Management of resilient urban integrated energy system: State-of-the-art and future directions.

The urban integrated energy system (UIES) is the fundamental infrastructure supporting the operation of resilient cities. The resilience of UIES plays a critical role in effectively responding to extreme events. We provide a comprehensive review on the management of resilient UIES. Firstly, we examine the existing studies on the resilience of UIES through quantitative and qualitative methodologies. Secondly, it points out that the coupling characteristics of UIES have a dual impact on resilience. The definition of UIES resilience can be understood from three perspectives, namely partial resilience versus total resilience, physical resilience versus digital resilience, and current resilience versus future resilience. Thirdly, this review summarizes the strategies for improving the resilience of UIES across three distinct stages, namely before, during, and after extreme events. The resilience of UIES can be enhanced by effective measures to prediction, adaptation, and assessment. Finally, the challenges faced by management of resilient UIES are presented and discussed, in terms of mitigating compound risks, modeling complex systems, addressing data collection and quality issue, and collaborating within multi stakeholders.

An osteoarthritis pathophysiological continuum revealed by molecular biomarkers.

We aimed to identify serum biomarkers that predict knee osteoarthritis (OA) before the appearance of radiographic abnormalities in a cohort of 200 women. As few as six serum peptides, corresponding to six proteins, reached AUC 77% probability to distinguish those who developed OA from age-matched individuals who did not develop OA up to 8 years later. Prediction based on these blood biomarkers was superior to traditional prediction based on age and BMI (AUC 51%) or knee pain (AUC 57%). These results identify a prolonged molecular derangement of joint tissue before the onset of radiographic OA abnormalities consistent with an unresolved acute phase response. Among all 24 protein biomarkers predicting incident knee OA, the majority (58%) also predicted knee OA progression, revealing the existence of a pathophysiological "OA continuum" based on considerable similarity in the molecular pathophysiology of the progression to incident OA and the progression of established OA.

A new multiregional carbon emissions forecasting model based on a multivariable information fusion mechanism and hybrid spatiotemporal graph convolution network.

Developing scientific and effective carbon emissions reduction policies relies heavily on precise carbon emission trend prediction. The existing complex spatiotemporal correlation and diverse range of influencing factors associated with multi-regional carbon emissions pose significant challenges to accurately modeling these trends. Under this constraint, this study is inspired by graph learning to establish a hybrid dynamic and static graph-based regional carbon emission network framework, which introduces a novel research standpoint for investigating short-term carbon emissions prediction (CEP). Specifically, a parallel framework of attribute-augmented dynamic multi-modal graph convolutional neural networks (ADMGCN) and temporal convolutional networks with adaptive fusion multi-scale receptive fields (AFMRFTCN) is proposed. The proposed model is evaluated against nineteen state-of-the-art models using daily carbon emission data from 30 regions in China, demonstrating its effectiveness in accurately predicting the trends of multi-regional carbon emissions. Conclusions are drawn as follows: First, especially in regions with marked periodicity, compared with the best baseline model, the mean absolute percentage error (MAPE) of our model is reduced by 20.19%. Second, incorporating graph convolutional neural networks (GCNs) with dynamic and static graphs is advantageous in extracting the spatial features of China's carbon emission network, which are influenced by geographical, economic, and industrial factors. Third, the parallel ADMGCN-AFMRFTCNs framework effectively captures the influence of external information on carbon emissions while mitigating the issue of low prediction accuracy resulting from univariate information. Fourth, the analysis reveals significant differences in the short-term (30-day) growth rate of carbon emissions among different regions. For example, Henan exhibits the highest growth rate (37.38%), while Guizhou has the lowest growth rate (-7.46%). It is valuable for policymakers and stakeholders seeking to identify regions with distinct emission patterns and prioritize mitigation efforts accordingly.

Multi-scenario reduction pathways and decoupling analysis of China's sectoral carbon emissions.

To achieve its goal of carbon emissions peak and neutrality, China requires synergistic efforts across all sectors. In this study, three scenarios-baseline, policy, and green low-carbon-were developed to explore the pathways for China's emissions reduction across sectors from 2020 to 2060, and the timing of decoupling economic growth from CO2. The results showed that, under these scenarios, China's carbon emissions peak in 2030, 2026, and 2025, with strong decoupling time, lagged one year behind peak attainment. The agriculture, forestry, livestock, and fishing (AFH) and mining and quarrying (MQ) sectors would be the first to achieve a carbon peak. Under all three scenarios, all of the other sectors-with the exception of electricity, gas, and water production and supply (EGW)-will achieve a carbon peak by 2030. Therefore, policymakers should set carbon peak goals based on sector characteristics and ensure energy security in the process of achieving carbon neutrality.

High-resolution electric power load data of an industrial park with multiple types of buildings in China.

Considering the growing demand for electricity in industrial parks, understanding their electric power load patterns is critical for improving energy efficiency and ensuring the rational utilization of energy resources. However, the detailed electric power load data of various buildings in industrial parks are rarely available and accessible, which hinders the related studies. In this context, we present the electric power load data of 6 years (from January 1, 2016 to December 31, 2021) for various types of buildings in an industrial park in Suzhou, China. The data are obtained from smart meters and have various time resolutions (i.e., 5 minutes, 30 minutes, and 1 hour). This work describes the data collection, processing process, and different imputation methods. The high-resolution electric power load data can be used for various research tasks, including load prediction, load pattern recognition, anomaly detection, and demand response strategy development.

A "best-in-class" systemic biomarker predictor of clinically relevant knee osteoarthritis structural and pain progression.

We aimed to identify markers in blood (serum) to predict clinically relevant knee osteoarthritis (OA) progression defined as the combination of both joint structure and pain worsening over 48 months. A set of 15 serum proteomic markers corresponding to 13 total proteins reached an area under the receiver operating characteristic curve (AUC) of 73% for distinguishing progressors from nonprogressors in a cohort of 596 individuals with knee OA. Prediction based on these blood markers was far better than traditional prediction based on baseline structural OA and pain severity (59%) or the current "best-in-class" biomarker for predicting OA progression, urinary carboxyl-terminal cross-linked telopeptide of type II collagen (58%). The generalizability of the marker set was confirmed in a second cohort of 86 individuals that yielded an AUC of 70% for distinguishing joint structural progressors. Blood is a readily accessible biospecimen whose analysis for these biomarkers could facilitate identification of individuals for clinical trial enrollment and those most in need of treatment.

Spatial and temporal evolution characteristics and spillover effects of China's regional carbon emissions.

China's carbon emissions account for approximately a quarter of the world's total greenhouse gas emissions. In 2020, China's fossil fuels accounted for approximately 85% of the primary energy demand, with coal alone accounting for 60%. Considering the severe global warming situation, it is necessary to reveal the spatial and temporal differences and analyze the spillover effects of carbon emissions between regions. In this study, a positive and significant spatial correlation between regional carbon emissions in China was found using an exploratory spatial data analysis. The spatial Durbin model was then utilized to explore the direct and spillover effects of factors that included economic growth, the energy intensity, and the level of technological innovation on regional carbon emissions. Whether a direct effect or a spillover effect, economic growth and improvements in the regional levels of technological innovation had significant inhibitory effects on carbon emissions both in the long term and in the short term. Specifically, an increase of 1% in the level of technological innovation led to a reduction of approximately 0.17% in the region's carbon emissions. However, a growth in the energy intensity will increase carbon emissions. In addition, an increase in the technological input intensity will lead to an increase in carbon emissions in local regions. However, an increase in neighboring regions will restrain carbon emissions in a local region. Based on these findings, it is recommended that the government accelerate regional innovation synergies and increase investment in clean energy technologies.

Air pollution impacts on public health: Evidence from 110 cities in Yangtze River Economic Belt of China.

This study intends to further reveal the relationship between air pollution and public health on a city scale in China and explore the spillover effect among cities. On the basis of collecting the panel data of 110 cities in the Yangtze River Economic Belt from 2010 to 2018, we establish a spatial econometric model to analyze the impacts of air pollution, economic development, and other factors on public health. According to the results, a significant spatial correlation exists between the public health and air pollution levels in all of the cities in the Yangtze River Economic Belt. Air pollution also shows a spillover effect among these cities; the relationships between the industrial fume (dust) emissions, industrial sulfur dioxide emissions, and particulate matter (PM 2.5) concentration and the public health level are not simple linear relationships, but instead U-shaped curvilinear relationships. The economic development in recent years has contributed to the improvement of the public health level of the cities in the Yangtze River Economic Belt. The economic development of their neighboring cities, however, has adversely affected the public health levels of these cities.

Impact of COVID-19 on private driving behavior: Evidence from electric vehicle charging data.

The COVID-19 pandemic has given rise to a major impact on traffic mobility. To implement preventive measures and manage transportation, understanding the transformation of private driving behavior during the pandemic is critical. A data-driven forecasting model is proposed to estimate daily charging demand in the absence of the COVID-19 pandemic by leveraging electric vehicle (EV) charging data from four cities in China. It serves as a benchmark for quantifying the impact of the COVID-19 pandemic on EV charging demand. A vector autoregressive (VAR) model is then used to investigate the dynamic relationship between the changes in charging demand and potential influencing factors. Potential influencing factors are selected from three aspects: public health data, public concern, and the level of industrial activity. The results show that the magnitude of the decline in EV charging demand varied by city during the pandemic. Furthermore, COVID-19 related factors such as daily hospitalizations and national confirmed cases are the primary causes of the decline in charging demand. The research framework of this paper can be generalized to analyze the changes in other driving behaviors during the pandemic. Finally, three policy implications are proposed to assist other countries in dealing with similar events and to stimulate the recovery of the transport system during the post-pandemic period.

"New normal" characteristics show in China's energy footprints and carbon footprints.

Around the 2010s, China's economy has entered a "new normal" stage-transitioning from an extensive to an intensive growth mode. This study aims to investigate whether China's energy and carbon footprints also show these "new normal" characteristics. We evaluate China's energy and carbon footprints of 42 sectors from 2007 to 2017. The "new normal" characteristics are reflected from three dimensions: trend, structure, and driving factor. The results show that while the growth rate of China's energy and carbon footprints has slowed down, the total footprints are still increasing. The footprints induced by consumption have gradually exceeded those induced by export, and the tertiary industrial sectors became critical nodes in footprint networks. Furthermore, economic structure and development level have been major drivers of energy and carbon footprint growth. The findings reveal that China's energy and carbon footprints show similar "new normal" characteristics as economic development. This supports the targeted formulation of China's future policies to achieve sustainable development.

From intention to behavior: Comprehending residents' waste sorting intention and behavior formation process.

Waste sorting is essential to address the current predicament of waste management. Though it is important, insufficient attention has been paid to explore residents' waste sorting intention and behavior and understand its formation process. To narrow the research gap, this research built a theoretical research model by adding personal moral norms and waste sorting knowledge into the theory of planned behavior to explicate residents' waste sorting intention and behavior formation process. Meanwhile, given the discrepancy between waste sorting intention and actual behavior, this research also explored the effect of external conditions, such as incentive measures, on this discrepancy. Based on survey data from 397 Chinese residents, this research found that attitudes, subjective norms, perceived behavioral control, personal moral norms and waste sorting knowledge were directly and significantly related to residents' waste sorting intention. Waste sorting knowledge also had an indirect influence on residents' waste sorting intention through attitudes and perceived behavioral control. Additionally, this research corroborated the discrepancy between waste sorting intention and behavior, and suggested that the link between intention and behavior was contingent on incentive measures. Incentive measures strengthened the effect of intention on behavior. This research is useful for understanding residents' waste sorting intention and behavior and valuable for encouraging residents to sort waste in their daily lives.

Establishment of two midgut cell lines from the fall armyworm, Spodoptera frugiperda (Lepidoptera: Noctuidae).

Two cell lines were generated from larval midguts of Spodoptera frugiperda and have been 26 passaged over 50 times. The CT/BCIRL-SfMG1-0611-KZ line was established from 27 trypsinized, minced whole midgut tissues: the CT/BCIRL-SfMG-0617-KZ line from isolated 28 midgut muscle tissue (containing some residual epithelial cells). Additional midgut cultures were 29 generated from isolated epithelial cells; some passaged not more than three times, which grew 30 very slowly and survived longer than 1 year. The continuously replicating cell lines contain 31 firmly adhering cells with different morphologies, including elongated, spherical, and/or 32 rectangular. The mean diameters of these cell lines are 9.3 ± 4.0 µm (SfMG1-0611) and 9.2 ± 3.9 33 µm (SfMG-0617). Growth curves for the two lines have relatively lengthy doubling times of 73.9 34 h and 50.4 h for SfMG1-0611 and SfMG-0617, respectively. We confirmed the identity of these 35 lines using DNA amplification fingerprinting (DAF-PCR) and noted that the DNA patterns for 36 each cell line were similar to their host tissues but distinctly different from other cell lines or 37 tissues from different insect species. Amplification of genomic DNA with species-specific 38 primers yielded DNA fragments of the expected sizes and with sequences nearly identical to 39 those from the S. frugiperda genome. Both cell lines were exposed to selected Bt Cry proteins 40 with minimal impact. These lines are currently available to researchers worldwide.

Decomposition analysis of China's CO2 emissions (2000-2016) and scenario analysis of its carbon intensity targets in 2020 and 2030.

To address the unprecedented increase in China's CO2 emissions over the past decades, the Chinese government has implemented many policies that are aimed at reducing carbon intensity. Applying the LMDI method, this study conducts a decomposition analysis of the drivers influencing China's CO2 emissions by examining the details of 41 industry sub-sectors during 2000-2016; further, it predicts the carbon intensity reduction potential in 2020 and 2030 based on various official policies and documents. We conclude that energy intensity was the primary indicator that reduced CO2 emissions, whereas the effects of carbon intensity, energy mix, and industrial structure were relatively minor. During the study period, the effect of industrial structure optimization on the change in CO2 emissions shifted from the promotion of emissions to their suppression, with the inhibiting influence becoming greater over time. Finally, scenario analysis indicated that CO2 intensity would decrease 21.5% by 2020 compared to the 2015 level, and the reduction target of 65% would be achieved fully in 2030 in the outlook scenario. Energy intensity is the largest contributor to the decrease in CO2 emissions during 2016-2020, whereas industrial structure optimization shows the greatest potential for environmental improvement during 2020-2030. This paper concludes that more stringent policies are essential to reducing CO2 emissions in the near future.

Prostaglandin actions in established insect cell lines.

Prostaglandins (PGs) are oxygenated metabolites of arachidonic acid (AA) and two other C20 polyunsaturated fatty acids that serve as biochemical signals mediating physiological functions. We reported that PGs influence protein expression in insect cell lines, which prompted the question: do PGs influence cell proliferation or viability in insect cell lines? Here, we report on the outcomes of experiments designed to address the question in cell lines from three insect orders: Hemiptera (squash bug, Anasa tristis, BCIRL-AtE-CLG15A), Coleoptera (red flour beetle, Tribolium castaneum, BCIRL-TcA-CLG1), and Lepidoptera (tobacco budworm, Heliothis virescens, BCIRL-HvAM1). Treating the insect cell lines with PGA1, PGA2, or PGD2 led to dose-dependent reductions in cell numbers. All three cell lines were sensitive to PGA1 and PGA2 (IC50s = 9.9 to 26.9 µM) and were less sensitive to PGD2 (IC50s = 31.6 to 104.7 µM). PG treatments also led to cell death at higher concentrations, as seen in mammalian cell lines. PGE1, PGE2, and PGF2α treatments did not influence AtE-CLG15A or HvAM1 cell numbers at lower concentrations, but led to dose-related reductions in TcA-CLG1 cells at higher concentrations. Similar treatments with pharmaceutical inhibitors of PG biosynthesis also led to reduced cell numbers: MAFP (inhibits phospholipase A2), indomethacin (inhibits PG biosynthesis), and esculetin (inhibits lipoxygenase). Because these pharmaceuticals are used to relieve inflammation and other medical issues in human medicine, they are not toxic to animal cells. We infer PGs are necessary in optimal quantities for ongoing homeostatic functions in established cell lines; in quantities outside the optimal concentrations, PGs are deleterious.

Howler monkey foraging ecology suggests convergent evolution of routine trichromacy as an adaptation for folivory.

Primates possess remarkably variable color vision, and the ecological and social factors shaping this variation remain heavily debated. Here, we test whether central tenants of the folivory hypothesis of routine trichromacy hold for the foraging ecology of howler monkeys. Howler monkeys (genus Alouatta) and paleotropical primates (Parvorder: Catarrhini) have independently acquired routine trichromacy through fixation of distinct mid- to long-wavelength-sensitive (M/LWS) opsin genes on the X-chromosome. The presence of routine trichromacy in howlers, while other diurnal neotropical monkeys (Platyrrhini) possess polymorphic trichromacy, is poorly understood. A selective force proposed to explain the evolution of routine trichromacy in catarrhines-reliance on young, red leaves-has received scant attention in howlers, a gap we fill in this study. We recorded diet, sequenced M/LWS opsin genes in four social groups of Alouatta palliata, and conducted colorimetric analysis of leaves consumed in Sector Santa Rosa, Costa Rica. For a majority of food species, including Ficus trees, an important resource year-round, young leaves were more chromatically conspicuous from mature leaves to trichromatic than to hypothetical dichromatic phenotypes. We found that 18% of opsin genes were MWS/LWS hybrids; when combined with previous research, the incidence of hybrid M/LWS opsins in this species is 13%. In visual models of food discrimination ability, the hybrid trichromatic phenotype performed slightly poorer than normal trichromacy, but substantially better than dichromacy. Our results provide support for the folivory hypothesis of routine trichromacy. Similar ecological pressures, that is, the search for young, reddish leaves, may have driven the independent evolution of routine trichromacy in primates on separate continents. We discuss our results in the context of balancing selection acting on New World monkey opsin genes and hypothesize that howlers experience stronger selection against dichromatic phenotypes than other sympatric species, which rely more heavily on cryptic foods.

Decision support for personalized cloud service selection through multi-attribute trustworthiness evaluation.

Facing a customer market with rising demands for cloud service dependability and security, trustworthiness evaluation techniques are becoming essential to cloud service selection. But these methods are out of the reach to most customers as they require considerable expertise. Additionally, since the cloud service evaluation is often a costly and time-consuming process, it is not practical to measure trustworthy attributes of all candidates for each customer. Many existing models cannot easily deal with cloud services which have very few historical records. In this paper, we propose a novel service selection approach in which the missing value prediction and the multi-attribute trustworthiness evaluation are commonly taken into account. By simply collecting limited historical records, the current approach is able to support the personalized trustworthy service selection. The experimental results also show that our approach performs much better than other competing ones with respect to the customer preference and expectation in trustworthiness assessment.