Glypican 4 down-regulation in pluripotent stem cells as a potential strategy to improve differentiation and to impair tumorigenicity of cell transplants

Recent advances in stem cell technologies have opened new avenues for the treatment of a number of diseases still lacking effective therapeutic options.Cell transplantation has emerged as among the most promising clinical intervention for disorders such as injuries,diabetes,liver diseases, neurodegeneration and heart failure （Lee et al., 2013; Forbes and Rosenthal, 2014; Tabar and Studer, 2014）.

A critique of Porter＇s cost leadership and differentiation strategies

Porter identifies high market share with cost leadership, citing GM as a successful practitioner of this strategy. However, GM became a market share leader in the American automobile industry due to a strategy of market segmentation, differentiation and a broad scope shaped during the 1920s. Porter argues that cost leadership and differentiation offer an equally viable path to competitive success. Nevertheless, a differentiation strategy based on superior quality compared to competition is more profitable than cost leadership strategy. It can lead a business to become a market share leader, and consequently even a low-cost leader. Research indicates that differentiation and cost leadership can co-exist. However, Porter insists that each generic strategy requires a different culture and a totally different philosophy. The problem is that Porter＇s generic strategies are too broad. It is not his logic that is flawed, but his basic premise that prescribes cost leadership strategy as the only route to market share leadership, and presents a narrow view of differentiation with a unique product--sold at a premium price--on the one hand, and a ＂standard, or no-frills＂ product on the other. Mintzburg （1988） says Porter＇s cost leadership strategy should be called ＂price differentiation＂： a strategy that is based on a lower price than that of the competition. He suggests that business strategy has two dimensions： differentiation and scope. Thus, setting scope aside, competitive strategy has only one component： differentiation. So, the key question is not whether to differentiate, but how？ First, make customer-perceived quality as the foundation of competitive strategy because it is far more critical to long-term success than any other factor. Second, serve the middle class by competing in the mid-price segment, offering better quality than the competition at a somewhat higher price. It is this path that can lead to market share leadership--a strategy that can be both profitable--and sustainable.

AERODYNAMIC OPTIMIZATION DESIGN OF LOW ASPECT RATIO TRANSONIC TURBINE STAGE

The advanced optimization method named as adaptive range differential evolution （ARDE） is developed. The optimization performance of ARDE is demonstrated using a typical mathematical test and compared with the standard genetic algorithm and differential evolution. Combined with parallel ARDE, surface modeling method and Navier-Stokes solution, a new automatic aerodynamic optimization method is presented. A low aspect ratio transonic turbine stage is optimized for the maximization of the isentropic efficiency with forty-one design variables in total. The coarse-grained parallel strategy is applied to accelerate the design process using 15 CPUs. The isentropic efficiency of the optimum design is 1.6% higher than that of the reference design. The aerodynamic performance of the optimal design is much better than that of the reference design.

Analyzing IKEA's International Investment in China Based on the Theory of International Production Compromise

This study analyzes IKEA's localized operation in China through the eclectic theory of international production.Firstly,the development history of IKEA is discussed along with its development in China.Secondly,IKEA's direct investment in China is analyzed from the perspective of IKEA's eclectic theory of direct investment in international production,the corporate ownership,internalization,and location advantages of the company,in addition to the challenges of IKEA5s investment and operation in China,hoping to enlighten the process of formulating overseas expansion strategies for foreign direct investment.This study aims to guide students to strengthen their skills in formulating and implementing strategies in regard to the international investment process of multinational companies・On the one hand,they can analyze the strategies used and challenges faced by IKEA in its international investment in China to stimulate their thinking on the international investment of Chinese enterprises;on the other hand,they can also strengthen their understanding of the international investment theory by analyzing IKEA5s international investment in China.This study hopes to enhance students5 understanding and application skills in regard to companies'transnational operations.

Harris Hawks Algorithm Incorporating Tuna Swarm Algorithm and Differential Variance Strategy

Because of the low convergence accuracy of the basic Harris Hawks algorithm,which quickly falls into the local optimal,a Harris Hawks algorithm combining tuna swarm algorithm and differential mutation strategy(TDHHO)is proposed.The escape energy factor of nonlinear periodic energy decline balances the ability of global exploration and regional development.The parabolic foraging approach of the tuna swarm algorithm is introduced to enhance the global exploration ability of the algorithm and accelerate the convergence speed.The difference variation strategy is used to mutate the individual position and calculate the fitness,and the fitness of the original individual position is compared.The greedy technique is used to select the one with better fitness of the objective function,which increases the diversity of the population and improves the possibility of the algorithm jumping out of the local extreme value.The test function tests the TDHHO algorithm,and compared with other optimization algorithms,the experimental results show that the convergence speed and optimization accuracy of the improved Harris Hawks are improved.Finally,the enhanced Harris Hawks algorithm is applied to engineering optimization and wireless sensor networks(WSN)coverage optimization problems,and the feasibility of the TDHHO algorithm in practical application is further verified.

An Improved Northern Goshawk Optimization Algorithm for Feature Selection

Feature Selection(FS)is an important data management technique that aims to minimize redundant information in a dataset.This work proposes DENGO,an improved version of the Northern Goshawk Optimization(NGO),to address the FS problem.The NGO is an efficient swarm-based algorithm that takes its inspiration from the predatory actions of the northern goshawk.In order to overcome the disadvantages that NGO is prone to local optimum trap,slow convergence speed and low convergence accuracy,two strategies are introduced in the original NGO to boost the effectiveness of NGO.Firstly,a learning strategy is proposed where search members learn by learning from the information gaps of other members of the population to enhance the algorithm's global search ability while improving the population diversity.Secondly,a hybrid differential strategy is proposed to improve the capability of the algorithm to escape from the trap of the local optimum by perturbing the individuals to improve convergence accuracy and speed.To prove the effectiveness of the suggested DENGO,it is measured against eleven advanced algorithms on the CEC2015 and CEC2017 benchmark functions,and the obtained results demonstrate that the DENGO has a stronger global exploration capability with higher convergence performance and stability.Subsequently,the proposed DENGO is used for FS,and the 29 benchmark datasets from the UCL database prove that the DENGO-based FS method equipped with higher classification accuracy and stability compared with eight other popular FS methods,and therefore,DENGO is considered to be one of the most prospective FS techniques.DENGO's code can be obtained at https://www.mathworks.com/matlabcentral/fileexchange/158811-project1.

中国重要农业文化遗产的空间分布特征及其差异性管理策略

Climate change and rapid urbanization pose significant challenges to the conservation and management of agricultural heritage systems,including decline in agricultural land,loss of labor,and ecosystem degradation.Although existing studies have proposed general strategies with theoretical guidance and specific strategies for particular systems to promote the conservation of agricultural heritage systems,there remains a large knowledge gap in effective and differentiated management strategies at the regional level.This is especially so in China because of the clear regional differences in the natural and socioeconomic conditions of the widely distributed China Nationally Important Agricultural Heritage Systems(China-NIAHS).In this study,we integrated multi-source data and spatial analysis to reveal the distribution characteristics of existing China-NIAHS and proposed differentiated management strategies.Results show that there are four clustering distribution zones of China-NIAHS,i.e.,the northwest clustering zone west of the Heihe-Tengchong Line(ZoneⅠ),the clustering belt with‘Northeast-Hebei-Shandong'as core(ZoneⅡ),the Yangtze River Delta clustering zone(ZoneⅢ),and the Hunan-Chongqing-Yunnan-Guizhou clustering zone(ZoneⅣ).Different management strategies are proposed for the China-NIAHS in each clustering zone.Specifically,ZoneⅠshould focus on maintaining their ecological functions and services,while ZoneⅡshould aim for livelihood supply,sustainable resource use,and ecological protection.For ZoneⅢ,rapid urbanization could become a positive driving force for China-NIAHS conservation through sustainable tourism and reasonable urban zoning.ZoneⅣshould emphasize the mutual support between characteristic product development and the brand effect of the China-NIAHS.These findings will help establish regional and targeted management strategies for China-NIAHS and provide a reference for the conservation of agricultural heritage systems in other countries.

Differential Evolution-Boosted Sine Cosine Golden Eagle Optimizer with Lévy Flight

Golden eagle optimizer(GEO)is a recently introduced nature-inspired metaheuristic algorithm,which simulates the spiral hunting behavior of golden eagles in nature.Regrettably,the GEO suffers from the challenges of low diversity,slow iteration speed,and stagnation in local optimization when dealing with complicated optimization problems.To ameliorate these deficiencies,an improved hybrid GEO called IGEO,combined with Lévy flight,sine cosine algorithm and differential evolution(DE)strategy,is developed in this paper.The Lévy flight strategy is introduced into the initial stage to increase the diversity of the golden eagle population and make the initial population more abundant;meanwhile,the sine-cosine function can enhance the exploration ability of GEO and decrease the possibility of GEO falling into the local optima.Furthermore,the DE strategy is used in the exploration and exploitation stage to improve accuracy and convergence speed of GEO.Finally,the superiority of the presented IGEO are comprehensively verified by comparing GEO and several state-of-the-art algorithms using(1)the CEC 2017 and CEC 2019 benchmark functions and(2)5 real-world engineering problems respectively.The comparison results demonstrate that the proposed IGEO is a powerful and attractive alternative for solving engineering optimization problems.

Quantum learning control using differential evolution with equally-mixed strategies

Learning control has been recognized as a powerful approach in quantum information technology. In this paper, we extend the application of differential evolution （DE） to design optimal control for various quantum systems. Various DE methods are introduced and analyzed, and EMSDE featuring in equally mixed strategies is employed for quantum control. Two classes of quantum control problems, including control of four-level open quantum ensembles and quantum superconducting systems, are investigated to demonstrate the performance of EMSDE for learning control of quantum systems. Numerical results verify the effectiveness of the FMSDE method for various quantum systems and show the potential for complex quantum control problems.