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Increasing crop yield and reducing environmental impacts are conductive to regional sustainable food production, while integrated evaluation of them is still lacking. To fill this gap, a new systematic analysis method was developed to quantify crop yield, economic input and environment impact of farmland. Taking the farmland ecosystems of Loess Plateau (LP) in China during 2014–2018 as a case, this new systematic analysis method was applied to quantify the ecological environment, systematic sustainability, and socioeconomic characteristics (indicators) of the farmland ecosystems in dry farming. Then, by exploring the relationships of these indicators and combining with crop switching analysis, the relatively unsustainable counties in farming and the improving potential of sustainable crop production was clarified, respectively. Several interesting results were explored. (1) The counties with the farmland of grey-water footprints exceeding 500 m3/t or soil erosion empowers exceeding 1.5E+12 sej/ha could be categorized as the relatively unsustainable counties in farming. (2) Maize and sorghum showed superior performance in terms of both environmental sustainability and crop productivity, and had promotion advantages in LP. (3) Crop switching could reduce grey water footprint and soil erosion of farmland by up to 27.41% and 35.14% respectively, and increase emergy sustainability index and crop yield by up to 10.35% and 19.90% respectively. The integrated systematic analysis method and crop switching method has high application value for regional sustainable crop production, especially for dryland regions with fragile ecological environment and prominent food demand-supply conflict. • A systematic analysis method integrating emergy and crop growth models was developed. • Relationships of natural, environmental, economic factors of farmland were explored. • Hotpot counties with relative unsustainable farmland ecosystems were identified. • Sustainable increase of regional grain yield can be achieved through crop switching. • Maize and sorghum have high promotion advantages in Loess Plateau, China. [ABSTRACT FROM AUTHOR]

Ammonia is a renewable energy medium appropriate for distant trading; therefore, many countries and companies have formulated ambitious strategies to develop energy transitions to use green ammonia for transportation systems. However, the associated social, economic and environmental impacts, and the overall viability and sustainability of these transitions are still a mystery, because of the lack of sufficiently complicated evaluations. To fill this gap, an integrated life cycle assessment and emergy evaluation (LCA-EME) method was developed and applied to synthesize, compare and recognize the hotspot nodes of resource depletion, emissions and impacts, and to quantify the exploitation and utilization efficiencies, environmental loadings and sustainability of the Australia-Japan telecoupling of wind power-based ammonia for electric vehicles (EV) and hydrogen fuel cell vehicles (FCV) used for passenger transportation, compared with two fossil fuel-based EV transportation systems. The results revealed that the transition to ammonia-based fuels can reduce nonrenewable energy consumption by >29.64% and Greenhouse Gas (GHG) emissions by >10.00%; however, the demand for emergy resources >2.03 times and biotic endpoint impacts >1.56 times, both of which mainly occurred in the sending subsystem of the telecoupling interaction. The results highlighted the necessity of internalizing the 'external' resource stress and its biotic impacts, increasing the utilization efficiency and the recycling rate of minerals and fresh water, and decreasing the endpoint impacts to guarantee the sustainability of the telecoupled energy transitions. Integrated LCA-EME was confirmed as a valuable tool for handling complex, multi-nodal nexus problems of telecoupling, which is widely needed for energy transition strategy making. • This is an "energy-water-material-emission-impact" multi-nodal nexus study of green ammonia. • The study is the first application of the telecoupling framework in energy conversions. • Conversion from fossil fuel to green ammonia can reduce GHG emissions but it increases potential biotic impacts. • Conversion from fossil fuel to green ammonia increases endpoint impacts in the sending area. [ABSTRACT FROM AUTHOR]

High temperature (HT) and drought stress (DS) pose serious limitations to wheat ( Triticum aestivum L.) grain development, which strongly influences grain yield and quality. In this study, we investigated changes in starch characteristics and starch granules of the high-yielding winter wheat cultivar Zhengmai 366 in response to HT and DS, both alone and in combination. HT, DS and the combination of these stressors led to a significant reduction in the proportion of A-type starch granules (>9.9 µm) to B-type starch granules (<9.9 µm), and the effects of HT and DS were significantly additive. The volume proportion of A-type starch granules (>9.9 µm) was positively correlated with the contents of AM, AP and starch as well as the grain yield (thousand grains weight) and was negatively correlated with the ratio of AM to AP, whereas there was a contrary correlation between the volume proportion of B-type starch granules (<9.9 µm) and these values. HT, DS and HT plus DS led to a significant reduction in the content of AP and starch and grain yield, but these treatments had less of an impact on AM content. DS reduced the size of the small starch granules, while HT reduced the size of the large granules. The changes in morphology and size distribution of starch granules led to a reduction in starch content under both stress conditions, consequently reducing grain yield. HT plus DS had a much greater influence on starch characteristics and yield than HT or DS alone. [ABSTRACT FROM AUTHOR]

Abstract: With the rapid economic and industrial development, the concentration of carbon dioxide in the atmosphere is increasing enormously, which has greatly affected the global climate and human living environment. At present, a lot of technologies have been applied to CO2 removal in fossil fuel-fired power plants, which are one of the main CO2 emission sources. But few researches have been done in the cement industry, which is the third largest CO2 emission source. There is no mature technology of CO2 removal in cement industry mentioned before. This paper proposes a feasible process for simultaneous removal of CO2, SO2 and NO x in the cement industry by NH3 scrubbing. As there is no ready steam source for the regeneration of CO2-rich loading solvent after absorption, a process with the final product of ammonium bicarbonate is developed. With the oxidative additive of NaClO2 added in the aqueous ammonium absorbent, the simultaneous removal of CO2, SO2 and NO x is feasible by NH3 scrubbing. The products of the process are mainly ammonium bicarbonate, ammonium sulfate and ammonium nitrate, which are all good fertilizers for crops and plants. The crystallization of NH4HCO3 is easier for storage and transportation than that for liquid carbon dioxide, which becomes more stable when dicyandiamide (DCD) is added. The thermodynamic analysis proves that the proposed process has the advantages of energy conservation and high thermodynamic perfection degree compared with the traditional ones. [Copyright &y& Elsevier]

OBJECTIVE The inability of pancreatic beta-cells to appropriately respond to glucose and secrete insulin are primary defects associated with beta-cell failure in type 2 diabetes. Mitochondrial dysfunction has been implicated as a key factor in the development of type 2 diabetes; however, a link between mitochondrial dysfunction and defective insulin secretion is unclear. RESEARCH DESIGN AND METHODS We investigated the changes in islet mitochondrial function and morphology during progression from insulin resistance (3 weeks old), immediately before hyperglycemia (5 weeks old), and after diabetes onset (10 weeks old) in transgenic MKR mice compared with controls. The molecular and protein changes at 10 weeks were determined using microarray and iTRAQ proteomic screens. RESULTS At 3 weeks, MKR mice were hyperinsulinemic but normoglycemic and beta-cells showed negligible mitochondrial or morphological changes. At 5 weeks, MKR islets displayed abrogated hyperpolarization of mitochondrial membrane potential (DeltaPsi(m)), reduced mitochondrial Ca(2+) uptake, slightly enlarged mitochondria, and reduced glucose-stimulated insulin secretion. By 10 weeks, MKR mice were hyperglycemic and hyperinsulinemic and beta-cells contained swollen mitochondria with disordered cristae. beta-Cells displayed impaired stimulus-secretion coupling including reduced hyperpolarization of DeltaPsi(m), impaired Ca(2+)-signaling, and reduced glucose-stimulated ATP/ADP and insulin release. Furthermore, decreased cytochrome c oxidase-dependent oxygen consumption and signs of oxidative stress were observed in diabetic islets. Protein profiling of diabetic islets revealed that 36 mitochondrial proteins were differentially expressed, including inner membrane proteins of the electron transport chain. CONCLUSIONS We provide novel evidence for a critical role of defective mitochondrial oxidative phosphorylation and morphology in the pathology of insulin resistance-induced beta-cell failure. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid machine learning model for predicting the pullback force in horizontal directional drilling (HDD) construction. The model combines the nondominated sorting genetic algorithm II (NSGA-II) and support vector machine (SVM). NSGA-II is used to optimize two hyperparameters in SVM. Different from other optimization algorithms, NSGA-II is a multi-objective optimizer, which considers prediction accuracy and stability. The proposed model is applied to two practical HDD projects in China. The prediction result is compared with the actual monitoring data, which shows that the mean absolute percentage errors (MAPE) are less than 7%. The primary conclusions are as follows: (1) The proposed model's accuracy and stability are better than those of the two benchmark models; (2) Machine learning models can predict the pullback force more accurately than the empirical model in the construction phase, and the maximum MAPE does not exceed 17%; (3) The running time of the proposed model is short, and it is feasible in practical application. Moreover, this paper discusses the practical application of machine learning models in HDD construction and the future development direction. • A hybrid machine learning model is proposed to predict pullback force during HDD construction. • Prediction accuracy and stability are considered. • Proposed model is applied to two HDD projects in China. [ABSTRACT FROM AUTHOR]

Electricity consumption has been affected due to worldwide lockdown policies against COVID-19. Many countries have pointed out that electricity supply security during the epidemic is critical to ensuring people's livelihood. Accurate prediction of electricity demand would act a more important role in ensuring energy security for all the countries. Although there have been many studies on electricity forecasting, they did not consider the pandemic, and many works only considered the prediction accuracy and ignored the stability. Driven by the above reasons, it is necessary to develop an electricity consumption prediction model that can be well applied in the pandemic. In this work, a hybrid prediction system is proposed with data processing, modelling, and optimization. An improved complete ensemble empirical mode decomposition with adaptive noise is used for data preprocessing, which overcomes the shortcomings of the original method; a multi-objective optimizer is adopted for ensuring the accuracy and stability; support vector machine is used as the prediction model. Taking daily electricity demand of US as an example, the results prove that the proposed hybrid models are superior to benchmark models in both prediction accuracy and stability. Moreover, selection of input parameters is discussed, and the results indicate that the model considering the daily infections has the highest prediction accuracy and stability, and it is proved that the proposed model has great potential in real-world applications. • A hybrid model is developed for predicting daily electricity demand during COVID-19. • The accuracy and stability of the new model are higher than those of benchmark models. • The proposed model also performs well in multi-step prediction. • The model that only considers daily infections has the best prediction performance. [ABSTRACT FROM AUTHOR]

Soil organisms play essential roles in maintaining multiple ecosystem processes, but our understanding of the dynamics of these communities during forest succession remains limited. In this study, the dynamics of soil organism communities were measured along a 3-step succession sequence of subtropical forests (i.e., a conifer forest, CF; a mixed conifer and broad-leaved forest, MF; and a monsoon evergreen broad-leaved forest, BF). The eco-exergy evaluation method was used as a complement to the classic community structure index system to reveal the holistic dynamics of the bio-thermodynamic health of soil organism communities in a forest succession series. Association between the self-organization of soil organisms, soil properties, and plant factors were explored through redundancy analyses (RDA). The results indicated that the biomass of soil microbes progressively increased in the dry season, from 0.75 g m−2 in CF to 1.75 g m−2 in BF. Microbial eco-exergy showed a similar pattern, while the community structure and the specific eco-exergy remained constant. Different trends for the seasons were observed for the soil fauna community, where the community biomass increased from 0.72 g m−2 to over 1.97 g m−2 in the dry season, but decreased from 3.94 g m−2 to 2.36 g m−2 in the wet season. Faunal eco-exergies followed a similar pattern. Consequently, the average annual biomass of the soil faunal community remained constant (2.17–2.39 g m−2) along the forest succession sequence, while the significant seasonal differences in both faunal biomass and eco-exergy observed at the early successional stage (CF) were insignificant in the middle and late forest successional stages (MF and BF). Both the dynamics of soil microbes and soil fauna were tightly correlated with tree biomass and with soil physicochemical properties, especially soil pH, moisture, total nitrogen, nitrate nitrogen, and organic matter content. Image 1 • Eco-exergy of soil microbe community gradually increased with forest succession. • Soil fauna community remained constant with subtropical forest succession. • Seasonal difference increased in soil microbes but decreased in soil fauna community. • Soil organisms were tightly correlated with tree biomass and soil properties. [ABSTRACT FROM AUTHOR]

The accurate prediction of energy price is critical to the energy market orientation, and it can provide a reference for policymakers and market participants. In practice, energy prices are affected by external factors, and their accurate prediction is challenging. This paper provides a systematic decade review of data-driven models for energy price prediction. Energy prices include four types: natural gas, crude oil, electricity, and carbon. Through the screening, 171 publications are reviewed in detail from the aspects of the basic model, the data cleaning method, and optimizer. Publishing time, model structure, prediction accuracy, prediction horizon, and input variables for energy price prediction are discussed. The main contributions and findings of this paper are as follows: (1) basic prediction models for energy price, data cleaning methods, and optimizers are classified and described; (2) the structure of the prediction model is finely classified, and it is inferred that the hybrid model and prediction architecture with multiple techniques are the focus of research and the development direction in the future; (3) root mean square error, mean absolute percentage error, and mean absolute error are the three most frequently used error indicators, and the maximum mean absolute percentage error is less than 0.2; (4) the ranges of data size and data division ratio for energy price prediction in different horizons are given, the proportion of the test set is usually in the range of 0.05–0.35; (5) the input variables for energy price prediction are summarized; (6) the data cleaning method has a more significant role in improving the accuracy of energy price prediction than the optimizer. • 171 energy price prediction-related publications are reviewed. • Input variables are classified and summarized. • Prediction accuracy and horizon are counted. • The proportion of the test set is summarized. • The impact of data cleaning method and optimizer on the basic model is compared. [ABSTRACT FROM AUTHOR]

Carbon dioxide transport plays a crucial role in carbon capture and storage systems. As an economical and convenient carrier, pipelines have huge advantages in the transport of carbon dioxide. In this paper, the development of carbon dioxide transport via pipeline is systematically reviewed from four aspects: pipeline design, process, risk and safety, standard and specification. The results reveal that there are many similarities between the carbon dioxide pipeline and the natural gas pipeline, but due to different gas compositions and transportation destinations, the transport process, design, and construction considerations are quite different. Especially in terms of management, the specifications and standards for carbon dioxide pipelines are still minimal, the techno-economic framework and integrity management system are still immature. In addition, this paper summaries the challenges and future directions of carbon dioxide transport via pipeline. It is concluded that the studies on the influence of impurities on phase equilibrium and corrosion mechanisms in carbon dioxide pipeline are challenges, and the new construction and detection technologies are also crucial. With the rapid development of computer science, some interdisciplinary applications such as artificial intelligence and digital twin will promote the development of carbon dioxide pipeline. • The progress of carbon dioxide pipeline technology is reviewed. • Review from four aspects: process, design, safety and specification. • The technical challenges and development direction are summarized. [ABSTRACT FROM AUTHOR]

Estimation of compressive strength of rubberized concrete is important for engineering safety. In this study, measured data (the compressive strength of rubberized concrete and its impacting factors) were collected by literature review (457 samples). In order to accurately predict the compressive strength of rubberized concrete, four machine learning models [artificial neural network (ANN), k-nearest neighbor (KNN), regression trees (RT), and random forests (RF)] were developed and compared to estimate the compressive strength of rubberized concrete, and the modeling results were compared with two traditional expressions. The model performance was evaluated using three performance indicators: the Nash–Sutcliffe efficiency coefficient (NSC), the root-mean-squared error (RMSE), and the mean absolute error (MAE). The results showed that the RT model performs the best, followed by the ANN and RF in the model training phase. In the model testing phase, the ANN model performs the best, followed by the RT, RF, and KNN. The overall results indicated that the ANN model performs the best, followed by RT and RF, and the KNN model performs the worst. The ANN and RT models outperformed the two traditional expressions. The tree-based models (RT and RF) and KNN model may not be applicative to estimate the compressive strength of rubberized concrete due to the generally poor performances in the model testing phase compared with that in the model training phase. The results showed that the traditional ANN model is sufficient for the accurate estimation of the compressive strength of rubberized concrete when the model is properly trained. The results in the present research can provide reference for the prediction of the compressive strength of rubberized concrete, which will benefit engineering management and safety as well. [ABSTRACT FROM AUTHOR]

The development of the wind power market has led various countries to begin shifting the construction of wind farms from land to offshore. Accurately predicting the short-term wind power of an offshore wind farm is significant for preventive control and scheduling. This paper proposes a novel two-stage hybrid model to predict short-term wind power. First, complete ensemble empirical mode decomposition with adaptive noise is used to preprocess the raw data and make it smoother. Second, an improved grey-box model is utilized to predict the wind power, where a multi-objective grey wolf optimizer is used to optimize the kernel-based nonlinear extension of the Arps decline model to ensure both prediction stability and accuracy. The historical wind power data for offshore wind farms in Belgium are taken as a case study, and the results indicate that the proposed model has higher prediction accuracy and stability than those of six benchmark models. Three critical issues related to this work are also considered. The following conclusions are reached: (1) Utilizing a denoising method and multi-objective optimizer effectively improves the prediction accuracy and stability of the original model. Specifically, the multi-objective optimizer improves the prediction performance; (2) The proposed model performs best at predicting wind power in autumn; (3) Because of the great variation in dimensions for the wind power time series, the mean absolute percentage error and root mean squared percentage error are not suitable as error evaluation indicators. • Two-stage hybrid model for predicting wind power of offshore wind farms. • Multi-objective algorithm optimizes the traditional kernel-based model. • Decomposition algorithm preprocesses raw data. • Rationality of the error indicators for the prediction results discussed. [ABSTRACT FROM AUTHOR]

Accurate prediction of building energy consumption is crucial for building energy management. However, the building energy consumption is affected by many factors and shows obvious nonlinear characteristics in the time series, which is difficult to predict. In this work, a novel hybrid model is proposed for predicting short-term building energy consumption. In this model, the raw data is decomposed into multiple smooth datasets using complete ensemble empirical mode decomposition with adaptive noise, and the building energy consumption is predicted by the traditional extreme gradient boosting. Taking the daily energy consumption of the City of Bloomington Intake Tower as the simulation object, the results show that the mean absolute percentage error of the proposed model is 4.85%, which is much lower than that of five benchmark models. The proposed model is also applied to the prediction of other parameters related to the energy consumption of the intake tower, and shows good prediction performance. Moreover, the influences of the sliding window length and data attributes on the prediction results are also discussed. • A novel model combines CEEMDAN and XGBoost is proposed. • Real-world case study based on 8 years' energy consumption is presented. • The improved model has only half of prediction error of XGBoost and outperforms other four models in the case study. [ABSTRACT FROM AUTHOR]