Population Forecast of China’s Rural Community Based on CFANGBM and Improved Aquila Optimizer Algorithm

Rural community population forecasting has important guiding significance to rural construction and development. In this study, a novel grey Bernoulli model combined with an improved Aquila Optimizer (IAO) was used to forecast rural community population in China. Firstly, this study improved the Aquila Optimizer by combining quasi-opposition learning strategy and wavelet mutation strategy, and proposed the new IAO algorithm. By comparing with other algorithms on CEC2017 test functions, the proposed IAO algorithm has the advantages of faster convergence speed and higher convergence accuracy. Secondly, based on the data of China’s rural community population from 1990 to 2019, a consistent fractional accumulation nonhomogeneous grey Bernoulli model called CFANGBM(1, 1, b, c) was established for rural population forecasting. The proposed IAO algorithm was used to optimize the parameters of the model, and then the rural population of China was predicted. Four error measures were used to evaluate the model, and by comparing with other forecasting models, the experimental results show that the proposed model had the smallest error between the forecasted value and the real value, which illustrates the effectiveness of using the IAO algorithm to solve CFANGBM(1, 1, b, c). At the end of this paper, the forecast data of China’s rural population from 2020 to 2024 are given for reference.

SOLUSI EKSAK MODEL EPIDEMI SUSCEPTIBLE-INFECTED-RECOVERED-DEATH

Model epidemi Susceptible-Infected-Recovered-Death (SIRD) adalah pengembangan dari model epidemi Susceptible-Infected-Removed (SIR) yang membagi kompartemen removed menjadi kompartemen recovered dan death. Dalam makalah ini dibahas kembali penurunan model SIRD. Selanjutnya dengan menggunakan persamaan Bernoulli, model tersebut diselesaikan untuk memperoleh solusi eksak dalam bentuk parametrik. Pengujian secara numerik untuk beberapa nilai parameter menunjukkan bahwa solusi numerik persis sama dengan solusi eksak.Kata Kunci: Solusi eksak, model epidemi Susceptible-Infected-Recovered-Death (SIRD), persamaan Bernoulli

Singularity of sparse random matrices: simple proofs

Consider a random $n\times n$ zero-one matrix with ‘sparsity’ p, sampled according to one of the following two models: either every entry is independently taken to be one with probability p (the ‘Bernoulli’ model) or each row is independently uniformly sampled from the set of all length-n zero-one vectors with exactly pn ones (the ‘combinatorial’ model). We give simple proofs of the (essentially best-possible) fact that in both models, if $\min(p,1-p)\geq (1+\varepsilon)\log n/n$ for any constant $\varepsilon>0$ , then our random matrix is nonsingular with probability $1-o(1)$ . In the Bernoulli model, this fact was already well known, but in the combinatorial model this resolves a conjecture of Aigner-Horev and Person.