Research on shipbuilding production logistics system combining MRP-Ⅱ with JIT

Shipbuilding production management and logistics control are studied so as to find an advanced shipbuilding production logistics management style.The necessity and feasibility to integrate MRP-Ⅱ(manufacture resource planning)and JIT(just-in-time)are researched properly following the comparison of MRP-Ⅱ and JIT and the analysis of shipbuilding production characteristics.A shipbuilding logistics system combining MRP-Ⅱ with JIT is set up and discussed according to a master production schedule(MPS),MRP and workshop-floor production control.The system is able to simplify the shipbuilding production plan,balance the workshop production,stabilize the supply,and inspire positivity and creativity so as to raise the shipbuilding production management level.

Distribution, residence time, autotrophic production, and heterotrophic removal of DOP in the Mirs Bay, northern South China Sea

The importance of dissolved organic phosphorus(DOP) as a potential nutrient source for primary producers in marine systems has been recognized for up to eight decades, but currently, the understanding of the biogeochemistry of DOP is in its infancy. In the present study, monthly data between 2000 and 2014 were used to analyze the temporal and spatial distributions of DOP in the Mir Bay, the northern South China Sea. The DOP residence time(TDOP) was also investigated using a simple regression analysis in combination with chlorophyll a(Chl a) measurements while excess DOP(ΔDOP), produced by the biogeochemical processes of autotrophic production and heterotrophic removal, was determined using a two-component mixing mass-balance model in combination with salinity measurements. The results showed that the DOP concentration was(0.017±0.010) mg/L higher in the surface-water compared with the bottom-water and higher in the inner Tolo Harbour and waters adjacent to Shatoujiao compared with the main zone of the bay. Although seasonal changes and annual variability in the DOP were small, the surface DOP concentration was higher in the wet season(April–September)than in the dry season(October–March) due to the impacts of seaward discharges and atmospheric deposition into the bay. Measurement and regression results showed that the DOP release rate from phytoplankton production was about 1.83(gP)/(gChl a) and the TDOP was about 7 d, which implied that the DOP cycle in the bay was rapid. The ΔDOP was calculated from the model to be about 0.000 mg/L in the main zone of the bay and about 0.002 mg/L in the inner Tolo Harbour and waters adjacent to Shaotoujiao, suggesting that the autotrophic production of DOP was almost balanced by the heterotrophic removal in the main zone of the bay and dominated in the inner Tolo Harbour and waters adjacent to Shaotoujiao. In conclusion, the Mirs Bay is very productive and fairly heterotrophic.

Interrupted Time Series Analysis of Military and Civilian Regimes in Nigeria: A Statistical Evidence from Gross Domestic Product (GDP)

Governments influence the economy by changing the level and types of taxes, the extent and composition of spending, and the degree and form of borrowing. Governments directly and indirectly influence the way resources are used in the economy. Higher taxes, fees, and greater regulations can stymie businesses or entire industries and the resulting impact is reflected on the country’s economy status (strong or weak). The growth rate of GDP is often used as an indicator of the general health of the economy. In broad terms, an increase in real GDP is interpreted as a sign that the economy is doing well. So it is important to study and pay more attention to country’s GDP growth rate. In this paper, an intervention analysis approach was applied to Nigeria GDP data in order to evaluate the performances of military and civilian rules in the country. Data on Nigeria GDP were collected and subjected to interrupted (intervention) time series model. Based on the Alkaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and sigma2 values, the interrupted time series model ARIMA (1, 1, 0) with exogenous variables (per capita per capita GDP, intervention, year and yearAfter) was identified as the best model amongst other competing models. It was observed that the intervention (civilian rule) was significant at the 10% level of significance in increasing the Nigeria GDP by 10B US$ on the average since 2005 till 2021 while controlling for the effects of other determinants. Also, the ARIMA (1, 1, 0) forecasts indicate that the Nigeria GDP will continue increasing during the civilian rule. As a result, changing from military rule to civilian rule in Nigeria significantly increased the GDP of the country.

Interrupted Time Series Analysis of Military and Civilian Regimes in Nigeria: A Statistical Evidence from Gross Domestic Product (GDP)

Governments influence the economy by changing the level and types of taxes, the extent and composition of spending, and the degree and form of borrowing. Governments directly and indirectly influence the way resources are used in the economy. Higher taxes, fees, and greater regulations can stymie businesses or entire industries and the resulting impact is reflected on the country’s economy status (strong or weak). The growth rate of GDP is often used as an indicator of the general health of the economy. In broad terms, an increase in real GDP is interpreted as a sign that the economy is doing well. So it is important to study and pay more attention to country’s GDP growth rate. In this paper, an intervention analysis approach was applied to Nigeria GDP data in order to evaluate the performances of military and civilian rules in the country. Data on Nigeria GDP were collected and subjected to interrupted (intervention) time series model. Based on the Alkaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and sigma2 values, the interrupted time series model ARIMA (1, 1, 0) with exogenous variables (per capita per capita GDP, intervention, year and yearAfter) was identified as the best model amongst other competing models. It was observed that the intervention (civilian rule) was significant at the 10% level of significance in increasing the Nigeria GDP by 10B US$ on the average since 2005 till 2021 while controlling for the effects of other determinants. Also, the ARIMA (1, 1, 0) forecasts indicate that the Nigeria GDP will continue increasing during the civilian rule. As a result, changing from military rule to civilian rule in Nigeria significantly increased the GDP of the country.

Water Requirement Rules and Production of Highland Barley in Different Sowing Time and Irrigation Levels

Water requirement rules and production of highland barley in different sowing time and irrigation levels were studied according to the data in field trial of 2016-2017.Based on analyzing the experimental results,the optimal irrigation scheduling and amount in the growth period of highland barley were proposed,which was of great significance to the unification of water saving and high yield of highland barley in the arid agricultural area of Tibet.

A Preliminary Study of Just-in-Time Methods for a Seamless Public Transportation Information Framework

A seamless multimodal transit system has been a goal for transportation planners and users. Owing to the developments of advanced public transportation and telecom technologies such as automatic vehicle location (AVL) and real-time passenger information system, key just-in-time (JIT) concepts can now be realized in process design and coordinated scheduling to shape a seamless multimodal transit system. JIT refers to a production system that times both movements of goods during production and delivery from suppliers together. It meets the same logic for seamless multimodal services. Therefore, this study innovatively analyzes possible public transportation technologies for JIT uses;moreover, to layout the possible application frame-work of a transportation technology based JIT multimodal transit system for main station area in Taipei city. With this innovative framework, multimodal transit services can deliver to the right place at right time.

Analysis of the Main Factors Influencing Food Production in China Based on Time Series Trend Chart

Based on the annual sample data on food production in China since the reform and opening up,we select 8 main factors influencing the total food production( growing area,application rate of chemical fertilizer,effective irrigation area,the affected area,total machinery power,food production cost index,food production price index,financial funds for supporting agriculture,farmers and countryside),and put them into categories of material input,resources and environment,and policy factors. Using the factor analysis,we carry out the multi-angle analysis of these typical influencing factors one by one through the time series trend chart. It is found that application rate of chemical fertilizer,the growing area of food crops and drought-affected area become the key factors affecting food production. On this basis,we set forth the corresponding recommendations for improving the comprehensive food production capacity.

Intentionally unilateral prostatic artery embolization:Patient selection,technique and potential benefits

BACKGROUND Prostatic artery embolization(PAE)is a promising but also technically demanding interventional radiologic treatment for symptomatic benign prostatic hyperplasia.Many technical challenges in PAE are associated with the complex anatomy of prostatic arteries(PAs)and with the systematic attempts to catheterize the PAs of both pelvic sides.Long procedure times and high radiation doses are often the result of these attempts and are considered significant disadvantages of PAE.The authors hypothesized that,in selected patients,these disadvantages could be mitigated by intentionally embolizing PAs of only one pelvic side.AIM To describe the authors’approach for intentionally unilateral PAE(IU-PAE)and its potential benefits.METHODS This was a single-center retrospective study of patients treated with IU-PAE during a period of 2 years.IU-PAE was applied in patients with opacification of more than half of the contralateral prostatic lobe after angiography of the ipsilateral PA(subgroup A),or with markedly asymmetric prostatic enlargement,with the dominant prostatic lobe occupying at least two thirds of the entire gland(subgroup B).All patients treated with IU-PAE also fulfilled at least one of the following criteria:Severe tortuosity or severe atheromatosis of the pelvic arteries,non-visualization,or visualization of a tiny(<1 mm)contralateral PA on preprocedural computed tomographic angiography.Intraprocedural contrast-enhanced ultrasonography(iCEUS)was applied to monitor prostatic infarction.IU-PAE patients were compared to a control group treated with bilateral PAE.RESULTS IU-PAE was performed in a total 13 patients(subgroup A,n=7;subgroup B,n=6).Dose-area product,fluoroscopy time and operation time in the IU-PAE group(9767.8μGy∙m^(2),30.3 minutes,64.0 minutes,respectively)were significantly shorter(45.4%,35.9%,45.8%respectively,P<0.01)compared to the control group.Clinical and imaging outcomes did not differ significantly between the IU-PAE group and the control group.In the 2 clinical failures of IU-PAE(both in subgroup A),the extent of prostatic infarction(demonstrated by iCEUS)was significantly smaller compared to the rest of the IU-PAE group.CONCLUSION In selected patients,IU-PAE is associated with comparable outcomes,but with lower radiation exposure and a shorter procedure compared to bilateral PAE.iCEUS could facilitate patient selection for IU-PAE.

Lag times in toe-to-heel air injection(THAI)operations explain underlying heavy oil production mechanisms

From a time value of revenue point of view,it is preferred that the time between reservoir stimulation and oil production response is small.Heavy oil combustion processes have a lag time between air injection and liquid production,but the common practice in production data analysis uses simultaneous injection and production data when seeking a relationship between them.In this research,the time scales of production for the Kerrobert toe-to-heel air injection(THAI)heavy oil project in Saskatchewan,Canada,is analyzed by using cross correlation analysis,i.e.time delay analysis between air injection and oil production.The results reveal two time scales with respect to production response with two distinctive recovery mechanisms:(1)a short time scale response(nearly instantaneous)where oil production peaks right after air injection(directly after opening production well)reflecting cold heavy oil production mechanisms,and(2)a longer time scale(of order of 100-300 days)response where peak production occurs associated with the collective phenomena of air injection,heat generating reactions,heat transfer,and finally,heated mobilized heavy oil drainage to the production well.This understanding of the two time scales and associated production mechanisms provides a basis for improving the performance of THAI.

Production performance forecasting method based on multivariate time series and vector autoregressive machine learning model for waterflooding reservoirs

A forecasting method of oil well production based on multivariate time series(MTS)and vector autoregressive(VAR)machine learning model for waterflooding reservoir is proposed,and an example application is carried out.This method first uses MTS analysis to optimize injection and production data on the basis of well pattern analysis.The oil production of different production wells and water injection of injection wells in the well group are regarded as mutually related time series.Then a VAR model is established to mine the linear relationship from MTS data and forecast the oil well production by model fitting.The analysis of history production data of waterflooding reservoirs shows that,compared with history matching results of numerical reservoir simulation,the production forecasting results from the machine learning model are more accurate,and uncertainty analysis can improve the safety of forecasting results.Furthermore,impulse response analysis can evaluate the oil production contribution of the injection well,which can provide theoretical guidance for adjustment of waterflooding development plan.

MIP Formulations and Metaheuristics for Multi-Item Capacitated Lot-Sizing Problem with Non-Customer Specific Production Time Windows and Setup Times

Our research focuses on the development of two cooperative approaches for resolution of the multi-item capacitated lot-sizing problems with time windows and setup times (MICLSP-TW-ST). In this paper we combine variable neighborhood search and accurate mixed integer programming (VNS-MIP) to solve MICLSP-TW-ST. It concerns so a particularly important and difficult problem in production planning. This problem is NP-hard in the strong sense. Moreover, it is very difficult to solve with an exact method;it is for that reason we have made use of the approximate methods. We improved the variable neighborhood search (VNS) algorithm, which is efficient for solving hard combinatorial optimization problems. This problem can be viewed as an optimization problem with mixed variables (binary variables and real variables). The new VNS algorithm was tested against 540 benchmark problems. The performance of most of our approaches was satisfactory and performed better than the algorithms already proposed in the literature.

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.

A Time Dependent Inventory Model for Exponential Demand Rate with Constant Production Where Shelf-Life of the Product Is Finite

In this paper a time dependent inventory model is developed on the basis of constant production rate and market demands which are exponentially decreasing. It advances in quest of total average optimum cost considering those products which have finite shelf-life. The model also considers the small amount of decay. Without having any sort of backlogs, production starts. Reaching at the desired level of inventories, it stops production. After that due to demands along with the deterioration of the items it initiates its depletion and after certain periods the inventory gets zero. The decay of the products is level dependent. The objective of this paper is to find out the optimum inventory cost and optimum time cycle. The model has also been justified with proving the convex property and by giving a numerical example.

High-Order Spatial FDTD Solver of Maxwell’s Equations for Terahertz Radiation Production

We applied a spatial high-order finite-difference-time-domain (HO-FDTD) scheme to solve 2D Maxwell’s equations in order to develop a fluid model employed to study the production of terahertz radiation by the filamentation of two femtosecond lasers in air plasma. We examined the performance of the applied scheme, in this context, we implemented the developed model to study selected phenomena in terahertz radiation production, such as the excitation energy and conversion efficiency of the produced THz radiation, in addition to the influence of the pulse chirping on properties of the produced radiation. The obtained numerical results have clarified that the applied HO-FDTD scheme is precisely accurate to solve Maxwell’s equations and sufficiently valid to study the production of terahertz radiation by the filamentation of two femtosecond lasers in air plasma.

RECONFIGURABLE PRODUCTION LINE MODELING AND SCHEDULING USING PETRI NETS AND GENETIC ALGORITHM

In response to the production capacity and functionality variations, a genetic algorithm （GA） embedded with deterministic timed Petri nets（DTPN） for reconfigurable production line（RPL） is proposed to solve its scheduling problem. The basic DTPN modules are presented to model the corresponding variable structures in RPL, and then the scheduling model of the whole RPL is constructed. And in the scheduling algorithm, firing sequences of the Petri nets model are used as chromosomes, thus the selection, crossover, and mutation operator do not deal with the elements in the problem space, but the elements of Petri nets model. Accordingly, all the algorithms for GA operations embedded with Petri nets model are proposed. Moreover, the new weighted single-objective optimization based on reconfiguration cost and E/T is used. The results of a DC motor RPL scheduling suggest that the presented DTPN-GA scheduling algorithm has a significant impact on RPL scheduling, and provide obvious improvements over the conventional scheduling method in practice that meets duedate, minimizes reconfiguration cost, and enhances cost effectivity.

Analysis and Forecast of MSW Production Based on the ARIMA Model in Beijing

Based on the data of MSW generation in Beijing from 2004 to 2012,an ARIMA model of time series analysis was established. By contrast of the modeling results of different yearly data,the forecast period was identified to be 10 years. The yearly production of MSW from 2015 to 2025 was forecasted by using SPSS 16. 0 software. Result shows that the forecasting effect of ARIMA( 1,0,1) model is relatively good,and it can be applied to prediction of MSW production in Beijing. In the next 10 years,the amount of MSW produced in Beijing is increasing,but the growth rate is not large. Is expected to 2025,the production of MSW will reach more than 9 million tons. Taking into account the MSW return,it is inferred that the production of MSW in Beijing in 2025 will be close to 10 million tons. In order to reduce the pressure of subsequent waste disposal facilities in Beijing,the government can increase the intensity of the recycling of waste materials.

A production prediction method of single well in water flooding oilfield based on integrated temporal convolutional network model

Since the oil production of single well in water flooding reservoir varies greatly and is hard to predict, an oil production prediction method of single well based on temporal convolutional network(TCN) is proposed and verified. This method is started from data processing, the correspondence between water injectors and oil producers is determined according to the influence radius of the water injectors, the influence degree of a water injector on an oil producer in the month concerned is added as a model feature, and a Random Forest(RF) model is built to fill the dynamic data of water flooding. The single well history is divided into 4 stages according to its water cut, that is, low water cut, middle water cut, high water cut and extra-high water cut stages. In each stage, a TCN based prediction model is established, hyperparameters of the model are optimized by the Sparrow Search Algorithm(SSA). Finally, the models of the 4 stages are integrated into one whole-life model of the well for production prediction. The application of this method in Daqing Oilfield, NE China shows that:(1) Compared with conventional data processing methods, the data obtained by this processing method are more close to the actual production, and the data set obtained is more authentic and complete.(2) The TCN model has higher prediction accuracy than other 11 models such as Long Short Term Memory(LSTM).(3) Compared with the conventional full-life-cycle models, the model of integrated stages can significantly reduce the error of production prediction.

Anomaly Detection in MODIS Land Products via Time Series Analysis

With remote sensing information products becoming increasingly varied and arguably improved, scientific applications of such products rely on their quality assessment. In an operational context such as the NASA (National Aeronautics and Space Administration) information production based on the MODIS (Moderate Resolution Imaging Spectroradiometer) instrument on board Earth Observing System (EOS) Terra and Aqua satellites, efficient ways of detecting product anomaly, i.e., to discriminate between product artifacts and real changes in Earth processes being monitored, are extremely important to assist and inform the user communities about potential unreliability in the products. A technique for anomaly detection, known as MAD (the median of absolute deviate from the median), in MODIS land products via time series analysis is described, which can handle intra- and in-ter-annual variation in the data by using MAD statistics of the original data and their first-order difference. This method is shown to be robust and work across major land products, including NDVI, active fire, snow cover, and surface reflectance, and its applicabil-ity to multi-disciplinary products is anticipated.

Prolonged prothrombin time at admission predicts poor clinical outcome in COVID-19 patients

BACKGROUND Supported by the National Natural Science Foundation of China,No.The prognostic value of coagulation disorder in coronavirus disease 2019(COVID-19)patients should be demonstrated.AIM To investigate the abnormalities of coagulation parameters in the patients with COVID-19 and their prognostic values.METHODS Consecutive patients admitted in the isolation ward of Renmin Hospital of Wuhan University from January 31 to February 5,2020 with confirmed COVID-19 were included.The primary outcomes were death and survival as of March 11.Demographics,vital signs,comorbidities and laboratory tests were collected and compared between those who died and survivors.Logistic regression analysis for prognostic factors was performed.Kaplan-Meier analysis was used to compare the estimated survival rate between patients with prolonged prothrombin time and normal prothrombin time.RESULTS The total number of patients with confirmed COVID-19 who were enrolled was 213.The median age was 62 years,and 95 patients(44.6%)were men.Fifty-one patients were critical(23.9%),79 patients were severe(37.1%)and 83 patients were moderate(39%).As of March 11,2020,99 patients were discharged(46.5%),79 patients(37.1%)stayed in the hospital and 35 patients(16.2%)died.Median time to death was 6(4-8)d,while median hospital stay was 32(22-36)d in survivors(P<0.001).More men(P=0.002)and elderly patients(P<0.001)were found in the group of those who died.The respiration rate at admission was higher in the group of those who died(P<0.001).The incidences of hypertension(P=0.028),cerebrovascular disease(P<0.001),chronic kidney disease(P=0.02)and chronic obstructive pulmonary disease(P<0.001)were higher in the group of those who died.Platelet count was decreased in the group of those who died(P=0.002)whereas prothrombin time(P<0.001),activated partial thromboplastin time(P=0.033),concentration of D-dimer(P<0.001)and fibrin degradation products(P<0.001)were increased in the group of those who died.Prothrombin time[odds ratio(OR):2.19,P=0.004],respiration rate(OR:1.223,P<0.001),age(OR:1.074,P<0.001)and fibrin degradation products concentration(OR:1.02,P=0.014)were predictors of death.The survival rate was significantly lower in patients with prolonge CONCLUSION Prothrombin time,concentration of fibrin degradation products,respiration rate and age were predictive factors for clinical outcomes of COVID-19 patients.