The study of productivity in the Bohai Sea-Ⅱ. Primary productivity and estimation of potential fish catch

-On the basis of the data obtained from the surveys in the Bohai Sea during 1982-1983, this paper analysed and discussed the distribution and seasonal variation of primary productivity in the Bohai Sea, and the correlations between the primary productivity and environmental factors. The organic carbon production and prospect of fishery production in the waters of this sea are estimated. It is shown that, there exists production patential in the Bohai Sea, the primary production is 112 gC/ (m2 ?a)the production of organic carbon being 10 million ton per year, the fishery yields 1 million ton and the maximum catch of sea products 0. 5 million ton. The results of the investigation can serve as the basic data for the exploitation, utilization and management of the fishery resources in the Bohai Sea.

Distribution of chlorophyU a and estimation of primary productivity in the eastern waters of Balingtang Channel in summer

Oceanographical features on both sides of Balingtang Channel (17°55′-20°06′N, 122°55′-126°57′E) were comprehensively investigated on board of R/V "Experiment 3" in June. 1984. The pre-sent paper reports the chlorophyll data collected and primary productivity estimated there. Water sam-ples were taken with a glass bottle of Model HQMat the depth of 0, 10. 25, 50, 75, 100 and 150m, separately. Chlorophyll was determined according to the spectrophotometry proposed by UNESCO(1966) and calculated with the trichromatic equations of Jeffrey-Humphrey(1975). Estimations ofprimary productivity were carried out using a simplified equation (Q = 1.5) given by Cadee(1975).

Enhancing production monitoring: A back allocation methodology to estimate well flow rates and assist well test scheduling

Production flow rates are crucial to make operational decisions,monitor,manage,and optimize oil and gas fields.Flow rates also have a financial importance to correctly allocate production to fiscal purposes required by regulatory agencies or to allocate production in fields owned by multiple operators.Despite its significance,usually only the total field production is measured in real time,which requires an alternative way to estimate wells'production.To address these challenges,this work presents a back allocation methodology that leverages real-time instrumentation,simulations,algorithms,and mathe-matical programming modeling to enhance well monitoring and assist in well test scheduling.The methodology comprises four modules:simulation,classification,error calculation,and optimization.These modules work together to characterize the flowline,wellbore,and reservoir,verify simulation outputs,minimize errors,and calculate flow rates while honoring the total platform flow rate.The well status generated through the classification module provides valuable information about the current condition of each well(i.e.if the well is deviating from the latest well test parameters),aiding in decision-making for well testing scheduling and prioritizing.The effectiveness of the methodology is demonstrated through its application to a representative offshore oil field with 14 producing wells and two years of daily production data.The results highlight the robustness of the methodology in properly classifying the wells and obtaining flow rates that honor the total platform flow rate.Furthermore,the methodology supports well test scheduling and provides reliable indicators for well conditions.By uti-lizing real-time data and advanced modeling techniques,this methodology enhances production monitoring and facilitates informed operational decision-making in the oil and gas industry.

Direction-of-arrival estimation of quasi-stationary signals using two-level Khatri-Rao subspace and four-level nested array

The Khatri-Rao(KR) subspace method is a high resolution method for direction-of-arrival(DOA) estimation.Combined with 2q level nested array,the KR subspace method can detect O(N2q) sources with N sensors.However,the method cannot be applicable to Gaussian sources when q is equal to or greater than 2 since it needs to use 2q-th order cumulants.In this work,a novel approach is presented to conduct DOA estimation by constructing a fourth order difference co-array.Unlike the existing DOA estimation method based on the KR product and 2q level nested array,the proposed method only uses second order statistics,so it can be employed to Gaussian sources as well as non-Gaussian sources.By exploiting a four-level nested array with N elements,our method can also identify O(N4) sources.In order to estimate the wideband signals,the proposed method is extended to the wideband scenarios.Simulation results demonstrate that,compared to the state of the art KR subspace based methods,the new method achieves higher resolution.

Time-Frequency Bandwidth Product Estimation of Sinusoidal Non-Linear Chirp Keying Scheme

Sine Non-linear Chirp Keying(SNCK) is a kind of high-efficient modulation scheme, which provides a potential new beamforming method in communication and radar systems. It has been proved to have advantages in some parameter estimation issues over conventional modulation schemes. In this paper, a novel transform termed as Discrete Sinusoidal Frequency Modulation transform(DSFMT) is proposed. Then, the DSFMT of SNCK signal is deduced and classified into three types, based on which, the time-bandwidth product is estimated by the proposed algorithm. Simulation results show that the noise has a signifi cant impact on the localization of the peak value and the time-bandwidth product can be estimated by using local ratio values when.

A Non-Destructive Time Series Model for the Estimation of Cherry Coffee Production

Coffee plays a key role in the generation of rural employment in Colombia.More than 785,000 workers are directly employed in this activity,which represents the 26%of all jobs in the agricultural sector.Colombian coffee growers estimate the production of cherry coffee with the main aim of planning the required activities,and resources(number of workers,required infrastructures),anticipating negotiations,estimating,price,and foreseeing losses of coffee production in a specific territory.These important processes can be affected by several factors that are not easy to predict(e.g.,weather variability,diseases,or plagues.).In this paper,we propose a non-destructive time series model,based on weather and crop management information,that estimate coffee production allowing coffee growers to improve their management of agricultural activities such as flowering calendars,harvesting seasons,definition of irrigation methods,nutrition calendars,and programming the times of concentration of production to define the amount of personnel needed for harvesting.The combination of time series and machine learning algorithms based on regression trees(XGBOOST,TR and RF)provides very positive results for the test dataset collected in real conditions for more than a year.The best results were obtained by the XGBOOST model(MAE=0.03;RMSE=0.01),and a difference of approximately 0.57%absolute to the main harvest of 2018.

An improved activity-based costing model for product cost estimation applied in a complex manufacturing environment

In order to evaluate and estimate me cosEs oi prouuct~ plt, uu^u e vironment properly, an improved activity-based costing （ABC） model is presented. By utilizing the input-output analysis method, the complex consumption relationships in a complex manufacturing environment are first expressed. The consumption characteristics （mainly presented by the activity rates） of all production activities are extracted by solving these relationships. Then with the con- sumption characteristics and operating parameters of these activities, the detailed cost consumption of a product in its manufacturing process is estimated. A case study is finally given based on the compressor products of a manufacturing company, and its effectiveness is shown. As the cost influ- ence of complex consumption relationships is fully considered, the limitation of traditional ABC method is overcome, and therefore a high accuracy in product cost estimation under the complex manufacturing environment can be achieved.

The depletion of nutrients and the estimation of the new production in the ice-edge of the Prydz Bay,Antarctica

The distributions and changes of nutrients in the Prydz Bay were investigated during CHINARE 6(the 6th Chinese National Antarctic Research Expedition) and CHINARE 9, and the new productions were estimated by using mass balance method. The results showed that in CHINARE 6, the depletion of NO 3 N, PO 4 P, SiO 3 Si were 1064.8, 69.2, 2196.9 mmol/m 2 ,respectively, the uptake ratios of N and Si to P were 15.3, 31.7, respectively, and new production was 992.4 mgC/(m 2·d) ; in CHINARE 9, the depletion of NO 3 N, PO 4 P were 551.9 , 41.2 mmol/m 2 , respectively, the uptake ratio of N to P was 13.4,and the new production was 390.1 mgC/(m 2·d) , indicating significant annual and/or seasonal variation. New productions were both greater than the primary production determined by 14 C trace method, demonstrating there existed rather higher new production in the Prydz Bay in austral summer. The uptake ratio of nutrients in the Prydz Bay, however, is different from the previous values obtained from the open sea, showing the regional difference of uptake and regeneration of nutrients in the Southern Ocean. Finally the annual change of new production and the characteristics of phytoplankton production were discussed.

A Cost-benefit Measuring Model of Green Products in Manufacturing Industry

The paper builds up a cost-benefit measuring model of green products in manufacturing industry throughout its full life cycle, which can quantify green products＇ cost and benefit completely and correctly under the circumstance of satisfying enterprise, customer, environment and society. It also puts forth an operable method to estimate social benefit by opportunity cost and establishes a profit maximization-programming model. The model can be applied to justify whether some kinds of green products should be developed and produced.

Generalized Class of Mean Estimators with Known Measures for Outliers Treatment

In estimation theory,the researchers have put their efforts to develop some estimators of population mean which may give more precise results when adopting ordinary least squares(OLS)method or robust regression techniques for estimating regression coefficients.But when the correlation is negative and the outliers are presented,the results can be distorted and the OLS-type estimators may give misleading estimates or highly biased estimates.Hence,this paper mainly focuses on such issues through the use of non-conventional measures of dispersion and a robust estimation method.Precisely,we have proposed generalized estimators by using the ancillary information of non-conventional measures of dispersion(Gini’s mean difference,Downton’s method and probabilityweighted moment)using ordinary least squares and then finally adopting the Huber M-estimation technique on the suggested estimators.The proposed estimators are investigated in the presence of outliers in both situations of negative and positive correlation between study and auxiliary variables.Theoretical comparisons and real data application are provided to show the strength of the proposed generalized estimators.It is found that the proposed generalized Huber-M-type estimators are more efficient than the suggested generalized estimators under the OLS estimation method considered in this study.The new proposed estimators will be useful in the future for data analysis and making decisions.

A New Regression Type Estimator and Its Application in Survey Sampling

In the present time, a large number of modified estimators have been proposed by authors to obtain efficiency. In this study, we suggested an alternative regression type estimator for estimating finite population means when there is either a positive or negative correlation between study variables and auxiliary variables. We obtained bias and mean square error equation of the proposed estimator ignoring the first-order approximation and found the theoretical conditions that make proposed estimator more efficient than simple random sampling mean estimator, product estimator and ratio estimator. In addition, these conditions are supported by a numerical example and it has been concluded that the proposed estimator performed better comparing with the usual simple random sampling mean estimator, ratio estimator and product estimator.

Exponentiated Generalized Inverse Flexible Weibull Distribution:Bayesian and Non-Bayesian Estimation Under Complete and TypeⅡCensored Samples with Applications

In this paper,a new 4-parameter exponentiated generalized inverse flexible Weibull distribution is proposed.Some of its statistical properties are studied.The aim of this paper is to estimate the model parameters via several approaches,namely,maximum likelihood,maximum product spacing and Bayesian.According to Bayesian approach,several techniques are used to get the Bayesian estimators,namely,standard error function,Linex loss function and entropy loss function.The estimation herein is based on complete and censored samples.Markov Chain Monte Carlo simulation is used to discuss the behavior of the estimators for each approach.Finally,two real data sets are analyzed to obtain the flexibility of the proposed model.