RL and AHP-Based Multi-Timescale Multi-Clock Source Time Synchronization for Distribution Power Internet of Things

Time synchronization(TS)is crucial for ensuring the secure and reliable functioning of the distribution power Internet of Things(IoT).Multi-clock source time synchronization(MTS)has significant advantages of high reliability and accuracy but still faces challenges such as optimization of the multi-clock source selection and the clock source weight calculation at different timescales,and the coupling of synchronization latency jitter and pulse phase difference.In this paper,the multi-timescale MTS model is conducted,and the reinforcement learning(RL)and analytic hierarchy process(AHP)-based multi-timescale MTS algorithm is designed to improve the weighted summation of synchronization latency jitter standard deviation and average pulse phase difference.Specifically,the multi-clock source selection is optimized based on Softmax in the large timescale,and the clock source weight calculation is optimized based on lower confidence bound-assisted AHP in the small timescale.Simulation shows that the proposed algorithm can effectively reduce time synchronization delay standard deviation and average pulse phase difference.

A Double-Timescale Reinforcement Learning Based Cloud-Edge Collaborative Framework for Decomposable Intelligent Services in Industrial Internet of Things

With the proportion of intelligent services in the industrial internet of things(IIoT)rising rapidly,its data dependency and decomposability increase the difficulty of scheduling computing resources.In this paper,we propose an intelligent service computing framework.In the framework,we take the long-term rewards of its important participants,edge service providers,as the optimization goal,which is related to service delay and computing cost.Considering the different update frequencies of data deployment and service offloading,double-timescale reinforcement learning is utilized in the framework.In the small-scale strategy,the frequent concurrency of services and the difference in service time lead to the fuzzy relationship between reward and action.To solve the fuzzy reward problem,a reward mapping-based reinforcement learning(RMRL)algorithm is proposed,which enables the agent to learn the relationship between reward and action more clearly.The large time scale strategy adopts the improved Monte Carlo tree search(MCTS)algorithm to improve the learning speed.The simulation results show that the strategy is superior to popular reinforcement learning algorithms such as double Q-learning(DDQN)and dueling Q-learning(dueling-DQN)in learning speed,and the reward is also increased by 14%.

Improving Child and Adolescent Mental Health Comprehensively in Challenging Times

Mental health,an integral component of well-being,encompasses more than the mere absence of illness(1).Improving the mental health of the next generation is essential,as it not only affects their health in adolescence and adulthood but also enables them to thrive in various aspects of life,such as learning,working,forming relationships,and contributing to society(2–3).Currently,China faces growing concerns regarding mental health issues among children and adolescents(4).Recognizing the gravity of this situation,the government has implemented various policies,including the Special Action Plan for Comprehensively Strengthening and Improving the Mental Health Work of Students in the New Era(2023–2025)(5).However,regional disparities and the complex interplay of factors influencing youth mental development are posing challenges to the effective implementation of these policies,highlighting the need for more integrated and impactful interventions.

The joint Laplace transforms for killed diffusion occupation times

The approach of Li and Zhou(2014)is adopted to find the Laplace transform of occupation time over interval(0,a)and joint occupation times over semi-infinite intervals(-∞,a)and(b,∞)for a time-homogeneous diffusion process up to an independent exponential time e_(q)for 0<a<b.The results are expressed in terms of solutions to the differential equations associated with the diffusion generator.Applying these results,we obtain explicit expressions on the Laplace transform of occupation time and joint occupation time for Brownian motion with drift.

Multivariate Time Series Anomaly Detection Based on Spatial-Temporal Network and Transformer in Industrial Internet of Things

In the Industrial Internet of Things(IIoT),sensors generate time series data to reflect the working state.When the systems are attacked,timely identification of outliers in time series is critical to ensure security.Although many anomaly detection methods have been proposed,the temporal correlation of the time series over the same sensor and the state(spatial)correlation between different sensors are rarely considered simultaneously in these methods.Owing to the superior capability of Transformer in learning time series features.This paper proposes a time series anomaly detection method based on a spatial-temporal network and an improved Transformer.Additionally,the methods based on graph neural networks typically include a graph structure learning module and an anomaly detection module,which are interdependent.However,in the initial phase of training,since neither of the modules has reached an optimal state,their performance may influence each other.This scenario makes the end-to-end training approach hard to effectively direct the learning trajectory of each module.This interdependence between the modules,coupled with the initial instability,may cause the model to find it hard to find the optimal solution during the training process,resulting in unsatisfactory results.We introduce an adaptive graph structure learning method to obtain the optimal model parameters and graph structure.Experiments on two publicly available datasets demonstrate that the proposed method attains higher anomaly detection results than other methods.

A Comparison of Turnaround-Times for Two Popular Specimen Types Used for Newborn Toxicology: Meconium and Umbilical Cord Tissue

Background: Prenatal exposure to illicit substances is responsible for several long-term negative health consequences. It is critical for healthcare professionals to know the extent and scope of prenatal substance exposure in their cases. Several studies exist with mixed results comparing the effectiveness of umbilical cord tissue (UCT) and meconium (MEC) as toxicology specimen types. The specific aim of this study is to compare the use of UCT and MEC regarding the time interval between the birth of the neonate, receipt of the specimen at the laboratory, and the hospital’s receipt of the final toxicology report. Method: The study queried de-identified results of 5358 consecutive UCT and 706 MEC from our laboratory. Results: The mean time from birth to receipt of the specimen at the laboratory for MEC and UCT was 4.5 days ± 2.9 days and 2.8 days ± 1.9 days, respectively. The mean time from birth to final report for MEC was 6.9 days ± 3.8 days, 5.7 days ± 3.3 days, and 8.4 days ± 3.8 days for all MEC specimens, negative MEC, and positive MEC, respectively. The mean time from birth to final report for UCT was 4.3 days ± 2.4 days, 3.5 days ± 2.2 days, and 5.4 days ± 2.2 days for all UCT, negative UCT and positive UCT, respectively. Discussion/Conclusion: Receipt of drug test results of the neonate prior to release from the hospital is critical. This study shows that UCT offers an advantage when results are needed quickly to make informed decisions about the health and well-being of newborns.

Characterization of Hydroxyapatite Extracted from Crab Shell Using the Hydrothermal Method with Varying Holding Times

Hydroxyapatite(HA)is a bio ceramic commonly utilized in bone tissue engineering due to its bioactive and osteoconductive properties.Crab shells are usually disregarded as waste material despite their significant CaCO_(3) content,and have not been widely utilized in the synthesis of HA.This study aims to synthesize and analyze HA derived from crab shells using the hydrothermal method with different durations of holding time.This study utilized precipitated calcium carbonate(PCC)derived from crab shells.With a hydrothermal reactor set at 160℃ and varying holding times of 14(HA_14),16(HA_16),and 18(HA_18)h,a PCC and(NH4)2HPO4 mixture was used to synthesize HA.The synthesis results were analyzed using scanning electron microscopy(SEM),fourier transform infrared spectroscopy(FTIR),and X-ray diffraction(XRD)tests.This study has accomplished the synthesis of HA from crab shells.Nonetheless,the final product of synthesis still contained CaCO_(3) as an impurity.The prolonged hydrothermal holding time of 14 to 18 h resulted in a reduction of impurities while increasing the percentage of crystal weight and crystallite size of HA.Specimen CH_18 is the best-quality product generated in this study.This specimen produced HA with the highest percentage of crystal weight and crystallite size compared to the other specimens.Furthermore,specimen CH_18 exhibited the lowest concentration of impurities.The Ca/P ratio in this specimen was also the closest to 1.67.The Ca/P ratio,crystallite size,and crystal weight percentage of this specimen are 1.54,19.06 nm,and 99.1%,respectively.

Picturing the Times

A man with his face painted to appear like a spirit,participates in the He Neak Ta ritual in Phum Boeung village,northwest of Phnom Penh,Cambodia,on June 11.Cambodian villagers took part in a rare traditional guardian spirit ceremony praying for good fortune,rain,and prosperity as part of efforts to preserve this ancient tradition.

Trends of the Times

BUILDING a resilient workforce of the future means understanding your workers.Over the past five years,the Adecco Group has interviewed over 82,000 employees across 25 countries-including China-to capture global workers’perspectives and equip employers with the tools to future-proof their workforce.These are some of the findings about the labor market trends that have transformed the world of work since 2020。

Picturing the Times

This photo taken on November 17 shows children holding candles during the celebrations of the Kalawfireworks festival in Kalaw township, Myanmar’s Southeastern Shan State. (SAI AUNG MAIN).

Picturing the Times

Girls with flowers on their heads at a celebration for the traditional Ngarot festival in West Java,Indonesia,on December 13,2023.The festival is a traditional ceremony to welcome the rice planting season.

Achieving Common Development in Step with the Times

As long as China and the EU adhere to strategic consensuses,keep in step with the times,and maintain mutual trust,they will be able to ensure the prosperity and stability of both sides.

Picturing the Times

Cherry blossoms at Qingchuan Pavilion in Wuhan,capital of central China’s Hubei Province,attract throngs of visitors on March 23.Illuminated by night lights,the cherry blossoms shone brightly,creating fantastic scenes of light and shadow with neon-clad bridges and landmark buildings along the Yangtze River.

Picturing the Times

Tourists sightsee on a boat in a massive lotus pond scenic area in Xinghua,Taizhou,east China’s Jiangsu Province,on August 5.When summer peaks,lotus flowers enter their blooming season,attracting throngs of tourists to admire their beauty.

Picturing the Times

On September 25,locals and tourists pose for photos with a giant decorative flower basket on Tiananmen Square in Beijing to celebrate the forthcoming National Day.The four Chinese characters adorning the basket mean"blessing the motherland."(IC PHOTO)

Optimal Multi-Timescale Scheduling of Integrated Energy Systems with Hybrid Energy Storage System Based on Lyapunov Optimization

The economic operation of integrated energy system(IES)faces new challenges such as multi-timescale characteristics of heterogeneous energy sources,and cooperative operation of hybrid energy storage system(HESS).To this end,this paper investigates the multi-timescale rolling opti-mization problem for IES integrated with HESS.Firstly,the architecture of IES with HESS is established,a comparative analysis is conducted to evaluate the advantages of the HESS over a single energy storage system(SESS)in stabilizing power fluctuations.Secondly,the dayahead and real-time scheduling cost functions of IES are established,the day-ahead scheduling mainly depends on operation costs of the components in IES,the real-time optimal scheduling adopts the Lya-punov optimization method to schedule the battery and hydrogen energy storage in each time slot,so as to minimize the real-time average scheduling operation cost,and the problem of day-ahead and real-time scheduling error,which caused by the uncertainty of the energy storage is solved by online optimization.Finally,the proposed model is verified to reduce the scheduling operation cost and the dispatching error by performing an arithmetic example analysis of the IES in Shanghai,which provides a reference for the safe and stable operation of the IES.

A Mixture Model Parameters Estimation Algorithm for Inter-Contact Times in Internet of Vehicles

Communication opportunities among vehicles are important for data transmission over the Internet of Vehicles(IoV).Mixture models are appropriate to describe complex spatial-temporal data.By calculating the expectation of hidden variables in vehicle communication,Expectation Maximization(EM)algorithm solves the maximum likelihood estimation of parameters,and then obtains the mixture model of vehicle communication opportunities.However,the EM algorithm requires multiple iterations and each iteration needs to process all the data.Thus its computational complexity is high.A parameter estimation algorithm with low computational complexity based on Bin Count(BC)and Differential Evolution(DE)(PEBCDE)is proposed.It overcomes the disadvantages of the EM algorithm in solving mixture models for big data.In order to reduce the computational complexity of the mixture models in the IoV,massive data are divided into relatively few time intervals and then counted.According to these few counted values,the parameters of the mixture model are obtained by using DE algorithm.Through modeling and analysis of simulation data and instance data,the PEBCDE algorithm is verified and discussed from two aspects,i.e.,accuracy and efficiency.The numerical solution of the probability distribution parameters is obtained,which further provides a more detailed statistical model for the distribution of the opportunity interval of the IoV.

Mechanism Study on the Effect of Retarder on Polyurethane Setting Time Based on Molecular Simulation

This study identified castor oil and phosphate ester as effective retarders through setting time,tensile,and flexural tests,and determined their optimal dosages.The mechanism by which phosphate ester affects the setting time of polyurethane was further investigated using molecular dynamics simulations.Fourier transform infrared spectroscopy was also employed to systematically study the physical and chemical interactions between phosphate esters and polyurethane materials.The results demonstrate that a 1%concentration of phosphate ester provides the most effective retarding effect with minimal impact on the strength of polyurethane.When phosphate ester is added to the B component of the two-component polyurethane system,its interaction energy with component A decreases,as do the diffusion coefficient and aggregation degree of component B on the surface of component A.This reduction in interaction slows the setting time.Additionally,the addition of phosphate ester to polyurethane leads to the disappearance or weakening of functional groups,indicating competitive interactions within the phosphate ester components that inhibit the reaction rate.

Impact of time between meniscal injury and isolated meniscus repair on post-operative outcomes:A systematic review

BACKGROUND Meniscal tears are one of the most common knee injuries.After the diagnosis of a meniscal tear has been made,there are several factors physicians use to guide clinical decision-making.The influence of time between injury and isolated meniscus repair on patient outcomes is not well described.Assessing this relationship is important as it may influence clinical decision-making and can add to the preoperative patient education process.We hypothesized that increasing the time from injury to meniscus surgery would worsen postoperative outcomes.AIM To investigate the current literature for data on the relationship between time between meniscus injury and repair on patient outcomes.METHODS PubMed,Academic Search Complete,MEDLINE,CINAHL,and SPORTDiscus were searched for studies published between January 1,1995 and July 13,2023 on isolated meniscus repair.Exclusion criteria included concomitant ligament surgery,incomplete outcomes or time to surgery data,and meniscectomies.Patient demographics,time to injury,and postoperative outcomes from each study were abstracted and analyzed.RESULTS Five studies met all inclusion and exclusion criteria.There were 204(121 male,83 female)patients included.Three of five(60%)studies determined that time between injury and surgery was not statistically significant for postoperative Lysholm scores(P=0.62),Tegner scores(P=0.46),failure rate(P=0.45,P=0.86),and International Knee Documentation Committee scores(P=0.65).Two of five(40%)studies found a statistically significant increase in Lysholm scores with shorter time to surgery(P=0.03)and a statistically significant association between progression of medial meniscus extrusion ratio(P=0.01)and increasing time to surgery.CONCLUSION Our results do not support the hypothesis that increased time from injury to isolated meniscus surgery worsens postoperative outcomes.Decision-making primarily based on injury interval is thus not recommended.

Prompt fission neutron uranium logging(II):dead-time effect of the neutron time spectrum

The acquisition of neutron time spectrum data plays a pivotal role in the precise quantification of uranium via prompt fission neutron uranium logging(PFNUL).However,the impact of the detector dead-time effect remains paramount in the accurate acquisition of the neutron time spectrum.Therefore,it is imperative for neutron logging instruments to establish a dead-time correction method that is not only uncomplicated but also practical and caters to various logging sites.This study has formulated an innovative equation for determining dead time and introduced a dead-time correction method for the neutron time spectrum,called the“dual flux method.”Using this approach,a logging instrument captures two neutron time spectra under disparate neutron fluxes.By carefully selecting specific“windows”on the neutron time spectrum,the dead time can be accurately ascertained.To substantiate its efficacy and discern the influencing factors,experiments were conducted utilizing a deuterium-tritium(D-T)neutron source,a Helium-3(3He)detector,and polyethylene shielding to collate and analyze the neutron time spectrum under varying neutron fluxes(at high voltages).The findings underscore that the“height”and“spacing”of the two windows are the most pivotal influencing factors.Notably,the“height”(fd)should surpass 2,and the“spacing”twd should exceed 200μs.The dead time of the 3 He detector determined in the experiment was 7.35μs.After the dead-time correction,the deviation of the decay coefficients from the theoretical values for the neutron time spectrum under varying neutron fluxes decreased from 12.4%to within 5%.Similarly,for the PFNUL instrument,the deviation in the decay coefficients decreased from 22.94 to 0.49%after correcting for the dead-time effect.These results demonstrate the exceptional efficacy of the proposed method in ensuring precise uranium quantification.The dual flux method was experimentally validated as a universal approach applicable to pulsed neutron logging instruments and holds immense significance for uranium exploration.