A New Class of Non-Quest-Newton Methods and Their Global Convergence with Goldstein Line Search

In this paper, on the basis of the DFP method a class of non-quasi-Newton methods is presented. Under some condition the global convergence property of these methods with Goldstein line search on uniformly convex objective function is proved

Competitive Strategies for China's Domestic Luxury Department Stores

Abstract China’s domestic luxury department stores have faced increasing competition from foreign competitors. The competition is mainly due to the liberalization of the retail industry by China’s government fulfilling their commitment to the WTO since 2004. Such liberalization attracts many foreign luxury department stores into China. By today, with the saturation of first tier cities’ market, they have begun expanding into the market of Chinese second tier cities. Except for the increased competition, it seems that China’s domestic luxury department stores also have faced many opportunities, because many new polices are launched by the Chinese government to motivate them to develop. The luxury goods market of the second tier city has its own unique characteristics. Zhengzhou as a classical example of second tier cities will be selected to study the competition and competitive strategies for China’s domestic luxury department stores. The purpose of this dissertation is to evaluate and identify the strategies of China’s domestic luxury department stores in the strong competition. In order to achieve this aim, several objectives are set. In the literature review, the author critically reviews the relevant literature about the topic, ranging from the industry positioning approach (Porter, 1980, 1985), the resource-based view (RBV) (Barney, 1991; Peteraf, 1993; Wernerfelt, 1984) to the dynamic capability approach (Teece et al., 1997) which are treated as the nature and causes of competitive advantages. However, these views above have been called into question as well in sometime. Moreover, Porter’s Five Forces Model and Generic Strategies are examined in this specific context. Furthermore, dynamic capabilities for the luxury department stores will be finally presented and discussed by the author. A qualitative methodology based on both primary and secondary data was justified as the research approach in this research, and the case study method is selected to collect consistent information about the research topic. Three different attributes of luxury department stores will be chose to support the research. Moreover, semi-structured interviews as the part of the case study will be used so as to allow the participants to provide detailed explanations regarding the research questions. The findings show that there is always no clear positioning for almost China’s luxury department stores, and they only focus on using the resources what they captured. For the industry environment, the bargaining power of different involvement has strikingly changed. With regard to competitive advantages and generic strategies, cost leadership strategy can be used by China’s luxury department stores as a hidden way. Moreover, domestic operators prefer to change the expansion model in the future development was not treated as a suitable approach of reconfiguration. Lastly, poor learning ability is a distinct weakness in their structure of strategy formulation

On the Self-extinction Time of Pool Fire in Closed Compartments

AbstractThe self-extinction time of pool fires in closed compartments was studied. Experiments were conducted in two bench-scale compartments with volumes of 0.75 m3 and 17.55 m3. It was found that the fire self-extinction time was proportional to the compartment volume but inversed to the pool area for n-heptane. The fuel mass loss rate in closed compartments was lower than that in the open space. The fire self-extinction occurred when the local oxygen mole fraction in the flame vicinity descended to a level of 10.7-15.3%. The mean remaining oxygen mole fraction at the self-extinction moment was about 14.1%. Based on the mass conservation of the oxygen, a model for predicting the self-extinction time of pool fires in closed compartments was developed. By defining a concept of the dimensionless fire volume, the dimensionless self-extinction time was proposed. The dimensionless self-extinction time is proportional to the difference between the initial and remaining oxygen mass fraction, fuel properties, such as heat of combustion and stoichiometric ratio etc., but inverses to the dimensionless fire volume and the integrated combustion coefficient. The predicted results showed a good agreement with the experimental results. The model also provides a good prediction for the results of NRL's t sts

Direct reprogramming of induced neural progenitors: a new promising strategy for AD treatment.

Alzheimer\u27s disease (AD) is a prominent form of dementia, characterized by aggregation of the amyloid β-peptide (Aβ) plaques and neurofibrillary tangles, loss of synapses and neurons, and degeneration of cognitive functions. Currently, although a variety of medications can relieve some of the symptoms, there is no cure for AD. Recent breakthroughs in the stem cell field provide promising strategies for AD treatment. Stem cells including embryonic stem cells (ESCs), neural stem cells (NSCs), mesenchymal stem cells (MSCs), and induced pluripotent stem cells (iPSCs) are potentials for AD treatment. However, the limitation of cell sources, safety issues, and ethical issues restrict their applications in AD. Recently, the direct reprogramming of induced neural progenitor cells (iNPCs) has shed light on the treatment of AD. In this review, we will discuss the latest progress, challenges, and potential applications of direct reprogramming in AD treatment

MGRO Recognition Algorithm-Based Artificial Potential Field for Mobile Robot Navigation

This paper describes a novel recognition algorithm which includes mean filter, Gaussian filter, Retinex enhancement method, and Ostu threshold segmentation method (MGRO) for the navigation of mobile robots with visual sensors. The approach includes obstacle visual recognition and navigation path planning. In the first part, a three-stage method for obstacle visual recognition is constructed. Stage 1 combines mean filtering and Gaussian filtering to remove random noise and Gauss noise in the environmental image. Stage 2 increases image contrast by using the Retinex enhancement method. Stage 3 uses the Ostu threshold segmentation method to achieve obstacle feature extraction. A navigation method based on the auxiliary visual information is constructed in the second part. The method is based on the artificial potential field (APF) method and is able to avoid falling into local minimum by changing the repulsion field function. Experimental results confirm that obstacle features can be extracted accurately and the mobile robot can avoid obstacles safely and arrive at target positions correctly

The Influence of Receiver Selection Strategy on Packet Success Probability in Ad Hoc Network

Considering the importance of the receiver (RX) selection strategy for the packet success probability (PSP) in ad hoc network, this paper probes into the PSPs with nearest RX selection strategy and farthest RX selection strategy and determines the number of hops with the two strategies. Next, the performance of the successful transmission probability (STP) and PSP were discussed through numerical simulation with the above mentioned two strategies. The simulation results show that the PSP is affected by the terminal density, the RX selection strategy, the packet length and the STP; the number of hops mainly depends on the terminal density, the RX selection strategy, the length between the source TX and the destination RX. Furthermore, the nearest RX selection strategy and the farthest RX selection strategy differ insignificantly in the packet transmission duration between source TX to destination RX at a small terminal density

DLPFA: Deep Learning based Persistent Fault Analysis against Block Ciphers

Deep learning techniques have been widely applied to side-channel analysis (SCA) in recent years and shown better performance compared with traditional methods. However, there has been little research dealing with deep learning techniques in fault analysis to date. This article undertakes the first study to introduce deep learning techniques into fault analysis to perform key recovery. We investigate the application of multi-layer perceptron (MLP) and convolutional neural network (CNN) in persistent fault analysis (PFA) and propose deep learning-based persistent fault analysis (DLPFA). DLPFA is first applied to advanced encryption standard (AES) to verify its availability. Then, to push the study further, we extend DLPFA to PRESENT, which is a lightweight substitution–permutation network (SPN)-based block cipher. The experimental results show that DLPFA can handle random faults and provide outstanding performance with a suitable selection of hyper-parameters

The optimization analysis of multi-type demand-side flexibility resources for renewable energy accommodation in electrical power systems

The significant increase in the proportion of renewable energy access has led to a burgeoning and urgent demand for flexible resources within the power system, and the prominence of flexible resources on the demand side has been underscored. The existing research on demand-side response primarily focuses on addressing peak load challenges, while the optimization of demand response for accommodating renewable energy remains inadequate. Considering this issue, the present study initially examines the flexibility mechanism offered by demand-side resources and proposes a calculation approach for determining the adjustable capacity of demand-side flexible resources. Furthermore, considering the limitations of the load that can be shifted, transferred, and curtailed, we develop a collaborative optimization operational model for accommodating renewable energy with multiple types of demand response. Finally, based on the provincial power grid example, the simulation study verifies the efficacy of a multi-type demand response. The results demonstrate that shiftable and transferable loads can effectively facilitate the accommodation of new energy, and their optimal allocation capacity ratios are 5.8% and 2.3%, respectively