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°C 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.

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.

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

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.

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.

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.

Times管理方案在心内科护士多参数心电监护仪警报疲劳中的应用

探讨Times管理方案在心内科护士多参数心电监护仪警报疲劳中的应用效果,以提高护士临床警报知识水平,改善警报管理态度和行为,减轻护士的警报疲劳,降低心电监护仪错误警报发生率。

Noise color influence on escape times in nonlinear oscillators-experimental and numerical results

The interplay between noise and nonlinearites can lead to escape dynamics.Associated nonlinear phe-nomena have been observed in various applications ranging from climatology to biology and engineering.For reasons of computational ease,in most studies,Gaussian white noise is used.However,this noise model is not physical due to the associated infinite energy content.Here,the authors present extensive experimental investigations and numerical simulations conducted to examine the impact of noise color on escape times in nonlinear oscillators.With a careful parameterization of the numerical simulations,the authors are able to make quantitative comparisons with experimental results.Through the experi-ments and simulations,it is illustrated that the noise color can drastically influence escape times and escape probability.

Prediction of impedance responses of protonic ceramic cells using artificial neural network tuned with the distribution of relaxation times

A deep-learning-based framework is proposed to predict the impedance response and underlying electrochemical behavior of the reversible protonic ceramic cell(PCC) across a wide variety of different operating conditions.Electrochemical impedance spectra(EIS) of PCCs were first acquired under a variety of opera ting conditions to provide a dataset containing 36 sets of EIS spectra for the model.An artificial neural network(ANN) was then trained to model the relationship between the cell operating condition and EIS response.Finally,ANN model-predicted EIS spectra were analyzed by the distribution of relaxation times(DRT) and compared to DRT spectra obtained from the experimental EIS data,enabling an assessment of the accumulative errors from the predicted EIS data vs the predicted DRT.We show that in certain cases,although the R^(2)of the predicted EIS curve may be> 0.98,the R^(2)of the predicted DRT may be as low as~0.3.This can lead to an inaccurate ANN prediction of the underlying time-resolved electrochemical response,although the apparent accuracy as evaluated from the EIS prediction may seem acceptable.After adjustment of the parameters of the ANN framework,the average R^(2)of the DRTs derived from the predicted EIS can be improved to 0.9667.Thus,we demonstrate that a properly tuned ANN model can be used as an effective tool to predict not only the EIS,but also the DRT of complex electrochemical systems.

Hybrid Flow Shop with Setup Times Scheduling Problem

The two-stage hybridflow shop problem under setup times is addressed in this paper.This problem is NP-Hard.on the other hand,the studied problem is modeling different real-life applications especially in manufacturing and high performance-computing.Tackling this kind of problem requires the development of adapted algorithms.In this context,a metaheuristic using the genetic algorithm and three heuristics are proposed in this paper.These approximate solutions are using the optimal solution of the parallel machines under release and delivery times.Indeed,these solutions are iterative procedures focusing each time on a particular stage where a parallel machines problem is called to be solved.The general solution is then a concatenation of all the solutions in each stage.In addition,three lower bounds based on the relaxation method are provided.These lower bounds present a means to evaluate the efficiency of the developed algorithms throughout the measurement of the relative gap.An experimental result is discussed to evaluate the performance of the developed algorithms.In total,8960 instances are implemented and tested to show the results given by the proposed lower bounds and heuristics.Several indicators are given to compare between algorithms.The results illustrated in this paper show the performance of the developed algorithms in terms of gap and running time.

Evolutionary timescale of chalcidoid wasps inferred from over one hundred mitochondrial genomes

Chalcidoidea is one of the most biologically diverse groups among Hymenoptera.Members are characterized by extraordinary parasitic lifestyles and extensive host ranges,among which several species attack plants or serve as pollinators.However,higher-level chalcidoid relationships remain controversial.Here,we performed mitochondrial phylogenomic analyses for major clades(18out of 25 families)of Chalcidoidea based on 139 mitochondrial genomes.The compositional heterogeneity and conflicting backbone relationships in Chalcidoidea were assessed using various datasets and tree inferences.Our phylogenetic results supported the monophyly of 16families and polyphyly of Aphelinidae and Pteromalidae.Our preferred topology recovered the relationship(Mymaridae+(Signiphoridae+Leucospidae)+(Chalcididae+((Perilampidae+Eucharitidae)+remaining Chalcidoidea))).The monophyly of Agaonidae and Sycophaginae was rejected,while the gall-associated((Megastigmidae+Ormyridae)+(Ormocerinae+Eurytomidae))relationship was supported in most results.A six-gene inversion may be a synapomorphy for most families,whereas other derived gene orders may introduce confusion in phylogenetic signals at deeper nodes.Dating estimates suggested that Chalcidoidea arose near the Jurassic/Cretaceous boundary and that two dynamic shifts in diversification occurred during the evolution of Chalcidoidea.We hypothesized that the potential codiversification between chalcidoids and their hosts may be crucial for accelerating the diversification of Chalcidoidea.Ancestral state reconstruction analyses supported the hypothesis that gallinducers were mainly derived from parasitoids of gallinducers,while other gall-inducers were derived from phytophagous groups.Taken together,these findings advance our understanding of mitochondrial genome evolution in the major interfamilial phylogeny of Chalcidoidea.

Reliability Analysis of Correlated Competitive and Dependent Components Considering Random Isolation Times

In the Internet of Things(IoT)system,relay communication is widely used to solve the problem of energy loss in long-distance transmission and improve transmission efficiency.In Body Sensor Network(BSN)systems,biosensors communicate with receiving devices through relay nodes to improve their limited energy efficiency.When the relay node fails,the biosensor can communicate directly with the receiving device by releasing more transmitting power.However,if the remaining battery power of the biosensor is insufficient to enable it to communicate directly with the receiving device,the biosensor will be isolated by the system.Therefore,a new combinatorial analysis method is proposed to analyze the influence of random isolation time(RIT)on system reliability,and the competition relationship between biosensor isolation and propagation failure is considered.This approach inherits the advantages of common combinatorial algorithms and provides a new approach to effectively address the impact of RIT on system reliability in IoT systems,which are affected by competing failures.Finally,the method is applied to the BSN system,and the effect of RIT on the system reliability is analyzed in detail.

Impacts of Increasing Model Resolutions and Shortening Forecast Lead Times on QPFs in South China During the Rainy Season

This study investigated the impacts of increasing model resolutions and shortening forecast lead times on the quantitative precipitation forecast(QPF)for heavy-rainfall events over south China during the rainy seasons in 2013-2020.The control experiment,where the analysis-forecast cycles run with model resolutions of about 3 km,was compared to a lower-resolution experiment with model resolutions of about 9 km,and a longer-term experiment activated 12 hours earlier.Rainfall forecasting in the presummer rainy season was significantly improved by improving model resolutions,with more improvements in cases with stronger synoptic-scale forcings.This is partially attributed to the improved initial conditions(ICs)and subsequent forecasts for low-level jets(LLJs).Forecasts of heavy rainfall induced by landfalling tropical cyclones(TCs)benefited from increasing model resolutions in the first 6 hours.Forecast improvements in rainfall due to shortening forecast lead times were more significant at earlier(1-6 h)and later(7-12 h)lead times for cases with stronger and weaker synoptic-scale forcings,respectively,due to the area-and case-dependent improvements in ICs for nonprecipitation variables.Specifically,significant improvements mainly presented over the northern South China Sea for low-level onshore wind of weak-forcing cases but over south China for LLJs of strong-forcing cases during the presummer rainy season,and over south China for all the nonprecipitation variables above the surface during the TC season.However,some disadvantages of higher-resolution and shorter-term forecasts in QPFs highlight the importance of developing ensemble forecasting with proper IC perturbations,which include the complementary advantages of lower-resolution and longer-term forecasts.

Vehicle Density Prediction in Low Quality Videos with Transformer Timeseries Prediction Model(TTPM)

Recent advancement in low-cost cameras has facilitated surveillance in various developing towns in India.The video obtained from such surveillance are of low quality.Still counting vehicles from such videos are necessity to avoid traf-fic congestion and allows drivers to plan their routes more precisely.On the other hand,detecting vehicles from such low quality videos are highly challenging with vision based methodologies.In this research a meticulous attempt is made to access low-quality videos to describe traffic in Salem town in India,which is mostly an un-attempted entity by most available sources.In this work profound Detection Transformer(DETR)model is used for object(vehicle)detection.Here vehicles are anticipated in a rush-hour traffic video using a set of loss functions that carry out bipartite coordinating among estimated and information acquired on real attributes.Every frame in the traffic footage has its date and time which is detected and retrieved using Tesseract Optical Character Recognition.The date and time extricated and perceived from the input image are incorporated with the length of the recognized objects acquired from the DETR model.This furnishes the vehicles report with timestamp.Transformer Timeseries Prediction Model(TTPM)is proposed to predict the density of the vehicle for future prediction,here the regular NLP layers have been removed and the encoding temporal layer has been modified.The proposed TTPM error rate outperforms the existing models with RMSE of 4.313 and MAE of 3.812.

Radiation therapy prior to a pancreaticoduodenectomy for adenocarcinoma is associated with longer operative times and higher blood loss

BACKGROUND Pancreatic adenocarcinoma is currently the fourth leading cause of cancer-related deaths in the United States.In patients with“borderline resectable”disease,current National Comprehensive Cancer Center guidelines recommend the use of neoadjuvant chemoradiation prior to a pancreaticoduodenectomy.Although neoadjuvant radiotherapy may improve negative margin resection rate,it is theorized that its administration increases operative times and complexity.AIM To investigate the association between neoadjuvant radiotherapy and 30-d morbidity and mortality outcomes among patients receiving a pancreaticoduodenectomy for pancreatic adenocarcinoma.METHODS Patients listed in the 2015-2019 National Surgery Quality Improvement Program data set,who received a pancreaticoduodenectomy for pancreatic adenocarcinoma,were divided into two groups based off neoadjuvant radiotherapy status.Multivariable regression was used to determine if there is a significant correlation between neoadjuvant radiotherapy,perioperative blood transfusion status,total operative time,and other perioperative outcomes.RESULTS Of the 11458 patients included in the study,1470(12.8%)underwent neoadjuvant radiotherapy.Patients who received neoadjuvant radiotherapy were significantly more likely to require a perioperative blood transfusion[adjusted odds ratio(aOR)=1.58,95%confidence interval(CI):1.37-1.82;P<0.001]and have longer surgeries(insulin receptor-related receptor=1.14,95%CI:1.11-1.16;P<0.001),while simultaneously having lower rates of organ space infections(aOR=0.80,95%CI:0.66-0.97;P=0.02)and pancreatic fistula formation(aOR=0.50,95%CI:0.40-0.63;P<0.001)compared to those who underwent surgery alone.CONCLUSION Neoadjuvant radiotherapy,while not associated with increased mortality,will impact the complexity of surgical resection in patients with pancreatic adenocarcinoma.

Adaptive Conditional Hazard Regression Modeling of Multiple Event Times

Recurrent event time data and more general multiple event time data are commonly analyzed using extensions of Cox regression, or proportional hazards regression, as used with single event time data. These methods treat covariates, either time-invariant or time-varying, as having multiplicative effects while general dependence on time is left un-estimated. An adaptive approach is formulated for analyzing multiple event time data. Conditional hazard rates are modeled in terms of dependence on both time and covariates using fractional polynomials restricted so that the conditional hazard rates are positive-valued and so that excess time probability functions (generalizing survival functions for single event times) are decreasing. Maximum likelihood is used to estimate parameters adjusting for right censored event times. Likelihood cross-validation (LCV) scores are used to compare models. Adaptive searches through alternate conditional hazard rate models are controlled by LCV scores combined with tolerance parameters. These searches identify effective models for the underlying multiple event time data. Conditional hazard regression is demonstrated using data on times between tumor recurrence for bladder cancer patients. Analyses of theory-based models for these data using extensions of Cox regression provide conflicting results on effects to treatment group and the initial number of tumors. On the other hand, fractional polynomial analyses of these theory-based models provide consistent results identifying significant effects to treatment group and initial number of tumors using both model-based and robust empirical tests. Adaptive analyses further identify distinct moderation by group of the effect of tumor order and an additive effect to group after controlling for nonlinear effects to initial number of tumors and tumor order. Results of example analyses indicate that adaptive conditional hazard rate modeling can generate useful insights into multiple event time data.

Adaptive Conditional Hazard Regression Modeling of Multiple Event Times

Recurrent event time data and more general multiple event time data are commonly analyzed using extensions of Cox regression, or proportional hazards regression, as used with single event time data. These methods treat covariates, either time-invariant or time-varying, as having multiplicative effects while general dependence on time is left un-estimated. An adaptive approach is formulated for analyzing multiple event time data. Conditional hazard rates are modeled in terms of dependence on both time and covariates using fractional polynomials restricted so that the conditional hazard rates are positive-valued and so that excess time probability functions (generalizing survival functions for single event times) are decreasing. Maximum likelihood is used to estimate parameters adjusting for right censored event times. Likelihood cross-validation (LCV) scores are used to compare models. Adaptive searches through alternate conditional hazard rate models are controlled by LCV scores combined with tolerance parameters. These searches identify effective models for the underlying multiple event time data. Conditional hazard regression is demonstrated using data on times between tumor recurrence for bladder cancer patients. Analyses of theory-based models for these data using extensions of Cox regression provide conflicting results on effects to treatment group and the initial number of tumors. On the other hand, fractional polynomial analyses of these theory-based models provide consistent results identifying significant effects to treatment group and initial number of tumors using both model-based and robust empirical tests. Adaptive analyses further identify distinct moderation by group of the effect of tumor order and an additive effect to group after controlling for nonlinear effects to initial number of tumors and tumor order. Results of example analyses indicate that adaptive conditional hazard rate modeling can generate useful insights into multiple event time data.

Confucius-The Storyofa Man Exemplary Masterof All Times-Confucius Culture Theme Exhibition

The exhibition presents the life of Confucius and the essence of his thoughts through plates,accompanied by replicas of related cultural objects and cultural creative products,with the purposeof helping local people understand the origin of Confucianism and presenting the modern charm of traditional culture.

Trend of the Times

The rise of the Chinese economy and China’s cultural confidence is driving the popularity of brands rooted in the unique Chinese culture.LITERALLY meaning a national wave or national trend,Guochao denotes a new consumer trend emerging in China in recent years.With the rise of the Chinese economy,domestic brands are embracing traditional cultural elements boosted by the Chinese pride in the achievements of the nation.Traditional culture has become a unique feature of Chinese brands going abroad and even foreign brands courting the Chinese market.From clothing and daily necessities to cell phones and cars。

Picturing the Times

On February 6,locals carry a Chinese sedan chair while performing the traditional fire-jumping dance to celebrate the Lantern Festival in Shanmei Village of Hui’an County,Quanzhou,Fujian Province.