An Improved Modification of Accelerated Double Direction and Double Step-Size Optimization Schemes

We propose an improved variant of the accelerated gradient optimization models for solving unconstrained minimization problems. Merging the positive features of either double direction, as well as double step size accelerated gradient models, we define an iterative method of a simpler form which is generally more effective. Performed convergence analysis shows that the defined iterative method is at least linearly convergent for uniformly convex and strictly convex functions. Numerical test results confirm the efficiency of the developed model regarding the CPU time, the number of iterations and the number of function evaluations metrics.

The CANDLE burnup strategy applied to small modular pressurized water reactor loading with fully ceramic microencapsulated fuel

For post-Fukushima nuclear power plants, there has been interested in accident-tolerant fuel (ATF) since it has better tolerant in the event of a severe accident. The fully ceramic microencapsulated (FCM) fuel is one kind of the ATF materials. In this study, the small modular pressurized water reactor (PWR) loading with FCM fuels was investigated, and the modified Constant Axial shape of Neutron flux, nuclide number densities and power shape During Life of Energy producing reactor (CANDLE) burnup strategy was successfully applied to such compact reactor core. To obtain ideal CANDLE shape, it’s necessary to set the infinity or enough length of the core height, but that is impossible for small compact core setting infinity or enough length of the core height. Due to the compact and finite core, the equilibrium state can only be maintained short periods and is not obvious, other than infinitely long active core to reach the long equilibrium state for ideal CANDLE. Consequently, the modified CANDLE shape would be presented. The approximate characteristics of CANDLE burnup are observed in the finite and compact core, and the power density and fuel burnup are selected as main characteristic of modified CANDLE burnup. In this study, firstly, lots of optimization schemes were discussed, and one of optimization schemes was chosen at last to demonstrate the modified CANDLE burnup strategy. Secondly, for chosen compact small rector core, the modified CANDLE burnup strategy is applied and presented. Consequently, the new characteristics of this reactor core can be discovered both in ignition region and in fertile region. The results show that application of CANDLE burnup strategy to small modular PWR loading with FCM fuels suppresses the excess reactivity effectively and reduces the risk of small PWR reactivity-induced accidents during the whole core life, which makes the reactor control more safety and simple.

Mammography Image-Based Diagnosis of Breast Cancer Using Machine Learning: A Pilot Study

A tumor is an abnormal tissue classified as either benign or malignant. A breast tumor is one of the most common tumors in women. Radiologists use mammograms to identify a breast tumor and classify it, which is a time-consuming process and prone to error due to the complexity of the tumor. In this study, we applied machine learning-based techniques to assist the radiologist in reading mammogram images and classifying the tumor in a very reasonable time interval. We extracted several features from the region of interest in the mammogram, which the radiologist manually annotated. These features are incorporated into a classification engine to train and build the proposed structure classification models. We used a dataset that was not previously seen in the model to evaluate the accuracy of the proposed system following the standard model evaluation schemes. Accordingly, this study found that various factors could affect the performance, which we avoided after experimenting all the possible ways. This study finally recommends using the optimized Support Vector Machine or Naïve Bayes, which produced 100% accuracy after integrating the feature selection and hyper-parameter optimization schemes.

Research on optimization design of men's suit considering users' perception

PurposeThis paper aims to identify the relationship between users' perception image, clothing design features and users' preference and propose a clothing design scheme based on users perception image and users' preference.Design/methodology/approachIn this paper, men's suit is composed into multiple design features under the design elements. Using the orthogonal experiment method, 16 schemes of the representative suit are designed. Through perception evaluation experiment, users' perception images and preference degree of the samples are obtained. By partial least squares (PLS) analysis method, the models between users' perception image, suit design features and users' preference are built.FindingsThe interrelationship between the three is identified by establishing PLS models between users' perception image, suit design features and users' preference. According to the coefficients of the models, the optimization schemes of men's suits considering users' perception image and preference are proposed. Verification results show that the optimization schemes are significantly better than other schemes.Originality/valueThe results of this paper can be used for consumer demand-oriented clothing design and provide references and methods for converting consumer's perceived needs into clothing design features.

Optimization of Microwave Resonator for Wireless Communication Based on Computer Technology

Wireless communication is a way of information transmission based on wireless network. It mainly includes microwave communication and satellite communication. There is no conductor between the receiving points of transmitting information. Microwave belongs to radio waves, its propagation distance is not far, but its transmission frequency and communication capacity is relatively high. Microwave resonators have been widely used and provide convenient conditions for wireless communication. In this paper, the status quo of wireless communication microwave resonator and some optimization schemes based on computer technology are described, and some obvious effects will be achieved after optimization based on computer technology are summarized.

Relationship Between the Cultivation and Incentive Mechanism of Senior Educational Talents

The attraction and mobility of senior educational talents hinge on the talent cultivation and incentive mechanism of various enterprises. If the mechanism is imperfect, the talents will be less active and creative in work, and the enterprises will underperform in the long term. This paper combines literature review, questionnaire survey, and mathematical analysis to summarize the current situation of the cultivation and incentive mechanism of senior educational talents in an enterprise in a region, and analyze the defects of the existing mechanism. The results show that: the senior educational talents have not received sufficient compensations or incentive measures; the employees do not have a smooth promotion channel; the enterprise fails to issue clear and innovative reward policies. To address these defects, this paper proposes improvement measures and optimization schemes: To boost the enthusiasm and creativity of employees in work, the enterprises should give full play to the personal values of senior educational talents, strive to introduce more excellent talents, and establish a sound salary distribution system and reasonable promotion polices. The research results lay a theoretical basis for enterprises to cultivate and absorb the necessary senior educational talents.

Synchronizability of Multi-Layer-Coupled Star-Composed Networks

In this paper, several multi-layer-coupled star-composed networks with similar symmetrical structures are defined by using the theory of graph operation. The supra-Laplacian matrix of the corresponding multi-layer networks is obtained according to the master stability equation (MSF). Two important indexes that reflect the synchronizability of these kinds of networks are derived in the case of bounded and unbounded synchronized regions. The relationships among the synchronizability, the number of layers, the length of the paths, the branchings, and the interlayer and intralayer coupling strengths in the two cases are studied. At the same time, the simulation experiments are carried out with the MATLAB software, and the simulated images of the two symmetrical structure networks’ synchronizability are compared. Finally, the factors affecting the synchronizability of multi-layer-coupled star-composed networks are found. On this basis, optimization schemes are given to improve the synchronizability of multi-layer-coupled star-composed networks and the influences of the number of central nodes on the networks’ synchronizability are further studied.

On biological flow networks: Antagonism between hydrodynamic and metabolic stimuli as driver of topological transitions.

A plethora of computational models have been developed in recent decades to account for the morphogenesis of complex biological fluid networks, such as capillary beds. Contemporary adaptation models are based on optimization schemes where networks react and adapt toward given flow patterns. Doing so, a system reduces dissipation and network volume, thereby altering its final form. Yet, recent numeric studies on network morphogenesis, incorporating uptake of metabolites by the embedding tissue, have indicated the conventional approach to be insufficient. Here, we systematically study a hybrid-model which combines the network adaptation schemes intended to generate space-filling perfusion as well as optimal filtration of metabolites. As a result, we find hydrodynamic stimuli (wall-shear stress) and filtration based stimuli (uptake of metabolites) to be antagonistic as hydrodynamically optimized systems have suboptimal uptake qualities and vice versa. We show that a switch between different optimization regimes is typically accompanied with a complex transition between topologically redundant meshes and spanning trees. Depending on the metabolite demand and uptake capabilities of the adaptating networks, we are further able to demonstrate the existence of nullity re-entrant behavior and the development of compromised phenotypes such as dangling non-perfused vessels and bottlenecks.

Asset Allocation via Machine Learning

In this paper, we document a novel machine learning-based numerical framework to solve static and dynamic portfolio optimization problems, with, potentially, an extremely large number of assets. The framework proposed applies to general constrained optimization problems and overcomes many major difficulties arising in current literature. We not only empirically test our methods in U.S. and China A-share equity markets, but also run a horse-race comparison of some optimization schemes documented in (Homescu, 2014). We record significant excess returns, relative to the selected benchmarks, in both U.S. and China equity markets using popular schemes solved by our framework, where the conditional expected returns are obtained via machine learning regression, inspired by (Gu, Kelly & Xiu, 2020) and (Leippold, Wang & Zhou, 2021), of future returns on pricing factors carefully chosen.