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Lead is an environmentally widespread neurotoxic pollutant. Although the neurotoxicity of lead has been found to be closely associated with metabolic disorders, the effects of short-chain fatty acids on the neurotoxicity of lead and its mechanisms have not yet been explored. In this study, the results of open field tests and Morris water maze tests demonstrated that chronic lead exposure caused learning and memory deficits and anxiety-like symptoms in mice. The serum butyric acid content of lead-treated mice decreased in a dose-dependent manner, and oral administration of butyrate significantly improved cognitive memory impairment and anxiety symptoms in lead-exposed mice. Moreover, butyrate alleviated neuroinflammation caused by lead exposure by inhibiting the STAT3 signaling in microglia. Butyrate also promoted the expression of acetyl-CoA synthetase ACSS2 in hippocampal neurons, thereby increasing the content of acetyl-CoA and restoring the expression of both histone H3K9ac and the downstream BDNF. We also found that the median butyric acid concentration in high-lead exposure humans was remarkably lower than that in the low-lead exposure humans (45.16 μg/L vs. 60.92 μg/L, P

Exposure to lead (Pb) and manganese (Mn) during early life influences neurodevelopment and increases the risk of neurodegenerative disorders. However, the level of developmental neurotoxicity due to combined exposure to the two metals remains unclear. Although the microbiota plays an essential part in the development of the nervous system via the gut–brain axis, there is a paucity of information regarding the interactions between exposure to Pb and Mn, the destruction of the microbiome, and neurodevelopmental impacts. To fill in this knowledge gap, we investigated the developmental neurotoxicity and effects on the microbiota of Pb (0.05 mg·L−1) alone and in combination with Mn (0.3 mg·L−1) in zebrafish larvae. Our results revealed that combined exposure precipitated higher malformation rates and lower locomotor activity levels than exposure to either Pb or Mn alone. Additionally, when we separated the combined exposure group from the other groups by applying unsupervised principal coordinates analysis (PCoA) and linear discriminant analysis (LEfSe) of microflora sequencing results, we observed extensive alterations in microbial abundances under combined-exposure conditions. Functional prediction analysis showed that combined exposure contributed to altered amino acid and lipid metabolism, and also that combined exposure to Pb and Mn reflected the greatest number of differentially activated biological pathways compared to the other three groups. ATP-binding cassette G (ABCG) genes and genes related to serotonin signaling and metabolism were altered following combined Pb and Mn exposure and exhibited disparate trends vis-à-vis Pb or Mn exposure alone. According to the results, the combined exposure to Pb and Mn led to more severe effects on both zebrafish locomotor activity and gut microbial composition. We suggest that the microbiota contributes to the combined neurotoxicity by increasing ABCG5 and ABCG8 gene expression.

BackgroundOccupational hazards such as solvents and noise in the electronics industry are serious. Although various occupational health risk assessment models have been applied in the electronics industry, they have only been used to assess the risks of individual job positions. Few existing studies have focused on the total risk level of critical risk factors in enterprises.MethodsTen electronics enterprises were selected for this study. Information, air samples and physical factor measurements were collected from the selected enterprises through on-site investigation, and then the data were collated and samples were tested according to the requirements of Chinese standards. The Occupational Health Risk Classification and Assessment Model (referred to as the Classification Model), the Occupational Health Risk Grading and Assessment Model (referred to as the Grading Model), and the Occupational Disease Hazard Evaluation Model were used to assess the risks of the enterprises. The correlations and differences between the three models were analyzed, and the results of the models were validated by the average risk level of all of the hazard factors.ResultsHazards with concentrations exceeding the Chinese occupational exposure limits (OELs) were methylene chloride, 1,2-dichloroethane, and noise. The exposure time of workers ranged from 1 to 11 h per day and the frequency of exposure ranged from 5 to 6 times per week. The risk ratios (RRs) of the Classification Model, the Grading Model and the Occupational Disease Hazard Evaluation Model were 0.70 ± 0.10, 0.34 ± 0.13, and 0.65 ± 0.21, respectively. The RRs for the three risk assessment models were statistically different (P < 0.001), and there were no correlations between them (P > 0.05). The average risk level of all of the hazard factors was 0.38 ± 0.18, which did not differ from the RRs of the Grading Model (P > 0.05).ConclusionsThe hazards of organic solvents and noise in the electronics industry are not negligible. The Grading Model offers a good reflection of the actual risk level of the electronics industry and has strong practicability.

Abstract As the proton exchange membrane fuel cell (PEMFC) is a nonlinear, time‐varying, multiple‐input multiple‐output system, an advanced controller with strong robustness and adaptability is required for controlling PEMFC stack temperature and achieve a high operation efficiency. In this paper, a data driven optimal controller is proposed for controlling the stack temperature, which is based on large‐scale deep reinforcement learning. In addition, a new deep reinforcement learning algorithm termed curriculum guidance strategy large‐scale dual‐delay deep deterministic policy gradient (CGS‐L4DPG) algorithm is proposed for this controller. The design of this algorithm introduces the concepts of the curriculum guidance strategy and imitation learning, and its inclusion improves the performance and robustness of the proposed controller. The simulation results show that, taking advantage of the high adaptability and robustness of CGS‐L4DPG algorithm, the proposed controller can more effectively control the PEMFC stack temperature than existing control algorithms.

Abstract To mismatch various power disturbances of interconnected power grid, an integrated generation‐control and power‐dispatch (IGCPD) for performance‐based frequency regulation market is proposed. Unlike the conventional automatic generation control with an independent controller and a power distributor, IGCPD can significantly improve the dynamic control performance via combining these two parts into an integrated decision agent. The IGCPD eliminates the waste of frequency regulation resources and repeated regulation of the units caused by the mismatch of the controller and power distributor. In addition, by setting the reward function reasonably, IGCPD can save regulatable capacity of fast units for regulation in the next large‐scale disturbance. In order to enhance the decision ability, a swarm intelligence‐based deep deterministic policy gradient (SI‐DDPG) algorithm is proposed to acquire the control knowledge and implement a high‐quality decision for this agent. Moreover, the swarm intelligence‐based training sample can achieve a high learning robustness for SI‐DDPG which solve the problem of low robustness in conventional deep reinforcement learning. The proposed technique is verified on an England two‐area load frequency control model and the west China two‐area LFC model. IGCPD improves the area control error control performance and reduce the frequency regulation mileage payment of the interconnected power grid.

With the participation in automatic generation control (AGC), a large-scale wind farm should distribute the real-time AGC signal to numerous wind turbines (WTs). This easily leads to an expensive computation for a high-quality dispatch scheme, especially considering the wake effect among WTs. To address this problem, a hierarchical power control (HPC) is constructed based on the geographical layout and electrical connection of all the WTs. Firstly, the real-time AGC signal of the whole wind farm is distributed to multiple decoupled groups in proportion of their regulation capacities. Secondly, the AGC signal of each group is distributed to multiple WTs via the data-driven surrogate-assisted optimization, which can dramatically reduce the computation time with a small number of time-consuming objective evaluations. Besides, a high-quality dispatch scheme can be acquired by the efficient local search based on the dynamic surrogate. The effectiveness of the proposed technique is thoroughly verified with different AGC signals under different wind speeds and directions.

Exposure to cadmium (Cd) can affect neurodevelopment and results in increased potential of developing neurodegenerative diseases during the early developmental stage of organisms, but the mechanisms through which exposure to environmentally relevant concentrations of Cd lead to developmental neurotoxicity remain unclear. Although we know that microbial community fixations overlap with the neurodevelopmental window during early development and that Cd-induced neurodevelopmental toxicity may be related to the disruption of microorganisms during early development, information on the effects of exposure to environmentally relevant Cd concentrations on gut microbiota disruption and neurodevelopment is scarce. Therefore, we established a model of zebrafish exposed to Cd (5 µg/L) to observe the changes in the gut microbiota, SCFAs, and free fatty acid receptor 2 (FFAR2) in zebrafish larvae exposed to Cd for 7 days. Our results indicated that there were significant changes in the gut microbial composition due to the exposure to Cd in zebrafish larvae. At the genus level, there were decreases in the relative abundances of Phascolarctobacterium, Candidatus Saccharimonas, and Blautia in the Cd group. Our analysis revealed that the acetic acid concentration was decreased (p > 0.05) while the isobutyric acid concentration was increased (p < 0.05). Further correlation analysis indicated a positive correlation between the content of acetic acid and the relative abundances of Phascolarctobacterium and Candidatus Saccharimonas (R = 0.842, p < 0.01; R = 0.767, p < 0.01), and a negative correlation between that of isobutyric acid and the relative abundance of Blautia glucerasea (R = −0.673, p < 0.05). FFAR2 needs to be activated by SCFAs to exert physiological effects, and acetic acid is its main ligand. The FFAR2 expression and the acetic acid concentration were decreased in the Cd group. We speculate that FFAR2 may be implicated in the regulatory mechanism of the gut–brain axis in Cd-induced neurodevelopmental toxicity.

With the wake effect between different wind turbines, a wind farm generally aims to achieve the maximum energy capture by implementing the optimal pitch angle and blade tip speed ratio under different wind speeds. During this process, the balance of fatigue load distribution is easily neglected because it is difficult to be considered, and, thus, a high maintenance cost results. Herein, a novel bi-objective optimal wind farm energy capture (OWFEC) is constructed via simultaneously taking the maximum power output and the balance of fatigue load distribution into account. To rapidly acquire the high-quality Pareto optimal solutions, the decomposition-based multi-classifier-assisted evolutionary algorithm is designed for the presented bi-objective OWFEC. In order to evaluate the effectiveness and performance of the proposed technique, the simulations are carried out with three different scales of wind farms, while five familiar Pareto-based meta-heuristic algorithms are introduced for performance comparison.

A novel culture evolution learning is presented to achieve an optimal carbon-energy combined-flow (OCECF). A shared responsibility of carbon emission between electricity producers and electricity consumers is introduced in OCECF, such that a double counting of carbon emission can be eliminated. The proposed algorithm is inspired from the culture construction, culture communication, and cultural inheritance in the human society. First, a culture matrix of each country is constructed by the Q-value matrix of Q-learning, which can be simultaneously updated by its people. Second, each country can learn the superior culture from other more-advanced countries, while this so-called culture communication can improve the quality of the obtained optimal solution. Finally, the culture inheritance can be achieved by transfer learning between different optimization tasks, thus the convergence can be dramatically accelerated. The performance of CEA has been evaluated for OCECF on two IEEE benchmark systems, and a practical urban power grid of southern China, respectively. Simulation results demonstrate that CEA has a high convergence stability and fast convergence, which is around 4.02 to 51.43 times faster than that of conventional heuristic algorithms.

This paper proposes a novel multi-agent bargaining learning (MABL) for the distributed energy hub economic dispatch (EHED) of multiple energy carrier systems (MECS). Distributed EHED is developed by extending the conventional economic dispatch (ED) into MECS in a distributed manner, in which each energy hub is regarded as a learning agent for self-scheduling. The classical Q-learning with associative memory is employed for knowledge learning of each agent, while the non-uniform mutation operator is adopted for handling the continuous control variables. To maximize the total payoff of all the energy hubs, the bargaining game is presented for achieving an effective coordination between the buyer agents and a seller agent, where the slack energy hub is designed as the seller agent and the others are the buyer agents. MABL has been thoroughly evaluated for the distributed EHED on a high-complex 39-hub MECS with 29 energy hub structures and 76 energy production units. Case studies verify the superior performance of MABL for the distributed EHED compared with six centralized heuristic optimization algorithms.

This paper aims to introduce a novel maximum power point tracking (MPPT) strategy called transfer reinforcement learning (TRL), associated with space decomposition for Photovoltaic (PV) systems under partial shading conditions (PSC). The space decomposition is used for constructing a hierarchical searching space of the control variable, thus the ability of the global search of TRL can be effectively increased. In order to satisfy a real-time MPPT with an ultra-short control cycle, the knowledge transfer is introduced to dramatically accelerate the searching speed of TRL through transferring the optimal knowledge matrices of the previous optimization tasks to a new optimization task. Four case studies are conducted to investigate the advantages of TRL compared with those of traditional incremental conductance (INC) and five other conventional meta-heuristic algorithms. The case studies include a start-up test, step change in solar irradiation with constant temperature, stepwise change in both temperature and solar irradiation, and a daily site profile of temperature and solar irradiation in Hong Kong.

This paper proposes a novel multi-searcher optimization (MSO) algorithm for the optimal energy dispatch (OED) of combined heat and power-thermal-wind-photovoltaic systems. The available power of wind turbine (WT) units and photovoltaic (PV) units is approximated with the probability density functions of wind speed and solar irradiance, respectively. The chaos theory is used to implement a wide global search, which can effectively avoid a low-quality local optimum for OED. Besides, a double-layer searcher is designed to guarantee fast convergence to a high-quality optimal solution. Finally, three benchmark functions and an energy system with 27 units are used for testing the performance of the MSO compared with nine other frequently used heuristic algorithms. The simulation results demonstrate that the proposed technique not only can solve the highly nonlinear, non-smooth, and non-convex OED problem of an energy system, but can also achieve a superior performance for the convergence speed and the optimum quality.

To reduce occurrences of emergency situations in large-scale interconnected power systems with large continuous disturbances, a preventive strategy for the automatic generation control (AGC) of power systems is proposed. To mitigate the curse of dimensionality that arises in conventional reinforcement learning algorithms, deep forest is applied to reinforcement learning. Therefore, deep forest reinforcement learning (DFRL) as a preventive strategy for AGC is proposed in this paper. The DFRL method consists of deep forest and multiple subsidiary reinforcement learning. The deep forest component of the DFRL is applied to predict the next systemic state of a power system, including emergency states and normal states. The multiple subsidiary reinforcement learning component, which includes reinforcement learning for emergency states and reinforcement learning for normal states, is applied to learn the features of the power system. The performance of the DFRL algorithm was compared to that of 10 other conventional AGC algorithms on a two-area load frequency control power system, a three-area power system, and the China Southern Power Grid. The DFRL method achieved the highest control performance. With this new method, both the occurrences of emergency situations and the curse of dimensionality can be simultaneously reduced.

This paper proposes a novel adaptive consensus algorithm (ACA) for distributed heat-electricity energy management (HEEM) of an islanded microgrid. In order to simultaneously satisfy the heat-electricity energy balance constraints, ACA is implemented with a switch between unified consensus and independent consensus according to the dynamic energy mismatches. The feasible operation region of a combined heat and power (CHP) unit is decomposed into eight searching sub-regions, thus its electricity and heat energy outputs can simultaneously match the incremental cost consensus requirement and the heat-electricity energy balance constraints. Case studies are thoroughly carried out to verify the performance of ACA for distributed HEEM of an islanded microgrid.