithms,the proposed joint algorithm improves schedulability by up to 50%.

An Early-Stop Adjoint Transient Sensitivity Analysis Method for Objective Functions Associated with Many Time Points

Transient sensitivity analysis aims to obtain the gradients of objective functions(circuit performance)with respect to design or variation parameters in a simulator,which can be widely used in yield analysis and circuit optimization,among others.However,the traditional method has a computational complexity of O(N^(2))for objective functions containing circuit states at N time points.The computational complexity is too expensive for large N,especially in time-frequency transform.This paper proposes a many-time-point sensitivity method to reduce the computational complexity to O(N)in multiparameter many-time-point cases.The paper demonstrates a derivation process that improves efficiency by weighting the transfer chain and multiplexing the backpropagation process.We also proposed an early-stop method to improve efficiency further under the premise of ensuring accuracy.The algorithm enables sensitivity calculation of performances involving thousands of time points,such as signal-to-noise and distortion ratio and total harmonic distortion,with significant speed improvements.

Time sensitive networking security:issues of precision time protocol and its implementation

Time Sensitive Networking(TSN)will be an integral component of industrial networking.Time synchronization in TSN is provided by the IEEE-1588,Precision Time Protocol(PTP)protocol.The standard,dating back to 2008,marginally addresses security aspects,notably not encompassing the frames designed for management purposes(Type Length Values or TLVs).In this work we show that the TLVs can be abused by an attacker to reconfigure,manipulate,or shut down time synchronization.The effects of such an attack can be serious,ranging from interruption of operations to actual unintended behavior of industrial devices,possibly resulting in physical damages or even harm to operators.The paper analyzes the root causes of this vulnerability,and provides concrete examples of attacks leveraging it to de-synchronize the clocks,showing that they can succeed with limited resources,realistically available to a malicious actor.

Gender difference in network relationship between inter-temporal decisions and prefrontal activation levels in internet gaming disorder

Background:Impulsivity and decision-making are key factors in addiction.However,little is known about how gender and time sensitivity affect impulsivity in internet gaming disorder(IGD).Objective:To investigate the gender difference of impulsive decision-making and relevant brain responses in IGD.Methods:We conducted a functional magnetic resonance imaging(fMRI)study with 123 participants,including 59 IGD individuals(26 females)and 64 matched recreational game users(RGUs,23 females).Participants performed a delay-discounting task during fMRI scanning.We examined gender-by-group effects on behavioral and neural measures to explore the preference for immediate over delayed rewards and the associated brain activity.We also investigated the network correlations between addiction severity and behavioral and neural measures,and analyzed the mediating role of brain activity in the link between delay discounting parameters and IGD severity.Results:We found significant gender-by-group interactions.The imaging results revealed gender-by-group interactions in the dor-solateral prefrontal cortex,medial frontal gyrus,and inferior frontal gyrus(IFG).Post hoc analysis indicated that,for females,RGUs showed higher activity than IGD individuals in these brain regions,while for males IGD individuals exhibited higher activity than RGUs.The activation in the left IFG mediated the relation between Internet Addiction Test score and discount rate in females.In males,the activation in the right dlPFC mediated the relation between IAT score and time sensitivity.Discussion:Our findings imply that male IGD participants demonstrate impaired intertemporal decisions associated with neural dysfunction.Influencing factors for impulsive decision-making in IGD diverge between males(time sensitivity)and females(discount rate).These findings augment our comprehension of the neural underpinnings of gender differences in IGD and bear significant implications for devising effective intervention strategies for treating people with IGD.

Improvising Personalized Travel Recommendation System with Recency Effects

A travel recommendation system based on social media activity provides a customized place of interest to accommodate user-specific needs and preferences. In general, the user’s inclination towards travel destinations is subject to change over time. In this project, we have analyzed users’ twitter data, as well as their friends and followers in a timely fashion to understand recent travel interest. A machine learning classifier identifies tweets relevant to travel. The travel tweets are then used to obtain personalized travel recommendations. Unlike most of the personalized recommendation systems, our proposed model takes into account a user’s most recent interest by incorporating time-sensitive recency weight into the model. Our proposed model has outperformed the existing personalized place of interest recommendation model, and the overall accuracy is 75.23%.