Generalized Bernoulli process: simulation, estimation, and application

A generalized Bernoulli process (GBP) is a stationary process consisting of binary variables that can capture long-memory property. In this paper, we propose a simulation method for a sample path of GBP and an estimation method for the parameters in GBP. Method of moments estimation and maximum likelihood estimation are compared through empirical results from simulation. Application of GBP in earthquake data during the years of 1800-2020 in the region of conterminous U.S. is provided.

Incoherent dimensionality in fisheries management: consequences of misaligned stock assessment and population boundaries

Fisheries policy inherently relies on an explicit definition of management boundaries that delineate the spatial extent over which stocks are assessed and regulations are implemented. However, management boundaries tend to be static and determined by politically negotiated or historically identified population (or multi-species) units, which create a potential disconnect with underlying, dynamic population structure. The consequences of incoherent management and population or stock boundaries were explored through the application of a two-area spatial simulation–estimation framework. Results highlight the importance of aligning management assessment areas with underlying population structure and processes, especially when fishing mortality is disproportionate to vulnerable biomass among management areas, demographic parameters (growth and maturity) are not homogenous within management areas, and connectivity (via recruitment or movement) unknowingly exists among management areas. Bias and risk were greater for assessments that incorrectly span multiple population segments (PSs) compared to assessments that cover a subset of a PS, and these results were exacerbated when there was connectivity between PSs. Directed studies and due consideration of critical PSs, spatially explicit models, and dynamic management options that help align management and population boundaries would likely reduce estimation biases and management risk, as would closely coordinated management that functions across population boundaries.

Cross-seasonal Experimental Study on the Comprehensive Performance of C-Si PV Window

PV windows can potentially produce electricity, reduce the air-conditioning load, and provide natural light. Cross-seasonal experimental tests for long period could eliminate the gap between real outdoor test and simulation estimation. In this research, the lighting-thermal-electricity performance of high-efficiency c-silicon PV windows was tested, and the improvement has been put forward according to the conditions. The long-term experiment was conducted cross different seasons, including summer, autumn, and winter seasons. The highest average power generation, 50W/m2, could be found in autumn. The average outside surface temperature of the PV window would reach 48 °C in sunny days in the autumn, which was higher than other seasons. Although the c-Si PV window maybe block the partial daylighting, the daylighting requirement still could be satisfied with the most days. Furthermore, the average Useful Daylight Illuminance was the highest in summer up to 0.79, and the average illumination uniform could be achieved at a high level in all seasons. For the improvement suggestions, some measures could be taken to reduce the indoor cooling load in summer. During winter, appropriate inner shading measures might be taken to prevent excessive illumination in the building, and allow the electricity and thermal performance of PV window.

A Laboratory Experimental and Reservoir Simulation Estimation on Minimum Miscible Pressure (MMP) for CO2 Injection in HIL Oil Field

As one of the Enhanced Oil Recovery (EOR) methods, CO2 injection is one of the methods used after secondary recovery to increase oil recovery. Based on the successful application of CO2 injection in some countries and the need of fulfillment of energy demand in Indonesia, CO2 injection can be the best method to be used for EOR. This method injects CO2 that has been observed in the laboratory through injection well to reservoir. In the reservoir, CO2 gas will be miscible with oil to decrease oil’s viscosity. But, to be miscible with oil and to avoid the reservoir problem, the Minimum Miscible Pressure (MMP) must be observed in the laboratory. Moreover, a reservoir simulation investigation must be performed to get a promising result. In this paper, the laboratory experimental and reservoir simulation on MMP to achieve CO2 gas miscibility on oil sample from HIL oil field has been performed. The MMP result from the laboratory experiment is 2385 psia and is increasing oil recovery up to 85% while the MMP from reservoir simulation is 2404 psia. With the differential value of just 19 psia or error of 0. 89%, this finding can be the basis for a recommendation to develop a CO2 project in the HIL oil field.

Performance evaluation of data-limited, length-based stock assessment methods

Performance evaluation of data-limited, length-based methods is instrumental in determining and quantifying their accuracy under various scenarios and in providing guidance about model applicability and limitations. We conducted a simulation–estimation analysis to compare the performance of four length-based stock assessment methods: length-based Thompson and Bell (TB), length-based spawning potential ratio (LBSPR), length-based integrated mixed effects (LIME), and length-based risk analysis (LBRA), under varying life history, exploitation status, and recruitment error scenarios. Across all scenarios, TB and LBSPR were the most consistent and accurate assessment methods. LBRA is highly biased, but precautionary, and LIME is more suitable for assessments with time-series longer than a year. All methods have difficulties when assessing short-lived species. The methods are less accurate in estimating the degree of recruitment overfishing when the stocks are severely overexploited, and inconsistent in determining growth overfishing when the stocks are underexploited. Increased recruitment error reduces precision but can decrease bias in estimations. This study highlights the importance of quantifying the accuracy of stock assessment methods and testing methods under different scenarios to determine their strengths and weaknesses and provides guidance on which methods to employ in various situations.