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A novel sub-class of S-adenosyl-l-methionine (SAM)-dependent methyltransferases catalyze atypical chemical transformations in the biosynthesis of anthracyclines. Exemplified by RdmB from Streptomyces purpurascens, it was found with 10-decarboxylative hydroxylation activity on anthracyclines. We herein investigated the catalytic activities of RdmB and discovered a previously unknown 4-O-methylation activity. The site-directed mutagenesis studies proved that the residue at position R307 and N260 are vital for the decarboxylative hydroxylation and 4-O-methylation, respectively, which define two distinct catalytic centers in RdmB. Furthermore, the multifunctionality of RdmB activity was found as cofactor-dependent and stepwise. Our findings expand the versatility and importance of methyltransferases and should aid studies to enrich the structural diversity and bioactivities of anthracyclines.

In order to improve the baking characteristics and nutritional quality of mung bean bread, mung bean sourdough was made by fermenting mung bean flour using Northwest leavening head, and then combined with yeast as a compound leavening agent to make mung bean bread. The microbial composition of the Northwest leavening head was analyzed to clarify the composition of its microflora, the Fourier transform infrared spectrometer and rheometer were used to analyze the changing pattern of the secondary structure and viscoelasticity of gluten proteins in the dough of the mung bean bread, and the texture meter was used in combination with the sensory assessment to evaluate the sensory quality of the mung bean bread. The results showed that the phytic acid content of the mung bean sourdough fermented in the Northwest fermentation head was reduced by 44.71% at 6 h of fermentation, and the pH and total titration acidity (TTA) were 6.29 and 11.03 mL, respectively. Compared with the control group, the bread with added mung bean sourdough showed a decrease in the modulus of elasticity and viscous modulus of the dough, an increase in the plasticity of the dough, the content of α-helices and β-folds in the secondary structure of proteins, and the stability of the dough. The antioxidant results showed that the scavenging ability of mung bean bread for both DPPH and ABTS+ free radicals also significantly increased (P

As a kind of complex system with strong nonlinearity, strong coupling, time variance, redundancy, and high dimension, the modeling, motion control, and stability analysis of an underwater vehicle-manipulator system(UVMS) are very challenging. In dynamics modeling, the traditional Lagrange equation is often used to model the complex UVMS with high degrees of freedom, which requires the operation of derivation and partial derivation of the generalized coordinates and their derivatives and will face the problems of a large amount of calculation and low modeling efficiency. Therefore, this paper proposed a simplified dynamics modeling method for UVMS with 6+n degree-of-freedom based on the quasi-Lagrange equation, which could reduce the amount of calculation in the symbolic formula derivation and improve the modeling efficiency and the accuracy of the results. Finally, the numerical simulation of a UVMS model was carried out with the physical parameters of the underwater vehicle BlueROV and the underwater manipulator Reach Alpha, and the simulation results verified the complex coupling of the UVMS. The dynamics model based on the proposed method had a clear equation form, which not only provided strong support for the study of control algorithms and the optimization of coupling forces but also a basis for the design of dynamics parameters and the study of trajectory planning.

Abstract Background Our facial skin hosts millions of microorganisms, primarily bacteria, crucial for skin health by maintaining the physical barrier, modulating immune response, and metabolizing bioactive materials. Aging significantly influences the composition and function of the facial microbiome, impacting skin immunity, hydration, and inflammation, highlighting potential avenues for interventions targeting aging-related facial microbes amidst changes in skin physiological properties. Results We conducted a multi-center and deep sequencing survey to investigate the intricate interplay of aging, skin physio-optical conditions, and facial microbiome. Leveraging a newly-generated dataset of 2737 species-level metagenome-assembled genomes (MAGs), our integrative analysis highlighted aging as the primary driver, influencing both facial microbiome composition and key skin characteristics, including moisture, sebum production, gloss, pH, elasticity, and sensitivity. Further mediation analysis revealed that skin characteristics significantly impacted the microbiome, mostly as a mediator of aging. Utilizing this dataset, we uncovered two consistent cutotypes across sampling cities and identified aging-related microbial MAGs. Additionally, a Facial Aging Index (FAI) was formulated based on the microbiome, uncovering the cutotype-dependent effects of unhealthy lifestyles on skin aging. Finally, we distinguished aging related microbial pathways influenced by lifestyles with cutotype-dependent effect. Conclusions Together, our findings emphasize aging’s central role in facial microbiome dynamics, and support personalized skin microbiome interventions by targeting lifestyle, skin properties, and aging-related microbial factors. Video Abstract

Heart disease is a common human disease with high morbidity and mortality. Timely and effective prevention and treatment is an urgent clinical problem. The pathogenesis of heart disease is complex and diverse, involving hypertension, diabetes, atherosclerosis, drug toxicity, thrombosis, infection and other aspects. LL-37, an endogenous peptide, is well known for its antimicrobial properties. In recent years, LL-37 has been found to have a variety of biological functions, including its role in the regulation of atherosclerosis, thrombosis, inflammatory responses, and cardiac hypertrophy. Engineered LL-37-related peptides were developed and proved to regulate the development of disease, which revealed its potential clinical application. A comprehensive review and summary of LL-37 is presented to clarify its role in heart disease and to provide a reference and direction for future research.

Abstract Background Venous thromboembolism (VTE) is a major complication of breast cancer surgical patients. Assessing VTE awareness enables medical staff to tailor educational programs that improve patient self-management and reduce VTE risk. Therefore, this study aimed to assess VTE awareness among breast cancer surgical patients and identify factors influencing their awareness level. Methods A multicenter cross-sectional study was conducted on breast cancer patients scheduled for surgery from May 2023 to November 2023. Data were collected using a general information form and a validated self-assessment questionnaire on VTE awareness for breast cancer surgical patients. Univariate analysis and multiple linear regression analysis were used to analyze the data. Results Of 1969 patients included, the term awareness rates for deep vein thrombosis and pulmonary embolism were 42.5% and 26.1%, respectively. Information about VTE was primarily obtained from doctors (30.4%), nurses (24.0%), and social media (23.3%). The overall average VTE awareness score was 1.55 ± 0.53, with the dimension of VTE preventive measures scoring highest, and VTE clinical symptoms/signs scoring lowest. Multivariate analysis identified education level, personal VTE history, chemotherapy and surgical history, and the hospital’s regional location as significant factors associated with VTE awareness level (p

The fermentation characteristics and aroma-producing properties of Lactobacilli could influence the flavour quality of fermented milk, an important influencing factor of consumers' preference. In this study, fermented milk was prepared using Lactobacillus helveticus and the dynamic changes in the sensory quality of fermented milk throught fermentation were to assess the dynamic changes in sensory quality of fermented milks throughout the fermentation process, including rheological properties and flavour profiles. Styrene, linalool, octanoic acid, and 1-nonanol were considered as the key flavour components during fermentation. The quality of the fermented milk tends to be stabilized after 24-h, showing the minimal off-flavour at 48-h and optimal fermented aroma at 72-h. Three prebiotics (inulin, Galactooligosaccharides and inulin mixed with Galactooligosaccharides) were added to Lactobacillus spiralis fermented milk separately, and the results showed that inulin mixed with Galactooligosaccharides was the most effective group in improving the organoleptic quality of the fermented milk. Overall, the experimental results provide deeper insights into the release and retention of aroma compounds during fermentation and scientific reference for broadening the application of prebiotics and flavour-producing Lactobacilli in fermented milk processing.

To investigate the effect of ultrasound assisted NaCl soaking on the structure and digestive characteristics of black beans, the scanning electron microscopy, infrared spectroscopy analysis, X-ray diffraction and other methods were used to analyze the mechanism of NaCl on the structure of black beans. And the thermal properties, water absorption, and swelling rate were used to clarify the changes in the properties of the black beans during the ripening process, and the in vitro digestive properties of starch were analyzed. The results showed that ultrasound-assisted NaCl soaking decreased the densification of black bean powder particles and made the gap between particles larger while reducing the damage to the starch crystallization region. With the increase of NaCl concentration, the swelling rate of black bean particles increased by 28%, 48%, and 56% compared to water soaking, and the hardness significantly decreased. The gelatinization degree increased to 40%, 43%, and 45%, and the hydrolysis rate of black bean starch decreased to 19.05%, 17.93%, and 17.48%, respectively. This indicated that sodium ions could inhibit starch digestion, the NaCl soaking treatment in this study would provide a theoretical basis for the development of low GI functional foods.

Steam explosion (SE) technology enhances the extraction efficiency of bioactive compounds and their physicochemical properties. This study compared the structural characteristics of κ-carrageenan extracted by SE-assisted alkali treatment and conventional alkali treatment from Eucheuma, as well as the quality attributes of the resulting jellies. The results indicate that SE treatment did not alter the species of carrageenan, but it significantly elevated the content of characteristic carrageenan groups, with the sulfate group content and 3,6-anhydrogalactose (3,6-AG) content increasing by 15.86% and 45.08%, respectively. The jellies prepared with κ-carrageenan following SE treatment demonstrated a more stable gel network structure, with an 80.9% increase in gel strength at a 1.5% κ-carrageenan concentration; the water precipitation rate of these jellies was minimized to 7.96 ± 0.69% when κ-carrageenan was added at a 1.7% concentration. These results suggest that SE treatment provides useful information for the application of κ-carrageenan in jelly.

This paper focuses on the scenario where relativistic electron nonlinear Thomson forward scattering is driven by circularly polarized laser pulses, using both theoretical analysis and numerical simulations. For the first time, we investigate how the magnetic flux density and beam waist radius jointly influence the spatiotemporal properties of radiation. For the superimposed field under different parameters, the forces exerted by the laser field and the applied magnetic field on electrons will show cooperative or competitive relationships at different moments of action. And the transformation of this relationship promotes the evolution of the electron radiation pattern in the superimposed field, which greatly changes the spatiotemporal properties of Thomson scattering. Based on this, completely new spatial distributions of radiation-ripple and lotus phenomena were discovered. In addition, the combination of parameters with the best radiation properties was selected through Big Data, and the quasi-periodicity of the azimuth angle ${{\phi }_{\bm{p}}}$ was discovered for the first time. These results will contribute to the understanding of the radiation mechanism of Thomson scattering in superimposed field and will be instructive for laboratory modulation of ultra-high-performance ${\bm{X}}/{\bm{\gamma }}$-rays.

In this paper, two parameters of the applied magnetic field—magnetic field strength and pulse length—are modified, the spatial properties of electron trajectories and radiation are studied, and conclusions are drawn. Under the premise that the radius of the laser pulse waist b0=4λ0, and that the peak amplitude a0=5 corresponds to the peak laser intensity IL=3.45×1019 W/cm2 of the electrons, obtaining high-energy and highly collimated X-rays can be realized by increasing the pulse length up to 6λ0 and increasing the magnitude of the applied magnetic field, or by continuously increasing the pulse length and applying a smaller magnetic field.

Ultrashort light pulses have strong research and application values, while nonlinear inverse Thomson scattering has been considered as a unique source of zepto-yoctosecond pulses. Here, the mechanism of nonlinear inverse Thomson scattering of a high-energy electron colliding with a tightly focused intense laser pulse is investigated through numerical simulation. The time-compression effect was proposed to explain the origin of ultrashort pulses and the nonlinear phenomenon of electron radiation in the time–space joint distribution. It is found that the time scale of electron radiation is orders of magnitude shorter than that of electron motion, and the increases in laser intensity and electron initial energy will result in stronger and shorter pulses. Yoctosecond pulses can be generated by a laser pulse with an intensity of 1.384×1020 W/cm2 and an electron with an initial energy of 51.1 MeV. These results provide theoretical and numerical basis for generating shorter light pulses.

In nuclear power plant (NPP) accidents, the containment is subject to high temperatures and high internal pressures, which may further trigger serious chain accidents such as core meltdown and hydrogen explosion, resulting in a significantly higher accident level. Therefore, studying the mechanical performance of a containment under high temperature and high internal pressure is relevant to the safety of NPPs. Based on similarity principles, the 1:3.2 scale model of a prestressed concrete containment vessel (PCCV) of a NPP was designed. The loading method, which considers the thermal-pressure coupling conditions, was used. The mechanical response of the PCCV was investigated with a simultaneous increase in internal pressure and temperature, and the failure mechanism of the PCCV under thermal-pressure coupling conditions was revealed.

The fermentation characteristics and aroma production properties of lactic acid bacteria can influence the flavor quality of fermented milk, which is one of the important factors influencing the consumer preference. In this study, fermented milk was prepared using Streptococcus thermophilus, and dynamic changes in its quality, including rheological properties and flavor characteristics, were evaluated throughout the fermentation process. The results showed that benzaldehyde, 2-undecanone, octanoic acid, n-hexanol and 2-nonanol were the key flavor components during the fermentation process. The quality of the fermented milk tends to be stabilized after 24-h, showing the minimal off-flavor and optimal fermented aroma at 48-h. Three prebiotics (inulin, Galactooligosaccharides and inulin mixed with Galactooligosaccharides) were added to Streptococcus thermophilus fermented milk separately, and the results showed that inulin was the most effective group in improving the organoleptic quality of the fermented milk. These findings contribute to our understanding of the release and retention of flavor compounds during fermentation and can be used as a scientific reference for the application of probiotics and flavor-producing lactic acid bacteria in fermented milk processing.

This study addresses the problem of prolonged precooling time and excessive weight loss of fruits and vegetables during postharvest processes. Ice slurry precooling system shows potential but faces challenges like low undercooling and ice blockage. Dispersants are introduced to optimize heat and moisture transfer as well as the flow characteristics of the ice slurry. This research investigates the effects of four dispersants on the performance of an ice slurry-based wet precooling system through experiments. Key parameters analyzed include temperature and humidity in the chamber, cooling rate, and weight loss rate of the tested load. Results show that the addition of dispersants effectively reduces the freezing temperature, enhancing the cooling rate in the system. Among the four dispersants tested, sodium diacetate yields the lowest freezing temperature of −6.4 °C and the highest cooling rate of 0.3 °C min−1. Furthermore, dispersants influence moisture transfer between ice slurry and circulating air, impacting the humidity within the precooling chamber. Notably, the addition of sodium diacetate results in the lowest weight loss rate of 2.74 %. This research shows the potential of using dispersants in ice slurry-based wet precooling systems to enhance the postharvest preservation of fruits and vegetables.

In order to improve the hydrophobicity of tapioca starch, and used it as a granular emulsifier to improve the emulsification properties of myofibrillar protein (MP). In this study, a hydrophobic modification of native tapioca starch (NTS) by octenyl succinate anhydride (OSA), which produced ocentyl succinic tapioca starch (OSTS) with different modification levels and degrees of substitution (DS), and the compound emulsions were prepared using OSTS and MP. The structural changes of the OSTS were analyzed by XRD, FT-IR, SEM, and PLM, and the emulsion index (EI), emulsifying activity index (EAI), emulsion stability index (ESI), Zeta-potential, particle size distribution, sulfhydryl content, and interface protein content of the emulsions were determined. The results showed that the hydrophobic groups were successfully introduced in the tapioca starch molecules, and the hydrophobic modification treatment had basically no effect on the crystal structure type of the tapioca starch particles. The EAI and ESI of the OSTS-MP compound emulsions improved significantly (P

Fucoidan refers to a group of sulphated polysaccharides obtained from brown seaweed, with numerous biological activities. In this study, fucoidan was fortified into Chinese steamed bread (CSB) at different concentrations (0, 1%, 3% and 5%) and the effect of fucoidan on the dough properties, structure properties and bioactivity were investigated. The results showed that fucoidan could change the viscosity of unfermented dough, and a high concentration of fucoidan could remove the free radicals produced by the SH–SS exchange reaction (GS-) in the dough, which significantly reduced the content of disulfide bond and reduced the expanded volume of fermented dough (p < 0.05). In addition, fucoidan forms a physical barrier on the surface of starch particles and hinders the reaction between protein-to-protein; therefore, fucoidan increased the hardness, gumminess and chewiness in CSB, and reduced the specific volume in CSB. Furthermore, the fucoidan-fortified CSB samples were found to have both the ability to significantly reduce the predicted glycemic index (pGI) (p < 0.05) and improve antioxidant activity (p < 0.05). Collectively, these findings could provide a theoretical basis for the applications of fucoidan as a functional component in fermented foods.

Most existing methods of constructing dynamic functional connectivity (dFC) network obtain the connectivity strength via the sliding window correlation (SWC) method, which estimates the connectivity strength at each time segment, rather than at each time point, and thus is difficult to produce accurate dFC network due to the influence of the window type and window width. Furthermore, the deep learning methods may not capture the discriminative spatio-temporal information that is closely related to disease, thus impacting the performance of mild cognitive impairment (MCI) identification. In this paper, a novel spatio-temporal attention-based bidirectional gated recurrent unit (STA-BiGRU) network is proposed to extract inherent spatio-temporal information from a dynamic adaptive functional connectivity (dAFC) network for MCI diagnosis. Specifically, we adopt a group lasso-based Kalman filter algorithm to obtain the dAFC network with more accurate connectivity strength at each time step. Then a spatial attention module with self-attention and a temporal attention module with multiple temporal attention vectors are incorporated into the BiGRU network to extract more discriminative disease-related spatio-temporal information. Finally, the spatio-temporal regularizations are employed to better guide the attention learning of STA-BiGRU network to enhance the robustness of the deep network. Experimental results show that the proposed framework achieves mean accuracies of 90.2%, 90.0%, and 81.5%, respectively, for three MCI classification tasks. This study provides a more effective deep spatio-temporal attention-based recurrent network and obtains good performance and interpretability of deep learning for psychiatry diagnosis research.

With the continuous progress of urbanization, determining the charging and discharging strategy for randomly parked electric vehicles to help the peak load shifting without affecting users’ travel is a key problem. This paper design a reinforcement learning-based method for the electric vehicle-assisted demand response management system. Specifically, we formalize the charging and discharging sequential decision problem of the parking lot into the Markov process, in which the state space is composed of the state of parking spaces, electric vehicles, and the total load. The charging and discharging decision of each parking space acts as the action space. The reward comprises the penalty term that guarantees the user’s travel and the sliding average value of the load representing peak load shifting. After that, we use a Deep Q-Network (DQN)-based reinforcement learning architecture to solve this problem. Finally, we conduct a comprehensive evaluation with real-world power usage data. The results show that our proposed method will reduce the peak load by 10% without affecting the travel plan of all electric vehicles. Compared with random charging and discharging scenarios, we have better performance in terms of state-of-charge (SoC) achievement rate and peak load shifting effect.

The energy trading market that can support free bidding among electricity users is currently the key method in smart grid demand response. Reinforcement learning is used to formulate optimal strategies for them to obtain optimal strategies. Non-etheless, the security problem raised by artificial intelligence technology has been paid more and more attention. For example, the neural network has been proved to be able to resist adversarial example attacks, thus affecting its training results. Considering that reinforcement learning is also widely used for training by neural networks, the security problem can not be ignored, especially in scenarios with high security requirements such as smart grids. To this end, we study the security issues in reinforcement learning-based bidding strategy method facing by the adversarial example. First of all, regarding to the electric vehicle double auction market, we formalize the bidding decision problem of EVs into a Markov Decision Process, so that reinforcement learning is used to solve this problem. Secondly, from the perspective of attackers, we have designed a local Fast Gradient Sign Method which affects the environment and the results of reinforcement learning by changing its own bidding. Then, from the perspective of the defender, we designed a reinforcement learning training network containing an attack identifier based on the deep neural network, so as to identify malicious injection attacks to resist against adversarial attacks. Finally, comprehensive simulations are conducted to verify our proposed method. The results shows that, our proposed attack method will reduce the auction profit by influencing reinforcement learning algorithm, and the protect method will be able to completely resist such attacks.

An efficient energy trading strategy is proven to have a vital role in reducing participants’ payment in the energy trading process of the power grid, which can greatly improve the operation efficiency of the power grid and the willingness of participants to take part in the energy trading. Nevertheless, with the increasing number of participants taking part in the energy trading, the stability and efficiency of the energy trading system are exposed to an extreme challenge. To address this issue, an actor-critic-based bidding strategy for energy trading participants is proposed in this paper. Specifically, we model the bidding strategy with sequential decision-making characteristics as a Markov decision process, which treats three elements, namely, total supply, total demand, and participants’ individual supply or demand, as the state and regards bidding price and volume as the action. In order to address the problem that the existing value-based reinforcement learning bidding strategy cannot be applied to the continuous action space environment, we propose an actor–critic architecture, which endows the actor the ability of learning the action execution and utilizes the critic to evaluate the long-term rewards conditioned by the current state–action pairs. Simulation results in energy trading scenarios with different numbers of participants indicate that the proposed method will obtain a higher cumulative reward than the traditional greedy method.

With the green-oriented transition of energy, electric vehicles (EVs) are being developed rapidly to replace fuel vehicles. In the face of large-scale EV access to the grid, real-time and effective charging management has become a key problem. Considering the charging characteristics of different EVs, we propose a real-time scheduling framework for charging stations with an electric vehicle aggregator (EVA) as the decision-making body. However, with multiple optimization objectives, it is challenging to formulate a real-time strategy to ensure each participant’s interests. Moreover, the uncertainty of renewable energy generation and user demand makes it difficult to establish the optimization model. In this paper, we model charging scheduling as a Markov decision process (MDP) based on deep reinforcement learning (DRL) to avoid the afore-mentioned problems. With a continuous action space, the MDP model is solved by the twin delayed deep deterministic policy gradient algorithm (TD3). While ensuring the maximum benefit of the EVA, we also ensure minimal fluctuation in the microgrid exchange power. To verify the effectiveness of the proposed method, we set up two comparative experiments, using the disorder charging method and deep deterministic policy gradient (DDPG) method, respectively. The results show that the strategy obtained by TD3 is optimal, which can reduce power purchase cost by 10.9% and reduce power fluctuations by 69.4%.

Steam explosion technology is an emerging pretreatment method that has shown great promise for food processing due to its ability to efficiently destroy the natural barrier structure of materials. This narrative review summarizes the principle of steam explosion technology, its similarities and differences with traditional screw extrusion technology, and the factors that affect the technology. In addition, we reviewed the applications in food processing by-products in recent years. The results of the current study indicate that moderate steam explosion treatment can improve the quality and extraction rate of the target products. Finally, we provided an outlook on the development of steam explosion technology with a reference for a wider application of this technology in the food processing field.

Palmer Amaranth (Amaranthus palmeri, Amaranthaceae) is one of the most competitive, troublesome, and noxious weeds causing significant yield reductions in various crops. A. palmeri was also a herbicide-resistant weed causing a serious eco-environmental problem. Given that the process of invasion is dynamic, the A. plamer invasion may already be quite severe where invasive species management and surveys are chronically lacking. Predicting the potential habitat of A. palmeri can help to develop effective measures for early warning and long-term detection. However, the invasive history and distribution patterns of A. palmeri in China remain largely unknown. Here, the invasive history and distribution patterns of A. palmeri from 1985 to 2022 in China were reconstructed, and then the potential geographical distribution of A. palmeri was predicted under current and future climate scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5) using the optimal MaxEnt model (V 3.4.4) and ArcGIS 10.8.2. The mean AUC values of A. palmeri were 0.967. Under the current climate conditions, the suitable habitat areas for A. palmeri reached 1,067,000 km2 in China and were mainly distributed in north and central China. Under the future scenarios, the highly suitable habitats were mainly distributed in Beijing, Tianjin, and Hebei. Under SSP2–4.5, the future suitable areas will reach the maximum and expand to 1,411,100 km2 in the 2060s. The centroid distribution would northwestward extend under future climate scenarios. The human footprint index, mean temperature of the warmest quarter (Bio_10), April wind speed (Wind_4), temperature seasonality (standard deviation × 100) (bio_4), topsoil gravel content (T_gravel), and precipitation of warmest quarter (Bio_18) were key environmental variables affecting distribution and growth of A. palmeri. Climate change would increase the risk of A. palmeri expanding to high latitudes. Our results will help in developing effective strategies for the early warning, prevention, control, and management of A. palmeri in China.

The smart grid greatly facilitates the transmission of power and information by integrating precise measurement technology and efficient decision support systems. However, deep integration of cyber and physical information entails multiple challenges to grid operation. False data injection attacks can directly interfere with the results of state estimation, which can cause the grid regulator to make wrong decisions and thus poses a huge threat to the stability and security of grid operation. To address this issue, we propose a detection approach against false data injection attacks for dynamic state estimation. The Kalman filter is used to dynamically estimate the state values from IEEE standard bus systems. A long short-term memory (LSTM) network is utilized to extract the sequential observations from states at multiple time steps. In addition, we transform the attack detection problem into supervised learning problem and propose a deep neural network-based detection approach to identify attacks. We evaluate the effectiveness of the proposed detection approach in multiple IEEE standard bus systems. The simulation results demonstrate that the proposed detection approach outperforms benchmarks in improving the detection accuracy of malicious attacks.

The combination of protein and flavonoids can ameliorate the problems of poor solubility and stability of flavonoids in utilization. In this study, soybean protein isolate pretreated by ultrasonication was selected as the embedding wall material, which was combined with luteolin to form a soybean protein isolate-luteolin nanodelivery system. The complexation effect and structural changes of soybean protein isolate (SPI) and ultrasonic pretreatment (100 W, 200 W, 300 W, 400 W and 500 W) of soybean protein isolate with luteolin (LUT) were compared, as well as the changes in digestion characteristics and antioxidant activity in vitro. The results showed that proper ultrasonic pretreatment increased the encapsulation efficacy, loading amount and solubility to 89.72%, 2.51 μg/mg and 90.56%. Appropriate ultrasonic pretreatment could make the particle size and the absolute value of ζ-potential of SPI-LUT nanodelivery system decrease and increase respectively. The FTIR and fluorescence results show that appropriate ultrasonic pretreatment could reduce α-helix, β-sheet and random coil, increase β-turn, and enhance fluorescence quenching. The thermodynamic evaluation results indicate that the ΔG 0 and ΔS > 0, so the interaction of LUT with the protein was spontaneous and mostly governed by hydrophobic interactions. The XRD results show that the LUT was amorphous and completely wrapped by SPI. The DSC results showed that ultrasonic pretreatment could improve the thermal stability of SPI-LUT nanodelivery system to 112.66 ± 1.69 °C. Digestion and antioxidant analysis showed that appropriate ultrasonic pretreatment increased the LUT release rate and DPPH clearance rate of SPI-LUT nanodelivery system to 89.40 % and 55.63 % respectively. This study is a preliminary source for the construction of an SPI nanodelivery system with ultrasound pretreatment and the deep processing and utilization of fat-soluble active substances.

Reliability-based design optimization (RBDO) is applied to handle the unavoidable uncertainties in engineering applications. To alleviate the huge computational burden in reliability analysis and design optimization, surrogate models are introduced to replace the implicit objective and performance functions. In this paper, the commonly used surrogate modeling methods and surrogate-assisted RBDO methods are reviewed and discussed. First, the existing reliability analysis methods, RBDO methods, commonly used surrogate models in RBDO, sample selection methods and accuracy evaluation methods of surrogate models are summarized and compared. Then the surrogate-assisted RBDO methods are classified into global modeling methods and local modeling methods. A classic two-dimensional RBDO numerical example are used to demonstrate the performance of representative global modeling method (Constraint Boundary Sampling, CBS) and local modeling method (Local Adaptive Sampling, LAS). The advantages and disadvantages of these two kinds of modeling methods are summarized and compared. Finally, summary and prospect of the surrogate–assisted RBDO methods are drown.

It is essential to understand the ultrasound-induced changes in assembly of proteins and polyphenols into non covalent nanocomplex. β-Lactoglobulin (LG) and chlorogenic acid (CA) with various biological activities can be combined to form food-grade nanocomplexes. This study systematically explored the role of high-intensity ultrasound pretreatment on the binding mechanisms of LG and CA, and the potential biological function for embedding curcumin (Cur). The scanning electron microscopy (SEM) revealed that ultrasound treatment could destroy the structure of LG, and the particle size of the protein was reduced to

Abstract Background The GRAS and oleaginous yeast Yarrowia lipolytica (Y. lipolytica) is an attractive cell factory for the production of chemicals and biofuels. The production of many natural products of commercial interest have been investigated in this cell factory by introducing heterologous biosynthetic pathways and by modifying the endogenous pathways. However, since natural products anabolism involves long pathways and complex regulation, re-channelling carbon into the product of target compounds is still a cumbersome work, and often resulting in low production performance. Results In this work, the carotenogenic genes contained carB and bi-functional carRP from Mucor circinelloides and carotenoid cleavage dioxygenase 1 (CCD1) from Petunia hybrida were introduced to Y. lipolytica and led to the low production of β-ionone of 3.5 mg/L. To further improve the β-ionone synthesis, we implemented a modular engineering strategy for the construction and optimization of a biosynthetic pathway for the overproduction of β-ionone in Y. lipolytica. The strategy involved the enhancement of the cytosolic acetyl-CoA supply and the increase of MVA pathway flux, yielding a β-ionone titer of 358 mg/L in shake-flask fermentation and approximately 1 g/L (~ 280-fold higher than the baseline strain) in fed-batch fermentation. Conclusions An efficient β-ionone producing GRAS Y. lipolytica platform was constructed by combining integrated overexpressed of heterologous and native genes. A modular engineering strategy involved the optimization pathway and fermentation condition was investigated in the engineered strain and the highest β-ionone titer reported to date by a cell factory was achieved. This effective strategy can be adapted to enhance the biosynthesis of other terpenoids in Y. lipolytica.

Abstract Background Glucoamylase enzyme is one of the most important enzymes. It catalyzes the hydrolysis of starch into soluble sugars. It was covalently immobilized onto κ-carrageenan gel beads after activation by using polyethylenimine (PEI) followed by glutaraldehyde (GA). All parameters in activation process were studied. Results The immobilized enzyme shows enhancement in temperature profile as the optimum temperature for the free enzyme was 60 °C, and it becomes 60–80 °C that means broader range and also shows stability in acidic conditions more than free enzyme. The apparent Km of immobilized enzyme, 147.46 mM, becomes higher than Km of the free one, 110 mM and the maximum reaction velocity (Vmax) values for the immobilized enzyme decreased from 2.28 to 1.11 μmol min−1. The immobilized enzyme can be reused and kept its activity (100%) till 11 successive cycles. The immobilization process steps were characterized by FTIR and SEM. Conclusions These results confirm the economic and biotechnical benefits of enzyme immobilization, particularly with regard to the number of enzyme reuses, which open the possibility of different industrial applications.

Autonomous parking is an active field of automatic driving in both industry and academia. Parking slot detection (PSD) based on a panoramic image can effectively improve the perception of a parking space and the surrounding environment, which enhances the convenience and safety of parking. The challenge of PSD implementation is identifying the parking slot in real-time based on images obtained from the around view monitoring (AVM) system, while maintaining high recognition accuracy. This paper proposes a real-time parking slot detection (RPSD) network based on semantic segmentation, which implements real-time parking slot detection on the panoramic surround view (PSV) dataset and avoids the constraint conditions of parking slots. The structural advantages of the proposed network achieve real-time semantic segmentation while effectively improving the detection accuracy of the PSV dataset. The cascade structure reduces the operating parameters of the whole network, ensuring real-time performance, and the fusion of coarse and detailed features extracted from the upper and lower layers improves segmentation accuracy. The experimental results show that the final mIoU of this work is 67.97% and the speed is up to 32.69 fps, which achieves state-of-the-art performance with the PSV dataset.

The coronavirus disease 19 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become the worst public health crisis in a century. However, knowledge about the dynamics of antibody responses in patients with COVID-19 is still poorly understood. In this study, we performed a serological study with serum specimens collected at the acute and the convalescent phases from 104 patients with severe COVID-19 who were part of the first wave of COVID-19 cases in Wuhan, China. Our findings revealed that neutralizing antibodies to SARS-CoV-2 are persistent for at least 6 months in patients with severe COVID-19, despite that IgG levels against the receptor binding domain (RBD) and nucleocapsid protein (N) IgG declined from the acute to the convalescent phase. Moreover, we demonstrate that the level of RBD-IgG is capable of correlating with SARS-CoV-2–neutralizing activities in COVID-19 serum. In summary, our findings identify the magnitude, functionality, and longevity of antibody responses in patients with COVID-19, which sheds light on the humoral immune response to COVID-19 and would be beneficial for developing vaccines.

“Fo Tiao Qiang” is a famous dish with Chinese characteristics. It is delicious, rich in materials, and high in nutritional value. Through physical and chemical analysis, electronic tongue, gas chromatography–ion mobility spectroscopy, and other technologies, the present study explored the quality characteristics and flavor differences of Fo Tiao Qiang by using different thawing methods (natural thawing, ultrasonic thawing, microwave thawing, and water bath thawing). The results show that the protein content was slightly higher in Fo Tiao Qiang with ultrasonic thawing than others. The fat content of the microwave-thawed Fo Tiao Qiang was significantly lower than the other three kinds of samples. After ultrasonic thawing, the number of free amino acids in the samples were the highest and the umami taste was the best. Compared with natural thawing, most of the flavor substances decreased in ultrasonic thawing, microwave thawing, and water bath thawing. However, several substances increased, such as alpha-terpineol, beta-phenylethyl alcohol, phenylacetaldehyde, cis-rose oxide, isobutyl acetate, and 2–3-pentanedione. This study revealed the changing laws of different thawing methods on the quality characteristics and flavor characteristics of Fo Tiao Qiang. It provides theoretical guidance for the industrial production and quality control of Fo Tiao Qiang.

Abstract Objective SARS‐CoV‐2 has caused a worldwide pandemic of COVID‐19. The existence of prolonged SARS‐CoV‐2 positivity (PP) has further increased the burden on the health system. Since T cells are vital for viral control, we aimed to evaluate the characteristics of T‐cell responses associated with PP. Methods We established a PP cohort and two age‐ and sex‐matched control cohorts: a regular clinical recovery (CR) cohort and a healthy donor (HD) cohort. The mean time for RNA negativity conversion in the PP cohort was markedly longer than that in the CR cohort (66.2 vs 25.3 days), while the time from illness onset to sampling was not significantly different. T‐cell responses in the PP cohort were assayed, analysed and compared with those in the CR and HD cohorts by flow cytometry and ELISpot analysis of peripheral blood mononuclear cells. Results Compared with the CR cohort, the proliferation, activation and functional potential of CD8+ and CD4+ T cells in the PP cohort were not significantly different. However, the frequencies and counts of Teff and Tem in CD8+ but not in CD4+ T cells of the PP cohort were prominently lower. Moreover, a weaker SARS‐CoV‐2 N protein‐specific IFN‐γ+ T‐cell response and a higher frequency of Tregs were detected in the PP cohort. Conclusion Suppressed CD8+ T‐cell differentiation is associated with PP and may be an indicator for the prediction of prolonged SARS‐CoV‐2 positivity in COVID‐19 patients. The association between suppressed CD8+ T‐cell differentiation and elevated Tregs warrants studies in the future.

SARS-coronavirus-2–induced immune dysregulation and inflammatory responses are involved in the pathogenesis of coronavirus disease-2019 (COVID-19). However, very little is known about immune cell and cytokine alterations in specific organs of COVID-19 patients. Here, we evaluated immune cells and cytokines in postmortem tissues, i.e., lungs, intestine, liver, kidneys, and spleen of three patients with COVID-19. Imaging mass cytometry revealed monocyte, macrophage, and dendritic cell (DC) infiltration in the lung, intestine, kidney, and liver tissues. Moreover, in patients with COVID-19, natural killer T cells infiltrated the liver, lungs, and intestine, whereas B cells infiltrated the kidneys, lungs, and intestine. CD11b+ macrophages and CD11c+ DCs also infiltrated the lungs and intestine, a phenomenon that was accompanied by overproduction of the immunosuppressive cytokine interleukin (IL)-10. However, CD11b+ macrophages and CD11c+ DCs in the lungs or intestine of COVID-19 patients did not express human leukocyte antigen DR isotype. In contrast, tumor necrosis factor (TNF)-α expression was higher in the lungs, intestine, liver, and kidneys, but not in the spleen, of all COVID-19 patients (compared to levels in controls). Collectively, these findings suggested that IL-10 and TNF-α as immunosuppressive and pro-inflammatory agents, respectively,—might be prognostic and could serve as therapeutic targets for COVID-19.

Unsupervised feature learning, as a promising tool for extracting features automatically, overcomes shortcomings of traditional feature extraction methods which generally take plenty of effort on designing features. Among various unsupervised feature learning methods, sparse filtering is an efficient and popular one owing to its simplicity with fewer hyper-parameters. However, the standard sparse filtering fails to consider the local structure of input samples, which may affect and restrict its feature learning ability. To address this deficiency, a new feature extraction approach named sparse filtering with local structure preserved (SF-Local) is proposed. The objective function of SF-Local is composed by the standard sparse filtering and a local structural regularization (LSR) which is formulated to preserve the local structure of the input samples. To design LSR, we adopt complexity-invariant distance to select neighbors for each sample rather than the widely used Euclidean distance, which is beneficial to establishing a reliable K-nearest graph. Based on SF-Local, an intelligent fault diagnosis method is developed by leveraging a supervised classifier. The developed diagnostic approach is applied to diagnose the bearing faults as well as its performance is evaluated on a bearing data set with a constant operating condition and a data set with variable operating conditions. Experimental results demonstrate the effectiveness and superiority of the proposed SF-Local.

State estimation plays a critical role in monitoring and managing operation of smart grid. Nonetheless, recent research efforts demonstrate that data integrity attacks are able to bypass the bad data detection mechanism and make the system operator obtain the misleading states of system, leading to massive economic losses. Particularly, data integrity attacks have become critical threats to the power grid. In this paper, we propose a deep-Q-network detection (DQND) scheme to defend against data integrity attacks in alternating current (AC) power systems. DQND is a deep reinforcement learning scheme, which avoids the problem of curse of dimension that conventional reinforcement learning schemes have. Our strategy in DQND applies a main network and a target network to learn the optimal defending strategy. To improve the learning efficiency, we propose the quantification of observation space and utilize the concept of slide window as well. The experimental evaluation results show that the DQND outperforms the existing deep reinforcement learning-based detection scheme in terms of detection accuracy and rapidity in the IEEE 9, 14, and 30 bus systems.

Zika virus (ZIKV) is a unique flavivirus with high tropism to the testes. ZIKV can persist in human semen for months and can cause testicular damage in male mice. However, the mechanisms through which ZIKV enters the testes remain unclear. In this study, we revealed that matrix metalloproteinase 9 (MMP9) was upregulated by ZIKV infection in cell culture and in A129 mice. Furthermore, using an in vitro Sertoli cell barrier model and MMP9-/- mice, we found that ZIKV infection directly affected the permeability of the blood-testis barrier (BTB), and knockout or inhibition of MMP9 reduced the effects of ZIKV on the Sertoli cell BTB, highlighting its role in ZIKV-induced disruption of the BTB. Interestingly, the protein levels of MMP9 were elevated by ZIKV nonstructural protein 1 (NS1) in primary mouse Sertoli cells (mSCs) and other cell lines. Moreover, the interaction between NS1 and MMP9 induced the K63-linked polyubiquitination of MMP9, which enhanced the stability of MMP9. The upregulated MMP9 level led to the degradation of essential proteins involved in the maintenance of the BTB, such as tight junction proteins (TJPs) and type Ⅳ collagens. Collectively, we concluded that ZIKV infection promoted the expression of MMP9 which was further stabilized by NS1 induced K63-linked polyubiquitination to affect the TJPs/ type Ⅳ collagen network, thereby disrupting the BTB and facilitating ZIKV entry into the testes.

Big data is considered to be the key to unlocking the next great waves of growth in productivity. The amount of collected data in our world has been exploding due to a number of new applications and technologies that permeate our daily lives, including mobile and social networking applications, and Internet of Thing-based smart-world systems (smart grid, smart transportation, smart cities, and so on). With the exponential growth of data, how to efficiently utilize the data becomes a critical issue. This calls for the development of a big data market that enables efficient data trading. Via pushing data as a kind of commodity into a digital market, the data owners and consumers are able to connect with each other, sharing and further increasing the utility of data. Nonetheless, to enable such an effective market for data trading, several challenges need to be addressed, such as determining proper pricing for the data to be sold or purchased, designing a trading platform and schemes to enable the maximization of social welfare of trading participants with efficiency and privacy preservation, and protecting the traded data from being resold to maintain the value of the data. In this paper, we conduct a comprehensive survey on the lifecycle of data and data trading. To be specific, we first study a variety of data pricing models, categorize them into different groups, and conduct a comprehensive comparison of the pros and cons of these models. Then, we focus on the design of data trading platforms and schemes, supporting efficient, secure, and privacy-preserving data trading. Finally, we review digital copyright protection mechanisms, including digital copyright identifier, digital rights management, digital encryption, watermarking, and others, and outline challenges in data protection in the data trading lifecycle.

Due to the NP-hard nature, the permutation flowshop scheduling problem (PFSSP) is a fundamental issue for Industry 4.0, especially under higher productivity, efficiency, and self-managing systems. This paper proposes an improved genetic-shuffled frog-leaping algorithm (IGSFLA) to solve the permutation flowshop scheduling problem. In the proposed IGSFLA, the optimal initial frog (individual) in the initialized group is generated according to the heuristic optimal-insert method with fitness constrain. The crossover mechanism is applied to both the subgroup and the global group to avoid the local optimal solutions and accelerate the evolution. To evolve the frogs with the same optimal fitness more outstanding, the disturbance mechanism is applied to obtain the optimal frog of the whole group at the initialization step and the optimal frog of the subgroup at the searching step. The mathematical model of PFSSP is established with the minimum production cycle (makespan) as the objective function, the fitness of frog is given, and the IGSFLA-based PFSSP is proposed. Experimental results have been given and analyzed, showing that IGSFLA not only provides the optimal scheduling performance but also converges effectively.

Corn starch was treated with raw moisture (30%), screw speed (160 rpm), extrusion temperature (30, 55, 80, 105, 130, and 155°C), and emulsifier (1.2%) to produce the starch extrudate particles in a twin screw extruder. The influence of extrusion processing and composition (starch and emulsifier content) on the viscosity, microstructure, solubility, swelling power, and thermal stability of starch extrudate particles was examined. The SEM results showed that the surface of starch extrudate particles was quite irregular and a fissure could be clearly observed. The X-ray intensities and relative crystallinity were decreased after extrusion treatment. The data highlighted that the gelatinisation temperature and solubility were increased and the gelatinisation enthalpy, viscosity, and swelling power were decreased. The emulsifiers present also influenced the thermal stability and solubility of starch extrudate particles, which were attributed to their ability to modify starch granule interactions. The thermogravimetric analysis and differential scanning calorimetry analysis indicated that the thermal stability of starch extrudate particles was improved after extrusion processing.

In the smart grid, measurement devices may be compromised by adversaries, and their operations could be disrupted by attacks. A number of schemes to efficiently and accurately detect these compromised devices remotely have been proposed. Nonetheless, most of the existing schemes detecting compromised devices depend on the incremental response time in the attestation process, which are sensitive to data transmission delay and lead to high computation and network overhead. To address the issue, in this paper, we propose a low-cost remote memory attestation scheme (LRMA), which can efficiently and accurately detect compromised smart meters considering real-time network delay and achieve low computation and network overhead. In LRMA, the impact of real-time network delay on detecting compromised nodes can be eliminated via investigating the time differences reported from relay nodes. Furthermore, the attestation frequency in LRMA is dynamically adjusted with the compromised probability of each node, and then, the total number of attestations could be reduced while low computation and network overhead can be achieved. Through a combination of extensive theoretical analysis and evaluations, our data demonstrate that our proposed scheme can achieve better detection capacity and lower computation and network overhead in comparison to existing schemes.

Enterovirus 71 (EV71) is a notable causative agent of hand, foot, and mouth disease in children, which is associated with an increased incidence of severe neurological disease and death, yet there is no specific treatment or vaccine for EV71 infections. In this study, the antiviral activity of gramine and 21 gramine derivatives against EV71 was investigated in cell-based assays. Eighteen derivatives displayed some degree of inhibitory effects against EV71, in that they could effectively inhibit virus-induced cytopathic effects (CPEs), but the anti-EV71 activity of the lead compound gramine was not observed. Studies on the preliminary modes of action showed that these compounds functioned by targeting the early stage of the EV71 lifecycle after viral entry, rather than inactivating the virus directly, inhibiting virus adsorption or affecting viral release from the cells. Among these derivatives, one (compound 4s) containing pyridine and benzothiazole units showed the most potency against EV71. Further studies demonstrated that derivative 4s could profoundly inhibit viral RNA replication, protein synthesis, and virus-induced apoptosis in RD cells. These results indicate that derivative 4s might be a feasible therapeutic agent against EV71 infection and that these gramine derivatives may provide promising lead scaffolds for the further design and synthesis of potential antiviral agents.

Diverse and varied cyber-attacks challenge the operation of the smart-world system that is supported by Internet-of-Things (IoT) (smart cities, smart grid, smart transportation, etc.) and must be carefully and thoughtfully addressed before widespread adoption of the smart-world system can be fully realized. Although a number of research efforts have been devoted to defending against these threats, a majority of existing schemes focus on the development of a specific defensive strategy to deal with specific, often singular threats. In this paper, we address the issue of coalitional attacks, which can be launched by multiple adversaries cooperatively against the smart-world system such as smart cities. Particularly, we propose a game-theory based model to capture the interaction among multiple adversaries, and quantify the capacity of the defender based on the extended Iterated Public Goods Game (IPGG) model. In the formalized game model, in each round of the attack, a participant can either cooperate by participating in the coalitional attack, or defect by standing aside. In our work, we consider the generic defensive strategy that has a probability to detect the coalitional attack. When the coalitional attack is detected, all participating adversaries are penalized. The expected payoff of each participant is derived through the equalizer strategy that provides participants with competitive benefits. The multiple adversaries with the collusive strategy are also considered. Via a combination of theoretical analysis and experimentation, our results show that no matter which strategies the adversaries choose (random strategy, win-stay-lose-shift strategy, or even the adaptive equalizer strategy), our formalized game model is capable of enabling the defender to greatly reduce the maximum value of the expected average payoff to the adversaries via provisioning sufficient defensive resources, which is reflected by setting a proper penalty factor against the adversaries. In addition, we extend our game model and analyze the extortion strategy, which can enable one participant to obtain more payoff by extorting his/her opponents. The evaluation results show that the defender can combat this strategy by encouraging competition among the adversaries, and significantly suppress the total payoff of the adversaries via setting the proper penalty factor.

To achieve the purpose of energy conservation, various sleep scheduling approaches, such as duty cycle, are applied in wireless sensor networks (WSN). However, the duty-cycle mechanism results in data delivery latency, which is critical to monitor applications. To minimize the delay caused by sleeping nodes in the transmission path, we propose to “hop over” the sleeping nodes based on the range extension of cooperative transmission (CT). The transmission delay models for the random duty-cycled WSN and optimized fixed duty-cycled WSN under cooperative operation are formulated, and an algorithm named (delay-tolerant cooperative transmission DTCT) is presented for the selection of transmission modes to avoid waiting for the sleeping nodes to wake up. The energy consumption model under direct transmission (DT) and CT mode is also presented. Theoretical analysis shows that sleep latency can be greatly reduced in the cooperative scheme, and it is validated by simulations that it outperforms the traditional store-and-forward (DT) mode in delivery latency. Especially, CT reduces 67% and 14.3% of the transmission delay in random and optimized fixed low duty-cycled WSN, respectively, and DTCT algorithm saves energy by 11.29% in random low duty-cycled WSN.

Wind turbines are mainly located in harsh environment, and the maintenance is therefore very difficult. The wind turbine faults are mostly from the gear box, and the fault signal is generally nonlinear and nonstationary. The traditional fault diagnosis methods such as Fast Fourier transform (FFT) and the inverted frequency spectrum identification method based on FFT are not satisfactory in processing this kind of signal. This paper proposes a hybrid fault diagnosis method which combines the empirical mode decomposition (EMD) and wavelet transform. The vibration signal is analyzed through wavelet transform, and the aliasing in high-frequency signals is then addressed by conducting EMD to the original signals. The experimental results based on a specific wind turbine gear box demonstrate that this method can diagnose the faults and locate their positions accurately.

Due to the advantage of avoiding upstream disturbance and voltage fluctuation from a power transmission system, Islanded Micro-Grids (IMG) have attracted much attention. In this paper, we first propose a novel self-sufficient Cyber-Physical System (CPS) supported by Internet of Things (IoT) techniques, namely “archipelago micro-grid (MG)”, which integrates the power grid and sensor networks to make the grid operation effective and is comprised of multiple MGs while disconnected with the utility grid. The Electric Vehicles (EVs) are used to replace a portion of Conventional Vehicles (CVs) to reduce CO 2 emission and operation cost. Nonetheless, the intermittent nature and uncertainty of Renewable Energy Sources (RESs) remain a challenging issue in managing energy resources in the system. To address these issues, we formalize the optimal EV penetration problem as a two-stage Stochastic Optimal Penetration (SOP) model, which aims to minimize the emission and operation cost in the system. Uncertainties coming from RESs (e.g., wind, solar, and load demand) are considered in the stochastic model and random parameters to represent those uncertainties are captured by the Monte Carlo-based method. To enable the reasonable deployment of EVs in each MGs, we develop two scheduling schemes, namely Unlimited Coordinated Scheme (UCS) and Limited Coordinated Scheme (LCS), respectively. An extensive simulation study based on a modified 9 bus system with three MGs has been carried out to show the effectiveness of our proposed schemes. The evaluation data indicates that our proposed strategy can reduce both the environmental pollution created by CO 2 emissions and operation costs in UCS and LCS.