Research on Blockchain-Enabled Smart Grid for Anti-Theft Electricity Securing Peer-to-Peer Transactions in Modern Grids.

Electricity theft presents a significant financial burden to utility companies globally, amounting to trillions of dollars annually. This pressing issue underscores the need for transformative measures within the electrical grid. Accordingly, our study explores the integration of block chain technology into smart grids to combat electricity theft, improve grid efficiency, and facilitate renewable energy integration.

A stochastic model of preventive maintenance strategies for wind turbine gearboxes considering the incomplete maintenance.

In contemporary large wind farms, the combination of condition-based maintenance (CBM) and time-based maintenance (TBM) has become a prevalent approach in preventive maintenance, which is an indispensable part to ensure the safe, stable and environmental operation of equipment. However, the utilization of an inappropriate maintenance strategy may result in over-maintenance or under-maintenance, leading to unstable equipment operation. Furthermore, the majority of preventive maintenance involves replacement maintenance, which may have adverse effects on the performance of wind turbines with excessive maintenance time.

Condition-based opportunistic maintenance strategy for multi-component wind turbines by using stochastic differential equations.

The components of wind turbines are complex in structure and the working environment is harsh, which makes wind turbines face problems such as high failure rates and high maintenance costs. In this paper, the stochastic differential equation model has been established for the harsh operating environment of wind turbines, and used Brownian motion to simulate random disturbances; aiming at the problem of high failure rate of wind turbines, based on Weibull distribution, a new model has been established by combining operating time and equipment state to calculate the failure rate; in the analysis of monitoring data, the Higher-Order Moment method and Bayesian method were used to solve the parameters. The opportunity maintenance threshold curve and preventive maintenance threshold curve were obtained by analyzing Time-Based Maintenance and Condition-Based Maintenance.

Prediction of train wheel diameter based on Gaussian process regression optimized using a fast simulated annealing algorithm.

An algorithm to predict train wheel diameter based on Gaussian process regression (GPR) optimized using a fast simulated annealing algorithm (FSA-GPR) is proposed in this study to address the problem of dynamic decrease in wheel diameter with increase in mileage, which affects the measurement accuracy of train speed and location, as well as the hyper-parameter problem of the GPR in the traditional conjugate gradient algorithm. The algorithm proposed as well as other popular algorithms in the field, such as the traditional GPR algorithm, and GPR algorithms optimized using the artificial bee colony algorithm (ABC-GPR) or genetic algorithm (GA-GPR), were used to predict the wheel diameter of a DF11 train in a section of a railway during a period of major repairs. The results predicted by FSA-GPR was compared with other three algorithms as well as the real measured data from RMSE, MAE, R2 and Residual value.

Siting and sizing of distributed generators based on improved simulated annealing particle swarm optimization.

Distributed power grids generally contain multiple diverse types of distributed generators (DGs). Traditional particle swarm optimization (PSO) and simulated annealing PSO (SA-PSO) algorithms have some deficiencies in site selection and capacity determination of DGs, such as slow convergence speed and easily falling into local trap. In this paper, an improved SA-PSO (ISA-PSO) algorithm is proposed by introducing crossover and mutation operators of genetic algorithm (GA) into SA-PSO, so that the capabilities of the algorithm are well embodied in global searching and local exploration.

Clinical grade production and characterization of a fusion protein comprised of the chemokine CCL2-ligand genetically fused to a mutated and truncated form of the Shiga A1 subunit.

First generation chemokine ligand-Shiga A1 (SA1) fusion proteins (leukocyte population modulators, LPMs) were previously only obtained in small quantities due to the ribosomal inactivating protein properties of the SA1 moiety which inhibits protein synthesis in host cells. We therefore employed 4-aminopyrazolo[3,4-d]-pyrimidine, an inhibitor of Shiga A1, to allow the growth of these cells prior to induction and during the expression phase post-induction with IPTG. Scale-up allowed the production of gram quantities of clinical grade material of the lead candidate, OPL-CCL2-LPM.

Expression system for high levels of GAG lyase gene expression and study of the hepA upstream region in Flavobacterium heparinum.

A system for high-level expression of heparinase I, heparinase II, heparinase III, chondroitinase AC, and chondroitinase B in Flavobacterium heparinum is described. hepA, along with its regulatory region, as well as hepB, hepC, cslA, and cslB, cloned downstream of the hepA regulatory region, was integrated in the chromosome to yield stable transconjugant strains. The level of heparinase I and II expression from the transconjugant strains was approximately fivefold higher, while heparinase III expression was 10-fold higher than in wild-type F.

Development of a genetic system for the transfer of DNA into Flavobacterium heparinum.

Flavobacterium heparinum (now Pedobacter heparinus) is a Gram-negative soil bacterium which can produce yellow pigments. It synthesizes five enzymes that degrade glycosoaminoglycan molecules. The study of this unique bacterium has been limited by the absence of a genetic manipulation system.