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This paper aims to propose a deployment optimization and efficient synchronous acquisition method for compressive stress sensors used by stress distribution law research based on the genetic algorithm and numerical simulations. The authors established a new method of collecting the mining compressive stress-strain distribution data to address the problem of the number of sensors and to optimize the sensor locations in physical similarity simulations to improve the efficiency and accuracy of data collection.

First, numerical simulations were used to obtain the compressive stress distribution curve under specific mining conditions. Second, by comparing the mean square error between a fitted curve and simulation data for different numbers of sensors, a genetic algorithm was used to optimize the three-dimensional (3D) spatial deployment of sensors. Third, the authors designed an efficient synchronous acquisition module to allow distributed sensors to achieve synchronous and efficient acquisition of hundreds of data points through a built-in on-board database and a synchronous sampling communication structure.

The sensor deployment scheme was established through the genetic algorithm, A synchronous and selective data acquisition method was established for reduced the amount of sensor data required under synchronous acquisition and improved the system acquisition efficiency. The authors obtained a 3D compressive stress distribution when the advancement was 200 m on a large-scale 3D physical similarity simulation platform.

The proposed method provides a new optimization method for sensor deployment in physical similarity simulations, which improves the efficiency and accuracy of system data acquisition, providing accurate acquisition data for experimental data analysis.

In this study, a physical similarity simulation plays a significant role in the study of crack evolution and the gas migration mechanism. A sensor is deployed inside a comparable artificial rock formation to assure the accuracy of the experiment results. During the building of the simulated rock formation, a huge volume of acidic gas is released, causing numerous sensor measurement mistakes. Additionally, the gas concentration estimation approach is subject to uncertainty because of the complex rock formation environment. As a result, the purpose of this study is to introduce an adaptive Kalman filter approach to reduce observation noise, increase the accuracy of the gas concentration estimation model and, finally, determine the gas migration law.

First, based on the process of gas floatation-diffusion and seepage, the gas migration model is established according to Fick’s second law, and a simplified modeling method using diffusion flux instead of gas concentration is presented. Second, an adaptive Kalman filter algorithm is introduced to establish a gas concentration estimation model, taking into account the model uncertainty and the unknown measurement noise. Finally, according to a large-scale physical similarity simulation platform, a thorough experiment about gas migration is carried out to extract gas concentration variation data with certain ventilation techniques and to create a gas chart of the time-changing trend.

This approach is used to determine the changing process of gas distribution for a certain ventilation mode. The results match the rock fissure distribution condition derived from the microseismic monitoring data, proving the effectiveness of the approach.

For the first time in large-scale three-dimensional physical similarity simulations, the adaptive Kalman filter data processing method based on the inverse Wishart probability density function is used to solve the problem of an inaccurate process and measurement noise, laying the groundwork for studying the gas migration law and determining the gas migration mechanism.

This study was designed to assess the risk factors associated with hypertension (HTN) and diabetes among artisans in Ogun State, Nigeria. Evidences suggest increasing prevalence, incidences and morbidity of diabetes and HTN in Nigeria. However, the purpose of this study is to plan and prioritize effective intervention programs, there is need to provide data on the prevalence and risk factors for HTN and diabetes among local groups.

In total 300 apparently healthy artisans who have never been diagnosed of diabetes or HTN were randomly selected from five communities. A structured questionnaire was used in obtaining information on the personal characteristics of the respondents. An adapted dietary habit and lifestyle questionnaire were used to assess the dietary habits and lifestyle of the respondents. The WHO global activity questionnaire was adapted and used to gather information on the physical activity level of the respondents. Random blood glucose, blood pressure and anthropometric measurements were assessed using standard instruments. Chi-square (χ²), correlations and multinomial logistic regression analysis were performed to identify significant determinants of diabetes and HTN.

Mean age was 34.8 ± 9.9 and prevalence of diabetes and pre-diabetes were 1 and 4.7 per cent, respectively, while HTN and pre-HTN were 48.0 and 30.3 per cent, respectively. About half (55.7 per cent) of the respondents skip at least a meal daily and 31 per cent snack daily. Most (61.4 per cent) consume alcohol and 65.7 per cent engage in high physical activity. Abdominal obesity was significantly higher among women (p = 0.004). Using the chi-square analysis, age, abdominal obesity and educational status were factors found to be significantly associated with diabetes (p = 0.002; p = 0.007; p = 0.004) while age, gender, abdominal obesity and alcohol consumption had significant association with HTN. Although not statistically significant, respondents were 0.8, 1.0 and 1.1 times more likely to be diabetic with increasing body mass index, waist circumference (WC) and age (odd ratio (OR) = 0.78; confidence intervals (CI): 0.51-1.18; OR = 1.04; CI: 0.89-1.21; OR = 1.06; CI: 0.96-1.18, respectively). Abdominal obesity was significantly associated with HTN (OR = 1.08; CI: 1.03-1.13; p = 0.001). Also, older respondents were 1.1 times more likely of becoming hypertensive (OR = 1.07; CI: 1.02-1.11; p = 0.003). Increased risk of diabetes and HTN was found among respondents with increasing age and WC.

This study was cross-sectional in design; it cannot be used to establish a cause-effect relationship between diabetes, HTN and the observed variables (anthropometric characteristics, dietary habits and lifestyle risk factors). Because of the few numbers (1 per cent) of respondents identified to be diabetic several important risk factors could not be included in the model.

An understanding of the risk factors associated with diabetes and HTN among sub-groups in the population will help to plan effective interventions targeted at specific groups.

The findings of this study show the associated risk factors for diabetes and HTN among artisans in Ogun State.

The authors consider a dynamic emission-reduction technology investment decision-making problem for an emission-dependent dyadic supply chain consists of a manufacturer and a retailer under subsidy policy for carbon emission reduction. The consumers are assumed to prefer to low-carbon products and formulate a supply chain optimal control problem.

The authors adopt differential game to analyze investment strategies of cost subsidy coefficient with respect to vertical incentive of a manufacturer and a retailer. A comparison analysis under four different decision-making situations, including decentralized decision-making, centralized decision-making, maximizing social welfare, is obtained.

The results show that the economic benefit and environmental pressure have a win–win performance in centralized decision-making. In four different game models, equilibrium strategies, profits and social welfare show changing diversity and have a consistent development trend as time goes on.

The authors estimate the demand function is a linear function in this paper. According to the consumers’ preference to low-carbon products, consumer’s awareness meets the law of diminishing marginal utility like advertising goodwill accumulation. The carbon-sensitive coefficient might be a quadratic expression, which will complicate the problem and be consistent with reality.

It captures that there is a necessity to strengthen cooperation and exchange of carbon emission technology among the enterprises by simulation of different decision-makings when government granted cost subsidy.

The results provide significant guidelines for the supply chain to make decision-makings of emission-reduction technology investment and relevant government departments to determine emission subsidies costs.

An endogenous subsidies coefficient is produced by the social welfare function. Distinguished from previous study, it also considered the influences of carbon emission trade policy and consumer preference.