In this paper, we prove the random homogenization of general coercive non-convex Hamilton–Jacobi equations in the one dimensional case. This extends the result of Armstrong, Tran and Yu when the ...Hamiltonian has a separable form
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Food safety analysis is an important procedure to control food contamination and supervision. It is urgently needed to construct effective methods for on-site, fast, accurate and popular food safety ...sensing. Among them, microfluidic chip technology exhibits distinguish advantages in detection, including less sample consumption, fast detection, simple operation, multi-functional integration, small size, multiplex detection and portability. In this review, we introduce the classification, material, processing and application of the microfluidic chip in food safety sensing, in order to provide a good guide for food safety monitoring.
In this paper, an optimal design to minimize the cost of the fuel cell and supercapacitor in a fuel-cell electric vehicle is presented. It is assumed that the cost of the fuel cell and supercapacitor ...is a function of the number of units of each, respectively. The constraints on the number of fuel-cell units and supercapacitor units are derived according to the system requirement of maintaining stable dc-link voltage for all possible vehicle operations. These constraints are combined with the derived cost function to obtain the optimal number of fuel-cell units and supercapacitor units and the minimum cost. The cost, volume, and weight of the optimized fuel cell and supercapacitor of the powertrain and the fuel economy of the vehicle are evaluated. Simulation results are presented to verify the design
It is proved that for the 2-dimensional case with random shear flow of the G-equation model with strain term, the strain term reduces the front propagation. Also an improvement of the main result by ...Armstrong-Souganidis is provided.
Ginsenoside Rb1, the main active constituent of Panax ginseng, displays significant anti-inflammatory activity, although the mechanism has not been clearly unraveled. In this study, Rb1’s mechanism ...of anti-inflammatory effects were investigated.
The flow cytometry and enzyme-linked immunosorbent assay (ELISA) were empolyed to detect pro-inflammatory cytokines release. The related protein and gene expression was investigated by western blotting and qRT-PCR. The dimerization of TLR4 was measured by co-immunoprecipitation and molecular docking assays. Cellular thermal shift assay was used for the determination of the binding of Rb1 and TLR4. For animal moldels, LPS- or cantharidin-induced acute kidney injury, LPS-induced septic death, and dimethyl benzene-induced ear edema were employed to investigate Rb1’s anti-inflammatory activity in vivo.
Rb1 significantly decreased inflammatory cytokines release in LPS-stimulated RAW264.7 cells and BMDMs, as well as COX-2 and iNOS amounts. Rb1 reduced LPS-associated calcium influx, ROS production, and NO generation. The NF-κB and MAPK axes participated in Rb1’s anti-inflammatory effects. Molecular docking simulation indicated Rb1 bound to TLR4 to prevent TLR4 dimerization, as confirmed by co-immunoprecipitation and cellular thermal shift assay. Furthermore, MyD88 recruitment and TAK1 expression were altered by reduced TLR4 dimerization, indicating the TLR4-MyD88-NF-κB/MAPK pathways contributed to Rb1’s anti-inflammatory process. In animal models, Rb1 markedly alleviated LPS- or cantharidin-induced acute kidney injury, rescued LPS-induced septic mice from death, and inhibited dimethyl benzene-induced mouse ear edema.
Overall, these findings demonstrate Rb1 exhibits marked anti-inflammatory effects, suggesting Rb1 represents an optimal molecule for treating inflammatory diseases.
Ginsenoside Rb1 significantly exhibits an anti-inflammatory effect. Rb1 decreases inflammatory cytokine release in LPS-stimulated RAW264.7 cells and BMDMs. The NF-κB and MAPK axes participate in Rb1’s anti-inflammatory effects. Molecular docking simulation indicates that Rb1 binds to TLR4 to prevent TLR4 dimerization, as confirmed by co-immunoprecipitation and cellular thermal shift assay. Furthermore, MyD88 recruitment and TAK1 expression are altered by reduced TLR4 dimerization, indicating the TLR4-MyD88-NF-κB/MAPK pathways contribute to Rb1’s anti-inflammatory process. Display omitted
This paper presents a system that can harvest energy in the water and use the harvested energy to power electronic devices deployed in the water. The system consists of a microbial fuel cell (MFC) ...and a power management system. The MFC uses electrochemical reactions and bacteria that exist in the water to harvest energy and generate electricity. The power management system consisting of a charge pump, a super capacitor, two solid-state switches, and a boost converter accumulates the energy harvested by the MFC, stores the energy in the super capacitor, and bursts power to the load. The power management system also boosts the voltage of the MFC to a sufficient level for the electronic devices. The presented energy-harvesting system is self-powered, sustainable, environment friendly, and maintenance-free. The system has been tested and proven through experimental work.
Brain–computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEP) have received widespread attention due to their high information transmission rate, high accuracy, and rich ...instruction set. However, the performance of its identification methods strongly depends on the amount of calibration data for within-subject classification. Some studies use deep learning (DL) algorithms for inter-subject classification, which can reduce the calculation process, but there is still much room for improvement in performance compared with intra-subject classification. To solve these problems, an efficient SSVEP signal recognition deep learning network model e-SSVEPNet based on the soft saturation nonlinear module is proposed in this paper. The soft saturation nonlinear module uses a similar exponential calculation method for output when it is less than zero, improving robustness to noise. Under the conditions of the SSVEP data set, two sliding time window lengths (1 s and 0.5 s), and three training data sizes, this paper evaluates the proposed network model and compares it with other traditional and deep learning model baseline methods. The experimental results of the nonlinear module were classified and compared. A large number of experimental results show that the proposed network has the highest average accuracy of intra-subject classification on the SSVEP data set, improves the performance of SSVEP signal classification and recognition, and has higher decoding accuracy under short signals, so it has huge potential ability to realize high-speed SSVEP-based for BCI.
•A Stackelberg game considering the correlation among multi-time slots is proposed.•The time interval is divided into charging time interval and pricing time interval.•User satisfaction is added to ...the game model.•The game model with uncertainty is transformed into a convex optimization problem.
Electric vehicle (EV) has increasingly become an important part of the load of smart power grid in recent years. Since uncoordinated EV would stress the distributed grid system, the establishment of Photovoltaic (PV) charging station is of great significance to optimizing domestic power demand and developing a low-carbon society. In this paper, a dynamic pricing scheme based on Stackelberg game for EV charging station with PV system is proposed. Considering the uncertainty of electricity changes before and after charging, we divide one day into two kinds of time intervals: charging time interval and pricing time interval, and the demand response (DR) based on preference of EV user is also implemented. Moreover, the constraint reflecting the fluctuation (uncertainty) of power consumption caused by the charging continuity of EV is also introduced to the model. By analyzing the probability property of constraints, the game model with uncertainty is equivalently transformed into a convex game at the end. Additionally, the existence and uniqueness of Stackelberg Equilibrium (SE) is proved, so the optimal strategies of all participants are guaranteed. Finally, a distributed algorithm is designed to obtain the SE. The simulation results show that the pricing scheme proposed in this paper can better reduce the selling price of charging station and increase the profit of charging station.
This study presents a dynamic Bayesian game model designed to improve predictions of ecological uncertainties leading to natural disasters. It incorporates historical signal data on ecological ...indicators. Participants, acting as decision-makers, receive signals about an unknown parameter-observations of a random variable's realization values before a specific time, offering insights into ecological uncertainties. The essence of the model lies in its dynamic Bayesian updating, where beliefs about unknown parameters are refined with each new signal, enhancing predictive accuracy. The main focus of our paper is to theoretically validate this approach, by presenting a number of theorems that prove its precision and efficiency in improving uncertainty estimations. Simulation results validate the model's effectiveness in various scenarios, highlighting its role in refining natural disaster forecasts.
Alzheimer’s disease (AD) is a common neurodegenerative disease that often occurs in the elderly population. At present, most drugs for AD on the market are single-target drugs, which have achieved ...certain success in the treatment of AD. However, the efficacy and safety of single-target drugs have not achieved the expected results because AD is a multifactorial disease. Multi-targeted drugs act on multiple factors of the disease network to improve efficacy and reduce adverse reactions. Therefore, the search for effective dual-target or even multi-target drugs has become a new research trend. Many of results found that the dual-target inhibitors of the beta-site amyloid precursor protein cleaving enzyme 1 (BACE1) and acetylcholinesterase (AChE) found from traditional Chinese medicine have a good inhibitory effect on AD with fewer side effects. This article reviews sixty-six compounds extracted from Chinese medicinal herbs, which have inhibitory activity on BACE1 and AChE. This provides a theoretical basis for the further development of these compounds as dual-target inhibitors for the treatment of AD.
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•ADrequires the development of new molecules with multi-target drug potential.•AChE and BACE1 promotes neuron injury and inflammation in AD.•Summarize the sources, characteristics and anti-AD activities of natural products.•The data should be of great help to design and synthesize new anti-AD agents.
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