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In this paper, we introduce the notion of a left-symmetric bialgebroid as a geometric generalization of a left-symmetric bialgebra and construct a left-symmetric bialgebroid from a pseudo-Hessian manifold. We also introduce the notion of a Manin triple for left-symmetric algebroids, which is equivalent to a left-symmetric bialgebroid. The corresponding double structure is a pre-symplectic algebroid rather than a left-symmetric algebroid. In particular, we establish a relation between Maurer–Cartan type equations and Dirac structures of the pre-symplectic algebroid which is the corresponding double structure for a left-symmetric bialgebroid. [ABSTRACT FROM AUTHOR]

Based on the differential graded Lie algebra controlling deformations of an n -Lie algebra with a representation (called an n - LieRep pair), we construct a Lie n -algebra, whose Maurer-Cartan elements characterize relative Rota-Baxter operators on n - LieRep pairs. The notion of an n -pre-Lie algebra is introduced, which is the underlying algebraic structure of the relative Rota-Baxter operator. We give the cohomology of relative Rota-Baxter operators and study infinitesimal deformations and extensions of order m deformations to order m + 1 deformations of relative Rota-Baxter operators through the cohomology groups of relative Rota-Baxter operators. Moreover, we build the relation between the cohomology groups of relative Rota-Baxter operators on n - LieRep pairs and those on (n + 1) - LieRep pairs by certain linear functions. [ABSTRACT FROM AUTHOR]

• Considered the significant impact of the COVID-19 on electricity load of metropolitan, ISOs and countries. • Developed a novel method based on multi-task learning, temporal convolutional network and long-short-term memory network. • Used the real mobility data from Google and Apple and load data from large power enterprises and public institutions. • Provides a feasible solution for reliable load forecasting model training and forecasting in long-sequence input scenarios. • Implemented the Shapley additive explanations technology to enhance interpretability by visualizing the Shapley values. The spread of the global COVID-19 epidemic has resulted in significant shifts in electricity consumption compared to regular days. It is unknown if standard single-task, single-indicator load forecasting algorithms can accurately reflect COVID-19 load patterns. Power practitioners urgently want a simple, efficient, and accurate solution for anticipating reliable load. In this paper, we first propose a unique collaborative TCN-LSTM-MTL short-term load forecasting model based on mobility data, temporal convolutional networks, and multi-task learning. The addition of the parameter sharing layers and the structure with residual convolution improves the data input diversity of the forecasting model and enables the model to obtain a wider time series receptive field. Then, to demonstrate the usefulness of the mobility optimized TCN-LSTM-MTL, tests were conducted in three levels and twelve base regions using 19 different benchmark models. It is capable of controlling predicting mistakes to within 1 % in the majority of tasks. Finally, to rigorously explain the model, the Shapley additive explanations (SHAP) visual model interpretation technology based on game theory is introduced. It examines the TCN-LSTM-MTL model's internal mechanism at various time periods and establishes the validity of the mobility indicators as well as the asynchronous relationship between indicator significance and real contribution. [ABSTRACT FROM AUTHOR]

Investigation of the rheological characteristics of deep-sea sediments is very important for understanding submarine disasters. Three reconstituted deep-sea soils with different plasticity and activity values from the South Sea China are considered in this study. A series of steady rheological tests are conducted on samples of three soils with different water contents and temperatures by a rate-controlled rotational rheometer operated in a steady-state regime. Rheological results show that shear stress and apparent viscosity can be decreased by a maximum of 65% when the temperature varies from 1 °C to 25 °C, and the three deep-sea soils are typical shear thinning non-Newtonian fluids with yield stress. The state of the samples can be divided into three phases during the shearing process, which implies that with an increase in the shear strain rate, all samples first behave as a solid, then transition from solid to liquid, and last behave as a liquid. The three phases are distinguished by the static yield stress (SYS) and fluidic yield stress (FYS), and a simple method for dividing the three phases is proposed. The SYS and FYS decreased with increasing temperature and water content. The relationships between yield stresses (SYS and FYS) and soil properties (water content and activity) and temperature are established. In addition, a unified strength model for deep-sea sediments is suggested, and the validity of the model is verified by comparing the prediction results of the model and test results. The results of this study could be useful for deep-sea submarine mudflow and landslide modelling. • A simple method to determine the values of SYS and FYS in steady rheological shearing tests is proposed. • Model for SYS and FYS that considering influence of LI, activity and temperature is established. • A unified strength model that considering effects of shear rate, temperature, water content, and activity is proposed. [ABSTRACT FROM AUTHOR]

In this paper, first we modify the definition of a Hom-Lie algebroid introduced by Laurent-Gengoux and Teles and give its equivalent dual description. Many results that are parallel to Lie algebroids are given. In particular, we give the notion of a Hom-Poisson manifold and show that there is a Hom-Lie algebroid structure on the pullback of the cotangent bundle of a Hom-Poisson manifold. Then we give the notion of a Hom-Lie bialgebroid, which is a natural generalization of a purely Hom-Lie bialgebra and a Lie bialgebroid. We show that the base manifold of a Hom-Lie bialgebroid is a Hom-Poisson manifold. Finally, we introduce the notion of a Hom-Courant algebroid and show that the double of a Hom-Lie bialgebroid is a Hom-Courant algebroid. The underlying algebraic structure of a Hom-Courant algebroid is a Hom-Leibniz algebra, or a Hom-Lie 2-algebra. [ABSTRACT FROM AUTHOR]

• Considered the significant impact of the COVID-19 on electricity load. • Developed a novel method based on multi-task learning using parameters sharing layers. • Used the real mobility data from Google and Apple and load data from large power enterprises and public institutions. • The universally adaptable model can control the forecasting error at a low level in the COVID-19. • Implemented the Shapley additive explanations technology to enhance interpretability. Affected by the new coronavirus (COVID-19) pandemic, global energy production and consumption have changed a lot. It is unknown whether conventional short-term load forecasting methods based on single-task, single-region, and conventional indicators can accurately capture the load pattern during the COVID-19 and should be carefully studied. In this paper, we make the following contributions: 1) A mobility-optimized load forecasting method based on multi-task learning and long short-term memory network is innovatively proposed to alleviate the impact of the COVID-19 on short-term load forecasting. The incorporation of mobility data and data sharing layers potentially reduces the difficulty of capturing the load patterns and improves the generalization of the load forecasting models. 2) The real public data collected from multiple agencies and companies in the United States and European countries are used to conduct horizontal and vertical tests. These tests prove the failure of the conventional models and methods in the COVID-19 and demonstrate the high accuracy (error mostly less than 1%) and robustness of the proposed model. 3) The Shapley additive explanations technology based on game theory is innovatively introduced to improve the objectivity of the models. It visualizes that mobility indicators are of great help to the accurate load forecasting. Besides, the non-synchronous relationships between the indicators' correlations and contributions to the load have been proved. [ABSTRACT FROM AUTHOR]

In this paper, first we give the cohomology theory of a Poisson algebra with a module (called a PoiMod pair) and study the linear deformation theory of a PoiMod pair. We introduce the notion of a Nijenhuis structure on a PoiMod pair, which gives a trivial linear deformation. Then by adding compatibility conditions between Nijenhuis structures and O -operators, we introduce the notion of an O N -structure on a PoiMod pair and show that an O N -structure gives rise to a hierarchy of pairwise compatible O -operators. Finally we introduce the notions of PN- and Ω N -structures on Poisson algebras and study their relations. [ABSTRACT FROM AUTHOR]

• The effect of the aging of paper/pressboard on furfural content in oil was studied. • The effect of oil/paper/pressboard ratio on furfural- DP equation was studied. • The effect of oil replacement on aging evaluation results is corrected. The furfural analysis is an effective method to evaluate the aging state of transformer cellulose insulation. However, the existing research on the furfural analysis mainly focuses on 'oil-paper' system, without considering the oil-paper-pressboard system. In fact, the mass of insulating pressboard occupies a large proportion in the transformer, and there are obvious differences in the mass ratio of oil-paper-pressboard among transformers with different voltage levels, which will affect the effectiveness of furfural analysis. In addition, oil replacement is an oil treatment method to improve the insulation performance of oil, which will lead to the loss of furfural content in oil. In view of this, a series of experiments and studies were carried out to obtain the modified equation between furfural and degree of polymerization (DP) of paper/pressboard insulation with different oil-paper-pressboard mass ratios under oil replacement condition. Firstly, accelerated thermal aging experiment of insulation samples with various oil-paper-pressboard mass ratios was conducted in the lab, and the effect of oil-paper-pressboard mass ratio on furfural was analyzed. Then, the oil replacement experiments were performed, and the oil replacement compensation factor was proposed. Finally, a modified furfural -DP equation was established. This paper attempts to report a method for analyzing the effect of oil-paper-pressboard mass ratio and oil replacement on furfural- DP equation. These findings are expected to improve the application of furfural analysis in field transformers. [ABSTRACT FROM AUTHOR]

In this paper, first we introduce the notions of quasi-triangular pre-Lie bialgebras and factorizable pre-Lie bialgebras. A factorizable pre-Lie bialgebra leads to a factorization of the underlying pre-Lie algebra. We show that the symplectic double of a pre-Lie bialgebra naturally enjoys a factorizable pre-Lie bialgebra structure. Then we give the Rota-Baxter characterization of factorizable pre-Lie bialgebras. More precisely, we introduce the notion of quadratic Rota-Baxter pre-Lie algebras and show that there is a one-to-one correspondence between factorizable pre-Lie bialgebras and quadratic Rota-Baxter pre-Lie algebras. Finally, we develop the theories of matched pairs, bialgebras and Manin triples of Rota-Baxter pre-Lie algebras. In particular, a factorizable pre-Lie bialgebra gives rise to a Rota-Baxter pre-Lie bialgebra, and conversely a Rota-Baxter pre-Lie bialgebra gives rise to a factorizable pre-Lie bialgebra structure on the double space. [ABSTRACT FROM AUTHOR]

Mobile source emissions have significantly contributed to fine particulate matter (PM 2.5) pollution in urban atmospheric environments. Few studies have explored on-road vehicular PM 2.5 emissions and the health effects attributed to these emissions under different traffic conditions. Based on driving data obtained from 20000 taxi receipts, a motor vehicle emission simulator (MOVES) model was used to estimate the PM 2.5 emission levels of motor vehicles in the urban area within the Sixth Ring Road of Beijing (SRRB) on weekdays and weekends, respectively. Two different PM 2.5 exposure scenarios were further simulated using an atmospheric dispersion model. Subsequently, the health effects attributable to traffic-related PM 2.5 exposure were quantified by using exposure-response function to calculate the population acute morbidity and premature mortality during different time periods. We found that PM 2.5 emission levels of motor vehicles on normal weekdays were overall higher than those on weekends. The median vehicular PM 2.5 dispersion concentration in the study area was 2.68 μg/m3 on weekdays and 1.82 μg/m3 on weekends. Annually, there were 4435 premature deaths attributed to vehicle emissions (95% confidence interval (CI): 3655, 4904) under weekday exposure conditions, and this number sharply decreased to 3462 (95% CI: 3052, 4011) on weekends. Considering the hourly measurements, road traffic emissions have the greatest impact on public health during morning rush hour (8:00 a.m.). Total PM 2.5 emissions were closely associated with road traffic conditions and largely determined the magnitude of the health impacts caused by traffic-related PM 2.5 exposure. These findings provide information to aid in formulating reasonable public health policies to address vehicular PM 2.5 emission-induced health implications. Image 1 • PM 2.5 emissions of on-road urban vehicles in different time periods were estimated. • Health effects attributed to vehicular PM 2.5 were quantified with E-R functions. • People could suffer worse health effects exposed to vehicular PM 2.5 on weekdays. • Traffic situation greatly affects the PM 2.5 pollution and public health burden. [ABSTRACT FROM AUTHOR]

Identifying the anomalies of wind turbine (WT) and maintaining in time will improve the reliability of wind turbine and the efficiency of energy use, however it is difficult toidentify the wind turbine's abnormal operation by the traditional threshold settings because the anomalies can be induced by multiple factors. Therefore, this paper presents an anomaly identification model for wind turbine state parameters, and the model can identify abnormal state which the fluctuation range of the condition parametersis within the SCADA alarm threshold. The main work is as follows: 1) in order to increase the accuracy of the prediction model, a novel BPNN model integrated genetic algorithm (GA) was employed to optimize the training method (called GABP method), data samples, and input parameter selection, respectively; 2) on this basis, the distribution characteristics of state parameter prediction errors were depicted by a T-location scale (TLS) distribution with the shift factor and elastic coefficient; 3)error abnormal index (EAI) is defined to quantify the abnormal level of the prediction error, which is used as an indicator of the wind turbine anomaly. The proposed method has been applied on areal 1.5 MW wind turbine, and the analysis shows that the proposed method is effective in wind turbine anomaly identification. [ABSTRACT FROM AUTHOR]

Finding out the transformer incipient faults and their development trend has always been a central issue for electric power companies. In this paper, a novel approach combing wavelet technique with least squares support vector machine (LS-SVM) for forecasting of dissolved gases in oil-immersed power transformers has been proposed. The algorithm of particle swarm optimization (PSO) with mutation is developed to optimize the parameters of constructed wavelet LS-SVM regression (W-LSSVR). The existence of admissible wavelet kernels is proven by theoretic analysis. Evaluation of forecasting performance is based upon the measures of mean absolute percentage error (MAPE) and squared correlation coefficient (r 2 ). On the basis of the proposed approach, a procedure is put forward to serve as an effective tool and experimental results show that this approach is capable of forecasting the dissolved gas contents accurately. Comparing with the back propagation neural network (BPNN), the radial basis function neural network (RBFNN), the generalized regression neural network (GRNN), and the SVM regression (SVR) in two practical cases (taken hydrogen as an example here), the MAPEs of the proposed approach are significantly better than that of the four methods (5.4238% vs 19.1458%, 11.7361%, 7.7395%, 8.3248%; 2.1567% vs 18.9453%, 10.2451%, 7.8636%, 2.4628%) respectively. [ABSTRACT FROM AUTHOR]

In the electrical industry, there are many hazardous gases that pollute the environment and even jeopardize human health, so timely detection and effective control of these hazardous gases is of great significance. In this work, the gas-sensitive properties of Pd-modified g-C 3 N 4 interface for each hazardous gas molecule were investigated from a microscopic viewpoint, taking the hazardous gases (CO, NO x) that may be generated in the power industry as the detection target. Then, the performance of Pd-modifiedg-C 3 N 4 was evaluated for practical applications as a gas sensor material. Novelly, an unconventional means was designed to briefly predict the effect of humidity on the adsorption properties of this sensor material. The final results found that Pd-modified g-C 3 N 4 is most suitable as a potential gas-sensitizing material for NO 2 gas sensors, followed by CO. Interestingly, Pd-modified g-C 3 N 4 is less suitable as a potential gas-sensitizing material for NO gas sensors, but has the potential to be used as a NO cleaner (adsorbent). Unconventional simulation explorations of humidity effects show that in practical applications Pd-modified g-C 3 N 4 remains a promising material for gas sensing in specific humidity environments. This work reveals the origin of the excellent properties of Pd-modified g-C 3 N 4 as a gas sensor material and provides new ideas for the detection and treatment of these three hazardous gases. [Display omitted] • The sensing properties of Pd-modified g-C 3 N 4 interface were evaluated for CO, NO x. • A new experiment for the prediction of humidity-influenced interface performance was designed. • This work enriches new schemes for the preparation of g–C 3 N 4 –based gas sensors. • This work provides new ideas for addressing hazardous gases generated in the electrical industry. [ABSTRACT FROM AUTHOR]

Insulation performance testing of gas-insulated switchgear (GIS) and treatment of SF 6 decomposition products under partial discharge are two crucial studies. In this paper, the adsorption behavior and sensor properties of five characteristic decomposition gases of SF 6 (HF, H 2 S, SO 2 , SOF 2 , SO 2 F 2) on intrinsic and CoO-doped SnSe monolayers are investigated in detail based on density-functional theory. The results showed that the adsorption behaviors of HF, H 2 S, SOF 2 and SO 2 F 2 on SnSe monolayers were all physisorption among which the best adsorption was SO 2 with an adsorption energy of −0.618 eV, which is a weak chemisorption. After doping with CoO, the adsorption capacity of SnSe monolayer for five gases was significantly enhanced, in which the adsorption energies for SO 2 and SOF 2 reached −1.356 eV and −1.175 eV, respectively. In addition, the conductivity of the system is greatly improved, with the bandgap changing from 1.129 eV to 0 eV. The microscopic mechanism of the interaction of gas molecules with CoO-SnSe monolayers has been revealed by energy band structure (E g), density of states (DOS), Milligan charge analysis (∆ Q) and electrostatic potential. Finally, the sensitivity (S) and desorption time (τ) of the five adsorption systems were calculated to illustrate the macroscopic gas-sensitive properties of the system. This work will help to explore the application of CoO-SnSe monolayers in SF 6 decomposition gas sensing detection and adsorption treatment. [Display omitted] • Doping greatly improves the conductivity of the system, and the adsorption effect of doped system on SO 2 and SOF 2 is significantly improved. • The change of electronic characteristics of doped system is expounded. • The sensing characteristics of adsorption system are calculated, and the possibility of SOF 2 as the target gas for CoO-SnSe sensing device to evaluate GIS insulation performance is explored. [ABSTRACT FROM AUTHOR]

A pre-Lie-Rinehart algebra is an algebraic generalization of the notion of a left-symmetric algebroid. We construct pre-Lie-Rinehart algebras from r -matrices through Lie algebra actions. We study cohomologies of pre-Lie-Rinehart algebras and show that abelian extensions of pre-Lie-Rinehart algebras are classified by the second cohomology groups. We introduce the notion of crossed modules for pre-Lie-Rinehart algebras and show that they are classified by the third cohomology groups of pre-Lie-Rinehart algebras. At last, we use (pre-)Lie-Rinehart 2-algebras to characterize the crossed modules for (pre-)Lie Rinehart algebras. [ABSTRACT FROM AUTHOR]

Accurate description of wind speed probability distribution is an essential part of wind farm planning and dispatching. The two-parameter Weibull distribution is mostly used to fit unimodal wind speed probability distribution, but it suffers from low fitting accuracy and insufficient applicability. To solve these issues of the Weibull distribution, a novel distribution model, Falling Model, is proposed in this paper. In this model, the first Step is to "fall" the empirical wind speed distribution based on the Weibull. Then the probability distribution of falling form is fitted by a Cloud Model after segmentation. Finally, the segmented Cloud Models are superposed with Weibull to build the Falling Model. The goodness-of-fit (GoF) of the Falling Model, Weibull, Burr, and Gamma distributions was compared in a case study, and it showed that the Falling Model had the highest fitting accuracy. The sum of squared error of the GoF is 106 times better than the Weibull. Further, the applicability analysis of the Falling Model was performed using existing literature data, and it found that the Falling Model can be widely applied. The study shows that the unimodal distribution combines the advantages of both the Weibull and several Cloud Models. • The Falling Model is firstly proposed to fit the wind speed probability distribution. • The Falling Model has the highest fitting accuracy in this study. • The Falling Model can adapt to different distributions in various regions. [ABSTRACT FROM AUTHOR]

The spatial mismatch between energy resources and water resources has been widely concerned. Electricity trade drives virtual water flows from electricity production sides to consumption sides, which can aggravate or alleviate regional water scarcity. Investigating the historical evolution of virtual water transmission and its impacts on water scarcity benefits the sustainable development of energy and water. To reduce data uncertainties, in this study, we built provincial water intensity of electricity generation inventory considering the impacts of factors such as electricity generation types, cooling technologies and generator units. Besides, to investigate how the direction and quantity of virtual water flows aggravate or alleviate the regional water scarcity, the impact assessment model was built, and the impact assessment index was defined. The results showed that the water consumption of electricity generation and interprovincial virtual water transfer increased by 2.61 times and 5.12 times during 2005–2015, respectively. Changes in the amount of electricity generation and the electricity transmission structure were the two main drivers of increases in virtual water flows, contributing to a total increase of 85.28%. A large amount of virtual water was transferred from the southwest and the northwest to the eastern coastal areas. There were many "positive flows" from the southwest to Jiangsu-Zhejiang-Shanghai, while "negative flows" were concentrated in Xinjiang-Henan, Gansu-Qinghai and Gan-Shaanxi. The nodes with the largest impact assessment index value were Hubei, Sichuan and Shanghai, while the lowest were Gansu, Xinjiang and Qinghai. This study can provide a basis for optimizing the electricity generation mix and electricity transmission structure, alleviating the growing tension between energy and water, and realizing the sustainable development of water resources. • The provincial water intensity for electricity generation inventory is built. • The amount of virtual water flows increased 5.12 times from 2005 to 2014. • The virtual outflows of the southwestern regions alleviated national water scarcity. [ABSTRACT FROM AUTHOR]

Accurate and real-time prediction of gas concentrations is a critical component of intelligent prediction systems and holds significant importance for production safety and quality of life. However, due to the constraints of the network topology in gas sensor networks and the temporal nature of gas concentration variations, concentration prediction has always been considered a significant challenge. To simultaneously capture the spatiotemporal correlations in gas concentration data with spatiotemporal characteristics, this study proposes a fully-connected temporal multilayer graph convolutional network (FTM-GCN). This method combines multiple graph convolutional layers (MGC), fully connected layers, and gated recurrent units (GRU). FTM-GCN exhibits three prominent features: (1) It leverages MGC to learn the topology of gas sensor networks, enhancing the model's ability to graph data and capture spatial characteristics. MGC is incorporated to capture spatial features in the data. (2) GRU is employed to capture the dynamic changes in sensor network data and the temporal characteristics of the data. (3) Continuous fully-connected layers are introduced to enhance the model's performance. Experimental results demonstrate that FTM-GCN effectively captures the spatiotemporal correlations in spatiotemporal data across various prediction perspectives and outperforms GCN, GRU, T-GCN and STGCN. • Spatio-temporal prediction of graph-structured data using an electronic nose. • Successful prediction of ethanol concentrations in space-time. • A new deep learning model called FTM-GCN is proposed which is based on GCN. • The proposed FTM-GCN achieves outstanding regression prediction performance. [ABSTRACT FROM AUTHOR]

The failure envelope is commonly used to describe the ultimate bearing capacity under combined loading of offshore foundations. Based on different loading strategies, various methods such as side swipe, probe, sequential swipe, and enhanced swipe methods, have been presented to determine failure envelopes. To improve the accuracy and efficiency of calculation, a simplified modified swipe method is proposed in this paper, and its performance is compared against the other four loading methods in different geological profiles with the influential factors analysed. The performance of different methods under complex conditions and parameter sensitivity are also discussed. It has been found the elastic modulus of soil affects the shape of the failure envelope when the same loading displacement is applied, and that the calculation accuracy of the simplified modified swipe method can be ensured by increasing the modulus or enhancing the loading ratio. The general comparison illustrates that the simplified modified swipe method is effective in terms of accuracy, speed, and ease of implementation and is therefore recommended as an attractive and convenient solution for obtaining failure envelopes. [ABSTRACT FROM AUTHOR]

Along with electricity transmission, virtual water is transferred from electricity generation provinces to load hubs, reallocating spatial water resources and impacting regional water scarcity. To assess whether virtual water in electricity transmission mitigated or aggravated national water scarcity, a rationality assessment model was proposed from energy-water nexus perspective. The interprovincial virtual water flows were calculated based on provincial water intensity and electricity transmission considering the provincial electricity generation mix. The flow rationality index and node rationality index were proposed and defined to evaluate the net benefit of virtual water flows and provinces, respectively, according to the interprovincial differences in water stress index. The results show that: 1) a large amount of virtual water was transferred from the northwest and southwest provinces to the southeast provinces and Beijing-Tianjin-Hebei region; 2) the flow with the highest flow rationality index is Inner Mongolia-to-Hebei (29.8), while the lowest one is Shanxi-to-Hubei (−102.5); 3) the node with the highest node rationality index is Hebei (32.0), while the lowest one is Shanxi (−104.7). By quantifying the impacts of electricity transmission on provincial and national water stress, this study aims to provide guidance for national virtual water strategy by electricity transmission to mitigate national water scarcity. • Virtual water transfers embodied in China's interprovincial electricity trade is calculated in 2014. • A rationality assessment model is proposed to evaluate whether the water transfers positively or negatively impacts overall water scarcity for a region. • Based rationality assessment model, the rationality index for flows and provinces is analyzed. [ABSTRACT FROM AUTHOR]

Abstract The sources, levels and health risks of volatile organic compounds emitted from the entire furniture manufacturing process in workshops were investigated. A probabilistic health assessment model was established to estimate the health risks of workers and managers caused by VOCs (volatile organic compounds) based on the inhalation risk model and the Monte Carlo method. Sensitivity analyses and uncertainty analyses were implemented to screen out the crucial input parameters and to identify the possible influential factors in the evaluation process. The results demonstrate that the highest concentration of VOCs occurs in the topcoating process, followed by the veneering, undercoating, edge bonding, polishing varnish, drying, and paint mixing processes. Overall, a total of 17 VOCs, including aromatic hydrocarbons, alkanes, halogenated alkanes, ketones, and saturated aliphatic esters, exist in 7 VOC emission processes in the wood processing workshop and the painting workshop. For health impacts, workers in the topcoating, undercoating, and veneering positions suffer the greatest carcinogenic risks, with mean values above 1.00E-05, which mainly originate from ethylbenzene, dichloromethane, and benzene. The carcinogenic risks in other positions are relatively lower and are in the sequence of polishing varnish, paint mixing, drying, and edge bonding. Sensitivity analyses indicated that exposure duration had the greatest impact on carcinogenic and non-carcinogenic risk, with an average contribution of 0.78 and 0.93, respectively. These comprehensive evaluations for exposure level and health risk can yield insight into the occupational hazards of VOC emissions and will contribute to informed decision-making related to abatement measures and risk management in furniture manufacturing workplaces. Graphical abstract Image Highlights • The VOCs emission processes were firstly investigated in furniture manufacturing. • A total of 17 VOCs during 7 emission processes were identified and qualified. • Probabilistic risks were estimated by Monte Carlo method and inhalation risk model. • Topcoating shows the greatest risk and ethylbenzene should be prior controlled. • Exposure duration have greatest impact on carcinogenic and non-carcinogenic risk. [ABSTRACT FROM AUTHOR]

With the advancements in materials and pattern recognition algorithms, electronic nose (E-nose) has been continuously developing and finding increasingly widespread applications. However, E-nose is prone to drift during practical usage. Currently, the primary methods for addressing the issue of sensor drift in sample classification are semi-supervised learning and unsupervised learning. The implementation of supervised learning has been difficult in the past due to the requirement for a large number of up-to-date samples to retrain the models. But now, this challenge can be overcome with the help of meta-learning. We applied the model-agnostic meta-learning (MAML) architecture from meta-learning and designed a residual classifier with learnable per-parameter learning rates. Leveraging the ability of 'learning to learn', our method do not require a large amount of target domain data and there is no need to repeat extensive computations when transferring to any other target domain compared to other algorithms, resulting it highly deployable. Through the evaluation of multiple experiments, we demonstrate the feasibility of our approach and achieve very high classification accuracy on a publicly available long-term drift dataset compared with other methods. • The MAML architecture in meta-learning is utilized to address the issue of gas sensor drift. • Learning a learning rate and direction for each parameter is conducted in our architecture. In the outer loop phase of our method, different learning rates are also applied to different parameters. • Compared to other algorithms, our model only requires two samples per class for knowledge transfer, without the need for extensive repeated training. [ABSTRACT FROM AUTHOR]

• Fe3+ modified Bronsted acidity and CTS altered hydrophilicity for NaY catalysts. • Fe3+ exchanged NaY display shortened Si-OH and electronically reconfigured Fe3+ sites. • CTS modified FeY materials show 18-fold activity improvement. • CTS modified FeY catalysts display remarkable stability. Solketal is known as a promising non-BPA plasticizer. However, fast and robust synthesis of solketal from bio-oxygenates remains a grand challenge, as strong surface polarization often deactivates acidic catalysts. We proposed and validated a dual designing strategy, involving (1) Fe3+ modified Bronsted acidity and (2) chlorotrimethylsilane (CTS) altered hydrophilicity for enhanced activity and stability of NaY catalysts. As one of the key findings, Fe3+ exchanged NaYs display an unusual shortened Si-OH bond and electronically reconfigured Fe3+ sites for a 4-fold activity enhancement. Furthermore, CTS modified FeY materials further alleviate surface passivation of glycerol and water, leading to an 18-fold activity improvement and better durability for acetalization of glycerol. CTS modified FeY catalysts display remarkable stability against varies impurity molecules. The dual modification methodology provides useful information on rational design of durable catalysts for synthesis of various other non-BPA derivatives. [ABSTRACT FROM AUTHOR]

The volume of landfill gas (LFG) produced during municipal solid waste (MSW) decomposition in local landfills has increased rapidly in recent years. To promote circular economy (CE) applications through resource recovery, this article investigates the feasibility of utilizing LFG as a sustainable energy to power data centers (DCs) in Xiamen (China) and evaluates the implications of waste heat reuse on the energy consumption of DC. The objective of this study was to investigate the environmental impacts and economic benefits of this scheme based on a SFC model. With respect to its novelty, LFG is utilized as an energy supply to DCs, while the waste heat generated in the energy production is recovered. The socio-economic benefits generated in this process are also elaborated. It was found that a large number of MSW was completely recycled to generate energy. The LFG enterprise could benefit from energy sale, while the energy satisfied the annual demand for DC power consumption. Utilizing this energy output resulted in the reduction of primary energy and less CO₂ emissions. By reusing the waste heat for domestic heating, the energy efficiency could be improved, while primary energy could be further saved, avoiding more CO₂ emissions. This implies that utilizing LFG output and waste heat to power DCs not only facilitate resource recovery from unused resources to improve energy efficiency and obtain socio-economic benefits, but also promote the digitalization of DCs and sustainable development of urban cities in the framework of CE. [Display omitted] • LFG can be used to power data centers (DCs) in order to promote CE. • The recovery of waste heat from LFG generation reduced CO 2 emission of 8.46 × 108kg. • Within 15 years, 4.97 × 10 10 kg of MSW can generate 4.65 × 109 kW ⋅ h of electricity. • The recovery of waste heat benefited landfill operators and DC firms financially. [ABSTRACT FROM AUTHOR]

• A multi-objective energy-water nexus optimization framework is established. • The provincial economic trade's impact on energy and water stress is quantified. • The economic development level is the key to optimize the resources reallocation. • The rapid trade growth among provinces may aggravate the risk of resource scarcity. With China's vast territory and high regional heterogeneity in the spatial energy and water resources distribution, provincial-level embodied energy (EE) and virtual water (VW) reallocation are critical to regional energy and water recourse management but unfortunately, remain far from completed. In this paper, considering the heterogeneity of energy and water resources endowments among provinces, we constructed a dual objectives optimization framework to minimize national energy stress and water stress. Also, we evaluated the EE and VW flows' synergy impact on mitigating energy and water stress. We found that the spatial resources reallocation has a significant role on mitigating energy and water stress in high-stress regions. The results show that the net energy inflow can increase by 33.5%, and the net water inflow can increase by 42.1% in high-stress regions. The EE and VW flows' impact on mitigating resource stress is affected by the spatial resource endowments' heterogeneity. The flows that have the most positive impact on mitigating regional energy and water stress are Shanxi-Jiangsu and Jiangxi-Shanghai, respectively, while the most negative flows are Jiangsu-Henan and Xinjiang-Heilongjiang, respectively. The EE and VW flows among provinces have synergy in mitigating energy and water stress. Policy makers should not only focus on the local resources' consumption, but on the inter-provincial recourse flow and spatial resource endowments' heterogeneity. Inter-provincial cooperation is recommended, with reducing the scale of EE and VW trade that has negative impact, which can mitigate regional energy and water stress. [ABSTRACT FROM AUTHOR]

Research findings on the relationship between adverse childhood experiences (ACEs) and the risk of cancer were inconsistent. The purpose of this study was to perform a quantitative synthesis of the preceding research findings. System review and meta-analysis. Electronic database of PubMed, Embase, Web of Science and Cochrane Library were systematically searched to identify relevant observational studies published not later than September 9, 2020. Specifically, original articles that reported the risk of cancer in adult populations that experienced ACEs before the age of 18 were selected. All pooled odds ratios (ORs) and 95 % confidence intervals (CIs) were calculated using a random-effects model. Publication bias was examined using funnel plots, and sensitivity analysis was used to explore the stability of the pooled results. A total of 18 studies involving 406,210 participants were included in this review. Individuals with 2 or 3 kinds of ACEs (OR = 1.35, 95%CI:1.12,1.62) or at least 4 ACEs (OR = 2.17, 95%CI: 1.76,2.68) were at increased risk of cancer when compared with individuals with no ACEs. Of the different types of ACEs examined, physical abuse (OR = 1.23, 95%CI:1.05,1.43), sexual abuse (OR = 1.26, 95%CI:1.02,1.56), exposure to intimate partner violence (OR = 1.26, 95%CI:1.12,1.41) and financial difficulties in the family (OR = 1.16, 95%CI:1.00,1.33) were associated with the risk of any cancer. These findings suggest that multiple ACEs may be a risk factor for cancer development. Therefore, prevention of ACEs and interventions for supporting those affected by ACEs are necessary. [ABSTRACT FROM AUTHOR]

Expansion of electric grid has induced increasing stress on regional water resources. Yet, few insights have been gained into the network dynamics of virtual water flows embodied in interprovincial electricity transmission. Here, we calculated the interprovincial transfer of virtual water and virtual scarce water associated with electricity transmissions in China during 2005–2014, considering both thermal power and renewable energies. To track the network dynamics of virtual water, we identified control and dependence relationships between provinces related to exchanging virtual water, using network control analysis. We found that the magnitudes of virtual water and virtual scarce water transferred from western provinces to the rest of China have increased five-fold between 2005 and 2014. The transfers of virtual water exacerbated the water scarcity in northwestern provinces over time, while the transfers between southwestern provinces mitigated the local water scarcity. Southwestern provinces have strong control over the Yangtze River Delta region due to the supply of virtual water in hydroelectricity transmission inbetween. In contrast, the interdependence among northwestern provinces has been strengthened over time. Gansu, Xinjiang and Inner Mongolia have become more dominant in controlling the virtual water appropriated by Beijing–Tianjin–Hebei region and Shandong over this period. We suggest that the dynamics of electricity-induced virtual water transfers across regions be regularly tracked to address water scarcity during constructing power plants and adjusting electricity dispatches. • Virtual water in electricity transmission has increased five-fold between 2005 and 2014. • Thermal power has a greater impact on virtual scarce water than virtual water. • Expansion of renewable energies in power system mitigated water scarcity in China. • The flow of virtual water has been controlled by more regions over time. [ABSTRACT FROM AUTHOR]

• A multi-objective optimization model is established for electric power sector. • Water consumption and carbon emissions from power generation are quantified. • Tradeoffs between water and carbon in electricity mix are evaluated. • Strategies for optimizing electricity mix and tasks distribution are proposed. Electric power sector is a significant water consumer and a major source of carbon emissions in China. Optimizing electricity mix is critical for maintaining a stable energy supply while addressing water stress and carbon emissions associated with power generation. In this study, we developed an optimization model with the dual objectives of mitigating the risk of water scarcity and cutting carbon emissions, by adjusting the electricity mix and power generation tasks of provincial grids. Using the weighted sum method, the optimization of grids is simulated based on a scenario analysis within specified equality and boundary constraints. We quantified the impact of the spatial distribution of thermal power and power generated from clean energies (hydropower, wind power, nuclear power, and solar power) on virtual water consumption and carbon emissions at a provincial level in China. The results suggested that transferring part of power generation tasks from water-deficient areas (e.g., Shandong and Jiangsu) to water-sufficient areas (e.g., Yunnan and Guangdong) could significantly mitigate water scarcity. Water consumption of power generation could be reduced by 34.7% in highly water-deficient areas if a water-prioritized scenario is implemented. Carbon emissions related to power generation could be reduced nationwide under a carbon-prioritized scenario and a water-carbon-balanced scenario. This model could provide a synergistic perspective for improving regional water and carbon performances based on changes in electricity mix. [ABSTRACT FROM AUTHOR]

China is the largest electricity consumers worldwide and the coal-dominated electricity system has led to increasing pressure on climate change and air pollution. Understanding the direct and indirect electricity consumption in China's economy is a prerequisite for proper energy conservation policy formulation. Therefore, this study drew the electricity consumption flows within the domestic supply chain network of China from the on-site electricity consumption to the final demand from 2007 to 2015. Specifically, hotspots of sectors and supply chain paths were identified by adopting power-of-pull analysis and structural path analysis, respectively. We find that Production and Supply of Electricity and Heat was the most critical sector that pulls other sectors' electricity consumption, but its impact has been declining during 2007–2015. The most critical supply chain paths originated from the Manufacture of Nonmetallic Mineral Products and Metal Smelting and Rolling , went through Construction and ultimately ended in capital formation, given that around 40% of the electricity consumption was driven by investment. Moreover, the electricity consumption attributed to each production layer for most sectors were more evenly distributed in 2015 than that in 2007, manifesting that the supply chain relationships for most sectors have become more complex. Our results highlighted the importance of indirect energy management and supply chain engagement in the electricity conservation strategies in China. • Electricity use from production to consumption via supply chains was investigated. • The temporal change of critical sectors' pulling power was shown. • Investment accounted for around 40% of total electricity consumption in China. • Most critical supply chain paths started from investment and ended at metals and non-metal minerals through construction. • Indirect energy and supply chain engagement should be highlighted in policy design. [ABSTRACT FROM AUTHOR]

• TRPV1 shows targeting potential in hepatocellular carcinoma. • TRPV1 gives rise to phenotypic plasticity in hepatocarcinogenesis. • Tumor initiating cells and biliary cells were cellular origins in TRPV1 induced HCC. • TRPV 1 exert its role via transcription factors Ovol 2 and Zeb 1 mediated by SOX 10. The landscape of cellular plasticity and sources with relevant niche signals in hepatocellular carcinoma is still obscure. Transient receptor potential vanilloid 1 (TRPV1), a non-selective cation channel, is involved in a variety of malignancies and overexpressed in hepatocellular carcinoma (HCC). We have investigated the role of TRPV1 in HCC from different angles by various experimental techniques, such as in vivo and in vitro experiments, and by bioinformatics analysis of data from genetic models induced by diethylnitrosamine (DEN), mice samples and human HCC samples. We find that TRPV1 knockout promotes to hepatocarcinogenesis and deconstructs the portal triad adjacent to tumor border that is contributed by originations of tumor initiating cells and biliary cells. Epithelial to mesenchymal transition (EMT) is involved and transcription factors Ovol2 and Zeb1 coordinated with Sox 10 drive gene expression in the event which is also confirmed by the expression of these proteins in human HCC samples. Treatment with TRPV1 agonist Capsaicin inhibits the growth of HCC cells in xenograft models. Our findings demonstrate that TRPV1 is a potential therapeutic target in human HCC and exerts effects on cellular plasticity with modulation of Ovol2, Zeb1 and Sox10. [ABSTRACT FROM AUTHOR]