Design of electronic cam for lower hook mechanism of fishing net-weaving machine based on polynomial fitting

The working efficiency and stability of the double hook-based fishing net-weaving machine is mainly determined by the lower hook mechanism. In this work, a new kind of lower hook mechanism, which is driven by four servo motors, is presented, and the electronic cam curve of the lower hook mechanism is introduced. First, cubic B-spline interpolation is used to get the basic motion path of the lower hook plate, and then the piecewise quintic polynomial fitting method is used to fit the motion path. Finally, self-adaptive mutation-based particle swarm optimization is put forward and used to obtain the optimal parameters of the quintic polynomial, which performs better compared with the other two particle swarm optimization algorithms in this study. Experiments suggest that the electronic cam curve generated by the piecewise quintic polynomial fitting has got 55.91% (horizontal motors) and 60.96% (vertical motors) optimization in maximum motor torque compared with curves generated by cubic B-spline interpolations. In addition, the new lower hook mechanism and its moving curve described in this paper improved the theoretical weaving speed of the fishing net-weaving machine, providing a basis for digital improvement of the knotted net-weaving industry.

Steam-Water Modelling and the Coal-Saving Scheduling Strategy of Combined Heat and Power Systems

China aims to peak carbon emissions by 2030. As a result, small-scale coal-fired combined heat and power (CHP) units and self-provided units are gradually shut down, and large-scale coal-fired CHP units are a solution to undertake the industrial heat loads. From the perspective of the industrial heat load allocation during the non-heating season, the problems regarding the coal-saving scheduling strategy of coal-fired CHP units are addressed. The steam-water equations of CHP units are established to analyze the heat-power coupling characteristics. The energy utilization efficiency, exergy efficiency and the coal consumption are analyzed. The optimization model of saving coal consumption is established and the adaptive mutation particle swarm optimization (AMPSO) is introduced to solve the above model. The 330 MW coal-fired CHP unit is taken as an example, and the results show that for the constant main flow rate, each increase of 1 t/h industrial steam extraction will reduce the power output by about 0.321 MW. The energy utilization efficiency and the exergy are mainly influenced by industrial steam supply and the power load, respectively. For the CHP system with two parallel CHP units, the unequal allocation of industrial heat load between two units saves more coal than equal allocation. The coal consumption can be reduced when the unit with lower power load undertakes more industrial heat load. In the typical day, the total coal consumption after optimization is 3203.92 tons, a decrease of 14.66 tons compared to the optimization before. The two CHP units in the case can benefit about 5,612,700 CHY extra in one year.

A Multimodal Improved Particle Swarm Optimization for High Dimensional Problems in Electromagnetic Devices

Particle Swarm Optimization (PSO) is a member of the swarm intelligence-based on a metaheuristic approach which is inspired by the natural deeds of bird flocking and fish schooling. In comparison to other traditional methods, the model of PSO is widely recognized as a simple algorithm and easy to implement. However, the traditional PSO’s have two primary issues: premature convergence and loss of diversity. These problems arise at the latter stages of the evolution process when dealing with high-dimensional, complex and electromagnetic inverse problems. To address these types of issues in the PSO approach, we proposed an Improved PSO (IPSO) which employs a dynamic control parameter as well as an adaptive mutation mechanism. The main proposal of the novel adaptive mutation operator is to prevent the diversity loss of the optimization process while the dynamic factor comprises the balance between exploration and exploitation in the search domain. The experimental outcomes achieved by solving complicated and extremely high-dimensional optimization problems were also validated on superconducting magnetic energy storage devices (SMES). According to numerical and experimental analysis, the IPSO delivers a better optimal solution than the other solutions described, particularly in the early computational evaluation of the generation.

An Internet of Things-Oriented Adaptive Mutation PSO-BPNN Algorithm to Assist the Construction of Entrepreneurship Evaluation Models for College Students

In this paper, the IoT-based adaptive mutation PSO-BPNN algorithm is used to conduct in-depth research and analysis of the entrepreneurship evaluation model for college students and practical applications. This paper details the principle, implementation, and characteristics of each BP algorithm and PSO algorithm. When classifying college students’ entrepreneurship evaluation based on BP neural network, because BP algorithm is a local optimization-seeking algorithm, it is easy to fall into local minima in the training phase of the network and the convergence speed is slow, which leads to the reduction of classifier recognition rate. To address the above problems, this paper proposes the algorithm of PSO optimized BP neural network (PSO-BPNN) and establishes a classification and recognition model based on this algorithm for college students’ entrepreneurship evaluation. The predicted values obtained from the particle swarm optimization neural network model are used to calculate the gray intervals, and the modeling samples are further screened using the gray intervals and the correlation principle, while the hyperspectral particle swarm optimization neural network model of soil organic matter based on the gray intervals is established afterward; and the estimation results are compared and analyzed with those of traditional modeling methods. The results showed that the coefficient of determination of the gray interval-based particle swarm optimization neural network model was 0.8826, and the average relative error was 3.572%, while the coefficient of determination of the particle swarm optimization neural network model was 0.853, and the average relative error was 4.34%; the average relative errors of the BP neural network model, support vector machine model, and multiple linear regression model were 8.79%, 6.717%, and 9.9%, respectively. The average relative errors of the BP neural network model, support vector machine model, and multiple linear regression model are 8.79%, 6.717%, and 9.468%, respectively. In general, the entrepreneurial ability of college students is at a good level (83.42 points), among which the entrepreneurial management ability score (84.30 points) and entrepreneurial spirit (84.16 points) are basically the same, while the entrepreneurial technology ability is relatively low (82.76 points), and the evaluation results are further verified by the double case analysis method. The current problems encountered by university students in entrepreneurship are mainly the lack of practicality, which indicates that universities, industries, and national strategy implementation levels are not sufficiently focused and collaborative in entrepreneurship development to varying degrees.

Optimization of Process Parameters on Surface Hardness and Energy Consumption in Milling of 7050 Aluminum Alloy using Enhanced NSGA-II

Aluminum alloy has high strength and light weight. It is widely used for aircraft fuselage, propellers and other parts which work under high load conditions. High-quality parts made of aluminum alloy processed by computerized numerical control (CNC) machine often have the characteristics of high cost in their processing. In order to achieve high surface quality and control processing costs, this article takes the workpiece surface hardness and machining energy consumption as targets. Intelligent optimization algorithm is used to find the optimal combination of milling parameters to obtain ideal targets. CNC milling parameter optimization is a multi-parameter, multi-objective, multi-constraint, discrete nonlinear optimization problem which is difficult to solve. For this challenge, an improved NSGA-II is presented, named enhanced population diversity NSGA-II (EPD-NSGA-II). EPD-NSGA-II is improved with the normal distribution crossover, adaptive mutation operator of differential evolution, crowding calculation method considering variance and modified elite retention strategy to achieve enhanced population diversity. 12 test functions are chosen for experimentation to verify the performance of the EPD-NSGA-II. The values of three evaluation indicators show that the proposed approach has good distribution and convergence performance. Finally, the approach is applied in the milling parameters optimization of 7050 aluminum alloy to get the optimal solutions. Results indicate that the EPD-NSGA-II is effective in optimizing the problem of milling parameters.

Efficient anaerobic consumption of D-xylose by E. coli BL21(DE3) via xylR adaptive mutation

Background Microorganisms can prioritize the uptake of different sugars depending on their metabolic needs and preferences. When both D-glucose and D-xylose are present in growth media, E. coli cells typically consume D-glucose first and then D-xylose. Similarly, when E. coli BL21(DE3) is provided with both D-glucose and D-xylose under anaerobic conditions, glucose is consumed first, whereas D-xylose is consumed very slowly. Results When BL21(DE3) was adaptively evolved via subculture, the consumption rate of D-xylose increased gradually. Strains JH001 and JH019, whose D-xylose consumption rate was faster, were isolated after subculture. Genome analysis of the JH001 and JH019 strains revealed that C91A (Q31K) and C740T (A247V) missense mutations in the xylR gene (which encodes the XylR transcriptional activator), respectively, controlled the expression of the xyl operon. RT-qPCR analyses demonstrated that the XylR mutation caused a 10.9-fold and 3.5-fold increase in the expression of the xylA (xylose isomerase) and xylF (xylose transporter) genes, respectively, in the adaptively evolved JH001 and JH019 strains. A C91A adaptive mutation was introduced into a new BL21(DE3) background via single-base genome editing, resulting in immediate and efficient D-xylose consumption. Conclusions Anaerobically-adapted BL21(DE3) cells were obtained through short-term adaptive evolution and xylR mutations responsible for faster D-xylose consumption were identified, which may aid in the improvement of microbial fermentation technology.

Application of Improved Differential Evolution Algorithm in Solving Equations

The problem of finding roots of equations has always been an important research problem in the fields of scientific and engineering calculations. For the standard differential evolution algorithm cannot balance the convergence speed and the accuracy of the solution, an improved differential evolution algorithm is proposed. First, the one-half rule is introduced in the mutation process, that is, half of the individuals perform differential evolutionary mutation, and the other half perform evolutionary strategy reorganization, which increases the diversity of the population and avoids premature convergence of the algorithm; Second, set up an adaptive mutation operator and a crossover operator to prevent the algorithm from falling into the local optimum and improve the accuracy of the solution. Finally, classical high-order algebraic equations and nonlinear equations are selected for testing, and compared with other algorithms. The results show that the improved algorithm has higher solution accuracy and robustness, and has a faster convergence speed. It has outstanding effects in finding roots of equations, and provides an effective method for engineering and scientific calculations.

Chikungunya virus infection: molecular biology, clinical characteristics, and epidemiology in Asian countries

Chikungunya virus (CHIKV) is a re-emerging mosquito-borne human pathogen that causes chikungunya fever, which is typically accompanied by severe joint pain. In Asia, serological evidence indicated that CHIKV first emerged in 1954. From the 1950’s to 2005, sporadic CHIKV infections were attributed to the Asian genotype. However, the massive outbreak of CHIKV in India and the Southwest Indian Ocean Islands in 2005 has since raised chikungunya as a worldwide public health concern. The virus is spreading globally, but mostly in tropical and subtropical regions, particularly in South and Southeast Asia. The emergence of the CHIKV East/Central/South African genotype-Indian Ocean lineage (ECSA-IOL) has caused large outbreaks in South and Southeast Asia affected more than a million people over a decade. Notably, the massive CHIKV outbreaks before 2016 and the more recent outbreak in Asia were driven by distinct ECSA lineages. The first significant CHIKV ECSA strains harbored the Aedes albopictus-adaptive mutation E1: A226V. More recently, another mass CHIKV ECSA outbreak in Asia started in India and spread beyond South and Southeast Asia to Kenya and Italy. This virus lacked the E1: A226V mutation but instead harbored two novel mutations (E1: K211E and E2: V264A) in an E1: 226A background, which enhanced its fitness in Aedes aegypti. The emergence of a novel ECSA strain may lead to a more widespread geographical distribution of CHIKV in the future. This review summarizes the current CHIKV situation in Asian countries and provides a general overview of the molecular virology, disease manifestation, diagnosis, prevalence, genotype distribution, evolutionary relationships, and epidemiology of CHIKV infection in Asian countries over the past 65 years. This knowledge is essential in guiding the epidemiological study, control, prevention of future CHIKV outbreaks, and the development of new vaccines and antivirals targeting CHIKV.

Mutation Y453F in the spike protein of SARS-CoV-2 enhances interaction with the mink ACE2 receptor for host adaption

COVID-19 patients transmitted SARS-CoV-2 to minks in the Netherlands in April 2020. Subsequently, the mink-associated virus (miSARS-CoV-2) spilled back over into humans. Genetic sequences of the miSARS-CoV-2 identified a new genetic variant known as “Cluster 5” that contained mutations in the spike protein. However, the functional properties of these “Cluster 5” mutations have not been well established. In this study, we found that the Y453F mutation located in the RBD domain of miSARS-CoV-2 is an adaptive mutation that enhances binding to mink ACE2 and other orthologs of Mustela species without compromising, and even enhancing, its ability to utilize human ACE2 as a receptor for entry. Structural analysis suggested that despite the similarity in the overall binding mode of SARS-CoV-2 RBD to human and mink ACE2, Y34 of mink ACE2 was better suited to interact with a Phe rather than a Tyr at position 453 of the viral RBD due to less steric clash and tighter hydrophobic-driven interaction. Additionally, the Y453F spike exhibited resistance to convalescent serum, posing a risk for vaccine development. Thus, our study suggests that since the initial transmission from humans, SARS-CoV-2 evolved to adapt to the mink host, leading to widespread circulation among minks while still retaining its ability to efficiently utilize human ACE2 for entry, thus allowing for transmission of the miSARS-CoV-2 back into humans. These findings underscore the importance of active surveillance of SARS-CoV-2 evolution in Mustela species and other susceptible hosts in order to prevent future outbreaks.