Screening and identification of an antagonistic bacterium strain LHB02 and the optimization of its fermentation conditions

A strain LHB02 with strong antibacterial activity against some aquatic pathogens was screened from the coastal marine sediment in Fujian province, China. LHB02 was identified as Bacillus subtilis, based on its 16S rRNA sequence, together with the morphological, physiological and biochemical characteristics. The antagonistic activity of strain LHB02 and its optimal fermentation conditions were also investigated. The results showed that LHB02 had strong antagonistic activity against 3 species of vibrios： Vibrio harveyi,Vibrio alginolyticus and Vibrio anguillarum, and the optimum fermentation conditions for the strain LHB02 were as follows： KB culture medium （peptone 20 g, glycerol 10 mL, K2HPO4 1.5 g, MgSO 4 .7H2O 1.5 g, H2O 1 000 mL）; temperature, 28 ℃; pH ,7.0; culture time, 36 h; and inoculation amount, 1.5% （v/v）.

Study on Establishment of cpSSR Marker Technology and Optimization of Its Reaction System in Jatropha curcas Linn

[ Objective] The aim of this study was to establish the optimum cpSSR-PCR system for Jatropha curcas Linn. [ Method] cpSSR-PCR amplification system for Jatropha curcas Linn influenced by five factors including Taq DNA polymerase, Mg^2＋ , DNA template, dNTP and primer were optimized from several levels. [ Result] The optimum concentration of 20 μl reaction system was 10 × Buffer, 2.00 mmol/L Mg^2＋ , 2 U/μl Taq DNA polymerase, 0.2 mmol/L dNTP, 0.2 μmol/L primer and 35 ng/μl DNA template. [ Conclusion] The optimum annealing temperature for cpSSR-PCR reaction system is 52 ℃, and the cpSSR reaction system is steady and reproducible.

Application of nanotechnology in a silver/graphite contact material and optimization of its physical and mechanical properties

By applying nanotechnology, a new type of silver/graphite (AgC) electricalcontact was fabricated and characterized. The AgC coating powders were obtained through high-energyball milling and reducer liquid spraying-coating method. The as-prepared powders were examined bytransmission electron microscope (TEM), scanning electron microscope (SEM), and X-ray diffraction(XRD). The results show that the thickness of graphite flakes milled for 10 h is about 50-60 nm andthe AgC coating powders exhibit flocculent structure with quite fine and homogeneous internalmicropores. XRD implies that the average crystalline size of silver in coating powders is about 50nm. The mechanical and physical properties of this newly developed AgC contact made from theabove-mentioned nanocrystalline powders by traditional powder metallurgy technique were measured.Compared with its counterparts made from other techniques, the properties of this new AgC contacthave been optimized. High surface energy and high-energy interfaces of the nanocrystalline AgCcoating powders provide powerful driving force for sintering densification. Moreover, the flocculentstructure of the powders is also an important factor to acquire fine density ratio.

Optimization of ITS rDNA Amplification for Fungi from Black Soil in North China

In order to optimize polymerase chain reaction（PCR） amplification of the internal transcribed spacer（ITS） rDNA region from fungi found in black soil in North China,an orthogonal experimental design [L16（45）] was used to evaluate five factors（template,Mg2＋,dNTP,Taq DNA polymerase,and primer） from four levels.Subsequently,the optimal annealing temperature,annealing time,extension time and cycle numbers were evaluated.The results showed that the optimized PCR solution for amplification of ITS region comprised 5 μL 10× buffer,30 ng soil DNA template,3.0 mmol·L-1 Mg2＋,0.2 mmol·L-1 dNTPs,0.1 μmol·L-1 each forward and reverse primer,and 2.0 U Taq enzyme in 50 μL reaction volume.The optimal thermal cycling protocol consisted of initial melting at 94℃ for 5 min,followed by 35 cycles at 94℃ for 30 s,56℃ for 30 s,72℃ for 90 s,and a final extension of 72℃ for 10 min.

DES Mutagenesis of Antagonistic Streptomyces of Botryosphaeria dothidea and Optimization of Its Fermentation Conditions

[ Objective] The paper wag to improve the antimicrobial effect of antagonistic Streptomyces on Botryosphaeria dothidea. [ Method ] Different concentrations of DES were adopted for mutagenic treatment of antagonistic Streptomyces F-58. After fermentation, the mutant strain numbered F-58-06 with genetic stability and good antimicrobial effect was selected among 200 mutant strains; the biological activity of the strain was determined with B. dothidea as the indicator fungus, and the best concentration of DES wag 2%. The fermentation conditions of antagonistic substances of Streptomyces strain F-58-06 produced in shake flasks were opti- mized using the method of single factor and orthogonal experimental design. [ Result] The optimal fermentation conditions were carbon source corn flour, nitrogen source KNO3, initial pH 7, loading volume 30 mL in 250 mL shake flask, inoculation volume 3%, incubation time 132 h. Based on orthogonal experiments, the best cultured medium ratio was corn flour 4%, KNO3 0.5%, CaCO3 0.2%, NaCI 0.35%, MgSO4 · 7H2O 0. 15%. [ Conclusion ] After mutation and optimization of culture medium, the antimicrobial activity of Streptomyces F-58 was greatly improved, and the average inhibition zone diameter was 1.35 times of that of the original strain.

Isolation, Purification, and Identification of a Novel HX-Producing Strain and Optimization of Its Fermentation Medium

Hexanoic acid (HX) is a crucial flavor compound and precursor of ethyl caproate (EA), which determines the quality of Chinese Luzhou-flavor liquor (CLFL). The isolation, purification, identification, and optimization of fermentation conditions of HX-producing bacteria are essential for industrial CLFL production. In this study, one strain of HX-producing bacterium was isolated from six candidate bacterial strains and identified as Clostridium sartagoneforme. Then, the growth characteristics and HX production of C. sartagoneforme were investigated. Sodium acetate medium was identified as the optimal fermentation medium from four candidate media. C. sartagoneforme yielded 800.85 ± 12.87 mg/100mL HX in sodium acetate medium. Then, to further optimize the formula of the fermentation medium, the carbon and nitrogen sources and inorganic salt component of the fermentation medium were investigated using HX yields as an optimization index. Optimization was performed with a single-factor experiment and the Taguchi design method. The single-factor experiment showed that the highest HX outputs were obtained when the sodium acetate medium contained 2.5 g/L yeast extract, 1.8 g/L KCl, 20 g/L sodium acetate, 15 mL/L ethanol, and 1.5 g/L glucose. In the orthogonal experiment designed using the Taguchi design method, HX yields reached 2018.29 ± 46.37 mg/100mL in sodium acetate medium that contained 3.5 g/L yeast extract, 1.8 g/L KCl, 25 g/L sodium acetate, and 15 mL/L ethanol.

An Improved JSO and Its Application in Spreader Optimization of Large Span Corridor Bridge

In this paper,given the shortcomings of jellyfish search algorithmwith low search ability in the early stage and easy to fall into local optimal solution,this paper introduces adaptive weight function and elite strategy,improving the global search scope in the early stage and the ability to refine the local development in the later stage.In the numerical study,the benchmark problem of dimensional optimization with a 10-bar truss structure and simultaneous dimensional shape optimization with a 15-bar truss structure is adopted,and the corresponding penalty method is used for constraint treatment.The test results show that the improved jellyfish search algorithm can provide better truss sections as well as weights.Because when the steel main truss of the large-span covered bridge is lifted,the site is limited and the large lifting equipment cannot enter the site,and the original structure does not meet the problem of stress concentration and large deformation of the bolt group,so the spreader is used to lift,and the improved jellyfish search algorithm is introduced into the design optimization of the spreader.The results show that the improved jellyfish algorithm can efficiently and accurately find out the optimal shape and weight of the spreader,and throughMidas Civil simulation,the spreader used canmeet the requirements of weight and safety.

Accelerated design of high-performance Mg-Mn-based magnesium alloys based on novel bayesian optimization

Magnesium(Mg),being the lightest structural metal,holds immense potential for widespread applications in various fields.The development of high-performance and cost-effective Mg alloys is crucial to further advancing their commercial utilization.With the rapid advancement of machine learning(ML)technology in recent years,the“data-driven''approach for alloy design has provided new perspectives and opportunities for enhancing the performance of Mg alloys.This paper introduces a novel regression-based Bayesian optimization active learning model(RBOALM)for the development of high-performance Mg-Mn-based wrought alloys.RBOALM employs active learning to automatically explore optimal alloy compositions and process parameters within predefined ranges,facilitating the discovery of superior alloy combinations.This model further integrates pre-established regression models as surrogate functions in Bayesian optimization,significantly enhancing the precision of the design process.Leveraging RBOALM,several new high-performance alloys have been successfully designed and prepared.Notably,after mechanical property testing of the designed alloys,the Mg-2.1Zn-2.0Mn-0.5Sn-0.1Ca alloy demonstrates exceptional mechanical properties,including an ultimate tensile strength of 406 MPa,a yield strength of 287 MPa,and a 23%fracture elongation.Furthermore,the Mg-2.7Mn-0.5Al-0.1Ca alloy exhibits an ultimate tensile strength of 211 MPa,coupled with a remarkable 41%fracture elongation.

Optimization Design of the Multi-Layer Cross-Sectional Layout of An Umbilical Based on the GA-GLM

Marine umbilical is one of the key equipment for subsea oil and gas exploitation,which is usually integrated by a great number of different functional components with multi-layers.The layout of these components directly affects manufacturing,operation and storage performances of the umbilical.For the multi-layer cross-sectional layout design of the umbilical,a quantifiable multi-objective optimization model is established according to the operation and storage requirements.Considering the manufacturing factors,the multi-layering strategy based on contact point identification is introduced for a great number of functional components.Then,the GA-GLM global optimization algorithm is proposed combining the genetic algorithm and the generalized multiplier method,and the selection operator of the genetic algorithm is improved based on the steepest descent method.Genetic algorithm is used to find the optimal solution in the global space,which can converge from any initial layout to the feasible layout solution.The feasible layout solution is taken as the initial value of the generalized multiplier method for fast and accurate solution.Finally,taking umbilicals with a great number of components as examples,the results show that the cross-sectional performance of the umbilical obtained by optimization algorithm is better and the solution efficiency is higher.Meanwhile,the multi-layering strategy is effective and feasible.The design method proposed in this paper can quickly obtain the optimal multi-layer cross-sectional layout,which replaces the manual design,and provides useful reference and guidance for the umbilical industry.

Web Layout Design of Large Cavity Structures Based on Topology Optimization

Large cavity structures are widely employed in aerospace engineering, such as thin-walled cylinders, blades andwings. Enhancing performance of aerial vehicles while reducing manufacturing costs and fuel consumptionhas become a focal point for contemporary researchers. Therefore, this paper aims to investigate the topologyoptimization of large cavity structures as a means to enhance their performance, safety, and efficiency. By usingthe variable density method, lightweight design is achieved without compromising structural strength. Theoptimization model considers both concentrated and distributed loads, and utilizes techniques like sensitivityfiltering and projection to obtain a robust optimized configuration. The mechanical properties are checked bycomparing the stress distribution and displacement of the unoptimized and optimized structures under the sameload. The results confirm that the optimized structures exhibit improved mechanical properties, thus offering keyinsights for engineering lightweight, high-strength large cavity structures.

Multi-objective optimization and evaluation of supercritical CO_(2) Brayton cycle for nuclear power generation

The supercritical CO_(2) Brayton cycle is considered a promising energy conversion system for Generation IV reactors for its simple layout,compact structure,and high cycle efficiency.Mathematical models of four Brayton cycle layouts are developed in this study for different reactors to reduce the cost and increase the thermohydraulic performance of nuclear power generation to promote the commercialization of nuclear energy.Parametric analysis,multi-objective optimizations,and four decision-making methods are applied to obtain each Brayton scheme’s optimal thermohydraulic and economic indexes.Results show that for the same design thermal power scale of reactors,the higher the core’s exit temperature,the better the Brayton cycle’s thermo-economic performance.Among the four-cycle layouts,the recompression cycle(RC)has the best overall performance,followed by the simple recuperation cycle(SR)and the intercooling cycle(IC),and the worst is the reheating cycle(RH).However,RH has the lowest total cost of investment(C_(tot))of$1619.85 million,and IC has the lowest levelized cost of energy(LCOE)of 0.012$/(kWh).The nuclear Brayton cycle system’s overall performance has been improved due to optimization.The performance of the molten salt reactor combined with the intercooling cycle(MSR-IC)scheme has the greatest improvement,with the net output power(W_(net)),thermal efficiencyη_(t),and exergy efficiency(η_(e))improved by 8.58%,8.58%,and 11.21%,respectively.The performance of the lead-cooled fast reactor combined with the simple recuperation cycle scheme was optimized to increase C_(tot) by 27.78%.In comparison,the internal rate of return(IRR)increased by only 7.8%,which is not friendly to investors with limited funds.For the nuclear Brayton cycle,the molten salt reactor combined with the recompression cycle scheme should receive priority,and the gas-cooled fast reactor combined with the reheating cycle scheme should be considered carefully.

Towards the performance limit of catenary meta-optics via field-driven optimization

Catenary optics enables metasurfaces with higher efficiency and wider bandwidth,and is highly anticipated in the imaging system,super-resolution lithography,and broadband absorbers.However,the periodic boundary approximation without considering aperiodic electromagnetic crosstalk poses challenges for catenary optical devices to reach their performance limits.Here,perfect control of both local geometric and propagation phases is realized through field-driven optimization,in which the field distribution is calculated under real boundary conditions.Different from other optimization methods requiring a mass of iterations,the proposed design method requires less than ten iterations to get the efficiency close to the optimal value.Based on the library of shape-optimized catenary structures,centimeter-scale devices can be designed in ten seconds,with the performance improved by ~15%.Furthermore,this method has the ability to extend catenary-like continuous structures to arbitrary polarization,including both linear and elliptical polarizations,which is difficult to achieve with traditional design methods.It provides a way for the development of catenary optics and serves as a potent tool for constructing high-performance optical devices.

Optimization of nursing interventions for postoperative mental status recovery in patients with cerebral hemorrhage

BACKGROUND Hypertensive cerebral hemorrhage(HCH),the most common chronic diseases,has become a topic of global public health discussions.AIM To investigate the role of rehabilitative nursing interventions in optimizing the postoperative mental status recovery phase and to provide clinical value for future rehabilitation of patients with HCH.METHODS This randomized controlled study included 120 patients with cerebral HCH who were contained to our neurosurgery department between May 2021–May 2023 as the participants.The participants have randomly sampled and grouped into the observation and control groups.The observation group received the rehabilitation nursing model,whereas the control group have given conventional nursing.The conscious state of the patients was assessed at 7,14,21,and 30 d postoperatively.After one month of care,sleep quality,anxiety,and depression were compared between the two groups.Patient and family satisfaction were assessed using a nursing care model.RESULTS The results showed that the state of consciousness scores of the patients in both groups significantly increased(P<0.05)after surgical treatment.From the 14th day onwards,differences in the state of consciousness scores between the two groups of patients began to appear(P<0.05).After one month of care,the sleep quality,anxiety state,and depression state of patients were significantly better in the observation group than in the control group(P<0.05).Satisfaction with nursing care was higher in the observation group than in the control group(P<0.05).CONCLUSION The rehabilitation nursing model has a more complete system compared to conventional nursing,which can effectively improve the postoperative quality of life of patients with cerebral hemorrhage and improve the efficiency of mental state recovery;however,further analysis and research are needed to provide more scientific evidence.

Development of Fixture Layout Optimization for Thin-Walled Parts:A Review

An increasing number of researchers have researched fixture layout optimization for thin-walled part assembly during the past decades.However,few papers systematically review these researches.By analyzing existing literature,this paper summarizes the process of fixture layout optimization and the methods applied.The process of optimization is made up of optimization objective setting,assembly variation/deformation modeling,and fixture layout optimization.This paper makes a review of the fixture layout for thin-walled parts according to these three steps.First,two different kinds of optimization objectives are introduced.Researchers usually consider in-plane variations or out-of-plane deformations when designing objectives.Then,modeling methods for assembly variation and deformation are divided into two categories:Mechanism-based and data-based methods.Several common methods are discussed respectively.After that,optimization algorithms are reviewed systematically.There are two kinds of optimization algorithms:Traditional nonlinear programming and heuristic algorithms.Finally,discussions on the current situation are provided.The research direction of fixture layout optimization in the future is discussed from three aspects:Objective setting,improving modeling accuracy and optimization algorithms.Also,a new research point for fixture layout optimization is discussed.This paper systematically reviews the research on fixture layout optimization for thin-walled parts,and provides a reference for future research in this field.

Application and optimization design of non-obstructive particle damping-phononic crystal vibration isolator in viaduct structure-borne noise reduction

The problems associated with vibrations of viaducts and low-frequency structural noise radiation caused by train excitation continue to increase in importance.A new floating-slab track vibration isolator-non-obstructive particle damping-phononic crystal vibration isolator is proposed herein,which uses the particle damping vibration absorption technology and bandgap vibration control theory.The vibration reduction performance of the NOPD-PCVI was analyzed from the perspective of vibration control.The paper explores the structure-borne noise reduction performance of the NOPD-PCVIs installed on different bridge structures under varying service conditions encountered in practical engineering applications.The load transferred to the bridge is obtained from a coupled train-FST-bridge analytical model considering the different structural parameters of bridges.The vibration responses are obtained using the finite element method,while the structural noise radiation is simulated using the frequency-domain boundary element method.Using the particle swarm optimization algorithm,the parameters of the NOPD-PCVI are optimized so that its frequency bandgap matches the dominant bridge structural noise frequency range.The noise reduction performance of the NOPD-PCVIs is compared to the steel-spring isolation under different service conditions.

Application of Stork Optimization Algorithm for Solving Sustainable Lot Size Optimization

The efficiency of businesses is often hindered by the challenges encountered in traditional Supply Chain Manage-ment(SCM),which is characterized by elevated risks due to inadequate accountability and transparency.To address these challenges and improve operations in green manufacturing,optimization algorithms play a crucial role in supporting decision-making processes.In this study,we propose a solution to the green lot size optimization issue by leveraging bio-inspired algorithms,notably the Stork Optimization Algorithm(SOA).The SOA draws inspiration from the hunting and winter migration strategies employed by storks in nature.The theoretical framework of SOA is elaborated and mathematically modeled through two distinct phases:exploration,based on migration simulation,and exploitation,based on hunting strategy simulation.To tackle the green lot size optimization issue,our methodology involved gathering real-world data,which was then transformed into a simplified function with multiple constraints aimed at optimizing total costs and minimizing CO_(2) emissions.This function served as input for the SOA model.Subsequently,the SOA model was applied to identify the optimal lot size that strikes a balance between cost-effectiveness and sustainability.Through extensive experimentation,we compared the performance of SOA with twelve established metaheuristic algorithms,consistently demonstrating that SOA outperformed the others.This study’s contribution lies in providing an effective solution to the sustainable lot-size optimization dilemma,thereby reducing environmental impact and enhancing supply chain efficiency.The simulation findings underscore that SOA consistently achieves superior outcomes compared to existing optimization methodologies,making it a promising approach for green manufacturing and sustainable supply chain management.

An Improved Artificial Rabbits Optimization Algorithm with Chaotic Local Search and Opposition-Based Learning for Engineering Problems and Its Applications in Breast Cancer Problem

Artificial rabbits optimization(ARO)is a recently proposed biology-based optimization algorithm inspired by the detour foraging and random hiding behavior of rabbits in nature.However,for solving optimization problems,the ARO algorithm shows slow convergence speed and can fall into local minima.To overcome these drawbacks,this paper proposes chaotic opposition-based learning ARO(COARO),an improved version of the ARO algorithm that incorporates opposition-based learning(OBL)and chaotic local search(CLS)techniques.By adding OBL to ARO,the convergence speed of the algorithm increases and it explores the search space better.Chaotic maps in CLS provide rapid convergence by scanning the search space efficiently,since their ergodicity and non-repetitive properties.The proposed COARO algorithm has been tested using thirty-three distinct benchmark functions.The outcomes have been compared with the most recent optimization algorithms.Additionally,the COARO algorithm’s problem-solving capabilities have been evaluated using six different engineering design problems and compared with various other algorithms.This study also introduces a binary variant of the continuous COARO algorithm,named BCOARO.The performance of BCOARO was evaluated on the breast cancer dataset.The effectiveness of BCOARO has been compared with different feature selection algorithms.The proposed BCOARO outperforms alternative algorithms,according to the findings obtained for real applications in terms of accuracy performance,and fitness value.Extensive experiments show that the COARO and BCOARO algorithms achieve promising results compared to other metaheuristic algorithms.

An Overview of Sequential Approximation in Topology Optimization of Continuum Structure

This paper offers an extensive overview of the utilization of sequential approximate optimization approaches in the context of numerically simulated large-scale continuum structures.These structures,commonly encountered in engineering applications,often involve complex objective and constraint functions that cannot be readily expressed as explicit functions of the design variables.As a result,sequential approximation techniques have emerged as the preferred strategy for addressing a wide array of topology optimization challenges.Over the past several decades,topology optimization methods have been advanced remarkably and successfully applied to solve engineering problems incorporating diverse physical backgrounds.In comparison to the large-scale equation solution,sensitivity analysis,graphics post-processing,etc.,the progress of the sequential approximation functions and their corresponding optimizersmake sluggish progress.Researchers,particularly novices,pay special attention to their difficulties with a particular problem.Thus,this paper provides an overview of sequential approximation functions,related literature on topology optimization methods,and their applications.Starting from optimality criteria and sequential linear programming,the other sequential approximate optimizations are introduced by employing Taylor expansion and intervening variables.In addition,recent advancements have led to the emergence of approaches such as Augmented Lagrange,sequential approximate integer,and non-gradient approximation are also introduced.By highlighting real-world applications and case studies,the paper not only demonstrates the practical relevance of these methods but also underscores the need for continued exploration in this area.Furthermore,to provide a comprehensive overview,this paper offers several novel developments that aim to illuminate potential directions for future research.

Determination of the Pile Drivability Using Random Forest Optimized by Particle Swarm Optimization and Bayesian Optimizer

Driven piles are used in many geological environments as a practical and convenient structural component.Hence,the determination of the drivability of piles is actually of great importance in complex geotechnical applications.Conventional methods of predicting pile drivability often rely on simplified physicalmodels or empirical formulas,whichmay lack accuracy or applicability in complex geological conditions.Therefore,this study presents a practical machine learning approach,namely a Random Forest(RF)optimized by Bayesian Optimization(BO)and Particle Swarm Optimization(PSO),which not only enhances prediction accuracy but also better adapts to varying geological environments to predict the drivability parameters of piles(i.e.,maximumcompressive stress,maximum tensile stress,and blow per foot).In addition,support vector regression,extreme gradient boosting,k nearest neighbor,and decision tree are also used and applied for comparison purposes.In order to train and test these models,among the 4072 datasets collected with 17model inputs,3258 datasets were randomly selected for training,and the remaining 814 datasets were used for model testing.Lastly,the results of these models were compared and evaluated using two performance indices,i.e.,the root mean square error(RMSE)and the coefficient of determination(R2).The results indicate that the optimized RF model achieved lower RMSE than other prediction models in predicting the three parameters,specifically 0.044,0.438,and 0.146;and higher R^(2) values than other implemented techniques,specifically 0.966,0.884,and 0.977.In addition,the sensitivity and uncertainty of the optimized RF model were analyzed using Sobol sensitivity analysis and Monte Carlo(MC)simulation.It can be concluded that the optimized RF model could be used to predict the performance of the pile,and it may provide a useful reference for solving some problems under similar engineering conditions.

Long-term operation optimization of circulating cooling water systems under fouling conditions

Fouling caused by excess metal ions in hard water can negatively impact the performance of the circulating cooling water system(CCWS)by depositing ions on the heat exchanger's surface.Currently,the operation optimization of CCWS often prioritizes short-term flow velocity optimization for minimizing power consumption,without considering fouling.However,low flow velocity promotes fouling.Therefore,it's crucial to balance fouling and energy/water conservation for optimal CCWS long-term operation.This study proposes a mixed-integer nonlinear programming(MINLP)model to achieve this goal.The model considers fouling in the pipeline,dynamic concentration cycle,and variable frequency drive to optimize the synergy between heat transfer,pressure drop,and fouling.By optimizing the concentration cycle of the CCWS,water conservation and fouling control can be achieved.The model can obtain the optimal operating parameters for different operation intervals,including the number of pumps,frequency,and valve local resistance coefficient.Sensitivity experiments on cycle and environmental temperature reveal that as the cycle increases,the marginal benefits of energy/water conservation decrease.In periods with minimal impact on fouling rate,energy/water conservation can be achieved by increasing the cycle while maintaining a low fouling rate.Overall,the proposed model has significant energy/water saving effects and can comprehensively optimize the CCWS through its incorporation of fouling and cycle optimization.