Intrinsic Spin Angular Momentum of Electron Relation to the Discrete Indivisible Quantum of Time Kshana or Moment

The frequency of any periodic event can be defined in terms of units of Time. Planck constructed a unit of time called the Plank time from other physical constants. Vyasa defined a natural unit of time, kshana, or moment based on the motion of a fundamental particle. It is the time taken by an elementary particle, to change its direction from east to north. According to Vyasa, kshana is discrete, exceedingly small, indivisible, and is a constant time quantum. When the intrinsic spin angular momentum of an electron was related to the angular momentum of a simple thin circular plate, spherical shell, and solid sphere model of an electron, we found that the value of kshana in seconds was equal to ten to a power of minus twenty-one second. The disc model for the spinning electron provides an accurate value of the number of kshanas per second as determined previously and compared with other spinning models of electrons. These results indicate that the disk-like model of spinning electrons is the correct model for electrons. Vyasa’s definition of kshana opens the possibility of a new foundation for the theory of physical time, and perspectives in theoretical and philosophical research.

Performance Degradation Prediction of Proton Exchange Membrane Fuel Cell Based on CEEMDAN-KPCA and DA-GRU Networks

In order to improve the performance degradation prediction accuracy of proton exchange membrane fuel cell(PEMFC),a fusion prediction method(CKDG)based on adaptive noise complete ensemble empirical mode decomposition(CEEMDAN),kernel principal component analysis(KPCA)and dual attention mechanism gated recurrent unit neural network(DA-GRU)was proposed.CEEMDAN and KPCA were used to extract the input feature data sequence,reduce the influence of random factors,and capture essential feature components to reduce the model complexity.The DA-GRU network helps to learn the feature mapping relationship of data in long time series and predict the changing trend of performance degradation data more accurately.The actual aging experimental data verify the performance of the CKDG method.The results show that under the steady-state condition of 20%training data prediction,the CKDA method can reduce the root mean square error(RMSE)by 52.7%and 34.6%,respectively,compared with the traditional LSTM and GRU neural networks.Compared with the simple DA-GRU network,RMSE is reduced by 15%,and the degree of over-fitting is reduced,which has higher accuracy.It also shows excellent prediction performance under the dynamic condition data set and has good universality.

Novel models for simulating maize growth based on thermal time and photothermal units: Applications under various mulching practices

Maize (Zea mays L.) is one of the three major food crops and an important source of carbohydrates for maintaining food security around the world.Plant height (H),stem diameter (SD),leaf area index (LAI) and dry matter (DM) are important growth parameters that influence maize production.However,the combined effect of temperature and light on maize growth is rarely considered in crop growth models.Ten maize growth models based on the modified logistic growth equation (Mlog) and the Mitscherlich growth equation (Mit) were proposed to simulate the H,SD,LAI and DM of maize under different mulching practices based on experimental data from 2015–2018.Either the accumulative growing degree-days (AGDD),helio thermal units (HTU),photothermal units (PTU) or photoperiod thermal units (PPTU,first proposed here) was used as a single driving factor in the models;or AGDD was combined with either accumulative actual solar hours (ASS),accumulative photoperiod response (APR,first proposed here) or accumulative maximum possible sunshine hours (ADL) as the dual driving factors in the models.The model performances were evaluated using seven statistical indicators and a global performance index.The results showed that the three mulching practices significantly increased the maize growth rates and the maximum values of the growth curves compared with non-mulching.Among the four single factor-driven models,the overall performance of the Mlog_(PTU)Model was the best,followed by the Mlog_(AGDD)Model.The Mlog_(PPTU)Model was better than the Mlog_(AGDD)Model in simulating SD and LAI.Among the 10 models,the overall performance of the Mlog_(AGDD–APR)Model was the best,followed by the Mlog_(AGDD–ASS)Model.Specifically,the Mlog_(AGDD–APR)Model performed the best in simulating H and LAI,while the Mlog_(AGDD–ADL)and Mlog_(AGDD–ASS)models performed the best in simulating SD and DM,respectively.In conclusion,the modified logistic growth equations with AGDD and either APR,ASS or ADL as the dual driving factors outperformed the commonly used modified logistic growth model with AGDD as a single driving factor in simulating maize growth.

Research on fault time prediction method for high speed rail BTM unit based on multi method interactive validation

The Balise Transmission Module(BTM)unit of the on-board train control system is a crucial component.Due to its unique installation position and complex environment,this unit has a higher fault rate within the on-board train control system.To conduct fault prediction for the BTM unit based on actual fault data,this study proposes a prediction method combining reliability statistics and machine learning,and achieves the fusion of prediction results from different dimensions through multi-method interactive validation.Firstly,a method for predicting equipment fault time targeting batch equipment is introduced.This method utilizes reliability statistics to construct a model of the remaining faultless operating time distribution considering uncertainty,thereby predicting the remaining faultless operating probability of the BTM unit.Secondly,considering the complexity of the BTM unit’s fault mechanism,the small sample size of fault cases,and the potential presence of multiple fault features in fault text records,an individual-oriented fault prediction method based on Bayesian-optimized Gradient Boosting Regression Tree(Bayes-GBRT)is proposed.This method achieves better prediction results compared to linear regression algorithms and random forest regression algorithms,with an average absolute error of only 0.224 years for predicting the fault time of this type of equipment.Finally,a multi-method interactive validation approach is proposed,enabling the fusion and validation of multi-dimensional results.The results indicate that the predicted fault time and the actual fault time conform to a log-normal distribution,and the parameter estimation results are basically consistent,verifying the accuracy and effectiveness of the prediction results.The above research findings can provide technical support for the maintenance and modification of BTM units,effectively reducing maintenance costs and ensuring the safe operation of high-speed railway,thus having practical engineering value for preventive maintenance.

Comprehensive applicability evaluation of four precipitation products at multiple spatiotemporal scales in Northwest China

Precipitation plays a crucial role in the water cycle of Northwest China.Obtaining accurate precipitation data is crucial for regional water resource management,hydrological forecasting,flood control and drought relief.Currently,the applicability of multi-source precipitation products for long time series in Northwest China has not been thoroughly evaluated.In this study,precipitation data from 183 meteorological stations in Northwest China from 1979 to 2020 were selected to assess the regional applicability of four precipitation products(the fifth generation of European Centre for Medium-Range Weather Forecasts(ECMWF)atmospheric reanalysis of the global climate(ERA5),Global Precipitation Climatology Centre(GPCC),Climatic Research Unit gridded Time Series Version 4.07(CRU TS v4.07,hereafter CRU),and Tropical Rainfall Measuring Mission(TRMM))based on the following statistical indicators:correlation coefficient,root mean square error(RMSE),relative bias(RB),mean absolute error(MAE),probability of detection(POD),false alarm ratio(FAR),and equitable threat score(ETS).The results showed that precipitation in Northwest China was generally high in the east and low in the west,and exhibited an increasing trend from 1979 to 2020.Compared with the station observations,ERA5 showed a larger spatial distribution difference than the other products.The overall overestimation of multi-year average precipitation was approximately 200.00 mm and the degree of overestimation increased with increasing precipitation intensity.The multi-year average precipitation of GPCC and CRU was relatively close to that of station observations.The trend of annual precipitation of TRMM was overestimated in high-altitude regions and the eastern part of Lanzhou with more precipitation.At the monthly scale,GPCC performed well but underestimated precipitation in the Tarim Basin(RB=-4.11%),while ERA5 and TRMM exhibited poor accuracy in high-altitude regions.ERA5 had a large bias(RB≥120.00%)in winter months and a strong dispersion(RMSE≥35.00 mm)in summer months.TRMM showed a relatively low correlation with station observations in winter months(correlation coefficients≤0.70).The capture performance analysis showed that ERA5,GPCC,and TRMM had lower POD and ETS values and higher FAR values in Northwest China as the precipitation intensity increased.ERA5 showed a high capture performance for small precipitation events and a slower decreasing trend of POD as the precipitation intensity increased.GPCC had the lowest FAR values.TRMM was statistically ineffective for predicting the occurrence of daily precipitation events.The findings provide a reference for data users to select appropriate datasets in Northwest China and for data developers to develop new precipitation products in the future.

Soil carbon pools of six ecological regions of the United States

Mineralisable soil organic carbon(SOC)pools vary with ecosystem type in response to changes in climate,vegetation and soil properties.Understanding the effect of climate and soil factors on SOC pools is critical for predicting change over time.Surface soil samples from six ecoregions of the United States were analyzed for permanganate oxidizable C(KMnO4-C)and mineralizable C pools.Variations of SOC ranged from 7.9 mg g^-1(Florida site)to 325 mg g^-1(Hawaii site).Mineralisable C pools and KMnO4-C were highest in soils from the Hawaii site.Mean annual precipitation explains SOC and resistant C pool variations.Clay content was related to mineralisable active C pools and bacterial abundance.Mean annual precipitation and clay content are potential variables for predicting changes in SOC pools at large spatial scales.

Time Step Issue in Unit Hydrograph for Improving Runoff Prediction in Small Catchments

Unit hydrograph is a very practical tool in runoff prediction which has been used since decades ago and to date it remains useful. Unit hydrograph method is applied in Way Kuala Garuntang, an ungauged catchment in Lampung Province, Indonesia. To derive an observed unit hydrograph it requires rainfall and water level data with fine time scale which are obtained from automatic gauges. Observed unit hydrograph has an advantage that it is possible to derive it for various time steps including those with time step less than an hour. In order to get a more accurate unit hydrograph, it is necessary to derive a unit hydrograph with small time step for a small catchment such as those used in this study. The study area includes Way Kuala Garuntang and its tributaries, i.e. Way Simpur, Way Awi with areas are 60.52 km2, 3.691 km2, and 9.846 km2 respectively. The results of this study highlight the importance of time step selection on unit hydrograph, which are shown to have a significant impact on the resulting unit hydrograph’s variables such as peak discharge and time to peak.

Stabilization strategy of a car-following model with multiple time delays of the drivers

An extended car-following model with multiple delays is constructed to describe driver's driving behavior.Through stability analysis,the stability condition of this uncontrolled model is given.To dampen the negative impact of the driver's multiple delays(i.e.,stability condition is not satisfied),a novel control strategy is proposed to assist the driver in adjusting vehicle operation.The control strategy consists of two parts:the design of control term as well as the design of the parameters in the term.Bifurcation analysis is performed to illustrate the necessity of the design of parameters in control terms.In the course of the design of parameters in the control term,we improve the definite integral stability method to reduce the iterations by incorporating the characteristics of bifurcation,which can determine the appropriate parameters in the control terms more quickly.Finally,in the case study,we validate the control strategy by utilizing measured data and configuring scenario,which is closer to the actual traffic.The results of validation show that the control strategy can effectively stabilize the unstable traffic flow caused by driver's delays.

Occurrence of Delirium and Length of Stay of Patients in the Intensive Care Unit

Introduction: The delirium has received little attention from professionals working in the intensive care unit, mainly due to the fact that this is, rarely, the primary reason for patient admission. Given the high prevalence of delirium in an intensive care environment, the current guidelines recommend the daily assessment of delirium and a multidisciplinary approach. Delirium is a frequent and severe form of acute brain dysfunction, as well as an important source of concern in critical care. Objective: To assess the occurrence of delirium and time of stay in the intensive care unit. Method: This is a quantitative, descriptive study, with a cross-sectional design, which was carried out in a university hospital located in the interior of the State of Rio de Janeiro. The sample consisted of 89 patients, of both sexes, aged between 24 and 92 years. The RASS and CAM-ICU scales were used to assess delirium. The data were collected every 12 hours, for 3 months, 7 days a week and in an uninterrupted manner. Results: Were evaluated 89 patients, of which 16 were excluded according to the scale criteria, leaving 73 patients. After evaluation, 22 patients were diagnosed with delirium and 51 patients without delirium. Of the patients who presented delirium, 13 deaths and 9 had high to the nursery. Of the patients who did not have delirium, 40 had high to the nursery and 11 deaths. Patients with delirium had an average hospital stay of 23.25 days and patients who did not have delirium had an average of 4.5 days hospitalization. Conclusion: We can infer that the longer the patient spends in the intensive care unit, the greater the chance of delirium occurring. Therefore, preventive and interventional measures are necessary to decrease the mortality rate in patients with delirium and early detection is an excellent tool to improve this outcome.

MODEL-BASED DEVELOPMENT OF REAL-TIME SOFTWARE SYSTEM FOR ELECTRONIC UNIT PUMP SYSTEM

A real-time operating system （RTOS）, also named OS, is designed based on the hardware platform of MC68376, and is implemented in the electronic control system for unit pump in diesel engine. A parallel and time-based task division method is introduced and the multi-task software architecture is built in the software system for electronic unit pump （EUP） system. The V-model software development process is used to control algorithm of each task. The simulation results of the hardware-in-the-loop simulation system （HILSS） and the engine experimental results show that the OS is an efficient real-time kernel, and can meet the real-time demands of EUP system; The built multi-task software system is real-time, determinate and reliable. V-model development is a good development process of control algorithms for EUP system, the control precision of control system can be ensured, and the development cycle and cost are also decreased.

Multi-relaxation-time lattice Boltzmann simulations of lid driven flows using graphics processing unit

Large eddy simulation （LES） using the Smagorinsky eddy viscosity model is added to the two-dimensional nine velocity components （D2Q9） lattice Boltzmann equation （LBE） with multi-relaxation-time （MRT） to simulate incompressible turbulent cavity flows with the Reynolds numbers up to 1 × 10^7. To improve the computation efficiency of LBM on the numerical simulations of turbulent flows, the massively parallel computing power from a graphic processing unit （GPU） with a computing unified device architecture （CUDA） is introduced into the MRT-LBE-LES model. The model performs well, compared with the results from others, with an increase of 76 times in computation efficiency. It appears that the higher the Reynolds numbers is, the smaller the Smagorinsky constant should be, if the lattice number is fixed. Also, for a selected high Reynolds number and a selected proper Smagorinsky constant, there is a minimum requirement for the lattice number so that the Smagorinsky eddy viscosity will not be excessively large.

基于Unity3D的油田抽油机三维可视化监控系统设计

针对抽油机监测形式单一、三维可视化程度低和联动性差的缺陷,引入信息―物理实时映射思想,开发了油田抽油机三维可视化监控系统。设计了抽油机数字空间,搭建虚实交互层设计架构;搭建三维可视化系统数据组成及关系框架;结合逆运动学,构建数学模型,基于五维模型和Unity 3D平台设计抽油机三维可视化系统总体架构;以游梁式抽油机为对象进行实验验证,实现了实时动态监测、虚实混合控制、运动轨迹追踪和在线预警等服务功能。为三维可视化监控在石油机械领域的落地应用和数字孪生系统的开发提供借鉴。

Optimization of drilling and blasting operations in an open pit mine——the SOMAIR experience

Drilling and blasting play vital roles in opencast mining. These operations not only affect the cost of production directly but as well and significantly, the overall operational costs. This research was carried out to find a possible way of optimizing the drilling and blasting operations in an open pit mine of Somair (Société des Mines de l’Air), in the Niger Republic. In order to optimize the drilling operation, the time taken by two drilling machines to accomplish the same task was analyzed statistically. The result indicates that the Down the Hole Hammer Drilling Rig (DMNo406) is more efficient than the Drill Master (DM405). The relative unit consumption of two explosives (Explus and Nitram 9), when used under the same operating conditions, were also considered and the results indicate Explus to be more economical per unit consumption with a range of 0.15 g/t–0.183 g/t, when compared with Nitram 9 with a unit consumption range of 0.19 g/t-0.24 g/t in the study area.

Aggregator-based demand response mechanism for electric vehicles participating in peak regulation in valley time of receiving-end power grid

With the increase in the power receiving proportion and an insufficient peak regulation capacity of the local units, the receiving-end power grid struggles to achieve peak regulation in valley time. To solve this problem while considering the potential of the large-scale charge load of electric vehicles(EVs), an aggregator-based demand response(DR) mechanism for EVs that are participating in the peak regulation in valley time is proposed in this study. In this aggregator-based DR mechanism, the profits for the power grid’s operation and the participation willingness of the EV owners are considered. Based on the characteristics of the EV charging process and the day-ahead unit generation scheduling, a rolling unit commitment model with the DR is established to maximize the social welfare. In addition, to improve the efficiency of the optimization problem solving process and to achieve communication between the independent system operator(ISO) and the aggregators, the clustering algorithm is utilized to extract typical EV charging patterns. Finally, the feasibility and benefits of the aggregator-based DR mechanism for saving the costs and reducing the peak-valley difference of the receiving-end power grid are verified through case studies.

Predicting Wavelet-Transformed Stock Prices Using a Vanishing Gradient Resilient Optimized Gated Recurrent Unit with a Time Lag

The development of accurate prediction models continues to be highly beneficial in myriad disciplines. Deep learning models have performed well in stock price prediction and give high accuracy. However, these models are largely affected by the vanishing gradient problem escalated by some activation functions. This study proposes the use of the Vanishing Gradient Resilient Optimized Gated Recurrent Unit (OGRU) model with a scaled mean Approximation Coefficient (AC) time lag which should counter slow convergence, vanishing gradient and large error metrics. This study employed the Rectified Linear Unit (ReLU), Hyperbolic Tangent (Tanh), Sigmoid and Exponential Linear Unit (ELU) activation functions. Real-life datasets including the daily Apple and 5-minute Netflix closing stock prices were used, and they were decomposed using the Stationary Wavelet Transform (SWT). The decomposed series formed a decomposed data model which was compared to an undecomposed data model with similar hyperparameters and different default lags. The Apple daily dataset performed well with a Default_1 lag, using an undecomposed data model and the ReLU, attaining 0.01312, 0.00854 and 3.67 minutes for RMSE, MAE and runtime. The Netflix data performed best with the MeanAC_42 lag, using decomposed data model and the ELU achieving 0.00620, 0.00487 and 3.01 minutes for the same metrics.

Response of Yield and Related Attributes of Upland Cotton to Weather Variables

Cotton growth and development is effected by various ecological issues like temperature fluctuations, distribution and quantity of rainfall, relative humidity and winds which are the climate change attributes. A field experiment was carried out to find out the response of cotton to weather variables in terms of total variation in yield and quality. The effect of planting times and thermal temperatures (cumulative heat units) on yield of 4 cotton cultivars viz;CIM-600, CIM-616, CIM-622 & CRIS-641 was evaluated. Plants were sown on 6 planting times during the year 2015-2016 and 2016-2017 in an experiment conducted in randomized complete block design having three replications. Cotton cultivars depicted significant variances for number of bolls plant-1, boll weight and seed cotton yield. The cultivar CIM-616 depicted the highest seed cotton yield of 2083.60 kg·ha-1 on interpretation of highest bolls and boll weight. Maximum seed cotton yield was noted in planting time from 1st April to 15th April whereas early and late planting decreases the seed cotton yield on account of less accretion of cumulative heat units. Regression analysis depicted that rise of one unit (15 days) from early to optimal date (15th March to 15th April) enhanced the seed cotton yield by 93.76 kg·ha-1 (y = -93.764x2 + 521.04x + 1364). Delayed planting also reduces the seed cotton yield with the same ratio. It is therefore established that cotton must be cultivated from 1st April to 1st May to harvest good production in this type of climate.

立足教材基础 培养学生能力——三年级下册Unit 4 Where’s the bird? (Story time)教学设计

【教材简解】本单元的核心教学内容是"询问物品的位置"。要求学生在一定情景下能正确使用核心句型Where’s...?It’s....进行交谈。Story time共有七个场景,主要描述小鸟停在教室里的不同位置。在对话中出现了本单元的重点句型,即询问及回答地点。教学应与日常生活联系起来,培养学生的主动性思维和与人交流的能力。【目标预设】1.能初步感知新的语言知识:(1)单词:a bird,a desk,a chair,in,on,under。

Coefficient of retardation time found out by polynomial fitting to predict creep behaviour of polystyrene

The universal creep function is successful in relating the creep （ε） to the ageing time （ta）, coefficient of retardation time （β）, and intrinsic time （t0）. The relation was used to treat the creep experimental data for polystyrene （PS） specimens at a given aged time and different stress levels. Comparing with “middle-point” method reported in the literatures, β is found out by another method “polynomial fitting” in this work. Then unified master lines were constructed with the treated data and curves according to the universal equation. The master lines can be used to predict the long-term creep behaviour and lifetime by extrapolating to a required ultimate strain.

Acceleration of optical coherence tomography signal processing by multi-graphics processing units

A multi-GPU system designed for high-speed,real-time signal processing of optical coherencetomography(OCT)is described herein.For the OCT data sampled in linear wave numbers,themaximum procesing rates reached 2.95 MHz for 1024-OCT and 1.96 MHz for 2048-OCT.Data sampled using linear wavelengths were re-sampled using a time-domain interpolation method and zero-padding interpolation method to improve image quality.The maximum processing rates for1024-OCT reached 2.16 MHz for the time-domain method and 1.26 MHz for the zero-paddingmethod.The maximum processing rates for 2048-0CT reached_1.58 MHz,and 0.68 MHz,respectively.This method is capable of high-speed,real-time processing for O CT systems.

Evaluation of the Acoustic Environment in a Protestant Church Based on Measurements of Acoustic Descriptors

The new guidelines of the Catholic Church are in line with the guidelines adopted by Protestant churches since the Reformation, unifying appreciation for the liturgical practices of preaching and congregational singing. These guidelines require that the room, in this case the church, provides appropriate acoustic characteristics, which can be characterized by acoustic descriptors such as Reverberation Time (RT), Clarity (C80) and Definition (D50). In this article, we analyzed the acoustic quality of a protestant church whose design tried to follow these guidelines. Our findings revealed the poor quality of the acoustic environment in terms of both speech intelligibility and music. These findings emphasized the need to adopt not only Reverberation Time but also other acoustic descriptors such as Clarity and Definition in church design.