UNASSIGNED: This study examines the impact of socioeconomic factors on calorie intake and macronutrient composition at the household level in Pakistan from 2006 to 2016, using data from the Household Integrated Income and Consumption Survey (HIICS). By applying a copula-based decomposition method, it identifies key drivers such as urbanization, household size, paternal education, income, and cultivation, highlighting their roles in dietary changes and implications for public health. The findings are crucial for understanding nutritional shifts and addressing non-communicable diseases.
UNASSIGNED: This study was conducted to assess the socioeconomic changes in total calorie intake per capita and calories obtained from macronutrients (fat, protein, and carbohydrates) at the household level in Pakistan.
UNASSIGNED: : Cross-sectional data were taken from 2 national-level surveys published by the Pakistan Bureau of Statistics: the Household Integrated Economic Survey 2006 (14,948 households) and the Household Integrated Income and Consumption Survey 2016 (7842 households). Participants were from all 4 provinces of Pakistan. A copula-based decomposition method was applied to decompose the 10-y change in the distribution (mean, median, and quartiles) of the total calorie intake per capita and calories obtained from macronutrients.
UNASSIGNED: The estimated results of decomposition revealed that total calorie intake per capita has increased on average and in the considered quartiles. The calories obtained from fat and carbohydrates have increased, whereas calories from protein have decreased, according to the distribution of the mean and quartile. The composition effect was negative for all outcome variables, and the main drivers of the composition effect were urbanization, household size, paternal education, income, and cultivation for all outcome variables.
UNASSIGNED: Household size and income are the most important covariates in an increase of total calories per capita and consumption of macronutrients, but urbanization, paternal education, and cultivation contribute negatively to the composition effect. Such findings are very important to inform researchers about nutritional change at the national level because the correlation between dietary change and risk factors for noncommunicable diseases such as heart disease and obesity is very strong.

The increasingly frequent occurrence of urban heatwaves has become a significant threat to human health. To quantitatively analyze changes in heatwave characteristics and to investigate the return periods of future heatwaves in Wuhan City, China, this study extracted 9 heatwave definitions and divided them into 3 mortality risk levels to identify and analyze historical observations and future projections of heatwaves. The copula functions were employed to derive the joint distribution of heatwave severity and duration and to analyze the co-occurrence return periods. The results demonstrate the following. (1) As the concentration of greenhouse gas emissions increases, the severity of heatwaves intensifies, and the occurrence of heatwaves increases significantly; moreover, a longer duration of heatwaves correlated with higher risk levels in each emission scenario. (2) Increasing concentrations of greenhouse gas emissions result in significantly shorter heatwave co-occurrence return periods at each level of risk. (3) In the 3 risk levels under each emission scenario, the co-occurrence return periods for heatwaves become longer as heatwave severity intensifies and duration increases. Under the influence of climate change, regional-specific early warning systems for heatwaves are necessary and crucial for policymakers to reduce heat-related mortality risks in the population, especially among vulnerable groups.

Droughts are increasingly frequent as the Earth warms, presenting adaptation challenges for ecosystems and human communities worldwide. A strategic environmental assessment (SEA) and the integration of adaptation strategies into policies, plans, and programs (PPP) are two important approaches for enhancing climate resilience and fostering sustainable development. This study developed an innovative approach to strengthen the SEA of droughts by quantifying the impacts of future temperature increases. A novel method for projecting drought events was integrated into the SEA process by leveraging multiple data sources, including atmospheric reanalysis, reconstructions, satellite-based observations, and model simulations. We identified drought conditions using terrestrial water storage (TWS) anomalies and applied a random forest (RF) model for disentangling the drivers behind drought events. We then set two global warming targets (2.0 °C and 2.5 °C) and analyzed drought changes under three shared socioeconomic pathways (SSP126, SSP370, SSP585). In a 2.0 °C warming world, over 50 % of the global surface will face increased drought risk. With an additional 0.5 °C increase, >60 % of the land will be prone to further drought escalation. We utilized copulas to build the joint distribution for drought duration and severity, estimating the joint return periods (JRP) for bivariate drought hazard. In tropical and subtropical regions, JRP reductions exceeding half are projected for >33 % of the regional land surface under 2.0 °C warming and for >50 % under 2.5 °C warming. Finally, we projected the impacts of drought events on population and gross domestic product (GDP). Among the three SSPs, under SSP370, population exposure is highest and GDP exposure is minimal under 2.0 °C warming. Global GDP and population risks from drought are projected to increase by 37 % and 24 %, respectively, as warming continues. This study enhances the accuracy of SEA in addressing drought risks and vulnerabilities, supporting climate-resilient planning and adaptive strategies.

We quantified the lag time of vegetation response to drought in the Pearl River basin (PRB) based on the standardized precipitation evapotranspiration index (SPEI) and normalized difference vegetation index (NDVI), and constructed a vegetation loss probability model under drought stress based on the Bayesian theory and two-dimensional joint distribution. We further quantitatively evaluated the spatial variations of loss probability of four vegetation types (evergreen broadleaf forest, mixed forest, grassland, and cropland) under different drought intensities. The results showed that the drought risk in eastern West River, the upper reaches of North River and East River, and southern Pearl River Delta was obviously higher than that in other regions during 1982-2020. The response time of vegetation to drought in high-altitude areas in the upper reaches of PRB (mostly<3 month) was generally shorter than that in low altitude areas (>8 month). Drought exacerbated the probability of vegetation loss, with higher vulnerability of mixed forest than the other three vegetation types. The loss probability of vegetation was lower in northwestern PRB than that in central PRB.
基于标准化降水蒸散指数(SPEI)和归一化植被指数(NDVI),在网格尺度上定量识别珠江流域植被对干旱响应的滞后时间;基于贝叶斯理论和二维联合分布构建了干旱胁迫下植被损失的条件概率模型,定量评估了不同干旱情景下4种植被类型(常绿阔叶林、混交林、草地、耕地)的损失风险的空间分异性。结果表明: 1982—2020年间,西江流域东部、北江和东江流域上游以及珠三角南部地区的干旱风险明显高于研究区的其他区域;上游高海拔地区植被对干旱的响应时间(大多<3个月)通常小于低海拔地区(>8个月);干旱加剧了植被的脆弱性,其中,混交林在不同干旱强度下的损失概率大于其他3种植被类型,表现出更高的脆弱性;流域西北部植被损失概率低于流域中部地区。.

Vibrational environments can cause drift or changes in Micro-Electro-Mechanical System (MEMS) gyroscope rotor parameters, potentially impacting their performance. To improve the effective use of MEMS gyroscopes, this study introduced a method for evaluating the reliability of parameter degradation under vibration. We analyzed the working principle of MEMS gyroscope rotors and investigated how vibration affects their parameters. Focusing on zero bias and scale factor as key performance indicators, we developed an accelerated degradation model using the distributional assumption method. We then collected degradation data for these parameters under various vibration conditions. Using the Copula function, we established a reliability assessment approach to evaluate the degradation of the MEMS gyroscope rotor\'s zero bias and scale factor under vibration, enabling the determination of reliability for these parameters. Experimental findings confirmed that increasing stress levels lead to reduced failure times and increased failure rates for MEMS gyroscope rotors, with significant changes observed in the zero bias parameter. Our evaluation method effectively characterizes changes in the reliability of the MEMS gyroscope rotor\'s scale factor and zero bias over time, providing valuable information for practical applications of MEMS gyroscopes.

Chongqing, as the last ecological barrier of the Upper Yangtze River, is constrained to achieve \"dual carbon\" goals due to imbalanced energy structure. Based on selecting the energy structure influencing factors through Copula function and Granger causality, a multi-dimensional dynamic support vector machine model (SSA-MFD-SVR-ARIMA) by adopting sparrow algorithm was constructed to predict the proportion of Chongqing\'s energy structure from 2021 to 2030 under the drive of green finance development, and an optimization path was obtained. The novel findings confirm that (1) the correlated contribution rate of Green Finance to optimizing Chongqing\'s Energy Structure is 10.8 %; (2) under the sustained growth rate of Green Finance at 4.5 %, the proportion of coal consumption will reach 40.03 % by 2030, and non-fossil energy consumption will account for 27 %. It confirms that Chongqing can achieve the Energy Development Plan assigned by the Central Government in 2025. The research proposes a four-dimensional optimized pathway from a financial perspective that includes green equity investments, digital finance for energy, financing environmental rights and interests, and developing an industry fund. Furthermore, our put forward the safeguard strategies for financing, innovation, linkage, and protection mechanisms of this pathway optimization.

Accurate forecasting of water quality variables in river systems is crucial for relevant administrators to identify potential water quality degradation issues and take countermeasures promptly. However, pure data-driven forecasting models are often insufficient to deal with the highly varying periodicity of water quality in today\'s more complex environment. This study presents a new holistic framework for time-series forecasting of water quality parameters by combining advanced deep learning algorithms (i.e., Long Short-Term Memory (LSTM) and Informer) with causal inference, time-frequency analysis, and uncertainty quantification. The framework was demonstrated for total nitrogen (TN) forecasting in the largest artificial lakes in Asia (i.e., the Danjiangkou Reservoir, China) with six-year monitoring data from January 2017 to June 2022. The results showed that the pre-processing techniques based on causal inference and wavelet decomposition can significantly improve the performance of deep learning algorithms. Compared to the individual LSTM and Informer models, wavelet-coupled approaches diminished well the apparent forecasting errors of TN concentrations, with 24.39%, 32.68%, and 41.26% reduction at most in the average, standard deviation, and maximum values of the errors, respectively. In addition, a post-processing algorithm based on the Copula function and Bayesian theory was designed to quantify the uncertainty of predictions. With the help of this algorithm, each deterministic prediction of our model can correspond to a range of possible outputs. The 95% forecast confidence interval covered almost all the observations, which proves a measure of the reliability and robustness of the predictions. This study provides rich scientific references for applying advanced data-driven methods in time-series forecasting tasks and a practical methodological framework for water resources management and similar projects.

In order to evaluate the impact of the Longyangxia Reservoir construction on the relationship between runoff and sediment load (RRSL) in the upper reaches of the Yellow River, the runoff and sediment load monitoring data are used to identify variation point by sliding correlation coefficient method. Then with Copula function, the various countering situations of runoff and sediment load before and after variation are proposed to innovatively reveal their changing relations. The results demonstrate that (1) the reservoir construction exerts a great impact on the RRSL in the upper reaches of the Yellow River with the occurring variation point in 1987. The correlation of runoff and sediment load is presented better before variation but tends to worse decrease after variation. (2) Either before or after variation, runoff follows the generalized extreme value distribution, while sediment load before variation obeys the normal distribution, but the lognormal distribution after variation. Meanwhile, Frank Copula function accurately simulates the RRSL before variation, whereas Clayton Copula function is selected after variation. (3) The probability of synchronous rich and poor runoff and sediment load is higher before variation. After variation, the RRSL decreases significantly; their synchronous probability decreases by 45.67%. Meanwhile, the asynchronous probability of their extreme events evidently increases. The joint recurrence and co-recurrence intervals before variation are smaller than those after variation, along with the decreasing of their volume peak. This study provides new knowledge of runoff and sediment load influenced by reservoir construction, and also offers guidance for flood control and sediment load-discharge schemes of reservoir.

Droughts are widespread in China and have brought considerable losses to the economy and society. Droughts are intricate, stochastic processes with multi-attributes (e.g., duration, severity, intensity, and return period). However, most drought assessments tend to focus on univariate drought characteristics, which are inadequate to describe the intrinsic characteristics of droughts due to the existence of correlations between drought attributes. In this study, we employed the standardized precipitation index to identify drought events using China\'s monthly gridded precipitation dataset from 1961 to 2020. Univariate and copula-based bivariate methods were then used to examine drought duration and severity on 3-, 6-, and 12-month time scales. Finally, we used the hierarchical cluster method to identify drought-prone regions in mainland China at various return periods. Results revealed that time scale played an essential role in the spatial heterogeneity of drought behaviors, such as average characteristics, joint probability, and risk regionalization. The main findings were as follows: (1) 3- and 6-month time scales yielded comparable regional drought features, but not 12-month time scales; (2) higher drought severity was associated with longer drought duration; (3) drought risk was higher in the northern Xinjiang, western Qinghai, southern Tibet, southwest China, and the middle and lower reaches of the Yangtze River, and lower in the southeastern coastal areas of China, the Changbai Mountains, and the Greater Khingan Mountains; (4) mainland China was divided into six subregions according to joint probabilities of drought duration and severity. Our study is expected to contribute to better drought risk assessment in mainland China.

To provide the theoretical basis for the engineering application of metakaolin as aluminum-rich pozzolanic ash materials, the promoting effect of metakaolin on the macro- and micro-mechanical properties of composite cement-based composite materials was explored under high-temperature steam and standard curing conditions. Analysis techniques, which involved thermogravimetric and nanoindentation coupled with scanning electron microscopy-energy dispersive X-ray spectroscopy, were used. To analyze the experimental data, the ACI empirical formula and the copula function were used. The correlation among the hydration degree of the MK-cement cementation system, the Al/Si of the C-(A)-S-H phase, and the nanomechanical properties of the C-(A)-S-H gel phase was investigated. According to prior research, the macroscopic mechanical properties and the substitution rate of the MK-cement composite cementitious system can be improved under high-temperature curing conditions in a short period, in which the optimum substitution rate of metakaolin is 20%. The ACI empirical formula was used to demonstrate the functional relationship between the metakaolin replacement rate, curing time, and compressive strength. The MK-cement cementation system can eliminate the defect phase, reduce the CH phase content, and then increase the C-(A)-S-H gel phase content and bulk density. The micro-mechanical properties of the C-(A)-S-H gel phase rises due to its phase content and Al/Si ratio. Furthermore, the copula function verifies the dependence of the nanomechanical properties of C-(A)-S-H gel and Al/Si.